Does informal economy reduce poverty?  Evidence from Morocco

Othmane BOURHABA and Mama HAMIMIDA  – Hassan II University Casablanca, Morocco


In Morocco, similarly to other developing countries, the informal economy represents a very large and growing share of production and employment. This paper aims to identify and analyse the relation between informality and the phenomenon of poverty using an empirical approach. This empirical analysis is based on a static and dynamic approach (GMM) by using panel data.

The results reveal that the empirical analysis leads to highlight the existence of a positive effect of the informal sector on the increase in the poverty rate in Morocco during the last three decades.

Keywords: Informal sector; Poverty; Morocco; Shadow economy; GMM

  1. Introduction

Does working in the informal economy make you poorer? Or does the informal economy only attract the poor? Poverty is one of the most serious problems facing developing countries and a number of transition economies. Although progress has been made by a number of countries, according to the World Bank, about 10% of the world’s population would live on less than $ 1.90 a day in 2015 ( 734 million people). The vast majority of this population is made up of the working poor, many of whom work in the informal economy. The high levels of poverty rates are two distinctive characteristics of Moroccan socio-economic situation. According to the World Bank database, 31.1% of Moroccan population lives below $ 5.50 a day. Thus, according to HCP (2017), subjective poverty in Morocco in 2014 was 45.1%.

In a parallel way, the expansion of the informal economy has become increasingly clear, infiltrating all fundamental aspects of socio-economic systems in Morocco. In fact, the size of the informal economy in Morocco constitutes between 30 and 40% of GDP [1]. On the one hand, the informal economy can contribute to development thanks to its role in reducing unemployment and promoting local economies, on the other hand, the informal economy is also seen as a sign of underdevelopment and unsustainability given the nature of poor working conditions and the precariousness of informal employment.

The prevalence of informal economies around the world has received continued attention over the past decade. However, both for Morocco and other developing countries, the link between poverty and informality has received little attention in the literature. It is not an easy relation, because the direction of causation and the degree of feedback between these two phenomena are not clear. This paper deals with this link and analyzes the possible relationship between variations in informality and poverty using econometric modeling.

At first, a literature review on the link between informality and poverty is presented in section 2. In a second place, the data and the empirical methodology used are presented in section 3, then the results obtained are shown in section 4. Finally, section 5 offers a discussion and conclusions.

  1. Informal Economy and Poverty: A Literature Review

The informal economy is debated in the context of poverty and social inequality. Individuals participating in this hidden part of the economy are trapped in the vicious cycle of poverty. This observation could be explained by several theoretical channels. Indeed, the informal sector has direct consequences on economic growth, the working conditions of individuals and public expenditure financed by taxes, elements which, in turn, affect poverty. The mechanisms through which the informal economy aggravates poverty are presented below.

Unlike studies that focus on the informality-inequality relationship, studies on the causal link between the informal sector and poverty are rather rare. Using data from India, Pradhan et al (1999) show an association between the incidence of poverty and participation in the informal sector. Measured on the basis of consumer spending, the authors show that 43% of participants in the informal sector are poor compared to only 6% in the formal sector. Sethuraman (1997) reports evidence from Latin American countries that clearly shows that, in general, the majority of the working poor are in the informal sector (66.2% in Bolivia, 66.4% in Brazil, 87, 1% in Panama, 57.4% percentage in Venezuela). It also shows that the incidence of extreme poverty is high in the informal sector. However, the association between poverty and participation in the informal sector does not apply uniformly to all types of workers. There are cases where the owners of informal production units have average incomes several times higher than the minimum wage, which allows to deducing a lower probability of poverty among them. Therefore, in many cases, it may be incorrect to claim that poverty is a defining characteristic of the informal sector as a whole.

Elijah and Uffort (2007) have shown that there is a causal link between the informal economy and poverty in developing countries. The authors found that the lack of employment in the formal economy, the high rate of corruption, economic hardship and lack of money to live on are the common causes of poverty and the informal economy in developing countries. Using a comparative analysis with data from 145 countries around the world, Elijah and Uffort (2007) reveal that the informal economy and poverty have no geographic border. They argue that although the size differs from country to another, the impacts of poverty and the informal economy are greater in poor countries than in highly developed countries. Nevertheless, the conclusions of this study are the result of a descriptive analysis. Indeed, to show the relationship between the informal sector and poverty, the authors limited themselves to compare the size of the informal sector and the poverty rate for several countries and over a time interval that differs from country to another.

Another more robust study, Nikopour and Habibullah (2010) who tested the causal relationship between the informal sector and poverty on a panel of 139 developing countries and 23 developed countries for a period of time between 1999 and 2007. The authors use the generalized least squares (GMM) method and the fixed and random effect method. Their empirical results suggest that the increase in the informal economy reduces poverty in developed countries, while it leads to an increase in poverty in developing countries.

De Martiis (2015) checked the relationship between the informal economy and the poverty rate using data on 33 OECD countries from 1999 to 2013. Unlike Nikopour and Habibullah (2010), the author assumes an inverse relationship between informality and poverty, that is, poverty has an effect on the informal sector. De Martiis (2015) used a fixed-effect estimation model and shows that poverty is negatively correlated with the informal economy.

The empirical literature review on the issue of the informal causality-poverty link rejects the hypothesis that the informal sector contributes to the eradication of poverty. According to the studies presented so far on different countries, the informal sector seems to trap its actors in a vicious cycle of poverty. Indeed, the informal sector offers poor quality jobs, poor employment and working conditions and therefore does not contribute much to poverty reduction. Among the active population, workers in the informal economy are poorly paid, underemployed, without social protection; their rights are less respected and they are excluded from social dialogue and decision-making. Thus, in the event of injury or illness, they have no protection or safety net to help them and their families survive on a reduced or cut income. According to this existing literature, it can be deduced that the poverty pushes people to enter the informal economy, and working in the informal economy means more poverty. In the next section, the case of Morocco is investigated to verify the causal link between poverty and informality.

  1. Description of the data and methodology

3.1. Data

The annual data are used to study the relationship between poverty and the informal sector. The data are obtained from the World Economic Outlook database. While the data for the variable of interest (size of the informal sector) come from the work of Elgin and Oztunali (2012). In this study, the panel data for 59 countries are used, with time intervals between 1981 and 2014, or 34 observations per country. The variables used in this study are:

Poverty”, which is an indicator measuring the poor population according to the national poverty line. According to the World Bank, this ratio represents the percentage of the population living below the national poverty line. National estimates are based on population-weighted estimates from household surveys. This indicator gives the level of the poor population with less than $ 3.10 per day in purchasing power parity based on 2011.

The variable of interest is: “Informal“, it represents the size of the informal sector in the different countries of the sample. To measure the size of the informal economy, the database built by Elgin and Oztunali (2012) are used. Based on a dynamic general equilibrium model, the authors succeeded in building a panel dataset of 161 countries over the period 1950 and 2009. The strong point of this database is that it is the largest in the literature, and this allows for more robust estimates. This variable will allow studying the impact of the informal sector on poverty.

Gross domestic product (GDP) per capita: converted to constant 2011 dollars is used to purchase power parity (PPP) rates. Data drawn from “World Development Indicators” published each year by the World Bank. It is a very useful indicator for observing the level of economic development in a country.

Education can have an effect on poverty, the rate of enrollment in tertiary studies (gross rate% of the population) are used as proxy for variable“Education”. The data come from the World Bank. According to the latter, the enrollment rate is calculated regardless of age and expressed as a percentage of the total population of the group of 5 years after graduation from high school.

The growth rate of the population is used in the model performed under the name of “Population“. According to the World Bank, this variable represents the exponential growth rate of the mid-year population of year t-1 to t, expressed as a percentage. The population is based on the de facto definition of the population which includes all residents regardless of their legal status or citizenship, with the exception of refugees who are not permanently established in their adopted country. These are generally considered to be part of their country of origin. The assumption is that the rate of population growth will have an impact on the increase in poverty. Therefore, the expected sign of the coefficient associated with this variable in the estimation would be positive. Table 1 provides a summary of the statistics for all the variables used in this study.

Table 1: Descriptive statistics

Variable Observations Average Median Min Max












Informal 1727 0.378 0.367 0.103 0.816
GDP_capita 1827 2705.04 1589.44 97.16 19941.45
Population 1971 1.429 1.561 -10.955 5.860
Education 1391 23.786 20.636 0.083 91.029

Source: author’s calculation


  • Methodology: Panel data estimation

An analysis with panel data are used in this study. Using this type of data in econometric research has several advantages. Panel data can, first, easily control unobserved individual heterogeneity, then, get more accurate results as they provide more observations and information, finally, track individual dynamics and therefore the before and after effects can easily be estimated (Hsiao (2003), Woodridge (2009) and Temple (2010)).

To determine the effect of the informal economy on poverty, a simple model inspired in the literature on the subject was constructed. The model is estimated econometrically through a static approach (OLS, fixed effect and random effect) and a dynamic approach (GMM).

The aim is to study the effect of the informal economy on poverty in Morocco. The absence of data for long periods pushed to resort to the panel of data for the developing countries and to use a qualitative variable “D” in the model to know the specific effect of informality on poverty for Morocco. This variable takes the value 1 for Morocco and the value 0 for all other countries.

  1. Static Model

First, for the results of the static model, three econometric techniques were carried out: the ordinary least squares panel technique (grouped), the fixed effects method (fixed effects) as well as the random effects method (random effects).

The ordinary least squares (OLS) method is a method for estimating unknown parameters in a linear regression model. This process minimizes the sum of the squares of the vertical distances between the responses observed in the data set and the responses predicted by the linear approximation. Estimates with fixed effects are expected from the differences within each country, while the estimation of random effects, integrates information on the different countries as well as on the different periods. Indeed, the random effects method is only valid if the country-specific effects are not correlated with the other explanatory variables. To do this, Hausman’s test (1978), which allows to assess whether this independence hypothesis is satisfied or not are used. The null hypothesis of this test is based on the independence between the explanatory variables and the errors. To do this, the Hausman test (1978) compares the variance-covariance matrix of the two estimators.

We will test six models, two for each technique, with and without the qualitative variable. The models used in the analysis take the following form:

Ordinary least squares (OLS):



Fixed effect model:



Random effect model:



Where i represents each country and t attributed to each time period, and    are country and time specific effects,  it is the poverty indicator,  indicates the size of the informal sector,  is GDP per capita,  represents the annual growth rate of the population, Education corresponds the rate of enrollment in tertiary studies, it is the qualitative variable which represents Morocco,  represents the interaction between the informal variable and the qualitative variable makes it possible to know the specific effect of informality on poverty for the case of Morocco and  is the error term.

  1. Dynamic model

In dynamic model, the GMM system introduced by Blundell and Bond (1998) is used to deal with the problem of presumed endogeneity between selected dependent variable and the independent variables. It is common in poverty regression for some of the explanatory variables to be endogenous. Endogeneity can bias estimates of how the variables independent of the equation affect the dependent variable in the model. There are two main sources of endogeneity such as: “unobservable heterogeneity” and “simultaneity”. To eliminate unobservable heterogeneity, classically fixed effect estimates are used. However, this estimate is only consistent when the characteristics or structures of the country are strictly exogenous. In other words, they are purely random observations over time and are not linked to the history of the country. But this assumption is unlikely to be valid in reality. Thus, while the estimation of OLS can be biased due to the fact that it ignores unobservable heterogeneity, the estimation with fixed effects can be biased since it neglects endogeneity.

The endogeneity problem could be resolved by choosing the GMM estimator to estimate the impacts of the informal sector on poverty in the context of a dynamic panel data model. The advantage of this methodology is that it eliminates any bias that might arise from ignorance of endogeneity, while providing theoretical and powerful instruments that account for simultaneity while eliminating any unobservable heterogeneity. It is preferable to use dynamic panel estimation in situations where there are unobservable factors that affect both the dependent variable and the explanatory variables, and some explanatory variables are strongly related to the past values ​​of the dependent variable. . This is likely to be the case in regressions of the impact of the informal sector on poverty. These identified complications are treated using the estimator of the generalized moment’s method of Arellano and Bond (1991). The GMM estimator of Arellano and Bond (1991) is usually called the GMM estimator with first standard differential. In addition, the augmented version of GMM is proposed by Arellano and Bover (1995) and Blundell and Bond (1998), known as the GMM system estimator.

The estimated dynamic GMM models can be written as follows:




The consistency of the GMM estimator depends on the validity of the moment conditions, which can be tested using two specification tests. The first is the Arellano-Bond autocorrelation test which tests if there is no second order correlation in the distribution. The second test, namely the Sargan test, this test is used to check the absence of correlation between the instrumental variables and the disturbances of the model. The test statistic can be calculated from the regression residuals of the instrumental variables by constructing a quadratic form based on the cross product of the residues and exogenous variables. (Sargan, 1988). We will, therefore, precede to the validation of these two specification tests after the estimation of our GMM model.

  1. Results

4.1. Static Model

Table 2 presents the results of the various estimates using OLS, fixed effects and random effects. For the three estimation techniques, the model is estimated with and without the qualitative variable. The static model is tested by numerous estimates of panel data in order to produce a model which gives robust results and optimal fit data.

The regression results of the ordinary least squares model show that all variables are significant except the variable representing the size of the informal sector. The R² is equal to 0.425, the variability of the explanatory variables explains 42.5% of the model. The unexpected results for the variable of interest lead to check the autocorrelation of the residuals. Consequently, the Durbin Watson (DW) test which allows to detect an autocorrelation of the errors of order 1 is used. Indeed, the DW [2] test affirms the existence of a strong autocorrelation for a p-value lower than 5%. This observation is true for the two models (1a) and (1b).

Obviously, the OLS model does not allow drawing conclusions because there is a strong correlation. Therefore, we also ran the fixed effect models and random which allows to solve this type of problem. As we explained in the methodology section, the Hausman specification test (1978) to discriminate between the fixed and random effect model is used. The results of Hausman’s test (1978) [3] confirm that the fixed-effects model is more robust than the random-effects model for this study.

Table 2. Estimation results

Variables  OLS model Fixed-effects model Random-effects model
  (1a)  (1b)   (2a)  (2b)               (3a)  (3b)  
Informal -4.741 -4.860 75.222*** 75.288*** 30.300*** 29.642 ***
(3.172) (3.150) (9.591) (9.601) (6.570) (3.013)
GDP_Capita    -0.0008***   -0.0008*** 0.0002 0.0002 -1.61e-05 -2.008e-05
(0.0001) (0.00016) (0.0001) (0.0001) (0.000162) (0.00016)
Population 2.150***     2.100*** 2.624*** 2.656*** 3.710*** 3.744***
(0.430) (0.428) (0.693) (0.698) (0.588) (0.589)
Education -0.151*** -0.160*** -0.090* -0.0901*   -0.130*** -0.139***
(0.034) (0.003) (0.037) (0.037) (0.036) (0.036)
D -7.052 14.057
(86.050) (59.533)
Informal*D -12.130 -67.820 -65.104*
(234.800) (154.530) (162.620)
Constant 18.500*** 18.960***        – 0.230 0.722**
(1.700) (1.698)        – (3.010) (3.013)










        0.308    0.315

Note: the standard error in parentheses                                                                           Source: author’s calculation

*** p <0.01, ** p <0.05, * p <0.1 indicates significance at 1%, 5% and 10% respectively.


The discussions on the results are therefore based on the results of the fixed effects model which is reported in column (2a) and (2b) of Table 2. The results reveal the expected relationship between the informal economy and poverty. The informal sector has a positive and significant effect on the increase in poverty in developing countries. The interaction term between the informal sector and the qualitative variable is not significant, which confirms the absence of specific effects of the informal sector in Morocco compared to other countries. Therefore, according to the results, the informal sector in Morocco leads to the amplification of poverty. The results show that the fixed effects model explains 28.8% of the variation in the dependent variable (poverty). The coefficients linked to the population growth variable in the two models (2a and 2b) show that education plays a significant role in reducing poverty in Morocco, as in other developing countries.

As expected, population growth generates more poverty and growth in per capita GDP has an insignificant effect on poverty.

Due to the potential problem of endogeneity between the size of the informal sector and poverty, the dynamic GMM model will also be estimated.

4.2. Dynamic Model

Table 3 shows the results of the estimation of the dynamic panel models. The use of the GMM model allowed us solving the problem of endogeneity and autocorrelation due to the presence of a lagged dependent variable in the explanatory variable. According to existing literature, the informal economy can be endogenous to poverty. Endogeneity can cause bias when estimating how the independent variables in the equation affect the dependent variable in the model. Thus, a specification test was carried out in order to obtain a model which gives more robust results.

Table 3: Results of GMM dynamic model estimates

  (4a)     (4b)     (4c)         
L1. Poverty                                                












Informal 9.108   10.245   60.001  
  (0.029)   (0.015)   (0.000)  
GDP_Capita   -0.0007   -0.0006   -0.0008  
  (0.002)   (0.004)   (0.000)  
Population     5.631   5.490   3.986  
  (0.000)   (0.000)   (0.000)  
Education 0.087   0.070   -0.014  
  (0.089)   (0.171)   (0.788)  
D   8.524   5.268  
    (0.076)   (0.282)  
Informal*D   -11.415   -22.919  
    (0.524)   (0.210)  
Informal ²      





















Sargan test  










Note: The p-value is presented in bracket                                                              Source: author’s calculation

The results of the GMM model reported in columns (4a), (4b) and (4c) of Table 3 confirm the positive and significant effect of the informal sector on the increase in poverty in all the countries studied. The interaction term between the informal sector and the qualitative variable is not significant in specifications (4b) and (4c), which confirms the absence of specific effects of the informal sector in Morocco compared to other countries. . Consequently, the informal sector in Morocco leads to the amplification of poverty.

In specification (4c) the “informal” variable squared (informal²) is integrated in ordre to study the linearity of the relationship between the “informal” variable and the “poverty” variable. The results of the estimated coefficients of “informal” and “informal²” indicate the existence of a non-linear relationship between the informal sector and poverty. Indeed, the variable “informal” has a positive effect while the variable “informal²” rather has a negative effect on the variable “Poverty”. This means that the “informal” variable has a concave impact on poverty. Basically, these results indicate the existence of a threshold effect from which the informal sector will make poverty worse.

The results also show that the coefficients linked to the variable of economic and demographic growth in the three specifications (4a, 4b and 4c) are significant. They also show that economic growth conduct to decrease the poverty rate while population growth generates more poverty. According to the estimations, the impact of education is not significant in reducing poverty in the countries studied.

The validity of the instruments is confirmed in all specifications (4a), (4b) and (4c). Arrelano-Bond’s tests (1991) show the presence of a first order negative autocorrelation, whereas the null hypothesis of absence of order 2 autocorrelation cannot be rejected. Thus, the specification retained in the three models and the validity of all the instruments used are accepted.

The results of the Sargan test in the GMM estimator are reported in Table 3. Based on the Sargan test statistics for all specifications, the p-value is high, indicating that the null absence hypothesis of over-identification restrictions fails to be rejected.

Therefore, Sargan test statistics indicate that (4a), (4b) and (4c) are well specified and that the instrument vector is appropriate.

  1. Discussion and Conclusion:

The study of phenomena such as the informal economy is, on the one hand, an important task, on the other, difficult challenge. The high level of informality in Morocco is an important social problem which affects not only those who live individually in poverty, but also has consequences for the whole social fabric. The objective of this present work was to investigate the effects of informal activities on poverty in Morocco. For this, a model that is estimated econometrically through a static approach (OLS, fixed effect and random effect) and a dynamic approach (GMM) is used. The results obtained through all of the econometric investigations indicate that the informal sector has impacts on poverty in Morocco.

Overall, the determinants of poverty in Morocco are multiple. The work suggested in this paper show the existence of an effect of the informal sector on the increase of the poverty rate in Morocco. Despite the existence of an empirical literature on the relationship between poverty and informality, very little evidence exists on the transmission channels of the effect of the informal sector on poverty. The estimate the presence of four possible channels of influence for the case of Morocco is estimated. First, wages in the informal sector are significantly lower than those in the formal economy due to the lack of regulation and compliance with labor law. Workers in the informal sector do not have access to the minimum wage, which allows employers to pay them substantially less than the minimum wage. As a result, workers in the informal sector do not earn enough to cross the poverty line and are often trapped in a vicious cycle of poverty and need.

Second, many economic agents operating in the informal economy are subject to specific vulnerabilities and insecurities and often experience serious decent work deficits due to the poor legal and institutional environment. In fact, the institutional and legal framework in Morocco generates a poor allocation of resources, thus favoring large companies, for example in the allocation of import licenses and foreign exchange for the import of raw materials. Business registration and formal requirements often involve very bureaucratic procedures, thus encouraging businesses to remain informal as the environment is not conducive to job creation or the development of small formal businesses. In the absence of institutionalized protections, within the framework of a legal and regulatory system or via a collective political power, which can alleviate poverty, the livelihoods of informal workers and their lives become precarious and do not allow them to develop their activities and to have more income to cross the poverty line. Pickerill (2011) estimates that foreign informal workers in Morocco suffer from many difficulties in obtaining credit and in administrative procedures, which hinders their will to develop their activities.

Third, the informal sector is the main employer of unskilled and low-skilled people with a low level of education in Morocco (HCP, 2014). The low level of education among economic agents operating in the informal sector has a negative effect on daily management and the development of informal economic activities. This has a direct impact on the productivity of the sector and therefore on the level of income. The fall in productivity due to the low level of education will therefore lead to an increase in poverty in the informal sector.

Fourth, the capitalist mode of production based on price competition leads to more poverty among informal workers. Indeed, attempts by formal businesses to reduce labor costs to increase competitiveness, their non-compliance with state economic regulations (notably taxes and social legislation), and global competition accompanied by an industrialization process (notably subcontracting chains and offshore industries) leads to outsourcing part of the production chain to the informal sector to gain competitiveness at the expense of informal labor. As the structuralist school maintains, the informal and formal economies are intrinsically linked. They see both informal enterprises and informal workers as subordinate to the interests of capitalist development, providing cheap goods and services. This increased competition and the exploitative relationships between formal and informal will further worsen the economic situation of those engaged in the informal economy.

In conclusion, the benefits of informal employment are often not sufficient, and the costs and risks are often too high for those who work informally to achieve an adequate standard of living throughout their lives. From a public policy perspective, since poverty and informality are interdependent, the results suggest that no public policy could effectively eliminate either of the two phenomena if nothing is done to mitigate both. It is clear, for example, that poverty reduction can only be sustainable if more formal and quantitative employment opportunities are created. This implies policies aimed at improving the functioning of labor markets, investing in education and increasing financial inclusion to allow informal workers to formalize.


[1]   Bourhaba et Hamimida(2016), Hassan et Schneider(2016), Elgin et Oztunali (2012), Schneider et al (2010) et Alaoui Moustain(2004)

 [2]    Modèle (1a) : DW = 0.5712, p-value < 2.2e-16         Modèle (1b) : DW = 0.5661, p-value < 2.2e-16 [3]     Modèle (2a) et (3a) : chisq = 25.0337, p-value = 4.953e-05          Modèle (2b) et (3b) : chisq = 25.5369, p-value = 0.0001097



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