How Does Informal Economy Affect Ecological Footprint?

Empirical Evidence from Saudi Arabia

MEHDI ABID

Department of Finance and Investment, College of Business,

Jouf University, Skaka,

SAUDI ARABIA

Department of Business Management, College of Business,

Jouf University, Skaka,

SAUDI ARABIA

Abstract: Given the increase in the informal economy in developing countries economies, a better

increase the informal economy. The Granger causality based on VECM approach shows bi-directional causality

between EFP and IFE in the short run and the long run. Therefore, the findings of this study can help policy

makers in Saudi Arabia and a number of countries with a large informal sector to better understand the role of

governance in reducing the IFE in order to improve the environmental quality.

Keywords: ARDL; Ecological footprint; Shadow economy; Saudi Arabia.

Received: May 28, 2022. Revised: October 28, 2022. Accepted: November 29, 2022. Published: December 31, 2022.

of millions of workers earning their living in

informal conditions ([2]; [3]). The term “informal

economy” covers a wide variety of situations and

phenomena. The informal economy manifests itself

in various forms from one country to another or

within economies. Formalization measures and

namely target 8.3. Indirectly, several other SDGs

on poverty (SDG 1), gender equality (SDG 5),

inequality (SDG 10), institutions (SDG 16), and

partnerships (SDG 17) are also important for

informality. Sustainable development is considered

environmental degradation and a set of

macroeconomic variables. The link between the

informal economy and environmental quality has

received little attention in the academic literature

perspective, developing countries have a large

informal economy as a percentage of formal gross

domestic product (GDP) ([14]). Studies that ignore

the informal economy can lead to biased

conclusions when it comes to the link between

however, because the informal sector is neglected

when it comes to environmental issues. Economic

activities have an environmental impact that is not

adequately represented by the formal economy.

Second, this study uses the Ecological Footprint

(henceforth EFP) as a proxy for environmental

degradation. Previous studies used CO2 emissions,

methane emissions and PM4 ([19]; [20]; [21]; [17])

as a proxy for environmental pollution. Although

directions between informality and EFP. Studies

that examined causal relations between economic

growth and environmental degradation without

acknowledging the influence of the informal

economy tend to be inconclusive. In Mexico, [23]

demonstrated a one-way causal relationship

between economic growth and environmental

pollution; in China and Malaysia, [24] confirmed it.

Meanwhile, in [25], [26], and [27] the authors

found that pollution is causally related to economic

and I (1). Unit root tests are therefore used in order

to avoid the inclusion of I(2) variables. In our

article, two types of unit root tests are used;

namely, the Augmented Dickey Fuller (ADF) and

the Phillips Perron (PP) test. Then, in order to

cointegration methods, this method has three

advantages. The first is that it does not require the

variables to be integrated in the same order of

integration, the variables can be integrated in the

same order or in a different order. In other words,

they can be I(1) and I(0) but never of an order

greater than one. The second advantage is that this

method is more efficient than the others in case the

sample sizes are small. The third and final

advantage is that the ARDL method provides

However, for economic interpretation reasons and

in order not to depart from the framework of the

problem we posed at the start, we are going to use

two models instead of five. This means that only

two variables will move to the left side of the

model and will occupy the role of the dependent

variable in the latter; namely, the ecological

footprint and the informal economy. The other

three remaining variables- i.e., economic growth,

trade openness and urbanization- will occupy the

,

,

and

(3)

) indicates

following a short-term shock. This coefficient must

be negative and significant to ensure the validity of

the models.

should be used with a view to estimating the long-

techniques lead to asymptotically distributed

estimators towards a normal distribution, with zero

mean and constant variance. Similarly, in [38] and

[39] the authors reach the same result using

FMOLS. However, Pderoni recognizes the

superiority of the DOLS method for estimating the

long-term relationship in the case of time series.

In the case where the variables of the model are

cointegrated, the meaning of the causality between

variables is measured through the Granger causality

values of X are useful to predict Y, and Y is said to

be Granger caused by X if helps to predict Y and

vice versa.

economy, ecological footprint, informal economy,

trade openness and urbanization in level and first

Table 1 illustrates the results of these tests which

consistently record that all the series are non-

stationary in level, and stationary in first difference.

They are therefore integrated of order one I(1), with

the exception of the opening commercial (TOP)

to study the impact of each explanatory variable on

each endogenous variable in Saudi Arabia.

3.2 The Bounds Cointegration Test

After determining the order of integration of the

different variables as well as the optimal lag of the

model, we use the ARDL approach for co-

integration in order to determine the long-term

relationship between the variables. For this, we use

The notation F(EFP/FE, IFE, TOP, URB) shows

that the dependent variable is EFP. We notice in

Table 2 that when EFP and IFE occupy the role of

dependent variables, the value of the Fisher statistic

existence of short- and long-term relationships

between the ecological footprint and its

determinants in Saudi Arabia, we estimate the

ARDL models corresponding to equations (1)-(2)

to verify the impact of these variables in the long

urbanization will lead to an increase of 0.064;

0.002 and 0.008% ecological footprint,

respectively. On the other hand, a 1% increase in

trade openness will lead to a 0.004% decrease in

the ecological footprint. In model 2, all the

ecological footprint and urbanization will lead to an

increase of 0.012 and 0.004% in the informal

economy. In contrast, a 1% increase in formal

economic growth and trade openness will lead to a

decrease of 0.002 and 0.005% in the informal

economy.

In the short run, we see in Table 3, which presents

the error-correction models, that in both models (1

and 2) the lagged error-correction terms (ECTt-1)

are all significant and with the desired negative

sign, which confirms the cointegrating relationships

82% and 65% of the imbalance coming from the

shocks of the previous years which is corrected and

converges towards the long-term equilibrium each

year for models (1) and (2). The results obtained

from the coefficients of the short-term dynamics

are displayed in Table 3. These results also show

similar trends to those observed for the long-term

estimates. The estimated short-term results indicate

that ecological footprints have a positive and

significant effect on ecological footprints.

of the impact is positive in all specifications of the

model, but its magnitude changes strongly

according to the regression model. Urbanization

has long-term effects on the ecological footprint in

Saudi Arabia, but in the short term, its impact is

insignificant. Our results confirm the conclusions

of [41] and [42].

To assess the robustness of our results, we

performed the four usual diagnostic tests on the

three estimated ARDL models. These tests are

the other hand, shows that there are no missing

variables or functional form issues in the model. In

addition, the stability tests of the cumulative sum of

recursive residuals (CUSUM) and the cumulative

sum of squares of recursive residuals (CUSUMSQ)

were applied to the four estimated models. As can

be seen in figures 1 and 2, the CUSUM and

CUSUMSQ statistics plots are well within the

critical limits, which implies that all the

coefficients of the four models considered are

results presented in Table 4 show that the

equilibrium adjustment coefficients of EFP and IFE

have a negative sign and are significant at the 1%

threshold. This suggests a dynamic of return to

equilibrium, following macroeconomic shocks,

which implies the existence of a long-term

bidirectional causality between the two variables.

For example in the short run, a 1% increase in EFP

per capita leads, all other things being equal, to a

short term. The impact of these control variables on

IFE and EFP in Saudi Arabia remains consistent

with theoretical predictions. First, increased trade

openness improves environmental quality. This

result is justified by the fact that trade could play a

positive role in this process by facilitating the

diffusion of environmentally friendly technologies

in Saudi Arabia. Of course, this would require

Saudi Arabia to be willing to remove barriers to the

import of modern technologies and environmental

structural adjustment in the formal sector following

trade reforms can have a negative effect – in the

short term – on the informal economy.

This study examined the relationship between

ecological footprint and informal economy of Saudi

Arabia between 1981 and 2017 using ARDL

increase pollution levels and induce environmental

degradation. Thus, the results show that the

ecological footprint has a positive effect on the

informal economy. This reveals that the size of the

informal economy is very large. Moreover, Granger