Gender Differences in the Impact of the Informal Economy on the Labor Market: Evidence From Middle Eastern Countries

Abstract

Although considerable discussion has been devoted to the macro determinants of labor market variables across genders, comparatively little attention has been given to the contribution of the informal economy to this market. This study was aimed at empirically investigating the impact of the size of the shadow or informal economy (IE) on labor market variables across genders in 12 Middle Eastern countries. The study used quarterly time series data on each country under investigation, covering 1991 to 2015. Phillips–Perron unit root tests were carried out to verify the stationarity of the examined economic series. An autoregressive distributed lag approach was adopted to conduct cointegration tests and estimate long-run regression coefficients and error correction terms. The results indicated that the IE served men and women differently across countries. Whereas this economy had a long-run positive relationship with men’s employment rates in Bahrain, Iran, Qatar, and Turkey, this relationship existed among women only in Israel. IE activities matter in the employment of men and women in Middle Eastern countries. The sizes of IEs in the labor market are a significant factor that favors men’s employment rather than that of women.

Keywords

informal economy labor market gender employment cointegration C22 J16

Introduction

The negative impact of low employment on an entire society is well known. In the Middle East, jobs and their quality have devastated sustainable economic growth and fair globalization. On the basis of labor market indicators, data have shown that employment in this region has grown at a slow pace and that such growth is highly uneven across countries. Moreover, unemployment rates have remained an increasing problem in almost all Middle Eastern countries throughout the 2000s.

Low employment can lead to reduced consumer confidence, consumer spending, and business investment (Dunkelberg, 1989; Dunphy et al., 2022), which can hamper economic growth and diminish the gross domestic product (GDP). These effects, in turn, can elevate poverty rates, increase inequality, and reduce the living standards of citizens. Therefore, policy attention and innovative strategies are needed because of global economic and financial crises.

Although the employment rate is a crucial indicator in understanding the state of the labor market, it provides only a partial picture of the overall employment situation in a given region. The shadow or informal economy (IE) that extensively proliferates in various developed and developing countries (Fedotov & Nevzorova, 2020; Navickas et al., 2019; Tanaka & Hashiguchi, 2020) is a significant source of private sector employment. This observation is supported by Dell’Anno and Solomon (2008), who stated that the informal sector provides employment opportunities to individuals who cannot find employment in the formal sector. Similarly, a report issued by the International Labor Organization (ILO, 2018) indicated that 61.2% of global labor is informally employed. Emerging and developing countries have higher shares of informal employment than developed nations.

There are many similarities in informal employment among different countries, but the volume and quality of IE activities are affected by economic, historical, social, and cultural factors. One aspect of the qualitative differences arising from these factors is the varying tendencies of genders to operate underground (Allen & Curington, 2014; Goel & Saunoris, 2017). For example, although women’s participation in paid employment has increased over the past two decades, gender segregation remains significant in the labor market, particularly in developing countries, where female employment is concentrated in low-quality, irregular, and informal jobs (Chen, 2006; Xaba et al., 2002). Women who might be disempowered to participate in formal economic activities may find it easier to seek employment in the informal sector or secure an assistantship in a small family enterprise because these jobs have low entry barriers (Bahramitash & Kazemipour, 2011; Chant & Pedwell, 2008; Djajić & Mesnard, 2015; Herwartz et al., 2015). Given these problems, a significant task is to investigate the relationship between the size of an IE and employment, unemployment, and self-employment rates, with a focus on gender differences.

Despite extensive studies on informal employment and its impact on the size of the shadow economy (Bosch and Pretel, 2015; Santoso & Sriyana, 2020; Shembavnekar, 2019; Williams & Franic, 2016), little is known about the gender dimension of the relationship between such size and labor market variables. Moreover, considering the rapid growth of the shadow economy in recent years due to neoliberal perspectives and women’s disadvantaged position in the economy, the overrepresentation of women in IEs reflects a vital gender dimension in the link between IE size and employment. These research gaps were addressed in the current work using data on 12 Middle Eastern countries. It contributes to the literature in a number of ways. First, previous empirical investigations focused on the role of the IE in European countries. Insufficient attention has been paid to the context of the Middle East, where employment and participation gaps between men and women are vast compared with the world average. Second, economic changes and the global competitiveness of production systems in the past few decades have differentially affected the positions of men and women in the IE. This study therefore examined the gender dimension of the relationship between the IE and employment, self-employment, and unemployment rates in Middle Eastern countries.

The remainder of the paper is organized as follows. Section 2 presents the theoretical background of the study and a review of related literature. Section 3 discusses the data, model, and estimation used to conduct the research. Section 4 provides the findings, and Section 5 concludes the paper.

Theoretical Background and Related Literature

Informal activities are described using a wide range of terms, including “shadow,” “hidden,” “underground,” and “unregulated” (Schneider & Bajada, 2018; Williams & Martinez, 2014). These activities were defined by Smith et al. (1985) as all transactions that circumvent or evade government regulation, taxation, and compliance. To this insight, Williams and Martinez (2014) added that informal employment is “unregistered by, or hidden from the state for tax or benefit purposes.” More broadly, the literature explains the existence of informal employment using two opposing theories. Comparative advantage theory defines informal employment as a voluntary choice of workers based on income or utility maximization (Smith, 1776). In contrast, segmentation theory regards informal employment as the last resort to escape involuntary unemployment (Piore, 1979). The opposition between these theories has been found in several empirical studies to be a valid avenue in which to compare informal employment in developed and developing countries (Gindling, 1991; Magnac, 1991; Pratap & Quintin, 2006). For instance, the informal sector seems legitimate in the latter, but it is regarded as a hidden and illegal endeavor in the former (Schneider & Bajada, 2018).

The significance and nature of the informal sector and informal employment have varied across different countries. The more significant the difference in labor costs between the formal and informal sectors, the higher the labor supply in the IE because of the greater tax burdens encountered in the formal economy (Buehn & Schneider, 2012; Webb et al., 2013).

The factors affecting the size of an IE have been widely studied over the past decades. The expansion of this economy, for example, can be significantly influenced by the quality of public institutions (Buehn & Schneider, 2012; Luong et al., 2020; Nguyen & Duong, 2021), the sizes of governments and tax burdens (Webb et al., 2013), and unemployment and self-employment (Blanton & Peksen, 2019; Davidescu, 2014; Hassan & Schneider, 2016). According to Dell’Anno et al. (2007) and Hassan and Schneider (2016), IE can be positively and significantly affected by self-employment. Davidescu and Dobre (2012) confirmed a causal association between the unemployment rate and the shadow economy in the US, while Tran (2021) confirmed unemployment as an essential driver of increased shadow economy activities in ASEAN countries by using DOLS and FMOLS. By contrast, Davidescu’s (2014) autoregressive distributed lag (ARDL) and SVAR analyses uncovered no long-run relationship between unemployment and the shadow economy in Romania.

As can be seen, various studies have examined the relationship between unemployment and self-employment and the magnitude of the IE, but little is known about how the volume of IE activities can affect employment, unemployment, and self-employment rates. The informal sector is an untapped reservoir of opportunities for employment and entrepreneurial capabilities and a measure against unemployment, particularly in developing countries (Charlot et al., 2015; Khuong et al., 2021). Its contribution to the labor market must therefore be evaluated. Note that this contribution may vary across genders because of differences in physical and educational abilities, social limitations, and differences in entry barriers into formal and informal sectors between males and females (Djajić & Mesnard, 2015; Otobe, 2017; Sengupta et al., 2013). Extant scholarship has not addressed how IE size can provide opportunities to men and women in the world of work.

Correspondingly, this study derived empirical evidence of the relationship of IE with employment, unemployment, and self-employment in the context of Middle Eastern countries. To this end, the following hypotheses were formulated:

Hypothesis 1:

There is a significant long-run relationship between the size of the IE and employment, but this relationship differs across genders.

Hypothesis 2:

There is a significant long-run relationship between the size of the IE and unemployment, but this link differs across genders.

Hypothesis 3:

There is a significant long-run relationship between the size of the IE and self-employment, but this association differs across genders.

Data and Methodology

Theoretical Setting

Previous empirical studies examined the macroeconomic relationship among economic growth, unemployment, employment rates, and the IE (Davidescu, 2015; Dell’Anno & Solomon, 2008; Saget, 2000). The IE is constituted by all economic activities that are deliberately hidden from official authorities. These activities and the incomes earned thus circumvent government regulation, taxation, or observation (Medina & Schneider, 2018). In the present research, the definition of IE excludes criminal, household, or charitable activities and reflects mainly productive economic activities that contribute to the GDP.

Over the past decades, various studies have investigated the macroeconomic relationship between formal and informal economies and employment, but few seem to consistently have a strong influence across different explorations. According to Okun’s law, the growth of the formal economy is positively related to employment level, but the effect of the IE on employment is ambiguous (Giles and Tedds, 2002; Dell'Anno & Solomon, 2008; Bajada & Schneider, 2009; Hassan & Schneider, 2016). On the one hand, the shadow economy can be positively related to economic growth and, thus, employment. On the other hand, it might also be negatively associated with employment given that it tends to reduce the number of individuals working in the informal economy (Dell’Anno et al., 2007). Furthermore, this relationship may vary across genders because of differences in education levels, social restrictions, and entry barriers (Goel & Saunoris, 2017; Sengupta et al., 2013).

This study’s primary focus and contribution lie in determining the relationship between the IE and labor market variables (employment, unemployment, and self-employment), especially with respect to any underlying gender differences. The estimation equation for labor market variables in the i^th country is a function of IE size and other controls (CV). Accordingly, the following functional relationship is proposed:

{LMV}_{i, g} = f (IEi, CVi)

(1)

i = 1,2,3, \dots

g = male, female

where LMV_i stands for the labor market variables in relation to gender, IE denotes the size of the informal or shadow economy, and CV is a control variable.

Following the literature and accounting for other important factors that affect labor market indicators, we used the GDP, government size (GOV), and the trade (TRADE) sector as control variables (Aydiner‐Avsar & Onaran, 2010; Fethi et al., 2013; Schneider, 2011). Because employment grows in accordance with the GDP, income level needed to be added as a primary determinant to equation (2). The government is also responsible for employment both in the public and private sectors as far as macroeconomic policies are concerned. Correspondingly, government spending was incorporated into equation (2) to represent the role of the government. Finally, trade activities (exports + imports) determine employment and were thus included in equation (2).

Therefore, equation (1) can be written in the form of a double logarithmic regression to obtain the growth effects of regressors on a regressand:

{l n L M V}_{i, g}_{t} = β_{0} + β_{1} ({l n I E}_{i, t}) + β_{2} ({l n G D P}_{i, t}) + β_{3} ({l n G O V}_{i, t}) + β_{4} ({l n T R A D E}_{i, t}) + ε_{i, t}

(2)

Equation (2) might not be adjusted to a long-run equilibrium path, thus requiring the estimation of the error correction mechanism to observe how fast the dependent variables in equation (1) converge toward their long-run equilibrium levels. The following error correction model (ECM) is then put forward:

{Δ l n L M V}_{i, g}_{t} = β_{0} + β_{1} (Δ {n I E}_{i, t}) + β_{2} ({Δ l n G D P}_{i, t}) + β_{3} (Δ {l n G O V}_{i, t}) + β_{4} ({Δ l n T R A D E}_{i, t}) + {β_{4} ({Δ ε}_{i, t - 1}) + µ}_{t}

(3)

where Δ is the first difference operator of a logarithmic series, ε_t-1 is the one-lagged error correction term (ECT) standing for the speed of adjustment between the long- and short-run values of the dependent variable, and µ_t represents the error disturbance of the ECM.

Data

The data used in this study were quarterly figures spanning 1991:Q1 to 2015:Q4, as converted from annual statistics. Owing to data being available beginning from 1991, all the datasets were transformed into quarterly figures using the quadratic functions in EViews. Employment data by gender were obtained from the World Bank (2022), and such data were represented by four proxies: (1) female wage and salaried workers (% of female employment), (2) male wage and salaried workers (% of male employment), (3) self-employed females (% of female employment), and (4) self-employed males (% of male employment). Female unemployment (% of the female labor force) and male unemployment (% of the male labor force) were used as dependent variables for purposes of comparison. Data on the control variables—the GDP (at constant 2010 USD prices), the final consumption expenditure of the general government (at constant 2010 USD prices), and trade volume (% of the GDP)—were acquired also from the World Bank (2022). Finally, data on the size of the shadow economy (% of the GDP) were obtained from Medina and Schneider (2018). Given that the data on size covered up to 2015, the data range in this study was fixed to the 1991:Q1 to 2015:Q4 period.

Methodology

To estimate equations (2) and (3), we adopted the ARDL method used by Pesaran et al. (2001). The ARDL approach enables researchers to evaluate models with regressors of mixed integration orders as restricted to I (0) or I (1). However, the prior condition is that the dependent variable in models should be integrated into the first order, I (1), as per Pesaran et al. (2001). Bounds testing of level relationships, the long-run coefficients of equation (2), and the ECTs from equation (3) were estimated on the basis of the Case III option in Pesaran et al.’s (2001) work. This option includes unrestricted trends and unrestricted intercepts.

In the final step, Granger causality tests using the block exogeneity approach, impulse response functions, and variance decomposition analyses were performed as robustness checks of the results obtained via ARDL estimation.

Results

Table 1 presents the descriptive statistics of the series under consideration. As indicated by the statistics, the lowest average employment rates for both genders occurred in Egypt, Iran, and Turkey. The average employment value in the other Middle Eastern countries had no significant differences. Average self-employment was highest in Oman (8.68) and Saudi Arabia (7.80) for males and in Israel (6.82), Oman (6.62), and Turkey (5.48) for females. The IEs in Egypt (3.42), Lebanon (3.15), and Turkey (3.13) were much higher than those in the other Middle Eastern countries. Finally, the average GDP was highest in Kuwait (9.62) and Qatar (9.39) and lowest in Egypt (1.53).

Table 1.

Descriptive Statistics for Countries Aggregates.

Country	EMP_F	EMP_M	Self_F	Self_M	UNEMP_F (%)	UNEMP_M (%)	IE (% of GDP)	GDP (billion USD)	GOV (billion USD)	Trade (% of GDP)
Bahrain
Average	98.072	82.386	1.928	4.371	3.949	.487	19.343	41.8	3.070	151.175
Max	98.899	85.971	3.468	6.558	5.025	.860	22.490	56.0	5.080	193.214
Min	96.531	78.998	1.098	2.233	2.446	.045	16.630	30.9	1.610	111.535
Std.Dev.	7.558	2.459	.756	1.162	.552	.182	1.363	8.03	1.190	21.275
Egypt
Average	53.858	60.844	4.614	3.915	2.213	6.741	34.237	153.0	20.000	49.502
Max	69.017	66.629	5.305	4.351	2.558	9.862	36.850	242.0	31.700	72.892
Min	46.944	56.486	3.098	3.337	1.695	4.206	28.880	87.2	11.500	34.369
Std.Dev.	5.472	2.591	5.472	2.591	2.403	1.593	2.167	5.00	5.200	10.245
Iran
Average	51.920	54.495	4.807	4.550	1.831	9.730	17.880	373.0	47.300	43.561
Max	57.749	56.605	5.767	4.654	2.557	1.201	21.060	503.0	59.000	54.693
Min	42.324	53.455	4.225	4.339	1.328	8.264	14.520	258.0	33.800	28.804
Std.Dev.	4.238	.985	4.238	.985	2.630	.895	2.207	8.51	61.300	6.628
Israel
Average	93.175	84.200	6.824	1.579	1.069	9.490	22.013	188.0	47.300	67.718
Max	94.133	85.349	8.430	1.678	1.731	1.332	25.020	279.0	62.800	82.418
Min	91.569	83.217	5.866	1.465	5.145	4.758	19.180	104.0	37.800	57.331
Std.Dev.	.820	.504	.820	.504	3.127	2.321	1.693	4.91	7.030	7.459
Jordan
Average	96.591	82.806	3.408	1.719	2.429	1.265	17.376	19.100	2.800	121.339
Max	96.681	83.129	3.564	1.739	3.108	1.856	21.120	30.900	5.000	146.841
Min	96.434	82.607	3.318	1.687	1.986	9.985	13.440	8.090	1.220	88.208
Std.Dev.	.062	.123	.063	.123	3.452	2.372	2.661	6.930	1.220	13.429
Kuwait
Average	99.457	96.971	.542	3.028	2.865	.871	19.312	9.62	NA	94.412
Max	99.702	98.792	1.671	4.470	5.275	2.169	22.070	138.0	NA	170.492
Min	98.328	95.530	.297	1.207	1.248	.091	15.710	3.63	NA	80.864
Std.Dev.	.272	.754	.272	.755	1.097	.592	1.758	2.97	NA	11.821
Lebanon
Average	83.331	56.230	1.666	4.376	1.017	7.097	31.580	2.71	3.630	77.392
Max	85.078	57.329	1.882	4.570	1.108	8.873	36.680	4.25	7.070	107.876
Min	81.174	54.295	1.492	4.267	9.545	4.846	24.630	1.17	8.380	47.661
Std.Dev.	.911	1.053	.910	1.053	.322	1.303	3.538	9.09	1.430	17.597
Oman
Average	93.380	91.312	6.620	8.688	9.881	4.252	19.933	4.55	NA	92.487
Max	95.681	96.550	1.016	1.461	1.343	5.430	23.910	7.07	NA	129.240
Min	89.832	85.389	4.318	3.440	7.594	2.080	15.520	2.69	NA	76.531
Std.Dev.	1.754	4.344	1.754	4.345	1.574	.911	2.113	1.15	NA	14.817
Qatar
Average	99.798	98.870	.201	1.129	3.672	.971	15.928	9.39	NA	91.332
Max	99.978	99.571	.526	1.595	6.221	2.484	19.000	169.0	NA	106.299
Min	99.463	98.414	.021	.428	.857	.058	12.150	3.58	NA	72.385
Std.Dev.	.185	.413	.184	.413	1.500	.766	2.057	4.83	NA	7.068
Saudi Arabia
Average	97.806	92.196	2.193	7.802	1.242	4.540	16.650	451.0	NA	74.578
Max	98.826	94.685	2.869	8.820	2.184	7.566	19.150	690.0	NA	97.588
Min	97.131	91.179	1.173	5.298	4.919	2.288	13.340	329.0	NA	55.051
Std.Dev.	.447	1.052	.447	1.052	5.555	1.519	2.009	108.0	NA	11.188
Turkey
Average	45.144	60.065	5.485	3.993	9.196	8.827	31.377	632.0	87.300	45.376
Max	62.199	69.677	7.054	4.821	1.281	1.273	35.990	1110.0	147.000	56.173
Min	29.451	51.784	3.780	3.032	5.841	6.413	27.330	363.0	48.500	30.007
Std.Dev.	9.384	5.062	9.384	5.062	1.912	1.492	2.672	208.0	29.500	6.499
UAE
Average	98.786	98.786	1.213	3.529	4.301	1.996	26.537	234.0	NA	136.562
Max	99.065	99.065	1.331	4.635	7.280	2.736	28.810	380.0	NA	178.513
Min	98.668	98.668	.934	3.156	2.095	1.264	22.020	125.0	NA	89.651
Std.Dev.	.113	.113	.113	.445	1.728	.369	1.963	7.59	NA	27.443

Note. NA, Not available.

Table 2, which presents the results of the Phillips & Perron (1988) unit root tests, shows that the regressors in equation (1) were of a mixed order of integration, whereas the dependent variables were integrated in the same order at I (1). Having the former in equation (1) enabled us to continue with the ARDL to detect level relationships in the equation and estimate long-run coefficients.

Table 2.

PP (1988) Unit Root Tests.

Country/Dependent var.	lnEMP_F	lnEMP_M	lnSelf_F	lnSelf_M	lnUnemp_F	LnUnemp_M	lnIE	lnGDP	lnGOV	lnTrade
Bahrain
PP (Level)	−2.105	−2.013	−2.365	−1.925	−2.274	−2.116	−1.447	−2.582	−2.017	−2.248
PP (1^st diff)	−5.402***	−4.459***	−5.752***	−5.655***	−5.232***	−5.517***	−3.671**	−5.458***	−3.773**	−4.797***
Egypt
PP (Level)	−2.267	−2.678	−2.295	−2.632	−3.387*	−1.907	−1.846	−2.214	−1.975	−1.281
PP (1^st diff)	−5.358***	−5.036***	−5.293***	−4.944***	−5.508***	−5.265***	−4.346***	−3.636**	−3.823**	−3.987**
Iran
PP (Level)	−.772	−1.726	.751	−1.680	−2.820	−2.561	−.482	−.949	−2.273	−1.776
PP (1^st diff)	−4.674***	−3.286*	−4.126***	−3.313*	−4.763***	−5.301***	−4.062***	−4.590***	−5.282***	−4.741***
Israel
PP (Level)	−2.812	−2.565	−.048	−.624	−1.300	−1.053	−3.162*	−3.167*	−1.466	−1.050
PP (1^st diff)	−5.649***	−5.022***	−3.741**	−3.695**	−5.096***	−4.459***	−6.550***	−4.825***	−4.578***	−5.108***
Jordan
PP (Level)	−1.524	−2.146	−1.505	−2.138	−1.479	−1.809	−1.309	−3.223*	−1.553	−1.344
PP (1^st diff)	−5.131***	−5.161***	−5.145***	−5.153***	−5.153***	−4.032**	−3.986**	−6.421***	−5.335***	−4.285***
Kuwait
PP (Level)	−2.530	−2.230	−2.288	−1.659	−2.743	−3.326*	−2.155	−4.233***	-	−7.675***
PP (1^st diff)	−4.958***	−3.968**	−4.275***	−3.431*	−4.131***	−5.882***	−4.869***	−5.742***	-	−4.680***
Lebanon
PP (Level)	−1.908	−.406	−1.831	−.468	−3.422*	−2.136	−1.623	−3.303*	−4.436***	−1.938
PP (1^st diff)	−3.031	−4.247***	−3.023	−4.193***	−5.314***	−4.059***	−4.354***	−4.042**	−6.057***	−4.897***
Oman
PP (Level)	−1.438	−.679	−1.401	−1.592	−.710	−1.527	.800	−1.997	-	−2.291
PP (1^st diff)	−2.741	−2.336	−2.477	−2.159	−4.790***	−4.623***	−4.625***	−4.696***	-	−5.542***
Qatar
PP (Level)	−1.462	−1.716	−1.769	−1.404	−2.461	2.715	−1.871	−.933	-	−3.231*
PP (1^st diff)	−5.677***	−3.576**	−6.061***	−3.264*	−3.333*	−3.877**	−5.341***	−3.683**	-	−4.524***
Saudi Arabia
PP (Level)	−.820	−.049	−.793	.889	−1.921	−1.617	−1.055	−1.362	-	−1.522
PP (1^st diff)	−4.296***	−4.213***	−4.353***	−3.653**	−5.438***	−4.166***	−5.043***	−5.453***	-	−4.294**
Turkey
PP (Level)	−1.739	−2.219	−1.714	−.830	−2.539	−2.275	−2.900	−1.872	−2.792	−2.620
PP (1^st diff)	−5.108***	−5.809***	−4.062***	−5.761***	−5.433***	−4.757***	−5.820***	−5.391***	−5.712***	−5.056***
United Arab Emirates
PP (Level)	−.897	−.897	−.979	−.897	−.907	−1.179	−1.903	−1.919	-	−1.679
PP (1^st diff)	−3.746**	−3.746**	−3.708	−2.835	−3.214*	−3.949**	−4.078***	−5.005***	-	−3.915**

Note. *, **, and *** denote statistical significance at .01, .05, and .10 levels, respectively.

Table 3 presents the empirical results of the bounds F-tests and estimations of the long-run coefficients in equation (1) with diagnostic test results and ECTs. The main focus was on the relative signs and magnitudes of employment, self-employment, and unemployment coefficients across genders. The empirical results indicated long-run interactions between female employment and IE size in Bahrain, Israel, Jordan, and Turkey. IE size contributed negatively to female employment in Bahrain, Jordan, and Turkey but positively and significantly influenced such employment in Israel. In the other Middle Eastern countries, no cointegration or long-term relationships were found between female employment and IE. The results concerning males showed that IE size positively correlated with employment in Bahrain, Iran, Qatar, and Turkey but negatively contributed to such employment in Egypt and Jordan.

Table 3.

The ARDL Results.

Country/Dependent var.	lnIE	lnGDP	lnGOV	lnTrade	Adj. R²	Std.Err.	F-stat	DW	Bounds F stat.	ECT_t-1	χ²-Prob.
Bahrain
Female emp.	−.058*	.045*	.029*	.000	.681	.000	12.216*	1.762	11.804c	−.443*	.245
Male emp.	.073*	.095*	−.099***	.010	.581	.001	22.849*	2.075	4.621c	−.201*	.391
Self_Female	2.453*	−2.045*	−1.990**	.438***	.566	.048	8.356*	1.848	7.018c	−.463*	.669
Self_Male	−2.650*	1.387*	1.690*	−.024	.745	.025	16.394*	1.520	14.952c	−.335*	.284
Unemp_Female	−1.870*	−1.566*	3.908*	−.760*	.940	.010	52.726*	1.810	12.590c	−.521*	.446
Unemp_Male	−7.716*	−9.010*	5.709*	−2.957*	.945	.053	66.630*	1.546	39.850c	−.247*	.186
Egypt
Female emp.	−.060	1.166*	1.849*	−.207*	.547	.017	13.770*	1.953	7.994c	−.350*	.410
Male emp.	−.311**	−.504*	.020	−.049	.532	.005	16.620*	2.086	7.440c	−.188*	.385
Self_Female	−.005	−1.726*	−2.576*	.226*	.547	.024	13.771*	1.949	8.400c	−.345*	.549
Self_Male	.568**	.874*	−.033	.088***	.548	.008	17.682*	2.085	7.641c	−.186*	.408
Unemp_Female	.746	−.514	1.321**	.280**	.529	.027	11.677*	1.944	6.565c	−.307*	.672
Unemp_Male	-	-	-	-	-	-	-	-	2.027a	-	-
Iran
Female emp.	-	-	-	-	-	-	-	-	.904a	-	-
Male emp.	.207*	.289*	−.104*	.046*	.869	.000	15.490*	2.173	6.613c	−.155*	.227
Self_Female	-	-	-	-	-	-	-	-	.997a	-	-
Self_Male	−.240*	−.374*	.132*	−.053**	.862	.000	15.641*	2.235	5.822c	−.130*	.572
Unemp_Female	.583	.590	−.977*	.913*	.933	.008	27.043*	2.183	15.176c	−.381*	.266
Unemp_Male	-	-	-	-	-	-	-	-	1.955a	-	-
Israel
Female emp.	.031**	.030**	−.026***	.002	.493	.000	9.413*	2.122	5.828c	−.277*	.235
Male emp.	.045	.052	.258*	.041*	.623	.001	14.085*	2.152	5.071c	−.161*	.123
Self_Female	−.322**	−.344**	.346***	−.001	.518	.009	10.287*	2.136	6.606c	−.305*	.145
Self_Male	−.242	−.282	−1.373*	−.218*	.623	.005	14.096*	2.150	4.932c	−.158*	.128
Unemp_Female	-	-	-	-	-	-	-	-	1.802a	-	-
Unemp_Male	-	-	-	-	-	-	-	-	1.771a	-	-
Jordan
Female emp.	−.009**	−.008	−.006	−.001	.679	.000	4.930*	2.012	5.666c	−.145*	.433
Male emp.	−.048**	−.004	−.010**	−.040**	.806	.000	8.894*	2.095	6.786c	−.195*	.260
Self_Female	.315**	.316**	.028	.173	.806	.000	8.593*	2.125	5.304c	−.166*	.242
Self_Male	.234**	.021	.049**	.193**	.805	.000	8.824*	2.097	6.756c	−.195*	.255
Unemp_Female	.345	2.448*	−.825*	−.395***	.561	.017	12.072*	2.032	4.741c	−.200*	.425
Unemp_Male	1.262	−4.803*	.738**	−.422	.945	.005	36.179*	2.010	27.228c	−.106*	.572
Kuwait
Female emp.	-	-	-	-	-	-	-	-	2.854a	-	-
Male emp.	-	-	-	-	-	-	-	-	2.723a	-	-
Self_Female	3.352*	3.556	-	−3.199	.860	.031	19.982*	2.378	4.566c	−.093*	.390
Self_Male	-	-	-	-	-	-	-	-	2.485a	-	-
Unemp_Female	1.590**	2.360*	-	−.473	.654	.023	26.180*	2.019	5.656c	−.130*	.921
Unemp_Male	-	-	-	-	-	-	-	-	1.330a	-	-
Lebanon
Female emp.	−.042	−.007	−.003	−.046*	.818	.000	9.059*	2.020	6.460c	−.041*	.900
Male emp.	-	-	-	-	-	-	-	-	1.473a	-	-
Self_Female	.345	.145	.042	.379*	.828	.002	7.281*	1.995	7.376c	−.041*	.876
Self_Male	-	-	-	-	-	-	-	-	1.492a	-	-
Unemp_Female	.181	.500*	−.125*	.052	.651	.003	26.687*	2.007	6.583c	−.110*	.911
Unemp_Male	-	-	-	-	-	-	-	-	1.998a	-	-
Oman
Female emp.	-	-	-	-	-	-	-	-	2.162a	-	-
Male emp.	-	-	-	-	-	-	-	-	3.192a	-	-
Self_Female	-	-	-	-	-	-	-	-	2.488a	-	-
Self_Male	.480**	2.533*	-	.888*	.973	.002	9.783*	2.047	6.216c	−.046*	.840
Unemp_Female	-	-	-	-	-	-	-	-	3.567a	-	-
Unemp_Male	−1.615*	−.603	-	−.782*	.869	.009	9.325*	2.227	5.761c	−.051*	.223
Qatar
Female emp.	-	-	-	-	-	-	-	-	2.656a	-	-
Male emp.	.009*	.008*	-	.007*	.899	.000	6.872*	2.062	6.131c	−.135*	.760
Self_Female	−2.789**	1.564*	-	−1.815***	.919	.032	10.143*	2.120	5.219c	−.155*	.593
Self_Male	−1.229**	−.643*	-	−.965**	.887	.004	17.737*	2.199	8.655c	−.220*	.830
Unemp_Female	-	-	-	-	-	-	-	-	1.059a	-	-
Unemp_Male	−1.279	−2.454*	-	−2.931	.692	.043	24.219*	2.031	7.172c	−.099*	.742
Saudi Arabia
Female emp.	-	-	-	-	-	-	-	-	2.260a	-	-
Male emp.	−.026	.032**	-	−.017**	.966	.000	9.586*	2.109	5.834c	−.030*	.352
Self_Female	-	-	-	-	-	-	-	-	3.014a	-	-
Self_Male	.238	−.476**	-	.174***	.963	.001	16.303*	2.070	6.055c	−.022*	.140
Unemp_Female	-	-	-	-	-	-	-	-	2.358a	-	-
Unemp_Male	−.235	−.438	-	.966*	.826	.011	13.818*	2.174	5.374c	−.097*	.364
Turkey
Female emp.	−1.595**	−.441	−1.810**	−.054	.697	.005	25.340*	1.915	7.898c	−.051*	.612
Male emp.	.183*	.235*	.112*	.051*	.934	.000	36.424*	2.200	6.783c	−.396*	.676
Self_Female	1.087**	.157*	.965*	.274**	.856	.002	12.833*	2.130	6.512c	−.166*	.320
Self_Male	-	-	-	-	-	-	-	-	2.514a	-	-
Unemp_Female	3.745*	1.856**	−3.012*	−.077	.379	.040	15.704*	2.012	4.308c	−.198*	.113
Unemp_Male	7.059*	1.404*	−5.914*	−.243	.899	.014	19.577*	2.165	8.613c	−.275*	.116
United Arab Emirates
Female emp.	-	-	-	-	-	-	-	-	2.098a	-	-
Male emp.	-	-	-	-	-	-	-	-	2.163a	-	-
Self_Female	-	-	-	-	-	-	-	-	1.092a	-	-
Self_Male	1.307	−8.641**	-	2.570**	.842	.006	53.415*	2.063	4.702c	−.027*	.598
Unemp_Female	-	-	-	-	-	-	-	-	1.783a	-	-
Unemp_Male	−.481	−5.755*	-	3.266*	.837	.015	30.938*	2.275	8.716c	−.104*	.272

Note. *, **, and *** denote statistical significance at .01, .05, and .10 levels, respectively. Bounds F-stat is for the cointegration test in the case of each model setting. ECT_t-1 is one lagged error correction term for the speed of adjustment. χ²-Prob. Stands for the probability value of chi-square statistic for serial correlation test using Lagrange Multiplier approach. Blank cells indicate that cointegration cannot be confirmed in the related model settings; thus, long-term estimations cannot be generated.

Consistent with Hypothesis 1, our findings showed that the contribution of IE to employment varied across genders. For instance, a 1% increase in IE size raised male employment in Bahrain by .073%, but it also reduced female employment by .058%. This phenomenon might have arisen from differences in social and cultural limitations on work for men and women across countries, differences in the physical and educational abilities of men and women, differences in entry barriers into formal sectors, and differences in networks across genders (ILO, 2013; Sengupta et al., 2013).

Our study generated mixed results for genders and countries. In some studies, unemployment is positively related to IE size (Bajada & Schneider, 2005; Goel & Saunoris, 2017; Saafi et al., 2015). In the present study, a significant and positive long-run relationship existed between IE and female unemployment in Kuwait and Turkey. The size of the IE was negatively correlated with male unemployment in Bahrain and Oman and positively correlated with male employment in Turkey.

In developing countries, 72% of all workers are self-employed, but among this labor force, the share of informal employment is roughly equal between men and women (93%) (Bonnet et al., 2019). Our findings revealed a cointegration and long-term relationship between female self-employment and IE. Consistent with Hypothesis 3, IE size was positively correlated with female self-employment in Bahrain, Jordan, Kuwait, and Turkey and negatively correlated in Israel and Qatar. The size of the shadow economy was positively correlated with male self-employment in Egypt, Jordan, and Oman.

The Granger causality test results are presented in Table 4. Considerable evidence of bi-directionality in Qatar and Turkey was found, indicating that a high level of IE leads to increased employment and vice versa. In the case of female employment, unidirectional causalities ran from female employment to IE size in Iran, Israel, Jordan, and the UAE, but only in Turkey did unidirectional causality run from male employment to the IE. This finding implies that a faster employment rate promotes a higher share of IE contribution to the total GDP. It might be worth noting that the only country where unidirectional causality ran from the IE to employment was Iran for male employment.

Table 4.

Granger Causality Tests.

Country	EMPf → IE	IE → EMPf	EMPm→ IE	IE → EMPm	Conclusion
Country	EMPf → IE	IE → EMPf	EMPm→ IE	IE → EMPm	Female	Male
Bahrain	4.362	2.658	.093	4.305	-	-
Egypt	.634	.540	1.704	2.172	-	-
Iran	8.723**	1.152	.818	5.836***	EMP → IE	IE → EMP
Israel	9.679*	3.402	.555	2.873	EMP → IE	-
Jordan	5.122***	.094	2.159	2.778	EMP → IE	-
Kuwait	.206	.603	1.074	.290	-	-
Lebanon	2.332	1.403	2.599	1.079	-	-
Oman	1.266	.634	1.593	1.433	-	-
Qatar	10.449*	6.927**	7.705**	8.274**	EMP ⇔ IE	EMP ⇔ IE
Saudi Arabia	2.622	7.023**	3.432	2.622	IE → EMP	-
Turkey	7.080**	6.212**	12.753*	1.394	EMP ⇔ IE	EMP → IE
UAE	5.932**	2.872	1.072	2.075	EMP → IE	-

Note. *, **, and *** denote statistical significance at .01, .05, and .10 levels, respectively.

The results regarding impulse response functions (Figure 1) reflected significant responses of both male and female employment to shocks in the volume of IE activities when responses were generally negative over the studied periods. Finally, the variance decompositions (Table 5) showed that the forecast variances in employment levels across countries were typically explained at low levels by variations in IEs of slightly less than 20%.

Table 5.

Variance Decompositions.

Bahrain			Egypt		Iran
Period	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE
1	.000000	.000000	.000000	.000000	.000000	.000000
2	.426523	.043118	.060334	.101931	.029044	.083586
3	1.415451	.262171	.201287	.391132	.116824	.164052
4	2.821119	.813719	.408719	.888410	.273934	.178348
5	4.496362	1.844540	.669115	1.595897	.504056	.138741
6	6.312279	3.454399	.968577	2.496129	.804319	.104733
7	8.168092	5.672132	1.293146	3.555894	1.166890	.167114
8	9.991625	8.451371	1.629475	4.732401	1.580903	.435727
9	11.73503	11.68501	1.965591	5.979825	2.034212	1.027471
10	13.36921	15.23101	2.291510	7.254769	2.514718	2.053131

Israel			Jordan		Kuwait
Period	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE
1	.000000	.000000	.000000	.000000	.000000	.000000
2	.095989	.003349	.013978	.427081	.055891	.020148
3	.542258	.046588	.041939	1.253738	.165172	.066756
4	1.473019	.192257	.076233	2.231347	.294049	.134125
5	2.840935	.503685	.111311	3.190312	.416441	.217615
6	4.468875	1.029946	.143447	4.028885	.514042	.313117
7	6.148825	1.794565	.170503	4.699421	.577513	.416960
8	7.716959	2.789669	.191591	5.192454	.606910	.525877
9	9.080639	3.977944	.206714	5.522212	.610540	.637000
10	10.21030	5.301997	.216436	5.715336	.602107	.747844

Lebanon			Oman		Qatar
Period	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE
1	.000000	.000000	.000000	.000000	.000000	.000000
2	.178064	.116783	.003962	.083373	.485736	.731190
3	.540289	.323740	.004982	.286581	1.775021	2.469244
4	.999814	.544307	.002844	.596945	3.794477	4.956385
5	1.500741	.735794	.004119	1.006292	6.353840	7.915703
6	2.006649	.877709	.018414	1.508105	9.222581	11.09265
7	2.494551	.964460	.056329	2.096669	12.19099	14.28516
8	2.950708	.999830	.128282	2.766651	15.09972	17.35249
9	3.367649	.992794	.243845	3.512877	17.84431	20.20920
10	3.742060	.954512	.411386	4.330207	20.36666	22.81290

Saudi Arabia			Turkey		UAE
Period	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE	% of the forecast error variance of EMP_f explained by the changes in IE	% of the forecast error variance of EMP_m explained by the changes in IE
1	.000000	.000000	.000000	.000000	.000000	.000000
2	.624638	.021802	.257294	.035743	.003686	.005257
3	1.579511	.017614	1.230338	.208744	.052463	.046363
4	2.442980	.015847	3.096748	.582952	.564780	.165798
5	3.056208	.090297	5.741312	1.154674	.365749	.401827
6	3.392654	.316699	8.852490	1.865650	.854976	.780037
7	3.487590	.751167	12.08375	2.636116	1.425789	1.310503
8	3.400090	1.422655	15.16620	3.393772	1.125467	1.988999
9	3.193577	2.334161	17.94248	4.088552	1.897456	2.800433
10	2.926623	3.468458	20.35009	4.694631	1.457896	3.722958

Conclusion

To contribute to the body of research on the influence of certain factors on labor market variables, this study addressed the possible long-run relationship between IE size and employment, unemployment, and self-employment rates across genders in 12 Middle Eastern countries. Despite the vast amount of research on the determinants of employment and unemployment and gender differences in the labor market in recent years (Adenutsi, 2014; Folawewo & Adeboje, 2017; Pattanaik & Nayak, 2014), little is known about the impact of the size of an IE—as the primary source of employment—on labor market indicators.

Our results showed that the IE served men and women differently in the investigated Middle Eastern countries. Except for Israel, there was no cointegration or positive long-term relationship between female employment and IE size. Gender segregation in occupations and, in some Muslim countries, sexual segregation, better professional networks for men, and the economic structures of most Middle Eastern countries that heavily rely on oil eliminate the significant contribution of IE to female employment. With regard to self-employment, a significant positive long-run relationship between IE size and female self-employment was found in Turkey, Bahrain, Jordan, and Kuwait. This result held true for men in Egypt, Jordan, and Oman.

The literature on the relationship between unemployment and the shadow economy broadly concentrates on the contribution of unemployment level to the sizes of IEs (Bajada & Schneider, 2005; Manléon & Sardà, 2017). The opposite was reflected in our findings. Except for Bahrain, no cointegration or negative long-term relationship was found between unemployment and IE size for women.

The fact that the IE served men and women differently aligns with our hypothesis. National employment policies that increase employment rates could benefit from paying greater attention to gender equality principles and the formalization of the IE. An action plan is needed to promote more decent and stable jobs in the underground economy and facilitate its transformation into a formal sector.

Entrepreneurship development policies should be directed toward an effective housing program. Such a program and infrastructural services, such as electricity, power, and water for enterprises, are expected to advance the growth of IE enterprises, perhaps more strongly than formal sector corporations. Furthermore, non-governmental and community-based organizations primarily operate in the informal sector. Policies should enable non-formal organizations to play a more significant role in developing community-based work.

Our results regarding the influence of IE size on improved labor market indicators showed that the IE served men better than women in the examined Middle Eastern countries. Policy recommendations should consider the gender division of labor and gender-related issues in this region.

Limitations and Suggestions for Further Research

The limitations of this study are worth noting. Given that the World Bank data did not provide sufficient information about unregistered employment and self-employment, future work on the issue of interest should consider women that are unaccounted for in the labor markets of formal and informal economies. Furthermore, a micro-level or qualitative study might help shed light on how the labor market provides different employment opportunities to genders in the IE.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Salih Katircioğlu

References

Adenutsi

D. E.

(2014). Macroeconomic determinants of workers' remittances and compensation of employees in sub-Saharan Africa. The Journal of Developing Areas, 48(1), 337–360. https://doi.org/10.1353/jda.2014.0015

Allen

W. D.

Curington

W. P.

(2014). The self-employment of men and women: What are their motivations? Journal of Labor Research, 35(2), 143–161. https://doi.org/10.1007/s12122-014-9176-6

Aydiner‐Avsar

Onaran

(2010). The determinants of employment: A sectoral analysis for Turkey. The Developing Economies. Institute of Developing Economies, 48(2), 203–231.

Bahramitash

Kazemipour

(2011). Veiled economy: Gender and the informal sector. In Veiled employment: Islamism and the political economy of women’s employment in Iran (pp. 226–255). Syracuse University Press.

Bajada

Schneider

(2005). The shadow economies of the Asia-Pacific. Pacific Economic Review, 10(3), 379–401. https://doi.org/10.1111/j.1468-0106.2005.00280.x

Bajada

Schneider

(2009). Unemployment and the shadow economy in the OECD. Revue Économique, 60(5), 1033–1067. https://doi.org/10.3917/reco.605.1033

Blanton

R. G.

Peksen

(2019). Labor laws, and shadow economies: A cross-national assessment. Social Science Quarterly, 100(5), 1540–1565. https://doi.org/10.1111/ssqu.12685

Bonnet

Vanek

Chen

(2019). Women and men in the informal economy – a statistical brief. WIEGO.

Bosch

Esteban-Pretel

(2015). The labor market effects of introducing unemployment benefits in an economy with high informality. European Economic Review, 75, 1–17. https://doi.org/10.1016/j.euroecorev.2014.10.010

10.

Buehn

Schneider

(2012). Shadow economies around the world: Novel insights, accepted knowledge, and new estimates. International Tax and Public Finance, 19(1), 139–171. https://doi.org/10.1007/s10797-011-9187-7

11.

Chant

Pedwell

(2008). Women, gender and the informal economy: An assessment of ILO research and suggested ways forward. International Labour Organization.

12.

Charlot

Malherbe

Terra

(2015). Informality in developing economies: Regulation and fiscal policies. Journal of Economic Dynamics and Control, 51, 1–27. https://doi.org/10.1016/j.jedc.2014.09.031

13.

Chen

Vanek

Heintz

(2006). Informality, gender, and poverty: A global picture. Economic and Political Weekly, 41(21), 2131–2139. https://doi.org/10.2307/4418269

14.

Davidescu

A. A.

(2014). Investigating the impact of unemployment rate on the Romanian shadow economy. A complex approach based on ARDL and SVAR analysis. Romanian Journal of Economic Forecasting, 4(1), 109–127.

15.

Davidescu

A. A.

(2015). The relationship between shadow economy and unemployment rate. An ARDL causality analysis for the case of Romania. Romanian Statistical Review, National Institute of Statistics, Romania, 4(4), 46–62.

16.

Davidescu

A. A.

Dobre

(2012). The Causal relationship between the unemployment rate and US shadow economy. A Toda-Yamamoto approach. Journal of Social and Economic Statistics, 1(1), 21–34.

17.

Dell’Anno

Gómez-Antonio

Pardo

(2007). The shadow economy in three mediterranean countries: France, Spain and Greece. A MIMIC approach. Empirical Economics, 33(1), 51–84. https://doi.org/10.1007/s00181-006-0084-3

18.

Dell'Anno

Solomon

O. H.

(2008). Shadow economy and unemployment rate in USA: Is there a structural relationship? An empirical analysis. Applied Economics, 40(19), 2537–2555. https://doi.org/10.1080/00036840600970195

19.

Djajić

Mesnard

(2015). Guest workers in the underground economy. Labour Economics, 35(C), 53–62. https://doi.org/10.1016/j.labeco.2015.04.002

20.

Dunkelberg

W. C.

(1989). Analyzing consumer spending and debt. Business Economics, 24(3), 17–22.

21.

Dunphy

Miller

G. F.

Rice

Sunshine

McCord

Howard-Williams

Coronado

(2022). The impact of COVID-19 state closure orders on consumer spending, employment, and business revenue. Journal of Public Health Management and Practice, 28(1), 43–49. https://doi.org/10.1097/PHH.0000000000001376

22.

Fedotov

D. Y.

Nevzorova

E. N.

(2020). Intersectoral shadow economic linkages and their impact on tax evasion. Journal of Tax Reform, 6(1), 36–53. https://doi.org/10.15826/jtr.2020.6.1.074

23.

Fethi

Katircioğlu

Caglar

(2013). The role of the financial sector in the Turkish Cypriot economy: Evidence from bounds and causality tests. Turkish Studies, 14(3), 540–563.

24.

Folawewo

A. O.

Adeboje

O. M.

(2017). Macroeconomic determinants of unemployment: Empirical evidence from economic community of west African states. African Development Review, 29(2), 197–210. https://doi.org/10.1111/1467-8268.12250

25.

Giles

D. E.

Tedds

L. M.

Werkneh

(2002). The Canadian underground and measured economies: Granger causality results. Applied Economics, 34(18), 2347–2352. https://doi.org/10.1080/00036840210148021

26.

Gindling

T. H.

(1991). Labor market segmentation and the determination of wages in the public, private-formal, and informal sectors in San Jose, Costa Rica. Economic Development and Cultural Change, 39(3), 585–605. https://doi.org/10.1086/451892

27.

Goel

R. K.

Saunoris

J. W.

(2017). Unemployment and international shadow economy: Gender differences. Applied Economics, 49(58), 5828-5840. https://doi.org/10.1080/00036846.2017.1343452

28.

Hassan

Schneider

(2016). Modelling Egyptian shadow economy: A MIMIC model and A currency demand approach. Journal of Economics and Political Economy 2016, 3(2), 309–339. https://doi.org/10.1453/jepe.v3i2.788

29.

Herwartz

Tafenau

Schneider

(2015). One share fits all? Regional variations in the extent of the shadow economy in Europe. Regional Studies, 49(9), 1575–1587. https://doi.org/10.1080/00343404.2013.848034

30.

ILO . (2013). Global Employment Trends 2013: Recovering from a Second Jobs Dip. Geneva: International Labour Office, International Labour Organisation.

31.

ILO . (2018). Women and men in the informal economy: A statistical picture (3rd ed.). International Labour Office.

32.

Khuong

N. V.

Shabbir

M. S.

Sial

M. S.

Khanh

T. H. T.

(2021). Does the informal economy impede economic growth? Evidence from an emerging economy. Journal of Sustainable Finance & Investment, 11(2), 103–122. https://doi.org/10.1080/20430795.2020.1711501

33.

Luong

T. T. H.

Faculty of Finance-Banking Ho Chi Minh City Open University Nguyen

T. M.

Nguyen

T. A. N.

(2020). Rule of law, economic growth and shadow economy in transition countries. The Journal of Asian Finance, Economics and Business, 7(4), 145–154. https://doi.org/10.13106/jafeb.2020.vol7.no4.145

34.

Magnac

(1991). Segmented or competitive labor markets. Econometrica, 59(1), 165–187. https://doi.org/10.2307/2938245

35.

Manléon

Sardà

(2017). Unemployment and the shadow economy. Applied Economics, 37, 3729–3740. https://doi.org/10.1080/00036846.2016.1267844

36.

Medina

Schneider

(2018). Shadow economies around the world: What did we learn over the last 20 years? IMF Working Papers, 18(17), 1. https://doi.org/10.5089/9781484338636.001

37.

Navickas

Juščius

Navickas

(2019). Determinants of shadow economy in Eastern European countries. Scientific Annals of Economics and Business, 66(1), 1–14. https://doi.org/10.2478/saeb-2019-0002

38.

Nguyen

D. V.

Duong

M. T. H.

(2021). Shadow economy, corruption, and economic growth: An analysis of BRICS countries. The Journal of Asian Finance, Economics and Business, 8(4), 665–672.

39.

Otobe

(2017). Gender and the informal economy key challenges and policy response. ILO Working Papers, (994974592902676).

40.

Pattanaik

Nayak

N. C.

(2014). Macroeconomic determinants of employment intensity of growth in India. Margin: The Journal of Applied Economic Research, 8(2), 137–154. https://doi.org/10.1177/0973801013519997

41.

Pesaran

M. H.

Shin

Smith

R. J.

(2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289–326.

42.

Phillips

P. C. B.

Perron

(1988). Testing for a unit root in Time Series Regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335

43.

Piore

M. J.

(1979). Birds of passage: Migrant labor in industrial societies. Cambridge University Press.

44.

Pratap

Quintin

(2006). Are labor markets segmented in developing countries? A semiparametric approach. European Economic Review, 50(7), 1817–1841. https://doi.org/10.1016/j.euroecorev.2005.06.004

45.

Saafi

Farhat

Mohamed

M. B. H.

(2015). Testing the relationships between shadow economy and unemployment: Empirical evidence from linear and nonlinear tests. Studies in Nonlinear Dynamics and Econometrics, 19(5), 585–608. https://doi.org/10.1515/snde-2014-0021

46.

Saget

(2000). Can the level of employment be explained by GDP growth in transition countries? (Theory versus the quality of data). Labour, 14(4), 623–643. https://doi.org/10.1111/1467-9914.00149

47.

Santoso

R. P.

Sriyana

(2020). The effect of idiosyncratic shocks on labor market outcomes of informal households in Indonesia. Jurnal Ekonomi Malaysia, 54(2), 13–27. https://doi.org/10.17576/JEM-2020-5402-2

48.

Schneider

(Ed.). (2011). Handbook on the shadow economy. Cheltenham, UK: Edward Elgar.

49.

Schneider

Bajada

(2018). An international comparison of underground economic activity. Size, causes and consequences of the underground economy (pp. 73–106). Routledge.

50.

Sengupta

Datta

S. K.

Mondal

(2013). Women's entrepreneurial abilities: A study in the Indian informal service sector. Journal of Entrepreneurship, 22(2), 223–243. https://doi.org/10.1177/0971355713490622

51.

Shembavnekar

Halder

P. J.

Santosh

Ravindra

(2019). Economic reforms, labor markets, and formal sector employment: Evidence from India. Indian journal of surgical oncology, 10(1), 31–36. https://doi.org/10.1007/s13193-018-0747-5

52.

Smith

(1776). An inquiry into the nature and causes of the wealth of nations. W. Strahan and T. Cadell.

53.

Smith

J. D.

(1985). Market motives in the informal economy. In Gaertner

w: W.

Wenig

(Eds.), The economics of the shadow economy (15). Studies in Contemporary Economics.

54.

Tanaka

Kato

Tanaka

Morita

Matsuda

Takahashi

Nakahata

Umeda

Hiramatsu

Adachi

Takita

Nakazawa

(2020). Agglomeration economies in the formal and informal sectors: A Bayesian spatial approach. Molecular therapy oncolytics, 18(1), 37–46. https://doi.org/10.1016/j.omto.2020.05.013

55.

Tran

T. K. P.

(2021). Unemployment and shadow economy in ASEAN countries. The Journal of Asian Finance, Economics and Business, 8(11), 41–46. https://doi.org/10.13106/jafeb.2021.vol8.no11.0041

56.

Webb

J. W.

Bruton

G. D.

Tihanyi

Ireland

R. D.

(2013). Research on entrepreneurship in the informal economy: Framing a research agenda. Journal of Business Venturing, 28(5), 598–614. https://doi.org/10.1016/j.jbusvent.2012.05.003

57.

Williams

Martinez

(2014). Is the informal economy an incubator for new enterprise creation? A gender perspective. International Journal of Entrepreneurial Behavior and Research, 20(1), 4–19. https://doi.org/10.1108/ijebr-05-2013-0075

58.

Williams

C. C.

Franic

(2016). Beyond a deterrence approach towards the undeclared economy: Some lessons from Bulgaria. Journal of Balkan and Near Eastern Studies, 18(1), 90–106. https://doi.org/10.1080/19448953.2015.1094269

59.

World Bank (2022). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators (Retrieved at June 20, 2022).

60.

Xaba

Horn

Motala

Singh

(2002). Informal sector in Sub-Saharan Africa. International Labour Organization.