Economic Journal of Emerging Markets, 17.
2025, 95-109 Economic Journal of Emerging Markets Available at https://journal.
id/jep Econ.
Emerg.
Mark.
Informal economy, institutional quality, and socioeconomic conditions in African countries Tolulope Osinubi*.
Munacinga Simatele Department of Economics.
University of Fort Hare.
South Africa * Corresponding Author: oyeneyetolu@yahoo.
Article Info Abstract Article history:
Received 04 November 2024 Accepted 26 February 2025 Published 28 April 2025 Purpose Ai This paper examines the impact of the informal economy and institutional quality on socioeconomic conditions in 35 African countries from 2000 to 2022.
JEL Classification Code:
C23.
E26.
O17
AuthorAos email:
msmatele@ufh.
DOI:
20885/ejem.
Methods Ai The study employs Driscoll-Kraay.
Fully Modified Ordinary Least Squares.
Method of Moments Quantile Regression.
Dynamic Panel Threshold, and Dumitrescu-Hurlin (D-H) Granger noncausality techniques.
Findings Ai The findings indicate that the informal economy significantly worsens socioeconomic conditions, whereas stronger institutional quality, evident in factors such as government stability and corruption control, enhances these outcomes.
A critical institutional quality threshold of 5.
is established, suggesting that countries with institutional quality above this level experience substantial improvements in socioeconomic conditions.
Unidirectional causality from the informal economy to socioeconomic conditions and a bidirectional relationship between institutional quality and socioeconomic outcomes are also noted.
Implication Ai Enhancing institutional quality is essential for promoting economic development and improving overall well-being in African and similar countries.
Addressing institutional weaknesses could enable these countries to exceed the quality threshold and achieve better socioeconomic Originality Ai This research differs from previous ones by investigating the effects of both informality and institutional quality within a threshold framework on socioeconomic situations in African countries.
Furthermore, it includes a socioeconomic conditions index that combines three subcomponents: poverty, unemployment, and consumer confidence.
Additionally, the study employs various measures of institutional quality to explore their differing impacts on socioeconomic conditions.
Keywords Ai informality.
socioeconomic conditions.
Africa Introduction The informal economy in Africa presents a multifaceted challenge intricately tied to the socioeconomic conditions across the continent (Dada et al.
, 2022.
Hart, 2.
Informality, defined as economic activities not regulated by the state, constitutes a significant share of the African economy (Medina & Schneider, 2.
It includes a wide range of activities, from street vending to informal financial services, and has become a vital component of survival for many African families (UNDP, 2.
Despite its prevalence, the informal economy is often characterised by low
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Economic Journal of Emerging Markets, 17.
2025, 95-109 productivity, inadequate social protection, and limited access to finance (UNDP, 2022.
World Bank, 2.
As a result, informality is a complex issue that is both a consequence of and a contributor to African socioeconomic challenges.
The prevalence of informality in Africa can be examined through multiple theoretical The dualistic labor market theory, originating with Lewis .
and extended by Fields .
, proposes that the informal sector acts as a residual labor market, absorbing individuals who cannot secure formal employment.
Similarly, the structural articulation theory, advanced by Castells and Portes .
, attributes informality to economic structures that cannot provide adequate formal employment opportunities (Ajide & Dada, 2024.
In the African context, where economic diversification is limited and formal sectors struggle to absorb a growing labor force, the structural articulation theory offers a compelling explanation for the persistence of informality.
Empirical studies from countries like Kenya and Ghana (Chen, 2012.
Becker, 2.
establish how limited opportunities in the formal sector and economic stagnation contribute to informal activities, supporting the structural articulation argument.
This phenomenon is exacerbated by high levels of underemployment and unemployment, which drive individuals into informal work as a last resort (Gymez & Irewole, 2023.
Ogbonna et al.
, 2.
Furthermore, empirical research in South Africa (Cichello & Rogan, 2.
reveals that the informal sector functions as a buffer for unemployed individuals, reinforcing the dualistic labor market perspective.
A third perspective emanates from the neoliberal approach, which perceives informality as a voluntary choice made by individuals seeking to avoid the constraints of formal regulation (De Soto, 1.
This perspective is also applicable in Africa, where excessive regulation, corruption, and bureaucratic inefficiencies often make formalisation costly and unattractive for small businesses (Canelas, 2019.
Xu et al.
, 2.
Consequently, many entrepreneurs opt to operate informally, where they can avoid taxes and regulatory burdens, even if it means sacrificing certain protections and benefits associated with formal employment (Dada et al.
, 2.
These theories all accentuate the vital role of institutions in shaping informality (Ofori et , 2023.
Ujunwa et al.
, 2.
Institutions, defined as the formal and informal rules governing economic, political, and social interactions (North, 1.
, impact the incentives and constraints faced by economic agents.
Poor institutional qualityAicharacterized by corruption, lack of government accountability, and weak rule of lawAioften leads to higher levels of informality as individuals seek ways to navigate an unpredictable environment (Olaniyi & Odhiambo, 2.
Institutional quality is a key factor in understanding the prevalence of informality (Kranl, 2.
and its impact on socioeconomic outcomes (Fagbemi et al.
, 2.
According to institutional theory (North, 1.
, strong institutions promote trust, reduce transaction costs, and facilitate economic exchanges, encouraging formalization.
On the contrary, weak institutions increase uncertainty and costs associated with formal economic activity, leading to an expansion of the informal economy.
In Africa, where institutional weaknesses are widespread, informality becomes a rational response to the challenges the formal regulatory environment presents.
For instance, in countries with insecure property rights, individuals may prefer to operate informally rather than risk losing assets to corrupt officials or bureaucratic inefficiencies.
Strong institutions create an environment conducive to economic growth, improve access to services, and reduce inequality (North 1990.
Olaniyi & Odhiambo 2.
On the other hand, weak institutions exacerbate inequality and poverty by restraining opportunities and access to resources (Fagbemi & Asongu, 2.
In Africa, weak institutional quality is closely linked to poor socioeconomic conditions, as evidenced by low scores on socioeconomic indicators, widespread poverty, and high unemployment rates (Sarsani, 2011.
Darin-Mattsson, 2017.
Galal, 2024a, 2024.
Studies establish that countries with stronger institutional frameworks tend to have lower levels of informality, higher economic growth rates, and improved socioeconomic outcomes (Olaniyi & Odhiambo, 2024.
Fagbemi et al.
, 2.
For example.
Osinubi et al.
discovered that good governance can help reduce poverty and unemployment, improving overall socioeconomic conditions.
Relatively, countries like Botswana and Rwanda, which have relatively stronger institutions, experience lower levels of informality than countries with weaker institutions, such as South Sudan and Zimbabwe (Bolarinwa & Simatele, 2.
Informal economy, institutional quality, and socioeconomic conditions .
(Osinubi and Simatel.
The nexus between informality and socioeconomic outcomes and between institutions and socioeconomic outcomes has been nonlinear (Bolarinwa & Simatele, 2023.
Ochi et al.
, 2023.
Porta & Shleifer, 2.
This implies that the effect of these factors on socioeconomic outcomes does not follow a simple linear relationship.
Instead, there appears to be a threshold level of institutional quality that countries must reach before they begin to see substantial reductions in informality and improvements in socioeconomic outcomes.
While the general outlook on informality is often pejorative, it is essential to recognise the role of the informal economy as a safety net for people with low incomes.
In many African countries, the informal sector provides livelihoods for millions of people who would otherwise be It provides economic security in environments where formal employment opportunities are scarce and social safety nets are weak or non-existent.
According to Bolarinwa and Simatele .
, the informal economy plays a vital role in poverty reduction in low- and middle-income African nations despite its productivity and income stability limitations.
This dual role of informality, both a consequence of institutional weakness and a source of resilience for vulnerable populations, highlights the issue's complexity and underscores the need for nuanced policy approaches.
Studies by Diallo et al.
and Sahnoun and Abdennadher .
also support the argument that the informal economy contributes to reducing unemployment for many urban poor and rural populations despite the challenges faced by the sector.
Understanding the link between socioeconomic outcomes, institutional quality, and informality in Africa is crucial in this context.
Although significant research has examined the role of institutions, these studies often rely on narrow measures of institutional quality.
For example.
Jamil et al.
and Fagbemi et al.
use limited indicators like governance.
Likewise.
Ochi et al.
Widiastuti et al.
Aby Ndjiy .
, and Shabbir et al.
focus on individual socioeconomic indicators such as employment or poverty.
This paper argues that while these studies provide valuable insights, policy interventions could benefit from understanding a broader range of institutional quality measures and their impact on comprehensive socioeconomic conditions.
This paper aims to investigate the relationship between informality and socioeconomic outcomes on the one hand and between institutional quality and socioeconomic outcomes on the other hand in a threshold framework.
To this end, the paper makes various contributions to the literature.
First, it extends the work of Fagbemi et al.
by using a comprehensive measure of institutional quality, encompassing five indicators: government stability, control of corruption, law and order, democratic accountability, and bureaucratic quality.
This broader approach provides a detailed analysis of how these components impact African socioeconomic conditions, addressing the region's high level of informality (Ajide & Dada, 2024b.
Dada et al.
, 2.
Second, the study examines the distributional effects of informality and institutional quality on socioeconomic conditions using Machado and Silva's .
quantile regression approach, which allows for heterogeneous impacts across different population segments (Olaniyi & Odhiambo, 2.
Third, it identifies the institutional quality threshold necessary to alleviate the adverse effects of informality.
Finally, by considering the reverse causality between informality, institutional quality, and socioeconomic conditions (Bolarinwa & Simatele, 2023.
Pham, 2.
, this study offers a nuanced understanding of the complex interactions that shape the informal economy and its impact on socioeconomic outcomes.
Methods Data This study uses a panel dataset comprising 35 African countries1 from 2000 to 2020, relying on quantitative methods to comprehensively analyze the effects of informality and institutional quality on socioeconomic outcomes.
Data sources include the World Bank, the International Country Risk Guide (ICRG) Database, and national statistical agencies (Elgin et al.
, 2.
Informal output is 1 The 35 African countries include Algeria.
Angola.
Botswana.
Burkina Faso.
Cameroon.
Democratic Republic of Congo.
Republic of Congo.
Cote dAoIvoire.
Egypt.
Ethiopia.
Gabon.
Gambia.
Ghana.
Guinea.
Guinea-Bissau.
Kenya.
Liberia.
Libya.
Madagascar.
Malawi.
Mali.
Morocco.
Mozambique.
Namibia.
Niger.
Nigeria.
Senegal.
Sierra Leone.
South Africa.
Tanzania.
Togo.
Tunisia.
Uganda.
Zambia, and Zimbabwe.
Economic Journal of Emerging Markets, 17.
2025, 95-109 measured as a percentage of official GDP using the Multiple Indicators Multiple Causes (MIMIC) model, following Elgin et al.
Socioeconomic conditions are assessed using an index that measures the risk of socioeconomic dissatisfaction, ranging from 0 .
ighest ris.
to 12 .
owest ris.
An institutional quality index is constructed using five indicators: government stability, control of corruption, law and order, democratic accountability, and bureaucratic quality (Fagbemi et al.
, 2.
These indicators assess the overall institutional environment in each country and its impact on The institutional variables are rescaled from 0 to 10, with higher values indicating stronger institutional quality (Aluko & Ibrahim, 2021.
Tang et al.
, 2.
The institutional variables provide a comprehensive assessment of governance.
Government stability reflects the government's capacity to implement policies effectively, while law and order capture the strength of legal institutions and public adherence to rules.
Democratic accountability measures government responsiveness to citizens, fostering trust and compliance.
Bureaucratic quality minimizes the adverse effects of political instability on public service delivery.
These indicators offer a robust measure of institutional quality, facilitating a detailed analysis of how governance impacts informality and socioeconomic outcomes.
The control variables include GDP .
onstant 2015 US$), inflation, and access to electricity to capture other macroeconomic factors that may influence informality and socioeconomic conditions.
The descriptions of the variables are presented in Table 1.
Table 1: Variables Description Variable Informal Economy Socioeconomic Conditions Control of Corruption Democratic Accountability Law and Order Government Stability Bureaucratic Quality Institutional Quality Index Economic Growth Inflation Electricity Symbol Measurement IFE Multiple indicators multiple causes model-based (MIMIC) estimates of informal output (% of official GDP) SOC AuA measure of the socioeconomic pressures at work in society that could constrain government action or fuel social dissatisfactionAy.
AuThe risk rating assigned is the sum of three subcomponents: Unemployment.
Consumer Confidence, and Poverty and each of them has a maximum score of four points and a minimum score of zero pointAy.
A score of four points equates to very low risk and a score of zero points to very high riskAy.
It is on a scale of 0 -12.
COR
AuA measure of corruption within the political system that is a threat to foreign investment by distorting the economic and financial environment, reducing the efficiency of government and business by enabling people to assume positions of power through patronage rather than ability, and introducing inherent instability into the political processAy.
It is on a scale of 0-6.
DAC
AuA measure of, not just whether there are free and fair elections, but how responsive government is to its peopleAy.
It is on a scale of 0-6.
LAO
AuTwo measures comprising one risk component.
Each subcomponent equals half of the total.
The AulawAy sub-component assesses the strength and impartiality of the legal system, and the AuorderAy sub-component assesses popular observance of the lawAy.
It is on a scale of 0-6.
GOS
AuA measure of both of the government's ability to carry out its declared program.
and its ability to stay in office.
The risk rating assigned is the sum of three subcomponents: Government Unity.
Legislative Strength, and Popular SupportAy.
It is on a scale of 0-12.
BUQ
AuInstitutional strength and quality of the bureaucracy is a shock absorber that tends to minimize revisions of policy when governments changeAy.
It is on a scale of 0-4.
IQI
Average of COR.
DAC.
LAO.
GOS, and BUQ
GDP
Natural logarithm of GDP .
onstant 2015 US$) INF
ELE
Inflation, consumer prices .
nnual %) Access to electricity (% of populatio.
Source Elgin et al.
ICRG
ICRG
AuthorsAo
Computations from ICRG
WDI
Informal economy, institutional quality, and socioeconomic conditions .
(Osinubi and Simatel.
Model Specification The study's analytical framework is grounded in institutional theory, which emphasizes the role of institutional quality in determining economic outcomes (North, 1.
The present study is consistent with the studies of Osinubi and Simatele .
Bolarinwa and Simatele .
Pham .
, and Fagbemi et al.
on the effects of informal economy and institutional quality on socioeconomic conditions in African countries.
However, the model is modified to include a few variables that can influence socioeconomic conditions, as shown in Equation 1.
ycIycCyaycnyc = yu0 1 yayayaycnyc 2 yaycAycIycnyc 3 yayaycEycnyc 4 yaycAyaycnyc 5 yayayaycnyc yuAycnyc where SOC.
IFE.
INS.
GDP.
INF, and ELE indicate socioeconomic conditions, informal economy, different indicators of institutional quality (IQI .
nstitutional quality inde.
COR .
ontrol of corruptio.
DAC .
emocratic accountabilit.
LAO .
aw and orde.
GOS .
overnment stabilit.
, and BUQ .
ureaucratic qualit.
), real GDP, inflation, and electricity, respectively.
i and t represent the countries and the study period, yu0 is the intercept, 1 -5 are the parameters to be estimated, and yuA is the error term.
Real GDP, inflation, and access to electricity are included in the model because they have been established in the literature to have significant effects on socioeconomic conditions and the informal economy (Ajide & Dada, 2024b.
Bolarinwa & Simatele, 2023.
Pham, 2022.
Fagbemi et al.
, 2021.
Sahnoun & Abdennadher, 2.
Most crucially, the indicators of institutional quality variables would be introduced into the equation step-by-step, estimating six alternative models, each of which would handle one of the indicators.
Method of Analysis Equation 1 will be evaluated using Driscoll-Kraay.
Fully Modified Ordinary Least Squares, and Methods of Moments Quantile Regression approaches.
The study employs Machado and Silva's .
quantile regression approach to assess the distributional effects of informality and institutional quality on socioeconomic conditions.
This approach captures heterogeneous impacts across population segments, allowing for a deeper understanding beyond average effects (Olaniyi & Odhiambo, 2.
The different methods are important because they solve the issues of endogeneity, cross-sectional dependence, serial correlation, heterogeneity, nonlinearity, and distributional effects (Olaniyi & Odhiambo, 2.
Additionally, the study investigates the level of institutional quality needed to mitigate the adverse effects of informality on socioeconomic conditions using a dynamic panel threshold model by Seo et al.
and Seo and Shin .
This method is better than the static method since it considers nonlinear relationships and a real-world dynamic perspective, as well as the behaviour of variables before and after the threshold (Olaniyi & Odhiambo, 2.
The study employs a panel Granger non-causality approach developed by Dumitescu and Hurlin .
to determine the causal relationships among the informal economy, institutional quality, and socioeconomic conditions.
This method is significant since it accounts for crosectional dependence and heterogeneity among African countries.
The D-H is a bivariate causality test based on the panel vector autoregressive (VAR) modeling approach.
Results and Discussion Cross-Sectional Dependence and Panel Unit Root Tests Most African countries are related economically, socially, or politically.
This suggests that a shock in one of the countries can spread to other related countries.
As a result, the series must be adjusted for cross-sectional dependence.
This is done by employing Breusch and Pagan .
Pesaran et .
Baltagi et al.
, and Pesaran .
CD tests.
The results of the four CD tests in Table 2 show that African countries are highly interconnected.
Notably, the Breusch-Pagan LM.
Bias-corrected scaled LM, and Pesaran scaled LM statistics do not apply to LAO and BUQ because the values are nearly the same for each country across the study period.
Thus, the study uses unit root tests and estimation procedures that account for cross-sectional dependence.
Tables 3 .
ntercept alon.
and 4 .
ntercept and tren.
show the panel unit root tests devised by Pesaran Economic Journal of Emerging Markets, 17.
2025, 95-109 .
: Im-Pesaran-Shim, cross-sectionally augmented IPS (CIPS), and cross-sectionally augmented Dickey-Fuller (CADF).
The IPS test does not account for the presence of CD, but CIPS and CADF do.
However.
CIPS outperforms CADF when the variables are cross-sectionally Some variables are stationary at the level, while others are stationary at the first difference, according to the many tests used.
As a result of the differing results from the different techniques, all the variables would be stationary at first difference.
According to Olaniyi and Odhiambo .
, the mixed orders of integration demonstrate that the variables act divergently in the short term.
As a result, there is a need to investigate the presence of long-term relationships.
Table 2: Cross-Sectional Dependence Tests Variable Breusch-Pagan LM
Pesaran scaled LM
SOC
143***
459***
IFE
867***
892***
IQI
777***
819***
COR
694***
954***
DAC
050***
544***
LAO
GOS
074***
218***
BUQ
GDP
13***
931***
INF
293***
605***
ELE
907***
459***
Note: *** indicates significance at 0.
Bias-corrected scaled LM
584***
017***
936***
079***
669***
343***
056***
730***
584***
Pesaran CD
865***
841***
385***
410***
904***
770***
182***
412***
410***
084***
110***
Table 3.
Panel Unit Root Tests (Intercept Onl.
IPS Test
Variable Level 1st Difference SOC
444***
408***
IFE
091***
IQI
027***
837***
COR
733***
284***
DAC
019***
674***
LAO
465***
724***
GOS
848***
328***
BUQ
271***
GDP
955***
INF
965***
878***
ELE
858***
Note: *** indicates significance at 0.
CIPS Test Level 1st Difference 795*** 806*** 690*** 811*** 430*** 303*** 583*** 363*** 670*** 921*** 957*** 736*** 972*** 594*** CADF Test Level 1st Difference 173*** 701*** 067*** 861*** 236*** 164*** 891*** 144*** 804*** 834*** 290*** 565*** 943*** 216*** 311*** Table 4.
Panel Unit Root Tests (Intercept and Tren.
IPS Test
CIPS Test
Variable Level 1st Difference Level 1st Difference SOC
135***
880***
950***
IFE
589***
961***
IQI
364***
346***
227***
COR
771***
148***
714***
DAC
907***
025***
963***
LAO
1595***
470***
762***
GOS
359***
809***
857***
509***
BUQ
088***
GDP
283***
278***
INF
0558***
548***
345***
970***
ELE
450***
067***
648***
Note: *** and ** indicate significance at 0.
01 and 0.
05 levels.
CADF Test Level 1st Difference 909*** 890*** 178*** 476*** 050*** 650*** 968*** 907*** 685*** 863*** 196*** 233*** 214*** Informal economy, institutional quality, and socioeconomic conditions .
(Osinubi and Simatel.
Slope Homogeneity and Cointegration Tests The next step is to check for slope homogeneity and cointegration among variables.
Table 5 shows Pesaran and Yamagata's .
slope homogeneity test findings.
From the table, the null hypothesis of slope homogeneity is rejected, implying the existence of slope heterogeneity across African Table 6 illustrates the cointegration test results using the method described by Persyn and Westerlund .
This test controls heterogeneity and cross-sectional dependence.
Three of the statistics corroborate the existence of cointegration, meaning that the variables will converge with time.
Table 5.
Slope Homogeneity Test Model SOC = f(IFE.
IQI.
GDP.
INF.
ELE) SOC = f(IFE.
COR.
GDP.
INF.
ELE) SOC = f(IFE.
DAC.
GDP.
INF.
ELE) SOC = f(IFE.
LAO.
GDP.
INF.
ELE) SOC = f(IFE.
GOS.
GDP.
INF.
ELE) SOC = f(IFE.
BUQ.
GDP.
INF.
ELE) Note: *** indicates significance at 0.
863***
251***
561***
819***
429***
686***
yuuCycayccyc
529***
004***
384***
475***
772***
088***
Table 6.
Panel Cointegration Test Statistic Value 885*** 781*** 938*** Note: *** indicates significance at 0.
Z-value p-value Robust p-value Empirical Findings and Policy Recommendations The study employs Driscoll-Kraay regression to account for cross-sectional dependence and heterogeneity .
ee Table .
Fully Modified Least Squares regression in Table 8 to take care of serial correlation and endogeneity, and Method of Moments Quantile Regression .
ee Table .
to account for heterogeneous distribution effects across quantiles.
Also, the study uses a Dynamic Panel Threshold Regression as shown in Table 10 to investigate the threshold value of institutional quality in the association between informal economy and socioeconomic conditions in African countries.
The estimates from the three methodologies reveal that the informal economy has a considerable and negative impact on African socioeconomic conditions, whether using bundle or unbundle measures of institutions.
Specifically, the quantile regression estimates show that greater informal economic activity damages socioeconomic conditions across the quantiles (Q10-Q.
Studies by Osinubi and Simatele .
Gasparini and Torbarolli .
Loayza et al.
, and KrstiN and Sanfey .
support this finding by indicating that increased informal economic activities result in lower socioeconomic conditions.
The negative impacts of the informal economy on socioeconomic results have significant consequences for policy development.
The results demonstrate that elevated levels of informality correlate with inadequate earnings, inconsistent incomes, and restricted access to basic services.
Consequently, mitigating informality should be a primary policy aim for African countries.
Attention must be on formalizing informal businesses by implementing targeted incentives, including streamlined registration procedures, tax advantages, and access to financing.
Also, enhancing social protection systems for informal workers may lessen the adverse effects of informality by bolstering job security and income stability.
Development organizations and NGOs could be instrumental in formulating and executing initiatives that enable the transition from informality to formality, ensuring that marginalized groups are not overlooked.
A strong institution is expected to create a conducive environment and provide the resources needed to improve socioeconomic conditions.
From the three estimations, institutional Economic Journal of Emerging Markets, 17.
2025, 95-109 quality, irrespective of its measurement, significantly affects African socioeconomic conditions.
The finding aligns with Fagbemi et al.
, who argue that strong governance can help improve socioeconomic conditions by lowering unemployment, inequality, and poverty levels.
The significant relationship between institutional quality and socioeconomic outcomes indicates that prioritizing investment in institutional development is essential for African countries.
The results suggest that enhancements in factors such as governmental stability, corruption control, law enforcement, democratic accountability, and bureaucracy effectiveness substantially improve socioeconomic conditions.
Table 10 shows the findings for the threshold effect of institutional quality in the relationship between the informal economy and socioeconomic conditions.
The linearity tests indicate the presence of nonlinearity and thresholds of institutional quality in the relationship between the informal economy and socioeconomic conditions, as the null hypothesis of linearity is rejected for all measures of institutional quality.
The overall institutional quality, often known as the institutional quality index, has a threshold value of 5.
Other indices of institutional quality have threshold values of 4.
035, 3.
330, 3.
499, 7.
610, and 2.
929, for corruption control, democratic accountability, law and order, government stability, and bureaucratic quality, respectively.
According to the findings, the thresholds for overall institutional quality, corruption, and government stability are higher than their average values, 4.
843, 3.
406, and 6.
878, respectively.
contrast, those for democratic accountability, law and order, and bureaucratic quality are lower, 505, 5.
140, and 3.
239, respectively.
The threshold of institutional quality yields results like those of Olaniyi and Odhiambo .
, who discovered a threshold of 5.
281 before institutional quality can enhance natural resource rents in spurring renewable energy transitions in resource-rich African nations.
Considering that the average IQI in the data is 4.
483, achieving and maintaining institutional quality levels beyond established criteria, such as the IQI threshold of 5.
282, is essential for significantly enhancing socioeconomic outcomes.
Development organizations can facilitate these initiatives by offering technical and financial help to improve governance frameworks and public sector The nonlinear correlation between institutional quality and socioeconomic outcomes, characterized by thresholds, indicates that a one-size-fits-all approach may be ineffective.
Policymakers must customize their tactics according to their country's existing level of institutional Countries operating below the established thresholds should prioritize foundational enhancements, including the development of law and order and the mitigation of corruption, to foster a stable and predictable environment.
Countries that have exceeded these criteria should implement strategies to maintain and enhance institutional quality to promote equitable growth.
These distinct tactics will assist nations in optimizing the advantages of institutional development and strengthening the overall socioeconomic environment.
Finally, the study confirms a one-way causality from the informal economy to socioeconomic conditions and a two-way causality between institutional quality and socioeconomic conditions, as displayed in Table 11.
The one-way causal relationship supports evidence from Makame and Christine .
Sahnoun and Abdennadher .
Diallo et al.
Loayza et al.
, and KrstiN and Sanfey .
For the case of institutional quality, the findings are in tandem with the study of Fagbemi et al.
, who observed that socioeconomic factors influence governance and vice versa.
Alternative Control Variables To further strengthen our estimates, we employ other control variables in investigating the effects of the informal economy and institutional quality on socioeconomic conditions.
The variables are foreign direct investment, urban population, and sanitation, which have been documented in the literature as factors that can influence socioeconomic conditions, especially poverty, unemployment, and income inequality (Bolarinwa & Simatele, 2023.
Gymez & Irewole, 2023.
Ochi et al.
, 2023.
Pham.
Fagbemi et al.
, 2.
The estimates from the three techniques, as shown in Tables A and B in the appendix, are consistent with the earlier findings that the informal economy and institutional quality worsen and improve socioeconomic conditions, respectively, in African countries.
Informal economy, institutional quality, and socioeconomic conditions .
(Osinubi and Simatel.
Table 7.
Driscoll and Kraay Estimates Variable Model 1
076***
384***
Model 2
104***
Model 3
106***
IFE
IQI
COR
DAC
061***
LAO
GOS
BUQ
GDP
INF
ELE
Constant F-statistic 160*** 44.
880***
750***
Number of observations Number of groups Note: ***, **, and * indicate significance at 0.
1, 0.
5, and 0.
10 levels.
Model 4
104***
126***
Model 5
Model 6
108***
168***
400***
99***
76***
Table 8.
Fully Modified Least Squares Estimates Variable Model 1
Model 2
Model 3
Model 4
IFE
050***
062***
059***
IQI
805***
COR
DAC
LAO
302***
GOS
BUQ
GDP
242***
INF
ELE
027***
028***
034***
024***
Constant
679***
283***
770***
405***
Note: ***, **, and * indicate significance at 0.
1, 0.
5, and 0.
10 levels.
Model 5
070***
Model 6
061***
386***
029***
297***
028***
840***
Table 9.
Method of Moments Quantile Regression Estimates Variable IFE
IQI
GDP
INF
ELE
Constant
IFE
COR
GDP
INF
ELE
Constant
IFE
DAC
GDP
INF
ELE
Constant
IFE
LAO
GDP
INF
ELE
Constant
Quantiles Q30
Q40
Q50
Q60
Q70
MODEL 1
044*** -0.
016*** -0.
019*** -0.
028*** -0.
035*** -0.
040*** -0.
046*** -0.
050*** -0.
056***
692*** -0.
698*** 0.
696*** 0.
695*** 0.
693*** 0.
692*** 0.
691*** 0.
690***
197*** 0.
045*** -0.
271*** -0.
245*** -0.
225*** -0.
210*** -0.
193*** -0.
181*** -0.
166***
001** 0.
023*** -0.
004*** 0.
031*** 0.
028*** 0.
026*** 0.
025*** 0.
023*** 0.
022*** 0.
020***
660*** 0.
956*** 5.
200*** 5.
393*** 5.
534*** 5.
696*** 5.
807*** 5.
949***
MODEL 2
056*** -0.
011*** -0.
038*** -0.
044*** -0.
049*** -0.
053*** -0.
057*** -0.
061*** -0.
064***
313*** -0.
325*** 0.
321*** 0.
318*** 0.
315*** 0.
313*** 0.
311*** 0.
309***
154*** 0.
169*** -0.
164*** -0.
160*** -0.
156*** -0.
153*** -0.
150*** -0.
147***
001** -0.
001** 0.
001** -0.
001** -0.
002*** -0.
002***
024*** -0.
002* 0.
027*** 0.
026*** 0.
025*** 0.
024*** 0.
024*** 0.
023*** 0.
023***
383*** 1.
094** 5.
524*** 6.
138*** 6.
669*** 7.
089*** 7.
478*** 7.
834*** 8.
153***
MODEL 3
069*** -0.
011*** -0.
049*** -0.
056*** -0.
061*** -0.
065*** -0.
069*** -0.
073*** -0.
076***
090*** -0.
102*** 0.
098*** 0.
095*** 0.
092*** 0.
089*** 0.
087*** 0.
085***
206*** 0.
235*** -0.
225*** -0.
218*** -0.
212*** -0.
205*** -0.
200*** -0.
195***
001*** 0.
003*** 0.
002*** 0.
002*** 0.
001** 0.
026*** -0.
003*** 0.
032*** 0.
030*** 0.
028*** 0.
027*** 0.
026*** 0.
025*** 0.
024***
596*** 1.
073*** 7.
760*** 8.
377*** 8.
865*** 9.
249*** 9.
675*** 9.
992*** 10.
324***
MODEL 4
068*** -0.
009*** -0.
051*** -0.
057*** -0.
062*** -0.
066*** -0.
069*** -0.
071*** -0.
074***
270*** -0.
290*** 0.
283*** 0.
277*** 0.
272*** 0.
269*** 0.
266*** 0.
263***
150*** 0.
166*** -0.
160*** -0.
55*** -0.
152*** -0.
149*** -0.
147*** -0.
144***
001** 0.
021*** -0.
003*** 0.
026*** 0.
024*** 0.
022*** 0.
021*** 0.
020*** 0.
019*** 0.
019***
589*** 1.
069** 5.
674*** 6.
300*** 6.
920*** 7.
368*** 7.
675*** 7.
957*** 8.
287***
Location Scale
Q10
Q20
Q80
Q90
061***
689***
152***
019***
086***
068***
687***
131***
017***
278***
068***
307***
144***
002***
022***
519***
073***
304***
140***
002***
021***
027***
081***
082***
188***
022***
780***
087***
078***
179***
002***
020***
346***
078***
258***
141***
017***
698***
084***
252***
136***
016***
305***
Economic Journal of Emerging Markets, 17.
2025, 95-109
Quantiles Q30
Q40
Q50
Q60
Q70
MODEL 5
IFE
070*** -0.
011*** -0.
050*** -0.
057*** -0.
063*** -0.
067*** -0.
071*** -0.
074*** -0.
078***
GOS
266*** 0.
225*** 0.
240*** 0.
251*** 0.
260*** 0.
267*** 0.
273*** 0.
283***
GDP
173*** -0.
157*** -0.
163*** -0.
167*** -0.
171*** -0.
174*** -0.
176*** -0.
180***
INF
001*** 0.
002** 0.
ELE
025*** -0.
026*** 0.
025*** 0.
025*** 0.
025*** 0.
025*** 0.
025*** 0.
024***
Constant 7.
586*** 1.
308*** 5.
256*** 6.
113*** 6.
772*** 7.
283*** 7.
659*** 8.
027*** 8.
572***
MODEL 6
IFE
061*** -0.
008*** -0.
048*** -0.
052*** -0.
056*** -0.
058*** -0.
061*** -0.
064*** -0.
067***
BUQ
192*** -0.
200*** 0.
197*** 0.
195*** 0.
194*** 0.
192*** 0.
191*** 0.
189***
GDP
219*** 0.
043** -0.
289*** -0.
264*** -0.
245*** -0.
233*** -0.
220*** -0.
205*** -0.
188***
INF
002*** 0.
002*** 0.
001** 0.
001*** 0.
002***
ELE
022*** -0.
028*** 0.
026*** 0.
024*** 0.
023*** 0.
022*** 0.
020*** 0.
019***
Constant 9.
639*** 0.
272*** 9.
191*** 9.
351*** 9.
471*** 9.
547*** 9.
632*** 9.
728*** 9.
830***
Variable Location Scale
Q10
Q20
Q80
Q90
082***
290***
183***
001***
024***
020***
087***
300***
187***
002***
024***
561***
070***
187***
171***
003***
017***
938***
074***
185***
149***
004***
015***
079***
Note: ***, **, and * indicate significance at 0.
1, 0.
5, and 0.
10 levels.
Table 10.
Dynamic Panel Threshold Estimates IQI
COR
DAC
LAO
GOS
BUQ
Lower Regime .
aycAycIycnyc O yuU) Lagged SOC
883***
396***
310***
655***
893***
250***
IFE
161***
050***
156***
163***
IQI
COR
205***
DAC
LAO
256***
GOS
219***
BUQ
Upper Regime .
aycAycIycnyc > yuU) Lagged SOC
751***
724***
317***
328***
IFE
101***
128***
228***
IQI
COR
692***
DAC
LAO
543***
GOS
341***
BUQ
Constant 567*** -0.
1772*** -11.
535*** 8.
415***
844***
Threshold Value 282*** 035*** 499*** 610*** Linearity Test
000***
000***
000***
000***
000***
000***
(Bootstrapped p-valu.
Note: ***, **, and * indicate significance at 0.
1, 0.
5, and 0.
10 levels.
1000 bootstrap iterations are used to compute its p-values.
Table 11.
Causality test IFE does not Granger cause SOC SOC does not Granger cause IFE IQI does not Granger cause SOC SOC does not Granger cause IQI Note: *** indicates significance at 0.
Wald statistics 459*** 863*** 150*** P-value Decision Reject Accept Reject Reject Conclusion The study investigates the effects of informal economy and institutional quality on socioeconomic conditions in 35 African countries between 2000 and 2022, with the view to determining the threshold of institutional quality in the relationship between informal economy and socioeconomic conditions and the direction of causality between the variables.
The study employs Driscoll-Kraay.
Fully Modified Ordinary Least Squares.
Method of Moments Quantile Regression.
Dynamic Panel Threshold, and Dumitrescu-Hurlin non-causality Granger techniques.
The findings from the first Informal economy, institutional quality, and socioeconomic conditions .
(Osinubi and Simatel.
three estimation techniques show that the informal economy has a negative and significant effect on African socioeconomic conditions.
In contrast, institutional quality, regardless of how it is measured, has a substantial and positive impact on African socioeconomic conditions.
As a result, the study concludes that the informal economy and institutional quality retards and improves socioeconomic conditions, respectively.
Thus, this study provides strong evidence for the importance of institutional quality in influencing the informal economy and its impact on African socioeconomic situations.
Identifying a precise threshold for institutional quality is a helpful guide for policymakers looking to eliminate informality and improve socioeconomic results.
Institutional strengthening, improved governance, economic stability promotion, and infrastructure service enhancement are critical measures for attaining inclusive growth and lowering informality across the continent.
However, one drawback of the study is that it did not test for the interactive effect of institutions and informality, which could provide more insight into how these variables influence socioeconomic outcomes.
Furthermore, the study is based on aggregated data, which may mask key micro-level differences and limit the analysis's granularity.
Future studies might examine the interaction effects of institutional quality and informality and the function of other potential moderators like education and technological advancement in defining the informal sector.
Addressing these shortcomings could contribute to a complete understanding of the interactions between institutions, informality, and socioeconomic development in Africa.
References