Submitted:
04 May 2026
Posted:
05 May 2026
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Abstract
This study examines the determinants of informality in ASEAN-5 (Indonesia, Malaysia, the Philippines, Thailand, and Singapore) using Partial Least Squares Structural Equation Modeling (PLS-SEM) on panel data from 2015–2022. Five hypotheses tested the effects of institutional quality, social protection, labor market policy, economic growth, and technological advancement (as mediator). Results show that institutional quality significantly reduces informality (β = –0.378; p = 0.015), while social protection, labor market policy, and economic growth exert positive and significant effects, reflecting policy design–implementation gaps and growth patterns that fail to generate formal employment. Technological advancement does not mediate the growth–informality relationship (β = 0.011; p = 0.335). The model explains 88.8% of the variance in informality (adjusted R² = 0.888). Policy implications highlight the need for stronger institutions, inclusive social protection, adaptive labor regulations, and digitalization integrated with e‑governance to foster formalization.
Keywords:
ASEAN-5
; informal economy
; institutional quality
; PLS-SEM
; digital governance
I. Introduction
Despite sustained economic growth and regional integration, the informal economy remains a persistent feature of Southeast Asia. In the ASEAN-5 (Indonesia, Malaysia, the Philippines, Thailand, and Singapore), diverse development trajectories, institutional capacities, and policy frameworks shape the extent of formalization. Informality challenges inclusive development, fiscal sustainability, and labor protection, as rapid expansion has not consistently translated into formal employment.
Structural characteristics—such as sectoral composition, firm size distribution, and labor absorption capacity—interact with governance quality to determine whether growth generates formal jobs or reinforces low-productivity informal work. Informality is multidimensional, encompassing unregistered enterprises, undeclared employment, and emerging digital gig work, driven by macroeconomic shocks, sectoral demand, administrative burdens, and enforcement gaps.
This study integrates institutional, policy, economic, and technological domains within a unified PLS-SEM framework to explain cross-country differences and assess whether digitalization can transform growth into formalization.
II. Methods
This study uses panel data for 2015–2022 across the ASEAN-5 and applies Partial Least Squares Structural Equation Modeling (PLS-SEM) to estimate both measurement and structural components of the model. PLS-SEM is appropriate for models with latent constructs and smaller cross-sectional samples, and for situations where the research objective emphasizes prediction and variance explanation. The analysis first refines measurement scales by removing indicators with inadequate outer loadings to ensure convergent validity and then evaluates structural relationships through bootstrapping to obtain robust significance estimates for path coefficients.
Indicator selection prioritized internationally comparable proxies: governance and rule-of-law measures for Institutional Quality; national social-protection expenditure or coverage metrics for Social Protection; legal minimum-wage or active labor program coverage for Labor Market Policy; GDP growth and related per-capita growth indicators for Economic Growth; and broadband subscriptions and related ICT metrics for Technological Advancement. Informal Economy was operationalized as the estimated share of informal employment or informal-sector value added as a percent of GDP. Where indicators produced poor loadings, items were excluded to preserve construct validity, and single-indicator constructs were retained only when they represented established, high-quality national-level proxies.
Model estimation followed a two-stage approach. The measurement model assessment included outer loadings, Average Variance Extracted (AVE), composite reliability, and discriminant validity checks. The structural model evaluation included multicollinearity diagnostics (VIF), coefficient of determination (R²) for endogenous constructs, and hypothesis testing using bootstrapped standard errors and T-statistics. Mediation of Economic Growth’s effect on the Informal Economy by Technological Advancement was tested through inspection of indirect path coefficients and bootstrap confidence intervals.
2.1. Research Design and Objectives
A quantitative explanatory design employing PLS-SEM was used to estimate measurement and structural relationships among latent constructs. Objectives: (1) estimate direct effects of Institutional Quality (IQ), Social Protection (SP), Labor Market Policy (LMP), and Economic Growth (EG) on Informal Economy (IE); (2) evaluate Technological Advancement (TA) as a mediator between EG and IE; (3) assess measurement validity and model explanatory power across ASEAN-5 for 2015–2022.
2.2. Data and Variable Operationalization
Data: secondary panel data from international sources (World Bank WDI, IMF FAS, ILO statistics) for 2015–2022 across five countries.
Latent constructs and indicators:
- Institutional Quality (IQ): governance effectiveness, political stability/absence of violence, regulatory quality, rule of law (initial indicators; low-loading items removed during measurement refinement).
- Social Protection (SP): public social protection expenditure or coverage (single indicator).
- Labor Market Policy (LMP): minimum wage/legal coverage or active labour program proxy (single indicator).
- Economic Growth (EG): GDP growth per capita and related macro indicators (revised to retain high-loading indicators).
- Technological Advancement (TA): broadband subscriptions (BCS), ICT access, mobile banking transactions (revised to BCS as retained indicator).
- Informal Economy (IE): estimated size of informal employment or informal value added (% GDP) (single indicator).
2.3. Analytical Procedure
PLS-SEM steps followed established guidelines: (a) measurement model assessment (convergent validity, discriminant validity, internal consistency), (b) structural model evaluation (collinearity assessment, path significance via bootstrapping, coefficient of determination). Indicator retention followed outer loading thresholds (> 0.5) with removal of negative/low-loading items. Bootstrapping used to obtain T-statistics and p-values; significance threshold set at p < 0.05 or T > 1.96. Variance Inflation Factor (VIF) assessed multicollinearity.
III. Results
3.1. Measurement Model
After refinement, Institutional Quality and Economic Growth retained strong multi-indicator constructs, while Social Protection, Labor Market Policy, and Informal Economy were represented by single high-quality proxies. Convergent validity and reliability were achieved, with AVE and Composite Reliability values exceeding conventional thresholds. Discriminant validity was confirmed using the Fornell–Larcker criterion.
Table 1.
Measurement model: outer loadings, AVE, composite reliability.
| Construct | Item | Outer Loading | AVE | Composite Reliability |
|---|---|---|---|---|
| Institutional Quality | GE | 0.986 | 0.966 | 0.991 |
| Institutional Quality | PSAV | 0.986 | 0.966 | 0.991 |
| Institutional Quality | RQ | 0.983 | 0.966 | 0.991 |
| Institutional Quality | ROF | 0.976 | 0.966 | 0.991 |
| Social Protection | SP | 1.000 | 1.000 | 1.000 |
| Labor Market Policy | LMP | 1.000 | 1.000 | 1.000 |
| Economic Growth | EG2 | 0.969 | 0.929 | 0.963 |
| Economic Growth | EG3 | 0.958 | 0.929 | 0.963 |
| Technological Advancement | BCS | 1.000 | 1.000 | 1.000 |
| Informal Economy | IE | 1.000 | 1.000 | 1.000 |
Notes: Indicators DOC, VAC, EG1, EG4, ICTA, MBDF were removed for low/negative outer loadings during measurement refinement.
3.2. Structural Model and Diagnostics
Variance Inflation Factors (VIF) indicated acceptable multicollinearity across predictors, with Economic Growth showing sensitivity (VIF > 10) but remaining within robustness checks. The model demonstrated strong explanatory power: Informal Economy R² = 0.902 (adjusted R² = 0.888), while Technological Advancement exhibited weak variance explained (R² = 0.025).
Table 2.
Variance Inflation Factors (VIF).
| Variable | Informal Economy | Technological Advancement |
|---|---|---|
| Institutional Quality | 8.169 | — |
| Social Protection | 4.515 | — |
| Labor Market Policy | 8.388 | — |
| Economic Growth | 11.024 | 1.000 |
| Technological Advancement | 2.426 | — |
All VIF values reported below or near conventional thresholds; EG’s VIF exceeds 10 in the Informal Economy equation, indicating potential collinearity sensitivity to interpret alongside robustness checks.
Table 3.
Coefficient of determination (R²).
| Variable | R² | Adjusted R² |
|---|---|---|
| Technological Advancement | 0.025 | −0.001 |
| Informal Economy | 0.902 | 0.888 |
3.3. Hypothesis Testing
The results reveal a clear pattern:
- Institutional Quality → Informal Economy: significant negative effect (β = –0.378; p = 0.015), confirming governance as a formalization anchor.
- Social Protection → Informal Economy: positive and significant (β = 0.364; p < 0.001), contrary to expectations.
- Labor Market Policy → Informal Economy: positive and significant (β = 0.641; p < 0.001), indicating unintended effects.
- Economic Growth → Informal Economy: positive and significant (β = 1.107; p < 0.001), suggesting growth without inclusive formalization.
- Economic Growth → Technological Advancement → Informal Economy (mediated): insignificant (β = 0.011; p = 0.335), showing no mediation effect.
Sensitivity tests (leave-one-country-out) confirmed the robustness of these findings: Institutional Quality consistently reduced informality, while Social Protection, Labor Market Policy, and Economic Growth remained positively associated with informality across specifications.
Table 4.
Path coefficients, T-statistics, p-values, decisions.
| Hypothesis | Path | Coefficient (β) | T-Statistic | P-Value | Decision |
|---|---|---|---|---|---|
| H1 | IQ → IE | −0.378 | 2.172 | 0.015 | Supported |
| H2 | SP → IE | 0.364 | 3.608 | 0.000 | Not supported |
| H3 | LMP → IE | 0.641 | 5.165 | 0.000 | Not supported |
| H4 | EG → IE | 1.107 | 6.473 | 0.000 | Not supported |
| H5 | EG → TA → IE (indirect) | 0.011 | 0.425 | 0.335 | Not supported |
Sensitivity tests (leave-one-country-out) confirm core pattern: IQ negative and significant across specifications; positive coefficients for SP, LMP, and EG persist with varying magnitudes.
IV. Discussion
The negative effect of Institutional Quality on informality confirms the central role of governance, regulatory clarity, and rule-of-law enforcement in shaping incentives to formalize. Institutional improvements lower transaction costs and reduce rent-seeking and uncertainty, making registration and compliance more attractive. In the ASEAN-5, countries with higher governance indicators exhibit smaller formal–informal gaps, illustrating how institutional depth operates as a precondition for policy instruments to be effective.
The positive relationships observed for Social Protection and Labor Market Policy reflect implementation and design mismatches rather than inherent policy failure. Aggregate increases in social-protection spending can coexist with large informal populations when coverage is skewed toward formal workers or when contribution-based schemes exclude the informal self-employed. Similarly, stringent labor regulations without proportional enforcement or complementary incentives may push employers and workers toward informal arrangements to avoid compliance costs. Economic Growth’s positive association with informality in this period signals growth that is quantitatively strong but qualitatively insufficient to generate formal jobs—expansion concentrated in low-productivity services, self-employment, and digitized gig activities can raise output while leaving formality unchanged or even reduced.
Technology’s inability to mediate formalization underscores that digital diffusion alone cannot substitute for governance integration. Broadband and platform adoption expand market access and transaction options but do not automatically create links to tax systems, labor records, or social-protection registries. Without interoperable digital IDs, e-registration platforms, and e-taxation systems that connect economic actors to formal institutions, technology may amplify informal digital work rather than steer it toward formal compliance.
These findings collectively suggest that policy instruments should be conceived and implemented in an integrated fashion: institutional reforms must accompany social-policy redesign, labor-market flexibility measures, and digital governance frameworks to translate growth and technology into formalization.
4.1. Institutional Quality as the Principal Formalization Pathway
The negative and significant IQ→IE path confirms that governance strength, regulatory quality, rule of law, and administrative effectiveness are fundamental to formalization. Strong institutions lower compliance costs and increase predictability for firms and workers, consistent with institutional theory and comparative evidence across the ASEAN-5 where countries with higher governance indicators have smaller informal sectors.
4.2. Interpretation of Unexpected Positive Coefficients
Positive associations of SP, LMP, and EG with informality likely reflect structural and implementation dynamics:
- Social Protection: aggregate increases may be skewed toward formal workers or represent expanded spending without inclusive enrollment mechanisms, producing an observed positive association when informal populations remain excluded.
- Labor Market Policy: stringent or costly regulations with weak enforcement incentivize informal arrangements, particularly among micro-enterprises unable to absorb compliance costs.
- Economic Growth: growth concentrated in low-productivity, service-sector expansions and platform-mediated gig activities can raise GDP while formal employment remains limited, producing growth that is not qualitatively inclusive.
These patterns underscore that policy content, enforcement, and institutional integration determine whether social protection, labor regulations, and growth contribute to formalization.
4.3. Why Did Technology Not Mediate Formalization
TA did not mediate EG→IE, reflecting that digital diffusion alone is insufficient for formalization unless coupled with e-governance, interoperable digital IDs, e-registration, and e-taxation systems. Rapid fintech and platform adoption may broaden economic participation but can also expand digital informality absent institutional integration that links digital economic activity to formal registration and social protection frameworks.
4.4. Policy Interpretation for ASEAN-5
The evidence suggests prioritizing institutional deepening and designing policy instruments that are administratively feasible for informal and micro firms (graduated compliance, digitalized simplified registration, portable contributions). Digital strategies must be integrated with governance reforms to convert technology into a formalization accelerator.
V. Conclusions and Recommendations
Conclusion
This study demonstrates that institutional quality is the decisive determinant of informality across ASEAN-5. It is the only factor with a significant negative effect, confirming governance as the anchor of formalization. In contrast, social protection, labor-market policy, and economic growth show positive associations with informality, reflecting design–implementation gaps and non-inclusive growth patterns. Technological advancement, measured primarily through broadband access, does not mediate the growth–informality relationship. The PLS-SEM model explains 88.8% of the variance in informality, underscoring the explanatory strength of institutional and policy variables.
Policy Recommendation
To foster formalization, ASEAN-5 governments should prioritize integrated policy packages:
- Institutional deepening: simplify business entry, strengthen regulatory predictability, enforce rule of law, and expand e-government services.
- Inclusive social protection: redesign financing to include non-contributory and portable schemes; leverage digital IDs and mobile-based contribution systems to reach informal workers.
- Adaptive labor-market policy: move from rigid uniform rules to graduated compliance; provide incentives for SMEs to formalize through cost-effective procedures.
- Quality-driven growth: promote industrial upgrading and formal job creation through fiscal incentives and targeted public investment.
- Digital governance alignment: harmonize e-registration, e-taxation, and digital ID systems to ensure technology accelerates formalization rather than expanding digital informality.
- Regional monitoring: establish an ASEAN Informality Observatory to benchmark progress and facilitate peer learning.
Academic Recommendations
Future research should employ longitudinal SEM with longer time horizons, integrate micro-level survey data, and develop composite indices linking institutional readiness with digital inclusion. This will deepen understanding of the mechanisms driving informality and guide more effective policy design.
VI. Limitations and Suggestions for Future Research
Limitations This study faces several constraints that should be acknowledged:
- Indicator availability: Some constructs (Social Protection, Labor Market Policy, Informal Economy) rely on single national-level proxies due to data limitations, reducing latent-variable nuance.
- Measurement choices: Removal of low-loading indicators improved validity but may have excluded theoretically relevant dimensions.
- Country sample: The analysis is limited to ASEAN-5, restricting generalizability beyond the region.
- Temporal horizon: The 2015–2022 period includes pandemic disruptions and rapid digital acceleration, which may reflect short-run rather than long-run dynamics.
- Omitted variables: Factors such as tax administration, sectoral heterogeneity, and micro-level firm behavior are not fully captured.
- Endogeneity risks: While PLS-SEM emphasizes prediction, causal interpretations remain tentative given possible reverse causality and omitted variables.
Future Research To deepen understanding of informality, future studies should:
- Employ longitudinal SEM with longer time horizons and country fixed effects to capture institutional and digital transformation.
- Integrate micro-level survey data and qualitative case studies to unpack mechanisms behind positive coefficients.
- Develop composite indices linking institutional readiness and digital inclusion to guide policy priorities.
- Expand the sample beyond ASEAN-5 to test generalizability across diverse regional contexts.
Acknowledgments
The authors thank academic mentors, institutional data providers, and peer reviewers who provided inputs during research development.
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