The researchers analyzed Ecuador’s carrying-capacity factor, or LCF, as the ratio of biocapacity to the ecological footprint. In level terms, an LCF above 1 means that ecosystems’ regenerative capacity exceeds the resource demand represented by the ecological footprint; a value below 1 means the opposite. The series averaged 1.35 over the study period, with a standard deviation of 0.29, but moved from above 1.9 in the early 1990s to close to unity in recent years. The analysis used annual national data from the Global Footprint Network, World Bank and V-Dem databases. GDP per capita was measured in constant 2015 US dollars, public-sector corruption was scored on a scale from 0 to 1, the informal economy was estimated as a share of GDP, and agricultural employment was measured as a percentage of total employment.

Because the dataset contained only 32 annual observations, the team used several econometric procedures designed to accommodate short samples and gradual changes in relationships. Fourier-augmented unit-root tests indicated that all variables were integrated of order one after first differencing, while their optimal Fourier frequencies allowed smooth changes in the series’ trajectories to be represented without requiring break dates to be specified in advance. Bayer–Hanck combined cointegration tests and a Residual Augmented Least Squares autoregressive distributed Fourier lag model, or RALS-FADL, both rejected the null hypothesis of no long-run equilibrium relationship. Fully Modified Ordinary Least Squares and Canonical Cointegration Regression were then used as complementary estimators, while Fourier–Toda–Yamamoto tests examined temporal causality and wavelet coherence assessed how relationships varied across periods and frequency scales.

The clearest result supported the Load Capacity Curve hypothesis. In both long-run models, the coefficient on log GDP per capita was negative, while the coefficient on its squared term was positive, producing the predicted U-shape. Under Fully Modified Ordinary Least Squares, the linear income coefficient was −1.17 and the quadratic coefficient was 0.07, both significant at the 1% level. Canonical Cointegration Regression produced coefficients of −0.84 and 0.05, respectively, also significant at the 1% level. The pattern indicates that income growth was associated with declining carrying capacity below a turning point and improving carrying capacity beyond it. Component estimates suggest that higher income initially increased the ecological footprint but later reduced it, while biocapacity followed the reverse pattern, supporting the interpretation of the aggregate relationship without establishing a mechanism by itself.

Public-sector corruption was negatively associated with the LCF in both estimators. Its coefficient was −0.61 under Fully Modified Ordinary Least Squares and −0.41 under Canonical Cointegration Regression, with both estimates significant at the 1% level. Decomposing the ratio showed that corruption was positively associated with the ecological footprint, while its negative association with biocapacity was not statistically significant. The analysis therefore points mainly toward greater ecological demand as the component linking corruption and weaker sustainability. The study treats this as an association rather than proof of a mechanism: corruption may weaken regulatory enforcement or accountability, but the national data do not directly measure how specific governance failures altered resource use.

The informal economy also had a negative long-run association with carrying capacity, significant at the 5% level in both estimators. The estimated coefficients were −0.03 and −0.02. Unlike the corruption result, the component analysis placed informality on both sides of the ratio: it was positively associated with the ecological footprint and negatively associated with biocapacity, with all four component estimates significant at the 1% level. This pattern is consistent with a combination of greater demand for resources and weaker regenerative capacity, although the causality tests did not establish a robust directional relationship after bootstrap correction. Informality was itself an indirect estimate generated by a two-sector dynamic general-equilibrium model in which households allocate labor between formal and informal activities, adding measurement uncertainty to the interpretation.

Agricultural employment produced the study’s most counterintuitive result. Its coefficient was positive at 0.05 in both long-run estimators and significant at the 1% level, rejecting the hypothesis that it would reduce the LCF. However, the component analysis showed negative associations with both the ecological footprint and biocapacity. Reduced ecological demand would tend to improve the ratio, whereas lower biocapacity would tend to weaken it, so the positive overall coefficient cannot be assigned to either component alone. The Fourier–Toda–Yamamoto test did identify a robust temporal relationship running from agricultural employment toward the LCF, and wavelet analysis found a positive, temporally ordered relationship mainly during 2010–2015 in frequency bands 2 and 3. These patterns do not prove that farming itself improves sustainability; they may reflect productivity gains, technology adoption or other structural changes that the aggregate model cannot isolate.

Time and frequency analyses showed that the relationships were not constant throughout the 32-year study period. Wavelet coherence identified a negative relationship between GDP per capita and the LCF mainly from the late 1990s to around 2018, with arrows consistent with income preceding the LCF. A similar pattern appeared for the quadratic income term beginning in 1993. Corruption and the LCF were significantly coherent chiefly between 2007 and 2012, but the arrows did not establish clear temporal precedence. Informality preceded a negative LCF relationship during 2000–2007, mainly in frequency bands 3 to 5, while agricultural employment preceded the positive relationship observed during 2010–2015. These period-specific findings show why a single annual coefficient can summarize several distinct phases rather than one invariant relationship.

The causality results added a further distinction between association and temporal direction. Neither the linear nor quadratic income term showed robust causality toward the LCF; significance instead ran from the LCF toward income and its quadratic term under both Wald and bootstrap procedures. Corruption was bidirectional, with significance under both procedures in each direction. Informality showed no robust causality toward the LCF, while the reverse direction failed to survive bootstrap correction. Agricultural employment showed robust causality toward the LCF, but not in the opposite direction. These tests identify precedence in a time-series model rather than definitive intervention effects, so they should not be read as proof that changing one variable would cause a specified change in another.

The findings suggest that Ecuador’s environmental trajectory cannot be attributed to income alone. The U-shaped income relationship indicates that economic development can coincide with either ecological deterioration or improved carrying capacity depending on the stage reached, while the negative associations with corruption and informality point to institutional conditions associated with how much pressure production places on natural systems. Agricultural policy presents the subtler challenge: higher agricultural employment coincided with a stronger LCF, yet both components of that ratio moved in opposing directions. The study consequently favors productivity, land and water efficiency, and environmentally efficient production rather than a simplistic interpretation of agricultural employment as either beneficial or harmful.

Several limitations constrain the findings. The analysis used a single country, a small annual sample and aggregated national data, limiting generalizability and obscuring differences among regions and industries. Public-sector corruption was an expert-based index rather than a directly observed measure, and informal output was model-estimated rather than directly recorded. The study also cannot establish that a particular institutional or productive change caused the observed shifts, although the Fourier and wavelet methods were designed to reveal smooth structural changes that conventional tests might miss. Future research could test the same relationships with regional and sector-level data and add variables measuring technology, productivity, energy efficiency and resource-use practices. Within these limits, the analysis offers a detailed national-scale account of how Ecuador’s ecological capacity, institutional quality and productive structure moved together from 1991 to 2022.

Subject of Research: Relationships among income, corruption, informality, agricultural employment and ecological carrying capacity in Ecuador from 1991 to 2022

Article Title: The role of public sector corruption, informal economy and agricultural employment on the load capacity factor: Testing the LCC hypothesis for Ecuador

Article References: Toledo, E., Conde, F., Alvarado, R., Tillaguango, B., Işık, C., & Rehman, A. (2026). The role of public sector corruption, informal economy and agricultural employment on the load capacity factor: Testing the LCC hypothesis for Ecuador. Environmental and Sustainability Indicators, 32, Article 101565. https://doi.org/10.1016/j.indic.2026.101565

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101565

Keywords: Ecuador, ecological carrying capacity, Load Capacity Curve, economic growth, public-sector corruption, informal economy, agricultural employment, biocapacity, ecological footprint, time-series analysis, role, public