Integrated RUSEC–MONASH CGE Modeling for Scenario Analysis of Agricultural Labor Demand and Interpretation of Employment Quality Dimensions Under Technological and Structural Changes in Kazakhstan

2026

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DOI 10.3390/su182010571
Достоверность данных 70 средняя
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Аннотация

(1) Background: Agricultural labor demand in transition economies is shaped by price incentives, technological modernization, migration, and structural labor-market change. This study examines their implications for agricultural labor demand and selected employment quality dimensions in Kazakhstan. From a sustainability perspective, the analysis focuses on the socio-economic capacity of agricultural labor markets to adapt to technological and structural change while maintaining productive employment, skill development, regional mobility, and inclusive rural development. Employment quality is represented by remuneration, productivity, skill structure, and labor mobility rather than by a composite worker-level index. (2) Methods: A calibrated RUSEC–MONASH CGE-based scenario framework combines a balanced Social Accounting Matrix, CES production structure, price/TFP employment-response equation, relative-wage migration equation, and calibrated Markov transition structure. (3) Results: A +20% agricultural price shock increases employment from 1200 to 1380 workers (+15.0%), while the same shock combined with a +5% TFP improvement yields 1296 workers (+8.0%). These results are deterministic single-period counterfactuals relative to the 2025 benchmark and are not future-dated forecasts or measures of predictive accuracy. Migration responses are governed by φ = 0.37–0.61. (4) Conclusions: The framework supports conditional scenario analysis and interpretation of selected employment quality dimensions but does not directly estimate overall job quality, working-poverty incidence, causal policy effects, or statistically validated forecasts. Numerical outcomes should therefore be interpreted only as internally consistent conditional scenarios rather than predictive estimates.