Technical-actuarial proposal of an occupational risk model for the vulnerable informal working population in rural areas

Social protection systems are essential for mitigating poverty and income insecurity, particularly in developing countries where informal employment is prevalent. In Colombia, rural workers face higher rates of informality, poverty, and occupational risks, yet current institutional mechanisms inadequately address their protection needs. This study estimates the fiscal impact of a public programme designed to provide occupational risk insurance for vulnerable informal workers in rural Colombia. The programme comprises three intervention lines: (i) Rural Dignity, which subsidises occupational risk insurance for the poorest; (ii) Subsidy for entry into the General System of Occupational Risks (SGRL); and (iii) Identification of SGRL evaders. An actuarial modelling approach was applied using demographic and labour data from official national sources, including GEIH/CNPV (DANE), and Sisbén IV. Premiums and fiscal impacts were projected over a five-year period (2024–2028) under three claim scenarios (optimistic, base, pessimistic). Each intervention line was analysed independently to estimate the population at risk, coverage levels, and financial requirements. Additionally, for the first time in the literature, a national mortality table for vulnerable informal workers in rural areas was estimated. By 2028, the Rural Dignity scheme is projected to cover over 2.1 million people annually, with annual subsidy costs ranging from COP 201,654 million (optimistic) to COP 205,613 million (pessimistic). The Subsidy for entry into the SGRL would benefit over 520,000 individuals by 2028, with an estimated cost of COP 427,800 million. The Identification of SGRL evaders could target over 820,000 individuals, although this line does not entail direct subsidies. The total projected fiscal impact of the programme in 2028 ranges from COP 629,454 million to COP 633,413 million (less than 0.05% of Colombia’s GDP in 2024). Findings underscore the need for targeted insurance mechanisms supported by accurate actuarial models and robust data systems.