Smallholder farmers in flood-prone agricultural regions face severe flood risks that threaten household welfare and long-term economic stability. This study evaluates the causal impact of crop insurance on post-disaster coping mechanisms among smallholder farming households in Laguna, Philippines (N=135). Methodologically, the study implements propensity score matching as its baseline framework, and extends the analysis using a multi-dimensional Mahalanobis distance nearest-neighbor matching algorithm (nn=3) to address localized perfect prediction anomalies inherent in the baseline logistic covariates. Internal validity is rigorously assessed through a Rosenbaum bounds sensitivity analysis to evaluate the vulnerability of the matched pairs to hidden unobserved endogeneity. The empirical results reveal a structural hierarchy in post-disaster household coping mechanisms. Crop insurance exerts a negative and statistically significant effect on immediate food consumption shocks (ATT=-0.236, ρ=0.030), reducing the probability of severe household food security contractions by 23.61 percentage points. This welfare-stabilizing effect proves highly resilient against hidden selection bias, remaining statistically robust up to an extreme log-odds threshold (γ>2.0). Conversely, the treatment effect on reducing the probability of cuts in domestic non-food expenditures is marginally insignificant (ATT=-0.167, ρ=0.119), while the probability of post-flood debt accumulation remains statistically unaltered (ATT=0.031, p=0.753). These findings indicate that although current indemnity programs effectively serve as short-term consumption-smoothing shields that protect food security, they fail to alleviate systemic credit dependencies. Because conventional crop insurance schemes are calibrated to variable production inputs rather than comprehensive asset recovery, policy frameworks must transition toward parametric, asset-based coverage bundled with formal credit instruments to strengthen comprehensive post flood disaster coping mechanisms.

1. [1] Intergovernmental Panel on Climate Change (IPCC), “Global warming of 1.5°C: An IPCC special report,” 2018.
2. [2] M. Carter, A. de Janvry, E. Sadoulet, and A. Sarris, “Index insurance for developing country agriculture: A reassessment,” Annu. Rev. Resour. Econ., Vol.9, pp. 421-438, 2017. https://doi.org/10.1146/annurev-resource-100516-053352
3. [3] J. M. Flores-Contró, K. Henshaw, S.-H. Loke, S. Arnold, and C. Constantinescu, “Subsidising inclusive insurance to reduce poverty,” arXiv:2103.17255, 2024. https://doi.org/10.48550/arXiv.2103.17255
4. [4] D. Biswal and C. S. Bahinipati, “Why are farmers not insuring crops against risks in India? A review,” Prog. Disaster Sci., Vol.15, Article No.100241, 2022. https://doi.org/10.1016/j.pdisas.2022.100241
5. [5] A. C. C. Rola and N. J. V. B. Querijero, “Efficiency and effectiveness of the Philippine Crop Insurance Corporation’s rice crop insurance program: The case of Laguna Province, Philippines,” J. Public Aff. Dev., Vol.4, pp. 29-49, 2017.
6. [6] A. Medina, “Assessment of PCIC insurance program in Pampanga,” Unpublished undergraduate thesis, University of the Philippines Los Baños, 2016.
7. [7] A. Kumar, S. Saroj, and A. K. Mishra, “Crop insurance and crop productivity: Evidence from rice farmers in eastern India,” IFPRI Discussion Paper, No.1996, 2021. https://doi.org/10.2499/p15738coll2.134239
8. [8] M. Caliendo and S. Kopeinig, “Some practical guidance for the implementation of propensity score matching,” J. Econ. Surv., Vol.22, No.1, pp. 31-72, 2008. https://doi.org/10.1111/j.1467-6419.2007.00527.x
9. [9] P. R. Rosenbaum and D. B. Rubin, “The central role of the propensity score in observational studies for causal effects,” Biometrika, Vol.70, No.1, pp. 41-55, 1983. https://doi.org/10.1093/biomet/70.1.41
10. [10] J. A. Smith and P. E. Todd, “Does matching overcome LaLonde’s critique of nonexperimental estimators?,” J. Econom., Vol.125, Nos.1-2, pp. 305-353. 2005. https://doi.org/10.1016/j.jeconom.2004.04.011
11. [11] Z. Zhao, “Using matching to estimate treatment effects: Data requirements, matching metrics, and Monte Carlo evidence,” Rev. Econ. Stat., Vol.86, No.1, pp. 91-107, 2004. https://doi.org/10.1162/003465304323023705
12. [12] M. M. Garrido et al., “Methods for constructing and assessing propensity scores,” Health Serv. Res., Vol.49, No.5, pp. 1701-1720, 2014. https://doi.org/10.1111/1475-6773.12182
13. [13] J. K. B. Arias, M. E. T. Mendoza, V. G. Ballaran, and R. A. Dorado, “Household vulnerability to climate change in selected municipalities in Laguna, Philippines,” Economy and Environment Program for Southeast Asia (EEPSEA) Research Report, 2014. https://eepsea.net/pub/rr/2014-RR1_Arias%20et%20alHH_web.pdf [Accessed September 8, 2025]
14. [14] P. Nepomuceno, “Agri, infra damage due to ‘Ulysses’ reaches P16-B,” Philippine News Agency, November 27, 2020. https://www.pna.gov.ph/articles/1123152 [Accessed September 8, 2025]
15. [15] Provincial Disaster Risk Reduction Management Office of Laguna (PDRRMO), “Typhoon ‘Ulysses’ updated report December 1, 2020 (1500H),” 2020.
16. [16] M. A. O. Catelo et al., “Impact assessment of floods and drought in selected agricultural municipalities in Laguna, Philippines,” Terminal Report. Department of Science and Technology-Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development (DOST-PCAARRD), 2024.