Flood disaster is a recurrent phenomenon in India. The geo-climatic conditions are a major cause for higher flood damages in Indian states. This study employs the pooled mean group (PMG) approach to examine economic growth in the aftermath of floods in 14 non-special category states in India using flood disaster data over the period 1981–2011. The PMG estimates show that flood impacts in terms of area affected, the population affected, and economic losses due to floods decline real per capita GSDP growth in the long run after taking into account growth-enhancing factors. Moreover, instrumental variables (IV)-two-stage least-squares (2SLS) estimates also confirm that states experience lower real per capita GSDP growth due to higher flood impacts. The results, further, show that higher urbanization and enrollment of higher education increases real per capita GSDP growth in the long run. For robustness check, the study investigates the impact of floods on real per capita GSDP growth after taking into account government capital expenditure and per capita power consumption. The PMG and IV-2SLS estimates produce same results and confirm that flood impacts reduce real per capita GSDP growth, while government capital expenditure and per capita power consumption enhances real per capita GSDP growth. Overall, results confirm that flood management policies are essential to minimize the adverse impacts of floods on the growth of real per capita GSDP.
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There are some limitations to estimating damages due to flood by the respective State Governments in India. We argue that damages due to floods are not accurately measured in various states of India for the following reasons. First, in the case of developing economies like India, there is inadequate insurance coverage for private properties such as house and crops. Second, the State Governments do not follow specific criteria to estimate the exact amount of disaster damages. Finally, the State Governments submit inflated disaster damage figures to the Central Government to receive high disaster grants.
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We have taken lagged one period value of urbanization to mitigate potential endogeneity bias.
Tables 5 and 11 show the F-Stat in the first stage regression, and their p values are significant, which implies the validity of the instrument. In other words, the instrument variable, “population density” is correlated with the endogenous variables such as “area affected,” “the population affected,” and “economic losses due to floods,” but uncorrelated with the error terms. In the 2SLS method, the p value of the F-Stat is not sufficient to conclude the validity of the instrument. To ensure the validity of the instrument, F-Stat should be more than 10 (Stock et al. 2002). If F-Stat is less than 10, it implies that the instrument is weakly correlated with endogenous variables, and in that case, the IV-2SLS produces the biased estimates. Poi (2006) and Stock et al. (2002) suggested that the limited information maximum likelihood (LIML) model performs better than the 2SLS method. In our case, F-Stat is less than 10, except for model 3 in Table 11. This is the limitation of our instrument, and therefore, we estimate the LIML model to reduce the biasedness of our estimates. Our estimates produce the same results. Results are available upon request.
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We gratefully acknowledge two anonymous reviewers for their comments and suggestions. We also thank Prof. B. N. Goldar, Dr. Rashmi Rekha Barua, and Dr. Parul Bhardwaj for valuable comments and suggestions. An earlier draft was presented at 4th International Conference on South Asian Economic Development, Faculty of Economics, South Asian University, New Delhi, February 22–23, 2018. This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors. This manuscript is a part of Mr. Yashobanta Parida’s PhD thesis.
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In our econometric analysis, we have used 14 major non-special category states for the following reasons. First, special category states in India are heavily dependent on central government assistance because these states are unable to generate adequate revenues to meet their expenditures. Second, these states marginally contribute to national GDP due to an inadequate expansion of manufacturing, services, construction sectors, and due to the poor resource base. Third, these states are historically disadvantaged as compared to other states in terms of different socioeconomic indicators such as higher vulnerable population (i.e., tribal population), the geographical location of the states, inadequate infrastructural facilities as well as low population density. Fourth, the financial market is inadequate to enhance further economic growth due to poor resource allocation. Fifth, according to the Normal Central Assistance (NCA), special category state receives 90% grant and 10% loans from the central government, while the ratio between grants and loans is 30:70 for non-special category states.
Moreover, there are 17 non-special category states in India since 2011. We have considered 14 major states and did not consider 3 non-special categories such as Goa, Chhattisgarh, and Jharkhand for the following reasons. For Goa and Chhattisgarh, the data on the area affected and the population affected by floods are not available and data on economic loss due to flood are available for 7 years over the period 1981–2011. In the case of Jharkhand, flood impact data such as area affected, the population affected, and economic loss due to flood data are not available. In sum, due to the reasons described, we could not include 3 non-special categories states in our analysis (see Tables 8, 9, 10, 11).
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Parida, Y., Saini, S. & Chowdhury, J.R. Economic growth in the aftermath of floods in Indian states. Environ Dev Sustain (2020). https://doi.org/10.1007/s10668-020-00595-3
- Per capita GSDP growth
- Damage due to flood
- PMG estimation