Short-Run Impacts of Floods: A Case Study from India

Robert C. M. Beyer; Abhinav Narayanan; Gogol Mitra Thakur

doi:10.1515/bejm-2023-0195

Article

Short-Run Impacts of Floods: A Case Study from India

Robert C. M. Beyer , Abhinav Narayanan and Gogol Mitra Thakur

Published/Copyright: May 22, 2025

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From the journal The B.E. Journal of Macroeconomics Volume 25 Issue 1

Abstract

This paper examines the short-run economic impacts of the 2018 Kerala flood, the third most severe flood in India since 1900, utilizing a variety of monthly data. During the disaster, both household income and expenditure declined significantly, hitting their lowest levels three months after the onset of the event. Expenditure then quickly rebounded to pre-disaster levels, in line with changes in ATM transactions. Household income in contrast surged significantly above pre-disaster levels, propelled by markedly higher wage income. Finally, households borrowed more for housing and consumer durables and aggregate credit increased. We provide indirect evidence that the increase in wage income may be linked to reconstruction efforts and the tightening of the labor market. The findings highlight that while the immediate economic impact of disasters can be severe, reconstruction efforts and government support can be crucial in accelerating economic recovery in the aftermath of natural disasters.

Keywords: floods; household behavior; consumption; reconstruction

JEL Classification: Q54; E23; E21; D12; R22

Corresponding author: Gogol Mitra Thakur, Centre for Development Studies, Thiruvananthapuram, India, E-mail: gogol@cds.edu

The views and opinions expressed in this paper are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management, the Reserve Bank of India, or any other institution which the authors may be affiliated with. The authors gratefully acknowledge comments and suggestions from two anonymous referees, Martin Rama and Norman Loayza. This paper has also benefited from feedback provided by participants at the 2022 “100 Years of Economic Development” Conference at Cornell University, the 2021 South Asian Economic Development Conference organized by the South Asian University, the 2021 Economic Theory and Policy Conference organized by the National Institute of Public Finance and Policy, the 2019 Annual Conference on Economic Growth and Development organized by the Indian Statistical Institute, and of seminars at the Centre for Development Studies (Thiruvananthapuram), the Indian Institute of Management (Kolkata), the Indian Institute of Management (Lucknow), the New Castle University Business School, and the Fourth Meeting of the Finance and Macroeconomics Group, India. The authors thank the National Payments Corporation of India for providing the data on ATM transactions. Previous versions of this paper have been circulated under the titles Natural Calamities and Household Finance: Evidence from Kerala Floods, Natural Disaster and the Economy: Evidence from the Kerala floods of 2018 and Natural Disasters and Economic Dynamics: Evidence from the Kerala Floods.

Appendix A: Figures

Figure A.1:

Actual and normal monsoon rainfall in Kerala: 2012–2019. Notes: This figure plots the actual and normal rainfall in Kerala during monsoon months (June–September) for each year from 2012 to 2019. Normal rainfall is the long period average based on past 50 years of data as reported by the IMD. Source: Authors’ calculation based on data from India Meteorological Department.

Figure A.2:

Rainfall during June–Dec 2018. (a) Kerala, (b) Karnataka, Kerala and Tamil Nadu. Notes: Panel (a) compares daily actual and normal rainfall in Kerala during June to December of 2018. Normal rainfall is the long period average based on past 50 years of data as reported by the IMD. Panel (b) compares daily actual rainfall in Karnataka, Kerala and Tamil Nadu during the same period. Source: India Meteorological Department.

Figure A.3:

Monthly effects on household expenditure and income (results from the trimmed sample). (a) Total expenditure, (b) total income. Notes: This figure plots β _ts obtained from estimating equation (2) on the trimmed household sample described in Section 5.3. Outcome variables in panels (a) and (b) are natural logarithms of household total expenditure and income (in per capita terms) respectively. May 2018 is the base month. Standard errors are clustered at the district-month level. The vertical lines are 95 percent confidence intervals. Source: Authors’ calculation based on data form the Consumer Pyramids Household Surveys (CPHS) database, Centre for Monitoring Indian Economy.

Appendix B: Tables

Table B.1:

District-wise allotment of state disaster response funds.

District	ln(Relief)	ln(Repair)
Alappuzha	5.42	4.25
Ernakulam	5.49	3.90
Idukki	4.42	3.99
Kannur	2.71	2.07
Kasaragod	3.28	1.61
Kollam	3.02	1.73
Kottayam	4.91	3.01
Kozhikode	3.95	2.56
Malappuram	3.71	1.98
Palakkad	3.52	2.87
Pathanamthitta	5.03	3.73
Thiruvananthapur	2.76	1.51
Thrissur	5.53	3.93
Wayanad	4.60	4.80

Notes: Relief and Repair, respectively, are the per capita assistance funding allocated to the districts of Kerala by the Government of Kerala through government orders G.O. (Rt) No. 460/2018/DMD and G.O. (Rt) No. 677/2018/DMD, issued on August 27 and December 13, 2018 respectively. ln = natural logarithm. Source: Authors’ calculation using government orders G.O. (Rt) No. 460/2018/DMD and G.O. (Rt) No. 677/2018/DMD of the Government of Kerala and the Census.

Table B.2:

Effect on household total, food and non-essential expenditures.

	(1)	(2)	(3)
	Total	Food	Non-essential
	expenditure	expenditure	expenditure
Affected*Oct17	−0.151^*	0.042	−0.236^*
	(0.069)	(0.076)	(0.106)
Affected*Nov17	−0.049	0.031	−0.103
	(0.060)	(0.054)	(0.107)
Affected*Dec17	0.022	0.082⁺	0.024
	(0.056)	(0.049)	(0.107)
Affected*Jan18	−0.076	0.047	0.017
	(0.057)	(0.049)	(0.104)
Affected*Feb18	−0.006	0.030	0.036
	(0.054)	(0.045)	(0.096)
Affected*Mar18	0.008	0.025	0.021
	(0.046)	(0.038)	(0.087)
Affected*Apr18	0.028	0.050	0.065
	(0.055)	(0.044)	(0.095)
Affected*Jun18	−0.009	−0.080	−0.077
	(0.060)	(0.063)	(0.112)
Affected*Jul18	−0.092	−0.105	−0.130
	(0.063)	(0.067)	(0.106)
Affected*Aug18	−0.100⁺	−0.122^*	−0.142
	(0.057)	(0.060)	(0.098)
Affected*Sep18	−0.143⁺	−0.093	−0.139
	(0.073)	(0.067)	(0.116)
Affected*Oct18	−0.094	−0.004	−0.062
	(0.066)	(0.055)	(0.111)
Affected*Nov18	−0.078	0.015	0.038
	(0.073)	(0.054)	(0.105)
Affected*Dec18	0.016	0.081	0.108
	(0.068)	(0.056)	(0.114)
Affected*Jan19	0.052	0.061	0.124
	(0.070)	(0.061)	(0.122)
Affected*Feb19	0.082	0.065	0.144
	(0.074)	(0.065)	(0.119)
Affected*Mar19	0.046	0.024	0.028
	(0.076)	(0.066)	(0.124)
Affected*Apr19	0.102	0.042	0.120
	(0.085)	(0.072)	(0.140)
Affected*May19	0.097	0.050	0.090
	(0.088)	(0.075)	(0.142)
N	123,663	123,663	103,161
Cluster	468	468	468
R ²	0.73	0.76	0.53

Notes: Columns (1)–(3) of this table respectively estimates of equation (2) for natural logarithms of total, food and non-essential expenditure of households (in per capita terms). These results are from the baseline household sample described in Section 4.3. Non-essential expenditure consists of expenditure on appliances, restaurants, recreational activities, health, and beauty enhancement products and services. Affected is 1 for households in Kerala and 0 for households in districts of Karnataka and Tamil Nadu that border Kerala. Affected*Month t denote the interaction dummies for Affected and Month t. Affected*May18 is omitted. The specification includes household and month fixed effects and district-month time trends. Standard errors are clustered at the district-month level. ⁺ p < 0.10, ^* p < 0.05. Source: Authors’ calculation based on data form the Consumer Pyramids Household Surveys (CPHS) database, Centre for Monitoring Indian Economy.

Table B.3:

Effect on household total, wage and non-wage incomes.

	(1)	(2)	(3)
	Total	Wage	Non-wage
	income	income	income
Affected*Oct17	−0.045	−0.022	−0.293⁺
	(0.068)	(0.063)	(0.170)
Affected*Nov17	−0.040	−0.017	−0.344^*
	(0.058)	(0.051)	(0.145)
Affected*Dec17	−0.027	−0.004	−0.188
	(0.055)	(0.047)	(0.128)
Affected*Jan18	−0.011	0.011	−0.246^*
	(0.052)	(0.044)	(0.112)
Affected*Feb18	0.006	0.037	−0.134
	(0.050)	(0.042)	(0.102)
Affected*Mar18	−0.009	0.016	−0.129
	(0.060)	(0.054)	(0.090)
Affected*Apr18	0.015	0.030	−0.059
	(0.058)	(0.048)	(0.063)
Affected*Jun18	−0.017	−0.032	−0.071
	(0.060)	(0.053)	(0.073)
Affected*Jul18	−0.059	−0.065	−0.197
	(0.056)	(0.046)	(0.138)
Affected*Aug18	−0.051	−0.056	−0.462^*
	(0.052)	(0.045)	(0.113)
Affected*Sep18	0.031	0.041	−0.421^*
	(0.056)	(0.048)	(0.124)
Affected*Oct18	0.107⁺	0.133^*	−0.454^*
	(0.055)	(0.049)	(0.146)
Affected*Nov18	0.182^*	0.199^*	−0.287⁺
	(0.066)	(0.056)	(0.154)
Affected*Dec18	0.194^*	0.196^*	−0.327⁺
	(0.063)	(0.058)	(0.174)
Affected*Jan19	0.178^*	0.175^*	−0.147
	(0.065)	(0.059)	(0.191)
Affected*Feb19	0.180^*	0.170^*	−0.290
	(0.065)	(0.060)	(0.216)
Affected*Mar19	0.230^*	0.219^*	−0.325
	(0.067)	(0.065)	(0.232)
Affected*Apr19	0.235^*	0.240^*	−0.518^*
	(0.071)	(0.068)	(0.250)
Affected*May19	0.252^*	0.264^*	−0.573^*
	(0.074)	(0.071)	(0.279)
N	123,585	101,037	62,507
Cluster	468	468	448
R ²	0.74	0.76	0.85

Notes: Columns (1)–(3) of this table respectively report estimates equation (2) for natural logarithms total, wage and non-wage income of households (in per capita terms). These results are from the baseline household sample described in Section 4.3. Non-wage income is all income other than labor income. Affected is 1 for households in Kerala and 0 for households in districts of Karnataka and Tamil Nadu that border Kerala. Affected*Month t denote the interaction dummies for Affected and Month t. Affected*May18 is omitted. The specification includes household and month fixed effects and district-month time trends. Standard errors are clustered at the district-month level. ⁺ p < 0.10, ^* p < 0.05. Source: Authors’ calculation based on data form the Consumer Pyramids Household Surveys (CPHS), Centre for Monitoring Indian Economy.

Table B.4:

Summary statistics (trimmed household sample).

	(1)	(2)	(3)	(4)
	Control	Treatment	Difference	P-value
Total income (in ln)	8.41	8.59	−0.18	0
Wage income (in ln)	8.38	8.54	−0.17	0
Non-wage income (in ln)	5.03	7.61	−2.57	0
Total expenditure (in ln)	7.89	8.25	−0.36	0
Food expenditure (in ln)	7.14	7.35	−0.21	0

Notes: All summary statistics are based on the period June 2017 to May 2018. Columns (1) and (2) report means for control and treated units restively. Column (3) reports mean difference and column (4) reports p-values the for test of mean difference. ln = natural logarithm. Household variables are expressed in terms of per household member. Source: Authors’ calculation based on data on household variables from the Consumer Pyramids Household Surveys (CPHS) database maintained by the Centre for Monitoring Indian Economy.

Table B.5:

Household expenditure and income during the floods (trimmed sample).

	(1)	(2)	(3)	(4)	(5)	(6)
	Total	Food	Non-essential	Total	Wage	Non-wage
	expenditure	expenditure	expenditure	income	income	income
Affected*Flood	−0.174^***	−0.122^***	−0.265^***	−0.076^**	−0.080^**	−0.238^***
	(0.035)	(0.031)	(0.063)	(0.027)	(0.026)	(0.070)
HH FE	Yes	Yes	Yes	Yes	Yes	Yes
Month*Year FE	Yes	Yes	Yes	Yes	Yes	Yes
District*Month Trend	Yes	Yes	Yes	Yes	Yes	Yes
N	47,161	47,161	36,075	47,120	40,954	24,021
Cluster	252	252	252	252	252	241
R ²	0.76	0.82	0.65	0.77	0.81	0.88

Notes: Columns (1)–(6) in this table report estimates of equation (1) respectively for natural logarithms of total expenditure, food expenditure, non-essential expenditure, total income, wage income and non-wage income of households (in per capita terms) using the trimmed household sample described in Section 5.3. Affected = 1 for households in Kerala and Flood = 1 for months June, July and August 2018. HH = households and FE = fixed effects. Standard errors are clustered at the district-month level.^* p < 0.05, ^** p < 0.01, ^*** p < 0.001. Source: Authors’ calculation based on data form the Consumer Pyramids Household Surveys (CPHS) database maintained by the Centre for Monitoring Indian Economy.

Table B.6:

Effect on ATM transactions.

	(1)	(2)
	Amount	Count
Affected*Oct17	−0.097^*	−0.082^*
	(0.028)	(0.024)
Affected*Nov17	0.012	−0.018
	(0.032)	(0.028)
Affected*Dec17	−0.016	0.008
	(0.027)	(0.025)
Affected*Jan18	−0.014	−0.015
	(0.026)	(0.024)
Affected*Feb18	−0.049^*	−0.033
	(0.025)	(0.022)
Affected*Mar18	−0.014	−0.015
	(0.026)	(0.024)
Affected*Apr18	−0.025	−0.014
	(0.027)	(0.025)
Affected*Jun18	−0.027	−0.012
	(0.026)	(0.024)
Affected*Jul18	−0.038	−0.026
	(0.027)	(0.023)
Affected*Aug18	0.013	−0.004
	(0.036)	(0.032)
Affected*Sep18	−0.098^*	−0.056^*
	(0.026)	(0.023)
Affected*Oct18	−0.091^*	−0.069^*
	(0.028)	(0.026)
Affected*Nov18	−0.039	−0.009
	(0.034)	(0.026)
Affected*Dec18	0.015	0.051^*
	(0.028)	(0.024)
Affected*Jan19	0.025	0.031
	(0.029)	(0.025)
Affected*Feb19	−0.030	−0.002
	(0.028)	(0.024)
Affected*Mar19	−0.056⁺	−0.015
	(0.030)	(0.025)
Affected*Apr19	−0.008	0.065^*
	(0.033)	(0.027)
Affected*May19	0.002	0.032
	(0.032)	(0.027)
N	38,642	38,659
Cluster	500	500
R ²	0.96	0.96

Notes: Columns (1)–(2) of this table respectively report estimates equation (3) respectively for natural logarithms of amount and count of ATM transactions at the postal code level. Affected is 1 for postal codes in Kerala and 0 for postal codes in districts of Karnataka and Tamil Nadu that border Kerala. Affected*Month t denote the interaction dummies for Affected and Month t. Affected*May18 is omitted. The specification includes pincode and month fixed effects and district-month time trends. Standard errors are clustered at the district-month level. ⁺ p < 0.10, ^* p < 0.05. Source: Authors’ calculation based on data form the National Payments Corporation of India.

Table B.7:

Effect on credit and deposits.

	(1)	(2)
	Credit	Deposit
Affected*CY17:Q4	0.000	−0.009
	(0.008)	(0.005)
Affected*CY18:Q1	−0.012	0.003
	(0.017)	(0.037)
Affected*CY18:Q3	0.002	0.004
	(0.005)	(0.004)
Affected*CY18:Q4	0.009	0.006
	(0.006)	(0.004)
Affected*CY19:Q1	0.024^*	−0.022^***
	(0.010)	(0.006)
N	456	456
Districts	76	76

Notes: Columns (1)–(2) of this table respectively report estimates equation (5) respectively for natural logarithms of credit and deposit of scheduled commercial banks at the district level. Affected is 1 for districts of Kerala and 0 for districts of Karnataka and Tamil Nadu. CY = Calender Year and Q = Quarter. Affected*Quarter t denote the interaction dummies for Affected and Quarter t. Affected*CY18:Q2 is omitted. The specification includes district and quarter fixed effects and state-quarter time trends. Standard errors are clustered at the district level. ^* p < 0.05, ^** p < 0.01, ^*** p < 0.001. Source: Authors’ calculation based on data form the Reserve Bank of India.

Table B.8:

Quantile regression results for wage income.

	(1)	(2)	(3)	(4)	(5)
	10th	25th	50th	75th	90th
Affected*Oct17	−0.081^*	−0.059^*	−0.025	0.005	0.024
	(0.031)	(0.022)	(0.019)	(0.029)	(0.038)
Affected*Nov17	−0.092^*	−0.068^*	−0.031⁺	0.002	0.023
	(0.029)	(0.021)	(0.018)	(0.027)	(0.035)
Affected*Dec17	−0.069^*	−0.048^*	−0.017	0.012	0.030
	(0.027)	(0.020)	(0.017)	(0.025)	(0.033)
Affected*Jan18	−0.033	−0.015	0.011	0.036	0.051
	(0.025)	(0.018)	(0.016)	(0.024)	(0.031)
Affected*Feb18	0.020	0.028	0.040^*	0.052^*	0.059^*
	(0.024)	(0.017)	(0.015)	(0.022)	(0.029)
Affected*Mar18	0.012	0.018	0.026⁺	0.034	0.039
	(0.023)	(0.016)	(0.014)	(0.021)	(0.028)
Affected*Apr18	0.019	0.021	0.024⁺	0.027	0.029
	(0.022)	(0.016)	(0.013)	(0.020)	(0.026)
Affected*Jun18	−0.043^*	−0.039^*	−0.033^*	−0.028	−0.024
	(0.020)	(0.015)	(0.012)	(0.019)	(0.025)
Affected*Jul18	−0.093^*	−0.081^*	−0.064^*	−0.049^*	−0.039⁺
	(0.019)	(0.014)	(0.012)	(0.018)	(0.024)
Affected*Aug18	−0.123^*	−0.097^*	−0.056^*	−0.020	0.003
	(0.019)	(0.014)	(0.012)	(0.018)	(0.023)
Affected*Sep18	0.014	0.028^*	0.050^*	0.071^*	0.083^*
	(0.019)	(0.014)	(0.012)	(0.018)	(0.023)
Affected*Oct18	0.157^*	0.159^*	0.161^*	0.163^*	0.165^*
	(0.018)	(0.013)	(0.011)	(0.017)	(0.023)
Affected*Nov18	0.220^*	0.211^*	0.198^*	0.185^*	0.178^*
	(0.019)	(0.014)	(0.012)	(0.018)	(0.023)
Affected*Dec18	0.234^*	0.218^*	0.193^*	0.171^*	0.157^*
	(0.020)	(0.014)	(0.012)	(0.019)	(0.024)
Affected*Jan19	0.221^*	0.208^*	0.187^*	0.169^*	0.158^*
	(0.020)	(0.015)	(0.013)	(0.019)	(0.025)
Affected*Feb19	0.188^*	0.180^*	0.169^*	0.158^*	0.152^*
	(0.021)	(0.015)	(0.013)	(0.020)	(0.026)
Affected*Mar19	0.233^*	0.225^*	0.213^*	0.201^*	0.194^*
	(0.022)	(0.016)	(0.014)	(0.021)	(0.027)
Affected*Apr19	0.262^*	0.249^*	0.229^*	0.210^*	0.199^*
	(0.024)	(0.017)	(0.015)	(0.022)	(0.029)
Affected*May19	0.275^*	0.263^*	0.246^*	0.230^*	0.220^*
	(0.001)	(0.000)	(0.001)	(0.001)	(0.001)
N	101,079	101,079	101,079	101,079	101,079

Notes: Columns (1)–(5) report estimates of equation (7) for natural logarithms of household wage income (in per family member terms) separately for the 10th, 25th, 50th, 75th, and 90th percentiles of the income distribution respectively. These results are from the baseline household sample. Affected is 1 for households in Kerala and 0 for households in districts of Karnataka and Tamil Nadu that border Kerala. Affected*Month t denote the interaction dummies for Affected and Month t. Affected*May18 is omitted. The specification includes household and month fixed effects and district-month time trends. Standard errors are in parenthesis. ⁺ p < 0.10, ^* p < 0.05. Source: Authors’ calculation based on data form the Consumer Pyramids Household Surveys (CPHS), Centre for Monitoring Indian Economy.

Table B.9:

Quantile regression results for household total income.

	(1)	(2)	(3)	(4)	(5)
	10th	25th	50th	75th	90th
Affected*Oct17	−0.144^*	−0.109^*	−0.059^*	−0.014	0.016
	(0.037)	(0.027)	(0.019)	(0.024)	(0.033)
Affected*Nov17	−0.155^*	−0.119^*	−0.066^*	−0.019	0.012
	(0.035)	(0.025)	(0.018)	(0.023)	(0.031)
Affected*Dec17	−0.144^*	−0.106^*	−0.052^*	−0.003	0.030
	(0.033)	(0.024)	(0.017)	(0.022)	(0.029)
Affected*Jan18	−0.103^*	−0.070^*	−0.023	0.020	0.049⁺
	(0.031)	(0.023)	(0.016)	(0.020)	(0.027)
Affected*Feb18	−0.051⁺	−0.030	0.001	0.028	0.047⁺
	(0.029)	(0.021)	(0.015)	(0.019)	(0.026)
Affected*Mar18	−0.040	−0.025	−0.004	0.015	0.028
	(0.028)	(0.020)	(0.014)	(0.018)	(0.024)
Affected*Apr18	−0.020	−0.010	0.005	0.018	0.027
	(0.026)	(0.019)	(0.013)	(0.017)	(0.023)
Affected*Jun18	−0.032	−0.026	−0.017	−0.008	−0.003
	(0.024)	(0.018)	(0.012)	(0.016)	(0.021)
Affected*Jul18	−0.099^*	−0.084^*	−0.060^*	−0.040^*	−0.026
	(0.024)	(0.017)	(0.012)	(0.016)	(0.021)
Affected*Aug18	−0.143^*	−0.110^*	−0.063^*	−0.020	0.009
	(0.023)	(0.017)	(0.012)	(0.015)	(0.020)
Affected*Sep18	−0.037⁺	−0.011	0.027^*	0.061^*	0.083^*
	(0.022)	(0.016)	(0.011)	(0.015)	(0.020)
Affected*Oct18	0.078^*	0.099^*	0.128^*	0.155^*	0.173^*
	(0.022)	(0.016)	(0.011)	(0.014)	(0.019)
Affected*Nov18	0.147^*	0.155^*	0.167^*	0.177^*	0.184^*
	(0.022)	(0.016)	(0.011)	(0.015)	(0.020)
Affected*Dec18	0.200^*	0.193^*	0.184^*	0.175^*	0.170^*
	(0.023)	(0.017)	(0.012)	(0.015)	(0.021)
Affected*Jan19	0.208^*	0.200^*	0.190^*	0.181^*	0.174^*
	(0.024)	(0.018)	(0.012)	(0.016)	(0.021)
Affected*Feb19	0.181^*	0.178^*	0.174^*	0.171^*	0.168^*
	(0.025)	(0.018)	(0.013)	(0.016)	(0.022)
Affected*Mar19	0.221^*	0.218^*	0.213^*	0.209^*	0.206^*
	(0.027)	(0.019)	(0.013)	(0.017)	(0.023)
Affected*Apr19	0.234^*	0.226^*	0.216^*	0.206^*	0.200^*
	(0.028)	(0.020)	(0.014)	(0.018)	(0.024)
Affected*May19	0.247^*	0.238^*	0.224^*	0.212^*	0.204^*
	(0.001)	(0.000)	(0.000)	(0.000)	(0.000)
N	123,585	123,585	123,585	123,585	123,585

Notes: Columns (1)–(5) of this table report estimates of equation (7) for natural logarithms of household total income (in per capita terms) separately for the 10th, 25th, 50th, 75th, and 90th percentiles of the income distribution respectively. These results are from the baseline household sample. Affected is 1 for households in Kerala and 0 for households in districts of Karnataka and Tamil Nadu that border Kerala. Affected*Month t denote the interaction dummies for Affected and Month t. Affected*May18 is omitted. The specification includes household and month fixed effects and district-month time trends. Standard errors are in parenthesis. ⁺ p < 0.10, ^* p < 0.05. Source: Authors’ calculation based on data form the Consumer Pyramids Household Surveys (CPHS), Centre for Monitoring Indian Economy.

Table B.10:

Quantile regression results for household total expenditure.

	(1)	(2)	(3)	(4)	(5)
	10th	25th	50th	75th	90th
Affected*Oct17	−0.201	−0.194	−0.184	−0.176^*	−0.170
	(0.445)	(0.319)	(0.158)	(0.060)	(0.122)
Affected*Nov17	−0.120	−0.111	−0.098	−0.086	−0.078
	(0.420)	(0.302)	(0.150)	(0.057)	(0.115)
Affected*Dec17	−0.002	−0.014	−0.030	−0.044	−0.053
	(0.394)	(0.283)	(0.140)	(0.053)	(0.108)
Affected*Jan18	−0.097	−0.099	−0.102	−0.104^*	−0.105
	(0.371)	(0.266)	(0.132)	(0.050)	(0.101)
Affected*Feb18	−0.091	−0.084	−0.075	−0.066	−0.061
	(0.347)	(0.249)	(0.124)	(0.047)	(0.095)
Affected*Mar18	−0.004	−0.009	−0.015	−0.020	−0.023
	(0.332)	(0.238)	(0.118)	(0.045)	(0.091)
Affected*Apr18	0.034	0.027	0.017	0.009	0.003
	(0.318)	(0.228)	(0.113)	(0.043)	(0.087)
Affected*Jun18	−0.065	−0.054	−0.039	−0.025	−0.016
	(0.309)	(0.222)	(0.110)	(0.042)	(0.084)
Affected*Jul18	−0.177	−0.137	−0.083	−0.035	−0.002
	(0.320)	(0.230)	(0.114)	(0.043)	(0.088)
Affected*Aug18	−0.193	−0.169	−0.138	−0.111^*	−0.092
	(0.289)	(0.207)	(0.103)	(0.039)	(0.079)
Affected*Sep18	−0.242	−0.216	−0.182⁺	−0.152^*	−0.131⁺
	(0.291)	(0.209)	(0.104)	(0.039)	(0.080)
Affected*Oct18	−0.173	−0.155	−0.131	−0.110^*	−0.096
	(0.273)	(0.196)	(0.097)	(0.037)	(0.075)
Affected*Nov18	−0.145	−0.141	−0.136	−0.131^*	−0.128⁺
	(0.268)	(0.193)	(0.096)	(0.036)	(0.073)
Affected*Dec18	0.078	0.051	0.016	−0.015	−0.037
	(0.289)	(0.207)	(0.103)	(0.039)	(0.079)
Affected*Jan19	0.158	0.127	0.087	0.051	0.027
	(0.304)	(0.218)	(0.108)	(0.041)	(0.083)
Affected*Feb19	0.137	0.118	0.093	0.071⁺	0.055
	(0.305)	(0.219)	(0.109)	(0.041)	(0.083)
Affected*Mar19	0.048	0.046	0.042	0.040	0.038
	(0.314)	(0.226)	(0.112)	(0.042)	(0.086)
Affected*Apr19	0.058	0.038	0.011	−0.013	−0.029
	(0.336)	(0.241)	(0.120)	(0.045)	(0.092)
Affected*May19	−0.015^*	−0.014^*	−0.014^*	−0.014^*	−0.014^*
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
N	123,663	123,663	123,663	123,663	123,663

Notes: Columns (1)–(5) of this table report estimates of equation (7) for natural logarithms of household total expenditure (in per capita terms) separately for the 10th, 25th, 50th, 75th, and 90th percentiles of the income distribution respectively. These results are from the baseline household sample. Affected is 1 for households in Kerala and 0 for households in districts of Karnataka and Tamil Nadu that border Kerala. Affected*Month t denote the interaction dummies for Affected and Month t. Affected*May18 is omitted. The specification includes household and month fixed effects and district-month time trends. Standard errors are in parenthesis. ⁺ p < 0.10, ^* p < 0.05. Source: Authors’ calculation based on data form the Consumer Pyramids Household Surveys (CPHS), Centre for Monitoring Indian Economy.

Table B.11:

Allocation of disaster response funds and household income growth: II.

	(1)	(2)	(3)	(4)	(5)
	Jan19	Feb19	Mar19	Apr19	May19
Relief p.c (in ln)	−0.051	0.093	−3.767^***	−2.248^*	−3.895^***
	(0.938)	(0.964)	(0.972)	(0.984)	(0.972)
Income change (Aug–May)	−0.552^***	−0.532^***	−0.466^***	−0.403^***	−0.401^***
	(0.028)	(0.028)	(0.028)	(0.026)	(0.028)
Controls	Yes	Yes	Yes	Yes	Yes
N	3,451	3,450	3,406	3,425	3,381

Notes: This table is a continuation of Table 5 in Section 7. Relief p.c. = per capita assistance fund allotted to the districts of Kerala by the Government of Kerala on 27th August 2018. ln = natural logarithm. Columns (1)–(5) report estimates of equation (8) for months January, February, March, April and May of 2019. Robust standard errors in parenthesis. ^* p < 0.05, ^** p < 0.01, ^*** p < 0.001. Source: Authors’ calculation using data from the Consumer Pyramids Household Surveys (CPHS) database of the Centre for Monitoring Indian Economy, government order G.O. (Rt) No. 460/2018/DMD of the Government of Kerala, the Census and the Reserve Bank of India.

Table B.12:

Allocation of disaster response funds and household income growth: III.

	(1)	(2)	(3)	(4)	(5)
	Jan19	Feb19	Mar19	Apr19	May19
Repair p.c. (Ln)	2.787^**	2.390^*	−1.213	0.356	−1.065
	(0.967)	(0.998)	(1.011)	(1.028)	(1.026)
Income change (Aug–May)	−0.545^***	−0.527^***	−0.462^***	−0.398^***	−0.397^***
	(0.027)	(0.028)	(0.028)	(0.027)	(0.028)
Controls	Yes	Yes	Yes	Yes	Yes
N	3,451	3,450	3,406	3,425	3,381

Notes: Repair p.c. = per capita assistance fund allotted to the districts of Kerala by the Government of Kerala on December 13, 2018 specifically for the repair of houses damaged during the flood. ln = natural logarithm. This table reports results from estimating equation (8) with Repair p.c. instead of Relief p.c. Columns (1)–(5) report the results for the months of January, February, March, April and May of 2019. Robust standard errors in parenthesis. ^* p < 0.05, ^** p < 0.01, ^*** p < 0.001. Source: Authors’ calculation using data from the Consumer Pyramids Household Surveys (CPHS) database of the Centre for Monitoring Indian Economy, government order G.O. (Rt) No. 677/2018/DMD of the Government of Kerala, the Census and the Reserve Bank of India.

References

Acconcia, A., G. Corsetti, and S. Simonelli. 2020. “Liquidity and Consumption: Evidence from Three Post-Earthquakes Reconstruction Programs in Italy.” American Economic Journal: Macroeconomics 12: 3.10.1257/mac.20180190Search in Google Scholar

Akter, S., and B. Mallick. 2013. “The Poverty – Vulnerability – Resilience Nexus: Evidence from Bangladesh.” Ecological Economics 96: 114–24. https://doi.org/10.1016/j.ecolecon.2013.10.008.Search in Google Scholar

Albala-Bertrand, J. M. 1993. Political Economy of Large Natural Disasters. Oxford, UK: Clarendon Press.10.1093/oso/9780198287650.001.0001Search in Google Scholar

Arouri, M., C. Nguyen, and A. B. Youssef. 2015. “Natural Disasters, Household Welfare, and Resilience: Evidence from Rural Vietnam.” World Development 70: 59–77. https://doi.org/10.1016/j.worlddev.2014.12.017.Search in Google Scholar

Bandyopadhyay, S., and E. Skoufias. 2015. “Rainfall Variability, Occupational Choice, and Welfare in Rural Bangladesh.” Review of Economics of the Household 13 (3): 589–634. https://doi.org/10.1007/s11150-013-9203-z.Search in Google Scholar

Bui, A. T., M. Dungey, C. V. Nguyen, and T. P. Pham. 2014. “The Impact of Natural Disasters on Household Income, Expenditure, Poverty and Inequality: Evidence from Vietnam.” Applied Economics 46 (15): 1751–66. https://doi.org/10.1080/00036846.2014.884706.Search in Google Scholar

Cavallo, E., and I. Noy. 2011. “Natural Disasters and the Economy – A Survey.” International Review of Environmental and Resource Economics 5 (1): 63–102. https://doi.org/10.1561/101.00000039.Search in Google Scholar

Cavallo, E., S. Galiani, I. Noy, and J. Pantano. 2013. “Catastrophic Natural Disasters and Economic Growth.” The Review of Economics and Statistics 95 (5): 1549–61. https://doi.org/10.1162/rest_a_00413.Search in Google Scholar

Chodorow-Reich, G., G. Gopinath, P. Mishra, and A. Narayanan. 2019. “Cash and the Economy: Evidence from India’s Demonetization.” Quarterly Journal of Economics 135 (1): 57–103. https://doi.org/10.1093/qje/qjz027.Search in Google Scholar

Coffman, M., and I. Noy. 2012. “Hurricane Iniki: Measuring the Long-Term Economic Impact of a Natural Disaster Using Synthetic Control.” Environment and Development Economics 17 (2): 187–205. https://doi.org/10.1017/s1355770x11000350.Search in Google Scholar

Cuaresma, C. J., J. Hlouskova, and M. Oberstriner. 2008. “Natural Disasters as Creative Destruction? Evidence from Developing Countries.” Economic Inquiry 46 (2): 214–26. https://doi.org/10.1111/j.1465-7295.2007.00063.x.Search in Google Scholar

CWC. 2018. Study Report: Kerala Floods of 2018. Report. Central Water Commission, Government of India.Search in Google Scholar

Das, S., L. Gadenne, T. Nandi, and R. Warwick. 2023. “Does Going Cashless Make You Tax-Rich? Evidence from India’s Demonetization Experiment.” Journal of Public Economics 224: 104907. https://doi.org/10.1016/j.jpubeco.2023.104907.Search in Google Scholar

Felbermayr, G., and J. Gröschl. 2014. “Naturally Negative: The Growth Effects of Natural Disasters.” Journal of Development Economics 111: 92–106. https://doi.org/10.1016/j.jdeveco.2014.07.004.Search in Google Scholar

Felbermayr, G., J. Gröschl, M. Sanders, V. Schippers, and T. Steinwachs. 2022. “The Economic Impact of Weather Anomalies.” World Development 151: 105745. https://doi.org/10.1016/j.worlddev.2021.105745.Search in Google Scholar

Fomby, T., Y. Ikeda, and N. V. Loayza. 2013. “The Growth Aftermath of Natural Disasters.” Journal of Applied Econometrics 28 (3): 412–34. https://doi.org/10.1002/jae.1273.Search in Google Scholar

Gallagher, J., and D. Hartley. 2017. “Household Finance After a Natural Disaster: The Case of Hurricane Katrina.” American Economic Journal: Economic Policy 9 (3): 199–228. https://doi.org/10.1257/pol.20140273.Search in Google Scholar

GOK-UNDP. 2018. Kerala: Post Disaster Needs Assessment (PDNA). Report. Government of Kerala and UNDP.Search in Google Scholar

Goswami, B. N., V. Venugopal, D. Sengupta, M. S. Madhusoodanan, and P. K. Xavier. 2006. “Increasing Trend of Extreme Rain Events Over India in a Warming Environment.” Science 314 (5804): 1442–5. https://doi.org/10.1126/science.1132027.Search in Google Scholar

Heger, M. P., and E. Neumayer. 2019. “The Impact of the Indian Ocean Tsunami on Aceh’s Long-Term Economic Growth.” Journal of Development Economics 141: 102365. https://doi.org/10.1016/j.jdeveco.2019.06.008.Search in Google Scholar

Hsiang, S. M., and A. S. Jina. 2014. The Causal Effect of Environmental Catastrophe on Long-Run Economic Growth: Evidence from 6,700 Cyclones. Working Paper 20352. National Bureau of Economic Research.10.3386/w20352Search in Google Scholar

Jaramillo, C. R. H. 2009. Do Natural Disasters Have Long-Term Effects on Growth? Documentos CEDE 006647. Universidad de los Andes - CEDE.10.2139/ssrn.1543453Search in Google Scholar

Karim, A. 2018. “The Household Response to Persistent Natural Disasters: Evidence from Bangladesh.” World Development 103: 40–59. https://doi.org/10.1016/j.worlddev.2017.10.026.Search in Google Scholar

Kirchberger, M. 2017. “Natural Disasters and Labor Markets.” Journal of Development Economics 125: 40–58. https://doi.org/10.1016/j.jdeveco.2016.11.002.Search in Google Scholar

Kocornick-Mina, A., T. K. J. McDermott, G. Michaels, and F. Rauch. 2020. “Flooded Cities.” American Economic Journal: Applied Economics 12: 2.10.1257/app.20170066Search in Google Scholar

Kurosaki, T. 2015. “Vulnerability of Household Consumption to Floods and Droughts in Developing Countries: Evidence from Pakistan.” Environment and Development Economics 20 (2): 209–35. https://doi.org/10.1017/s1355770x14000357.Search in Google Scholar

Loayza, N. V., E. Olaberría, J. Rigolini, and L. Christiaensen. 2012. “Natural Disasters and Growth: Going Beyond the Averages.” World Development 40 (7): 1317–36. https://doi.org/10.1016/j.worlddev.2012.03.002.Search in Google Scholar

Mottaleb, K. A., S. Mohanty, H. T. K. Hoang, and R. M. Rejesus. 2013. “The Effects of Natural Disasters on Farm Household Income and Expenditures: A Study on Rice Farmers in Bangladesh.” Agricultural Systems 121: 43–52. https://doi.org/10.1016/j.agsy.2013.06.003.Search in Google Scholar

Noy, I. 2009. “The Macroeconomic Consequences of Disasters.” Journal of Development Economics 88 (2): 221–31. https://doi.org/10.1016/j.jdeveco.2008.02.005.Search in Google Scholar

Parida, J. K., and K. R. Raman. 2021. In-Migration, Informal Employment and Urbanization in Kerala. Report. State Planning Board, Government of Kerala.Search in Google Scholar

Parida, Y., S. Saini, and J. R. Chowdhury. 2020. “Economic Growth in Aftermath of Floods in Indian States.” Environment, Development and Sustainability 23 (1): 535–61. https://doi.org/10.1007/s10668-020-00595-3.Search in Google Scholar

Patnaik, I., R. Sane, and A. Shah. 2019. Chennai 2015: A Novel Approach to Measuring Impact of a Natural Disaster. Working Paper 285. National Institute of Public Finance and Policy.Search in Google Scholar

Raddatz, C. 2007. “Are External Shocks Responsible for the Instability of Output in Low-Income Countries?” Journal of Development Economics 84 (1): 155–87. https://doi.org/10.1016/j.jdeveco.2006.11.001.Search in Google Scholar

Raddatz, C. 2009. “The Wrath of God: Macroeconomic Costs of Natural Disasters.” Policy Research working paper WPS 5039. The World Bank.10.1596/1813-9450-5039Search in Google Scholar

Raman, K. R. 2020. “Ecospatiality: Transforming Kerala’s Post-Flood ‘Riskscapes’.” Cambridge Journal of Regions, Economy and Society 13 (2): 319–41. https://doi.org/10.1093/cjres/rsaa023.Search in Google Scholar

Roxy, M. K., S. Ghosh, A. Pathak, R. Athulya, M. Mujumdar, R. Murtugudde, P. Terray, and M. Rajeevan. 2017. “A Threefold Rise in Widespread Extreme Rain Events over Central India.” Nature Communications 8: 1–11. https://doi.org/10.1038/s41467-017-00744-9.Search in Google Scholar

Sawada, Y., and S. Shimizutani. 2008. “How Do People Cope with Natural Disasters? Evidence from the Great Hanshin-Awaji (Kobe) Earthquake in 1995.” Journal of Money, Credit, and Banking 40 (2-3): 463–88. https://doi.org/10.1111/j.1538-4616.2008.00122.x.Search in Google Scholar

Skidmore, M., and H. Toya. 2002. “Do Natural Disasters Promote Long-Run Growth?” Economic Inquiry 40 (4): 664–87. https://doi.org/10.1093/ei/40.4.664.Search in Google Scholar

Strobl, E. 2011. “The Economic Growth Impact of Hurricanes: Evidence from U.S. Coastal Counties.” The Review of Economics and Statistics 93 (2): 575–89. https://doi.org/10.1162/rest_a_00082.Search in Google Scholar

Received: 2023-11-24

Accepted: 2025-04-18

Published Online: 2025-05-22

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Articles in the same Issue

https://doi.org/10.1515/bejm-2023-0195

Keywords for this article

floods; household behavior; consumption; reconstruction