metadata: authors: 0: author: Beyer,Robert Carl Michael
1: author: Narayanan,Abhinav
2: author: Thakur,Gogol Mitra
count: India,World
docna: 0: docna: Natural Disasters and Economic Dynamics :
            Evidence from the Kerala Floods
docty: Policy Research Working Paper
abstracts: cdata!: Exceptionally high rainfall in the
            Indian state of Kerala caused major flooding in 2018. This
            paper estimates the short-run causal impact of the disaster
            on the economy, using a difference-in-difference approach.
            Monthly nighttime light intensity, a proxy for aggregate
            economic activity, suggests that activity declined for three
            months during the disaster but boomed subsequently.
            Automated teller machine transactions, a proxy for consumer
            demand, declined and credit disbursal increased, with
            households borrowing more for housing and less for
            consumption. In line with other results, both household
            income and expenditure declined during the floods. Despite a
            strong wage recovery after the floods, spending remained
            lower relative to the unaffected districts. The paper argues
            that increased labor demand due to reconstruction efforts
            increased wages after the floods and provides corroborating
            evidence: (i) rural labor markets tightened, (ii) poorer
            households benefited more, and (iii) wages increased most
            where government relief was strongest. The findings confirm
            the presence of interesting economic dynamics during and
            right after natural disasters that remain in the shadow when
            analyzed with annual data.
display_title: Natural Disasters and Economic
            Dynamics : Evidence from the Kerala Floods
listing_relative_url: /research/2022/06/33841529/natural-disasters-economic-dynamics-evidence-kerala-floods
topicv3: Climate Change m1326829 567,Flood m1503315 3216,Natural Disaster m1503325 3138
publishtoextweb_dt: 2022-06-13T00:00:00Z
dois: 10.1596/1813-9450-10084
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1501628
1: author_key: NA
2: author_key: NA
model_extractions: Policy Research Working Paper 10084

# Natural Disasters and Economic Dynamics

Evidence from the Kerala Floods _Robert C. M. Beyer Abhinav Narayanan Gogol Mitra Thakur_ Macroeconomics, Trade and Investment Global Practice June 2022
Policy Research Working Paper 10084

# **Abstract**

Exceptionally high rainfall in the Indian state of Kerala caused major flooding in 2018. This paper estimates the short-run causal impact of the disaster on the economy, using a difference-in-difference approach. Monthly nighttime light intensity, a proxy for aggregate economic activity, suggests that activity declined for three months during the disaster but boomed subsequently. Automated teller machine transactions, a proxy for consumer demand, declined and credit disbursal increased, with households borrowing more for housing and less for consumption. In line with other results, both household income and expenditure declined during the floods. Despite a strong wage recovery after the floods, spending remained lower relative to the unaffected districts. The paper argues that increased labor demand due to reconstruction efforts increased wages after the floods and provides corroborating evidence: (i) rural labor markets tightened, (ii) poorer households benefited more, and (iii) wages increased most where government relief was strongest. The findings confirm the presence of interesting economic dynamics during and right after natural disasters that remain in the shadow when analyzed with annual data.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rcmbeyer@worldbank.org, abhinav.narayanan@gmail.com, and gogol@cds.ac.in.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Natural Disasters and Economic Dynamics:

# Evidence from the Kerala Floods<sup>_∗_</sup>

Robert C. M. Beyer<sup>_†_</sup> Abhinav Narayanan<sup>_‡_</sup> Gogol Mitra Thakur<sup>_§_</sup>

**JEL Codes:** Q54, R22, D12, O44, E21, E23

**Keywords:** natural disasters, aggregate activity, household behavior, spatial analysis

> _∗_ The views and opinions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, the Asian Infrastructure Investment Bank, or any other institution with which the authors may be affiliated. This paper has benefited from comments and suggestions from Martin Rama, Norman Loayza, and Rangeet Gosh, as well as from participants of the 2019 Annual Conference on Economic Growth and Development organized by the Indian Statistical Institute, 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, 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. Previous versions of this paper have been circulated under the titles _Natural Calamities and Household Finance: Evidence from Kerala Floods_ and _Natural Disaster and the Economy: Evidence from the Kerala floods of 2018_ .

> _†_ World Bank. Email: rcmbeyer@worldbank.org.

> _‡_ Asian Infrastructure Investment Bank. Email: abhinav.narayanan@gmail.com.

> _§_ Centre for Development Studies, Thiruvananthapuram, India. Email: gogol@cds.ac.in.
# **1. Introduction**

Natural disasters can cause large economic damage both by directly impacting the population and their resources, as well as by indirectly affecting production capacity and economic decisions. Economic losses due to natural disasters are estimated to have been close to US$ 3,000 billion between 1998 and 2017 (UNISDR 2018). Over three-quarters of the damage have been caused by climate-related disasters, with the United States suffering the most, followed by China, Japan, and India. In Japan and the United States, most of the damage has been caused by earthquakes and storms, while for China and India, the major cause has been floods. Globally, floods account for nearly half of all natural disasters.<sup>1</sup> In India, more than 300 floods have been recorded since 1950, killing at least 75,000 people and rendering more than 900 million individuals injured, homeless, or otherwise affected.

The number of extreme rainfall events resulting in landslides, flash floods and crop damage has been increasing in India over time (Goswami et al. 2006; Roxy et al. 2017). The incidence of natural disasters has been growing in other parts of the world too. In the Asia-Pacific region, for example, the number of natural disasters grew from an average of 11 per country during the 1970s to more than 28 in the 2000s (Cavallo and Noy 2011). With accelerating climate change, the increase in natural disasters and flooding is likely to speed up.

In this paper, we study the short-run impact of a major flood in India on the economy and economic agents using several monthly and quarterly outcome variables. Kerala, a state in Southern India, experienced severe rainfall, landslides, and floods between June and August 2018. These were the worst floods in the state since 1924 and the third worst in India since 1900. A total of 504 people died and 23 million people were directly affected.<sup>2</sup> Since

> 1The publicly available Emergency Events Database (EM-DAT), maintained by the Center for Research on the Epidemiology of Disasters, is the most widely used database in economic research related to natural disasters. EM-DAT defines a disaster as a situation or event that overwhelms local capacity, necessitating national or international assistance and is often unforeseen and sudden. See https:www.emdat.be and Cavallo and Noy (2011) for more information.

> 2https://www.emdat.be/cred-crunch-53-flash-floods-sharing-field-experience-kerala

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no natural disasters occurred in the neighboring states of Karnataka and Tamil Nadu, we can causally estimate the impacts of the Kerala floods on economic activity and household income and expenditure using a difference-in-difference estimation strategy.

We find that monthly nighttime light intensity, a proxy for aggregate economic activity, declined for three months during the disaster but increased subsequently, suggesting an economic boom likely due to reconstruction efforts. Automated teller machine (ATM) transactions, which proxy consumer demand, declined for six months and credit disbursal increased, with households borrowing more for housing and less for consumption. Income and expenditure declined strongly during the floods, but while expenditure remained lower in the medium-term, wage income recovered quickly. In September, right after the disaster, wages in affected districts started exceeding wages in unaffected districts. We provide indirect evidence that the overshooting can be attributed to reconstruction efforts. First, data on India’s largest public work program, the Mahatma Gandhi National Rural Employment Guarantee Act 2005 (MGNREGA), suggest that rural labor markets tightened. Second, poorer households benefited more from increasing wages. Third, wages increased most where government relief was strongest, providing corroborative evidence on the link between reconstruction and increased wages.

The next section provides background on the Kerala floods and section 3 discusses related literature. Section 4 describes the data and summary statistics. Section 5 presents the analysis of aggregate economic activity and credit. Section 6 analyzes household expenditure and income, and section 7 analyzes the impact on household balance sheets. Finally, section 8 concludes.

# **2. The Kerala Floods of 2018**

Every year in June, the southwest monsoon enters India and Kerala is usually the first state to be hit. From June 1 to August 19, 2018, Kerala received 2,335 millimeters (mm) of rain, which was about 46 percent above normal. Higher than normal rainfall in June and July led to the first onset of floods toward the end of July. Rainfall further intensified in August, 3
with Kerala receiving total rainfall of 755 mm between August 1 and 19, about 163 percent above normal. Nearly half of the rain occurred in just three days, August 15 to 17, about 700 percent above normal.<sup>3</sup> Due to the continuous rain from June onward, reservoirs were already filled to capacity at that time, so that gates of 35 dams on upstream rivers had to be opened. The heavy rainfall and the opening of the gates, as well as the geography of Kerala with a steep descent from the eastern hill districts atop the Western Ghats to low-lying coastal areas facing the Arabian Sea, caused disastrous flooding in 13 of the state’s 14 districts. The extreme rainfall also caused severe landslides, particularly in the hill districts of Idukki and Wayanad. The Indian government categorized the disaster as a level 3 calamity or “calamity of severe nature.” According to the government’s post disaster needs assessment, seven districts - Alappuzha, Ernakulam, Idukki, Kottayam, Pathanamthitha, Thrissur and Wayanad - were flooded entirely (GOK-UNDP 2018).

Due to the geography of Kerala, the areas that received the heaviest rainfall did not suffer the most from flooding. Figure A.2 in the appendix shows few flooded areas in the eastern hill region, which received the heaviest rainfall in August. Most of the flooded areas were in the low-lying coastal areas around the backwaters. Flooding in these areas was aggravated by adverse tidal conditions and sustained strong onshore winds that elevated the sea level and hampered the outflow of flood water. As a result, many of the low-lying areas were for more than two weeks.<sup>4</sup> Floods are frequent in some parts of India. Some of the most affected regions include Central India, stretching from Gujrat and Maharashtra in the West to Odisha on the east coast, and eastern states such as Bihar, West Bengal, and Assam. In regions with frequent floods, households and firms internalize them in their decisions and consequently they do not offer themselves to clearly identify the impact of a flood. In line with this, Kocornick-Mina et al. (2020) find permanent relocation of economic activity in response to large floods only

> 3Figure A.1, in the appendix, plots the daily actual and normal rainfall in Kerala from June to December 2018 using data from the Indian Meteorological Department.

> 4 See Raman (2020) on how geographical features of Kerala shaped the course of this disaster.

4
in recently populated urban areas, likely due to substantial learning effects. The incidence of extreme rainfall is much lower in southern states such as Karnataka, Kerala, and Tamil Nadu (Roxy et al. 2017). There is no recent precedence for rainfall as in 2018 in Kerala, and the only other recorded floods of comparable magnitudes are those during the monsoons in 1924 and 1961 (CWC 2018). This makes the 2018 Kerala floods truly exogenous and supports our identification strategy.

# **3. Related Literature**

This section briefly discusses the literature on the impacts of natural disasters on aggregate economic activity and household finances.

# **3.1 Natural Disasters and Economic Activity**

Most papers quantifying the impact of disasters on economic activity are cross-country studies that use data on the occurrence of or damage caused by natural disasters, from the Emergency Events Database (EM-DAT).<sup>5</sup> Skidmore and Toya (2002) find a statistically highly significant positive correlation between the incidence of climatic disasters and longrun growth of real gross domestic product (GDP) per capita. They speculate that disasters may spur investment in updating the capital stock and that adopting newer technology could promote long-run growth.<sup>6</sup> Jaramillo (2009), however, finds mixed evidence on the long-run growth impacts of different disasters depending on the type of disaster, the measure, and the category of the country in terms of disaster vulnerability. Parida, Saini, and Chowdhury (2020) show that floods decrease economic growth of Indian states in the long run. Cavallo et al. (2013) find that very large disasters, if followed by radical political upheavals, lead to a

> 5EM-DAT records the incidence of more than 24,000 disaster events across the globe from 1900 to the present and contains data on the number people affected, injured, and killed in these events as well as estimates of property damage and loss of livestock. A less frequently used but similar source is NatCatSERVICE.

> 6This so-called “creative destruction” effect of natural disasters has been probed further in the literature. For example Cuaresma, Hlouskova, and Oberstriner (2008) examine if trade facilitates such a process following natural disasters but find that only richer countries benefit from capital upgrading while disasters tend to reduce technology spillovers between industrialized and developing countries.

5
persistent decline in GDP per capita.<sup>7</sup> Noy and Nualsri (2007) argue that a negative shock to human capital (loss of human lives) due to natural disasters has a significant negative effect on the medium-run (five year average) growth rate of real per capita GDP, while a similar shock to physical capital has a positive but insignificant effect. Loayza et al. (2012) find that the intensity of floods (measured using the fraction of the population affected) has a significant positive impact on medium-run GDP growth. Fomby, Ikeda, and Loayza (2013) find that the cumulative effect of droughts on GDP per capita in developing countries is negative, while it is positive for floods. Both Loayza et al. (2012) and Fomby, Ikeda, and Loayza (2013) attribute the positive impact of floods to possible productivity-enhancing effects in terms of improved land fertility and irrigation.

In contrast to the mixed findings on the medium- to long-run impacts of natural disasters, there is a broad consensus that their short-run impacts are negative. For example, Raddatz (2007) finds that the incidence of climatic disasters (floods, droughts, extreme temperature, and windstorms) and humanitarian disasters (famines and epidemics) has large, negative, and statistically significant impacts on the real GDP of low-income countries in the short run (one year after the event). Similarly, Noy (2009) argues that a negative shock to the capital stock due to property damage caused by natural disasters negatively affects the growth of real GDP in the same year. Adverse impacts of natural disasters on output are also documented by Hochrainer (2009). Raddatz (2009) notes that most of the negative impact of climate disasters on economic growth is realized in the year of the disaster itself. Felbermayr et al. (2022) investigate how weather anomalies affect economic activity approximated by nighttime light emissions. They find significant effects on local growth and significant spatial spillovers, both driven by lower income regions. Studies differentiating the impact on growth in developing and advanced countries consistently show that developing countries are more sensitive and vulnerable to natural disasters (Noy 2009; Raddatz 2009; Loayza et al. 2012; Fomby, Ikeda, and Loayza 2013).

> 7They define large natural disasters using the distribution of number of people killed due to natural disaster from EM-DAT.

6
The use of disaster data from databases such as EM-DAT and NatCatSERVICE for estimating the macroeconomic consequences of natural disasters has been criticized for possible issues related to endogeneity and measurement.<sup>8</sup> An alternative is to use the physical strength of disaster events to measure their intensity. Albala-Bertrand (1993), in a seminal contribution, collected data on 28 events across 26 countries between 1960 and 1979 and argues that natural disasters have a positive effect on GDP. Strobl (2011) constructs a “Hurricane Disaster Index,” by combining estimates of monetary losses, wind speed, as well as local exposure, and finds that hurricane strikes significantly reduced the annual economic growth of coastal counties in the United States between 1970 and 2005. Hsiang and Jina (2014) show that tropical cyclones cause permanently lower levels of GDP, using a data set containing information on the exposure of all countries to all known cyclones between 1950 and 2008. Felbermayr and Gr ̈oschl (2014) construct a data set of physical intensities of earthquakes, volcanic eruptions, storms, floods, droughts, and extreme temperature events from primary geophysical and meteorological information. They find that, on an average, natural disasters have had a substantial, negative impact on the growth of per capita GDP in the years subsequent to the disasters, but that there is a recovery later.<sup>9</sup> Another approach consists of studying specific disasters in case studies. Disasters are treated as “natural experiments” for causal estimation of their impacts on different variables of interest. On the one hand, Coffman and Noy (2012) find that even 18 years after Hurricane Iniki hit Hawaii in 1992, Kauai’s economy had not fully recovered. On the other hand, Heger and Neumayer (2019) find that the 2004 Indian Ocean tsunami depressed economic growth in the Indonesian province of Aceh only for one year after the disaster. They argue that growth was considerably boosted subsequently by reconstruction efforts.

We contribute to this literature by causally estimating the impact of the Kerala floods on district-level economic activity, proxied by monthly nighttime lights and ATM transactions.

> 8See, for example, Felbermayr and Gr ̈oschl (2014).

> 9Both Felbermayr and Gr ̈oschl (2014) and Hsiang and Jina (2014) reject the notion of growth-enhancing creative destruction of natural disasters.

7
In contrast to the rest of the literature, the high frequency of the data allows us to focus on the short-run dynamics during and right after the disaster. The results show that local-level aggregate activity declined for some months but then recovered quickly and even overshot within the first year.

# **3.2 Natural Disasters and Household Finances**

Amid developing countries’ high vulnerability to natural disasters, an important concern is their impact on rural households. Many papers focus on Southeast Asia and South Asia, two particularly vulnerable regions in terms of natural disasters.<sup>10</sup> Bui et al. (2014) and Arouri, Nguyen, and Youssef (2015), for example, find that natural disasters reduced household expenditure and income in Vietnam in the 2000s. Lohmann and Lechtenfeld (2015) show that droughts deteriorated health conditions and increased the share of health expenditure in rural Vietnam between 2007 and 2013. Karim (2018) finds that recurrent flood risks negatively affect the income and non-food expenditure of agricultural households. In line with these findings, Kurosaki (2015) shows that floods significantly reduce the consumption of rural households in Pakistan. In contrast, Bandyopadhyay and Skoufias (2015) find that proneness to floods does not affect the consumption of rural households in Bangladesh, but rainfall variability in non-flood-prone subdistricts has a negative effect. This strand of the literature highlights access to microcredit, internal remittances, education, social allowances, and land ownership as potential mitigating factors.<sup>11</sup> Case studies of specific disasters contribute to a better understanding of their impact on household finances and consumption. Akter and Mallick (2013), for example, find that while poor households in Bangladesh suffered greater damage during Cyclone Aila than others due to low-quality housing and distant cyclone shelters, they experienced higher income

> 10They tend to use cross-sectional or longitudinal data from primary surveys or nationally representative household surveys such as the Vietnam Household Living Standards Survey, the Household Income and Expenditure Survey of Bangladesh, or the Indonesian Family Life Survey.

> 11See, for example, Arouri, Nguyen, and Youssef (2015), Karim (2018), and Kurosaki (2015).

8
growth and were more likely to find employment after the cyclone.<sup>12</sup> Mottaleb et al. (2013) compare paddy-farming households in moderately and severely devastated districts during Cyclone Aila in Bangladesh and find that the latter spent more on food and health but less on children’s education after the cyclone. Kirchberger (2017) finds that the Yogyakarta earthquake in Indonesia increased the incomes of agricultural workers because their wages increased as labor moved from agriculture to the construction sector.

A number of important papers from the early 1990s show that rural farming households in developing countries mitigate idiosyncratic income shocks and smooth consumption using community-based credit arrangements and dis-saving/saving as coping mechanisms.<sup>13</sup> Sawada and Shimizutani (2008) find that only households with enough collateralizable assets could maintain their consumption by borrowing after the Kobe earthquake of 1995 in Japan, whereas others were subject to a binding borrowing constraint that adversely affected their consumption. Gallagher and Hartley (2017) show that Hurricane Katrina in the United States reduced household debt in most flooded blocks in New Orleans as homeowners used flood insurance claims to lower their mortgage debts. Patnaik, Sane, and Shah (2019) find that the 2015 Chennai flood in India increased household consumption in the months immediately after the event and argue that households financed it by reducing asset purchases and savings. Acconcia, Corsetti, and Simonelli (2020) also find evidence of a temporary increase in homeowners’ consumption after three major earthquakes in Italy, which they attribute to increased liquidity positions of homeowners due to receipt of public funds for reconstruction. Yao, Xu, and Zhang (2019) argue that natural disasters may have different psychological effects on households depending on the severity of suffering; they show that the 2008 Wenchuan earthquake in China had differential impacts on time preference and consequently on household savings in moderately and severely affected counties.

> 12Akter and Mallick (2013) conducted a survey in 12 of the worst hit villages of coastal Bangladesh one year after the cyclone and construct pre- and post-cyclone scenarios.

> 13These include Paxson (1992), Townsend (1994), Udry (1994), and Udry (1995). Shocks include rainfall variability and farm damages caused by various factors, such as wind, animal invasion, weeds, and poor germination caused by flooding.

9
We contribute to this literature by tracking monthly changes in household expenditure and income following the Kerala floods. The results in section 6 show a persistent decline in household expenditure, particularly non-essential expenditure, despite a quick recovery and subsequent increase household income. The flood also affected the composition of household debt and savings. In section 7, we show that households were more likely to borrow for housing and medical purposes, and less likely to borrow for consumption.

# **4. Data, Samples, and Summary Statistics**

Our analysis employs numerous district-level and household-level variables from various sources. This section describes the data, discusses the sample selection, and reports summary statistics.

# **4.1 Data**

# _Nighttime Lights_

Nighttime lights are a widely used proxy for aggregate economic activity.<sup>14</sup> Felbermayr et al. (2022) use nighttime lights to investigate the impact of weather anomalies on economic activity. In India, they have been used to analyze the spatial impact of demonetization (Chodorow-Reich et al. 2020), regional convergence (Chanda and Kabiraj 2020), and the heterogeneous impact of the COVID-19 pandemic (Beyer, Franco-Bedoya, and Galdo 2021, Beyer, Jain, and Sinha 2021). To translate changes in nighttime lights into changes in economic activity, we rely on the quarterly elasticity estimated by Beyer, Hu, and Yao (2022). Although nighttime lights data are very noisy over time, they are useful for difference-indifference analyses since most of the noise is common across the treated and untreated units.

We extract district level nighttime light data from the Visible and Infrared Imaging Suite Day Night Band (VIIRS-DNB) Cloud Free Monthly Composites (version 1) provided by the Earth Observation Group at the Colorado School of Mines.<sup>15</sup> The monthly composite

> 14For a summary of the literature, see Donaldson and Storeygard (2016).

> 15We employ the VIIRS Cloud Mask (VCM) configuration starting from April 2012. For information on the different versions, visit https://eogdata.mines.edu/products/vnl/. Due to a wider radiometric detection

10
still includes ephemeral light, for example from fires and gas flaring, which we filter out. Following Beyer et al. (2018) and Beyer, Franco-Bedoya, and Galdo (2021), we identify different clusters by removing monthly outliers, averaging the brightness of cells over time, and clustering areas based on their light intensity.<sup>16</sup> We then define a background noise mask based on these clusters and only include lights outside the mask. In practice, this approach amounts to setting to zero cells that are distant from homogeneous bright cores.<sup>17</sup> The cleaned monthly data are aggregated to the district level and standardized by area.

# _ATM Transactions_

We obtained monthly data on ATM transactions at the Postal Index Number (PIN code) level from the National Payments Corporation of India, which is an umbrella organization set up by the Reserve Bank of India for operating retail electronic payment and settlement systems.

# _Credit Disbursements and Deposits of Scheduled Commercial Banks_

We use district-level credit and deposits data from the Reserve Bank of India, which are available at quarterly frequency. The scheduled commercial banks (SCBs) provide these data as part of the Basic Statistical Returns.

# _Household Income and Expenditure_

We obtained data on household-level income, expenditure, and their subcategories from the Consumer Pyramids Household Surveys (CPHS) database maintained by the Centre for Monitoring Indian Economy. This database is based on a large-scale survey that includes around 160,000 households covering all Indian states and union territories. The Centre for Monitoring Indian Economy visits each household once every four months and records

> range and onboard calibration correcting for saturation and blooming effects, the data are more comparable over time than previous nighttime light products.

> 16The advantage of using a background mask is shown clearly by Gibson and Boe-Gibson (2021b), who compare results using masked and unmasked VIIRS data, for both within and between estimators on a panel of about 3,000 U.S. counties.

> 17Our approach basically replicates the approach of Elvidge et al. (2017) for monthly data.

11
information for the three months preceding the date of the survey. Our unit of observation is the household and we obtained information on monthly income and expenditure over time for each household. The CPHS database also includes qualitative information on household borrowing, savings, and asset ownership. The CPHS provides these data at a wave frequency (four months); however, different households are surveyed in different months within a wave. _MGNREGA_ We obtained monthly data on the numbers of households that worked and those that demanded work under the MGNREGA for all the districts in Kerala, Tamil Nadu and Karnataka from the MGNREGA Public Data Portal maintained by the Ministry of Rural Development. MGNREGA is a demand-driven wage employment program. It provides at least 100 days of guaranteed employment in a financial year to every household residing in a rural area and covers all adult members of rural households who volunteer to do unskilled manual work. A surplus of households that demand work over those who work under MGNREGA reflects excess labor supply in rural labor markets. Declining excess demand for MGNREGA employment from households indicates a tightening of labor market conditions. We later examine the impact of the Kerala floods on the difference between the number of households that worked and number of households that demanded work under MGNREGA.

# _Flood Relief_

We collected district-wise flood relief data from a scanned copy of a report issued by the Disaster Management (A) Department of the Government of Kerala, obtained from the official web portal of the Government of Kerala.<sup>18</sup> It contains the allotment of state disaster response funds sanctioned for 18 items - including agricultural crop loss, ex gratia, food and clothing, and repair of damaged houses - to district collectors in all 14 districts of Kerala. We normalize the relief amounts using district-wise population data from the Census. Every district in Kerala received some amount of assistance and the amount varied from around 250

> 18The report is G.O. (Rt) No. 460/2018/DMD, issued by the Disaster Management (A) Department of the Government of Kerala, dated Thiruvanthapuram, August 27, 2018.

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Table 1: Nightlights, ATM transactions, Credit and Deposits, and MGNREGA

||(1)<br>Treatment|(2)<br>Control|
|---|---|---|
|**ATM transactions (INR, millions)**|39.56<br>(2.957)|60.04<br>(4.042)|
|N (pincodes)|1,400|693|
|**Lights per square kilometer (nanowatts)**|1.794|7.038|
||(2.502)|(23.86)|
|N (districts)|14|62|
|**Credit (INR, billions)**|190.6|211.0|
||(181.9)|(613.6)|
|**Deposit (INR, billions)**|307.0|243.0|
||(219.5)|(731.5)|
|N (districts)|14|62|
|Total households worked under MGNREGA (1)|35,209.1<br>(26,113.9)|33,257.4<br>(39,267.8)|
|Total households demanded work under MGNREGA (2)|38,929.7<br>(26,826.1)|36,330.4<br>(39,926.6)|
|**Diference** (2) - (1)|3,720.6|3,073.1|
||(3,477.9)|(2,921.7)|
|N (districts)|14|62|

Note: Summary statistics are based on the period June 2017 to May 2018. For credit and deposits, the sample period pertains to the relevant quarters. Standard deviations are in parentheses. MGNREGA = Mahatma Gandhi National Rural Employment Guarantee Act 2005 Table 1 presents the summary statistics for nighttime lights, ATM transactions (in logs), as well as credit and deposits for all the pin codes/districts and all pin codes/districts in the treated and control groups. On average, control districts have a much higher nighttime light intensity than treated ones, consistent with higher per capita state domestic product in Karnataka and Tamil Nadu and more advanced industrialization, especially in Tamil Nadu. The control districts also have higher ATM transactions. The amount of outstanding credit is comparable between the two groups, and the treated districts have somewhat higher deposits. Table 1 also reports the average numbers of households that worked and demanded work under MGNREGA in the three states. In all of them, employment demanded exceeded actual employment from June 2017 to May 2018, indicating excess supply in the rural labor market.

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Table 2: Household Income and Expenditure in INR

||Treatment|Control|
|---|---|---|
|Total income|5965.6|5140.5|
||(524.9)|(383.8)|
|Wage income|4344.2|4549.9|
||(386.6)|(363.6)|
|Total expenditure|4266.4|2833.0|
||(136.7)|(183.9)|
|Food expenditure|1713.7|1404.0|
||(65.18)|(46.69)|
|Nonessential expenditure|322.3|112.7|
||(17.55)|(24.53)|
|Number of households|3403|3214|

Note: Summary statistics are based on the period June 2017 to May 2018. Total income; wage income; and total, food and nonessential expenditures are expressed in terms of per household member. Standard deviations are in parentheses.

Our baseline household sample comprises 6,617 households, of which 51.4 percent are in Kerala and the rest are in Tamil Nadu (34.8 percent) and Karnataka (13.7 percent). Table 2 reports the summary statistics for the key household level variables (in logs). Treated households (those in Kerala) earn and spend on average more than those in the control districts (those in Tamil Nadu and Karnataka).

# **5. Impact on Aggregate Economic Activity and Credit**

This section examines the impact of the disaster on aggregate economic activity, consumer demand, and credit. We examine the impact on monthly nighttime light intensity, monthly ATM transactions, and outstanding quarterly credit by estimating the following equation: The left-hand side represents the log of the dependent variables. The cross-section unit _i_ refers to districts for nighttime light intensity and credit, and to pin codes for ATM trans15
actions. Time period _t_ refers to months for nighttime light intensity and ATM transactions, and to quarters for credit. We define May 2018 as the base month and the quarter containing May 2018 as the base quarter. The sample covers January to December 2018 for monthly data, and the fourth quarter (Q4) of calendar year (CY) 2017 to Q4 of CY 2018 for quarterly data. The right-hand side includes the interaction between the dummies of units located in Kerala and months before and after the floods. We include cross-section fixed effects ( _γi_ ) that absorb any characteristics specific to a district or pin code that are constant over time, that is, not changing within a year. Among others, they account for differences related to infrastructure, financial outreach, and cash use intensity. We also include time fixed effects ( _δt_ ) that absorb any aggregate macroeconomic shocks that may affect economic activity in both and districts.

# **5.1 Nighttime Lights**

Figure 2 shows the effect of the disaster on nighttime lights intensity. Two caveats stand out immediately: first, nighttime lights are a very noisy measure of economic activity and the standard deviations of the month-treatment interaction terms are very large; second, the large positive and highly statistically significant coefficients for January, February, and March violate the parallel trend assumption. However, while the coefficient for April is still large, it is not statistically significant even at the 5 percent level. We hence assume that without the floods, parallel trends would have held starting in April onward. With the onset of the rains, light intensity in Kerala declined below that in the control group. In July and August, light intensity was 13 and 11 percent lower, respectively, with both coefficients statistically significant at the 0.1 percent level. From September onward, however, nighttime light intensity exceeded that of the control group. Between September and December, it was on average 23 percent higher. While highly uncertain, these results point to a substantial negative impact on economic activity during the disaster, but a very sudden and strong boom subsequently. The results are very much in line with what Heger, Julca, and Paddison 16
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# **6. Impact on Household Expenditure and Income**

This section presents the impacts of the floods on household income and expenditure. We estimate the following specification: where ln _yit_ represents the log of per capita expenditure or income (or their subcategories) for household _i_ in month _t_ . The right-hand side of equation (2) contains the interaction term between the disaster months and the dummy for households residing in Kerala. The sample period includes all months of 2018, and May 2018 is the base month. Household and month fixed effects are denoted by _γi_ and _δt_ , respectively. Household fixed effects control for household-level characteristics that are constant over time and may affect household income like caste, religion, gender, and occupation. The month fixed effects control for any aggregate shock that affects all households. To estimate the impact on household expenditure (and its subcategories), we also control for household income as it is an important driver of household expenditure. The results reported in this and the subsequent sections are from our baseline sample, which includes households located in all the districts in Kerala and the districts

> 21Deposits did not increase in Kerala compared with the neighboring states. The results are available upon request.

20
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# **6.3 Fast Wage Growth after the Floods**

Section 6.2 showed that wages in Kerala increased relative to wages in unaffected bordering districts in Karnataka and Tamil Nadu in the post-flood months. In line with the results on aggregate economic activity, we hypothesize that a reconstruction boom increased wages. Kerala is heavily dependent on migrant labor from the rest of the country, particularly for menial jobs across sectors.<sup>26</sup> Hence, higher aggregate demand due to the reconstruction efforts may have increased wages.

Unfortunately, to the best of our knowledge, no data are available to test this hypothesis directly. Thus, we need to rely on indirect evidence. If our hypothesis is correct, we expect a tightening of labor market conditions in the post-flood period, that low-income earners benefited more, and that wages increased most where government relief efforts were strongest. In the following, we test these three implications one by one.<sup>27</sup>

# _Tightening Labor Market Conditions_

We use information on the number of households that actually provided employment under the MGNREGA scheme and the number of households that demanded employment by registering for the scheme. MGNREGA only covers rural employment under the government’s employment guarantee scheme, but it still provides information on labor market conditions. The need to repair rural infrastructure after the disaster is likely to have increased MGNREGA employment. There is usually a positive difference between those registered and those actually working. If this difference widens, it suggests that there is an increasing excess supply of labor. If the difference decreases, it suggests a tightening of rural labor markets. We compute the difference for each month for each of the districts and run the same difference-in-difference specification as before with districts in Kerala as the treatment 26A study sponsored by the State Planning Board of the Government of Kerala estimates that migrant workers from other states in the country constituted 26.3 percent of the total workforce in Kerala in 2017-18. The largest migrant receiving sector was the construction sector. See Parida and Raman (2021) for more details.

> 27We also analyzed whether agricultural yields in Kerala differed from those in unaffected areas after the disaster, but do not find any evidence that they did.

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where _qτ_ denotes quantile _τ_ of the income distribution. Most of the terms in this equation are the same as before, including household and month fixed effects. Our coefficients of interest are the _βtτ_ ’s, which capture the difference between the variable of interest between households in Kerala and the control group in month _t_ relative to the base month, May 2018, in quantile _τ_ of the income distribution.

Table A.9 in the appendix shows that although the income of poorer households declined more during the floods, they benefited much more from the subsequent boom than richer households. This is very much in line with a construction boom and replicates the experience of Cyclone Aila and Aceh.

# _Government Relief_

The Government of Kerala provided assistance towards immediate relief and rebuilding of damaged houses. It is likely that the reconstruction efforts were strongest in districts that received the most relief. In turn, households living in these districts may have seen faster wage growth. To test this hypothesis, we use the following specification: for all _t ∈{_ Sep-18,Oct-18,Nov-18,Dec-18 _}_ . The left-hand side represents the change in income of household _i_ located in district _d_ between month _t_ and August 2018. On the right-hand side, ln( _Reliefd_ ) is logarithm of _Reliefd_ , which is state disaster response funds allotted to district _d_ in per capita terms by the Government of Kerala on August 28, 2018. _Reliefd_ may not be an exhaustive measure of government relief but it captures the immediate government assistance to various districts. The coefficient _β_ 1 will show if households in districts with larger per capita allocation of disaster relief funds (as on 28/08/2018) also experienced higher increases in income between August 2018 and month _t_ . We include the change in credit between FY 2019: Q2 and FY 2019:Q3 in district _d_ to control for possible 27
effect of district-level credit expansion by SCBs. Since the initial impact may also impact the recovery, we also control for the average income change of household _i_ between May 2018 and August 2018: ( _yid,Aug_ 18 _− yid,May_ 18). Finally, we also control for household characteristics (age, caste, religion, and profession).

Table 3 reports the results for income and expenditure recovery. We find that immediate government assistance, as captured by the variable _Reliefd_ , seems to have contributed significantly toward income recovery in all the months from September to December 2018. Specifically, 1 percent higher per capita assistance is associated with roughly 4 percentage points additional income growth between August and December. This is again in line with the hypothesis, providing an additional indication that the fast wage growth was related to reconstruction after the disaster.

Table 3: Allocation of Disaster Response Funds and Household Income Growth

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Sep-18|Oct-18|Nov-18|Dec-18|
|Relief p.c(Log)|4.910<sup>_∗∗∗_</sup>|0.348|3.535<sup>_∗∗∗_</sup>|4.003<sup>_∗∗∗_</sup>|
||(0.783)|(0.945)|(1.049)|(1.100)|
|Income change (Aug-May)|-0.239<sup>_∗∗∗_</sup>|-0.378<sup>_∗∗∗_</sup>|-0.529<sup>_∗∗∗_</sup>|-0.649<sup>_∗∗∗_</sup>|
||(0.030)|(0.030)|(0.029)|(0.029)|
|Controls|Yes|Yes|Yes|Yes|
|N|2112|2045|1993|1975|

Note: Standard errors are clustered at the household level. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001

# **7. Impact on Household Balance Sheet Items**

Examining household balance sheet items, such as borrowing and saving, contributes to a better understanding of how households responded to the disaster. In this section, we use qualitative information on household borrowing and saving, from the CPHS, to estimate linear probability models of the following specification: 28
where _i_ denotes household and _t_ denotes month ranging from January to December 2018. _P_ indicates probability. _Z_ ( _k_ ) _it_ are dummies, which take the value one if household _i_ in month _t_ has outstanding borrowing for purpose _k_ , or outstanding saving in instrument _k_ . _Post_ is a dummy that takes the value one for all the months post May 2018. We are interested in the coefficients of the interaction term between _Post_ and the dummies indicating whether household _i_ resides in Kerala. Coefficient _β_ 1 captures the relative difference in probability of _Z_ ( _k_ ) _it_ = 1 between households affected by the disaster and the control group.

The increase in wages after the disaster without an equivalent increase in expenditure suggests that households in Kerala increased savings. Nonetheless, a mixed picture emerges for outstanding savings of households in various instruments. Table 4 reports the estimates for five instruments: commercial bank deposits, Post Office accounts, business, gold and real estate.<sup>28</sup> The coefficient in column (2) is highly significant and positive suggesting that post-May 2018, households in Kerala were more likely to have invested in post office savings schemes.<sup>29</sup> At the same time, however, the highly significant but negative coefficients in columns (1) and (4) indicate that they were less likely to have investments in fixed or recurring deposits in commercial banks and gold. It is likely that some households had to dissolve existing savings to repair damaged property.

Table 5 reports the estimates of equation (5) for household outstanding borrowing for various purposes. The coefficient on borrowing for consumption in column (4) is negative and statistically significant at the 0.1 percent level. Thus, consistent with the decline in their consumption expenditure, households in Kerala were also less likely to have outstanding borrowing for consumption expenditure after May 2018.<sup>30</sup> However, columns (1) and (3)

> 28Commercial bank deposits include fixed or recurring deposits; Post Office transactions include savings schemes offered by the Department of Post of the Government of India, such as the Post Office savings accounts, Post Office time deposit accounts, and so forth; business refers to any investment in any private business; gold includes investment in gold funds or assets such as gold bars and jewelry; and real estate refers to investment in a house, plot of land, apartment, bungalow or farmhouse.

> 29Post Office savings schemes are popular and completely risk-free instruments for small savings. In general, they offer higher interest rates than similar schemes of banks.

> 30Consistent with declining consumption expenditure, households were less likely to have acquired durable assets such as a house, refrigerator, television, personal computer, car, two-wheeler or cattle during this

29
Table 4: Probability That Households Have Savings

||(1)<br>FD|(2)<br>POS|(3)<br>Business|(4)<br>Gold|(5)<br>Real Estate|
|---|---|---|---|---|---|
|Kerala*Post|-0.160<sup>_∗∗∗_</sup>|0.208<sup>_∗∗∗_</sup>|-0.003|-0.019<sup>_∗∗∗_</sup>|0.009|
||(0.017)|(0.017)|(0.003)|(0.005)|(0.006)|
|HH FE|Yes|Yes|Yes|Yes|Yes|
|Month*Year FE|Yes|Yes|Yes|Yes|Yes|
|Kerala*Timetrend|Yes|Yes|Yes|Yes|Yes|
|N|26264|26255|26265|26267|26267|
|Households|7967|7964|7967|7968|7968|

Note: Standard errors are clustered at the Household level. Kerala*Post denotes the interaction dummy for households residing in Kerala during the months post May 2018. FE = fixed effects; HH = households; FD = commercial bank deposits; POS = Post Office accounts. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 Table 5: Probability That Households Had Outstanding Borrowing for Six Purposes

||(1)<br>Housing|(2)<br>Education|(3)<br>Medical|(4)<br>Consumption|(5)<br>CD|(6)<br>Business|
|---|---|---|---|---|---|---|
|Kerala*Post|0.030<sup>_∗∗∗_</sup>|0.003|0.016<sup>_∗_</sup>|-0.081<sup>_∗∗∗_</sup>|-0.009|0.001|
||(0.008)|(0.006)|(0.007)|(0.020)|(0.012)|(0.006)|
|HH FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Month*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Kerala*Timetrend|Yes|Yes|Yes|Yes|Yes|Yes|
|N|26,267|26,267|26,267|26,267|26,267|26,267|
|Households|7,968|7,968|7,968|7,968|7,968|7,968|

Note: Standard errors are clustered at the household level. Kerala*Post denotes the interaction dummy for household residing in Kerala for the months post May 2018. HH = households; FE = fixed effects; CD = commercial bank loans. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 in table 5 show that they were more likely to have outstanding borrowing for housing and medical purposes. In particular, the coefficient of housing in column (1) is highly statistically significant. Outstanding borrowing for housing includes both construction of new residential property and renovation. The increase in this probability likely reflects the need to rebuild or repair damage to residential properties due to the disaster as well as inadequate or delayed receipt of relief funding.

period. The coefficient of the interaction term from estimates of equation (5) for households acquiring these assets in the past 120 days is highly significant and negative. Results are available upon request.

30
# **8. Conclusion**

In this paper, we causally estimated the impact of a major natural disaster in India - the Kerala floods of 2018 - on district-level economic activity, household income and expenditure, as well as household balance sheets. Extreme rainfall between June and August 2018 caused landslides and floods, in particular, disastrous flash floods during the middle of August, and affected the entire state of Kerala. We used a difference-in-difference estimation strategy exploiting the fact that Kerala’s neighboring states, Karnataka and Tamil Nadu, were not affected by this disaster.

We showed that nighttime light intensity in the affected area declined for three months during the disaster but boomed subsequently. Analysis using an elasticity between nighttime light intensity and economic activity from Beyer, Hu, and Yao (2022) suggests that economic activity decreased by 7.7 percent during the disaster, but increased by 14.8 percent during the post-disaster boom. However, the actual elasticity between nighttime lights and economic activity in Kerala may be quite different, and the elasticity could have potentially changed during the flood. The GDP conversions are hence very uncertain and need to be interpreted with caution. ATM transactions in affected areas declined from June to October 2018 relative to those in unaffected areas, suggesting lower consumer demand. These findings support the consensus in the literature that natural disasters have adverse macroeconomic effects in the short run. Further, we provided evidence that credit disbursed by SCBs increased in districts affected by the disaster between September and December, with households borrowing more for housing and less for consumption.

At the household level, we found that both income and expenditure in Kerala declined from June to August relative to households in the bordering districts. Household income recovered by September and then kept increasing during the remaining sample period. However, expenditure continued to remain lower for the entire period, driven by lower spending on non-essentials. The negative impact of this disaster was initially more severe in the districts 31
with the heaviest rainfall (Idukki, Kottayam and Kollam), but had more long-lasting effects in the districts with the worst floods (Alappuzha, Kottayam, Ernakulam and Thrissur).

We argue that increased labor demand due to reconstruction efforts could have increased wage income after the floods and provide corroborating evidence: (i) rural labor markets tightened, (ii) poorer households benefited more, and (iii) wages increased most where government relief was strongest. Immediate relief provided by the Government of Kerala after the disaster was a major factor in the recovery of household income. Specifically, we found that a 1 percent higher assistance per capita contributed roughly 4 percentage points higher household income between August and December.

Based on annual data, Raddatz (2009) concludes that most of the impacts of natural disasters materialize within the first year. Our analysis based on monthly and quarterly data shows that the negative impact can be even shorter and that there are interesting economic dynamics during and right after a disaster within the first year.

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