The Cost of Being Under the Weather: - 麻省理工学院人工智能研究专业

The Cost of Being Under the Weather:
Droughts, Floods, and Health-Care
Costs in Sri Lanka
Diana De Alwis and Ilan Noy∗

We measure the impact of extreme weather events—droughts and ﬂoods—on
health-care utilization and expenditures in Sri Lanka. We ﬁnd that frequently
occurring local ﬂoods and droughts impose a signiﬁcant health risk when
individuals are directly exposed to these hazards. Individuals are also at risk
when their communities are exposed even if they themselves are unaffected.
These impacts, especially the indirect spillover effects to households not directly
做作的, are associated with land use in affected regions and access to sanitation
and hygiene. 最后, both direct and indirect health risks associated with
ﬂoods and droughts have an economic cost: our estimates suggest that Sri
Lanka spends $19 million per year directly on health-care costs associated with ﬂoods and droughts. This cost is divided almost equally between the public purse and households, 和 83% of it spent on ﬂood-related health care and the rest on drought-related health care. 在斯里兰卡, both the frequency and intensity of droughts and ﬂoods are likely to increase because of climatic change. 最后, the health burden associated with these events will likely increase. 关键词: drought, ﬂood, health-care costs, health impact, Sri Lanka JEL codes: I15, Q54 l D o w n o a d e d f r o m h t t p : / / 直接的 . 米特 . / e d u a d e v / 文章 – p d l f / / / / 3 6 2 1 8 5 1 6 4 4 2 0 3 a d e v _ a _ 0 0 1 3 6 压力 . / 来宾来访 0 8 九月 2 0 2 3 我. Introduction Extreme weather events or disasters can potentially lead to signiﬁcant and adverse health outcomes. There are myriad ways in which disasters can lead to a deterioration of health and to the economic challenges associated with this deterioration. In many places, climate change is predicted to increase both the frequency and intensity of extreme events, such as heat waves, drought, storms, and ﬂoods (Elsner, Kossin, and Jagger 2008; Emanuel 2005; Intergovernmental Panel on Climate Change 2014). The ﬁnancial costs of the health burden associated with such ∗Diana De Alwis (corresponding author): Research Associate, School of Economics and Finance, Victoria University of Wellington. 电子邮件: Diana.dealwis@vuw.ac.nz; Ilan Noy: Professor of Economics, School of Economics and Finance, Victoria University of Wellington. 电子邮件: ilan.noy@vuw.ac.nz. The authors would like to thank the managing editor and the journal referees for helpful comments and suggestions. ADB recognizes “Vietnam” as Viet Nam. The usual ADB disclaimer applies. Asian Development Review, 卷. 36, 不. 2, PP. 185–214 https://doi.org/10.1162/adev_a_00136 © 2019 Asian Development Bank and Asian Development Bank Institute. 根据知识共享署名发布 3.0 国际的 (抄送 3.0) 执照. 186 Asian Development Review events could increase as well (Yonson 2018). This health risk will grow signiﬁcantly if global warming continues unabated, the economic burden of climate-induced health risks goes unchecked, and the investment to avoid these costs is not made. Maybe surprisingly, there is a paucity of quantitative evidence on the extent of the current cost burden of health risks associated with extreme weather events (Smith et al. 2014, United Nations International Strategy for Disaster Reduction 2011). This paper therefore examines the impact of such risks on health-care utilization and costs (public and private), by focusing on ﬂoods and droughts and their effects on the health sector in Sri Lanka. Extreme weather events cause physical injuries, but they may also increase health risks, including stress-related ailments, communicable diseases, and indirect mortality (Cook et al. 2008; Heutel, 磨坊主, and Molitor 2017; Philipsborn et al. 2016). 例如, increasing intensity of rainfall and subsequent ﬂoods likely elevate the risk of waterborne and vector-borne diseases, while extreme heat can cause deaths due to heat stress and increase the incidence of cardiovascular and respiratory diseases. Droughts decrease food production and, in poor regions, may result in malnutrition and its associated health risks. Floods and droughts can also cause health spillovers into unaffected populations in disaster-affected regions since health consequences occur through complex interactions. These interactions include the impaired ability of the health system to reduce these risks and the adverse economic consequences borne by indirectly affected households through reduced potential income and the strain on public services provision (Smith et al. 2014, Nomura et al. 2016, Noy and Patel 2014). Health consequences can vary with individual characteristics (年龄, 教育, 收入, and occupation) and the community-wide socioeconomic and political context (the health-care system, national and international involvement, public security concerns, and public health policy). Changes in land use, urbanization, 贸易, and travel are other drivers that can affect the spread of diseases in the aftermath of extreme events (Sutherst 2004). 例如, changes in land use can increase the risk of infectious diseases (McFarlane, Sleigh, and McMichael 2013; Eisenberg et al. 2007). Higher population densities with inadequate urban infrastructure, changes in vegetation and ground cover, deforestation, and artiﬁcial water storage facilities can all determine the link between adverse events and the spread of diseases (Sutherst 2004; Cheong, Leitão, and Lakes 2016; Kweka, Kimaro, and Munga 2016; Berazneva and Byker 2017; Deryugina et al. 2017). Our analysis uses a cross section of households from the national Sri Lankan household income and expenditure survey of almost 80,000 individuals conducted during 2012–2013. We match this survey data with disaster, meteorological, and land use data across the 25 administrative districts in the country to assist us in identifying the links in question. We ultimately aim to quantify the cost burden of providing more health-care services associated with extreme weather events. l 从http下载 : / / 直接的 . 米特 . / e d u a d e v / 文章 – p d l f / / / / 3 6 2 1 8 5 1 6 4 4 2 0 3 a d e v _ a _ 0 0 1 3 6 压力 / . 来宾来访 0 8 九月 2 0 2 3 The Cost of Being under the Weather 187 The ﬁndings of this paper can also inform us about the additional future cost burden we should expect should climate change predictions materialize and lead to a signiﬁcant change in the likelihood and intensity of extreme weather events. Without accounting for these health-care costs, we are potentially underestimating the beneﬁts of disaster risk reduction and climate mitigation policies. The next section discusses the relevant literature, section III describes the Sri Lankan context, and section IV focuses on the methodology and data used in this study. Sections V and VI describe the results and their robustness, 分别, and section VII concludes with some relevant caveats and policy implications. 二. Related Literature: The Health Impact of Disasters Nomura et al. (2016) 分析过的 28 peer-reviewed observational studies on mid- and long-term health impacts of major disasters in the postemergency period (3 months or more after the event). The studies address seven health outcomes: mortality (discussed in 4 学习), suicide (1), mental and behavioral disorders (17), diseases of the circulatory system (4), infectious and parasitic diseases (2), nutritional diseases (1), and biometric measures such as blood pressure (4). In the authors’ metastudy, these health impacts are inﬂuenced by 35 factors related to the socioeconomic and political context, personal characteristics, and intermediating factors (例如, behavioral responses, health system functioning, sanitation, food supply, and psychosocial circumstances). In online Appendix 1, we describe in detail the main diseases relating to both inpatient and outpatient treatments in Sri Lanka and some of the related epidemiological literature that examined the determinants of disease outbreaks. 最终, we are interested in the economic burden that disasters impose via the increasing incidence of diseases and the increasing need to provide both inpatient and outpatient health services. 在斯里兰卡, much of health care is provided by the government (and paid for by tax revenue). Some services are provided by both the private and public sectors, with the private sector usually serving the high-end market. 像这样, market prices of various services frequently do not exist or are rather inaccurate in proxying for well-being (福利) costs associated with these services. Studies in health economics attempt to understand the total welfare cost of health care in terms of three components: resource costs (costs of health and nonhealth goods and services used in medical treatments), lost productivity due to an illness, and the disutility that accompanies many inﬂictions (experienced pain and inconvenience). We focus on the ﬁrst component. When deriving the health costs of infectious diseases, a number of studies focusing on malaria have found a substantial increase in household and public sector expenditures for preventing and treating the disease. 例如, two studies identiﬁed a decrease in labor inputs and low school attendance due to malaria (Chima, 古德曼, and Mills 2003; Malaney, Spielman, and Sachs 2004). l 从http下载 : / / 直接的 . 米特 . / e d u a d e v / 文章 – p d l f / / / / 3 6 2 1 8 5 1 6 4 4 2 0 3 a d e v _ a _ 0 0 1 3 6 压力 . / 来宾来访 0 8 九月 2 0 2 3 188 Asian Development Review Bleakley (2010) observed higher earnings among people who were born just after the eradication of malaria in the United States, enabling a calculation of the previous cost associated with malaria there. Using the estimated costs of the disease, and assuming that these costs are equivalent to a beneﬁt should the disease be prevented, other studies have calculated the beneﬁt–cost ratios for malarial prevention interventions (例如, Mills and Shillcutt 2004). Another strand of this literature examined pandemics. 例如, Smith et al. (2009) modeled the economic impact of inﬂuenza in the United Kingdom, while another study examined the impact on income associated with an outbreak of severe acute respiratory syndrome (SARS) (Keogh-Brown and Smith 2008). Research in poorer countries identiﬁed, 例如, the direct cost of illnesses due to waterborne diseases in Pakistan (Malik et al. 2012) or the overall economic burden of waterborne diseases in the South Paciﬁc (Asian Development Bank 2014). 有, 然而, only a limited amount of work evaluating the health cost burden associated speciﬁcally with extreme natural hazard events such as ﬂoods and droughts (Merson, 黑色的, and Mills 2006; Intergovernmental Panel on Climate Change 2014; 戴尔, 琼斯, and Olken 2014). The available literature on this topic can be grouped into three types of studies: 健康影响, adaptation costs, and health economics evaluation. This last strand uses different monetary valuation methods such as the value of statistical life, disability-adjusted life years, treatment cost estimations, household health expenditure measures, and preventive health provision cost estimates (例如, Noy 2016). 例如, when isolating the health impact of a 1°C increase in global annual temperature, Bosello, Roson, and Tol (2006) estimate the costs for attributed cases using a multicountry general equilibrium model. Mortality due to vector-borne diseases (such as malaria, dengue, and schistosomiasis) is calculated ﬁrst using temperature, 疾病, and associated mortality risks as parametrized in previous studies, and then the associated health costs in terms of death avoidance are calculated using treatment costs as reported by the World Health Organization. These provide inputs into the authors’ general equilibrium model. Kovats, Lloyd, and Watkiss (2011) also use a modeling approach to estimate the marginal effect of climate change in 27 European Union countries by quantifying the value of lives lost due to heat mortality, deaths from additional cases of salmonella, and fatalities due to coastal ﬂoods. The estimates produced from these models inevitably depend on the many assumptions associated with their construction. Statistical quantiﬁcation of observed data provides a different approach that is less structural and assumption dependent. Knowlton et al. (2011), 例如, attempt to calculate the cost of health impacts associated with events that can be related to climate change—ozone air pollution, heat waves, hurricanes, outbreaks of infectious diseases, river ﬂooding, and wildﬁres—for over a decade in the United States. Mortality and l D o w n o a d e d f r o m h t t p : / / 直接的 . 米特 . / e d u a d e v / 文章 – p d l f / / / / 3 6 2 1 8 5 1 6 4 4 2 0 3 a d e v _ a _ 0 0 1 3 6 压力 / . 来宾来访 0 8 九月 2 0 2 3 The Cost of Being under the Weather 189 morbidity from such events are measured using epidemiological studies, aggregate public health data, and extrapolations when required. These are then matched with statistical estimates of the value of life, medical care costs, and lost productivity. In low- and middle-income countries, micro-observational approaches are more common and probably more accurate. Lohmann and Lechtenfeld (2015), 例如, empirically estimate the household-level impact of a drought on health expenditure in Viet Nam by ﬁrst estimating an illness and drought shock model, aggregating drought-associated illnesses at the household level, and then regressing household health expenditure on the instrumented illness measure. This study identiﬁed a 9% 到 17% health expenditure burden on households due to drought-related health shocks. Our study uses a similar microeconometric approach to reveal more insights into the health economic impact of ﬂoods and droughts at the individual household level. Another segment of the literature estimates the costs of adapting to climate change-related differences in health risks. These studies focus on preventing treatment costs of diarrheal cases for Europe and Central Asia (世界卫生组织 2013); the total net cost savings in disease treatment (Agrawala et al. 2009); preventing the risk of malaria and diarrheal diseases using preventive service costs in Europe (Ebi 2008); evaluation of cardiovascular and respiratory disease treatment due to air pollution (Hutton 2008); and waterborne disease vaccination programs (Goossens et al. 2008, Melliez et al. 2008). 三、. Background on Natural Hazards and Health in Sri Lanka Sri Lanka has a land area of 65,610 square kilometers. Rainfall is largely associated with tropical monsoons, but rain also occurs in other seasons. The mean annual rainfall varies from under 900 millimeters in the driest parts (southeastern and northwestern regions) to over 5,000 millimeters in the wettest parts (western slopes of the Central Highlands). The mean annual temperature of the lowlands varies between 26.5°C and 28.5°C. In the highlands, the temperature can fall to 15.9oC.1 The country has an irregular topography comprising a broad coastal plain and a central mountainous area rising to elevations of 2,500 meters. This topography and differences in regional climates are underlying causes of the variation in agroecological zones that are identiﬁed based on variation in rainfall and its seasonal distribution, soil, and altitude. 关于 33% of the land is covered 1The island is divided into three climatic zones based on the annual rainfall: dry, wet, and intermediate. The location of the southern part of the central highlands causes interception of monsoonal rains from the southwest and creates a “rain shadow” on the other side. This has given rise to an ever-wet region which receives abundant rainfall from two monsoons and a dry zone that receives rainfall from only the northeast monsoon. The northeast dry zone is characterized by long spells of drought during other months. See Department of Meteorology. http://www .meteo.gov.lk/ (访问过 6 十一月 2015). l 从http下载 : / / 直接的 . 米特 . / e d u a d e v / 文章 – p d l f / / / / 3 6 2 1 8 5 1 6 4 4 2 0 3 a d e v _ a _ 0 0 1 3 6 压力 / . 来宾来访 0 8 九月 2 0 2 3 190 Asian Development Review with forest, 43% is used for agriculture (permanent and temporary crops), 和 4% is surface water bodies.2 Sri Lanka is affected by numerous disasters. The most frequent weather-related disasters are ﬂoods, cyclones, and droughts. For the period 1974–2008, the Sri Lankan government reported 1,397 ﬂood events; 1,263 instances of cyclones, strong winds, surges, and gales; 和 285 drought events (Disaster Management Centre 2010).3 The seasonal distribution of ﬂoods shows two peaks: one from April to June and the other from October to December, representing the two monsoon seasons. Sri Lanka is a lower-middle-income country, with a per capita income of $11,500 (purchasing power parity) and a population of 20.9 百万 2015,
according to World Bank data. Sri Lanka has made considerable progress on
infectious
immunization against
diseases in recent years include vector-borne ones, such as dengue and leptospirosis,
and diseases transmitted orally through contamination of food or water, 这样的
as diarrhea (dysentery), hepatitis, and typhoid fever (Ministry of Health 2012a,
2012乙). 关于 18% of the population suffer from chronic diseases and 15% 从
acute diseases (联合国 2015, Department of Census and Statistics 2014).

infectious diseases. 仍然,

the most prevalent

During 2012–2013,

the government reported more than 64,000 案例
of dengue, a vector-borne (mosquito) viral disease, 和 270 reported deaths.
Leptospirosis is the second-highest prevalent disease. Caused by bacteria and
transmitted mainly by rodents, there were almost 7,000 cases of leptospirosis and
almost 100 deaths in the same time period (Ministry of Health 2012a, 2013).
There are more reported outbreaks of both diseases during high-rainfall months.
Mumps, measles, and chicken pox are other common infectious diseases. 这
national communicable disease surveillance undertaken in 2012 also reported
80,660 outpatient visits for inﬂuenza-like illnesses and 2,580 inpatients for severe
respiratory tract infections (Ministry of Health 2012b). 在过去的几年中,
inﬂuenza in Sri Lanka has been generally observed from April to June and again
from November to January.4

Health care in Sri Lanka is mainly provided by the public sector. Total health
expenditure accounts for 3.3% of total gross domestic product. According to World
Bank data, health expenditure in Sri Lanka in 2015 is comparable to those of
Bangladesh and the Philippines, which are in the same income category, 并
upper-middle-income countries such as Thailand.

2Sri Lanka has many major river basins as well as a large number of artiﬁcial reservoirs. See World Data

Atlas. https://knoema.com/atlas/Sri-Lanka/topics/Land-Use/Area/Inland-water (访问过 19 行进 2016).

3By far the worst disaster experienced in Sri Lanka since its independence was the Boxing Day tsunami in
2004 (following an earthquake in Indonesia). Details about this event are available from numerous sources. De Alwis
and Noy (2019) document the tsunami’s long-term impact on Sri Lankan households.

4Sri Lanka faced an outbreak of inﬂuenza (mainly due to the H1N1 virus) 在 2015, causing 74 deaths (世界

Health Organization 2015).

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The Cost of Being under the Weather 191

The government health sector is predominantly ﬁnanced from general
revenue taxation, while private sector ﬁnancing is from out-of-pocket spending,
private insurance, enterprise direct payments, insurance paid for by enterprises,
and contributions from nonproﬁt organizations. Public sector health care is
universally accessible to the entire population and is almost wholly free of charge.
Annual per capita total expenditure (from all sources) 是 $105, for which the government contribution is $62 (Institute for Health Policy 2015). 根据
这 2013 national health accounts, the largest health expenditure is attributed to the
treatment of noncommunicable diseases (35%) followed by infectious and parasitic
疾病 (22%). Reproductive health services accounted for nearly 10% of health
expenditures, while injuries required 7.7%. Classiﬁed by the way health care is
delivered and based on government health sector data, inpatient care accounted for
37.1% of total health expenditure by the public sector, and outpatient treatment with
medical products (例如, medicines) 曾是 46.5%. Inpatient care is mainly provided by
the public sector (Institute for Health Policy 2015).
在此背景下, this study attempts to

(我)

quantify the individual health risk attributable to ﬂoods and droughts,

(二)

quantify health spillovers from ﬂood- and drought-affected populations to
those not directly affected and identify the associated trigger factors, 和

(三、)

identify the costs associated with the health-related disaster
identiﬁed in (我) 和 (二) for both the private and public health sectors.

影响

IV. Data and Methodology

Our data come from the National Household Income and Expenditure
民意调查 (NHIES) conducted between June 2012 and July 2013. The data include
information on whether each household member received inpatient hospital
treatment in the past year and visited a hospital (private or public) for outpatient
treatment in the previous month.5 The survey questionnaire also posed a question
on whether the households were affected in the past year by a ﬂood or drought.
We combine this data with ﬂood and drought information compiled in a separate
national database to identify our treatment variables for each district, 那是,

5In a study about health expenditure surveys, 徐等. (2009) specify the standard recall period as 1 month for
frequent health expenditures and 1 year for infrequent ones, including hospitalizations. 像这样, the Sri Lanka survey
follows the global practice. O’Donnell et al. (2008) investigate health expenditures in Asia and argue that recall
mistakes most likely do not bias their estimations (IE。, they are not systematically biased). We note that because the
outpatient data request a recall of the past month, and because utilization of outpatient services might not be evenly
distributed throughout the year, it is impossible to directly compare the extrapolated data from the survey with the
aggregate numbers available at the end of each year from the Ministry of Health.

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192 Asian Development Review

桌子 1. Data Summary

Variables
性别 (dummy for male = 1)
年龄 (年)
教育 (年)
Ethnicity Singhalese (dummy)
Ethnicity Tamil (dummy)
就业 (dummy)
Employer (dummy)
Own family worker (dummy)
Reside in rural sector (dummy)
Reside in estate sector (dummy)
Outpatient visit at least once last month
Inpatient visit at least once last year
Flood affected last year (dummy for self-reported)
Flood affected last year (dummy for district-wide ﬂood)
Drought affected (dummy for self-reported)
Drought affected last year (in affected district)
Flood affected last month (in affected district)
Drought affected last month (in affected district)
Flood spillover
Drought spillover
Households toilet shared (dummy)
Households toilet public (dummy)
Households drinking water well (dummy)
Households drinking water open sources (dummy)
Agricultural water retention area (% of land in district)
Natural water retention area (% of land in district)
Household income (Sri Lanka rupees)
Household health expenditure (Sri Lanka rupees)

意思是

Std. Dev.

最小

Max

0.48
32.60
8
0.65
0.34
0.23
0.01
0.12
0.65
0.10
0.28
0.09
0.04
0.72
0.03
0.32
0.11
0.14

30
68
0.06
0.04
0.48
0.18
11.09
4.98
29,790
1,544

0.50
21.50
4.70
0.48
0.47
0.42
0.80
0.33
0.48
0.29
0.45
0.28
0.20
0.45
0.17
0.47
0.31
0.35

46
46
0.24
0.19
0.49
0.38
5.73
3.24
31,656
13,645

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
−3,750
0

1
99
19
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
23.7
18.6
324,275
1,103,400

笔记: 有 79,381 observations.
来源: Authors’ estimates of National Household Income and Expenditure Survey 2012/2013 数据.

whether districts were affected by ﬂood and drought in the past year or in the month
before the NHIES survey was undertaken in the 25 administrative districts across
国家. District-level land use data come from the district proﬁles maintained
by the Sri Lanka Census and Statistics Department. We also use district land use
data to identify how land use affects the health-care costs associated with ﬂoods
and droughts.

The summary statistics for our sample (桌子 1) 显示 28% of household
members sought outpatient treatment in the previous month and 9% sought inpatient
treatment in the previous year.6 About 4% reported they were affected by a ﬂood

6Inpatient care generally refers to any medical service that requires admission into a hospital and is typical
for more serious ailments and trauma. Outpatient care, 另一方面, is any medical service that does not require
a prolonged stay at a facility. This can include routine services such as checkups or visits to clinics (even more
involved emergency care procedures are included, so long as the hospital or facility allows the patient to leave on
the same day). 在斯里兰卡, there are accident and emergency care units (A&E units) in secondary and tertiary
care hospitals (大约 120 hospitals) that allow patients to stay a maximum of 4 小时; after which the patient is

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The Cost of Being under the Weather 193

和 3% by a drought in the past year. In the month before the survey was conducted,
11% (14%) were residing in districts affected by ﬂoods (droughts).7

We estimate individual (inpatient and outpatient) health impacts using a
probit model speciﬁcation. Our outcome variable is a binomial response for
inpatient or outpatient treatment. The empirical model speciﬁcation is

Yid = β1Zid + β2Did + β3DS pillid + β5 [Zid ∗ Did] + β6 [Zid ∗ DS pillid]

+ δm + γd + Uid

(1)

In the benchmark model, Yid is the dependent variable—a dummy variable for
hospital inpatient or outpatient treatment, and the unit observed is for household
i in district d. Did is the ﬂood or drought variable (a “treatment” binary indicator)
and the demographic and household covariates. Zid are incorporated to control for
heterogeneity of health outcomes due to structural factors. Month ﬁxed effects (δm)
and district ﬁxed effects (γd) are incorporated to control for seasonality and district
heterogeneity, 分别, in some of the reported speciﬁcations (when the district-
level land-use measures Xd are not included). The coefﬁcient of interest is β2, 哪个
denotes the marginal effect of ﬂoods and droughts on the probability of needing
inpatient or outpatient treatment. Uid controls for unobserved variation and is
assumed to be independent and identically distributed with mean 0. To isolate health
vulnerability to ﬂoods and droughts based on structural factors (age groups, rural
and urban sectors, household sanitation), model speciﬁcations incorporating the
interaction of treatment with structural factors β5[Zid ∗ Did] + β6[Zid ∗ DS pillid]
are estimated.

The previous literature ﬁnds that both ﬂoods and droughts affect human
health directly (例如, deaths; injuries; 心理健康; and cardiovascular, respiratory,
and kidney diseases) and through indirect pathways (例如, vector-borne and
waterborne diseases). Both can lead the affected population to seek inpatient or
outpatient health-care services (see more details in online Appendix 1). 作为
health impacts associated with disasters are hypothesized to be mediated through
other characteristics (vulnerabilities such as limited household sanitation), 这些
can also affect households that are not directly impacted. These spillovers may lead

admitted to a continuum care unit, short stay unit, or intensive care unit, depending on care needs and the expected
length of the patient’s stay. These are then classiﬁed as inpatient care. Admission to an A&E unit is decided locally
(at the local facility) or by a senior medical ofﬁcer at the A&乙. All other hospitals (965 hospitals) have emergency
care rooms (inpatient). 所以, we can expect that only sometimes will an intravenous ﬂuids (IV) treatment be
given to a patient and not be classiﬁed as inpatient. 尤其, while larger (城市的) hospitals have good emergency
care services, the smaller hospitals in rural areas that are more vulnerable to droughts and ﬂoods do not have such
facilities for outpatient IV delivery (Wimalaratne et al. 2017).

7因此, the majority of residents in affected districts do not report being affected by either ﬂoods or droughts.
For ﬂoods, these nonaffected households may live farther away from waterways and reservoirs that were ﬂooded.
For droughts, these households might live in areas of the district that were less affected by the drought, 或他们的
agricultural land might be irrigated, or they might not work in agriculture, and therefore the drought had no direct
observable impact on their lives.

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194 Asian Development Review

to impaired health outcomes for people who are not directly affected by a ﬂood or
drought but live in the vicinity of directly affected households. To identify these
spillovers, we estimate the model including a variable (DS pillid) that deﬁnes a
separate treatment group for those people who live in ﬂood- or drought-affected
districts but did not self-report as being affected by a ﬂood or drought in the
survey questionnaire. β3 is the coefﬁcient of interest to quantify the indirect health
spillovers associated with these natural hazards. To identify how land use factors
may induce disaster-triggered health risks, we incorporated these into the estimation
还有; in these speciﬁcations, the district ﬁxed effects are replaced with these
district-level measures (Xd), as shown in equation (2):

Yid = β1Zid + β2Did + β3DS pillid + β4Xd + β5 [ZidXd ∗ Did]

+ β6 [ZidXd ∗ DS pillid] + Uid

(2)

To identify how the external household-speciﬁc and district-level factors may
induce disaster-triggered health risks, we incorporated these into the estimation in
several interaction terms. In these speciﬁcations in equation (2), interaction terms
of the disaster measure and the district-level factors are also introduced to the model
(ZidXd ∗ Did) to examine the causal connection between these factors and disaster
exposure and between the same factors and the disaster spillover indicator (ZidXd ∗
DS pillid). β5 and β6 are therefore the coefﬁcients of interest in equation (2) 那
identify the answer to our second question.8

很遗憾, interpreting interaction terms in nonlinear regressions is not
直截了当, as the marginal impact of a variable depends on the values that
other variables take. 实际上, even the sign of the coefﬁcient of the interaction term
may depend on the level of other independent variables and may even change along
their distribution (Hoetker 2007). We present our results on the interaction effects
in a series of graphs that describe the marginal effect at various points. To construct
these ﬁgures, we employ the Stata command routine developed and described in
诺顿, 王, and Ai (2004).

In order to estimate the private cost of health impacts due to natural hazards,
we use the household health expenditure data collected in the survey. Most health
care in Sri Lanka is provided by the public sector (which is free). 然而,
many households choose to use the private sector instead (because of queues for
specialists or because of a perceived difference in the quality of service) and much
of the expenditure on medicines is paid privately. The monthly household health
expenditure for a member experiencing inpatient treatment (at least once in the

8We also estimated a more restricted model: Yid = β1 + β2Zid + β3Did + γd + Uid . This model does not
include the hypothesized spillover effects (directly unaffected households that reside in affected districts). 结果
for these regressions are available from the online Appendix: https://sites.google.com/site/noyeconomics/research
/natural-disasters.

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The Cost of Being under the Weather 195

last year) and receiving outpatient treatment (in the past month) is derived from
estimating the household health expenditure model shown in equation (3):

Yhd = β1 + β2Xihd + β3Iihd + γd + Uid

(3)

Yhd is the household health expenditure and Iihd is the inpatient or outpatient i in
family h and district d. γd is the district dummy variable to control for district
heterogeneity in health costs. Using equation (3), we can then estimate the average
private health-care costs associated with both inpatient and outpatient treatment.

最后, the total public costs of health care due to ﬂoods and droughts are
calculated using the average per capita public health expenditure for inpatient and
outpatient treatment in each district. These numbers are reported in the national
health accounts of Sri Lanka (Institute for Health Policy 2015).

In the last step, the marginal effects estimated in our models are used to
predict the number of inpatients and outpatients associated with extreme weather
events at the district level. The estimated ﬁgures are used in conjunction with the
per capita public and private health expenditure costs, estimated as described in
方程 (3), to calculate the overall health-care costs of ﬂoods and droughts for
each Sri Lankan district.

We note that our main identifying assumption, if we were to argue that
causality is identiﬁed, would be to assume that the shocks are randomly distributed.
那
自从, obviously, some areas are more prone to disasters than others,
assumption is too restrictive, and it is possible that people “sort out” according
to their willingness to take on disaster risk. Since mobility is not that high,
especially in between rural areas, we do not believe that this is a major source of
bias in our estimates.9 Still, a strict interpretation of our model would argue that
we are identifying only correlations between disaster occurrence and health-care
utilization. We retain this interpretation in the rest of the paper.

V. 结果

We estimate our models (1) 和 (2) separately for inpatient and outpatient
关心. 桌子 2 provides the results for the inpatient model based on equation (1),
桌子 3 for the inpatient model based on equation (2), 和表 4 和表
12 in the online Appendix for outpatient services (using equations [1] 和 [2],
分别). All of these are discussed separately in each of the sections below.

Health Impacts of Extreme Weather: Inpatient Care

Estimates of the parameters for equation (1) are provided in Table 2. In all
columns, controls for demographic factors are included, and the results for their

9There is signiﬁcant movement of people from rural areas to urban centers.

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196 Asian Development Review

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The Cost of Being under the Weather 197

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The Cost of Being under the Weather 199

coefﬁcients are presented in the online Appendix. The basic speciﬁcations are
presented in columns (我) 和 (二), which include self-reported and spillover ﬂood
and drought binary indicators and month ﬁxed effects in column (二). In these
结果, we ﬁnd that having been directly affected by ﬂoods or living in a community
affected by ﬂoods increases the probability of needing inpatient care by about 2
百分点, while the impact for those directly affected by a drought is about
4 百分点.

Columns (三、)–(六) 表中 2 include hygienic factors (shared or public toilet
indicators and access to drinking water) and combinations of month and district
ﬁxed effects. Throughout the estimations in columns (三、)–(v), we consistently
observe that the likelihood of receiving inpatient treatments associated with direct
exposure to ﬂooding increases by about 2 百分点. The spillover risk,
once we control for other factors, is lower by about 1 percentage point and less
consistently estimated. Relying on either shared or public toilets (the default being
private ones) is associated with increased inpatient treatment, as is drinking water
that comes only from wells. 出奇, unsafe drinking water (as reported in the
survey) is not associated with increased use of inpatient services. The presence of
water bodies is investigated in column (六)—we ﬁnd that reservoirs are associated
with increased use of inpatient care, but the magnitude of this coefﬁcient is quite
小的. We ﬁnd no association between the presence of natural water bodies and
inpatient services.

表中 3, we divide the population sample we have into several subsamples
and estimate these separately. 尤其, we estimate rural households, 城市的
households, and those residing in estates (the ﬁrst three columns in Table 3).10 在
the last two subsamples (columns 4 和 5), we separate the sample according to age
(at the median age). Maybe not surprisingly, the impact of ﬂoods is higher for rural
households than it is for urban households in terms of inpatient health treatments.
This is also true for droughts, though the coefﬁcient estimates for rural households
are not statistically signiﬁcant. 出奇, the coefﬁcient for the drought spillover
指标, which is statistically signiﬁcant, is twice as large for the urban sample as
it is for the rural sample.

More important than these distinctions between rural and urban are the
estimated coefﬁcients in the estate sector. These are much larger for droughts,
suggesting that this population, already the poorest and most disadvantaged, 还
suffers from a much higher need for inpatient care as a consequence of droughts
(and spillover from ﬂoods). Also notable is that the impacts of hygiene and water
on the estate sector are also both larger and more statistically signiﬁcant, 这是
surprising given that the size of the estate sample is much smaller. This is a further

10Estate sector consists of all plantations that are 20 acres or more in extent and have 10 or more resident

laborers. Estate laborers reside in the plantation areas.

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200 Asian Development Review

indication of the intensity of the impact of natural hazards on health utilization in
the estate sector.

The differences between the estimated coefﬁcients for the young and old
populations are less pronounced. 然而, we do note that the impact of both
hazards on inpatient health services appears to be higher for the older subsample
and also more statistically signiﬁcant.

In an additional set of regressions, we investigate the interaction effects of
the occurrence of ﬂoods and droughts using hygiene and water as interaction terms.
The interpretation of interaction effects in limited dependent variable models is
more involved and, as Norton, 王, and Ai (2004) 展示, frequently misestimated
and misunderstood. We follow their recommendation and present these results in a
series of graphs discussed below in section V.C.

乙.

Health Impacts of Extreme Weather: Outpatient Care

桌子 4 presents the impact of ﬂoods and droughts on the likelihood
treatment, similar to the presentation of results for inpatient
of outpatient
hospitalizations in Table 2. The dependent variable in Table 4 is whether a
household member used outpatient services in the previous month, and the main
variable of interest is whether a district-wide ﬂood occurred during that same
月. We no longer have the data available to allow us to separate those that were
directly and indirectly (spillovers) 做作的.

Unlike earlier results (for inpatient care), we no longer observe that
households that live in a district that was ﬂooded are signiﬁcantly more likely to
require outpatient services. The results in all the regressions for the district-wide
ﬂood measure are always statistically not signiﬁcantly different from zero. 一
explanation for this lack of statistical signiﬁcance is that our ﬂood indictor is no
longer identiﬁed precisely, so that it erroneously identiﬁes many households that
were not actually affected by ﬂoods.

Droughts are a more spatially widespread hazard, and therefore our
identifying independent variable (district-wide exposure) is more relevant
在
这个上下文. We indeed ﬁnd more consistent results for the drought-treatment
variable—the coefﬁcient
in most of the estimates is both statistically and
economically signiﬁcant, with droughts increasing the likelihood of outpatient
treatment in the following month by 1–4 percentage points. 这是, 然而, 重要的
to note that once we estimate the full model with all controls, neither the ﬂood nor
the drought indicators retain their statistical signiﬁcance.

The estimated model consistently shows that households that share toilet
facilities with other families are at a signiﬁcantly higher risk of requiring outpatient
health treatment (irrespective of their weather-hazard exposure). When households
do not possess an in-house source for drinking water, evidence of their need for
outpatient health services is less consistent (columns [三、]–[六]). Where the presence

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The Cost of Being under the Weather 201

of water bodies is included in the estimation, the presence of artiﬁcial reservoirs is
associated with an increased probability of requiring outpatient health-care services,
while the presence of natural water bodies is associated with the opposite (in both
cases the results are statistically signiﬁcant and not very large; 柱子 [六]).

Interactions of the Hazard Variables with Hygiene Controls

As stated earlier, the magnitude and even the sign of the interaction effects
are difﬁcult to present because in nonlinear models these depend on the level of
all the variables. As suggested by Norton, 王, and Ai (2004), the easiest way to
present these interactions is through a series of graphs where the coefﬁcient size is
presented on the vertical axis while the estimated probability of the event (在这个
案件, seeking inpatient or outpatient care) is presented on the horizontal axis. 我们
note that there might be multiple combinations of independent variables that lead to
a similar estimated probability, and the size of the interaction coefﬁcient associated
with each one of these combinations might be different.

These interaction effects for inpatient care are presented in the ﬁgure. 在
each case, the companion ﬁgure to each of the estimated interaction effect (每
estimated probability) describes the statistical signiﬁcance of these results, 与
5% signiﬁcance threshold noted in the graph. Examining the inpatient model, 为了
例子, the interaction between having shared toilets and being affected by ﬂoods
(self-reported) appear to be negative, but it is not statistically signiﬁcant for any
estimated probability. More nuanced and more difﬁcult to interpret is the interaction
effect between the same ﬂood-affected measure and having access to a public
toilet. 在这种情况下, the results appear to be statistically signiﬁcant for estimated
probabilities >0.2, but the sign of the coefﬁcient associated with this interaction
can be either negative or positive for different combinations of the independent
variables yielding these larger estimated probabilities.

全面的, the estimated interaction effects in most cases are not consistently
statistically signiﬁcant and of the same sign all across the range of associated
probabilities. Exceptions are few but worth noting. A household that is indirectly
affected by ﬂooding and has access only to a well or unsafe drinking water
faces a higher likelihood of needing inpatient care for the whole distribution of
estimated probabilities. Rural households that are exposed to ﬂood risk also appear
to experience much larger impacts (this is a result we only reported using different
subsamples in this section). All interaction effects of ﬂoods and droughts on seeking
inpatient health are available in online Appendix 14.

The ﬁgure available in online Appendix 15 presents the interaction effects
for outpatient care. 在这种情况下, none of the interaction effects are statistically
signiﬁcant. This might be because there are no interactions, or because our
identiﬁcation of hazard exposure at the district level is not precise enough, 和我们一样
discussed in section V.B.

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202 Asian Development Review

数字 1.

Interaction Effects of Floods and Droughts on Seeking Inpatient Health Care

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District-Level Health Costs of Floods and Droughts

桌子 5 provides information about the estimation speciﬁcation described
in equation (3). In these speciﬁcations, we estimate the average increase in health

The Cost of Being under the Weather 203

数字 1. Continued.

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expenditures at the household level associated with an episode of inpatient or
outpatient health service utilization. Not very surprisingly, we note that inpatient
care is on average about 3 times as costly for a household as it is for outpatient
关心 (柱子 [三、]). Other interesting observations that arise out of these estimates

204 Asian Development Review

数字 1. Continued.

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来源: Authors’ estimates.

is that the expenditure associated with males and older patients are on average
更高. Households with higher socioeconomic status (better educated, belong to the
Sinhalese majority, have higher income, and live in an urban area) are all associated

The Cost of Being under the Weather 205

桌子 5. Private Health-Care Costs (per month, for inpatient and outpatient services)

Variables

Inpatient (至少
once last year)
Outpatient (至少
once last month)

(我)

(二)

(三、)

1,720.18***

(166.900)

1,602.20***

(176.430)

709.30***

(111.000)

502.40***

(113.030)

180.70*

Male or female
(dummy)
年龄 (年)
教育 (年)
Sinhalese (dummy)
Tamil (dummy)
就业 (dummy) −486.24***
Employer (dummy) −564.42
Own family worker

10.92***
64.71***
449.64
227.57

−710.62***

(104.100)

171.33*

(104.030)

169.90*

(103.980)

10.82***
58.54***
469.45
170.25

(2.550)
(11.420)
(753.520)
(753.580)
(131.590) −509.70***
(607.370) −553.99
(163.470) −716.05***

8.47***
63.16***
467.38
543.61
−458.47***

(2.530)
(11.340)
(750.890)
(753.200)
(131.130)
(607.110) −5,484.53
(163.220)

−546.22***

(2.560)
(11.420)
(753.470)
(760.990)
(131.570)
(606.810)
(164.440)

(dummy)
Rural sector
(dummy)
Estate sector
(dummy)
Total income
(斯里兰卡
rupees)

Time to hospital
持续的

观察结果
R-squared
F-statistic
Degrees of freedom

−290.97**

(121.340) −305.15***

(121.310)

−179.87***

(127.300)

−778.76***

(197.290) −755.90***

(197.150)

−672.13***

(224.020)

0.02***

(0.002)

0.02***

(0.001)

0.02***

(0.001)

−29.90***
31.31

(7.770) −30.72***

(760.310)

49.19

(7.760)

−26.81
(758.890) −1,149.43

(8.030)
(890.560)

79,381
0.005
32.85
13

79,381
0.006
37.32
13

79,381
0.01
36.26
14

Notes: Robust standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Source: Authors’ estimates. with more health expenditures. Low expenditures are especially associated with the estate (plantation) sector and, maybe obviously, those that live in communities that are more distant from hospitals. We note that while all of these results are statistically signiﬁcant, the overall explanatory power of the model is quite minimal. In order to assess the overall costs associated with health services provided to a hazard-impacted population, we need to measure the population’s vulnerability to ﬂood- and drought-related utilization of health services across districts. The estimates provided in Table 6 are calculated by multiplying the district population and the point estimates of the disaster shock variable (marginal effect of ﬂoods and droughts on health services utilization) as estimated in the regressions detailed above. Table 7 shows the total cost estimates due to droughts and ﬂoods, separated for the costs associated with the private and public sectors. The estimates are based l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 2 1 8 5 1 6 4 4 2 0 3 a d e v _ a _ 0 0 1 3 6 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 206 Asian Development Review t n e i t a p t u O d e t a i c o s s A - t h g u o r D h t n o M r e p t s o C e r a C r a e Y r e p t s o C e r a C t n e i t a p n I d e t a i c o s s A - d o o l F s k s i R h t l a e H d e t a l e R - t h g u o r D d n a - d o o l F o t y t i l i b a r e n l u V n o i t a l u p o P l e v e L - t c i r t s i D . 6 e l b a T e t a v i r P r o t c e s c i l b u P r o t c e s t n e i t a p t u o t n e i t a p t u o t s o c e r a c t s o c e r a c ) $ ( 1 8 5 7 6 2 , 1 2 8 5 6 2 , 0 2 0 1 4 1 , 8 9 6 8 5 1 , 1 6 8 5 5 , 7 5 8 1 8 , 8 5 6 2 2 1 , 4 5 7 3 9 , 4 1 1 9 6 , 8 3 5 7 6 , 0 8 4 1 1 , 8 6 8 9 1 , 6 4 6 0 1 , 9 7 0 3 1 , 1 3 8 0 6 , 0 8 0 5 7 , 3 1 8 3 4 , 0 2 0 8 8 , 7 8 1 9 9 , 1 2 7 6 4 , 4 4 0 4 9 , 1 1 9 1 5 , 7 4 5 6 8 1 , 7 1 9 6 9 , 0 8 3 5 2 1 , ) $ ( 6 1 7 8 4 1 , 9 4 9 4 5 , 6 4 2 3 7 , 8 2 4 2 8 , 7 6 4 5 1 , 9 0 6 7 1 , 0 2 7 0 5 , 2 0 7 5 2 , 6 6 2 0 2 , 3 9 2 9 2 , 2 4 2 5 , 1 5 3 7 , 5 5 2 5 , 5 6 1 3 , 7 6 0 3 2 , 1 9 3 8 2 , 9 6 4 2 1 , 4 8 7 0 6 , 4 9 4 0 2 , 4 3 7 9 3 , 9 9 7 7 1 , 8 8 6 1 4 , 8 1 4 7 1 , 5 2 0 9 3 , 4 1 6 6 2 , s t n e i t a p t u O l a t o t f i n o i t a l u p o p d e t c e f f a s i t h g u o r d y b e t a v i r P r o t c e s t n e i t a p n i t s o c e r a c ) $ ( c i l b u P r o t c e s t n e i t a p n i t s o c e r a c ) $ ( 4 9 2 , 9 6 8 3 8 , 8 6 9 1 5 , 6 3 7 9 0 , 1 4 6 6 4 , 4 1 8 9 1 , 1 2 4 6 7 , 1 3 9 7 2 , 4 2 8 9 8 , 7 1 0 9 4 , 7 1 3 7 9 , 2 5 4 1 , 5 7 5 7 , 2 7 8 3 , 3 3 5 7 , 5 1 3 4 4 , 9 1 6 4 3 , 1 1 9 0 3 , 8 4 4 9 7 , 2 2 6 8 6 , 5 2 9 9 0 , 2 1 4 5 3 , 4 2 3 4 4 , 3 1 9 6 4 , 2 3 8 9 0 , 5 2 8 3 6 , 4 8 2 5 6 7 , 2 8 2 9 0 0 , 0 5 1 4 1 8 , 8 6 1 2 2 4 , 9 5 5 7 0 , 7 8 7 7 4 , 0 3 1 1 3 7 , 9 9 9 1 5 , 3 7 4 4 8 , 1 7 2 1 2 , 2 1 4 3 1 , 1 2 5 2 3 , 1 1 3 1 9 , 3 1 8 0 7 , 4 6 6 6 8 , 9 7 6 0 6 , 6 4 9 3 4 , 8 9 1 1 3 6 , 3 9 0 1 5 , 5 0 1 9 9 6 , 9 4 0 2 2 , 5 5 9 3 0 , 0 0 1 3 7 3 , 3 3 1 5 9 0 , 3 0 1 9 5 4 , 8 7 4 8 8 7 , 6 7 1 4 6 6 , 5 3 2 6 0 2 , 5 6 2 4 6 7 , 9 4 2 5 6 , 6 5 1 8 1 , 3 6 1 2 9 6 , 2 8 4 0 2 , 5 6 9 4 2 , 4 9 4 6 8 , 6 1 9 4 6 , 3 2 7 0 9 , 6 1 3 8 1 , 0 1 8 1 2 , 4 7 4 4 3 , 1 9 8 1 1 , 0 4 4 6 5 , 5 9 1 5 3 9 , 5 6 9 3 8 , 7 2 1 6 6 2 , 7 5 9 3 0 , 6 5 5 2 1 , 4 3 1 7 5 5 , 5 2 1 4 2 6 , 5 8 s t n e i t a p n I h c a e f i t c i r t s i d s e c n e i r e p x e d o o ﬂ a 8 9 0 , 3 2 6 4 9 , 2 2 3 7 1 , 2 1 9 9 6 , 3 1 2 2 8 , 4 6 6 0 , 7 8 8 5 , 0 1 3 9 0 , 8 6 6 9 , 5 0 3 8 , 5 1 9 9 5 1 7 , 1 9 1 9 9 2 1 , 1 1 5 2 , 5 1 8 4 , 6 2 8 7 , 3 8 9 5 , 7 2 6 5 , 8 3 3 0 , 4 8 1 1 , 8 1 8 4 , 4 3 0 1 , 6 1 6 6 3 , 8 3 2 8 , 0 1 e t a v i r P r o t c e s t n e i t a p n i t s o c e r a c ) $ ( c i l b u P r o t c e s t n e i t a p n i t s o c e r a c ) $ ( s t n e i t a p n I l a t o t f i n o i t a l u p o p y l t c e r i d s i d e t c e f f a 7 7 2 , 9 6 5 1 3 5 , 5 6 5 8 1 0 , 0 0 3 8 2 6 , 7 3 3 4 4 8 , 8 1 1 0 5 1 , 4 7 1 3 5 9 , 0 6 2 1 6 4 , 9 9 1 9 3 0 , 7 4 1 7 8 6 , 3 4 1 4 2 4 , 4 2 8 6 2 , 2 4 0 5 6 , 2 2 6 2 8 , 7 2 7 1 4 , 9 2 1 2 3 7 , 9 5 1 2 1 2 , 3 9 7 7 8 , 6 9 3 1 6 2 , 7 8 1 0 2 0 , 1 1 2 8 9 3 , 9 9 7 7 0 , 0 0 2 9 3 4 , 0 1 1 5 4 7 , 6 6 2 0 9 1 , 6 0 2 8 1 9 , 6 5 9 7 7 5 , 3 5 3 7 2 3 , 1 7 4 3 1 4 , 0 3 5 8 2 5 , 9 9 5 0 3 , 3 1 1 3 6 3 , 6 2 3 4 8 3 , 5 6 1 8 0 4 , 0 3 1 7 9 4 , 8 8 1 8 2 7 , 3 3 8 9 2 , 7 4 5 1 8 , 3 3 6 6 3 , 0 2 5 3 4 , 8 4 1 8 8 6 , 2 8 1 6 3 2 , 0 8 7 2 1 , 1 9 3 0 7 8 , 1 3 1 7 7 6 , 5 5 2 3 3 5 , 4 1 1 0 5 2 , 8 6 2 9 7 0 , 2 1 1 4 1 1 , 1 5 2 8 4 2 , 1 7 1 6 9 1 , 6 4 2 9 8 , 5 4 6 4 3 , 4 2 8 9 3 , 7 2 4 4 6 , 9 2 3 1 , 4 1 6 7 1 , 1 2 6 8 1 , 6 1 2 3 9 , 1 1 0 6 6 , 1 1 2 8 9 , 1 0 3 4 , 3 8 3 8 , 1 8 5 2 , 2 2 0 5 , 0 1 2 6 9 , 2 1 4 6 5 , 7 6 0 2 , 2 3 6 9 1 , 5 1 4 2 1 , 7 1 6 6 0 , 8 2 6 9 , 8 6 3 2 , 6 1 6 4 6 , 1 2 2 3 7 , 6 1 o b m o l o C a h a p m a G a r a t u l a K y d n a K e l a t a M t c i r t s i D a y i l e a r a w u N a t o t n a b m a H a r a t a M e l l a G r a n n a M a n f f a J a y i n u v a V u v i t a l u M i h c h c o n i l i K a o l a c i t t a B a r a p m A e e l a m o c n i r T a l a g e n u r u K m a l t t u P a r u p a h d a r u n A a w u r a n n o l o P a l a g a r e n o M a l l u d a B e c n i v o r P n r e t s e W l a r t n e C n r e h t u o S n r e h t r o N n r e t s e W h t r o N l a r t n e C h t r o N a v U n r e t s a E e l l a g e K a r u p a n t a R a w u m a g a r a b a S . s e t a m i t s e ’ s r o h t u A : e c r u o S l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 2 1 8 5 1 6 4 4 2 0 3 a d e v _ a _ 0 0 1 3 6 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 t n e i t a p t u O d e t a i c o s s A - t h g u o r D h t n o M r e p t s o C e r a C r a e Y r e p t s o C e r a C t n e i t a p n I d e t a i c o s s A - d o o l F ) $ ( s t h g u o r D d n a s d o o l F f o t s o C h t l a e H c i l b u P . 7 e l b a T l a t o T t s o C r e p a t i p a c c i l b u p t n e i t a p t u o t n e i t a p t u o a t i p a c c i l b u p r e P e t a v i r P d n a e t a v i r P r o t c e s c i l b u P r o t c e s r e P e t a v i r P d n a a t i p a C t s o c t s o c r o t c e s t s o c e r a c t s o c e r a c t s o c t s o c r o t c e s e t a v i r P r o t c e s t n e i t a p n i t s o c e r a c c i l b u P r o t c e s r e P t n e i t a p n i a t i p a c e t a v i r P d n a c i l b u p t s o c e r a c t s o c t s o c r o t c e s e t a v i r P r o t c e s c i l b u P r o t c e s t n e i t a p n i t s o c e r a c t n e i t a p n i t s o c e r a c e c n e i r e p x e s t c i r t s i d l l a f I s t c i r t s i d l l a f i s r e v o l l i p s h t l a e H t c i r t s i d h c a e n i n o i t a l u p o p l a t o t e h t f I t h g u o r d a d o o ﬂ a e c n e i r e p x e d o o ﬂ a y b d e t c e f f a y l t c e r i d s i The Cost of Being under the Weather 207 0 1 . 0 1 . 9 0 . 0 1 . 9 0 . 9 0 . 0 1 . 0 1 . 8 0 . 9 0 . 0 2 0 . 0 2 0 . 0 2 0 . 0 2 0 . 0 2 0 . 0 2 0 . 0 2 0 . 0 2 0 . 0 2 0 . 0 2 0 . 2 3 3 1 5 9 , 0 2 9 1 1 4 , 4 1 2 2 8 3 , 7 1 9 2 7 1 , 1 5 6 3 4 2 , 4 4 8 5 5 3 , 1 4 4 3 0 2 , 1 6 0 5 0 2 , 6 3 9 7 8 2 , 2 2 4 , 4 7 6 6 1 4 , 6 9 2 6 2 5 , 5 8 2 2 1 6 , 2 2 1 4 2 7 , 9 7 1 7 6 5 , 4 7 2 8 0 9 , 5 4 1 5 5 9 , 5 4 1 7 9 2 , 2 2 2 0 1 9 , 6 7 2 4 0 5 , 5 1 1 8 8 6 , 6 9 5 0 3 , 0 5 8 2 9 , 3 6 7 7 2 , 1 8 3 3 5 , 7 5 6 0 1 , 9 5 9 3 6 , 5 6 , 7 1 3 4 1 2 3 , 5 2 4 7 , 4 3 2 1 9 8 , 6 6 8 0 3 . 0 0 3 . 0 0 2 . 0 0 3 . 0 0 3 . 0 0 2 . 0 0 3 . 0 0 3 . 0 0 2 . 0 0 3 . 0 3 3 9 , 6 8 6 4 0 8 , 4 1 6 0 8 2 , 2 9 2 0 6 8 , 6 9 3 8 6 5 , 3 5 5 4 1 3 , 0 4 3 3 2 4 , 5 4 3 8 4 6 , 7 4 4 4 2 3 , 8 0 6 , 1 3 1 4 , 7 1 7 1 1 3 , 5 1 3 7 2 7 , 3 0 3 7 2 4 , 0 3 1 0 8 1 , 1 9 1 9 6 0 , 2 9 2 9 0 2 , 5 5 1 8 5 2 , 5 5 1 8 6 4 , 6 3 2 1 1 9 , 0 9 8 3 2 6 , 1 7 3 7 7 0 , 1 1 3 2 5 8 , 1 6 1 0 8 6 , 5 0 2 8 9 4 , 1 6 2 5 0 1 , 5 8 1 4 6 1 , 0 9 1 1 8 1 , 1 1 2 3 5 1 , 6 8 2 , 5 2 6 0 , 7 9 4 , 2 1 9 0 , 9 8 7 , 2 0 6 . 0 0 5 . 0 0 5 . 0 0 6 . 0 0 5 . 0 0 5 . 0 0 5 . 0 0 5 . 0 0 5 . 0 0 5 . 0 7 4 6 , 6 1 2 , 3 6 6 8 , 3 7 3 , 1 8 0 6 , 9 2 2 , 1 9 5 5 , 4 8 5 0 2 7 , 3 9 7 5 3 1 , 7 0 1 , 1 8 2 6 , 0 8 6 6 4 8 , 0 9 6 7 9 2 , 5 9 8 1 2 6 , 0 3 6 4 5 4 , 7 0 6 5 5 8 , 0 6 2 0 6 3 , 2 8 3 8 3 1 , 4 8 5 8 1 4 , 0 1 3 7 1 5 , 0 1 3 5 3 9 , 2 7 4 5 4 2 , 3 4 7 4 5 1 , 2 2 6 4 0 7 , 3 2 3 0 6 3 , 1 1 4 7 9 9 , 2 2 5 0 1 2 , 0 7 3 9 2 3 , 0 8 3 2 6 3 , 2 2 4 5 2 8 , 4 3 4 , 1 2 2 8 , 1 8 7 , 1 e c n i v o r P n r e t s e W l a r t n e C n r e h t u o S n r e h t r o N n r e t s a E n r e t s e W h t r o N l a r t n e C h t r o N a v U a w u m a g a r a b a S 7 0 3 , 2 7 5 , 0 1 4 2 1 , 4 9 9 , 4 3 8 1 , 8 7 5 , 5 l a t o T l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 2 1 8 5 1 6 4 4 2 0 3 a d e v _ a _ 0 0 1 3 6 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . 3 1 0 2 n i e t a r e g n a h c x e e g a r e v a e h t s a w h c i h w , 0 3 1 s R L S = 1 $ s i n o i s r e v n o c y c n e r r u C : s e t o N . s e t a m i t s e ’ s r o h t u A : e c r u o S 208 Asian Development Review on Sri Lanka’s population census of 2012. Public health costs are based on the reported district-level per capita health expenditure, while the private costs were estimated in Table 5. The estimated realization of the district-level health burden is derived from the population in each district in each year and from whether districts were actually exposed to a ﬂood or drought in the same year. Finally, the online Appendix also presents the same results on a map of Sri Lanka, identifying the costs associated with both inpatient and outpatient care at the district level and in per capita terms. VI. Robustness The self-reported binary treatment variable we use does not provide detailed information on the severity of the treatment. It is also possible that self-reported treatment is motivated by factors other than the damage intensity, such as the hope of becoming eligible for disaster relief, and therefore might be inaccurate.11 When examined against district-level administrative data on disasters, the indicator matches well—all affected districts reported self-reported treatment were also locations where people self-reported as affected.12 Certain self-reported households, however, were in districts that were not reported as affected by a disaster in the administrative data. This is not necessarily an indication of any misreporting as the aggregate datasets are frequently criticized for not reporting on local events that were destructive in a very limited geographic area and therefore did not cause that much damage in the aggregate (even if the loss for affected households was very high). The district-level ﬂood and drought impact reported in the administrative data is reasonably matched with the district-level rainfall data and, accordingly, provides further evidence that the treatment variable we use is not overtly biased. We also include speciﬁcations in the online Appendix that use measured rainfall data; the results of these speciﬁcations (when treatment is identiﬁed by district-measured rainfall) are very similar. Similarly, there may be problems with the self-reported health outcome variable used in the analysis. This variable provides only limited information, because it reports only on whether there was an inpatient or outpatient visit at least once in the past year (or month), even though more than one visit could have occurred within that year (or month). This can cause an underestimation of the health risk due to disasters in our analysis—estimates reveal only the association of exposure to extreme weather and the likelihood of seeking inpatient and outpatient health care at least once in the past year (or month). The estimated costs of health 11In reality, of course, the survey and the disaster relief program are completely independent from each other. The two programs are implemented by different administrative authorities reporting to different ministries. 12This conclusion is in contrast with a ﬁnding from Bangladesh, where the congruence between self-reports and objective observations is less reassuring (Guiteras, Jina, and Mobarak 2015). l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 2 1 8 5 1 6 4 4 2 0 3 a d e v _ a _ 0 0 1 3 6 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 The Cost of Being under the Weather 209 care after a disaster may still be biased if frequently affected households take (costly) adaptation measures or if frequent disasters cause people to relocate to other areas. If adaptation is similar at the district level, the district ﬁxed effects in our model control for any district-level adaptations. VII. Conclusions, Caveats, and Climate Change This study’s objective was to determine the economic costs associated with extreme weather impacts on health care. The most obvious ﬁnding emerging from our analysis is that frequently occurring local ﬂoods and droughts appear to impose a signiﬁcant health risk when individuals are directly exposed to these hazards, and that this exposure sometimes requires even higher hospitalization rates. Our observations are not surprising given that Sri Lanka experiences a high incidence of several infectious diseases (e.g., large numbers of leptospirosis and dengue cases) that are related to ﬂoods and droughts and that require affected people to seek health-care services (see online Appendix 1). Those impacts, and especially the indirect spillover effects to households that are not directly affected by the hazard, are at least partly associated with land use in the affected environs of the hazard and with the household’s access to sanitation and hygiene. Why sanitation and hygiene are important in mediating the impact of ﬂoods and droughts probably does not need explaining. The most likely causal story behind our observations about land use interacting with both ﬂoods and droughts is that both disasters lead to a higher likelihood of contaminants and infections being transmitted (most likely orally or through contact) when artiﬁcial reservoirs are prevalent in the affected area as they interact with the water available for human consumption.13 The health spillovers we identiﬁed almost always appear to be associated with household sanitation and hygienic conditions. Health spillovers due to ﬂoods are associated with households using unsafe drinking water sources (wells and other unsafe sources). It seems that ﬂooding increases the likelihood of contamination of public water sources. Other possible epidemiological explanations for our spillover ﬁnding is the increased presence of disease- transmitting vectors (e.g., mosquitos) in the aftermath of ﬂoods, an increase that also affects households that were not directly damaged by the event. Finally, both direct and indirect risks of ﬂoods and droughts on individual health have an economic cost and, consequently, a welfare loss associated with it. Overall, our estimates suggest that Sri Lanka spends at least $19 million per year on health-care costs associated with ﬂoods and droughts. This cost is divided almost equally between the public and household sectors, with 83% of it spent on ﬂood-related health care and the rest on drought-related health care. Worryingly, our calculations show that the health burden is distributed spatially so that the highest 13It is important to note that Sri Lanka has many artiﬁcial reservoirs, some dating back many centuries. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 2 1 8 5 1 6 4 4 2 0 3 a d e v _ a _ 0 0 1 3 6 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 210 Asian Development Review health burden due to ﬂoods and droughts is borne by the Western and Central provinces followed by the Southern and North Western provinces. The total per capita burden is almost equal across all regions. The Western province is the richest region in the country—it has nearly double the monthly per capita income when compared to the poorest one, and it also bears the highest health burden associated with ﬂoods and droughts (online Appendix 16). It is worth noting that the estimated health expenditure burden quantiﬁed in this paper is only a part of the full economic cost of this health burden. The cost in this paper is estimated in terms of direct public and household expenditure on disease treatment, not the full accounting of costs. Underestimation of actual costs is likely since household members presumably experience reduced productivity and reduced ability to generate income during their treatment. Equally, the opportunity cost of government spending resources on these health costs is probably substantial, as the opportunities for more productive ﬁscal expenditures are more numerous in countries with a low capital base and one that is rapidly developing (as is the case in Sri Lanka). Our estimated drought effect may also be underestimated since droughts cause longer-term effects beyond 1 year, while our estimates focus only on same-year health expenditures. Finally, regional climate model projections for future temperatures predict increases for Sri Lanka: 1°C–1.1°C by 2030, and 2.3°C–3.6°C by 2080. Accordingly, precipitation is likely to increase by 3.6%–11% by 2030, and 31.3%–39.6% by 2080 (Ahmed and Suphachalasai 2014). Studies also predict higher frequencies of high intensity rainfall events causing ﬂoods and dry periods generating drought conditions (Ministry of Environment 2010). In short, both the frequency and the intensity of droughts and ﬂoods are projected to increase because of climatic change, though the magnitude of these increases is as yet unknown. Consequently, the health burden of these events is only likely to increase, further demanding precious resources that are required elsewhere in a rapidly growing but still relatively poor country. 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Appendix 1: Epidemiological Literature on the Links between Disasters and Diseases Appendix 2: Agricultural-Ecological Regions in Sri Lanka Appendix 3: Selected Cases of Notiﬁable Diseases in Sri Lanka Appendix 4: Chronic Illnesses in Sri Lanka Appendix 5: Seasonality of Diseases, 2012 Appendix 6: Seasonality of Diseases, 2013 Appendix 7: Monthly Reported Dengue Cases in Sri Lanka Appendix 8: District-Level per Capita Public Health Expenditure (Sri Lanka rupees) Appendix 9: Diseases in Sri Lanka Appendix 10: Inpatient and Outpatient Private and Public Health Costs (percentage of total costs) Appendix 11: Restricted Models Appendix 12: Immediate Health Effects of Floods: Outpatient Treatments for Different Groups Appendix 13: Rainfall Speciﬁcation Model Appendix 14: Interaction Effects for Floods and Droughts on Seeking Outpatient Health Care Appendix 15: Health Care Cost Maps Due to Floods and Droughts l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 6 2 1 8 5 1 6 4 4 2 0 3 a d e v _ a _ 0 0 1 3 6 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3
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