Beyond the heat: The mental health toll of temperature and humidity in India
Manuela Fritz
1 TUM School of Social Science and Technology, Professorship for Global Health, Munich, Germany
2 University of Passau, Chair of Development Economics, Passau, Germany
Abstract
Evidence on the heat-mental health nexus remains mixed. I show that this can be partly explained by previous studies focusing solely on temperature while neglecting temperature-humidity interactions. Using a measure that considers both indicators (wet bulb temperature), I assess the causal link between extreme heat and mental health, and its heterogeneity across socioeconomic indicators. I combine self-reported depression and anxiety levels from three Indian WHO-SAGE survey waves with climate data, leveraging quasi-random variation in heat exposure due to survey timing and location. The results reveal that extreme heatincreases the risk of depression but not of anxiety. Importantly, these effects are consistently smaller when humidity is not considered. Finally, the study provides evidence that the District Mental Health Program plays a protective role in mitigating adverse mental health effects. The findings suggest that the costs induced by climate change need to account for the economic consequences of deteriorated mental health. Keywords:
Mental health, depression, heatwaves, wet bulb temperature, socioeconomic heterogeneity, India,
District Mental Health Program. JEL Codes:
I1, I15, I31, Q54. Acknowledgments:
I gratefully acknowledge funding from the Deutsche Forschungsgemeinschaft (DFG, German Re search Foundation) under the Project Number 537845359. ∗ Corresponding author: Manuela Fritz, TUM School of Social Sciencesand Technology. Richard Wagner-Straße 1, 80333 Munich, Germany.
Specifically, according to the Intergovernmental Panel of Climate Change (IPCC), the region belongs to those in which the combination of high temperature and high humidity levels will lead to heat exposure thatinduces extreme risks to human health. Similarly, LoPalo et al. argue that the predicted loss in GDP due to health and productivity losses driven by climate change in India has been substantially underestimated since humidity was not accounted for. Yet, so far, the interaction between high temperaturesand high humidity levels has not received particular attention in the literature assessing the link between heat and (mental) health in general andin India in particular; previous studies either did notinclude humidity levels at all or only as control variable. Moreover, the evidence for the heat-mental health relationship for LMICs so far focuses primarily on the effects of extreme heat on suicides ratesand hospital admissions, mostly driven by exacerbation
1 Recent research has explored the impact of heat on worker productivity and aggregate GDP, migration, judicial decisions, violence and conflict, and cognitive performance. 1 of already existing mental illnesses. For this link, the literature finds robust evidence for a relationship between extreme temperaturesand exacerbated illness. For example, increased suicide rates due to increased temperatures have been documentedin India, Mexico, or China, andincreases in mental health related hospital admissions have been recordedin Vietnam and China. The literature is much more limited andinconclusive when it comes to self-reported mental health states. Understanding the effects of heat on such “lower level” mental health outcomes is important since they will most likely affect a much larger share of the population (also those without prior existing mental health illnesses). While Pailler and Tsaneva in the context of India find that temperature
(and rainfall) variability reduces the self-reported ability to cope with one’s life, they do not find any effects on key depressive symptoms. Similarly, Xue et al. do not find a significant relationship between mean temperature levelsand self-rated mental health scores in China. In contrast, Zhang
et al. show that heatwaves in East China reduce self-reported happiness levels. One factor explaining these mixed results could be that mental disorders remain stigmatized, hence might be
under-reportedin interviewsand might not be capturedin an analysis based on survey responses
. This is also true in the Indian context. Another reason might be that simply focusing on temperature and ignoring humidity levels has led to an underestimation of the actual effects; a channel that I explore in this study. Specifically, I
argue that previous studies have underestimated the heat-mental health nexus by only focusing on temperature and neglecting the interaction with high humidity levels. I address this shortcoming by using a temperature measure thatincorporates the interaction between heat and humidity, namely the wet bulb temperature, which provides a more accurate prediction of actual heat stress, and show that there is a robust link between extreme wet bulb temper aturesand and adverse mental health outcomes measured as self-reported depressive symptoms. For identification, I rely on the temporal and geographical variation in the roll-out of the Indian
WHO-SAGE surveys. Under the assumption that the survey roll-out was independent of the weather conditions (which is plausible, given the strict time protocol such large-scale studies follow), the variation in the respondents’ exposure to the frequency and severity of heat events in the 30 days preceding the survey interview can be considered as good as random. I make use of a “temperature bin regression” approach, as it has become standardin the literature, but also use multiple other heatindicators to capture effects at both the extensive andintensive margin. This study most closely links to the studies of Carleton and Pailler and Tsaneva,
showing for the context of India that heatincreases suicidesand decreases self-reported mental well being. Both studies, however, do not consider humidity levelsand focus on the longer-term effects of heat events in the previous agricultural season on mental health. In contrast, I rely on exposure to recent heat events over a one-month horizon and consider heat and humidity jointly, which allows me to focus on short-term physiological mechanisms as a plausible explanation between heat and mental health outcomes. I find that a heatwave in the 30 days preceding the survey increases the probability of suffering from severe or extreme depression by 24% at the extensive margin (exposure to a heatwave lasting two or more days) and by 6% at the intensive margin (one additional heat
day). I find no significant effects on anxiety levels.
Crucially, these effects for depression become substantially smaller in size and significance or disappear completely when only temperature levels
2 are used as the explanatory variable, without accounting for the temperature-humidity interaction. I then investigate whether an existing mental health intervention in India – the District Mental
Health Program – has the potential to address some of the negative mental health impacts of heat. Making use of the staggered roll-out of this program across the country, I find that the effect size of the heat-mental health relationship is significantly smaller in districts that had access to the program,
suggesting a protective role of the program in mitigating adverse mental health effects. The remainder of this paper is organized as follows.
In Section 2, I provide details about the dif ferent data sources. Section 3 presents the empirical strategy. This is followed by the presentation of the results in Section 4, including robustness checks, an investigation of the time dynamics, and heterogeneity assessment across a range of socioeconomic indicators. In Section 5, I discuss potential channels through which heat affects mental health outcomes in the given context. Section 6 investi gates the effectiveness of the District Mental Health Program for alleviating the heat-mental health
link. In Section 7, I discuss the resultsand their implicationsand conclude. 2 Data
To identify the impact of high wet bulb temperatures on mental health outcome, I use individual level survey data from three cross-sectional waves of the Indian WHO Study on Global Ageing and
Adult Health (WHO-SAGE) and merge them with data on the weather conditions during the 30 days preceding the interview date. 2.1 Mental health outcomes: WHO-SAGE study
The Indian WHO-SAGE study is a nationally representative household survey with the objective to collectinformation on adult health and well-being with a focus on the population above the age of 50
. Additionally, comparison samples for individuals aged 18-49 were includedin the survey. The baseline survey was collected as part of the WHO World Health
Survey and served as sampling frame for two follow-up surveys in 2007 and 2015. I employ all three cross-sectional waves. Geo-coded locations of the primary sampling units (PSUs, which are villages in rural areasand wards in urban areas) are drawn from the WHS and are merged to the PSUs in follow-up Waves 1 and 2. For two PSUs, no geo-locations
were available in Wave 0 and four new PSUs were addedin Wave 1, yet without available geo-coded
locations, resulting in a sample of 371 PSUs with geo-locations observedin all three waves. All three survey waves were conductedin six Indian states (Assam, Karnataka, Maharashtra, Ra jasthan, Uttar Pradesh and West Bengal), which together account for almost 50% of the Indian popu lation. In Wave 0, around 10,300 individuals were interviewed, with follow-up sample sizes of around
11,000 in Wave 1 and around 9,000 in Wave 2. Face-to-face interviews were used to obtain information about the individuals’ subjective and objective health status, including health state descriptions, pres
2 Apart of the WHO-SAGE study was designed as longitudinal study with panel individuals. However, the publicly available data do not contain an individual identifier that would allow to link individuals across survey waves. This was also confirmed through e-mail correspondence with the Indian International Institute for Population Sciences (IIPS). Therefore, I employ only the cross-sectional dimension. The IIPS is currently working on the provision of necessary information to build a panel data set, but this was not yet available at the time this study was conducted. Also, a third follow-up has been conductedin 2018/2019, but data are not yet publicly available. 3 ence of chronic conditions, subjective well-being and quality of life, access to health care and health care utilization. The study further included physical and cognition tests, such as vision tests, blood
pressure, and verbal and digit recall tests. Additionally, individual and householdinformation about demographic and socioeconomic characteristics, such as age, gender, marital status, assets, income and expenditures were collected. The main variables of interest are the self-reported mental health states. Specifically, I make use of indicators of depressive and anxiety symptoms during the last 30 days. Respondents were asked
“Overall in the last 30 days, how much of a problem did you have with feeling sad, low, or depressed”
and “Overall in the last 30 days, how much of a problem did you have with worry or anxiety”. An swers could be in the range of 1 (none) and 5 (extreme). For both health states, I define a binary indicator equal to one if individuals reported to suffer from severe or extreme depression or from severe or extreme anxiety. Table 1, Panel 1, presents an overview of the characteristics of the respondents, pooled over all three survey waves. The sample consists of slightly more women than men. The mean age is 48 years,
with a standard deviation of 17 years. Respondents have on average 4.6 years of education, 77% are married and 75% live in a rural location. Seventy-five percent have access to electricity and about 43%
suffer from a chronic health condition. The mean depression score is 1.82, which is lower than the mean anxiety score of 2.01, indicating that respondents suffered more from worry/anxiety than from depression. Similarly, 8% reported to have suffered from severe or extreme depression in the last 30 days, whereas 13% reported to have suffered from extreme or severe worry/anxiety in the last 30 days. 2.2 Weather, climate and environmental data
2.2.1 Wet bulb temperature
The information about the exact date of the survey interview together with the geo-locations of the
PSUs allows to match the survey data with meteorological data on the temperature and humidity conditions during the 30 days preceding the survey. This paper focuses on wet bulb temperature as opposed to dry bulb temperature (the “conventional” air temperature), with the wet bulb temper ature being a non-linear function of dry bulb temperature, moderated by relative humidity levels. At 100% humidity, dry bulb and wet bulb temperature are identical, whereas wet bulb temperature readings are always lower than dry bulb temperature if humidity levels are below 100%. Wet bulb temperature is the lowest temperature that can be reached through cooling by the evaporation of
water. Intuitively, wet bulb temperature is the temperature that will be reached if a thermometer is coveredin wet cloth. The higher the water saturation in the air, the less water can evaporate and the less cooling is possible. For humans, this means that the cooling process through sweating is less effective the higher the humidity level at a given temperature. A wet bulb temperature of 35° is considered the limitin which humans could no longer survive for a sustained period of
time.
3 The following chronic conditions are included: arthritis, stroke, angina, diabetes, chronic lung diseases, and hypertension. 4 The information on the wet bulb temperature are extracted from the NASA GLDAS Noah Land
Surface Model, which is a gridded data set with a spatial resolution of
0.25° × 0.25° and a 3-hourly temporal resolution. While this data set does not contain the wet bulb temperature directly, I use the information on dry bulb temperature, air pressure, and specific hu midity to calculate wet bulb temperature, as specifiedin Appendix A. The 3-hourly observation are aggregated to daily averages by taking the mean of all eight observations per 24-hour interval. The re sulting daily temperature grids are merged to the respective PSU by using inverse-distance weighting of the four nearest grid points to each PSU.
I also construct the long-run average wet bulb tempera ture and its standard deviation for a given day over the years 2000-2016, which are used to control for
long-term climate conditions. Figure 1 exemplary presents the 2003 wet bulb and dry bulb temperature trendin Bharatpur in the state Rajastan. It shows that not only the levels are different (as explained above), but that also the trendin the two temperature measures occasionally differs significantly. In particular, while the highest dry bulb temperatures are reachedin June, the highest wet bulb temperatures are only reachedin August and September, when humidity levels are highest (indicated by almost identical dry bulb and wet bulb temperatures). Figure 1: Wet bulb and dry bulb temperature in 2003 in Rajasthan
2.2.2 Control variables
From the same NASA data set and using the same approach as above, I obtain information on aver age wind speed. I further control for rainfall levelsand air pollution, to be able to identify the pure
4 To construct the long-run daily wet bulb temperature, I use the monthly observations from the GLDAS Noah Land Surface Model over the period 2000-2016, calculate the long-run monthly meansand weigh them by the number of days in the respective month in the last 30 days. 5 heat effect. Data on daily rainfall levels are obtained from the Indian Meteorological Department
(IMD), which similarly provides the data in the form of a gridded data set with a spatial resolution of
0.25° × 0.25°. Information on air pollution is drawn from the NASA MERRA-2 data set,
which is available on a 0.5° × 0.625° grid and with an hourly time resolution (Global Modeling and
Assimilation Office (GMAO), 2015). These hourly observations are averaged to daily observationsand surface particulate matter concentration is calculated by using the formula displayedin
Appendix A.
The assignment of daily grids to the respective PSU is analogous to the inverse distance weighting procedure described above. Lastly, I obtain information about the Koeppen-Geiger climate classifications from Beck et al. . Koeppen-Geiger classifications specify different global climate regions according to their sea sonal temperature and rainfall patterns. The employed data set provides for each 0.1° × 0.1° degree grid of the world the respective climate classifications for the period 1990-2010. I use this high resolu tion data set to assign each PSU its corresponding climate classification by using the Koeppen-Geiger
climate classifications of the nearest grid point. Figure 2 presents the Koeppen-Geiger climate zones of India together with the geographical dis tribution of the PSUs.
Note that climate zones are not homogeneous within states (or even district)
borders. Therefore, I control for climate region fixed-effects in the empirical specification below (in addition to state fixed-effects), resulting in a comparison of individuals that are on average exposed to the same climate conditions, but differ in the exposure to heat during the 30 days preceding the
survey. Table 1, Panel 2, presents the summary statistics of the temperature and climate variables (sum mary statistics for dry bulb temperatures are presentedin Appendix B). The average daily mean wet bulb temperature is about 22°C and reaches maximum values of more than 28°C. Average rainfall levels in the last 30
days range between 0 and 1355 mm and deviate between -740 mm and + 766 mm from the long-run
10 year average monthly rainfall.
Mean daily levels of air pollution over the last 30 days are very high
. by international standards, with a mean of 34 µg/m
6 Table 1: Summary statistics
N Mean SD Min Max
Panel 1: Individual level variables Depression (scale) 29,541 1.82 0.99 1 5 Severe or extreme depression 29,541 0.08 0.28 0 1 Worry/Anxiety (scale) 29,494 2.01 1.08 1 5 Severe or extreme anxiety 29,494 0.13 0.33 0 1 Male 29,719 0.45 0.50 0 1 Age 29,718 48.36 17.13 15 106 Years of education 28,124 4.61 5.07 0 30 Married 29,718 0.77 0.42 0 1 Chronic condition 29,514 0.43 0.49 0 1 Ever diagnosed with depression 29,299 0.06 0.25 0 1 Rural 30,300 0.75 0.43 0 1 Access to electricity 29,767 0.75 0.43 0 1 Household owns a bike 30,177 0.61 0.49 0 1 Household owns a car 29,058 0.11 0.31 0 1
Panel 2: Temperature and climate variables Mean wet bulb temperature (°C) 21.86 3.99 3.62 28.37
Temperature bins Days <16.5°C 5.08 8.39 0 30 Days 16.5°C-18°C 2.92 3.88 0 18 Days 18°C-19.5°C 2.74 3.64 0 21 Days 19.5°C-21°C 3.03 4.12 0 27 Days 21°C-22.5°C 3.77 5.33 0 29 Days 22.5°C-24°C 3.52 5.02 0 29 Days 24°C-25.5°C 4.13 6.51 0 30 Days 25.5°C-27°C 3.77 6.80 0 28 Days >27°C 1.01 3.26 0 23
Consecutive days >27°C 0.57 1.69 0 11 Heatwave 0.12 0.32 0 1 Heatwave 0.08 0.28 0 1 Heatwave 0.02 0.14 0 1
Rainfall, last 30 days (mm) 111.71 184.94 0 1355.76 Rainfall deviation from long-run mean, last 30 days (mm) 6.04 88.17 -746.14 766.25 Average wind speed, last 30 days (m/s) 2.97 1.06 0.93 7.50 Average air pollution, last 30 days 34.67 11.28 7.03 72.57
Koeppen-Geiger climate classifications Am 0.09 0.29 0 1
Aw 0.22 0.42 0 1
BWh 0.05 0.22 0 1
BSh 0.28 0.45 0 1
Cwa 0.35 0.48 0 1
Cwb 0.01 0.07 0 1
Survey waves Survey wave 0, year 2003 0.34 0.47 0 1 Survey wave 1, year 2007 0.37 0.48 0 1 Survey wave 2, year 2015 0.29 0.46 0 1
Notes: The Koeppen-Geiger climate classification system describes the following climate regions: Am : tropical – monsoon, Aw : tropical – savanna, dry winter, BWh : dry – arid desert – hot, BSh : dry – semiarid steppe – hot, Cwa : temperate – dry winter – hot summer, Cwb ; temperate – dry winter – warm
summer. 7 Figure 2: Koeppen-Geiger Climate classificationsand distribution of sampling clusters - The Koeppen-Geiger climate classifications in which the sampling clusters fall are: Am : tropical – monsoon, Aw : tropical – savanna, dry winter, BWh : dry – arid desert – hot, BSh : dry – semi-arid steppe – hot, Cwa : temperate – dry winter – hot summer, Cwb ; temperate – dry winter – warm summer. 3 Empirical strategy
Identification comes from the variation in exposure to different wet bulb temperatures in the 30 days preceding the survey conditional on a series of geographical and time fixed-effects. I make use of several different empirical specifications to capture the effects of wet bulb heat on mental health at the extensive andintensive margin. I start with an empirical specification which has become standardin the climate literature. Specifically, I use a non-parametric approach, for which I define nine 1.5°C wet bulb temperature binsand use the number of days that fell in each of these bins in the 30 days prior of the survey
interview. This allows to estimate the effect of one additional extremely hot day relative to a day in a given reference category on the state of mental health over the last 30 days. The top bin is chosen to correspond to the 95% percentile of the mean wet bulb temperature distribution over the three survey waves.
Within the sample, on average one day over the last 30 days reached such
temperatures. The empirical specification reads
8 Mental idc =
̸
9 � α t Wetbulb [t] dc [+] [ β] [X] [idc] [+] [ γ] [Z] [dc] [+] [ δ] [ym] [ +] [ ζ] [s] [ +] [ η] [z] [ +] [ ϵ] [idc]
t =1 ,t =5 ̸
̸
where Mental idc is one of the two binary indicators of severe or extreme mental health issues (depres sion or worry/anxiety) over the last 30 days for individual i interviewed at day din PSU c . Wetbulb [t] dc [is]
the number of days that fell in temperature range tin the previous 30 days of day din PSU c. To be precise, the temperature bin variables will be identical for individuals that were interviewedin the same PSU at the same day, but will vary if individuals in the same PSU
were interviewed on different days, or individuals were interviewed on the same day butin different
PSUs. The specification further includes a series of individual and household controls X (age, gender,
years of education, marital status, rural or urban location, access to electricity, whether the individ ual suffers from any chronic health condition, household assetsand housing characteristics). Z dc are other weather conditions that could potentially affect mental health, specifically, I control for the rain fall deviation from the 10-year long-run rainfall in the last 30 days, the wind speed averaged over the last 30 days, and the amount of particulate matter averaged over the last 30 days. Moreover, I
control for the long-run mean wet bulb temperature and its standard deviation averaged over the last 30 days. Thereby, the effects can be interpreted as the effect of one more heat day holding constant the temperature and its variation that would be usually observedin the given 30 day pe riod.
Lastly, δ ym, ζ sand η z are year-month, state and climate zone fixed-effects. State
fixed-effects are the choice of regional fixed-effects, since health policies are commonly implemented at state/UT level in India. Year-month fixed effects account for unobserved shocks affecting mental health specific to a given year-month. Standard errors are clustered at the PSU level. Whereas the first empirical approach allows to determine the mental health effects at the intensive margin (i.e. one more heat day), I am also interestedin the effects at the extensive margin (i.e. being exposed to a heatwave lasting multiple days). To explore these effects, I define a binary heatwave indicator Heatwave [>] dc equal to one if an individual interviewed on day din PSU c was exposed to two or more consecutive days with a mean wet bulb temperature above 27°C. The empirical specification then reads
Mental idc = θHeatwave [>] dc + ϑ X idc + κ Z dc + λ ym + µ s + ν z + ε idc
All control variables remain similar to equation. I am using different temperature and daily cut offs as well as the continuous number of consecutive heat days to test for robustness. To test my presumption that wet bulb temperatures have a different effect than dry bulb tempera tures, I re-run all regressions with similar dry bulb indicators. Given that dry bulb temperatures are always higher than wet bulb temperaturesand its distribution has a larger standard deviation, I employ different temperature binsand binary heatwave indicators chosen as follows. The highest temperature bin for the wet bulb specification was chosen to correspond to the 95 [th] percentile of the mean wet bulb temperature distribution within the data over the three survey waves. The corresponding dry bulb temperature is 37°C, which I use to define the top bin for the dry bulb specification. As a different cut off, I choose the 99 [th] percentile of the dry bulb temperature distribution observedin the data, corresponding to 39°C. Additionally, to account for the larger spread of the dry bulb temperature, I use 11 instead of 9 temperature bins. Summary statics for the respective binsand binary heatwave indicators are presentedin Table B1 in Appendix B.
They show that the chosen cut-offs gen erate comparable bins, with on average 1.62 days within the last 30 days falling in the highest bin with a dry bulb temperature above 37°C. 4 Results
4.1 Main results
Figure 3 presents the results from the wet bulb temperature bin regression separately for depression
(blue) and anxiety (red). Each coefficient plot is interpreted as the percentage point change in the likelihood of suffering from severe/extreme anxiety or depression that is observed if one additional day within the last 30 days had a wet bulb temperature in the range plotted on the x-axis instead of one day with a mean wet bulb temperature range between 21°-22.5°C. The results show that the probability of having suffered from severe/extreme depression in the last 30 days significantly increases with higher wet bulb temperatures. This is especially pronounced if wet bulb temperatures surpass 27°C: one more day above 27°C (instead of a day in the reference temperature range) increases the probability of having suffered from severe/extreme depression by
0.5 percentage points. In relative terms, this corresponds to an increase of 6% from a mean prevalence of 8.47%. Also for one more day in the temperature range between 25.5°C and 27°C, a significantincrease of 0.25 percentage points can be observed. These results are similarly present when I use the continuous depression score as outcome variable: one more day above 27°C increases the depression score by 0.017 points. The effects remain significant and become smaller (larger) when
I shift the temperature bins down (up) by 1°, i.e. such that the highest bin becomes 26°.
The results are shown in tables C2 and C3. In contrast, I do not find any significant robust effects of temperature on the probability of having suffered from extreme/severe anxiety in the last 30 days. While the point estimates for two top bins are positive, they are not statistically significant and also some of the lower bins pointin a positive
direction, rejecting a clear relationship between high wet bulb temperaturesand anxiety levels. There is also no significant effect when the continuous anxiety score is used as outcome variable. For this reason, I focus in the remainder solely on depression. Table 2 presents the results for depression using alternative heatindicators. Column uses only the number of days above 27°C as explanatory variable (without any other temperature bins)
and confirms the main results. One more heat day above 27° in the last 30 days increases the likelihood of having suffered from extreme or severe depression by 0.3 percentage points. In Column, I focus on the number of consecutive days above 27°C. The results show that consecutive heat days indeed are worse for mental health than the same number of heat days which are not necessarily consecutive; if the number of consecutive days above 27° increases by one day, itincreases the likelihood of having suffered from extreme or severe depression by 0.64 percentage points. Column presents the main results for the binary heatwave indicator; individuals that were exposed to two or more consecutive days with a mean wet bulb temperature above 27°C were two percentage points more likely to have
5 The results for anxiety using alternative heatindicators are shown in Table C4 in Appendix C. 10 suffered from severe/extreme depression in the last 30 days. If instead three or more days are used as the cut-off point, this effectincreases to 2.3 percentage points. In relative terms, these numbers corre spond to an increase of 24% and 27% relative to the mean prevalence of severe/extreme depression. When a heatwave is defined as 2 or more consecutive days above 28°C, the effect becomes as large as
5.8 percentage points. However, such extreme heat events are very rare. Only 2.1% of the individuals in the sample experienced such a heatwave in the last 30 days. Figure 3: Heat effects on depression and anxiety (wet bulb temperature) - The figure displays the effect of the number of days in a given wet bulb temperature range relative to the number of days with a wet bulb temperature in the reference category in the last 30 days on self-reported symptoms of depression and anxiety. Outcomes are binary variables; severe/extreme anxiety). Coefficients are multiplied by 100. 11 Table 2: Alternative heatindicators - Wet bulb
Sev./Extr. Sev./Extr. Sev./Extr. Sev./Extr.
Sev./Extr. Depression Depression Depression Depression Depression
Days ≥ 27°C (w/o other bins) 0.316 Consecutive days ≥ 27°C 0.642 Heatwave 1.994 Heatwave 2.324 Heatwave 5.880
Observations 27,287 27,287 27,287 27,287 27,287 R-squared 0.070 0.070 0.069 0.069 0.070 Weather Controls � � � � � Ind. Controls � � � � � Year-month FE � � � � � State FE � � � � � KGC FE � � � � �
of PSU 371 371 371 371 371 Mean of dependent variable 8.47% 8.47% 8.47% 8.47% 8.47%
Notes: Robust standard errors are clustered at the PSU level and presentedin parentheses. p<0.01, p<0.05, p<0.1. 4.2 Dry bulb temperature
Having established that exposure to high wet bulb temperatures increases the likelihood of suffering from severe/extreme depression, I now turn to the effects of dry bulb temperatures. Figure 4 displays the results for the temperature bin regressions with dry bulb temperature as main explanatory vari able. In graph (a), the highest dry bulb temperature bin corresponds to 37°C andin graph (b), the highest dry bulb temperature bin corresponds to 39°C. In both regression, average relative humidity over the last 30 days is included only as ordinary control variable. There is no strong relationship between the dry bulb temperature binsand both mental health out comes. For depression, the higher bins point towards an increase in the prevalence of severe/extreme
depression, but the highest bins are not not or only marginally significant. Only the coefficient for the temperature bin 37.5C°-39°C reaches significance at conventional levels. Yet, the effect size for this bin is half as large as the effect of the highest bin in the wet bulb specification. Similarly, almost all alternative dry bulb heatindicators show only a marginally significant or insignificant relationship with the depression outcome, as shown in Table 3. Only when using the binary heatwave indicator of 3 or more consecutive days above 37°C, I detect an increase of 2.5 per centage points in the prevalence of severe/extreme depression – half as large as the effect found for the wet bulb heatwave indicator. This suggests that earlier studies might have indeed underestimated the relationship between heat exposure and mental health. This could also explain some of the null-results of dry bulb heat on depression as foundin Pailler and Tsaneva. 12 Figure 4: Heat effects on depression and anxiety (dry bulb temperature) - The figure displays the effect of the number of days in a given dry bulb temperature range relative to the number of days with a dry bulb temperature in the reference category in the last 30 days on self-reported symptoms of depression and anxiety. Outcomes are binary variables; severe/extreme anxiety). Coefficients are multiplied by 100. 13 Table 3: Alternative heatindicators - Dry bulb
Sev./Extr. Sev./Extr. Sev./Extr. Sev./Extr. Sev./Extr. Sev./Extr. Sev./Extr.
Sev./Extr. Depression Depression Depression Depression Depression Depression Depression Depression
Days ≥ 37°C (w/o other bins) 0.097 Days ≥ 39°C (w/o other bins) -0.013 Heatwave 1.654 Heatwave 2.536 Heatwave 1.289 Heatwave -0.495 Consecutive days ≥ 37°C 0.194 Consecutive days ≥ 39°C -0.003 Observations 27,287 27,287 27,287 27,287 27,287 27,287 27,287 27,287 R-squared 0.069 0.069 0.070 0.070 0.069 0.069 0.070 0.070 Weather Controls � � � � � � � � Ind. Controls � � � � � � � � Year-month FE � � � � � � � � State FE � � � � � � � � KGC FE � � � � � � � �
of PSU 371 371 371 371 371 371 371 371 Mean of dependent variable 8.47% 8.47% 8.47% 8.47% 8.47% 8.47% 8.47% 8.47%
Notes: Robust standard errors are clustered at the PSU level and presentedin parentheses. p<0.01, p<0.05, p<0.1. 4.3 Robustness checksand dynamics
I conduct a series of robustness checks for both wet bulb temperature specifications; the temperature bin specification and the specification using the binary heatwave indicator. The results are presentedin Table D1 in Appendix D. For readability, I only show the coefficient for the highest temperature bin
for the bin regression, butin all regressions, the full set of bins is included. Column shows once again the baseline specification. In Column, district fixed-effects instead of state fixed-effects are includedin the regression to verify that the results are not driven by heterogeneity across districts. In Column, I include year and month fixed-effects separately instead of year-month fixed-effects, allowing for month-specific re occurring unobserved shocks that could affect depression outcomes. In Column and I drop the state and climate zone fixed-effects, respectively, to verify that results are not driven by the choice of fixed-effectsand whether regional heterogeneity might bias the results. In Column, I drop all other weather variablesandin Column I drop all individual and household controls to verify that the effects are not dependent on the choice of covariates, and lastly in Column, I use a logit model specification instead of a linear probability model to account for the bounded nature of the binary outcome variable. Overall, the results are very robust to these changes in the specifications, though the coefficient sizes slightly change in size in some specifications. Specifically, effect sizes become particularly large when no state or district fixed-effects are included), suggesting that there are indeed unobserved geographical factors that correlate with mental health outcomes. In contrast, effect sizes are smallest when other weather and climate conditions (rainfall, wind speed etc.) are not controlled
for), highlighting the importance of the interplay between different climate conditions. I next turn to the temporal dynamics of the heat effect on mental health. Specifically, I am inter estedin whether the effect of high wet bulb temperatures on depression is solely contemporaneous,
or also measurable over a longer time horizon. To explore these dynamics, I change the horizon over which I count the number of days within the different temperature bins. The results are shown in
Table D2. In Column, the bins are constructed only over the last two weeks before the respective interview took place, in Column, the horizon is shifted to week three and four before the interview, Column focuses on the complete second to last month
, and lastly, Column serves as placebo test and uses the temperature distribu tion of the following month to construct the temperature bins. Overall, the results suggest that the effect is not limited to a short time horizon, but sustained over a longer period of time. Specifically,
the coefficients for the highest temperature bin remain large and significant not only when focusing on the last 14 days before the interview, but even high temperatures two weeksand more than one months ago have a noticeable effect on the current mental health state.
Lastly, the placebo specifica tion in Column shows no statistically significant effect, confirming that the actual effects are not driven by statistical chance. 15 4.4 Heterogeneity
In the following, I assess the effects of heat exposure on mental health separately by working status,
gender, age, place of living and education level to identify vulnerable populations. Figure 5 presents the heterogeneous effects by these socioeconomic characteristics using the temperature bin regres sion approach. The coefficients represent the effect for the highest bin, stemming from the temperature bin regression. The analyses are well in line with previous studies indicating that working individuals, rural pop ulationsand those with lower education status are more prone to heat effects. In particular, in the context of India or other
LMICs, individuals with lower education andin rural areas are more likely to engage in agricultural activities or other physically demanding jobs outside, often have less resources to engage in adaptive measuresand are hence more exposed to extreme temperatures. Figure 5 shows that the point esti mates for working individuals, those living in rural areasand those with no education are larger than their comparison counterparts. However, only in the case of education, the point estimates are also statistically different from each other, calling for caution to interpret this as robust evidence for differ ences with respect to working statusand place of living.
In particular, in terms of place of living, the sample for the urban population is small in comparison to the rural sample,
which could partly explain the large confidence intervals. In terms of heterogeneous heat effects on mental health by gender, the existing literature is incon clusive. Whereas studies investigating heat effects on suicide rates mostly find larger effects for men
, studies on self-reported mental health outcomes often find larger effects for women. My results suggest that men and women are not differently affected by heat. While the point estimates are slightly larger for men, the effects are not statistically different from each other, which would allow for a definite conclusion. However, my results are in strong contrast to other studies when it comes to the heterogeneous effects by age. While previous studies find that especially elderly are prone to heat, also in terms of mental health outcomes, my results show that the effect is larger for younger cohorts below the age of 50, and the effect for those above the age of 50 is not statistically
significant. A potential reason could be that younger individuals are more likely to work and the age effect hence only reflects the working effect. However, the age difference remains even when limiting the sample to younger and older working individuals; the heat effect is larger and significant for young workers but not significant for older workers. Moreover, also for younger non-working individuals,
the effect remains similar in size and significance. Also, power issues cannot explain the differential effect since samples sizes for those below and above the age of 50 are almost equal (with the elderly sample being even larger). Another reason could be that the elderly sample contains many individuals that generally live longer than the average of their respective cohorts; the mean age in the 50+ sample is 62 and 36% of the individuals are 65 or older. Under the presumption that these individuals are generally healthier than the average), they also might be less prone to suffer under heat. And alternative explanation could be that mental health stigma is lower among younger
cohorts, and hence younger individuals are more likely to truthfully report mental health outcomes
16. However, what speaks against this hypothesis is that the mean prevalence of depression among those individuals below the age 50 in the sample is on average lower than the mean prevalence for those above the age of 50. Hence, while the reason remains purely
speculative, the findings are reason to concern; if especially younger and productive individuals are those most likely to suffer mentally from increasingly more frequent heatwaves, this can lead to large productivity losses in the future. Figure 5: Heterogeneous effects - The figure displays the heat effect for different socioeconomic subgroups using the temperature bin regression. Coefficients are from the top bin.
The outcome variable is binary and multiplied by 100, hence, coefficients are directly interpretable as percentage points. 17 5 Channels
I nextinvestigate different channels through which heat exposure can affect mental health. First,
high temperatures during the night can worsen sleeping patternsand thereby cause or exacerbate depressive symptomsand worsen mental well-being. Second, heat can also increase aggression, conflict and violence, which in turn are harmful to mental health. In the following, I explore in turn whether these two channels are of relevance in the given context. Within the scope of WHO-SAGE information about the level of conflict and tension with others over the last 30 days as well as about sleeping problems over the last 30 days were collected, allow ing to assess whether these two factors function as channels of the heat-mental health relationship. The questions for conflict and tension as well as for sleeping problems were designed similar to the depression and anxiety questions. Hence, similar to the main outcomes, I construct binary indicators equal to one if the individual reported to had encountered severe/extreme conflictsand tensions or
severe/extreme sleeping problems in the last 30 daysand regress the indicators on the wet bulb heat
indicators. The results are presentedin Table 4 and show that conflict and tensions tend to increase with extreme temperatures, whereas I find no effects on reported sleeping difficulties. This suggest thatincreased conflict and tension might be one channel through which extreme temperatures drive deterioration of mental health. This is further consolidated when including the conflict variable as moderating variable in the main depression regression; the effect size of heat on depressions is re duced by about 15% (both for the bin regression and binary heatwave indicator), while the correlation between conflict and depression is statistically significant (not shown in Table). Heatwaves during the main agricultural season are another likely channel of the heat-mental health nexus, since physically demanding activities, such as sowing or harvesting, are conducted dur ing extreme heat. Moreover, extreme heat during the main agricultural season might be correlated with the occurrence of droughts, which in turn mightincrease expectations of an adverse harvest outcome and poverty. Hence, both physiological mechanisms as well as economic considerations can be a driving force of the heat-mental health nexus. While disentangling the precise mechanism remains difficult, the focus of my analysis on exposure to heat events in the last 30 days renders a physiological mechanism the more plausible explanation than economic expectations which might only unfoldin the longer-run. Moreover, since crops that are planted during the main agricultural season (Kharif) are more sensitive to water shortages than high temperatures, and rainfall levels are held constantin the analysis, the effect captured by the wet bulb temperature rather reflects the physiological channel of exposure to heat stress on mental health. In Table 5, I split the sample by urban and rural population andinteract the temperature binsand the binary heatwave indicator with a binary variable for the main Indian agricultural season Kharif. Kharif lasts from June to October and crops are sown when the Indian south-west monsoon rains
start. The results in Column and show that the non-significant effect of wet bulb temperature on mental health in urban areas is not heterogeneous over the agricultural cycle. In contrast, in rural
areas, the mental health consequences due to high wet bulb temperatures are stronger in the Kharif
18 season. In fact, the linear coefficients for both heatindicators even turn negative (implying an im provementin mental health), whereas the interaction term is large in absolute termsand statistically
significant. Hence, the effect of high wet bulb temperatures on the likelihood of suffering from se vere or extreme depression is largest during the main agricultural season, suggesting that continued exposure to heat due to outside and physically demanding activities amplifies the effect of high wet bulb temperatures on mental health. Table 4: Channels - Conflict and sleep
Conflict Conflict Conflict Sleep Sleep Sleep
Days >27°C 0.298 0.152 Heatwave 1.374 -0.597 Heatwave 5.724 1.517
Observations 27,278 27,278 27,278 27,300 27,300 27,300 R-squared 0.079 0.078 0.079 0.074 0.074 0.074 of PSU 371 371 371 371 371 371 Mean of dependent variable 9.66% 9.66% 9.66% 9.22% 9.22% 9.22%
Notes: All regressions include weather controls, individual controls, year-month fixed-effects, state fixedeffectsand climate zone fixed effects. Regressions for temperature binsand) include all temperature bins but only the top bin is shown in the table. Robust standard errors are clustered at the PSU level and shown in parentheses.
p<0.01, p<0.05, p<0.1. 19 Table 5: Channels - Kharif season
Sev./Extr. Sev./Extr. Sev./Extr. Sev./Extr. Depression Depression Depression Depression Urban Urban Rural Rural
Days >27°C -1.134 -1.272 Days >27°C Kharif 1.538 1.992 Heatwave -2.331 -4.621 Heatwave Kharif 2.969 8.083
Observations 6,751 6,751 20,535 20,535 R-squared 0.107 0.104 0.067 0.064 of PSUs 89 89 282 282 Mean of dependent variable 6.76% 6.76% 9.03% 9.03%
Notes: All regressions include weather controls, individual controls, year-month fixedeffects, state fixed-effectsand climate zone fixed effects. The temperature bin regressionsand) include all binsand its interactions with the Kharif dummy, but on the top bin is shown in the table. The Kharif dummy is absorbed by the year-month fixed effects. Robust standard errors are clustered at the PSU level and presentedin parentheses.
p<0.01, p<0.05, p<0.1. 6 Effectiveness of the Indian District Mental Health Program in reducing negative heatwave effects
This section explores whether an existing mental health program in India has the potential to effec tively counteract the negative mental health impacts of heatwaves. Since the early 1980s, the Indian
Ministry of Health and Family Welfare started to implement programsandinitiatives to promote mental health among its population. The National Mental Health Program (NMHP) was initiatedin
1982 and as part of this program the District Mental Health Program (DMHP) was integratedinto the national program in 1996. The DMHP targeted the imple mentation of community-based mental health services, including the training of community mental health workers, early detection and treatment programs as well as awareness generation and the in tegration of mental health interventionsand programs within the public health system. Starting as a pilot projectin four districts in 1996, it has since then been rolled outin a staggered design across almost the entire country and is predicted to cover 767 districts by the end of 2025. By the end of 2014, 32 districts within the WHO data sample were covered under the DMHP; 4 already before Wave 0, 16 were coveredin between Wave 0 and Wave 1, and 12 in between Wave 1 and Wave 2). In the following, I assess whether the program was able to counteract some of the previously recorded negative mental health effects of heatwaves by providing improved access to mental health care andincreased awareness about mental health
issues. To do so, I modify equation and to include a binary indicator equal to one if district i
20 was covered under the DMHP in year t, and its interaction term with the temperature bins or heat wave indicator, respectively. Moreover, I include district-fixed effects in the specification to account for the program being rolled out on the district- and not state-level. The linear term of the DMHP in dicator then represents the general (difference-in-difference) correlation between DMHP availability and mental health outcomes, while the interaction term can be interpreted as the mediating effect of program availability on the heatwave-mental health link. Note that this should not be interpreted as causal effect, but rather as correlational evidence, given that the roll-out of the program might have been targeted to purposely chosen districts first. The results of this analyses are presentedin Table 6 – with the bin specification in Column and the binary indicator in Column. The coefficient for the linear DMHP term is large in absolute termsand significant, suggesting that the program indeed was rolled out first to districts where rates of depression (or generally rates of mental illness) were high to begin with. Also, the heatindicators remain significant, with effect sizes very similar to those in the main results. The main coefficients of interest are the two interaction terms between the heatindicatorsand the DMHP variable. Both have a negative sign and the coefficientin the bin regression reaches statistical significance. Hence, it appears that the mental health consequences of extreme heat are to some extent alleviated if individuals have access to the DMHP, suggesting a protective role of the program in mitigating adverse mental health
effect. This also speaks in favor of integrating specifically climate-sensitive mental health components within the existing framework of the DMHP.
Nevertheless, given the steady increase in the coverage rate of the program, it remains unanswered whether this alleviating effect remained once the program was rolled-out to the entire country, which the current data do not allow to address. Table 6: District Mental Health Program
Sev./Extr. Sev./Extr. . Depression Depression
DMHP 5.373 3.050 Days >27°C 0.363 Days >27°C DMHP -0.558 Heatwave 2.182 Heatwave DMHP -0.362
Observations 27,287 27,287 R-squared 0.087 0.085 District fixed-effects � � of PSU 371 371 Mean of dependent variable 8.47% 8.47%
Notes: All regressions include weather controls, individual controls, year-month fixed-effects, district fixed-effectsand climate zone fixed-effects. The temperature bin regression) includes all temperature binsand their interactions with the DMHP indicator, but only the top bin is shown in the table. Robust standard errors are clustered at the PSU level and shown in parentheses.
By highlighting the compounded risks posed by heat and humidity,
it advocates for integrating mental health into broader climate resilience strategies, in particular in
LMICs in South- and Southeast Asia, where high humidity exacerbates the physiological and psy chological burdens of extreme heat. Declaration of generative AI and AI-assisted technologies in the writing process
During the preparation of this work the author used ChatGTP in order to shorten the manuscript and to improve the readability and language of the manuscript.