Embed Size (px)
DESCRIPTION
Heat wave – a big killer 1. Mortality & morbidity -- the most lethal weather phenomenon (biggest killer of all natural calamities) -- Fatality counts: -- on average 400/year in USA, in 2003 in Central Europe, in 2003 heat waves in India (AP),… in 2010 in East Europe and Russian (50000 in Russia) -- WMO predicts heat related fatalities will double in less than 20 years 2. Productivity loss -- less talked about, few studies % output loss / 1 ⁰ C rise in temperature in Caribbean and Central America of which, 0.1% loss is in agriculture and 2.4% loss in industries due to thermal stress of workers (Hsiang, 2010 (PNAS); Dell, Jones, and Olken, 2009 (NBER WP); Jones and Olken, 2008 (AER)
Citation preview
Importance of changing food habits in adapting to heat waves: Observations from private
adaptation cost estimates
Saudamini Das,1.Institute of Economic Growth,
2.SSN College, DU3.Fellow, SANDEE
Economics of Climate, Food, Energy and Environment (CECFEE) workshop, Indian Statistical Institute, Delhi, 2nd Nov 2015
Heat waves• Extended period of hot and humid weather
• Relatively defined : -- 28 deg C in Denmark -- 32 deg C in NW USA -- 40 deg C in Australia -- 45 deg C in India ,….
• Heat stress: if core body temperature > 37deg C or skin temperature > 35 deg C
Heat wave – a big killer1. Mortality & morbidity -- the most lethal weather phenomenon (biggest killer of all natural calamities) -- Fatality counts: -- on average 400/year in USA, -- 35000 in 2003 in Central Europe, -- 3000 in 2003 heat waves in India (AP),… -- 70000 in 2010 in East Europe and Russian (50000 in Russia) -- WMO predicts heat related fatalities will double in less than 20 years
2. Productivity loss -- less talked about, few studies -- 2.5% output loss / 1⁰C rise in temperature in Caribbean and Central
America of which, 0.1% loss is in agriculture and 2.4% loss in industries due to thermal stress of workers
(Hsiang, 2010 (PNAS); Dell, Jones, and Olken, 2009 (NBER WP); Jones and Olken, 2008 (AER)
Temperature affect productivity• Thermal stress reduce productivity (Ergonomics studies) ---- productivity may fall by 5–7% at the elevated indoor temperatures, ---- 2% decline in productivity for 1⁰C increase in temp (Niemela et al.,
2002; Lan et al., 2010; Mahamed & Srinavin, 2005)
• High temperature reduce work efficiency in open environment ( Economic studies)
--- Beyond 26°C, manual workers worked about 1.8 percent less for
every1⁰C rise in temp (Zivin and Neidell, 2011) --- Effective labor supply (composite of labor hours, task performance and
effort) decrease for temperature variations beyond biological optimum (Heat and Park, 2014)
.
Heat Wave in Odisha
Heat Wave Management in Odisha after 1998
• Regular: Dissemination of HW warning, Jal Chhatra, Health facility, rescheduling of work hours, school timing, bus timing etc.
• HW management under Disaster Risk Management (DRM) Program (2002 onwards)
• Awareness Campaign (more intensive after 2007)
• Private (reactionary adaptation: buying AC, using car in place of scooter – income based)
• Physiological Adaptation
What I study?
• Coping strategy of low income urban workers to heat stress
-- effect on time allocation to different work -- change in consumption -- change to living place (both short run and long run) -- any other effect, like change in occupation etc.
Equilibrium under heat wave
Z1 : leisure; Z2 :housework
Z2*: Minimum possible time needed for house work
AB: PPC for Z1 and Z2,
e1: summer equilibrium,
e2: heat wave equilibrium.
e*
Theoretical Model
),( 21 ZZUU
)(11
*
)( TTcZhl 0)(0)( 11 ZhandZh
22 Zl
321 lllL
}2,1{, iZX iii
AZpZpIcZhZLwIllLwIwl TT
222111
0)(
1202103 ])([)(*
with
T is actual daytime maximum temperature,T* is maximum bearable temperature‘c’ is adaptation parameter
(Input requirement)
(Budget constraint)
Equilibrium under heat waves
)()()(
2
1
22
11)(
1
2
1
*
workhouserest
pwpcZhw
ZU
ZU
TT
Situation 1: T = T*, )( 11 Zhl
:
*
)( 11 TTc
Zhl
Situation 2: T >T*, *
)( 11TTcZhl
:
2
1&Z
Z
MUMU
112
1& withMUMU
Z
Z
Situation 3: T<T* 222
1& withMUMU
Z
Z
Time allocation
• Let time allocation be
--- l1τ, l2τ, and l3τ on a normal day with temp Tτ
--- l1, l2, l3 on a heat wave day with temp T and T>Tτ
• Then, we expect --- l1 > l1τ, --- l2 < l2τ, and --- l3 ≤ l3τ
as l1τ+ l2τ+l3τ= l1+ l2+ l3.
• Examine
332211 ,, llandllll
to measure heat wave effect on labor supply
Annualise expenditure on consumer durables
])1(
11[
*
n
ii
r
rPA
A is annualised value, P is market price of ith asset,n is life span and r is rate of interest
Sampling• Purposive random sampling • 15 workers --- 10 different types of work in open environment -- 2
cities in India
• Response on labour allocation are elicited indirectly through recall ---- ‘what activities you did during 7AM to 9AM today (yesterday) vs. what
activities you did during 7AM to 9AM on the heat wave day’
• Collect information on activities engaged during each 2 hours in between 7AM to 11PM.
• Put activities into 3 category: rest, family work, and work.
Study area
Sample features
020406080
100120140160180200
Less than20
20 - 40 40 - 60 60 - 80 Eighty orMore
Education level
15.72
27.7647.83
5.69 3.01
IlliteratePrimaryMatriculateGraduatePost graduate
Occupation
Vegetable/Fruit Seller
Cobbler
Construction Worker
Coolie (Manual Labour in Transport or Other sectors)
Rickshaw/Trolley Drivers
Auto Driver
Taxi Driver
Mobile marketing and Sales executive
Vendors (Mobile sellers of Household items in trolleys)
Owners & workers in open retail enterprises (Temp Stall)
Age groups
Annual Income
0.010.020.030.040.050.060.0
Less than10000
10000 -20000
20000 -50000
50000 -100000
100000 - 300000
Above300000
Income range
Freq
uenc
y(%
)
Males:94%; Hindus: 84%; Gereral:18%; SC: 30%; OBC:43%
Awareness campaign and behaviour
Type of behaviour change Number of AdaptersDrink more water 0.73
Eat cucumber and onion 0.65
Carry water bottle when travelling
0.64
Carry umbrella 0.63
Wear light colour cloths 0.26
Wear shows/slippers 0.21
• Aware of dos and don’ts : 0.99
Health effect
Type of health problems Reported sufferingRise_Body_Temp 0.15Respiration_Prob 0.08Unconscious 0.05Blurred_Vision 0.08Nausea 0.11Body_Ache 0.04Tiredness 0.12Hospitalised_heat_attack 0.15
Average effect on time allocationOccupation Change in average time allocation during a
heat wave day compared to normal summer day (in hours)
Regular outside work
House work Rest
Vegetable/Fruit Seller -0.73 -0.22 0.95
Cobbler -1.23 -0.47 1.70Construction Worker -1.54 -0.38 1.93
Coolie (Manual Labour in Transport or Other sectors)
-1.62 -1.02 2.64
Rickshaw/Trolley Drivers -1.65 -0.52 2.17
Auto Driver -1.06 -0.21 1.28Taxi Driver -1.52 -0.68 2.2
Mobile marketing and Sales executive -0.74 -0.37 1.1
Vendors (Mobile sellers of Household items in trolleys)
-1.26 -0.18 1.44
Owners & workers in open retail enterprises (Temp Stall)
-0.53 -0.58 1.11
Average change -1.19 -0.46 1.65
SURE Results
Explanatory Variables
Dep var: Ratio of time allocated to
work on a heat wave day compared
to a normal summer day
Dep var: Ratio of time allocated
to rest on a heat wave day
compared to a normal summer day
City 0.123*** (6.00) -0.006 (0.08)
occu_vendor 0.031 (1.12) -0.033 (0.34)
occu_manual_work -0.079*** (3.36) 0.214*** (2.62)
occu_driver -0.031 (1.17) 0.197** (2.15)
distance_workplace -0.004* (1.74) -0.002 (0.84)
workplace_congested -0.024 (1.2) 0.001 (0.98)
workplace_tree -0.019 (0.88) 0.142** (1.89)
profession_change_heat 0.096* (1.75) -0.325* (1.69)
illiterate 0.012 (0.49) -0.012 (0.14)
hospitalized_heat_attack -0.014 (0.59) 0.123 (1.47)
health_problem -0.031* (1.68) -0.092 (1.42)
income_medium -0.095** (2.58) 0.191 (1.49)
income_high -0.108** (2.42) 0.132 (0.85)
_cons 0.989*** (21.91) 1.023*** (6.52)
RMSE 0.14 0.49
R 2 0.2611 0.10
Extra expenditure on food and other non-durablesOccupation Average approximate
annual income (in INR)a
Average increase in monthly routine expenditure due to heat wave days (in
INR)b
Vegetable/Fruit Seller 63667 810 (0.15)Cobbler 57903 597 (0.12)
Construction Worker 47667 450 (0.11)Coolie (Manual Labour in Transport or
Other sectors)59828 597 (0.12)
Rickshaw/Trolley Drivers 57833 557 (0.12)Auto Driver 110161 694 (0.08)Taxi Driver 86333 612 (0.09)
Mobile marketing and Sales executive 200000 693 (0.04)
Vendors (Mobile sellers of Household items in trolleys)
78036 339 (0.05)
Owners & workers in open retail enterprises (Temp Stall)
95323 639 (0.08)
Average for all groups 85833 601 (0.08)Average extra expenditure per heat wave day 100 (0.014)
Expenditure on durable goodsType of heat wave averted activities at home
Sample proportion who reported to have undertaken these changes due to heat waves
Possible life span (years)
Annual average cost of the activity using Eq. 10 (in INR)
Wipe floor repeatedly using cooling ingredients
0.33 1 320
Put thick layers of paddy straw on roof to stop transmission of heat
0.54 1 482
Use thick curtains 0.14 5 126
Insulate walls with paint, mud 0.017 10 152
Purchase fans 0.92 15 151.29
Purchase Air/water cooler 0.05 10 631
Purchase fridge 0.04 20 729.47
Purchase Air Conditioner 0.003 20 1833.34
Weighted annual average for a sample household
592
Private cost of adaptation per heat wave day
Extra expenditures
Non-durable (food and electricity)
Rs100 ($1.7) 51%
Durable Rs49 ($0.8) 25%
Work time loss Rs46 ($0.7) 24%
Total loss / hw day Rs195 ($3.25)
• Rs25.12 million per heat wave day or Rs3.01billion per annum at 2012-13 prices for the state of Odisha
Policy
• So far government programs have targeted reducing the health effect (both mortality and morbidity), economic loss or just work time loss is not yet recognised.
• Income supplement with Awareness campaign
• Subsidized electricity
• Some subsidy on food commonly consumed during summer
THANKS
