LT Week 4 Flashcards
Motivation
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
To estimate the effect of climate change (specifically temperature changes) on global mortality risk.
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
Special settings
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
“Economics literature tends to lack emperical findings, made worse by need to project economic effects that are both long run and at global scale (due to role of climate change on future)
Attempts for multi-century and global nature known as “intergrated assessment models”” but tend not to consider adaptation to gradual climate change”
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
Theory
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
“Emperical estimation of the global damages of climate change, accounting for the costs and benefits of adaptation, for a single sector of the economy using micro data
Use approach of mortality risks associated with temperature change
Uses estimates for regions to compute mortality PSCC to avoid the changes in mortality risk caused by additional metric ton of CO2.”
Impactc of climate change differ between developing and developed countries, as there is heterogenous upadte of adaptive technology & heterogenous investment
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
Empirical design
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
“1) Estimate regressions to infer age-specific mortality-temperature relationships using annual subnational mortality stats from 40 countries, covering 38% of global population. Benefits of adaptation and income growth estimated through allowing mortality-temp function to vary with long-run climate and income per capita. Allows prediction of mortality-temperature relationship across locations where the mortality data is missing, yielding global results
2) Combine regression results with standard future predictions of climate, income and population to project future climate change (fatality rates and monetised value). Create revealed-preference model to infer sum of costs, based on assumptions that people will make adaptation investments until MB=MC.
3) Use estimates to compute global marginal willingess to pay (MWTP) to avoid alteration of mortality risk due to temperature change per additional metric ton of CO2. Known as partial social cost of carbon (PSCC) as only considers temperature change and not other elements of climate change (i.e. storms)
Accounts for difference in costs of climate change across globe and different adaptations across location (i.e. Pakistan versus Alaska). Develop estimates for 24k regions at average size of a US county.”
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
Data
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
Assembled data set composed of historical mortality records, historical climate data, future projections of climate, population and income across globe
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
Key findings
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
- Hotter and poorer countries today have higher mortality from hot days
- Countries that are hotter and poorer today have higher risk of mortality from climate change in the future
- Higher income can protect from heat, but adaptation is costly
“1) U-shaped relationship where extremely cold and hot temperatures increase mortality rates (especially for 65+ year olds. Heterogenous across planet. Find both income and warmer long-run climate moderate mortality sensitivity to temperature. Estimate that an extra very hot day (35 degrees +) effect on mortality for >64 year olds is 50% larger in regions where mortality data is unavailable –> data might understate due to reliance on wealthier economies which report data
2) Mean estimate of projected increase in global mortality rate due to climate change is 73 deaths per 100,000 at end of century under high-emissions scenario (similar rate to current global mortality of all cancers). Unequally distributed across globe, with 17% increase expected in Ghana due to more very hot days versus 15% decrease in Germany due to milder winters. Failure to account for climate adaptation and income growth would lead to overstating mortality rate of climate change by factor of 3. Full mortality risk of climate change estimated at 3.2% of global GDP by end of century, with poor countries experiencing this cost disproportionately through deaths.
3) Estimates find PSCC of $36.60 with high-emmissions scenario (but with high uncertainty)”
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
Interpretation / policy implications
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
“Temperature-related mortality risk from climate change substantially greater than previously understood
Paper highlights role for systematic empirical analysis of effects of climate change in the future”
However,
For the poorest countries today, there may be many more pressing short-term issues that need to be dealt with.
Unless addressed now, these issues will constrain their abilities to grow in the next few decades – even before the worst of climate change hits. So despite being most adversely affected by climate change in the future, the net present value of fighting hunger could still be higher than adapting to future climate change
Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (Carleton et al., 2015) (2022)
Motivation
Balboni et al 2024: Weathering Pover
Balboni et al 2024: Weathering Poverty
Special settings
Balboni et al 2024: Weathering Pover
Use high resolution satellite data to study how floods and droughts affect ultra-poor communities in Bangladesh. Like papers from last term, use the TUP scheme - so make sure you are clear on that
Balboni et al 2024: Weathering Poverty
Theory
Balboni et al 2024: Weathering Poverty
To mitigate risk, poor household might adopt costly coping strategies by diverting their investments towards less risky activities, ultimately reducing their long term earning potential
Reason why magnitude of climate change development varies from developed to developing:
* Occupational structures in developing countries are skewed towards weather-dependent sectors
* Larger shares lack insurance or credit to smooth productivity or health-related shocks, amplifying the negative impacts on health from a weather shock
Balboni et al 2024: Weathering Poverty
Empirical design
Balboni et al 2024: Weathering Poverty
“Use high resolution satellite data to study how floods and droughts affect ultra poor communities in Bangladesh. Combining satellite data and TUP household data, the researchers evaluate the direct impact of climate shocks on household-level outcomes. To do this, they look at the effect of BRAC’s TUP program on mitigating the magnitude of these shocks for beneficiary households, in a difference-in-difference setting.
Outcomes of interest: (i) total expenditure, (ii) food expenditure, (iii) non-food expenditure, (iv) productive assets 10, (v) other assets 11, (vi) loan, and (vii) savings.
Equation 1 is key here:
- Ti = 1 if household I lives in a treated village and 0 otherwise
- C(natural disaster) is a continuous variable that measures how many dekads (10 day intervals) of drought does this village experience in the last 36 dekads. Gamma coefficient on this term measures the impact of being highly exposed to climate events during 2011.
Last coefficient measures the additional difference between program beneficiaries respond to higher severity of natural disasters. This interaction coefficient compares outcomes of ultra-poor households treated by BRAC’s TUP program and residing in villages experiencing major climate shocks in 2011 with program beneficiaries whose villages are spared
”
Balboni et al 2024: Weathering Poverty
Data
Balboni et al 2024: Weathering Poverty
”
BRAC’s Targeting the Ultra-poor Program (TUP) - Data on those living in villages that received this program covers 23,000 households living in 1,309 villages in the 13 poorest districts of the country. 1/2 assigned to treatment Graduation program comprising: asset transfer, training, coaching, cash transfer, social inclusion Survey representative of entire village. Baseline in 2007, 5 waves until 2018
Of these households over 6000 are extremely poor, half of which are randomly selected to receive a large asset transfer in 2007
Satellite data on floods and droughts at a very high resolution
Drought Data – obtained from the Food and Agriculture Organization: Vegetation Health Index database. Data available from 2000 to 2018 for intervals spanning 10 days. (VHI) VHI combines “greenness” of surface and temperature deviation from historical minimum Every 10 days, 1 × 1 kilometer
“
Balboni et al 2024: Weathering Poverty
Key findings
Balboni et al 2024: Weathering Poverty
“The difference between the blue and red estimates (see Figure IV which is the key result of this paper) shows the heterogeneous impact of climate shocks on program beneficiaries compared to non-beneficiaries, within the set of affected villages. The results indicate that, on average, beneficiaries are more resilient to climate shocks, which is supported by the fact
that red dots are in general higher than the blue dots. For the consumption and saving,
the beneficiaries now can perfectly offset the negative impact of the climate shock.
In the following Table panel (a), paper presents the results when they control for historical shocks (i.e. the number of combined shock dekads between Jan 1, 2000 and Dec 31, 2006), since the past shocks might correlated with the future shocks. Panel (a) has a very similar pattern as the main specification in Figure 4.
Document three important facts:
First, natural disasters affect households’ ability to save, access external finance, and invest. Productive investment, measured as the level of productive assets, is approximately 25% lower after a climate event, with droughts being mostly responsible for this negative effect.
Second, a poverty reduction program (BRAC’s TUP Program) based on large asset transfers allows households to shield against climate risk thus enhancing their resilience to these shocks.
Third, they nuance this finding by showing that the shielding effect is muted for the poorest beneficiaries who are exposed to climate shocks before receiving the transfer
Ex ante, highly exposed households react by consuming more and investing less relative to those who are less exposed “
Balboni et al 2024: Weathering Poverty
Interpretation / policy implications
Balboni et al 2024: Weathering Poverty
Poverty reduction programs that rely on the accumulation of productive capital could be complemented by insurance for this capital or conditional loans, so that households perceiving a high degree of risk would still be inclined to invest
Balboni et al 2024: Weathering Poverty
