Climate Change and Poverty Vulnerability

Transcription

1 Climate Change and Poverty Vulnerability Amer Ahmed The Poverty and Distributional Impacts of Climate Change PREM Knowledge and Learning Forum April 26, 2010 Based on findings from the DECAR project Climate Volatility and the Poor in Southern and Eastern Africa, supported by the Trust Fund for Environmentally and Socially Sustainable Development.

2 Human Systems Interaction with Other Policies Carbon Emissions Impacts Policy Responses Natural Systems Adaptation Financing Emissions Reduction

3 Agriculture as a Link Substantial literature on climate change, and on poverty; less done linking the two Agriculture is key mediator between climate change & poverty Sector most exposed to climate; temperature & precipitation fundamental to productivity Food most important consumption item for poor Concentration of poor in rural areas, direct and indirect dependence on farming for earnings

4 Yield Change (%) Climate and CO 2 Changes Agricultural productivity Average Global Yields vs. Temperatures, Temperature Change (ºC) Source: Lobell and Field, 2007

5 Source: WDR, 2010

6 Changing Climate Volatility Higher temperature and precipitation extremes in the future (IPCC, 2007) Extreme outcomes may be particularly important for agriculture (White et al, 2006; Mendelsohn et al, 2007) Greater climate volatility is already occurring (Easterling et al, 2000)

7 Implications for Poverty Extreme climate events will reduce agricultural output in the tropics and subtropics (Lobell et al, 2008; Battisti and Naylor, 2009) Food supply constraints Food insecurity influenced by forces that constrain people s access to food, not just availability (Sen, 1981) income & price effects important in poverty impacts of exogenous shocks, e.g. in Doha Development Agenda poverty studies

8 Income Effects Income is sensitive to sources For many rural poor, main endowment is unskilled labor Agriculture is unskilled labor intensive If output expands, unskilled wages rise; opposite for contraction Ambiguous impact of agricultural commodity price rise on non-farm rural households depends on earnings diversification, impacts on farm factor returns, and unskilled wages

9 Price Effects Higher food prices hurt all households, but hurt the poor relatively more due to large food budget share Lower crop prices may reduce incomes of rural net-sellers of crops Recent experience: 100 million additional poor due to global food price crisis between (Ivanic & Martin, 2009)

10 Historical Volatility in Grains Production and Prices in Tanzania 1 in 30 productivity draw from here Prices more volatile than production Source: Ahmed, Diffenbaugh, & Hertel (2009)

11 Computational Framework GTAP model used to elicit national price and earnings impacts of productivity shocks : land use by Agro-Ecological Zone factor market segmentation Micro-simulation module to evaluate household-level impacts at poverty line in 7 population strata across 16 countries in Asia, Latin America and Africa Survey data: Estimate earnings shares and density around poverty line Use estimated consumer demand system to predict consumption changes at poverty line Estimate change in stratum poverty due to combination of factor earnings and consumption impacts Combine into estimate of national poverty using shares of strata in national poverty headcount

12 Sensitivity of Poverty Rates to 1 in 30 Climate Extreme 1 in 30 draw worsens poverty Source: Ahmed, Diffenbaugh, & Hertel (2009)

13 %D in Poverty by Household Stratum due to 1 in 30 Climate Extreme Table 2: Percent change in poverty due to once-in-30 year climate extreme by stratum and country Agricultural Non- Agricultural Socio-Economic Strata Urban Labor Rural Labor Transfer Urban Diverse Rural Diverse Bangladesh Brazil Chile Colombia Indonesia Mexico Mozambique Malawi Peru Philippines Thailand Tanzania Uganda Venezuela Vietnam Zambia Average Source: Authors simulations Source: Ahmed, Diffenbaugh, & Hertel (2009)

14 Changing Climate Extremes Consider three distinct agricultural productivity stressors: Wet: the percent of annual total precipitation due to events exceeding the th percentile Dry: the maximum number of consecutive dry days Heat: the heat wave duration index (the maximum period greater than 5 days with the daily maximum temperature greater than the normal) Draw on CMIP3 archive heavily used in the IPCC Fourth Assessment Report for estimates Compare 1971 to 2000 with and 2071 to 2100 period under the SRES A2 emissions scenario.

15 Source: Ahmed, Diffenbaugh, & Hertel (2009)

16 Percentage Change in Intensity of 1 in 30 Year Extreme Heat Wave, vs Source: Diffenbaugh, forthcoming

17 Percentage Change in Intensity of 1 in 30 Year Extreme Dry Spell, vs Source: Diffenbaugh, forthcoming

18 Tanzania: Average Maize Yield ( ) Source: Rowhani et al., mimeo

19 Sensitivity of Tanzanian Poverty to Climate Volatility Statistical analysis using data from 17 administrative regions: Maize, rice, and sorghum yields (tonnes/ha) Temperature (growing season mean in degrees C) Precipitation (growing season mean in mm/month) Predictions using data from 22 global climate models Yield variability increases in 10 cases out of 22 Use yield equation to translate historical and future climate into output changes

20 Distribution of Interannual % Changes in Tanzanian Grains Yield due to Climate Mass of distribution shifting left (more/larger outcomes with % decline in yield from previous year) median yield change Outcomes within box represent 75% of all predicted outcomes Source: Ahmed, Diffenbaugh, Hertel, Lobell, Ramankutty, Rowhani, & Rios (2009)

21 Sensitivity of Poverty in Tanzania to Climate Outcomes: Distribution of % Point Changes in National Poverty Rate Impact of most extreme harmful climate on poverty Impact of most extreme beneficial climate on poverty Outcomes within box represent 75% of all predicted outcomes Source: Ahmed, Diffenbaugh, Hertel, Lobell, Ramankutty, Rowhani, & Rios (2009)

22 Trade Policy Climate volatility effects interact with trade policy Concerns about greater trade & food security World price instability as a source of domestic price instability, price transmission e.g. Special Safeguard Mechanisms Export bans during food price crisis May block supply responses to higher international prices in long run (Mitra & Josling, 2009) Higher price volatility Tanzania has export ban on maize and other grains

23 SR Supply Shocks and Price Changes: Small Open Economy PRICE D SM S0 SX PM P0 PX QM QDM Q0 QDX QX QUANTITY

24 SR Supply Shocks and Price Changes: Export Ban PRICE D S0 SX PM P0 PX PEB Q0 QX QUANTITY

25 Price Volatility Due to Historical Grains Productivity Volatility* Global Volatility Tanzania Only Volatility Grains Other Crops Textiles Raw Material Livestock Fishing Vegetable Oils Processed Livestock Processed Rice Sugar Other Food & Beverages Grains price volatility explained mostly by domestic production volatility *Volatility = standard deviation of interannual % changes

26 Factor Incomes Volatility Due to Historical Grains Productivity Volatility* Global Grains Productivity Volatility 15% Increase in TZA Grains Prod. (75 th Percentile outcome) 2001 Regime Export Ban 2001 Regime Export Ban Land Rent Unskilled Labor - Agricultural Skilled Labor- Agricultural Unskilled Labor - Non-agricultural Skilled Labor- Non-Agricultural Capital- Agricultural Capital-Non- Agricultura Transfer Payments *Volatility = standard deviation of interannual % changes

27 Grains Revenue %D Revenue =%D Quantity + %D Price %D Revenue when there is a 15% Increase in TZA grains productivity ( good climate outcome ) Under 2001 trade regime, %D R = -1.9% Under export ban, %D R = -4.8% Ambiguous impact on rural agricultural households dependant on grains

28 General Take Homes Agriculture a key linkage between climate volatility & poverty vulnerability Poverty impacts depend on both income (sources of earnings) and price effects Future impacts are likely to be complex across countries, with great uncertainty in climate predictions Lessons to be learned from historical volatility Climate volatility can interact with existing policies (e.g. trade policies) to exacerbate impacts

29 Specific Take Homes Climate extremes damaging grains production Detrimental extremes may affect urban wage dependant poor more than rural agricultural poor Climate extremes may become more intense and frequent for many countries Agricultural variability has a wide range of possible impacts on poverty in Tanzania Will be higher in the 21 st Century than in the 20 th Century Export bans in Tanzania increase price volatility due to production variability Implications for rural agricultural households May get worse in future if climate change increases agricultural variability

30 Limitations Spatial coverage of climate data, e.g. weather stations Precision of models, e.g. growing season definitions Importance of looking at sub-national effects Need for national scale modeling