1. Catholic University of the Sacred Heart – Piacenza (IT)
Climate change, Weather Variability and Food Consumption: A Multidisciplinary study of Rural Uganda
Sara Lazzaroni
Catholic University of the Sacred Heart – Piacenza (IT)
March 18, 2013

2. Introduction (1) Research question
Does weather variability affect food consumption in Uganda?
Motivation
Current debate on the effects of climate change on poor households in developing countries (Crist, 2007; IPCC, 2001; Skoufias et al., 2011)
Effects of climate change in Uganda (NAPA, 2007; Magrath, 2008) and concerns for food security (Shively and Hao, 2012)
Slowness of the process (Nordhaus, 1993) and data availability constraints (Cooper et al., 2008)
Weather variability as marker of climate change (Stenseth et al., 2003) and importance of climatic shocks in affecting households welfare (Dercon et al. 2005; Kazianga and Udry, 2006; Tol, 2009)

3. Introduction (2) Theoretical and empirical analysis
Channels through which weather variations affect rural households welfare in general and in Uganda
Qualitative analysis (Magrath, 2008)
Analysis of agricultural sector performance (FAO, 2011)
Rural households data from LSMS panel dataset 2005/ /10 and rainfall (mm and no. rainy days) and temperatures data from Uganda Department of Meteorology (UDOM) for quantitative analysis
Triangulation: small effects of weather variations thanks to ex-ante production decisions, land ownership and non-consumption expenditures reduction.
Relevance
Rethinking of the literature on climate change
Multidisciplinarity and multiple research techniques
Focus on Uganda

4. Climatic shocks and welfare
Simple/complex extreme events aggregated (Anderson, 1994; IPCC, 2001)
Climatic shocks and their impact on welfare
Paxson (1992) on transitory income and savings
Dercon (2004) on food consumption (persistency)
Maccini and Young (2008) EC rainfall and female health, height,education
Asiimwe and Mpuga (2007) on total/agricultural income and consumption
Skoufias et al. (2011) expenditures and child health (BMI)
Income and consumption smoothing (Morduch, 1995)
Hisali et al. (2011) adaptation strategies to contrast climatic shocks
Kijima et al. (2006) on off-farm/farm labour supply and off-farm/farm income

5. Climatic shocks and welfare

6. Uganda: the country at a glance

7. Uganda: distribution of occupations

8. Qualitative analysis (Magrath, 2008)
Interview to the E.D. of the Karughe Farmers Partnership in Kasese district
“Because of the current weather changes the yields have completely gone down.
We used to have much more rainfall than we are having now […]
[T]his area is warmer than 20 years ago. […]
Now the March to June season in particular isn’t reliable […]
[Y]ou have to go for early maturing varieties and now people are trying to select these. That’s why some local varieties of pumpkins and cassava that need a lot of rain, even varieties of beans, have disappeared […]
Coffee isn’t doing badly, but it’s not doing well either – not like the 1970s when we harvested lots.”

9. Data on production

10. lnFCEh,s,r,t = α + βlnWDs,r,t-1 + γXh,s,r,t + μs + πr + ρt + εh,s,r,t
Empirical model (1)
lnFCEh,s,r,t = α + βlnWDs,r,t-1 + γXh,s,r,t + μs + πr + ρt + εh,s,r,t
where:
lnFCEh,s,r,p,t is the log of food consumption expenditures for household h assigned to synoptic station s in region r, surveyed in period t
lnWDs,r,p,t-1 is a vector with the log of the weather indicators levels in the season preceding the interview (Dercon, 2004)
Xh,s,r,t is a vector of household specific characteristics
μs is a synoptic station dummy
πr is a region dummy
ρt is a time dummy
εh,s,r,t is the error term
OLS fixed effects estimation, standard errors clustered by synoptic st.

11. Empirical model (2) Persistency
lnFCEh,s,r,t = α + β1lnWDs,r,t-1 + β2lnWDs,r,t-2 + γXh,s,r,t + μs + πr + ρt + εh,s,r,t
Heterogeneity of impacts
lnFCEh,s,r,t = α + β0lnWDs,r,t-1 + β1(lnWDs,r,t-1 ∙ Hh,s,r,t) + γXh,s,r,t + μs + πr + ρt + εh,s,r,t

12. Data World Bank LSMS panel dataset 2005/2006 – 2009/2010
3,123 households, 322/783 EAs, 77 districts
Rural households only (2,248 households)
Household surveyed in the same season in both rounds (488)
Data on households demographic composition, food consumption (7 days weekly recall) and house and land ownership
GoU, Department of Meteorology
Monthly data of rainfall, number of rainy days and max/min temperature and respective and long-term means
13 synoptic stations across the country (4N, 3E, 2C, 4W)

13. Households descriptive statistics

14. Households descriptive statistics

15. Weather variables (1)
2 agricultural seasons (dry+rainy) along the year
Weather variables calculated for the 2 seasons before the interview (one agricultural season to be covered)
log average weather indicator level in the season preceding the interview Dercon (2004)

16. Weather variables (2)
Month of interview
Previous season (-1)
Previous season (-2)
Match household data with synoptic stations by proximity (average distance 32 Km, st.dev. 23 Km)

17. Weather descriptive statistics (1)

18. Weather descriptive statistics (2)

19. Weather descriptive statistics (3)

20. Results (1)

21. Results (2)

22. Results (3)

23. Results (4)
If -10% rainfall variation, land size has to be greater than 5.93 hectares to completely insure the negative effects on food consumption

24. Conclusion
Weather variability does not seem to substantially affect household food consumption
Number of rainy days and minimum temperatures are relatively more important in affecting food consumption
Land ownership seems to mitigate the impact of a decrease in precipitations depending on size of rainfall reduction and size of land
Triangulation: ex-ante smoothing strategies
Cultivation of low-risk, low-income crops (i.e. Sweet potatoes)
Wealth effect of land
Catastrophic predictions about the effects of climate change
seem to be rather exaggerated

25. Caveats and further analysis
Missing values for land ownership
Synoptic stations data
Further exploration on the impact of weather variability on other types (value and/or share) of expenditures
Intra-regional/government transfers?

26. Further analysis

27. Thank you