I version of the draft law on renewable energy source: 20.12.2011

The implications of climate change for malaria have been the subject of several studies¹³. This paper adds to this literature by constructing a year-to-year Malaria Ecology Index based solely on ecological factors exogenous to human intervention, and demonstrates the predictive power of the index over the historical record of elimination and over within-country year-to-year fluctuations in malaria morbidity and mortality. Since the MEI represents a part of the basic reproduction number (R0) equation, and given that public health measures successfully curb a disease when they push the R0 below 1, then results predicting significant increases in MEI (up to a doubling of the mean MEI by 2090) have profound implications for malaria control. In places of the world where policy choices, resource allocation, and current technologies have struggled to reduce R0 to below 1 (not coincidentally those places where R0 is highest), a doubling of R0 due to climate change would mean that bringing the disease under control will be a much more challenging prospect. Finally, the relationship between the Malaria Ecology Index and malaria incidence and mortality suggest that a doubling of the MEI (a four-point increase from 4 to 8) would lead to a 92% (0.23*4) increase in malaria incidence, and a 148% (0.37*4) increase in mortality rates given calibration using 1990-2005 data. Considering that malaria accounts for over 300 million clinical cases today, and kills between 1-3 million people, such large increases in incidence and mortality rates represent significant burdens on the human population. Since malaria has been showed to exact a measurable economic burden^14,15 and to hinder the demographic transition¹⁶, a worsening of the malaria situation would further push many of the poorest societies away from thresholds they need to reach in order to break their poverty trap.

Improvements in future work will push forward in two directions. First, the Malaria Ecology Index can be calculated for more than one GCM’s scenario, or for an ensemble of

13 For example, Lindsay and Martens (1998), Hay et al. (2002), Bouma (2003).

14Gallup, John Luke and Jeffrey D. Sachs. “The Economic Burden of Malaria,” The Supplement to The American Journal of Tropical Medicine & Hygiene. Vol. 64, no. 1, 2, January/February 2001.

15 Sachs, Jeffrey D. and Pia Malaney. “The Economic and Social Burden of Malaria.” Nature Insight, Vol. 415, no. 6872, Feb. 7, 2002.

16 McCord, Gordon C., Dalton Conley and Jeffrey D. Sachs. “Improving Empirical Estimation of Demographic Drivers: Fertility, Child Mortality & Malaria Ecology.” Social Science Research Network Working Paper. July 2010.

scenarios. In addition, a robustness check would be to construct the MEI for the 20^th century using GCM output for the 20^th century instead of actual weather data, in order to make sure that differences in past and future MEI are not just due to the difference between actual weather data and a GCM model’s output. Secondly, the discussion above of long-term impacts of changes in malaria ecology assumes that no new technologies become available to fight the disease and that countries continue using available technologies at the present rate. In fact, however, economic growth and technological change between now, 2030 and 2090 will likely mitigate some of the effect of a worsening of malaria ecology. While my estimation of malaria ecology’s effect on incidence and mortality did include a flexible global time trend to capture some of the increases in public health investments over the period, a more complete model might attempt to jointly estimate malaria ecology and the role of public health spending directly. This would allow calculating the effect of climate change on malaria using the GCM’s predicted temperature and precipitation as well as the assumptions made in the GCM concerning income levels in 2030 and 2090. I leave such an exercise for future work.

Appendix

The table below presents the region-by-region analysis of the association between malaria ecology and malaria incidence. Sub-Saharan Africa shows the tightest correlation between the two variables. However, adding the other regions increases the strength of the association, indicating that regressing within regions might be resulting in a lack of power, thus explaining the lack of significance.

Table 4-3: Region-by-Region Analysis of Malaria Ecology and Incidence

One reason for why the malaria ecology index performs less well in Latin America is that while the index is calibrated for Plasmodium falciparum, malaria in Latin America is predominantly caused by Plasmodium vivax, which has a slightly different functional form in its temperature-dependent incubation period function. It is worth noting that the index predicts incidence better than mortality, and the likely reason is that malaria mortality data is notoriously poor: data are available for fewer countries than for incidence, and malaria-related death is frequently attributed to other factors. The paucity of good data is especially acute in sub-Saharan Africa, where many countries lack capacity for accurate national data collection. The insignificant results in some of the region-by-region estimates are likely due to insufficient power after year and country fixed effects reduce degrees of freedom, and secondly due to inflated standard errors resulting from measurement error in the dependent variable.

Independent Variable Sample N countries

Malaria Ecology Sub-Saharan Africa 0.16* 568 45 0.06

(1.68)

South Asia 1.94 95 6 0.14

(1.57)

East Asia & Pacific 0.05 211 14 0.14

(0.09)

Latin America & Caribbean -0.13 313 20 0.10

(-0.14)

Middle East & North Africa 0.08 149 10 0.08

(1.50)

All 0.15 1336 95 0.02

(1.17)

0.23** 1187 85 0.02

(2.20) t-statistic in parentheses, * indicates α = 0.10, ** α = 0.05

Regressions include country and year dummies and a constant (not reported)

Within R-squared computed without clustering

All except Middle East & North Africa

Regressions report robust standard errors clustered by country

within R-squared ln( Malaria cases per 1,000

population )

Chapter 5

Improving Empirical Estimation of Demographic