Climate variability and vulnerability to poverty in Nicaragua

ABSTRACT

This study considers the effect of climate variability on vulnerability to poverty in Nicaragua. It discusses how such vulnerability could be measured and which heterogeneous effects can be expected. A multilevel empirical framework is applied, linking per capita consumption to household, regional and climate characteristics. Results confirm a negative effect of climate variability on consumption per capita of Nicaraguan households. This suggests the need for stronger public policies and more resources in order to adapt to the effect of climate change. Furthermore, the poverty reduction attainments reached since the 1990s could be jeopardized if this vulnerability persists.

KEYWORDS:

• Poverty
• vulnerability
• economic development
• climate variability
• Nicaragua

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. We prefer to use the term ‘variability’ (short-term changes) instead of climate ‘change’ which refers to any long-term change in Earth's climate, or in the climate of a region or city. This includes warming, cooling and changes besides temperature.

2. The Global Climate Risk Index (2014) analyses the extent to which countries have been affected by the impacts of weather-related events. The Climate Risk Index may serve as a red flag for existing vulnerability that may further increase in regions where extreme weather events will become more frequent or more severe because of climate change (Kreft and Eckstein 2014).

3. Poverty is defined as whether households or individuals have sufficient resources or abilities today to meet their needs (World Bank 2010).

4. I.e. individual or household level shocks, such as death, illness, injury, unemployment, loss on investments, indebtedness.

5. I.e. community or regional shocks, such as natural disasters, epidemics or climate change.

6. The analysis consisted of the comparison between different temperature changes and their direct effect on GDP change.

7. According to the World Bank (2012), educational attainment and welfare outcomes are closely related in Nicaragua. Findings show that lack of education is one of the main explanatory factors for poverty. Broadly speaking, welfare gains are associated with increased schooling. On average, households headed by individuals with secondary education are able to consume 32 per cent more per capita than similar households headed by someone with no education. Furthermore, a household head with technical education raises household consumption by 37 per cent. Households headed by individuals with a tertiary education account for only 12 per cent of Nicaraguan households and show a 93 per cent gain in consumption per capita. Additional findings show that households with heads who have attained less than complete secondary education are very likely to be living in poverty if the head is the only source of family income.

8. Poverty maps provide reliable diagnosis on poverty dynamics obtained by inferring consumption and income values from survey data estimations and extrapolating them to census data (Elbers, Lanjouw, and Lanjouw 2003).

9. Which might occur whenever the proposed methodology is applied to hierarchical data structures, i.e. whenever variables from various levels (e.g. from the household and regional level) are introduced in the regressions (Gunther and Harttegen 2006).

10. The ICC (interclass correlation) presents the percentage that is explained by a second level of analysis “level 2”; usually when the value is greater than 10 per cent, the use of an explanation of the dependent variable in more than one level is required.

11. The World Bank (2012) also relied of LSMS data to assess the effects of the commodities crisis on the Nicaraguan economy.

12. INIDE (2009) General Household Survey Report measuring the standard of living.

13. Some of the advantages of using the consumption rather than income as a measure of wellness are: less fluctuation and information is more reliable.

14. Calculated by Serial “MEAN” Points “2004” and “2005” From Authors Graph Calculation (161.6065062 in 2005, –161.6065062 in 2004)

15. Calculated by Serial “MEAN” Points “2005” and “2006” From Authors Graph Calculation (161.6065062 in 2005, - 114.6736185 in 2006)

16. This is in line with the Clausius-Clapeyron physical law that states that the water-holding capacity of the atmosphere increases by about 7 percent for every 1 °C rise in temperature. Because precipitation comes mainly from weather systems that feed on the water vapour stored in the atmosphere, this has generally increased precipitation intensity and the risk of heavy rainfall. Basic theory, climate model simulations and empirical evidence all confirm that a warmer climate, owing to increased water vapour, lead to more intense precipitation events even when the total annual precipitation decreases slightly, and have prospects for even stronger events when the overall precipitation amounts increase. Warmer climate therefore increases the risks of both drought – where it is not raining – and floods – where it is – but at different times and/or places (IPCC 2011).

17. Used to quantify the severity of multicollinearity in the analysis. A general interpretation is that if VIF is greater than 10 then multicollinearity is high.