Abstract
In light of the expected increase in weather variability from climate change, we examine the impact of weather shocks, defined as rainfall or growing degree days more than a standard deviation from their respective long-run means, on household consumption per capita. The analyses suggest that both rainfall and temperature shocks affect both food and non-food consumption. Furthermore, the results show that a household’s ability to protect its consumption from weather shocks depends on the climate region and when in the agricultural year the shock occurs. Especially, households in arid climates are not fully protected from weather shocks occurring during the beginning of the wet season (April, May, June). The results highlight the necessity to account for the underlying climatic variation as well as to carefully define the shocks.
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Notes
According to the Intergovernmental Panel on Climate Change (IPCC) a narrow definition of climate refers to the statistical description in terms of the mean and variability of quantities such as temperature, precipitation, and wind over a period of time ranging from months to thousands of years. The norm is 30 years as defined by the World Meteorological Organization (WMO). Climate is different from weather that refers to atmospheric conditions in a given place at a specific time. The term “climate change” is used to indicate a significant variation (in a statistical sense) in either the mean state of the climate or in its variability for an extended period of time, usually decades or longer (Wilkinson 2006).
There are other channels through which weather may affect the well-being of individuals. For example, the changes in climate may increase (or decrease) the prevalence of certain diseases and thus have impacts on health outcomes. In Skoufias and Vinha (2011), we explore the impacts of weather shocks on children’s health as measured by their height-for-age in rural Mexico.
In general, households are better able to insure their consumption against idiosyncratic shocks, which are shocks that affect only a particular household, such as the death of a household member, than they are able to insure against covariant shocks, shocks that affect a large number of households in the same locality, such as weather-related shocks (Harrower and Hoddinott 2005).
The description of corn’s growth cycle is adapted from Neild and Newman.
Rural households are considered to be those that live in localities with less than 2,500 inhabitants.
MxFLS collects information on the value spent purchasing various categories of goods—food, dining out, healthcare, transportation, personal items, education, recreation, cleaning services, communications, toys/baby articles/childcare, kitchen items and bedding, clothing, tobacco, gambling, appliances and furniture, and other expenses—as well as the value of goods consumed from own production or received as gifts. It is not possible to estimate the value of goods consumed from own production since this value and the value of goods received from others are reported jointly.
There are several localities in each municipality. In MxFLS 1, only two municipalities had more than one locality sampled.
We use INEGI’s 2005 geographic definitions, which contains 2,451 municipalities.
Given that the agricultural year starts runs from October to September, the first agricultural year that we use is 1951, and we only use the last 3 months of the 1950 calendar year.
For other important crops in Mexico, the required GDDs are 2,400 for beans and 2,200–2,370 for sorghum. The GDD values are taken from The Institute of Agriculture and Natural Resources Cooperative Extension, University of Nebraska-Lincoln. Growing Degree Days & Crop Water Use. http://www.ianr.unl.edu/cropwatch/weather/gdd-et.html, Accessed July 22, 2010.
We use the Modified Growing Degree Days formula where the minimum and maximum temperatures are adjusted prior to taking the average. See for example Fraisse et al. (ND).
A particular month is coded as missing if none of the 20 closest weather stations reported data for five or more consecutive days.
The correlation of the six different weather shock variables for the MxFLS sample of municipalities is given in Table 10. The rainfall deviations from mean for the various periods (annual, wet season, and pre-canícula period) are positively correlated with annual rainfall and the wet season rainfall being very highly correlated. The GDD deviations from mean are all very highly correlated. Given the high correlations among the different time periods, we only include weather variables from one time period in each regression.
These averages imply that the closest station is less than 13 km from the municipal centroid and since stations closest to the centroid are given more weight in determining the weather the assigned values should be similar to the actual weather experienced at the municipal centroid.
Given the way in which the expenditure survey was administered, we are unable to separate the value of consumption from own production from the value of goods received as gifts. For about 7% of the rural households, more than 50% of their food comes from non-purchased sources. On average for a rural household, about 7% of all food comes from non-purchased sources.
The poverty line data were obtained from CONEVAL. Poverty lines, for both the urban and rural portions separately, are available for the following seven regions: Mexico City metropolitan area, Center-North, Center-South, North-Border, Northeast, Northwest, and South.
The asset index of the sum of whether the household owns land, a residence, another house, bicycle, motor vehicle, an electric device, a washing machine or a stove, a domestic appliance, machinery or a tractor, bulls or cows, horses or mules, pigs or goats, or poultry.
For example, Paxon (1992) finds seasonal consumption patterns.
The average minimum and maximum annual precipitations are 200 and 600 mm for the arid regions and 900 and 1,400 mm for the humid region.
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Acknowledgments
We wish to thank the four anonymous referees, Mariano Rabassa and Abla Safir, for useful comments in an earlier version of this paper, and Hector V. Conroy for interpolating the weather data.
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The findings, interpretations, and conclusions are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.
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Skoufias, E., Vinha, K. The impacts of climate variability on household welfare in rural Mexico. Popul Environ 34, 370–399 (2013). https://doi.org/10.1007/s11111-012-0167-3
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DOI: https://doi.org/10.1007/s11111-012-0167-3
Keywords
- Climate change
- Weather shocks
- Household welfare
- Consumption
- Food security
- Rural Mexico
- Livelihoods
- Environment