This research uses a rich panel data set of household surveys and external long-term flood data, extracted from satellite images, to complete a puzzling picture of the effects of floods on household welfare. Floods impose a mixed impact on households. On the one hand, the floods reduce household incomes dependent on natural sources; while on the other hand, floods push farmers out of the fields to seek extra incomes from non-agricultural activities. In addition, the floods significantly increase some types of expenditure. The finding of a lower household subjective wellbeing score reaffirms all these results. Further, this research shows the efforts that rural households are making to cope with the effects of flooding. They employ both formal and informal coping mechanisms; however, only financial remittances are shown to be significantly effective in providing relief.
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MODIS Flood Water (MFW), MODIS Surface Water (MSW): Currently these are only distributed as vector products (shapefile and kmz) for standard composites. MSW gives all land-based water (with a buffer into oceans) that was observed in the given product. MFW removes from MSW a reference or expected water layer, such that the remaining water is likely to be floodwater.
The OECD work on Measuring Progress and Well-Being (www. oecd.org/measuring progress) has been addressing these issues in the last few years. These efforts have led to the OECD Better Life Initiative, launched by the OECD Secretary-General on 24 May at the 2011 OECD Forum.
Secondary data for sampling on Thailand was available down to the village level; population density and agro-ecological conditions were assumed to be sufficiently homogeneous; sample design for Thailand is kept simple and aimed at obtaining a self-weighting sample. The provinces in Vietnam were purposively selected for the survey and are geographically more diverse than those in Thailand. While Dak Lak province is part of the landlocked Central Highland, Thua Thien-Hue, and Ha Tinh provinces extend from the coast to the mountainous border to Laos. In order to take into account this heterogeneity, strata for the first stages were defined as agro-ecological zones within the three provinces.
For each village, the interviewers recorded one coordinate during the village interviews. The coordinates were recorded in decimal degrees (latitude, longitude). For example, the geographical coordinates of the village Yang, sub-district Kham Duan, distric Krasang, province Buriam, Thai land is (15.0773638888889, 103.401458333333)
The dependency ratio is a measure showing the ratio of the number of dependents aged 0 to 14 and over the age of 65 to the total population aged 15 to 64.
The survey used a three-stage clustered sampling approach. Provinces were targeted, sub-districts were selected with probability proportional to population size (PPS), followed by a simple random PPS sample of two villages from each sampled sub-district. Lastly, households were randomly sampled with implicit stratification by household size. I account for the survey design using sample weights.
The set of control variables are slightly different in different analysis. For instance, while analysis of crops’ impacts is fully controlled by household’s characteristics, agriculture assets and land usage, the analysis of self-employment is only controlled by household’s characteristics and wealth, also infrastructure index of village when where households are living.
Using the same data sets, in the paper: Le Thi Ngoc Tu, Sebastian Vollmer, Felix Stips (2018). “The effects of floods on agricultural production: a mixed blessing”, we find that the effects of floods on agricultural production is mixed. While floods increase expenditures and reduce incomes, they can also increase rice productivity.
In Thailand, the Universal Coverage Scheme (USC) has followed a long string of efforts to improve equity in health. By 2001, the UCS was covering 48 million members and their families, leaving less than 2% of the Thai population without health insurance coverage - see Wagstaff and Manachotphong (2012).
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The author is grateful to participants in conferences and seminars of the Universities of Goettingen, Hannover, Frankfurt, York, and Columbia for their helpful comments and suggestions. For household data I gratefully thank Thailand Vietnam Socio-Economic Panel data within project: “DFG-FOR 756: Vulnerability to Poverty in Southeast Asia”, financed by the Deutsche Forschungsgemeinschaft (DFG). For assistance with flood data, I thank Dan Slayback at NASA/GSFC. For helpful writing comments, I thank Brian D Cuthbertson MA (Cantab), Dip Arch (Cantab), FRSA. For helpful empirical strategies comments, I thank to Felix Stips MA at University of Goettingen.
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Tu Le, T.N. Floods and Household Welfare: Evidence from Southeast Asia. EconDisCliCha 4, 145–170 (2020). https://doi.org/10.1007/s41885-019-00055-x
- Flood impacts
- Welfare impacts
- Income impacts
- Consumption impacts
- Geographic information systems (GIS)
- MODIS images
- MSC: 91B76.