Flood events

4.1.1 Nutrition indicators

The four types of models (individual-events (Table 9), joint models (Table 11) and combined flood-drought models (Table 25) and cyclone-flood-salinity models (Table 27)) were expected to show relatively consistent relationships with the nutritional and food security and food process indicators. The reality is a bit more complicated.

As far as the zlen (stunting) indicator is concerned no clear or consistent pattern emerges. There was a statistically significant negative coefficient at the seven-month lagged period under the 1998 individual test (Table 9) but it was not confirmed in any of the other models.

It may be that the 1998 flood was particularly severe and with a slow recovery which had an impact on the stunting of children. In sum, the floods we examined did not impact child stunting levels. Conjointly the qualitative data reveals that residents

of a flooded area may not engage in altered consumption patterns (e.g., eating less food and fewer meals) for more than a few months following a disaster, but they seek to protect child consumption.

In contrast to the zlen indicator, the zwfl indicator (wasting) had consistently negative (and mainly statistically significant) coefficients.

This was the case for the 1998 individual test (Table 9) where three, five and seven-month lagged periods were associated with statistically significant negative coefficients [the 2004 flood event was also associated with negative coefficients for zero to nine months after the event but these were not statistically significant].

This trend is also observed for the joint model (Table 11) where every level of flood severity and every lag-period are associated with negative coefficients. The five-month lagged period was statistically significant (p<0.01) throughout the whole series of tests. This finding was confirmed in the case of the combined model with drought (Table 25) and in the case of

the combined model with cyclones and salinity (Table 26). The results of the joint model also revealed that the coefficient values increased (in absolute values) with the severity of the events.

Based on these findings, it seems reasonable to conclude that these flood events in Bangladesh had a negative impact on wasting scores of children and that the effect was the strongest around five months after the event. Furthermore it seems that the impact increased with the severity of the flood event. The decrease in significant impact between five and seven months also fits with findings from the qualitative research that food availability and access begin to return to normal by seven months, although that varies by type of food.

Additionally, illnesses like diarrhoea are reported to increase during flooding and respondents frequently reported disposing of human waste in flood waters.

Maternal BMI does not demonstrate any clear or consistent pattern in relation to flooding. It seems reasonable to conclude that these floods were not associated with

altered BMI for women in Bangladesh.

This should be qualified however by findings from the qualitative analysis which revealed that in approximately half of the households interviewed, women are expected to reduce (or are actually reducing) their consumption during crisis periods. Flood event included in this analysis might not have been severe enough.

The results for both maternal and child dietary diversity indexes are consistent with one another. While the individual 2004 flood event (Table 9), the lower level of flood severity in the joint model (Table 11) and the drought-flood combined model (Table 25) had negative coefficients, both dd and DD indicators

demonstrate several statistically significant positive values across the models as well. This is especially true for the severe level of flood (20, 30, 50 days) for the joint model (Table 11), the drought-flood combined model (Table 25), and the flood-cyclones-salinity combined model (Table 27).

This inconsistent pattern would suggest that while for the smaller flood events, child and maternal dietary diversity indices could have

been negatively affected, for the larger flood events, was reversed and the dietary diversity indices seem to have been higher in flood-affected areas than in control areas. One possible explanation for this finding is suggested by the qualitative analysis where respondents reported that the availability of fruits and eggs are not severely affected by flood events;

that rice, dal and vegetables are still consistently consumed (although often in lower amounts –especially for the mother³⁰), and that fish is even more available and consumed more often during flooding than normally.

The overall picture in terms of nutrition for these households is therefore mixed. While we can confidently assert that these children in communities affected by flood events have a lower weight for height z-score than children in non-affected communities, the other nutrition indicators do not seems to show any negative impacts.

4.1.2 Food security and food price indicators

The food security and food price indicators were the next set of variables to be investigated and

30 Technically, the dietary diversity index may not be sufficiently sensitive to detect the change in amount of consumption of these items. For instance while all respondents report reduced vegetable consumption for approximately three months after a disaster until they are able to cultivate crops or obtain money, this reduction means that many people go from eating vegetables several times a day to once per day or an equal number of times but in smaller quantities. These changes would not be detected by the dietary diversity indicator.

were defined through four types of models: (i) individual-events (Table 10), (ii) joint models (Table 12) and combined models with (iii) drought (Table 26) and (iv) cyclone and salinity (Table 28). As for the nutrition indicators above, some of these indicators appear to display some clear and consistent patterns, while others do not.

The three food price indicators show a relatively coherent story where the coefficients are for the most part positive and statistically significant across the models, and across the lagged periods and the different levels of flood severity. This trend was first observed with the individual-event model and the 1998 event (Table 10) where PR and PFB indicators displayed positive values over the whole series of lagged periods (particularly strong around the seven- and nine-month lagged periods). PO was also positive for these periods but not significant.

This trend was confirmed by the joint model (Table 12) where the three food price indicators (PR, PO, and PFB) all show positive (and statistically very significant p<0.001 for all) values, and by the combined drought-flood (Table 26) and cyclone-flood-salinity (Table 28) models.

These various analyses indicate therefore that for the most part the prices of rice, oil and more generally food baskets are usually greatly affected by flood events, and that this effect lasts for up to nine months after the flood. This is also consistent with the finding from the qualitative analysis which reveals that while most food items are available at the local markets, their prices are higher than normal.

A closer look at the quantitative analyses also reveals other more nuanced patterns. The analysis shows in particular an increasing trend in the coefficients from the less severe to the more severe flood events, suggesting that the more severe the flood, the higher the price peak. The data also suggests that the prices of rice and food basket were at their highest levels five months after the event (which is when the wasting indicators are at their highest), while the price of soybean oil was at its highest level three months after the flood. This is especially clear in the joint model (Table 10) and the flood-drought combined model (Table 26).

The households’ food security indicators are not associated to these high price shocks in a coherent or consistent manner. We would in particular expect the FE (food

expenditure) indicator to show some strong positive values suggesting that households invest a larger share of their expenditure on food in order to cope with the increase in food price. It is the case for the 1998 event (Table 10) for the first 5 month periods following the event and to some extent for the joint model (Table 12) but in that case the positive coefficient is significant only during the period just following the flood and the three-month lagged period. For the combined drought-flood model (Table 26) the negative (statistically significant) coefficient seems to be more consistent as the severity of the events increase, but this is not observed for the cyclone-flood-salinity combined model.

On the basis of these results it is not clear that households affected by floods respond to the increase in price by reallocating a larger share of their income to food expenditures.

They may alter or limit their market purchase behaviour to keep costs down (thus maintaining or reducing the relative proportion of expenditure allocated to food).

As far as the FL (food loan) indicator is concerned, results are clearer but still not completely consistent. The individual model (Table 10) suggests

that households engaged in food loans only after seven months for the 1998 event but much earlier for the 2004 event (which seems counter-intuitive as the results for the other indicators showed that the 1998 event was more severe than the 2004 event). The results of the cyclone-flood-salinity combined model (Table 28) were also not as expected, demonstrating mainly (non-significant) negative values. In contrast the joint model (Table 12) displays a pattern that is more in line with what we could expect: the FL indicators were consistently positive throughout the whole period following the events (zero to nine months) and the majority of these positive values are statistically significant. The drought-flood model (Table 26) suggests a very similar pattern.

Overall the analysis tends to suggest that households affected by flood are likely to take more food loans than households that are not affected by flood events but the results of the different models are not totally consistent. Furthermore, the findings of the qualitative analysis do not necessarily provide clear answers to these inconsistencies. They indicate that a majority of households engage in food loans, which would tend to confirm the positive values observed

for the joint-events and the drought-flood model. These differences could be due to differences in the relief activities which followed these events.

In sum, as with for the nutritional indicators, the overall picture in terms of food security of households affected by flood is mixed. They do certainly face some challenges and times during floods are associated with harsh conditions. People do report that they often have to skip meals or reduce portions up to two to three months after the events.

But they also make clear that floods are ‘expected’ (seasonal) events and that they usually have prepared themselves for this harsh period.

To recall one of the interviewees’

responses: “To minimize flood time suffering, we try to save some money in the dry season, we also store some food. We store wheat, prepare and store puffed rice, store dry fuel and make a movable stove”. In that context, perhaps it is not surprising that the food security indicators did not necessarily show any strong or consistent patterns as the change in individual household indicators may be contingent on the households’

ability to prepare themselves as well as other households and possible community-level factors which have not been controlled for in the models.