The cyclone-salinity-flood combined models were fit for floods longer than 30 days; cyclones with a wind speed greater than 40 m/s; and for areas with any salinity problem. For salinity, we also recall that a simple difference in means (DiM) approach was applied instead of a DiD due to the slow onset nature of the process. For the
sake of space, we are reporting only the results of the models with flood events equal or longer than 30 days;
cyclone events with wind speed greater than 40 m/s and for all areas affected by saline intrusion (including all categories s1 to s4).
Nutrition indicators
Table 27 shows the results of the tests for the nutrition indicators.
The table suggests that for the
communities that live in areas where saline intrusion, flood and cyclones are all common, saline intrusion is associated with increased malnutrition (as measured by zlen, zwfl, and maternal BMI indicators).
The effect is highly statistically significant (p<0.001 for zlen and zwfl;
and p=0.012 for BMI). The effect on child and maternal dietary diversity seems to be on the opposite side,
however, with two strong positive values (p<0.001), suggesting that in these communities the dietary diversity of both child and mothers are higher than in the rest of the country.
Table 27. Combined salinity flood cyclone models(d) – nutrition indicators.
zlen zwfl BMI child dd Maternal DD
coef p-value coef p-value coef p-value coef p-value coef p-value -0.164 saline_all (a)
flood_d30 event (b) +3m
+5m +7m +9m
cyc_w40 event (c) +3m
Legend: Only statistically significant p-values are indicated (in bold), other non-statistically p-values are omitted. Coefficient values highlighted in light red indicate cases for which the DiD test suggests a worsening situation in relation to nutrition, that is, a lower zlen, zwfl, BMI, child dd or maternal DD in the affected (treatment) communities, compared to the non-affected (control) communities. Dark red values indicate cases where the worsening of the nutritional situation is statistically significant.
zlen: Length/Height for age z-score; zwfl: weight for Length/Height z-score; BMI: Maternal body mass index; dd: Child dietary diversity; DD: Maternal dietary diversity.
(a) all the upazilas affected by saline intrusion, irrespective of the level of saline concentration
(b) all upazila affected by flood events with water above the DL threshold (Danger level) for 30 days or more
(c) all upazilas affected by cyclone events with wind speed greater than 40 m/s
(d) The following variables were also included in the models (but not shown in the table) to control for individual and household effects: age and sex of child; mother age and education; livelihood strategies (farmer; labour; transport; salary; business); family size; birth order; source for drinking; use of latrine –see definitions in section 2.5.2. The notation +3m; +5m, +7m and +9m indicate 3-month, 5-month, 7-month and 9-month lagged periods respectively.
Flood seems to have a slightly stronger negative impact on zwfl (wasting) than on zlen (stunting).
In particular the zwfl coefficient values are negative and statistically significant for the period just after the events and for the five and seven-month lagged periods. The impact of flood on zlen is less clear. The coefficient shows a succession of positive and negative values, none of which is statistically significant.
Similar comment can be made for the BMI indicator, for which no clear story emerges. In contrast the dietary diversity indicators display some very clear pattern, with a continuous series of positive and statistically significant coefficients for both dd and DD across the lagged-periods.
These different results regarding the impact of floods are in line with the results of the combined flood-drought model and these of the joint flood-only model.
As far as cyclones are concerned, these appear to have a negative effect on the wasting (zwfl), starting just after the event and continuing for 3 lagged periods, that is, up to 7 months after the event. The most severe effect seems to occur at the 5-month lagged period when the negative coefficient is statistically highly significant (p=0.001). In contrast, cyclone events were seen to
decrease stunting, which is difficult to explain. All the values of the zlen coefficients become positive and statistically significant two months after the event and remain so until the seven-month lagged period. The results also shows a statistically significant negative effect of cyclone on maternal BMI just after the event (p=0.027). No clear pattern emerges between cyclone and dietary diversity. First data are missing for some of the rounds that followed the events, preventing us from conducting the test for that period. The three-month lagged period shows some strong positive values for both child and mother indicators (p=0.001 and p=0.007 respectively) but the next two rounds (five and seven-month lagged period) are negative with the value of the maternal DD indicator even showing some statistical significance (p=0.009) at 7-month lag. Both child and maternal indicators however become positive and significant at the nine-month lagged period.
Food security and food price indicators
Table 28 summarizes the results of the test for the food security and food price indicators. As far as the effect of saline intrusion is concerned, the tests indicate that communities affected by high salinity are
associated with statistically higher FL (food loan) and FE (food expenditure) levels than communities in the rest of the country (p<0.001 for two tests).
More counter-intuitive is the fact that PR (price of rice) and PO (soybean oil price) coefficients are negative in these communities (PR being actually statistically significant). Note that this last trend is however consistent with the results of the DiM presented above (Table 23).
The impact of flood on these communities is mixed. While the FE (food expenditure) appears statistically higher just after the flood the effect wanes away rapidly and becomes negative after 5 months.
This contrasts with the three food price indicators (PR, PO, and PFB) which all display positive coefficients after 3 months and statistically significant positive values from 5 months until 9 months after the event, suggesting that flood events do lead to higher food prices in the affected regions. A closer look at the coefficient suggests that the price peak occurs around the seven-month lagged period.
Finally the lower part of Table 28 highlights the effects that cyclone events have on the food security and food price indicators. Food loan (FL) values are consistently higher
in the affected communities than in non-affected communities (in fact statistically higher for all period except for the 5-month lag). The PR, PO, and PFB indicators indicate that
the food prices in the aftermath of cyclones are higher than they are in non-affected communities, especially during the first 5 months following the events (during these periods the
difference is actually statically highly significant for most of the tests). A look at the coefficients also suggests that the peak in these prices occurs around the three-month lag period.
Table 28. Combined salinity flood cyclone models(d) – food security and food price indicators
FL FE PR PO PFB
coef p-value coef p-value coef p-value coef p-value coef p-value 0.715 saline_all (a)
flood_d30 event (b) +3m
+5m +7m +9m
cyc_w40 event (c) +3m
Legend: Only statistically significant p-values are indicated (in bold), other non-statistically p-values are omitted. Coefficient values highlighted in light red indicate cases for which the test suggests a worsening situation in relation to food security, that is, a higher FL, FE, PR, PO, or PFB in the affected (treatment) communities, compared to the non-affected (control) communities. Dark red values indicate cases where the worsening of the food security or food price situation is statistically significant.
FL: food loan; FE: food expenditure; PR: rice price; PO: soybean oil price; PBF: price of food basket.
(a) all the upazilas affected by saline intrusion, irrespective of the level of saline concentration
(b) all upazila affected by flood events with water above the DL threshold (Danger level) for 30 days or more
(c) all upazilas affected by cyclone events with wind speed greater than 40 m/s
(d) The following variables were also included in the models (but not shown in the table) to control for individual and household effects: age and sex
of child; mother age and education; livelihood strategies (farmer; labour; transport; salary; business); family size; birth order; source for drinking; use of latrine –see definitions in section 2.5.2. The notation +3m; +5m, +7m and +9m indicate 3-month, 5-month, 7-month and 9-month lagged periods respectively.
3.2 Qualitative analysis
In addition to the quantitative analyses described in the previous section, qualitative data was collected in two regions using multiple methods including focus group discussions (FGDs), key informant interviews (KIIS), in-depth interviews (IDIs); one region is Gaibandha in the north-west part of the country where river bank erosion and floods are common and one is Satkhira in the southern coastal area where the local population has to cope with saline intrusion and cyclones on a regular basis.
Because the quantitative data reflects the time period from 1998-2006 and the qualitative data generally reflects the past decade, due to the tendency for respondents to recall more recent events, the findings from the qualitative data are useful for better understanding how people cope with disasters with regards to nutrition and food security, but do not speak specifically to the disaster events pinpointed by the quantitative analysis.
3.2.1 Salinity and cyclone-prone areas (example Satkhira)
In the cyclone-prone area of Satkhira, the local population deal with high salinity levels in the soil and water on a daily basis. Salinity levels are attributed to natural climate events, such as cyclones, drought, and heavy rainfall, but also to the development of shrimp farming. Respondents perceived shrimp farming to be the primary cause of high salinity levels and associated hardships, but climate events are viewed as exacerbating the problems on a temporary basis. In other words, the “usual” high levels of saline are even higher after an event, increasing the effects of high salinity, but also adding additional hardships. Therefore, the results in this section focus primarily on salinity because the responses to interview questions about climate events consistently centered around salinity, revealing it to be foremost in the minds of residents in the cyclone prone area of southwest Bangladesh.