Disaster-related Statistics 2015 Climate Change and Natural Disaster Perspectives
Bangladesh, 2015
BANGLADESH BUREAU OF STATISTICS
Identification
idno
BGD-BBS-DCS-2015-v01
Title
Disaster-related Statistics 2015 Climate Change and Natural Disaster Perspectives
Country
| Name | Country code |
|---|---|
| Bangladesh | BGD |
Abstract
Bangladesh is often said to be one of the most vulnerable countries of the world in terms of natural and anthropogenic hazards. As per theVorld Risk Report 2015"Bangladesh has been identified as the sixth most natural disaster-prone country among 173 countries in the world'. The geography and climate have made the country vulnerable to different meteorological, hydrological and geological hazards. At the same time, Bangladesh is one of the countries, most at risk from the adverse impacts of climate change and variability. Climate change will exacerbate many of the natural hazards the country already faces and bring about a significant challenge for future development. Climate change and natural disaster are leaving impacts on human livelihoods since long, this is the first time the BBS has taken initiative to generate baseline data to quantify the 'Bangladesh Disaster-related Statistics 2015: Climate Change and Natural Disaster Perspectives under the "Impact of Climate Change on Human Life (ICCHL) Programme". The ICCHL Programme aimed to conduct the household-based survey to generate the twelve main natural disasters due to climate change in the disaster prone areas of Bangladesh. All the 64 districts of the country were taken as the universe and aggregate of the mauzas/mahallas as the target population of the survey. Each district has treated as a stratum or domain. A two stage sampling design has been adopted in this survey. In the first stage, the level of stratification corresponds to the major geographic domains, in this case, district and within district, stratifications were done based on "type of natural disaster". A total of 4945 highest disaster prone mauzas/mahallas are selected out of 21,892 highest disaster prone mauzas/mahallas of the country using the random sampling. In the second stage, all the households of 4945 mauzas/mahallas have been listed with some basic characteristics from the selected mauzas/mahallas. A total 143,980 households have been selected following in the systematic random sampling (SRS), where every the mauzas/mahallas is treated as the primary sampling unit (PSU).
The survey result shows that the number of times disaster affected the households i.e. frequency, the highest percentage of household affected in one time is recorded as 56.52% followed by 26.57% are two times and 16.91% are three or more times. It is found that 24.44% household are affected by flood, 15.10% are cyclone-affected households, 10.59% are affected by thunderstorm, 10.49% are affected by drought, 9.84% are affected by water logging, 8.42% are by hailstorm, 6.13% are storm/tidal surge affected households respectively. At the divisional level, cyclone and storm/tidal surge were recorded only in Barisal, Chittagong and Khulna division. In the case of flood affected households, Sylhet division possesses the highest position (69.97 %) while Barisal division holds only 5.24 % households. In the case of water logging 34.88% households are found affected in Khulna division and only 0.65% households are found in Rajshahi division. While it comes to the non-working days during last natural disaster from the year of 2009-'14 it is found that slightly less than one-half of the households (48.46%) did not work from one to seven days, but the interesting point is that with gradual increase of number of days number of households decreases. It is also found that the average working days per household are only 12.13. Out of all categories of disaster, households affected by flood had the highest (17.63) non-working days. The second highest 16.86 non-working days per household is river/coastal erosion disaster. Moreover, the lowest 5.30 non-working days per household is hailstrom. In case of early warning and preparedness the result shows that out of the 6153344 affected, 1223835 household managed to get early warning. Highest proportion of the households (66.73%) got warning in cyclone, followed by storm or tidal surge 61.03% and flood 17.84% respectively. For the preparedness, out of the 1223835 households, 814450 households took necessary preparedness. The highest proportion of preparedness is taken by 88.48 % households in cyclone and the lowest 18.86 % is found in flood.
Out of the total BDT 184247.34 million damage and loss, Dhaka division is found to lead with 25.00% damage and loss followed closely by Barisal division (20.07%), Khulna division (15.86%), Rajshahi division (11.77%), Chittagong division (10.33%), Sylhet division (8.50%) and Rangpur division (8.47%) respectively.
In case of sickness due to disaster, share of male sickness is 52.40% (990769) compared to female 47.60% (899965). At the division level, it is found that the highest portion of 20.97% persons is reported in Dhaka followed by 16.17% in Rajshahi, 13.04% in Sylhet, 12.97% in Khulna, 12.65% in Chittagong, 12.12% in Barisal and the lowest 12.07% in Rangpur. In the disaster area, a total of 32908 persons are found injured of which 19126 (58.12%) are male and 13782 (41.88%) female. At division level, it is seen that the highest persons 17.80% is reported in Chittagong and the lowest 9.26% in Rangpur. It is important to note that male persons are the highest 11.81 % in Chittagong division and the lowest 4.15% in Rangpur division.
During disaster, the percentage of water borne disease affected households increase up to 94.20% and post disasters the percentage of households affected by water borne diseases are 91.65%. During disasters and post disasters the percentages of households affected by vector borne diseases are 5.80% and 8.35% respectively. It is found that children from Dhaka division are most vulnerable and become sick due to disasters with a percentage of 21.70% followed by Rajshahi (15.44%), Sylhet (13.77%), Barishal (13.24%), Chittagong (12.75%), Rangpur (12.14%) and Khulna division (10.96%).
Knowledge and perception about climate change is found to vary from division to division. It is observed from the survey that the highest 55.27% households felt it as long term change due to natural or manmade reasons followed by 16.06% responded who explained as sudden extreme events affecting human life, 13.83% did not know about it, 13.56% respondents categorized as regional change of temperature or rainfall and remaining 1.27% mentioned other ideas. The survey result shows that household from Dhaka division have best knowledge and perception about climate change 21.36% followed by Barisal (18.76%), Khulna (15.34%), Rajshahi (14.07%), Rangpur (11.20%), Chittagong (9.87%) and Sylhet division (9.40%) respectively.
The survey result shows that the number of times disaster affected the households i.e. frequency, the highest percentage of household affected in one time is recorded as 56.52% followed by 26.57% are two times and 16.91% are three or more times. It is found that 24.44% household are affected by flood, 15.10% are cyclone-affected households, 10.59% are affected by thunderstorm, 10.49% are affected by drought, 9.84% are affected by water logging, 8.42% are by hailstorm, 6.13% are storm/tidal surge affected households respectively. At the divisional level, cyclone and storm/tidal surge were recorded only in Barisal, Chittagong and Khulna division. In the case of flood affected households, Sylhet division possesses the highest position (69.97 %) while Barisal division holds only 5.24 % households. In the case of water logging 34.88% households are found affected in Khulna division and only 0.65% households are found in Rajshahi division. While it comes to the non-working days during last natural disaster from the year of 2009-'14 it is found that slightly less than one-half of the households (48.46%) did not work from one to seven days, but the interesting point is that with gradual increase of number of days number of households decreases. It is also found that the average working days per household are only 12.13. Out of all categories of disaster, households affected by flood had the highest (17.63) non-working days. The second highest 16.86 non-working days per household is river/coastal erosion disaster. Moreover, the lowest 5.30 non-working days per household is hailstrom. In case of early warning and preparedness the result shows that out of the 6153344 affected, 1223835 household managed to get early warning. Highest proportion of the households (66.73%) got warning in cyclone, followed by storm or tidal surge 61.03% and flood 17.84% respectively. For the preparedness, out of the 1223835 households, 814450 households took necessary preparedness. The highest proportion of preparedness is taken by 88.48 % households in cyclone and the lowest 18.86 % is found in flood.
Out of the total BDT 184247.34 million damage and loss, Dhaka division is found to lead with 25.00% damage and loss followed closely by Barisal division (20.07%), Khulna division (15.86%), Rajshahi division (11.77%), Chittagong division (10.33%), Sylhet division (8.50%) and Rangpur division (8.47%) respectively.
In case of sickness due to disaster, share of male sickness is 52.40% (990769) compared to female 47.60% (899965). At the division level, it is found that the highest portion of 20.97% persons is reported in Dhaka followed by 16.17% in Rajshahi, 13.04% in Sylhet, 12.97% in Khulna, 12.65% in Chittagong, 12.12% in Barisal and the lowest 12.07% in Rangpur. In the disaster area, a total of 32908 persons are found injured of which 19126 (58.12%) are male and 13782 (41.88%) female. At division level, it is seen that the highest persons 17.80% is reported in Chittagong and the lowest 9.26% in Rangpur. It is important to note that male persons are the highest 11.81 % in Chittagong division and the lowest 4.15% in Rangpur division.
During disaster, the percentage of water borne disease affected households increase up to 94.20% and post disasters the percentage of households affected by water borne diseases are 91.65%. During disasters and post disasters the percentages of households affected by vector borne diseases are 5.80% and 8.35% respectively. It is found that children from Dhaka division are most vulnerable and become sick due to disasters with a percentage of 21.70% followed by Rajshahi (15.44%), Sylhet (13.77%), Barishal (13.24%), Chittagong (12.75%), Rangpur (12.14%) and Khulna division (10.96%).
Knowledge and perception about climate change is found to vary from division to division. It is observed from the survey that the highest 55.27% households felt it as long term change due to natural or manmade reasons followed by 16.06% responded who explained as sudden extreme events affecting human life, 13.83% did not know about it, 13.56% respondents categorized as regional change of temperature or rainfall and remaining 1.27% mentioned other ideas. The survey result shows that household from Dhaka division have best knowledge and perception about climate change 21.36% followed by Barisal (18.76%), Khulna (15.34%), Rajshahi (14.07%), Rangpur (11.20%), Chittagong (9.87%) and Sylhet division (9.40%) respectively.
Kind of Data
Sample survey data [ssd]
Version
Version Date
2015-05
Coverage
Geographic Coverage
The survey captured various data and information of the sample households pertaining to their livelihood activities in relation to the direct and indirect impacts of climate change and natural disaster. It did not attempt to collect information on the climate parameters or components like temperature, rainfall or anything in relation to carbon emission, greenhouse gas etc.
Producers and sponsors
Authoring entity/Primary investigators
| Agency Name | Affiliation |
|---|---|
| BANGLADESH BUREAU OF STATISTICS | Statistics and Informatics Division, Ministry of Planning |
Funding Agency/Sponsor
| Name | Abbreviation |
|---|---|
| Statistics and Informatics Division | SID |
Sampling
Sampling Procedure
The quality of response collected by any statistical survey depends on methodology applied, that includes preparation of schema, construction of sampling frame, choice of sampling design and technique to draw the representative samples, preparation and execution of questionnaire design, methods used to collect data, methods used for data consistency and accuracy check, and adjustment of the sampling error etc. Statistical survey provides a means of measuring a population's characteristics, self-reported and observed behaviour, awareness of programmes, attitudes or opinions, and needs. An optimum balance of resource and desired level of accuracy also play an obvious role on such issues. While the sampling unit for this particular survey was determined to be household (HH) with head of the HH as the respondent, serious attention was needed in identification of the population itself. Since the survey objectives under Impact of Climate Change on Human Life (ICCHL) Programme did not include all the Hils of the country, a sampling frame was not readily available for this survey.
Sample Design: A sample design is the framework, or road map, that serves as the basis for the selection of a survey sample and affects many other important aspects of a survey as well. One must define a sampling frame that represents the population of interest, from which a sample is to be drawn. The sample design provides the basic plan and methodology for selecting the sample. This survey encountered the situation where the target population had not been properly identified in any prior instances and a sampling frame could not be prepared due to lack of adequate information. Hence, the primary activities of this survey consisted identification of the study population and construction of the sampling frame based on a pre-survey census of natural disaster status of the mauzas/mahallas/PSU under some pre-determined set of principles. The sampling methods described below administered in the population of the natural disaster prone mauzas/mahallas/PSU (lowest administrative unit) under 64 districts in Bangladesh.
Target Population: The entire country was considered as the universe and aggregate of the mauzas/mahallas/PSU (lowest administrative unit) that experienced at least one major natural disaster was taken as the target population of the survey.
Sampling frame: A sampling frame is a complete list of all the members of the population of the study. If there is no such sampling frame, then the survey is restricted to less satisfactory forms of samples, which cannot be randomly selected because not all individuals within that population will have the same probability of being selected for the sample. Hence, sampling technique has to be chosen and applied carefully. For this survey, no sampling frame was readily available and a sampling frame required constructing solely for this study. Keeping the objectives of the ICCHL survey in consideration, the list of mauzas/mahallas/PSU with at least one natural disaster reported considered as the sampling frame. The list of natural disaster mauzas/mahallas/PSU was prepared during a pre-survey census of mauzas/mahallas/PSU in November to December 2013. Mauzas/mahallas/PSU later considered as the Primary Sampling Unit (PSU) for the first stage.
Stratification: One of the most important features of an efficient sample design is the stratification of the sampling frame into homogeneous areas. For this survey, two levels of stratifications considered. The first level of stratification corresponds to the major geographic domains, in this case, District. Within district, stratifications were done based on "type of natural disaster". For each of the districts, the mauzas/mahallas/PSU affected by different types of natural disasters constituted a different sub-stratum within the concerned district. The allocated number of mauzas/mahallas/PSU for that district was again proportionately allocated for each of the stratum (natural disaster type).
Sample Design: A sample design is the framework, or road map, that serves as the basis for the selection of a survey sample and affects many other important aspects of a survey as well. One must define a sampling frame that represents the population of interest, from which a sample is to be drawn. The sample design provides the basic plan and methodology for selecting the sample. This survey encountered the situation where the target population had not been properly identified in any prior instances and a sampling frame could not be prepared due to lack of adequate information. Hence, the primary activities of this survey consisted identification of the study population and construction of the sampling frame based on a pre-survey census of natural disaster status of the mauzas/mahallas/PSU under some pre-determined set of principles. The sampling methods described below administered in the population of the natural disaster prone mauzas/mahallas/PSU (lowest administrative unit) under 64 districts in Bangladesh.
Target Population: The entire country was considered as the universe and aggregate of the mauzas/mahallas/PSU (lowest administrative unit) that experienced at least one major natural disaster was taken as the target population of the survey.
Sampling frame: A sampling frame is a complete list of all the members of the population of the study. If there is no such sampling frame, then the survey is restricted to less satisfactory forms of samples, which cannot be randomly selected because not all individuals within that population will have the same probability of being selected for the sample. Hence, sampling technique has to be chosen and applied carefully. For this survey, no sampling frame was readily available and a sampling frame required constructing solely for this study. Keeping the objectives of the ICCHL survey in consideration, the list of mauzas/mahallas/PSU with at least one natural disaster reported considered as the sampling frame. The list of natural disaster mauzas/mahallas/PSU was prepared during a pre-survey census of mauzas/mahallas/PSU in November to December 2013. Mauzas/mahallas/PSU later considered as the Primary Sampling Unit (PSU) for the first stage.
Stratification: One of the most important features of an efficient sample design is the stratification of the sampling frame into homogeneous areas. For this survey, two levels of stratifications considered. The first level of stratification corresponds to the major geographic domains, in this case, District. Within district, stratifications were done based on "type of natural disaster". For each of the districts, the mauzas/mahallas/PSU affected by different types of natural disasters constituted a different sub-stratum within the concerned district. The allocated number of mauzas/mahallas/PSU for that district was again proportionately allocated for each of the stratum (natural disaster type).
Data Collection
Dates of Data Collection (YYYY/MM/DD)
| Start date | End date |
|---|---|
| 2015-01-06 | 2015-01-12 |
Mode of data collection
Face-to-face [f2f]
Supervision
The data collection was carried out immediately after 04 days comprehensive training program. About 1,800 officers and employees of BBS from field offices as well as headquarters were employed in data collection in two phases due to shortage of human resources of BBS. In both the phases, the field operation conducted for a total of 45 days. At the time of field operation supervising officers and supervisors verified and edited the data that collected by the enumerators at the field level. Same enumerators and supervisors who engaged at the field operation attended in both phases. Besides, a total of 80 officers of BBS, most of which were Joint Director, Deputy Director, Statistical Officer, Divisional/District Statistical Offices were employed to supervise the data collection and to immediately take care of any untoward problem arouse during data collection in the fields. These supervising officers stayed at the field until the data collection was completed. Secretary, Statistics and Informatics Division, Director General (DG), Deputy Director General (DDG) and Director of National Accounting Wing, Directors (all), Joint Directors, Deputy Directors, BBS Head quarter closely and strongly monitored/supervised the data collection of the survey. Joint Directors, Deputy Directors and Statistical officers were engaged as supervising officer/coordinators at Divisional/District levels or master trainer of the survey. Supervisors (Upazila/Assistant Statistical Officer/Statistical Investigator) and Enumerators (Statistical Investigator, Statistical Assistant, Junior Statistical Assistant, DEO/CO etc.) who have worked round the clock to visit door-to-door and collect the baseline data. Programme Director coordinated all activities and arranged all supports required for successful completion of data collection.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Statistics and Informatics Division | SID | Ministry of Planning |
Data Processing
Cleaning Operations
Data editing refers the activity of checking and cleaning data that have already been collected from the field. After the completion of data collection, all questionnaires were brought to headquarter for further processing. A group of experienced staffs of BBS under the supervision of four officers of the survey team and about 60 editors and coders from different wing of BBS edited and coded all data manually. An instruction with the editing and coding guidelines was also prepared and editors and coders were provided four days training on these guidelines. Although some coding was done during data collection, but it was checked once again during data editing and coding.
Data Processing: Data processing involves a number of steps which are follows:
* Data entry,
* Appending and merging files;
* Data validation (further checking, editing, and imputation);
* Final decision on errors;
* Completion of data processing and generation of data files;
* Final documentations;
* Conversion of data files to another software; and
* Storage of all files
Data Processing: Data processing involves a number of steps which are follows:
* Data entry,
* Appending and merging files;
* Data validation (further checking, editing, and imputation);
* Final decision on errors;
* Completion of data processing and generation of data files;
* Final documentations;
* Conversion of data files to another software; and
* Storage of all files
Data access
Contact
| Name | Affiliation |
|---|---|
| BANGLADESH BUREAU OF STATISTICS | BBS |
Metadata production
Document ID
DDI-BGD-BBS-DCS-2015-v01
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| BANGLADESH BUREAU OF STATISTICS | BBS | Statistics and Informatics Division, Ministry of Planning | Documentation of the study |