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The 2021 Combined Sewer Overflow (CSO) spatial data layer shows the point locations of National Pollutant Discharge Elimination System (NPDES) permitted combined sewer overflow discharges. These outfalls occur in older cities in Massachusetts that still have areas of combined sewer and stormwater systems which overflow during heavy storm events. MassDEP has responsibility for ensuring that these systems, along with other sanitary sewer systems, are in compliance with the Clean Waters Act https://www.epa.gov/laws-regulations/summary-clean-water-act and the regulations adopted under 314 CMR 1.00 through 9.00 – please see https://www.mass.gov/regulations/314-CMR-9-401-water-quality-certification for more detail. Please refer to the Sanitary Sewer Systems and Combined Sewer Overflows guide for additional information.A list of active CSO locations was created and checked against lists contained in the NPDES permit for each CSO system along with lists contained in reports available from the systems online or on town websites. In some cases, when needed, CSO systems were contacted to verify active status of outfalls.Outfall locations are estimates based on the best available maps or coordinates and may contain errors and should only be used as a guide. For additional locational information please contact the CSO systems involved.Source Materials and Data AutomationMassDEP staff used information including maps and general descriptions of CSO locations found online and in NPDES permits to assist in mapping. This information was supplemented by maps and coordinates received from municipal staff responsible for CSO management in each of the systems involved, staff knowledge of outfall locations, maps found in system reports, and maps found on town web sites. MassDEP staff viewed the material and then mapped or verified the location of the CSO using the 2019 orthophoto as a basemap. Some systems were able to provide GIS data directly to MassDEP staff – in these cases, CSO locations were placed directly onto the provided points s or the systems provided point locations were used to verify MassDEP locations. When available, coordinates were entered directly and then checked against the orthophoto and other available maps for general accuracy. Last updated July, 2021.

FIELD

TYPE/WDTH

DEFINITION

NPDES_ID

C/50

Permit Number for the National Pollutant Discharge Elimination System (NPDES)

SYSTEMNAME

C/50

CSO System Name

OUTFALL_ID

C/15

Outfall number according to the Town or System

DEP_OUTFL_ID

C/15

Unique ID for the CSO which combines the Town or System abbreviation with the CSO Outfall No

RECVING_WATER

C/100

The body of water the CSO drains into

ADDRESS

C/120

General locational description of the CSO

TOWN

C/40

Standard (1-351) municipality name where the CSO is actually located

DEP_REGION

C/4

MassDEP Regional Office Code

L_TYPE

C/10

Type of location, i.e., estimated location, center of site, etc.

POINT_X

D

X Coordinate Decimal Degrees

POINT_Y

D

Y Coordinate Decimal Degrees

MAJOR_BASIN

C/100

Major Basin the outfall is located within

PERMIT_URL

C/200

URL to permit online

Data Universe

A list of active CSO locations was created and checked against lists contained in the NPDES permit for each CSO system along with lists contained in reports available from the systems online or on town websites. In some cases, when needed, CSO systems were contacted to verify active status of outfalls.CONTACTS AND TERMS OF USE

The CSOs contained in the CSO spatial data layer are based on a list provided by the MassDEP Bureau of Water Resources Wastewater program – for general questions please contact Lealdon Langley at (617) 574-6882. Locations are estimates and should only be used as a guide. For additional locational information please contact the CSO systems involved. For GIS related questions please contact the MassDEP GIS Program at (617) 292-5500.

Municipal governments in the Global South vary in their ability to provide not only complex social services, like environmentally proper solid waste disposal, but even simple services, like trash collection from the streets. This paper examines whether variation in service provision outcomes is associated with service-specific municipal administrative capacity, locally embedded civil society organization (CSO) presence, and collaborative governance for local planning and budgeting (or cogovernance). Using a panel dataset of Peruvian municipalities, I find that while all three factors are associated with better outcomes for simple services, only greater public administration capacity is associated with higher service outcomes when the service is more complex. This suggests that CSOs may face some difficulties to supplement the state in the provision of relatively complex services and that local cogovernance venues tend to prioritize more immediate service issues. These findings have policy implications for managing relatively complex services in Global South cities that struggle with service-specific administrative capacity and relatively complex service provision, particularly those with climate change consequences. They convey that strengthening this type of capacity at the office level is crucial to providing increasingly complex services, and engaging community-based CSOs and cogovernance venues may help as a strategy to address simple service delivery.

Please be advised that there are issues with the Small Area boundary dataset generalised to 20m which affect Small Area 268014010 in Ballygall D, Dublin City. The Small Area boundary dataset generalised to 20m is in the process of being revised and the updated datasets will be available as soon as the boundaries are amended. This feature layer was created using Census 2016 data produced by the Central Statistics Office (CSO) and Small Areas national boundary data (generalised to 20m) produced by Tailte Éireann. The layer represents Census 2016 theme 1.1, the total population across Ireland by sex and age. Attributes include population breakdown by age 0-19 by and sex, and population aged 20+ by sex and age group (e.g. Age 3 Males, Age 16 Females, Age 75-80 Males). Census 2016 theme 1 represents population by Sex, Age and Marital status. The Census is carried out every five years by the CSO to determine an account of every person in Ireland. The results provide information on a range of themes, such as, population, housing and education. The data were sourced from the CSO.

The Small Area Boundaries were created with the following credentials. National boundary dataset. Consistent sub-divisions of an ED. Created not to cross some natural features. Defined area with a minimum number of GeoDirectory building address points. Defined area initially created with minimum of 65 – approx. average of around 90 residential address points. Generated using two bespoke algorithms which incorporated the ED and Townland boundaries, ortho-photography, large scale vector data and GeoDirectory data. Before the 2011 census they were split in relation to motorways and dual carriageways. After the census some boundaries were merged and other divided to maintain privacy of the residential area occupants. They are available as generalised and non generalised boundary sets

Abstract

Since 1991, the country has been utilizing cross-sectional sample data to monitor the well-being of the Zambian population, as was the case with the 1996 and 1998 LCMS surveys. However, in 2002/2003 a different methodology was employed to collect and analyze data. The survey was designed to collect data for a period of 12 months.

The Living Conditions Monitoring Survey IV (LCMSIV) was intended to highlight and monitor the living conditions of the Zambian society. The survey included a set of priority indicators on poverty and living conditions to be repeated regularly.

The main objective of the Living Conditions Monitoring Survey IV (LCMSIV) is to provide the basis for comparison of poverty estimates derived from cross-sectional survey data. In addition, the survey provides a basis on which to: - - Monitor the impact of government policies and donor support on the well being of the Zambian population. - Monitor poverty and its distribution in Zambia. - Provide various users with a set of reliable indicators against which to monitor development. - Identify vulnerable groups in society and enhance targeting in policy implementation. - Develop new weights for the Consumer Price Indices and generate information that is required to produce National Accounts Statistics.

Geographic coverage

The Living Conditions Monitoring Survey IV had a nationwide coverage on a sample basis. It covered both rural and urban areas in all the nine provinces. The survey was designed to provide data for each and every district in Zambia.

Analysis unit

• Households
• Individuals

Universe

This survey was carried out under the provisions of the Census and Statistics Act, Chapter 425 of the Laws of Zambia. All persons residing in Zambia except for foreign diplomats accredited to embassies and high commissions at the time of the survey were required by this act to provide the necessary information.

Excluded from the sample were institutional populations in hospitals, boarding schools, colleges, universities, prisons, hotels, refugee camps, orphanages, military camps and bases and diplomats accredited to Zambia in embassies and high commissions. Private households living around these institutions and cooking separately were included such as teachers whose houses are within the premises of a school, doctors and other workers living on or around hospital premises, police living in police camps in separate houses, etc. Persons who were in hospitals, boarding schools, etc. but were usual members of households were included in their respective households. Ordinary workers other than diplomats working in embassies and high commissions were included in the survey also. Others with diplomatic status working in the UN, World Bank etc. were included. Also included were persons or households who live in institutionalized places such as hostels, lodges, etc. but cook separately. The major distinguishing factor between eligible and non eligible households in the survey is the cooking and eating separately versus food provided by an institution in a common/communal dining hall or eating place. The former cases were included while the latter were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Stratification and Allocation The sampling frame used for LCMSIV survey was developed from the 2000 census of population and housing. The country is administratively demarcated into 9 provinces, which are further divided into 72 districts. The districts are further subdivided into 155 constituencies, which are also divided into wards. Wards consist of Census Supervisory Areas (CSA), which are further subdivided into Standard Enumeration areas (SEAs). For the purposes of this survey, SEAs constituted the ultimate Primary Sampling Units (PSUs).In order to have equal precision in the estimates in all the districts and at the same time take into account variation in the sizes of the district, the survey adopted the Square Root sample allocation method, (Lesli Kish, 1987). This approach offers a better compromise between equal and proportional allocation methods in terms of reliability of both combined and separate estimates. The allocation of the sample points (PSUs) to rural and urban strata was almost proportional.A sample size of about 1,048 SEAs and approximately 20,000 households was drawn.

Sample Selection The LCMS IV employed a two-stage stratified cluster sample design whereby during the first stage, 1048 SEAs were selected with Probability Proportional to Estimated Size (PPES). The size measure was taken from the frame developed from the 2000 census of population and housing. During the second stage, households were systematically selected from an enumeration area listing. The survey was designed to provide reliable estimates at district, provincial, rural/urban and national levels. The LCMS IV survey commenced by listing all the households in the selected SEAs. In the case of rural SEAs, households were stratified according to their agricultural activity status. Therefore, there were four explicit strata created in each rural SEA namely, the Small Scale Stratum (SSS), the Medium Scale Stratum (MSS), the Large Scale Stratum (LSS) and the Non-agricultural Stratum (NAS). For the purposes of the LCMSIV survey, about 7, 5 and 3 households were supposed to be selected from the SSS, MSS and NAS, respectively. The large scale households were selected on a 100 percent basis. The urban SEAs were implicitly stratified into low cost, medium cost and high cost areas according to CSO's and local authority classification of residential areas. About 15 and 25 households were sampled from rural and urban SEAs, respectively.However, the number of rural households selected in some cases exceeded the desired sample size of 15 households due to the 100 percent sampling of large scale farming households.The formulae used in selecting SEAs is provided in section 2.3.3 of the Survey Report in External Resources.

Selection of Households The selection of households from various strata was preceded by assigning fully responding households sampling serial numbers. The circular systematic sampling method was used to select households. The method assumes that households are arranged in a circle (G. Kalton, 1983) and the following relationship applies:

Let N = nk, Where: N = Total number of households assigned sampling serial numbers in a stratum n = Total desired sample size to be drawn from a stratum in an SEA k = The sampling interval in a given SEA calculated as k=N/n.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires were used in the survey. These are:- 1. The Listing Booklet - for listing all the households residing in the selected Standard Enumeration Areas (SEAs) 2. The Main questionnaire - for collecting detailed information on all household members.

Cleaning operations

The data from the LCMSIV survey was processed and analysed using the CSPRO and the Statistical Analysis System (SAS) softwares respectively. Data entry was done from all the provincial offices with 100 percent verification, whilst data cleaning and analysis was undertaken at CSO’s headquarters

This is a geographical representation of the locations of CSO outfall points statewide. Combined Sewer Overflows (CSO) are sewers that are designed to collect rainwater runoff, domestic sewage, and industrial wastewater in the same pipe. Most of the time, combined sewer systems transport all of their wastewater to a sewage treatment plant, where it is treated and then discharged to a water body. During periods of heavy rainfall or snowmelt, however, the wastewater volume in a combined sewer system can exceed the capacity of the sewer collection system or treatment plant.This map data layer provides information regarding the location of permitted CSO Outfall Points, the applicable NJPDES Permit number, the assigned 3-digit discharge serial number, the latitude and longitude, the alternate name (such as a street address) of the CSO point, the municipality and Watershed Management Area (WMA) where the CSO outfall is located, a unique identifier for each point consisting of the permit number and outfall number, the receiving waterbody, the receiving treatment plant name and permit number, if the CSO outfall has solids and floatable treatment, a link for Discharge Monitoring Report (DMR) data, and a website.

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There is a requirement, as per Commission Implementing Regulation (EU) 2018/1799, to deliver Census data for the reference year 2021 to Eurostat. In September 2020, the Irish Government decided to postpone the scheduled April 2021 Census to April 2022 following a recommendation from CSO related to the impact of the Covid-19 pandemic. The CSO however has agreed that the office will still meet its legal requirement. It will base the Eurostat requirements on Census 2022 data, using administrative and other sources to appropriately adjust the data to reference year 2021. A (preliminary) headcount of usual residents at the 1 km² grid level (there are approximately 73,000 such square kilometres in Ireland) is required by Eurostat by 31^st December 2022. The data was produced in the following manner:

Initial preliminary Census estimate for April 2022 As part of the field operation for the 2022 Census, the CSO introduced a new smartphone-based application that allowed field staff to capture information about every dwelling in the country. This application facilitated the production of a preliminary population publication less than 12 weeks (June 23^rd) after census night (April 3^rd). The information includes data on the number of de facto occupants. This information is provisional, and the final file will not be completed until all collected paper forms are fully processed, which is expected to be around the end of January 2023. The provisional data should however be a very strong indicator of the final results.

The preliminary Census de facto population estimate was 5,123,536 persons, available at the 1 km² grid level. As we need the population on a usual resident basis, it was decided to adjust this estimated de facto population at the 1 km² grid level by applying the arithmetic differences between the 2016 usual resident and de facto population counts at that level to the de facto population for 2022. A ratio model, where rates of change of de facto to usual resident counts are applied instead of differences, was also considered but this led to more extreme adjustments, mainly where there was a large change in the population count of a cell between 2016 and 2022. This reduced the usual resident population to 5,101,268 for April 2022, a fall of 22,268 persons.

Temporary Absent Dwellings Census also provided data on the temporarily absent dwellings dataset (at 1 km² grid level), containing a count of persons usually resident in the State but whose entire household were abroad on census night and therefore not included in the de facto population count. This covers 33,365 temporarily absent dwellings with 50,749 temporarily absent persons across 9,138 grid cells. This category was not present in the 2016 figures so it was decided to include these absent persons as they meet the definition of usual residents and will be present in the final transmission, due March 2024. The resulting usually resident population count for 3^rd April 2022 was estimated as 5,152,671 persons.

Note that in a small number cases (80 grid cells), adjustments resulted in a negative cell value, but these were set to zero.

Final preliminary estimate

The CSO then adjusted this figure of estimated usual residents for 3^rd April 2022 back to the 3^rd December 2021 reference point by performing a reverse cohort-survival model.

Firstly, there are an estimated 21,528 births, some 12,405 deaths and approximately 63,595 inward and 25,730 outward migrants for the four-month period December 2021 to March 2022. This affects a total of approximately 123,000 persons, or about 2.4% in a total population of around 5.15 million persons. These population changes were ‘reversed’, as indicated below. Secondly, we also ‘reversed’ those persons who moved from their address within Ireland after December 3^rd 2021 to their Census April 3^rd 2022 address. Based on the selection method approximately 85,000 persons were moved to their previous address, representing about 1.7% of the population.

The steps in the process were:

Births We took the actual November 2015 to April 2016 births from Census 2016 with the variables grid reference, gender and NUTS3 as the sampling frame for the selection of births. Then, using data from table 19 in the Q1 2022 Vital Stats quarterly release (Table VSQ19 on Statbank), we derived the number of Q1 2022 births at NUTS3 by gender level. We also included a proportion of Q4 2021 births, taking one-third to represent December 2021. There are 21,528 births in total for the four-month period we are interested in (16,121 for Q1 2022 plus a third of the value of Q4 2021 which is 5,407), see table 2. Then, using the SAS procedure surveyselect, we selected, at random, the required number of births per strata from the frame and totalled up per grid reference. The resulting figure is the number of people removed from the Census 2021 grid totals, as these figures represent those born during December 2021 to March 2022.

We took the entire Census 2016 data with the variables grid reference, gender, NUTS3 and broad age group (0-14, 15-29, 30-49, 50-64, 65-84 and 85+) as the sampling frame for the selection of people to add back in who died between December 2020 and March 2022. This stratification results in 96 cells. This frame serves as a proxy for the distribution of deaths across the 1km grid square strata. Next, we obtained the Q4 2021 and Q1 2022 mortality data stratified by gender, NUTS3 and age group, provided by the Vital Stats statistician. The total number is 12,405 deaths for the four-month period of interest (9,535 for Q1 2022 plus one third of the value for Q4 2021 which is 8,626), see tables 3 and 4.

Then using the SAS procedure surveyselect, we selected, at random, the required number of deaths per strata from the frame and total up per grid reference. The resulting figure is simply the number of people added to the Census 2021 grid figures as summarised at the grid level, as they represent those who died during December 2021 to March 2022.

Inward and outward migrants

The processing of the inward and outward migrants essentially follows the same methodology in that we used Census 2016 as a sampling frame for the inclusion of those who emigrated in December 2021 and March 2022 and the exclusion of those who immigrated in the same period.

We took the Census 2016 with the variables grid reference, gender, NUTS3, broad nationality (Irish, UK, EU14 excl. IE, EU15 to 27 and Rest of the World) and broad age group (0-14, 15-29, 30-49, 50-64, 65-84 and 85+) as the sampling frame for the selection of migrants. Using the Q4 2021 and Q1 2022 migration data, we got the required inward and outward movers. The Population and Migration statistician provided the data at an individual level for our purposes. There are 63,780 inward migrators (53,403 in Q1 2022 and 10,377 taking one-third of the Q4 2021 values) and 25,730 outward migrators (19,779 in Q1 2022 and 5,951 taking one-third of the Q4 2021 values), see tables 5 to 7.

Then, using SAS procedure surveyselect, we selected, at random, the required number of inward and outward migrants per strata from the frame and sum over grid reference. Given that there will be more inward than outward migrants, the resulting figures will generally be negative i.e., the population will fall.

Ukrainian refugees There are no official statistics, but it was estimated that there were more than 23,000 Ukrainian refugees present in the State in April 3 2022. It is difficult to know the exact numbers captured by the Census until the full final dataset is available. Ukrainian refugees were to be counted as immigrants and usual residents (UR) on the census form unless an individual classed themselves as a visitor, in which case they were de facto (DF) residents. From the point of view of the procedure being described here, Ukrainians who are classified

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Geographic coverage

The ASI extends to the entire country

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to census scheme is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The ASI covers all factories registered under Sections 2m(i) and 2m(ii) of the Factories Act, 1948 i.e. those factories employing 10 or more workers using power; and those employing 20 or more workers without using power. The survey also covers bidi and cigar manufacturing establishments registered under the Bidi & Cigar Workers (Conditions of Employment) Act, 1966 with coverage as above. All electricity undertakings engaged in generation, transmission and distribution of electricity registered with the Central Electricity Authority (CEA) were covered under ASI irrespective of their employment size. Certain servicing units and activities like water supply, cold storage, repairing of motor vehicles and other consumer durables like watches etc. are covered under the Survey. Though servicing industries like motion picture production, personal services like laundry services, job dyeing, etc. are covered under the Survey but data are not tabulated, as these industries do not fall under the scope of industrial sector defined by the United Nations. Defence establishments, oil storage and distribution depots, restaurants, hotels, café and computer services and the technical training institutes, etc. are excluded from the purview of the Survey.

From ASI 1998-99, the electricity units registered with the CEA and the departmental units such as railway workshops, RTC workshops, Govt. Mints, sanitary, water supply, gas storage etc. are not covered, as there are alternative sources of their data compilation for the GDP estimates by the National Accounts Division of CSO.

Kind of data

Sample survey data [ssd]

Sampling procedure

The ASI frame is based on the lists of registered factory / units maintained by the Chief Inspector of Factories (CIF) in each state and those maintained by registration authorities in respect of bidi and cigar establishments and electricity undertakings. The frame is being revised and updated periodically by the Regional Offices of the Field Operations Division of NSSO in consultation with the Chief Inspector of Factories in the state. At the time of revision, the names of the de-registered factories are removed from the ASI frame and those of the newly registered factories are added. In updation, only new registrations are added to the existing frame. In spite of regular updating of the frame, quite a number of small-sized factories selected for the survey are found to be non-existing in the field and are termed as deleted factories. However, such factories are not taken into consideration for the purpose of tabulation and analysis in this report.

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Bivariate analysis of the relation of basic, underlying, and immediate factors with CSO.

Abstract

The main objective of the 2006 and 2010 LCMS surveys was to provide the basis for comparison of poverty estimates derived from cross-sectional survey data between 2006 and 2010.

In addition, the survey provides a basis on which to: - Monitor the impact of government policies on the well being of the Zambian population. - Monitor the level of poverty and its distribution in Zambia. - Provide various users with a set of reliable indicators against which to monitor - Identify vulnerable groups in society and enhance targeting in policy implementation.

Geographic coverage

In the LCMS 2010, all the 1000 sampled SEAs were enumerated representing 100 percent coverage at national level.

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure household members (usual residents) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample stratification and allocation The sampling frame used for the LCMS VI was developed from the 2000 Census of Population and Housing. The country is administratively demarcated into 9 provinces, which are further divided into 72 districts. The districts are further subdivided into 150 constituencies, which are in turn divided into wards. For the purposes of conducting CSO surveys, Wards are further divided into Census Supervisory Areas (CSA), which are further subdivided into Standard Enumeration areas (SEAs). For the purposes of this survey, SEAs constituted the Primary Sampling Units (PSUs). In order to have reasonable estimates at district level and at the same time take into account variation in the sizes of the districts, the survey adopted the Square Root sample allocation method, (Leslie Kish, 1987). This approach offers a compromise between equal and proportional allocation i.e. small sized strata (Districts) are allocated larger samples compared to proportional allocation. However, it should be pointed out that the sample size for the smallest districts is still fairly small, so it is important to examine the confidence intervals for the district-level estimates in order to determine whether the level of precision is adequate. The allocation of the sample points to rural and urban strata was done in such a way that it was proportional to their sizes in each district. Although this method was used, it was observed from the LCMS 2006 that the coefficient of variation (CV) of the poverty estimates was highest in districts which are predominantly urban and lowest in rural districts. This means that the sample size in some urban districts may have been inadequate to measure poverty with a good level of precision. That is, given the higher variability in the urban districts, a larger sample size would be required. Also some districts had very low CV estimates, indicating a higher level of precision for the poverty estimates. In order to try and improve the precision of the poverty estimates for the urban districts, the initial distribution of the sample was adjusted. It was necessary to increase the number of PSUs for some districts without increasing the budget and at the same time not compromising significantly the precision of the poverty estimates for rural areas. Rural districts which had the lowest CVs in the 2006 LCMS results had their sample size reduced, and these were in turn distributed to districts with the highest CVs. The distribution of the sample for the LCMS 2006 and LCMS 2010 were initially the same but changed after the later was adjusted. Table 2.1 in the Survey Report shows the allocation of PSUs in the survey.

Sample Selection The LCMS VI employed a two-stage stratified cluster sample design whereby during the first stage, 1000 SEAs were selected with Probability Proportional to Estimated Size (PPES) within the respective strata. The size measure was taken from the frame developed from the 2000 Census of Population and Housing. During the second stage, households were systematically selected from an enumeration area listing. The survey was designed to provide reliable estimates at the district, provincial, rural/urban and national levels. However, the reliability for some indicators may be limited for the smaller districts, given the limited sample size. This will be determined by the tabulation of sampling errors and confidence intervals.

Selection of households Listing of all the households in the selected SEAs was done before a sample of households to be interviewed was drawn. In the case of rural SEAs, households were stratified and listed according to their agricultural activity status. Therefore, there were four explicit strata created at the second sampling stage in each rural SEA namely, the Small Scale Stratum (SSS), the Medium Scale Stratum (MSS), the Large Scale Stratum (LSS) and the Non-agricultural Stratum (NAS). For the purposes of the LCMS VI, Seven, five and three households were selected from the SSS, MSS and NAS, respectively. The large scale households were selected on a 100 percent basis. The urban SEAs were explicitly stratified into low cost, medium cost and high cost areas according to CSO's and local authority classification of residential areas. From each rural and urban SEA, 15 and 25 households were selected, respectively. However, the number of rural households selected in some cases exceeded the prescribed sample size of 15 households depending on the availability of large scale farming households.The selection of households from various strata was preceded by assigning fully responding households sampling serial numbers. The circular systematic sampling method was used to select households. The method assumes that households are arranged in a circle (G. Kalton, 1983) and the following relationship applies: Let N = nk, Where: N = Total number of households assigned sampling serial numbers in a stratum n = Total desired sample size to be drawn from a stratum in an SEA k = The sampling interval in a given SEA calculated as k=N/n.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three types of questionnaires will be used in the survey. These are:- 1. The Listing Booklet - to be used for listing all the households residing in the selected Standard Enumeration Areas (SEAs) 2. The Main questionnaire - to be used for collecting detailed information on all household members in the selected households 3. The Prices questionnaire:- to be used to collect unit prices of various commodities. This information is vital for harmonising regional differences in prices

Cleaning operations

The Living Conditions Monitoring Survey data were entered using CSPro version 4.0 software. The LCMS 2010 application used a double entry system unlike the LCMS 2006 application which used single entry. The 2010 data entry was done by two teams, one team in the Provinces and another one at CSO headquarters. The data were then compared and matched by a team of matchers. Errors identified by matchers were corrected as a way of completing data entry. The major advantage of double entry (verification) is that data entry errors generated by the data entry operator are greatly minimized. The data were then exported to SAS, SPSS and Stata formats for data cleaning bulation and analysis.

Response rate

The household response rate was calculated as the ratio of originally selected households with completed interviews over the total number of households selected. The household response rate was also generally very high with a national average of 98 percent of the originally selected households for both survey periods.

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Data showing the names,address and activities of Civil Society Organisations in the state.

Hierarchical multiple logistic regression analysis to determine basic, underlying and immediate determinants of CSO.

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Abstract

The Zambia Demographic and Health Survey (ZDHS) is a nationally representative sample survey of women and men of reproductive age. The main objective is to provide information on levels and trends in fertility, childhood mortality, use of family planning methods, maternal and child health indicators including HIV/AIDS. This information is necessary for programme managers, policymakers, and implementers to monitor and evaluate the impact of existing programmes and to design new initiatives for health policies in Zambia.

The primary objectives of the 2013-14 ZDHS are: • To collect up-to-date information on fertility, infant and child mortality, and family planning. • To collect information on health-related matters such as breastfeeding, antenatal care, children’s immunisations, and childhood diseases. • To assess knowledge of contraceptive practices among women. • To assess the nutritional status of mothers and children. • To improve understanding of variations in HIV seroprevalence levels according to social and economic characteristics and behavioural risk factors. • To estimate levels of HIV incidence in the general population of adults. • To estimate unmet need for antiretroviral treatment.

In the case of HIV/AIDS, the testing component of the 2013-14 ZDHS was undertaken to provide information to address the monitoring and evaluation needs of government and nongovernmental programmes dealing with HIV/AIDS. It also provides programme managers and policymakers with the information they need to effectively plan and implement future interventions. The overall objective was to collect high-quality and representative data on knowledge, attitudes, and behaviours regarding HIV/AIDS and other STIs and on the prevalence and incidence of HIV among women and men.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2013-14 ZDHS was designed to provide estimates at the national and provincial levels, as well as for rural and urban areas within the provinces. This is the first time the ZDHS has been designed to provide estimates at such disaggregated levels for many of the survey indicators. The updated list of enumeration areas (EAs) for the 2010 Population and Housing Census provided the sampling frame for the survey. The frame comprises 25,631 EAs and 2,815,897 households. An EA is a convenient geographical area with an average size of 130 households or 600 people. For each EA, information is available on its location, type of residence (rural or urban), number of households, and total population. Each EA has a cartographical map with delimited boundaries and main landmarks of the area. A 2013-14 ZDHS cluster is essentially representative of an EA.

A representative sample of 18,052 households was drawn for the 2013-14 ZDHS. The survey used a two-stage stratified cluster sample design, with EAs (or clusters) selected during the first stage and households selected during the second stage. In the first stage, 722 EAs (305 in urban areas and 417 in rural areas) were selected with probability proportional to size. Zambia is now administratively divided into 10 provinces (Central, Copperbelt, Eastern, Luapula, Lusaka, Muchinga,2 Northern, North Western, Southern, and Western). Stratification was achieved by separating each province into urban and rural areas. Therefore, the 10 provinces were stratified into 20 sampling strata. In the second stage, a complete list of households served as the sampling frame in the selection of households for enumeration. An average of 25 households was selected in each EA. It was during the second stage of selection that a representative sample of 18,052 households was selected.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2013-14 ZDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. The three instruments were based on the questionnaires developed by the Demographic and Health Surveys Program and adapted to Zambia’s specific data needs. The questionnaires were translated into seven major languages: Bemba, Kaonde, Lozi, Lunda, Luvale, Nyanja, and Tonga. Questionnaires and field procedures were pretested prior to implementation of the main survey.

The Household Questionnaire was used to collect data such as: • Age, sex, marital status, and education of all usual members and visitors • Current school attendance and survivorship of parents among children under age 18 • Characteristics of the structural dwelling/housing unit • Sanitation facilities and source of water • Ownership of durable goods, land, and livestock • Ownership and use of mosquito nets The Household Questionnaire was also used to record biomarker data, including height and weight data for children and women and HIV and CD4 testing information for women and men. Data on age and sex of household members were used to identify the women and men eligible for individual interviews.

The Woman’s Questionnaire was used to collect information from all women age 15-49.

The Man’s Questionnaire was administered to all men age 15-59. It collected much of the same information as the Woman’s Questionnaire but it did not contain a detailed reproductive history or questions on maternal and child health or nutrition.

Cleaning operations

All questionnaires for the 2013-14 ZDHS were returned to the CSO headquarters in Lusaka for data processing, which consisted of office editing, coding of open-ended questions, data entry, and editing of computer-identified errors. Data processing staff included two data processing supervisors, 24 data entry clerks, five office editors, four secondary editors, one questionnaire administrator, and one biomarker administrator.

The processing of the data began in September 2013, one month after data collection commenced, and continued concurrently with the fieldwork. This offered an advantage because data were consistently checked and feedback was given to field teams, thereby improving data quality. Before being sent to the data processing centre in Lusaka, completed questionnaires were edited in the field by the field editors and checked by the supervisors. At the processing centre, data were edited and coded by office editors. Data were then entered using the CSPro computer package. All data were entered twice for 100 percent verification. This double entry of data enabled easy comparisons and identification of errors and inconsistencies. Inconsistencies were resolved by tallying the data with the paper questionnaire entries. Further inconsistencies that were identified were resolved through secondary editing of the data. The data files (excluding HIV testing data) were finalised in June 2014 after data cleaning.

Response rate

A total of 18,052 households were selected from 722 clusters, of which 16,258 were occupied at the time of the fieldwork. Of the occupied households, 15,920 were successfully interviewed, yielding a household response rate of 98 percent.

In the interviewed households, a total of 17,064 women age 15-49 were identified as eligible for individual interviews, and 96 percent of these women were successfully interviewed. A total of 16,209 men age 15-59 were identified as eligible for interviews, and 91 percent were successfully interviewed. Individual response rates were slightly lower in urban areas than in rural areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2014 Zambia DHS (ZDHS) to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2014 ZDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2014 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it

Abstract

The 2013-14 Afghanistan Living Conditions Survey (ALCS; previously known as NRVA - National Risk and Vulnerability Assessment) provides national and international stakeholders with information that is required for the development of policies and programmes. The survey was conducted by the Central Statistics Organization (CSO) of the Islamic Republic of Afghanistan and provides results that are representative at national and provincial level. It covered 20,786 households and 157,262 persons across the country, and is unique in the sense that it also includes the nomadic Kuchi population of Afghanistan. Another distinguishing feature of the survey was the continuous data collection during a cycle of 12 months, which captured important seasonal variation in a range of indicators. The survey was designed to cover a wide scope of development themes and indicators that were agreed upon in series of consultations with government departments and agencies, donors and international organisations.

Geographic coverage

National coverage, the survey was designed to produce representative estimates for the national and provincial levels, and for the Kuchi population.

Analysis unit

• Household
• Individual
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design of the Afghanistan ALCS 2013-14 was developed to produce results that are statistically reliable for most of the indicators at national and provincial level. In addition, the aim of the sampling design was to have representative estimates by season according to the Shamsi calendar used in Afghanistan, in order to capture seasonal fluctuations in a number of key indicators. The design developed for the 2013-14 survey round was a stratified, two-stage cluster approach. The sample distribution is sufficiently close to the national urban-rural distribution that separate analysis for these populations is justified.

Sample Frame The pre-census household listing that was conducted by CSO in 2003-05, updated in 2009 was used as the sampling frame. For three provinces, the sampling frame consisted of the Socio-Demographic and Economic Survey (SDES) household listings: Bamyan (data collected in 2010), Ghor and Daykundi (both with data collected in 2012). Prior to the fieldwork, the selected EAs - urban and rural - were visited for a mapping update of the households, on the basis of which the second sampling stage was implemented.

The sampling frame that was used for the Kuchi population was the 2003-04 National Multi-sectoral Assessment of Kuchi (NMAK-2004). Although far from perfect given the rate of settlement of Kuchis in recent years and ongoing discussion about the definition of Kuchi, this is the best frame available for this part of Afghanistan's population.

Sample size Analysis of previous NRVA rounds showed that a sample size of around 21 thousand households with a cluster size of ten households would produce sufficiently reliable estimates for most variables. Consequently, this sample size was maintained as the standard the 2013-14 ALCS.

For further details on sampling design, see the survey report.

Sampling deviation

Two major issues impeded the implementation of the sampling design during the fieldwork period.

One was the security situation in parts of the country. For in total 182 clusters (8.7 percent of the original 2,100 clusters) the coverage shifted in time or replacement clusters were selected. In addition, 19 clusters, representing 190 households, were not implemented and not replaced.

A second interference with the sampling design concerned delays in the fieldwork due to administrative, logistic and technical issues. This had the following implications: - The fieldwork was extended with three weeks in order to capture the full sample. - Information for winter time was collected in two different years (2013 and 2014) - The Kuchi sample was implemented in summer and autumn 2014, instead of in winter 2013-14 and summer 2014. - There was an underrepresentation of coverage during the spring season due to the presidential elections in that period.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two type of questionnaires were used to collect the survey data - Household and Male Shura questionnaires.

Cleaning operations

Manual Checking and Coding Data processing in CSO Headquarters was done in parallel to the fieldwork and started upon arrival of the first batch of completed questionnaires in February 2014. The first two data-processing stages consisted of manual checking and coding by a team of eight questionnaire editors and coders.

The tasks of editors include: - recording and archiving returned questionnaires - checking the completeness of the questionnaire batches and questionnaire forms - checking questionnaire answers for completeness, consistency, correctness and readability - correcting answers or completing missing answers for a limited and prescribed number of questions, including identification fields and some key questions - adding codes for missing values - completing an evaluation form on the basis of which the questionnaire batch would be dispatched to the questionnaire coders or returned to the field for renewed data collection.

Data Entry and Data Editing Data capture was done with a specially designed CSPro programme, which was piloted to ensure a smooth performance in real time. The data-entry system applied first data entry and dependent verification through double entry to avoid high levels of manual data capture errors. In addition, CSPro data-editing programmes were developed to identify errors and either perform automatic imputation or manual screen editing, or refer cases to data editors for further questionnaire verification and manual corrections.

CSO's data-entry section started entering the first round of data in February 2014. Progress was slow in the first weeks due to the operators' unfamiliarity with the complex questionnaires and fine-tuning of the elaborate data-processing programmes. With experience built up, the speed of data processing increased and in later months all data were entered and verified within two weeks from reception of questionnaires from the manual checking and coding section. Data capture and editing operations were completed in March 2015.

Response rate

Response rate was 99.98 percent. Non-response within clusters was very limited. Only 845 (4.1 percent) of the households in the visited clusters were not available or refused or were unable to participate. In 841 of these cases households were replaced by reserve households listed in the cluster reserve list, leaving 4 households unaccounted for (0.02 percent).

Data appraisal

Data Limitations The specific constraints in the Afghanistan context in terms of security problems, cultural barriers and local survey capacity induced some data limitations. The following observations should be taken into account when interpreting the results: - In 152 out of 2,100 clusters (7.2 percent), originally sampled clusters could not be covered, in most cases due to security reasons. For 148 of these cases, clusters were replaced. To the extent that the non-visited clusters may have profiles different from visited clusters, the final sample will give a bias in the results. This effect will have been larger at the provincial level for provinces with relatively large numbers of replacement, such as Ghazni, Helmand and Badakhshan. - Analysis of the population structure by sex and age shows under-enumeration of women and girls, as well as young children in general, especially infants. Coverage of the youngest age group was much better than in previous surveys, but significant numbers are still omitted. Cultural backgrounds related to the seclusion of women and high infant mortality are among likely reasons for these omissions. - The quality of age reporting in the Afghan population remains extremely poor, as indicated by large age heaping on ages with digits ending on 5 and 0 - Due to alleged security problems, work by female interviewers in Zabul was not allowed by the authorities. Consequently, the information on general living conditions, maternal- and child health, and gender is largely missing for this province. However, the food-security and child-labour modules in the female questionnaire were completed by male interviewers interviewing male respondents.