The Afghanistan Living Conditions Survey (ALCS) is one of the flagships of the Central Statistical Organization, as it is the longest standing and most comprehensive survey in the Afghan statistical system. It is now running for more than 10 years and has provided the Afghan Government, civil society, researchers and the international community with precious data on the living conditions of the Afghan population since its first iteration. During the course of its implementation, the survey as changed in scope and purpose, following the country's transformations, what has prompted the National Statistics and Information Authority (NSIA, formerly Central Statistics Organization) to let its name evolve as well. It had started as the National Risk and Vulnerability Assessment (NRVA) following a methodology of the United Nations Food and Agriculture Organisation, focusing on food security and poverty prevalence and was then heavily supported by the United Nations World Food Programme.
The survey is funded by the European Union with contributions from the World Food Programme, WFP. The present 2016-17 round of data collection is the sixth in a series, the previous surveys were conducted in 2003, 2005, 2007-08, 2011-12 and 2013-14. The survey covers the period April 2016 to March 2017 and comprises individual-, household- and community (Shura) information, as well as – in this round – information on market prices.
The Afghanistan Living Conditions Survey (previously known as NRVA - National Risk and Vulnerability Assessment) is the national multi-purpose survey of Afghanistan, conducted by the National Statistics and Information Authority (NSIA, formerly known as Central Statistics Organization) of Afghanistan.
The ALCS aims to assist the Government of Afghanistan and other stakeholders in making informed decisions in development planning and policy making, by collecting and analyzing data related to poverty, food security, employment, housing, health, education, population, gender and a wide range of other development issues. The sampling design of the survey allows representative results at the national and provincial level. Besides presenting a large set of recurrent development indicators and statistics, the present 2016-17 round has a specific focus on poverty, food security and disability.
Over the years the ALCS and NRVA surveys have been the country’s most important source of indicators for monitoring the Millennium Development Goals (MDGs). The ALCS will similarly serve as the main source for producing the set of indicators that were endorsed in March 2016 by the UN Statistical Commission to monitor the implementation of the 2030 Agenda for Sustainable Development. Although this set of Sustainable Development Goals (SDG) indicators was only finalized around the time the ALCS went into the field, required information for many new indicators was anticipated and accommodated in the questionnaire design. As a result, ALCS 2016-17 will be able to report on and set the baseline for 20 SDG indicators.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The 2016-2017 Afghanistan Living Conditions Survey covered the following topics:
- Household identification
- Household roster
- Housing and amenities
- Household assets
- Household income and expenditure
- Household shocks and coping strategies
- Persons who left the household
- General living conditions and household decisions
- Food security
- Child Labor
- Maternal and child health
MALE SHURA QUESTIONNAIRE
- Community identification
- Community development priorities
National coverage, the survey was designed to produce representative estimates for the national and provincial levels, and for the Kuchi population.
Producers and sponsors
National Statistics and Information Authority (NSIA)
Government of the Islamic Republic of Afghanistan
The sampling design of the ALCS 2016-17 ensured results that are representative at national and provincial level, for the Kuchi population and for Shamsi calendar seasons. In total, 35 strata were identified, 34 for the provinces of Afghanistan and one for the nomadic Kuchi population. Stratification by season was achieved by equal distribution of data collection over 12 months within the provinces. For the Kuchi population, the design only provided sampling in winter and late summer when communities tend to temporarily settle. The distribution of sampling areas per province was based on an optimal trade-off between precision at the national and provincial levels.
For seven provinces, the sampling frame for the resident population consisted of the household listing of the Socio-Demographic and Economic Survey (SDES): Bamyan, Ghor, Daykundi, Kapisa, Parwan, Samangan and Kabul. For all other provinces, the sampling frame depended on the pre-census household listing conducted by NSIA in 2003-05 and updated in 2009. Households were selected on the basis of a two-stage cluster design within each province. In the first sampling stage Enumeration Areas (EAs) were selected as Primary Sampling Units (PSUs) with probability proportional to EA size (PPS). Subsequently, in the second stage, ten households were selected as the Ultimate Sampling Unit (USU). The design thus provided data collection in on average 170 clusters (1,700 households) per month and 2,040 clusters (20,400 households) in the full year of data collection.
The Kuchi sample was designed on basis of the 2003-04 National Multi-sectoral Assessment of Kuchi (NMAK-2004). For this stratum, a community selection was implemented with PPS and a second stage selection with again a constant cluster size of ten households. The 60 clusters (600 households) for this stratum were divided between the summer and winter periods within the survey period, with 40 and 20 clusters, respectively.
Deviations from the Sample Design
The reality of survey taking in Afghanistan imposed a number of deviations from the sampling design. In the first three months of fieldwork, areas that were inaccessible due to insecurity were replaced by sampled areas that were scheduled for a later month, in the hope that over time security conditions would improve, and the original cluster interviews could still be conducted. In view of sustained levels of insecurity, from the fourth month of data collection onward, clusters in inaccessible areas were replaced by clusters drawn from a reserve sampling frame that excluded insecure districts.
Unit non-response in ALCS 2016-2017 occurred to the extent that sampled clusters were not visited, or that sampled households in selected clusters were not interviewed. Out of the 2,102 originally scheduled clusters, 294 (14 percent) were not visited. For 196 of these non-visited clusters, replacement clusters were sampled and visited. Although this ensured the approximation of the targeted sample size, it could not avoid the likely introduction of some bias, as the omitted clusters probably have a different profile than included clusters.
In the visited clusters - including replacement clusters - 1,021 households (5.1 percent of the total) could not be interviewed because - mostly - they were not found or because they refused or were unable to participate. For 1,019 of these non-response households (5.1 percent of the total), replacement households were sampled and interviewed. Since the household non-response is low and it can be expected that the replacement households provide a reasonable representation of the non-response households, this non-response error is considered of minor importance.
The overall unit non-response rate - including non-visited clusters and non-interviewed households, without replacement - is 14.0 percent.
Sample weights were calculated for up-scaling the surveyed households and persons to the total number of households and population in Afghanistan. The calculation was based on the official NSIA population estimate by province for January 2016 and average provincial household sizes derived from the survey. Annex IV gives an account of the backgrounds and technical details of the sampling design and implementation.
Dates of Data Collection
Data Collection Mode
For the ALCS 2016-2017, a total number 177 field staff was required in the 34 provinces. In 32 provinces, the team had the standard composition of two couples - each consisting of one male and one female interviewer - and one supervisor. Due to the larger populations, in Herat province three couples and one supervisor were required and in Kabul province, four couples and two supervisors.
Data Collection Notes
The ALCS 2016-17 period of data collection coincided with the Shamsi solar year 1395. Fieldwork started in April 2016 (Hamal 1395) and finished in April 2017 (Hamal 1396). An elaborate monitoring system was applied to check the progress and quality of data collection, including field monitoring by regional supervisors and ALCS staff from NSIA Headquarters, tracking of GPS coordinates and rapid data-quality assessments based on manual and computerized data checking. Feedback to interviewers and supervisors was done on a daily basis by telephone and through refresher trainings.
In view of recurrent security problems, a security strategy was applied, which includes mapping of insecure areas, security assessment in the field and consultation of other relevant information sources. Data collection areas that were considered insecure were substituted by other areas from the original sample or by areas from a reserve sample.
The tasks of the Regional Statistical Officers (RSOs) included checking a sample of the completed questionnaires, as a second level of quality control in the field after the checking by supervisors. For this purpose, specific check lists were developed. On a monthly basis, they transported batches of completed questionnaires and other survey documents back to NSIA Headquarters and took new field supplies to the provinces. The PSOs were responsible for the introduction of the field teams to the provincial and local authorities, for monitoring fieldwork progress and the security situation, and for verification of survey results in the field.
Further quality assurance during data collection was provided by seven members of the ALCS team at NSIA Headquarters, who conducted field monitoring missions every survey quarter. These monitors focussed specifically on those provinces from which questionnaires were returning with the most irregularities according to manual checking at Headquarters.
Provinces that faced most security challenges were Kapisa, Nangarhar, Paktya, Paktika, Wardak, Sar-e-Pul, Urozgan, Baghlan, Kunarha, Kunduz and Helmand. As a last resort, insecure areas were replaced by more secure areas. The security situation in Paktika did not allow data collection after month six.
Out of the 391 sampled districts and provincial centres of Afghanistan, in 342 (87 percent) information was collected. In total, information from 1,929 clusters was collected, against 2,042 clusters according to the sampling design (94 percent). Out of these, 1,557 clusters (81 percent) were covered as originally planned, while 176 (9 percent) were replaced with clusters from the reserve sample. Interviews of the remaining 196 clusters (10 percent) were conducted in the planned EAs, but in another month than originally planned. From the 60 Kuchi clusters, 55 were covered according to the sampling design, whereas for the five remaining the targeted Kuchi population could not be found in the field.
Since 2003, the successive survey rounds incorporated an increasing number of questions. This continued even to the extent that interview burden and workloads in data processing and analysis overreached the capacity of fieldworkers, respondents and NSIA staff. The need to compress all information requirements into one survey that was conducted at irregular intervals was reduced when the Afghanistan National Statistical Plan (ANSP) (CSO 2010) was formulated. The ANSP presented a medium-term perspective that anticipated the implementation of NRVA - now ALCS - as the national multi-purpose survey of Afghanistan on an annual basis. Rather than including all questions and topics every year, the principle of producing information on a rotating basis was introduced. While each survey round provides a core set of key indicators, successive rounds add or expand different modules to provide more detailed information on specific subjects. In the series of consultations with stakeholders in 2010, agreement was reached to re-design the ALCS data collection and questionnaires according to this rotation principle. This implied that information needs and survey implementation could be achieved in a more sustainable and efficient way.
The core of ALCS 2016-17 consist of a household questionnaire with 16 subject matter sections, 11 administered by male interviewers and answered by the male household representative (usually the head of household), and five asked by female interviewers from female respondents. In addition, the questionnaire includes three modules for identification and monitoring purposes. In the last five months of the fieldwork, one more module was added to test a methodology for water quality assessment.
On average the time required to answer the household questionnaire was one to one-and-a-half hour.
A data-entry programme in CSPro software has been developed to manually capture the survey data, applying first data entry and dependent verification through double data entry to minimise data-entry errors. In addition, CSPro data-editing programmes were applied 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. A final round of monthly data checking was performed by the project Data Processing Expert.
NSIA's data-entry section started entering the first month of data in June 2016. Usually, 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 May 2017.
Extensive programmes in Stata software were developed or updated to perform final data verification-, correction-, editing- and imputation procedures A full dataset was available in August 2017 in STATA and SPSS. A team of 15 national and international analysts contributed to the present Analysis Report.
Data processing in NSIA Headquarters was done in parallel to the fieldwork and started upon arrival of the first batch of completed questionnaires in May 2016. The first two data-processing stages consisted of manual checking and coding by a team of eight questionnaire editors and coders. The tasks of the questionnaire editors consisted of:
- 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.
The coders had the responsibility to add codes for textual answers to questions and variables about occupations, industries, provinces and countries, following the guidelines given in the training and in the provided coding manuals. For coding, international standard classifications were used. Subsequently, the questionnaire batch was submitted for data entry.
Estimates of Sampling Error
Statistics based on a sample, such as means and percentages, generally differ from the statistics based on the entire population, since the sample does not include all the units of that population. The sampling error refers to the difference between the statistics of the sample and that of the total population. Usually, this error cannot be directly observed or measured, but is estimated probabilistically.
The sampling error is generally measured in terms of the standard error for a particular statistic, which equals the square root of the variance of that statistic in the sample. Subsequently, the standard error can be used to calculate the confidence interval within which the true value of the statistic for the entire population can reasonably be assumed to fall: a value of the statistic produced from the sample 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.
Please refer to Table IX.2 (ALCS full report) in the Related Materials for an overview of standard errors and confidence intervals for selected key indicators. Since the sample design of ALCS 2016-17 is not simple random sampling, but a multi-stage stratified design, the linearization method is used for estimation of standard errors.
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 this report:
- In 304 out of 2,102 clusters (14 percent), originally sampled clusters could not be covered, in most cases due to security reasons. For 176 of these cases, clusters were replaced, bringing the number of visited clusters up to 1,926, 92 percent of the number originally planned. To the extent that the replaced and non-visited areas may have profiles different from visited areas, the final sample may give a bias in the results. This effect will have been larger at the provincial level for provinces with relatively large numbers of missing and replaced clusters, such as, respectively, Paktika, Nooristan, Urozgan, Helmand and Kapisa, Kunhara, Faryab, Paktya.
- 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.
Central Statistics Organization
Government of the Islamic Republic of Afghanistan
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
National Statistics and Information Authority (NSIA). Afghanistan- Living Conditions Survey (LCS) 2016-2017. Ref. AFG_2016_LCS_v01_M. Dataset downloaded from [URL] on [date].
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.