On 1 January 2016, the world officially began implementation of the 2030 Agenda for Sustainable Development—the transformative plan of action based on 17 Sustainable Development Goals—to address urgent global challenges over the next 15 years. The Sustainable Development Goals Database in UNdata presents data for the global SDG indicators that were compiled through the UN System in preparation for the Secretary-Generals annual report on “Progress towards the Sustainable Development Goals.” The data series respond to the revised global indicator framework that was agreed by the Statistical Commission at its forty-eighth session in March 2017. The database contains SDG indicator series and additional indicator series. The list of SDG indicators is subject to refinement by the United Nations Statistical Commission.

Abstract

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.

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 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.

Sampling deviation

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.

Mode of data collection

Face-to-face [f2f]

Research instrument

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.

Cleaning operations

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.

Response rate

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.

Sampling error estimates

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

In order to assist countries in measuring their SDG baselines and to measure future progress, the Bertelsmann Stiftung and the Sustainable Development Solutions Network (SDSN) jointly released the first SDG Index and Dashboards in July 2016. This report aims to achieve four main objectives: 1. Establish SDGs as a useful, operational tool for policy action. 2. Support national debates on prioritization and formulation of SDG implementation strategies. 3. Complement efforts to develop a robust SDG monitoring framework by the UN Statistical Commission. 4. Identify SDG data gaps, need for investments in statistical capacity and research, and new forms of data. The SDG Index and Dashboards is not officially endorsed by the UN National Assembly. http://sdgindex.org/assets/files/2018/Methodological%20Paper_v1_gst_jmm_Aug2018_FINAL_rev10_09.pdf

Total SDG Index scores are based on a slightly different basket of indicators for OECD countries & HICs compared with other countries. In order to assist countries in measuring their SDG baselines and to measure future progress, the Bertelsmann Stiftung and the Sustainable Development Solutions Network (SDSN) jointly released the first SDG Index and Dashboards in July 2016. This report aims to achieve four main objectives: 1. Establish SDGs as a useful, operational tool for policy action. 2. Support national debates on prioritization and formulation of SDG implementation strategies. 3. Complement efforts to develop a robust SDG monitoring framework by the UN Statistical Commission. 4. Identify SDG data gaps, need for investments in statistical capacity and research, and new forms of data. The SDG Index and Dashboards is not officially endorsed by the UN National Assembly. http://sdgindex.org/assets/files/2018/Methodological%20Paper_v1_gst_jmm_Aug2018_FINAL_rev10_09.pdf

The Travel Survey of Residents of Canada (TSRC) is a major source of data used to measure the size and status of Canada's tourism industry. It was developed to quantify the volume, the characteristics and the economic impact of domestic travel. For the system of national accounts, TSRC measures the size of domestic travel in Canada from the demand side. Since the beginning of 2005, the Travel Survey of Residents of Canada (TSRC) has been conducted to measure domestic travel in Canada. It replaces the Canadian Travel Survey (CTS). Featuring several definitional changes and a new questionnaire, this survey provides estimates of domestic travel that are more in line with the international guidelines recommended by the World Tourism Organization (WTO) and the United Nations Statistical Commission. In 2011, TSRC underwent a redesign. The Travel Survey of Residents of Canada is sponsored by Statistics Canada, the Canadian Tourism Commission, and the provincial governments. It measures the size of domestic travel in Canada from the demand side. The objectives of the survey are to provide information about the volume of trips and expenditures for Canadian residents by trip origin, destination, duration, type of accommodation used, trip reason, mode of travel, etc.; to provide information on travel incidence and to provide the socio-demographic profile of travellers and non-travellers. Estimates allow quarterly analysis at the national, provincial and tourism region level (with varying degrees of precision) on: total volume of same-day and overnight trips taken by the residents of Canada with destinations in Canada, same-day and overnight visits in Canada, main purpose of the trip/key activities on trip, spending on same-day and overnight trips taken in Canada by Canadian residents in total and by category of expenditure, modes of transportation (main/other) used on the trip, person-visits, household-visits, spending in total and by expense category for each location visited in Canada, person- and household-nights spent in each location visited in Canada, in total and by type of accommodation used, use of travel packages and associated spending and source of payment (household, government, private employer), demographics of adults that took or did not take trips, and travel party composition. The main users of the TSRC data are Statistics Canada, the Canadian Tourism Commission, the provinces, and tourism boards. Other users include the media, businesses, consultants and researchers.

ForwardWe are pleased to present the 2017 edition of the Sustainable Development Goals (SDG) Index and Dashboards that have been jointly developed by the Bertelsmann Stiftung and the Sustainable Development Solutions Network (SDSN). This year’s edition includes revised and additional metrics for the SDGs bringing the total to 99 indicators. We also introduce several refinements to the methodology and extend coverage from 149 to 157 of the 193 UN member states. Results are therefore not strictly comparable with the 2016 edition. We are grateful to the many organizations and individuals who have helped us improve the SDG Index and Dashboards.The SDGs are a universal agenda of sustainable development, calling on all nations to pursue a holistic strategy that combines economic development, social inclusion, and environmental sustainability. We are gratified that throughout the world, local and national governments are rallying around the goals, seeking ways to incorporate them into planning processes.Businesses, universities, and civil society are also recognizing that the SDGs and the Paris Climate Agreement (incorporated into the sustainable development agenda as SDG 13) require a new orientation of strategy and national planning.The purpose of the SDG Index and Dashboards is to assist countries to identify priorities for action, in order to achieve the 17 SDGs. The indicators and dashboards should help countries to pinpoint key implementation challenges and the overall index permits an assessment of progress towards the goals and a comparison with peer countries.We applaud the large number of countries stepping forward to make Voluntary National Reports on their progress in implementing the SDGs at the High-Level Political Forum. We also note that the design and implementation of the official SDG indicators is making significant progress following their formal adoption by the UN Statistics Commission. The SDG Index and Dashboards are complementary to official SDG monitoring. They are not an official product endorsed by any governments or the United Nations.Based on our scrutiny of the relevant data available for tracking the SDGs, the SDG Index and Dashboards present these data in a way that we believe and hope to be informative, insightful, and interesting for policy makers and the public.Where possible we use the official SDG indicators and fill gaps in data availability with variables published by reputable sources. We have constructed the various measures for each SDG so that they immediately indicate a country’s position on a 0-to-100 spectrum from the “worst” (score 0) to the “best” (score 100).The SDGs rightly emphasize a universal agenda that requires all countries – both rich and poor alike – to take decisive actions to support sustainable development. In this year’s report we note that development patterns of the rich countries may generate adverse “spillovers” that may hinder the ability of poorer countries’ to achieve the SDGs. For example, the high consumption levels, banking secrecy and tax havens, and weapons exports, by the rich countries may severely inhibit sustainable development in poorer and more vulnerable countries. On the other hand, international development financeby high-income donor nations also directly supports the SDGs.Many of the adverse spillovers tend to be neglected or poorly measured in official development statistics. The 2017 SDG Index and Dashboards therefore reviews the scientific and policy literature to identify the best available data for quantifying such complex spillovers. We show that there are indeed many such adverse global spillovers to consider and that they are indeed driven strongly by high-income countries. We believe that such adverse spillovers deserve much greater attention by national and international efforts to achieve the SDGs and by statistical agencies. We know that our report only is a starton such analyses and should be understood in that spirit.The SDG Index and Dashboards show that data on important SDG priorities are sometimes unavailable or out of date or not yet counted on the official list of indicators. Filling these gaps and ensuring that key measures are included among the official indicators will require improved metrics as well as more and better data. One priority for SDG implementation must therefore be to invest in strengthening data collection, choice of indicators, and statistical capacity in all countries.The 2017 SDG Index and Dashboards report generates “tough grading” for all countries, including the richest ones. We choose this approach not to be punitive or pessimistic about the prospects for dramatic improvements, but to draw attention to the most urgent SDG-related challenges facing each country for each SDG.We hope that in addition to governments, other SDG stakeholders will find this report interesting and useful. Business, civil society organizations, foundations, universities, the media, and others will all play a vital role in turning the SDGs into practical tools for explaining sustainable development, managing implementation, ensuring accountability, and reporting on progress at local, national, regional, and global levels. This report and the companion website (www.sdgindex.org) provide rich information to help inform these discussions.To support SDG implementation at local levels, the SDSN is launching a preliminary SDG Index and Dashboards for cities in the United States of America. Similar analyses can be conducted for cities and provinces elsewhere. We are also planning to work with SDSN partners to develop deeper indicators and new SDG Indices and Dashboards to focus on specific challenges in major regions around the world.In addition to the SDG Index and Dashboards report, Bertelsmann Stiftung is contributing to many SDGs with its operational and data-related work to promote social inclusion, improve education, shape democracy, advance society, promote health, vitalize culture and strengthen economies. For example, our assessment at the local level (Monitor Nachhaltige Kommune) analyzes the sustainability of German local communities. We also undertake monitoring projects on health, education, social cohesion, and governance to identify best practices.We look forward to the opportunity to improve the quality and coverage of the SDG Index and Dashboards, including ways to understand trend data. We encourage and welcome feedback on the usefulness and limitations of the SDG Index andDashboards, and advice from all parts of the global community on how the report can be made more useful and accurate in the coming years.Aart de Geus,Chairman and CEO, Bertelsmann StiftungJeffrey Sachs,Director, Sustainable Development Solutions Network