Throughout the Common Era, Japan's population saw relatively steady growth between each century. Failed invasions and distance from Asia's mainland meant that Japan was unaffected by many pandemics, primarily bubonic plague, therefore its development was not drastically impeded in the same way as areas such as China or Europe. Additionally, religious practices meant that hygiene was prioritized much more in Japan than in other regions, and dietary customs saw lower rates of meat consumption and regular boiling of water in meals or tea; both of these factors contributed to lower rates of infection for many parasitic or water-borne diseases. Fewer international conflicts and domestic stability also saw lower mortality in this regard, and Japan was an considered an outlier by Asian standards, as some shifting trends associated with the demographic transition (such as lower child mortality and fertility) began taking place in the 17th century; much earlier time than anywhere else in the world. Yet the most significant changes came in the 20th century, as Japan's advanced healthcare and sanitation systems saw drastic reductions in mortality. Challenges Japan's isolation meant that, when pandemics did arrive, the population had less protection and viruses could have higher mortality rates; smallpox has been cited as the deadliest of these pandemics, although increased international contact in the late 19th century brought new viruses, and population growth slowed. Earlier isolation also meant that crop failure or food shortages could leave large sections of the population vulnerable, and, as mentioned, the Japanese diet contained relatively little meat, therefore there was a higher reliance on crops and vegetables. It is believed that the shortage of arable land and the acidity of the soil due to volcanic activity meant that agriculture was more challenging in Japan than on the Asian mainland. For most of history, paddy fields were the most efficient source of food production in Japan, but the challenging nature of this form of agriculture and changes in employment trends gradually led to an increased reliance in imported crops. Post-Sakoku Japan Distance from the Asian mainland was not the only reason for Japan's isolation; from 1603 to 1853, under the Tokugawa shogunate, international trade was restricted, migration abroad was forbidden, and most foreign interaction was centered around Nagasaki. American neo-imperialism then forced Japan to open trade with the west, and Japan became an imperial power by the early-1900s. Japanese expansion began with a series of military victories against China and Russia at the turn of the century, and the annexation of Taiwan, Korea, and Manchuria by the 1930s, before things escalated further during its invasion of China and the Second World War. Despite its involvement in so many wars, the majority of conflicts involving Japan were overseas, therefore civilian casualties were much lower than those suffered by other Asian countries during this time. After Japan's defeat in 1945, its imperial ambitions were abandoned, it developed strong economic ties with the West, and had the fastest economic growth of any industrial country in the post-WWII period. Today, Japan is one of the most demographically advanced countries in the world, with the highest life expectancy in most years. However, its population has been in a steady decline for over a decade, and low fertility and an over-aged society are considered some of the biggest challenges to Japanese society today.

Urban data deficits in developing countries impede evidence-based planning and policy. Could energy data be used to overcome this challenge by serving as a local proxy for living standards or economic activity in large urban areas? To answer this question, we examine the potential of georeferenced residential electricity meter data and night-time lights (NTL) data in the megacity of Karachi, Pakistan. First, we use nationally representative survey data to establish a strong association between electricity consumption and household living standards. Second, we compare gridded radiance values from NTL data with a unique dataset containing georeferenced median monthly electricity consumption values for over 2 million individual households in the city. Finally, we develop a model to explain intra-urban variation in radiance values using proxy measures of economic activity from Open Street Map. Overall, we find that NTL data are a poor proxy for living standards but do capture spatial variation in population density and economic activity. By contrast, electricity data are an excellent proxy for living standards and could be used more widely to inform policy and support poverty research in cities in low- and middle-income countries.

Abstract

Over the past decade, Albania has been seeking to develop the framework for a market economy and more open society. It has faced severe internal and external challenges in the interim – extremely low income levels and a lack of basic infrastructure, the rapid collapse of output and inflation rise after the shift in regime in 1991, the turmoil during the 1997 pyramid crisis, and the social and economic shocks accompanying the 1999 Kosovo crisis. In the face of these challenges, Albania has made notable progress in creating conditions conducive to growth and poverty reduction.

In the process leading to its first Poverty Reduction Strategy (that is the National Strategy for Socioeconomic Development, now renamed the National Strategy for Development and Integration), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis the information it needs to inform policy-making.

Multi-purpose household surveys are one of the main sources of information to determine living conditions and measure the poverty situation of a country. They provide an indispensable tool to assist policy-makers in monitoring and targeting social programs. In its first phase (2001-2006), this monitoring system included the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys.

The Population and Housing Census (PHC) conducted in April 2001, provided the country with a much needed updated sampling frame which is one of the building blocks for the household survey structure. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a subsample of LSMS households (in 2003, and 2004), drawing heavily on the 2001 census information.

A poverty profile based on 2002 data showed that some 25 percent of the population are poor, with many others vulnerable to poverty due to their incomes being close to the poverty threshold. Income related poverty is compounded by poor access to basic infrastructure (regular supply of electricity, clean water), education and health services, housing, etc.

The 2005 LSMS was in the field between May and early July, with an additional visit to agricultural households in October, 2005. The survey work was undertaken by the Living Standards unit of INSTAT, with the technical assistance of the World Bank.

Geographic coverage

National coverage. Domains: Tirana, other urban, rural; Agro-ecological areas (coastal, central, mountain)

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Republic of Albania is divided geographically into 12 Prefectures (Prefekturat). The latter are divided into Districts (Rrethet) which are, in turn, divided into Cities (Qyteti) and Communes (Komunat). The Communes contain all the rural villages and the very small cities. For census purposes, the cities and the villages have been divided into enumeration areas (EAs).

1. Sampling frame

The Enumeration Areas (EA) that make up the sampling frame come from the April 2001 General Census of Population and Housing. The EAs in the frame are classified by Prefecture, District, City or Commune. The frame also contains, for every EA, the number of Housing Units (HU), the number of occupied HUs, the number of unoccupied HUs, the number of households, and the population. We are using occupied dwellings and not total number of dwellings since many EAs contain a large number of empty dwellings.

A detailed study of the list of census EAs shows that many have zero population. In order to obtain EAs with the minimum of 50 and the maximum of 120 occupied housing units, the EAs with zero population have been taken off the sampling frame. Since the sizes of the EAs varied from 0 to 395 HUs, the smaller EAs (with less than 50 HU) have been collapsed with geographically adjacent ones and the largest EAs (with more than 120 HU) have been split into two or more EAs. Subsequently, maps identifying the boundaries of every split and collapsed EA were prepared. Given that the 2002 LSMS has been conducted less than a year after the April 2001 census, a listing operation to update the sample EAs was not conducted in the field. However, since the level of construction is very high in the city of Tirana and its suburbs, a quick count of the 75 sample EAs selected in Tirana was carried out followed by a listing operation. The check of the listing based on the Census data revealed two types of discrepancies: - HUs had become invalid, i.e. vacant, nonresidential, demolished, seasonally occupied, etc. - Instead of one small building (with one or two HU), a new one with 15 HUs was identified.

During of the listing update process, HUs identified as invalid were taken off the frame. In the case of a new building, these new HUs were entered with a new sequential code. The listing sheets prepared during the listing operation in Tirana, become the sampling frame for the final stage of selection of 12 HU which has to be interviewed. The unit of analysis and the unit of observation is the household. The universe under study consists of all the households in the Republic of Albania. We have used the Housing Unit (defined as the space occupied by one household) as the sampling unit, instead of the household, because the HU is more permanent and easier to identify in the field.

1. Sample Size

In the LSMS the sample size is 450 EA and in each EA 8 households were selected. So the total sample size of the LSMS is 3600 households. In addition, since a certain level of nonresponse is expected, 4 reserve units were selected in each sample EA.

1. Stratification

The sampling frame has been divided in three regions (strata) 1. Coastal Area 2. Central Area 3. Mountain Area and Tirana (urban and other urban) is consider as a separate strata.

The first three strata were divided into major cities (the most important cities in the region), other urban (the rest of cities in the region), and rural. In each more importance was given to the major cities and rural areas. We have selected 10 EA for each major city and 65 EAs (75 EAs for Mountain Area) for each region. In the city of Tirana and its suburbs, implicit stratification was used to improve the efficiency of the sample design.

1. Procedure for the Selection of Housing Units

A fixed number of valid dwelling units (12) was selected systematically and with equal probability from the Listing Form pertaining to Tirana and from the Census forms for the other areas. Once the 12 HUs were selected, 4 of them were chosen at random and kept as reserve units. The selected HUs were numbered within the EA and identified with a circle around the number in the listing form, as well as a circle on the maps. The reserve sample (units 9 to 12) were identified from R1 to R4 during data collection to emphasize the fact that they were reserve units.

Two copies of the sample listing sheets and two copies of maps for each EA were printed. The first copy of the listing sheet and the map were given to the supervisor and included the 12 HU, the second copy was given to the enumerator. The enumerator only received the 8 dwelling units, not the reserve ones. Each time the enumerator needed a reserve HU, he/she had to ask the supervisor and explain the reason why a reserve unit was needed. This process helped determine the reason why reserve units were used and provided more control on their use.

In the field the enumerator registered the occupancy status of every unit: - occupied as principal residence - vacant - under construction (not occupied) - demolished or abandoned (not occupied) - seasonally occupied

In the case that one HU was found to be invalid, the enumerator used the first reserve unit (identified with the code R1). In the case that in one EA more than 4 DU selected were invalid, other units from that EA chosen at random by headquarter (in Tirana) were selected as replacement units to keep the enumerator load constant and maintain a uniform sample size in each EA. Before identifying the invalid HUs, the interviewer had to note the interview status of each visit for all the units for which an interview was attempted, whether these are original units or reserve units. This was done to determine the interview status: interview completed, nonresponse, refusal, etc. In other words, this will allow identifying: the completed interviews (responses obtained), the incomplete but usable ones (responses obtained), the incomplete ones but not usable (nonresponse), the refusals (nonresponse) and the "not at home" (nonresponse). Subsequently, the invalid units identified were substituted with the available reserves, always maintaining the sample of 8 HUs.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four survey instruments were used to collect information for the 2005 Albania LSMS: a household questionnaire, a diary for recording household food consumption, a community questionnaire, and a price questionnaire.

The household questionnaire included all the core LSMS modules as defined in Grosh and Glewwe (2000)1, plus additional modules on migration, fertility, subjective poverty, agriculture, non-farm enterprises, and social capital. Geographical referencing data on the longitude and latitude of each household were also recorded using portable GPS devices. Geo-referencing will enable a more efficient spatial link among the different surveys of the system, as well as between the survey households and other geo-referenced information.

The choice of the modules was aimed at matching as much as

Abstract

The 2021 NPHC is tthe first census conducted under the federal structure of Nepal. The main census enumeration was originally scheduled to take place over 15 days- from June 8 to 22, 2021, but due to the COVID-19 pandemic, the enumeration was postponed for five months. Once the impact of the pandemic subsided, the enumeration was carried out according to a new work plan for a 15 dya period from November 11 to 25, 2021.

This report contains statistical tables at the national, provincial, district and municipal levels, derived from the topics covered in the census questionaires. The work of the analyzing the data in detail is still in progress. The report provides insights into the different aspects of the census operation, including its procedure, concepts, methodology, quality control, logistics, communication, data processing, challenges faced, and other management aspects.

This census slightly differs from the previous censuses mainly due to the following activities: i. three modes of data collection (CAPI, PAPI and e-census); ii. a full count of all questions instead of sampling for certain questions, as was done in the previous two censuses, iii. collaboration with Ministry of Health and Population to ascertain the likely maternal mortality cases reported in the census by skilled health personnel; iv. data processing within its premises; v. recuitment of fresh youths as supervisor and enumerators; and vi. using school teachers as master trainers, especially for the local level training of enumerators.

The objectives of the 2021 Population Census were:

a) to develop a set of benchmark data for different purposes. b) to provide distribution of population by demographic, social and economic characteristics. c) to provide data for small administrative areas of the country on population and housing characteristics. d) to provide reliable frames for different types of sample surveys. e) to provide many demographic indicators like birth rates, death rates and migration rates. f) to project population for the coming years.

The total population of Nepal, as of the census day (25 November 2021) is 29,164,578, of which the number of males is 14,253,551 (48.87 %) and the number of females is 14,911,027 (51.13 %). Accordingly, the sex ratio is 95.59 males per 100 females. Annual average population growth rate is 0.92 percent in 2021.

Geographic coverage

National Level, Ecological belt, Urban and Rural, Province, District, Municipality, Ward Level

Analysis unit

The census results provide information up to the ward level (the lowest administrative level of Nepal), household and indivisual.

Universe

The census covered all modified de jure household members (usual residents)

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f] and online

Research instrument

In this census three main questionnaires were developed for data collection. The first was the Listing Form deveoped mainly for capturing the basic household informatioin in each Enumeration area of the whole country. The second questionnaire was the main questionnaire with eight major Sections as mentioned hereunder.

Listing Questionaire Section 1. Introduction Section 2. House information Section 3. Household information Section 4. Agriculture and livestock information Section 5. Other information

Main Questionaire Section 1. Introduction Section 2. Household Information Section 3. Individual Information Section 4. Educational Information Section 5. Migration Section 6. Fertility Section 7.Disability Section 8. Economic Activity

For the first time, the NPHC, 2021 brougt a Community Questionnaire aiming at capturing the socio-economic and demographic characteristics of the Wards (the lowest administrative division under Rural/Urban Municipalities). The Community Questionnaire contains 6 Chapters. The information derived from community questionnaire is expected to validate (cross checks) certain information collected from main questionnaire.

Community questionaire Section 1. Introduction Section 2. Basic information of wards Section 3. Caste and mother tongue information Section 4. Current status of service within wards Section 5. Access of urban services and facilities within wards Section 6. Status of Disaster Risk

It is noteworty that the digital version of questionnare was applied in collecting data within the selected municipalities of Kathmandu Valley. Enumerators mobilized in Kathmandu Valley were well trained to use tablets. Besides, online mode of data collection was adpoted for all the Nepalese Diplomatic Agencies located abroad.

Cleaning operations

For the concistency of data required logics were set in the data entry programme. For the processing and analysis of data SPSS and STATA programme were employed.

In 2025, there are six countries, all in Sub-Saharan Africa, where the average woman of childbearing age can expect to have between 5-6 children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of almost six children per woman, Chad is the country with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

DHIS2 is a web-based platform primarily used in developing countries, ensuring reliable data and aiding decentralized decision-making. The Ministry of Health and Population has greatly emphasized using DHIS2 for data entry and reporting. However, studies regarding health workers’ experiences on DHIS2 and the utilization of data at the local level remain limited. Therefore, this study aims to investigate the usage and practical experience of DHIS2 at the local levels of Gandaki province, Nepal. An exploratory qualitative study was conducted in the Gandaki province from February to August 2023. We conducted twenty in-depth interviews among the DHIS2 users at local levels, health posts, and provincial health directorate using in-depth interview guidelines. The study participants were selected purposively. Thematic analysis was conducted to analyze the data, and NVivo was used to facilitate data analysis. Health professionals demonstrated dedication and commitment to use DHIS2 for reporting. DHIS2 has facilitated timely reporting, data storage, data analysis and visualization, feedback and communication mechanisms, and service delivery. Users’ self-motivation and support from the local and provincial levels and regular review and program-specific review meetings were major facilitators for DHIS2 use. Similarly, technical issues, poor internet connectivity, power outages, and inexperienced health professionals were the significant challenges to using DHIS2. The basic and refresher training needed improvement at all levels, and learning materials were unavailable in health facilities. In addition, the data utilization at the local level in various actions was unsatisfactory despite sufficient data. Health professionals have been facilitated by DHIS2 in various actions. Capacity building of health professionals on data analysis and interpretations, continued onsite coaching, reliable internet connectivity, availability of learning materials, and improved server capacity are needed to enhance the performance of DHIS2 at the local level.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

Abstract

Jamaica conducted its Fourteenth Census of Population and Housing in 2011. Under the provisions of the Statistics Act, the Statistical Institute of Jamaica (STATIN) is vested with the authority to conduct any census in Jamaica. The census plays an essential role in all elements of the national statistical system, including the economic and social components. Census statistics are used as benchmarks for statistical compilation or as a sampling frame for household sample surveys. The national statistical system of almost every country relies on sample surveys for efficient and reliable data collection. Without the sampling frame derived from the population and housing census, the national statistical system would face difficulties in providing current reliable official statistics.

While recognizing the importance of the census however, countries are faced with serious resource constraints. Census taking in Jamaica faces not only the challenge of limited resources but an apathetic public which views official data collection with suspicion and even hostility. Despite a vibrant publicity programme for Census 2011, the level of cooperation particularly in some urban centres was disappointing. Worker attitude also presented problems as in a number of cases workers had to be relieved of their duties due to poor and or unproductive work. There was not always sufficient recognition of the fact that remuneration was for work done.

Geographic coverage

National coverage

Analysis unit

• Individuals;
• Household.

Universe

The 2011 census, like all since 1943, was conducted on a 'de jure' basis. The 'de jure' count includes all persons, Jamaicans and non-Jamaicans whose usual place of residence was in Jamaica even if they were temporarily (less than six months) abroad at the time of the census.

The following groups were excluded: (i) All Jamaicans (including diplomatic personnel) who were away from the country for six months or more; (ii) All visitors to Jamaica who are usual residents of other countries; and (iii) All foreign diplomatic personnel located in Jamaica.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The census design included a Post Enumeration Survey planned as a coverage assessment tool. The Post Enumeration Survey was conducted during the period September 7-30, 2011 in all parishes and covered a 5 per cent sample of census EDs. A total of 286 EDs were eventually canvassed.

Jamaica was divided into 5,776 geographic units called enumeration districts (EDs) for the purpose of data collection during the 2011 Population and Housing Census. Each ED is an independent unit which shares common boundaries with contiguous EDs. The number of dwellings/households contained in the ED (estimated before the census) was the primary determination of the size of an ED. This was approximately 150 dwellings/households in urban areas and 100 in rural areas. Each ED was designed to be of a size that would ensure an equitable work load for each census taker, and because dwellings are more widely spaced in rural areas than in urban areas, rural EDs usually contained fewer dwellings/households than their urban counterpart. When grouped together, enumeration districts reconstitute larger divisions; special area, constituency and the parish.

Mode of data collection

Face-to-face [f2f]

Research instrument

One questionnaire was developed for use in the census to collect information from individuals and one for households. The topics included on the census questionnaire were as follows:

Individual: - Age - Sex - Relationship to Head of Household - Religious Affiliation - Ethnic origin - Marital and Union Status - Educational Attainment - Physical and Mental Limitations - Birthplace and Residence - Training - Economic Activity and Social Welfare - Fertility - Transportation - Information and Communication Technology.

Household: - Type of Unit - Material of outer walls - Material of Roofing - Number of rooms - Tenure of Land and Dwelling - Availability and Type of Kitchen, Bathroom and Toilet Facilities - Method of Disposal of Solid Waste - Source of Water for Domestic Use - Source of Drinking Water - Type of Lighting - Type of Fuel used for Cooking - Availability of Telephone and other Communication Devices and facilities - Migration and Mortality.

Cleaning operations

The data collecting method utilised was the "interviewer Method" One census taker was assigned to each enumeration district (to be defined) to list every building in the area assigned. Where the building was found to be the living quarters of an individual or a group of individuals the form was completed for each household and each person. Each census taker worked with a household form and an individual form. Consideration was also given to the enumeration of persons who live in institutions as well as persons who were located on the streets and this was taken into account in the design.

The processing of the census returns is a massive undertaking for which STATIN sought to utilize modern technology for this phase. The data processing of the questionnaires was out-sourced to XSOMO International Ltd., who was required to produce the electronic data in a database format and images of the questionnaires. Scanning of the forms which began in June 2011 ended on January 31, 2012. The data editing and cleaning were done using software developed internally and shared via the intranet. The validity and consistency checks which followed have been completed for those variables which have been included in this report. A full and clean database, from which tables on all census topics will be produced, is expected by December 2012.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely designed modules in the Employer Survey aim to assess the structure of the labor force; the skills (cognitive skills, behavior and personality traits, and job-relevant skills) currently being used; the skills that employers look for when hiring new workers; the propensity of firms to provide training (including satisfaction with education, training, and levels of specific skills) and the link between skills and compensation and promotion. The survey also captures background characteristics (size, legal form, industry, full time vs. non-standard employment and occupational breakdown), performance (revenues, wages and other costs, profits and scope of market), key labor market challenges and their ranking relative to other challenges, and job skill requirements of the firms being interviewed.

The questionnaire can be adapted to address a sample of firms in both informal and formal sectors, with varying sizes and industry classifications.

Geographic coverage

Capital Belgrade and other urban areas.

Analysis unit

The units of analysis are establishments or workplaces - a single location at which one or more employees work. The larger legal entity may include multiple establishments. The firms on the list will have been randomly chosen, with probability proportional to the number of employees in the firm.

Universe

The universe of the study are non-government businesses registered with Serbian Business Register Agency from 2013, with at least five employees from the following sectors: Manufacturing, Trade and Other Services.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling objective of the survey was to obtain interviews from 1000 non-government enterprise workplaces in the capital and urban regions of Serbia. Firms with less than five employees were excluded from the target population.

Two-stage stratified random sampling was used in the survey. A list of businesses registered with Serbian Business Register Agency from 2013 served as the sampling frame.

Detailed information about sampling is available in the Serbia Employer Survey Design Planning Report and Serbia Employer Survey Weighting Procedure, provided as Related Material.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire for the STEP Employer Survey consists of five modules:

Section 1 - Work Force Section 2 - Skills Used Section 3 - Hiring Practices Section 4 - Training and Compensation Section 5 - Background

In the case of Serbia, the questionnaire was adapted to the Serbian context and published in English and Serbian. It has been provided as Related Material.

Cleaning operations

STEP Data Management Process:

1) Raw data is sent by the survey firm

2) The World Bank (WB) STEP team runs data checks on the Questionnaire data. Comments and questions are sent back to the survey firm.

3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data.

4) The WB STEP team again check to make sure the data files are clean. This might require additional iterations with the survey firm.

5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies.

Response rate

An overall response rate of 48% was achieved in Serbia STEP Survey. Detailed distribution of responses by stratum can be found in the document Serbia Employer Survey Weighting Procedure, available as Related Material.

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.

Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.

Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.

Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet

Liberia is one of the poorest countries, ranking 180th out of the 190 countries in the World Bank’s development database. Based on the national poverty line, 59 percent of Liberians were poor in 2016, the latest year for which household survey data is available. According to World Bank estimations, about 6 out of 10 Liberians continue to live in poverty. Broader welfare measures tell a similar story: Liberia ranked 177th out of 193 countries on the UN Human Development Index and the UN Gender Inequality Index in 2022. Low human development is exemplified by Liberia’s score of 0.32 on the World Bank’s measure of human capital, suggesting that a newborn child will only reach 32 percent of their potential productivity as an adult under current conditions of healthcare and education. Poverty is more prevalent in rural areas, and its incidence increases with distance from the capital, Monrovia, highlighting Liberia’s severe spatial challenges. Rapid population growth, deforestation, and the accelerating impacts of climate change are degrading the country’s abundant natural capital, a dynamic which, in turn, is increasingly tied to the persistence of poverty. Pervasive food insecurity contributes to the high rate of child stunting and to malnutrition more generally. Inadequate sanitation heightens the risk of communicable disease.

Abstract

The Survey on Income and Living Conditions, introduced as part of the European Union harmonisation efforts, aims to produce data on income distribution, relative poverty by income, living conditions and social exclusion comparable with European Union member states. The study which uses a panel survey method is repeated every year and monitors sample of household members for four years. Every year, the study attempts to obtain two datasets: cross-sectional and panel.

The Income and Living Conditions Survey 2012 has been conducted to provide annual and regular cross-sectional data to answer questions such as:

• How equally is the income in the country distributed and how has it changed as compared to the previous years?
• How many poor people are there in the country and how do they distribute across regions? How has this situation changed as compared to the previous years?
• Who is poor? Has there been a change over time?
• How has this gap between the poor and the rich evolved over time?
• What kind of a change or transition occurs in the incomes of individuals and households? How does the direction of this change depends on characteristics and circumstances, does it decline or grow?
• How is the income distributed across sectors, types of income and household characteristics?
• How do people's living conditions change or improve over time?
• The study also aims to provide panel data to calculate indicators such as persistent income poverty and to measure net changes over time.

The cross-sectional database 2012 is documented here.

Geographic coverage

All settlements within the borders of the Republic of Turkey have been included.

Universe

All household members living in households within the borders of the Republic of Turkey. However, the study excludes the population defined as institutional population living in hospices, elderly homes, prisons, military barracks, private hospitals and in childcare centres. Migrant population has also been excluded due to practical challenges.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling method: Stratified, multi-stage, clustered sampling.

Sampling unit: Household.

Sampling framework: Sampling framework has been derived from 2 sources:

1. For the settlements with municipal status; General Building Census conducted in 2000 by TurkStat and Numbering Study (conducted in 2000) Form Population 1 data have been used.
2. For the settlements without municipal status (Villages); data of General Population Census conducted in 2000 have been used to select the blocks which constituted the sampling unit of the first stage.

Selection of sample households: for the purposes of the study which used a two-staged sampling design; entire Turkey has been divided into blocks which covered 100 households each.

• At the first stage, blocks were selected as the first stage sampling unit
• At the second stage, households were selected from among the previously selected blocks as the final sampling unit. Prior to the selection of sample households, addresses at the blocks were updated through an "address screening study"

Sample size: Annual sampling size is 13,414 households in respect of the estimation, objectives and targeted variables of the study and in consideration of the attritions in the sample.

Substitution principle: Substitution has not been used as the sample size had been calculated by taking account of non-response.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• Household registry form: The form filled at the beginning of the survey provides brief information on access to the address of the household, condition of the household and of the survey. Moreover, following the first field application, modalities are identified for filling in the monitoring forms if the households included in the panel survey move home.

• Personal registry form: These forms aim to identify basic demographic characteristics of the household members, changes that occur in the status of household membership of the individuals included in the panel survey, reasons for their leaving the household, the date of their departure etc. as well as individuals who join the household.

• Household and personal follow-up form: There is need for following up the households which have moved home and the sample individuals who have left the household to join or found another one. Household and personal follow-up forms are used to identify their new addresses and access their contact information.

• Household questionnaire: These forms attempt to collect information on the type of the occupied dwelling, status of ownership, information relating to the dwelling (number of rooms, the space actually used, heating system, dwelling facilities, goods owned etc), problems of the dwelling of the neighbourhood, status of indebtedness, rent payments, expenditures for the dwelling, the extent to which households are able to meet their general economic and basic needs and incomes earned at household level.

• Personal questionnaire: These forms attempt to collect information on education, health, employment and marital status of the household members aged 15 and over, as well as the dates of employment and incomes earned during the reference year.

Over the past decade, Albania has been seeking to develop the framework for a market economy and more open society. It has faced severe internal and external challenges in the interim – extremely low income levels and a lack of basic infrastructure, the rapid collapse of output and inflation rise after the shift in regime in 1991, the turmoil during the 1997 pyramid crisis, and the social and economic shocks accompanying the 1999 Kosovo crisis. In the face of these challenges, Albania has made notable progress in creating conditions conducive to growth and poverty reduction. In the process leading to its first Poverty Reduction Strategy (that is the National Strategy for Socioeconomic Development, now renamed the National Strategy for Development and Integration), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis the information it needs to inform policy-making. Multi-purpose household surveys are one of the main sources of information to determine living conditions and measure the poverty situation of a country. They provide an indispensable tool to assist policy-makers in monitoring and targeting social programs. In its first phase (2001-2006), this monitoring system included the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys. The Population and Housing Census (PHC) conducted in April 2001, provided the country with a much needed updated sampling frame which is one of the building blocks for the household survey structure. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a subsample of LSMS households (in 2003, and 2004), drawing heavily on the 2001 census information. A poverty profile based on 2002 data showed that some 25 percent of the population are poor, with many others vulnerable to poverty due to their incomes being close to the poverty threshold. Income related poverty is compounded by poor access to basic infrastructure (regular supply of electricity, clean water), education and health services, housing, etc. The 2005 LSMS was in the field between May and early July, with an additional visit to agricultural households in October, 2005. The survey work was undertaken by the Living Standards unit of INSTAT, with the technical assistance of the World Bank.

Abstract

The Survey on Income and Living Conditions, introduced as part of the European Union harmonisation efforts, aims to produce data on income distribution, relative poverty by income, living conditions and social exclusion comparable with European Union member states. The study which uses a panel survey method is repeated every year and monitors sample of household members for four years. Every year, the study attempts to obtain two datasets: cross-sectional and panel.

The Income and Living Conditions Survey 2009 has been conducted to provide annual and regular cross-sectional data to answer questions such as:

• How equally is the income in the country distributed and how has it changed as compared to the previous years?
• How many poor people are there in the country and how do they distribute across regions? How has this situation changed as compared to the previous years?
• Who is poor? Has there been a change over time?
• How has this gap between the poor and the rich evolved over time?
• What kind of a change or transition occurs in the incomes of individuals and households? How does the direction of this change depends on characteristics and circumstances, does it decline or grow?
• How is the income distributed across sectors, types of income and household characteristics?
• How do people's living conditions change or improve over time?
• The study also aims to provide panel data to calculate indicators such as persistent income poverty and to measure net changes over time.

The cross-sectional database 2009 is documented here.

Geographic coverage

All settlements within the borders of the Republic of Turkey have been included.

Universe

All household members living in households within the borders of the Republic of Turkey. However, the study excludes the population defined as institutional population living in hospices, elderly homes, prisons, military barracks, private hospitals and in childcare centres. Migrant population has also been excluded due to practical challenges.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling method: Stratified, multi-stage, clustered sampling.

Sampling unit: Household.

Sampling framework: Sampling framework has been derived from two sources:

1. For the settlements with municipal status; General Building Census conducted in 2000 by TurkStat and Numbering Study (conducted in 2000) Form Population 1 data have been used.
2. For the settlements without municipal status (Villages); data of General Population Census conducted in 2000 have been used to select the blocks which constituted the sampling unit of the first stage.

Selection of sample households: for the purposes of the study which used a two-staged sampling design; entire Turkey has been divided into blocks which covered 100 households each.

• At the first stage, blocks were selected as the first stage sampling unit
• At the second stage, households were selected from among the previously selected blocks as the final sampling unit. Prior to the selection of sample households, addresses at the blocks were updated through an "address screening study"

Sample size: Annual sampling size is 13,414 households in respect of the estimation, objectives and targeted variables of the study and in consideration of the attritions in the sample.

Substitution principle: Substitution has not been used as the sample size had been calculated by taking account of non-response.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• Household registry form The form filled at the beginning of the survey provides brief information on access to the address of the household, condition of the household and of the survey. Moreover, following the first field application, modalities are identified for filling in the monitoring forms if the households included in the panel survey move home.

• Personal registry form These forms aim to identify basic demographic characteristics of the household members, changes that occur in the status of household membership of the individuals included in the panel survey, reasons for their leaving the household, the date of their departure etc. as well as individuals who join the household.

• Household and personal follow-up form There is need for following up the households which have moved home and the sample individuals who have left the household to join or found another one. Household and personal follow-up forms are used to identify their new addresses and access their contact information.

• Household questionnaire These forms attempt to collect information on the type of the occupied dwelling, status of ownership, information relating to the dwelling (number of rooms, the space actually used, heating system, dwelling facilities, goods owned etc), problems of the dwelling of the neighbourhood, status of indebtedness, rent payments, expenditures for the dwelling, the extent to which households are able to meet their general economic and basic needs and incomes earned at household level.

• Personal questionnaire These forms attempt to collect information on education, health, employment and marital status of the household members aged 15 and over, as well as the dates of employment and incomes earned during the reference year.

Link to this report's codebookExecutive SummaryThe world is still in the midst of the worst public health crisis in a century. Mobility restriction measures taken to respond to the COVID-19 threat have led to a global economic crisis, with massive job losses and major impacts amounting to a significant setback in the world’s progress towards achieving the SDGs, especially for poor countries and vulnerable population groups. In line with SDG 3 (Good Health and Well-Being), all countries need to strengthen the resilience of their health systems and their disease and pandemic prevention programs. Besides greater investments, the crisis has highlighted the need for better measurement and reporting to track disease and pandemic prevention programs, healthcare system preparedness, and resilience to pandemics.This report presents a special edition of the SDG Index and Dashboards, in which Uruguay is benchmarked against OECD countries using a specific set of SDG indicators available for these countries. Due to time lags in data generation and reporting, however, the SDG Index and Dashboards for Uruguay do not reflect the impact of COVID-19. The projection of country trajectories based on recent progress (business-as-usual scenarios) may therefore not provide a realistic sense of the likely future, as COVID-19 is likely to alter trajectories relating to many SDGs. Nevertheless, the Index and Dashboards remain useful for understanding, goal by goal, the progress of Uruguay compared to these other countries. The SDG data and the Six Transformations Framework presented in this report help to identify the key vulnerabilities and challenges that Uruguay was facing before the COVID-19 crisis and provide a useful framework to inform its long-term recovery from COVID-19.Uruguay ranks 30th of the 39 countries covered in this special edition. Its overall score is, however, above the average for OECD countries in the Latin America and Caribbean region and only slightly below the population-weighted average of OECD countries overall. Uruguay performs well and is showing progress on most of the socio-economic goals (SDGs 1–10) although progress is lagging on SDG 4 (Quality Education), SDG 9 (Industry, Innovation and Infrastructure) and SDG 10 (Reduced Inequalities). As with other OECD countries, and particularly the OECD countries in the Latin America and Caribbean region, further effort is needed to meet goals related to sustainable consumption and production, or to climate and biodiversity (SDGs 12 to 15), and to address governance and security issues covered under SDG 16 (Peace, Justice and Strong Institutions).As part of its commitment to the 2030 Agenda, Uruguay has already submitted four voluntary national reviews to the UN High Level Political Forum: in 2017, 2018, 2019 and 2021. Incorporating exhaustive statistical data, these comprehensive reports show Uruguay’s progress on the 17 SDGs and provide detailed information on regulatory frameworks and specific actions contributing to progress towards each goal. The government’s recent submission of the 2021 voluntary national review, which incorporates the results in this report, presents an opportunity to reinforce Uruguay’s commitment to the 2030 Agenda by defining strategies to address remaining challenges and further accelerate progress.Reliable, relevant and timely information is essential to successfully align national strategies to the SDGs: to identify priorities, mobilize resources, measure results and ensure transparency. Uruguay must encourage and advance the strategic use of data and digital technologies towards improving its policies for sustainable development.Achieving the SDGs requires closing the financing gap. The private sector plays a key role in mobilizing resources for sustained economic growth and contributing to social inclusion and environmental protection. The private sector contributes directly to SDG 12 (Responsible Consumption and Production) and indirectly, through its actions and financing, to the achievement of all 17 SDGs. Uruguay has already started to move in this direction, initiating the country’s first private issuance of green bonds to finance sustainable investment portfolios. Uruguay’s Central Bank has now joined the Network for Greening the Financial System, and the Uruguayan Private Banks Association has established a sustainability committee to accelerate the transition towards sustainable finance in the banking system.

There are approximately 8.16 billion people living in the world today, a figure that shows a dramatic increase since the beginning of the Common Era. Since the 1970s, the global population has also more than doubled in size. It is estimated that the world's population will reach and surpass 10 billion people by 2060 and plateau at around 10.3 billion in the 2080s, before it then begins to fall. Asia When it comes to number of inhabitants per continent, Asia is the most populous continent in the world by a significant margin, with roughly 60 percent of the world's population living there. Similar to other global regions, a quarter of inhabitants in Asia are under 15 years of age. The most populous nations in the world are India and China respectively; each inhabit more than three times the amount of people than the third-ranked United States. 10 of the 20 most populous countries in the world are found in Asia. Africa Interestingly, the top 20 countries with highest population growth rate are mainly countries in Africa. This is due to the present stage of Sub-Saharan Africa's demographic transition, where mortality rates are falling significantly, although fertility rates are yet to drop and match this. As much of Asia is nearing the end of its demographic transition, population growth is predicted to be much slower in this century than in the previous; in contrast, Africa's population is expected to reach almost four billion by the year 2100. Unlike demographic transitions in other continents, Africa's population development is being influenced by climate change on a scale unseen by most other global regions. Rising temperatures are exacerbating challenges such as poor sanitation, lack of infrastructure, and political instability, which have historically hindered societal progress. It remains to be seen how Africa and the world at large adapts to this crisis as it continues to cause drought, desertification, natural disasters, and climate migration across the region.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

As of February 2025, India was the country with the largest offline population worldwide. The South Asian country had over 651 million people without internet connection. China ranked second, with around 311.9 million people not connected to the internet. Despite these large shares of the disconnected population in these countries, China and India ranked first and second, respectively, as countries with the highest number of internet users worldwide. Internet access in Africa In 2023, Africa lagged behind other global regions regarding internet penetration rate, as only 37 percent of the continent’s population accessed the web. In contrast, around 91 percent of Europe’s population were internet users. This is heavily influenced by the infrastructure development in the region. However, some improvements are forecasted, as by 2028, the internet penetration rate in Africa will be at an estimated 48.15 percent. Global internet access challenges: disruptions and restrictions Government internet shutdowns around the world are another challenge for internet access. Between 2015 and the first half of 2023, 172 local internet connection disruptions occurred due to protests globally. Moreover, according to a 2023report on internet freedom, almost four out of ten global internet users were deprived of essential freedoms on online platforms. In 2023, 76 new restrictions on internet usage were implemented worldwide. Asia led in imposing these restrictions, accounting for approximately 55 cases across various countries in the region.

In 2013, the total number of women aged 15 to 49 years worldwide was *** billion. In 2017 it was estimated that this number would increase to almost ****billion by 2025. The U.S. accounted for a small proportion of the total number of women globally in 2013 with just **** million. Global demographics In 2024, the total global population at approximately **** billion people. In 2024, the continent with the largest proportion of the global population was Asia, followed by Africa. While North America and Oceania were some of the least populated areas of the world. The age distribution of the population varies by region as well. For example, the percentage of the global population between the ages of 15 and 64 years varies between ** percent and ** percent. Women’s health worldwide Women face different health challenges depending on the region and country. One important global health issue is maternal mortality. The country with the highest maternal mortality rate in 2023 was Nigeria. Chad had the seventh-highest estimated birth rate in 2024 and was the country with the second-highest maternal mortality rate. The United States has one of the highest maternal mortality rates when compared to similarly developed countries.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely designed modules in the Employer Survey aim to assess the structure of the labor force; the skills (cognitive skills, behavior and personality traits, and job-relevant skills) currently being used; the skills that employers look for when hiring new workers; the propensity of firms to provide training (including satisfaction with education, training, and levels of specific skills) and the link between skills and compensation and promotion. The survey also captures background characteristics (size, legal form, industry, full time vs. non-standard employment and occupational breakdown), performance (revenues, wages and other costs, profits and scope of market), key labor market challenges and their ranking relative to other challenges, and job skill requirements of the firms being interviewed.

The questionnaire can be adapted to address a sample of firms in both informal and formal sectors, with varying sizes and industry classifications.

Geographic coverage

Capital Nairobi and other urban areas.

Analysis unit

The units of analysis are establishments or workplaces - a single location at which one or more employees work. The larger legal entity may include multiple establishments. The firms on the list will have been randomly chosen, with probability proportional to the number of employees in the firm.

Universe

The universe of the study are non-government businesses registered with the Kenya National Bureau of Statistics (KNBS), from 2016. Firms with at least five employees were selected from the following sectors: Manufacturing, Trade and Other Services.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling objective of the survey was to obtain interviews from 500 non-government enterprise workplaces in the capital and urban regions of Kenya. Firms with less than five employees were excluded from the target population.

Two-stage stratified random sampling was used in the survey. A list of businesses registered with the Kenya National Bureau of Statistics (KNBS) from 2016, served as the sampling frame.

Detailed information about sampling is available in the Kenya Employer Survey Design Planning Report and Kenya Employer Survey Weighting Procedure, provided as Related Material.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Questionnaire for the STEP Employer Survey consists of five modules:

Section 1 - Work Force Section 2 - Skills Used Section 3 - Hiring Practices Section 4 - Training and Compensation Section 5 - Background

In the case of Kenya, the questionnaire was adapted to the Kenya context and published in English and Swahili. It has been provided as Related Material.

Cleaning operations

STEP Data Management Process:

1) Raw data is sent by the survey firm

2) The World Bank (WB) STEP team runs data checks on the Questionnaire data. Comments and questions are sent back to the survey firm.

3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data.

4) The WB STEP team again check to make sure the data files are clean. This might require additional iterations with the survey firm.

5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies.

Response rate

An overall response rate of 72% was achieved in Kenya STEP Survey. Detailed distribution of responses by stratum can be found in the document Kenya Employer Survey Weighting Procedure, available as Related Material.