The population density in Rwanda amounted to 553.35 people in 2022. Between 1961 and 2022, the population density rose by 427.42 people, though the increase followed an uneven trajectory rather than a consistent upward trend.

Rwanda RW: Population Density: People per Square Km data was reported at 494.869 Person/sq km in 2017. This records an increase from the previous number of 483.077 Person/sq km for 2016. Rwanda RW: Population Density: People per Square Km data is updated yearly, averaging 255.367 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 494.869 Person/sq km in 2017 and a record low of 121.447 Person/sq km in 1961. Rwanda RW: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Rwanda – Table RW.World Bank: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted Average;

In 2024, the population density in Africa was 51.3 inhabitants per square kilometer. From 2000 onwards, the density of the population on the continent has increased annually. Moreover, the average number of people living within a square kilometer was expected to increase to around 58.5 by 2030. Mauritius, Rwanda, and Burundi were the African countries with the highest population density as of 2023.

In 2022, the population density in Uganda increased by seven inhabitants per square kilometer (+3.06 percent) compared to 2021. While the growth is slowing down, with 235.95 inhabitants per square kilometer, the population density is at its peak in the observed period. Notably, the population density continuously increased over the last years.Population density is calculated by dividing the total population by the total land area, to show the average number of people living there per square kilometer of land.Find more key insights for the population density in countries like Ethiopia and Rwanda.

Abstract

Child labour is a critical issue in Rwanda. This issue is considered as a socialization factor and gives rise to the problem of children’s participation in improving socioeconomic life of their respective households, and even in improving that of the country. The purpose of the National Child Labour Survey is to generate data on child labour (including education, economic and non-economic activities) and embark on the process of creating a database containing both qualitative and quantitative data on the child labour phenomenon in Rwanda.

These objectives unfold as follows: -To collect information on characteristics, nature, extent and reasons influencing childlabour in Rwanda and assess working conditions and impact on children health, education and normal development of children; -To create a database (quantitative and qualitative) on child labour which can be regularly updated through additional surveys and other administrative documents; -To carry out a comprehensive analysis of the conditions of children in employment in Rwanda by presenting the structure of activities, working conditions and their effects on children.

The 2008 Rwanda National Child Labour Survey (2008-RNCLS) focused on children aged 5-17 years living in ordinary households countrywide. Street children and those living in institutions like prisons, hospitals, children living in orphanages and others not living in ordinary households were not focused on in the survey. Only households having children aged 5 -17 years were targeted by the 2008-RNCLS. In total, 5,510 households were selected to constitute the survey sample and among them 5,084 were successfully identified and interviewed during the field survey, which is a response rate of nearly 92%.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The National Child Labour Survey has covered children aged 5 to 17 years living in ordinary households countrywide.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the National Child Labour Survey is a stratified two-stage sampling. The primary sampling stage is a cluster which is constituted of one enumeration block (EB) as it was designed in 2002 Rwanda General Census of Population and Housing.

At first stage, enumeration areas constructed on the basis of the 2002 Rwanda General Census of Population and Housing served as sampling frame for the drawing of lots of a sample of 300 enumeration blocks or clusters with the distribution of 10 enumeration blocks per District. The drawing of lots has been carried out with a proportional probability to the size of each cluster. The size has been measured on the basis of the number of households having children aged 5 to 17 years that constitute each enumeration block, according to the 2002 Rwanda General Census of Population and Housing. To pick those households, one has considered the households which had children under 13 years old in 2002.

For the second stage where the sampling unit was a household, a household list was first updated in each EB selected at primary stage and then a constant number of households have been selected from the number of households that have children aged from 5 to 17 years, identified during households listing in each EB.

Sampling deviation

No deviation from the sapmling design.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire comprises of three main parts. The first part covers all household members and allows to collect general information on their socio-demographic characteristics, their economic activities and on perceptions of parents or guardians of children about child labour.

While the second part is about household characteristics and allow to collect information on housing and accommodation.

The third part focuses only on children and provides information on their education and their working status. It also provides information on the health and welfare of children in employment as well as their safety in the workplace. The questionnaire is appended to this report.

Cleaning operations

The coding of the 2008-RNCLS questionnaires was carried out from 23rd to 29th, July 2008. Previously, a cross checking of filled questionnaires was done from the first filled questionnaires received and was pursued during the coding.

Response rate

The sample of the 2008-RNCLS initially comprised of 5,510 households. Following some non response cases, the number of households which were interviewed was lower than had been anticipated in the sample. Non-response cases were due to many reasons including the lack of household location, the change of location of a household, the absence of household 13 members for interview, etc. In total 5,085 households were visited and interviewed during the 2008-RNCLS, representing a response rate of 92.3%.

Data appraisal

A series of data quality tables and graphs are available to review the quality of the data and include the following: - NUMBER OF ENUMERATION BLOCS AND HOUSEHOLDS SELECTED BY PROVINCE........................................................................................................................................................................11 - DISTRIBUTION OF SURVEYED POPULATION BY SEX AND AGE GROUP. ..................................................................................................................................................................................................14 - DISTRIBUTION OF SURVEYED POPULATION UNDER 18 YEARS OLD BY SEX AND AGE. ....................................................................................................................................................................16 - DISTRIBUTION OF HOUSEHOLDS (IN %) BY PROVINCE ACCORDING TO HOUSEHOLD SIZE ........ ...................................................................................................................................................17 - DISTRIBUTION OF HOUSEHOLDS BY ECONOMIC SECTORS OF HEADS OF HOUSEHOLD..................................................................................................................................................................18 - DISTRIBUTION OF HOUSEHOLDS BY PROVINCES ACCORDING TO WELFARE QUINTILE....................................................................................................................................................................19 - DISTRIBUTION OF CHILDREN AGED 5 - 17 YEARS BY STATUS OF ACTIVITY...........................................................................................................................................................................................22 - ACTIVITY STATUS OF CHILDREN AGED 5 - 17 YEARS BY SEX AND PROVINCE ...................................................................................................................................................................................23 - NUMBER AND PERCENTAGE OF CHILDREN AGED 7 - 17 YEARS ATTENDING SCHOOL ...................................................................................................................................................................25 - PERCENTAGE OF CHILDREN INVOLVED IN HOUSEHOLD CHORES BY AGE, SEX AND PROVINCE ................................................................................................................................................25 - AVERAGE NUMBER OF WORKING HOURS IN HOUSEHOLD CHORES FOR CHILDREN AGED 7-17 YEARS BY SEX, AGE GROUP AND SSHOOL ATTENDANCE...................................29 - NUMBER AND PERCENTAGE OF CHILDREN IN EMPLOYMENT AGED 5-17 YEARS BY SECTORS OF ECONOMIC ACTIVITIES AND PROVINCE ……...................................................... 30
- DISTRIBUTION (IN %) OF CHILDREN IN EMPLOYMENT BY SECTORS OF ACTIVITY AND SEX ............................................................................................................................................................30 - NUMBER AND PERCENTAGE OF CHILDREN IN EMPLOYMENT AGED 5-17 YEARS BY STATUS IN EMPLOYMENT....................................................................................................................31
- HOURS WORKED PER WEEK BY CHILDREN IN EMPLOYMENT BY STATUS OF ACTIVITY, SEX, AGE GROUP AND PROVINCE ...............................................................................................30 - NUMBER AND PERCENTAGE OF CHILD LABOURERS AGED 5-17 YEARS BY SEX, AGE GROUP AND PROVINCE .....................................................................................................................34 - NUMBER AND PERCENTAGE OF CHILDREN AGED 5-17 YEARS CARRYING OUT HAZARDOUS WORK BY SEX, AGE GROUP AND PROVINCE ................................................................36 - NUMBER AND PERCENTAGE OF CHILD LABOURERS AGED 5-17 YEARS BY BRANCHES OF ACTIVITIES, AGE GROUP, AND SEX......................................................................................38 - NUMBER AND PERCENTAGE OF CHILDREN AGED 5-17 YEARS INVOLVED IN HAZARDOUS WORK BY BRANCH OF ACTIVITIES, AGE GROUP, AND SEX..........................................40 - NUMBER AND PERCENTAGE OF CHILD LABOURERS BY STATUS IN EMPLOYMENT............................................................................................................................................................................42 - WORKPLACE OF CHILD LABOURERS BY SEX AND AGE GROUP

BackgroundEvaluations of health systems strengthening (HSS) interventions using observational data are rarely used for causal inference due to limited data availability. Routinely collected national data allow use of quasi-experimental designs such as interrupted time series (ITS). Rwanda has invested in a robust electronic health management information system (HMIS) that captures monthly healthcare utilization data. We used ITS to evaluate impact of an HSS intervention to improve primary health care facility readiness on health service utilization in two rural districts of Rwanda.MethodsWe used controlled ITS analysis to compare changes in healthcare utilization at health centers (HC) that received the intervention (n = 13) to propensity score matched non-intervention health centers in Rwanda (n = 86) from January 2008 to December 2012. HC support included infrastructure renovation, salary support, medical equipment, referral network strengthening, and clinical training. Baseline quarterly mean outpatient visit rates and population density were used to model propensity scores. The intervention began in May 2010 and was implemented over a twelve-month period. We used monthly healthcare utilization data from the national Rwandan HMIS to study changes in the (1) number of facility deliveries per 10,000 women, (2) number of referrals for high risk pregnancy per 100,000 women, and (3) the number of outpatient visits performed per 1,000 catchment population.ResultsPHIT HC experienced significantly higher monthly delivery rates post-HSS during the April-June season than comparison (3.19/10,000, 95% CI: [0.27, 6.10]). In 2010, this represented a 13% relative increase, and in 2011, this represented a 23% relative increase. The post-HSS change in monthly rate of high-risk pregnancies referred increased slightly in intervention compared to control HC (0.03/10,000, 95% CI: [-0.007, 0.06]). There was a small immediate post-HSS increase in outpatient visit rates in intervention compared to control HC (6.64/1,000, 95% CI: [-13.52, 26.81]).ConclusionWe failed to find strong evidence of post-HSS increases in outpatient visit rates or referral rates at health centers, which could be explained by small sample size and high baseline nation-wide health service coverage. However, our findings demonstrate that high quality routinely collected health facility data combined with ITS can be used for rigorous policy evaluation in resource-limited settings.

The population density in Seychelles increased by 44.8 inhabitants per square kilometer (+20.76 percent) compared to the previous year. With 260.6 inhabitants per square kilometer, the population density thereby reached its highest value in the observed period. Notably, the population density continuously increased over the last years.Population density is calculated by dividing the total population by the total land area, to show the average number of people living there per square kilometer of land.Find more key insights for the population density in countries like Rwanda and Kenya.

In 2023, the share of urban population in Tanzania increased by 0.7 percentage points (+1.91 percent) compared to 2022. Therefore, the share in Tanzania reached a peak in 2023 with 37.41 percent. Notably, the share continuously increased over the last years.A population may be defined as urban depending on the size (population or area) or population density of the village, town, or city. The urbanization rate then refers to the share of the total population who live in an urban setting. International comparisons may be inconsistent due to differing parameters for what constitutes an urban center.Find more key insights for the share of urban population in countries like Seychelles and Rwanda.

Mauritius had the highest population density level in Africa as of 2023, with nearly *** inhabitants per square kilometer. The country has also one of the smallest territories on the continent, which contributes to the high density. As a matter of fact, the majority of African countries with the largest concentration of people per square kilometer have the smallest geographical area as well. The exception is Nigeria, which ranks among the largest territorial countries in Africa and is very densely populated at the same time. After all, Nigeria has also the largest population on the continent.