The biggest receivers of remittances in the world included India, Mexico, and China in 2023, with each country receiving several billions worth of dollars. This is according to a database that tries to model money sent internationally from one party to another. Remittances typically refer to money sent from migrant workers back home to family and friends, although there are other forms of this. Remittances can, for example, include pensioners who have a second home in a foreign country. Nevertheless, Asia Pacific is often referred to as the main receiver of remittances.

This map is part of SDGs Today. Please see sdgstoday.orgInternational migration has significant implications for countries’ economic growth, and remittances are an important factor on the economy. Typically sent by migrant workers to family and friends in their home countries, remittances are transfers of money that are often a large source of income for recipients. Remittances are comparable to international aid and represent one of the largest financial flows to developing countries, impacting both economic development and poverty alleviation. Compiled by the World Bank, this dataset measures officially-recorded remittance inflows (remittances received) per country in 2020. In 2020, the global remittance inflow was $666,223,000,000. Data is based off of the International Monetary Fund’s (IMF) Balance of Payment Statistics, which are updated annually. Remittance amounts are calculated as the sum of personal transfers, compensation of employees, and migrants’ transfers from IMF data. For some countries, remittance figures may come from central banks or other official sources.

Remittances sent to Africa went largely towards Egypt, Nigeria, and Morocco in 2021, with each country receiving at least 10 billion billions worth of dollars. This is according to a database that tries to model money sent internationally from one party to another. Remittances typically refer to money sent from migrant workers back home to family and friends, although there are other forms of this. Remittances can, for example, include pensioners who have a second home in a foreign country. Nevertheless, Asia Pacific - not Africa - is often referred to as the main receiver of remittances.

Remittances sent to Europe went largely towards France and Germany in 2021, with each country receiving at least 20 billion billions worth of dollars. This is according to a database that tries to model money sent internationally from one party to another. Remittances typically refer to money sent from migrant workers back home to family and friends, although there are other forms of this. Remittances can, for example, include pensioners who have a second home in foreign country. Nevertheless, Asia Pacific - not Europe - is often referred to as the main receiver of remittances.

This feature layer is part of SDGs Today. Please see sdgstoday.orgInternational migration has significant implications for countries’ economic growth, and remittances are an important factor on the economy. Typically sent by migrant workers to family and friends in their home countries, remittances are transfers of money that are often a large source of income for recipients. Remittances are comparable to international aid and represent one of the largest financial flows to developing countries, impacting both economic development and poverty alleviation. Compiled by the World Bank, this dataset measures officially-recorded remittance inflows (remittances received) per country in 2020. In 2020, the global remittance inflow was $666,223,000,000. Data is based off of the International Monetary Fund’s (IMF) Balance of Payment Statistics, which are updated annually. Remittance amounts are calculated as the sum of personal transfers, compensation of employees, and migrants’ transfers from IMF data. For some countries, remittance figures may come from central banks or other official sources.

Abstract

The main objective of the assignment is to contribute to the improvement of migration and remittances impact on development in sub-Saharan Africa. Some specific objectives are to provide better understandings of migration and remittances through household surveys. By enhancing the knowledge regarding migrant remittances sending or receiving, these data will be a reliable input for policy-makers. The survey will provide information on migrant and non-migrant households' socioeconomic characteristics and how they send remittances back home. Appropriate approach and methodology will be applied to carry out the survey activities for high quality data.

Geographic coverage

Ten most intensive migration provinces in Burkina Faso: Banwa, Boulgou, Boulkiemdé, Kadiogo, Namentenga, Passoré , Sanmatenga, Sourou, Tuy and Yatenga.

Analysis unit

• Household
• Individual

Universe

Households from the geographic coverage

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling approach was chosen to focus on international migration and remittances. It has ensured an adequate representation of migration density in Burkina Faso. First we used the Burkina Faso Census 2006 that includes questions on demographic and migration. Then we calculated the incidence of international migration for each of the 45 provinces in Burkina Faso. We chose the largest 10 provinces which have the highest international migration incidence. The universe for this survey is the households that are placed in these provinces. After chosen the universe of households, we have adopted a two stages sampling: i) village/town sector and ii) household. Migration scope is different depending on the province or rural/urban area in Burkina Faso. We determine the number of village to be selected in each province according to the international migration incidence of each province. In total we selected 78 villages out of 2,273 villages for the 10 provinces.

Village town sector selection

The first stage of sampling consisted of villages and towns selection. We randomly selected the village/town sector within each province with equal probability. We have considered a village/town sector sample size of 27 households. We distributed the 27 households according to the following rule: 9 households with international migrants, 9 households with internal migrants and 9 households with no migrants. Thus, the whole sample is composed of 78 villages/towns sectors for 2,106 households. Considering migration density in each province, a number of villages/town sectors were randomly selected by province. In big town as Ouagadougou, each sector was considered as a village.

Household selection

The second sampling stage was household selection. Households were randomly selected after doing an exhaustive and quick household census in the selected village. For the villages in rural areas, we listed all the households in the village. The listing survey was not done for all households in town: we have randomly selected about 120 households in each selected town sector around a random start point. This number is extended if needed in order to attend the household type quotas. We have determined this point by using the town addressing codes. The process consisted of selecting randomly a street code and an avenue code which constituted the two elements of the start point. The main objective of the census is the household sampling. It provides information on the head household name, household size and household category (households without migrants, households with internal migrants and households with international migrants). The sub sample size for each household category is 9 in selected village/town sector. These households are randomly selected among the total households of the category from the quick census.

Mode of data collection

Face-to-face [f2f]

Research instrument

8 section questionnaire designed in English by the World Bank Migration and Remittance Team with the collaboration of the survey implementation team staff. This questionnaire has been translated into French by the survey team staff and submitted to pre-test and pilot in area with similar migration and remittance characteristics to the survey zone.

Cleaning operations

Data have been cleaned using STATA software. The double entry process was the first level of dataset quality control. Several consistency and cross checking rounds have been done in order to ensure a high dataset quality. This process often needed to check error using the hard completed questionnaires. Very few errors have been unsolved. We made no imputation for missing data in the dataset.

Response rate

Most of the questionnaires (99%) have been completed at the first visit. The non response rate is very low: 4 households out of 2106 have not responded. This score could be explained by the census process during which the high experienced team prepared the household for interview if it coma to be selected.

Abstract

The Human Sciences Research Council (HSRC) carried out the Migration and Remittances Survey in South Africa for the World Bank in collaboration with the African Development Bank. The primary mandate of the HSRC in this project was to come up with a migration database that includes both immigrants and emigrants. The specific activities included: · A household survey with a view of producing a detailed demographic/economic database of immigrants, emigrants and non migrants · The collation and preparation of a data set based on the survey · The production of basic primary statistics for the analysis of migration and remittance behaviour in South Africa.

Like many other African countries, South Africa lacks reliable census or other data on migrants (immigrants and emigrants), and on flows of resources that accompanies movement of people. This is so because a large proportion of African immigrants are in the country undocumented. A special effort was therefore made to design a household survey that would cover sufficient numbers and proportions of immigrants, and still conform to the principles of probability sampling. The approach that was followed gives a representative picture of migration in 2 provinces, Limpopo and Gauteng, which should be reflective of migration behaviour and its impacts in South Africa.

Geographic coverage

Two provinces: Gauteng and Limpopo

Limpopo is the main corridor for migration from African countries to the north of South Africa while Gauteng is the main port of entry as it has the largest airport in Africa. Gauteng is a destination for internal and international migrants because it has three large metropolitan cities with a great economic potential and reputation for offering employment, accommodations and access to many different opportunities within a distance of 56 km. These two provinces therefore were expected to accommodate most African migrants in South Africa, co-existing with a large host population.

Analysis unit

• Household
• Individual

Universe

The target group consists of households in all communities. The survey will be conducted among metro and non-metro households. Non-metro households include those in: - small towns, - secondary cities, - peri-urban settlements and - deep rural areas. From each selected household, one adult respondent will be selected to participate in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Migration data for South Africa are available for 2007 only at the level of local governments or municipalities from the 2007 Census; for smaller areas called "sub places" (SPs) only as recently as the 2001 census, and for the desired EAs only back so far as the Census of 1996. In sum, there was no single source that provided recent data on the five types of migrants of principal interest at the level of the Enumeration Area, which was the area for which data were needed to draw the sample since it was going to be necessary to identify migrant and non-migrant households in the sample areas in order to oversample those with migrants for interview.

In an attempt to overcome the data limitations referred to above, it was necessary to adopt a novel approach to the design of the sample for the World Bank's household migration survey in South Africa, to identify EAs with a high probability of finding immigrants and those with a low probability. This required the combined use of the three sources of data described above. The starting point was the CS 2007 survey, which provided data on migration at a local government level, classifying each local government cluster in terms of migration level, taking into account the types of migrants identified. The researchers then spatially zoomed in from these clusters to the so-called sub-places (SPs) from the 2001 Census to classifying SP clusters by migration level. Finally, the 1996 Census data were used to zoom in even further down to the EA level, using the 1996 census data on migration levels of various typed, to identify the final level of clusters for the survey, namely the spatially small EAs (each typically containing about 200 households, and hence amenable to the listing operation in the field).

A higher score or weight was attached to the 2007 Community Survey municipality-level (MN) data than to the Census 2001 sub-place (SP) data, which in turn was given a greater weight than the 1996 enumerator area (EA) data. The latter was derived exclusively from the Census 1996 EA data, but has then been reallocated to the 2001 EAs proportional to geographical size. Although these weights are purely arbitrary since it was composed from different sources, they give an indication of the relevant importance attached to the different migrant categories. These weighted migrant proportions (secondary strata), therefore constituted the second level of clusters for sampling purposes.

In addition, a system of weighting or scoring the different persons by migrant type was applied to ensure that the likelihood of finding migrants would be optimised. As part of this procedure, recent migrants (who had migrated in the preceding five years) received a higher score than lifetime migrants (who had not migrated during the preceding five years). Similarly, a higher score was attached to international immigrants (both recent and lifetime, who had come to SA from abroad) than to internal migrants (who had only moved within SA's borders). A greater weight also applied to inter-provincial (internal) than to intra-provincial migrants (who only moved within the same South African province).

How the three data sources were combined to provide overall scores for EA can be briefly described. First, in each of the two provinces, all local government units were given migration scores according to the numbers or relative proportions of the population classified in the various categories of migrants (with non-migrants given a score of 1.0. Migrants were assigned higher scores according to their priority, with international migrants given higher scores than internal migrants and recent migrants higher scores than lifetime migrants. Then within the local governments, sub-places were assigned scores assigned on the basis of inter vs. intra-provincial migrants using the 2001 census data. Each SP area in a local government was thus assigned a value which was the product of its local government score (the same for all SPs in the local government) and its own SP score. The third and final stage was to develop relative migration scores for all the EAs from the 1996 census by similarly weighting the proportions of migrants (and non-migrants, assigned always 1.0) of each type. The the final migration score for an EA is the product of its own EA score from 1996, the SP score of which it is a part (assigned to all the EAs within the SP), and the local government score from the 2007 survey.

Based on all the above principles the set of weights or scores was developed.

In sum, we multiplied the proportion of populations of each migrant type, or their incidence, by the appropriate final corresponding EA scores for persons of each type in the EA (based on multiplying the three weights together), to obtain the overall score for each EA. This takes into account the distribution of persons in the EA according to migration status in 1996, the SP score of the EA in 2001, and the local government score (in which the EA is located) from 2007. Finally, all EAs in each province were then classified into quartiles, prior to sampling from the quartiles.

From the EAs so classified, the sampling took the form of selecting EAs, i.e., primary sampling units (PSUs, which in this case are also Ultimate Sampling Units, since this is a single stage sample), according to their classification into quartiles. The proportions selected from each quartile are based on the range of EA-level scores which are assumed to reflect weighted probabilities of finding desired migrants in each EA. To enhance the likelihood of finding migrants, much higher proportions of EAs were selected into the sample from the quartiles with the higher scores compared to the lower scores (disproportionate sampling). The decision on the most appropriate categorisations was informed by the observed migration levels in the two provinces of the study area during 2007, 2001 and 1996, analysed at the lowest spatial level for which migration data was available in each case.

Because of the differences in their characteristics it was decided that the provinces of Gauteng and Limpopo should each be regarded as an explicit stratum for sampling purposes. These two provinces therefore represented the primary explicit strata. It was decided to select an equal number of EAs from these two primary strata.

The migration-level categories referred to above were treated as secondary explicit strata to ensure optimal coverage of each in the sample. The distribution of migration levels was then used to draw EAs in such a way that greater preference could be given to areas with higher proportions of migrants in general, but especially immigrants (note the relative scores assigned to each type of person above). The proportion of EAs selected into the sample from the quartiles draws upon the relative mean weighted migrant scores (referred to as proportions) found below the table, but this is a coincidence and not necessary, as any disproportionate sampling of EAs from the quartiles could be done, since it would be rectified in the weighting at the end for the analysis.

The resultant proportions of migrants then led to the following proportional allocation of sampled EAs (Quartile 1: 5 per cent (instead of 25% as in an equal distribution), Quartile 2: 15 per cent (instead

In 2023, the personal remittances received in Nigeria decreased by roughly 0.6 billion U.S. dollars (-2.9 percent) since 2022. The incoming personal remittances in Nigeria peaked in 2018, when the figure stood at 24 billion U.S. dollars.Personal remittances refer to personal transfers and compensation of employees. The former includes all current transfers between resident and nonresident individuals, while the latter refers to the income of workers who are employed in an economy where they are not resident, and of residents employed by nonresident entities. These include border, seasonal, and other short-term workers.Find more statistics on other topics about Nigeria with key insights such as value of personal remittances paid, national gross income per capita, and gross national income (GNI).