Ten-year outcome measures used for the cost-effectiveness analyses, assuming a 10% annual migration rate.

Migration is a complex and multifaceted phenomenon that has significant social, economic, and political implications. Net migration, which is the difference between the number of people entering and leaving a country, is an important measure of the movement of people across borders. Net migration can be influenced by a variety of factors, including economic opportunities, political stability, social networks, and cultural ties.

The net migration per country dataset provides a comprehensive overview of the net migration of each country. The dataset includes information on the number of immigrants, emigrants, and net migration, covering all countries in the world. It is compiled from various sources, including national statistical agencies, international organizations such as the United Nations (UN), and other relevant data sources.

The net migration per country dataset can be used by researchers, policymakers, and the general public to gain insight into the patterns and trends of migration across the world and to compare the relative levels of migration across different countries and regions. It can also be used to monitor changes in migration patterns over time and to evaluate the effectiveness of migration policies and strategies.

Overall, the net migration per country dataset is an important resource for understanding the dynamics of migration and for developing policies and strategies that promote safe, orderly, and regular migration for the benefit of migrants, sending countries, and receiving countries alike.

In 2022, companies worldwide claim that since migrating to the cloud they have not experienced changes in security operations costs, portraying efficiency in their cloud security and compliance processes. An increase ****** percent in GxP audit scores was noticed by the surveyed companies alongside a reduction of ***** percent in spend to manage adverse events.

Abstract

These data consist of a long-term follow-up of applicants to a migration visa lottery. Tongan households were surveyed as migrants in New Zealand, or non-migrants in Tonga. It was used to examine the long-term impacts of international migration by comparing immigrants who had successful ballot entries in a migration lottery program, and first moved almost a decade ago, with people who had unsuccessful entries into those same ballots. It was additionally used to study how migrating from a poor country to a rich country affects economic beliefs, preference parameters, and household decision-making efficiency. In a ten-year follow-up survey of applicants to a migration lottery program we elicit risk and time preferences and pro-market beliefs for the migrants and the unsuccessful applicants. The successful and the unsuccessful applicants are each linked to closest relative households, who would stay in the home country if the applicant moved, to play lab-in-the-field games that measure intra-family trust and the efficiency of intra-family decision-making.

Geographic coverage

The survey covers Tongans who applied to the 2002-05 Pacific Access Category migration visa program, along with linked households of their family members. This involved surveying in both New Zealand and Tonga (along with a small number of surveys of movers to third countries).

Analysis unit

Data are collected at both the individual and household level

Kind of data

Sample survey data [ssd]

Sampling procedure

Our population of interest consists of entrants to the 2002 to 2005 PAC migration lotteries. There were a total of 4,696 principal applicants of whom 367 were randomly selected as ballot winners (figure 2). Official records provided by the New Zealand immigration authorities in late 2012 show that 307 of these winners (84%) had residency applications approved and had ever migrated to New Zealand. The remaining 60 ballot winners did not migrate and are thus non-compliers to the treatment of migration.

Our main survey involved an extensive face-to-face interview, which also collected anthropometrics, blood pressure, peak lung flow, and included lab-in-the-field games. Of the 307 principal applicants ever migrating to New Zealand, 133 completed the full survey between late 2013 and the end of 2014. In order to bolster our sample size, in early 2015 we fielded a shortened survey that did not include health measurements or the lab-in-field games. This was mainly done as a telephone interview and was designed to reach those who had on-migrated beyond New Zealand or were located in parts of New Zealand that were impractical for face-to-face interviewing, although we also learned, through snowball effects, of more migrants in our face-to-face survey area and gave them the short survey as well. Overall, 61 additional ballot winners who had ever migrated to New Zealand were given the short survey, including 11 who had now on-migrated to Australia (ten) and the UK (one). In total, we were able to survey 194 households with principal applicants who ever migrated to New Zealand after winning the ballot.

We had even less information available for the ballot losers and non-compliers since these individuals had not filled out residency applications. We therefore used the same surveying approach for these groups as we had in our previous survey, which was to sample from the same villages in Tonga from which our migrants originated. Out of 4329 ballot losers, 143 were administered the long form survey and 39 the short survey (of which nine had subsequently moved to New Zealand through alternative pathways, including by winning a later round of the PAC lottery). Finances limited us to this relatively small sample, but, based on our previous research, we judged that it would give us enough power to measure economically significant impacts. An advantage of surveying from the same origin villages is that we can implicitly control for any unobserved characteristics that vary spatially in Tonga. Finally, we have a small sample of nine non-compliers; six who received the long survey and three the short survey. This is out of a population of 60 non-compliers, which hence made it difficult to find many individuals in this group.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four separate questionnaires were administered: - a survey for migrant households in New Zealand - a survey for non-migrant households in Tonga - a survey of linked partner households - a short survey

The DEMIG-QuantMig Migration Policy Database tracks more than 7'600 migration policy changes enacted by 31 European (EU and non-EU) countries for the period 1990 to 2020. This database extendeds and updates the DEMIG POLICY database (https://www.migrationinstitute.org/data/demig-data/demig-policy) and follows the same methodology. The policy measures are coded according to the policy area and migrant group targeted, as well as the change in restrictiveness they introduce in the existing legal system. The database allows for both quantitative and qualitative research on the long-term evolution and effectiveness of migration policies.

Debates about migration are often in the news. People quote numbers about how many people are entering and leaving different countries. Governments need to plan and manage public resources based on how their own populations are changing.

Informed discussions and effective policymaking rely on good migration data. But how much do we really know about migration, and where do estimates come from?

In this article, I look at how countries and international agencies define different forms of migration, how they estimate the number of people moving in and out of countries, and how accurate these estimates are.

Migrants without legal status make up a small portion of the overall immigrant population. Most high-income countries and some middle-income ones have a solid understanding of how many immigrants live there. Tracking the exact flows of people moving in and out is trickier, but governments can reliably monitor long-term trends to understand the bigger picture.

Who is considered an international migrant? In the United Nations statistics, an international migrant is defined as “a person who moves to a country other than that of his or her usual residence for at least a year, so that the country of destination effectively becomes his or her new country of usual residence”.1

For example, an Argentinian person who spends nine months studying in the United States wouldn’t count as a long-term immigrant in the US. But an Argentinian person who moves to the US for two years would. Even if someone gains citizenship in their new country, they are still considered an immigrant in migration statistics.

The same applies in reverse for emigrants: someone leaving their home country for more than a year is considered a long-term emigrant for the country they’ve left. This does not change if they acquire citizenship in another country. Some national governments may have definitions that differ from the UN recommendations.

What about illegal migration? “Illegal migration” refers to the movement of people outside the legal rules for entering or leaving a country. There isn’t a single agreed-upon definition, but it generally involves people who breach immigration laws. Some refer to this as irregular or unauthorized migration.

There are three types of migrants who don’t have a legal immigration status. First, those who cross borders without the right legal permissions. Second, those who enter a country legally but stay after their visa or permission expires. Third, some migrants have legal permission to stay but work in violation of employment restrictions — for example, students who work more hours than their visa allows.

Tracking illegal migration is difficult. In regions with free movement, like the European Union, it’s particularly challenging. For example, someone could move from Germany to France, live there without registering, and go uncounted in official migration records.2 The rise of remote work has made it easier for people to live in different countries without registering as employees or taxpayers.

Abstract

The study includes data and materials (do files, survey instruments) necessary for the replication of the paper: "Unilateral Facilitation Does Not Raise International Migration from the Philippines" by Emily A. Beam, David McKenzie, Dean Yang. According to the study, signifcant income gains from migrating from poorer to richer countries have motivated unilateral (source-country) policies facilitating labor emigration. However, their effectiveness is unknown. The investigators conducted a large-scale randomized experiment in the Philippines testing the impact of unilaterally facilitating international labor migration. Their most intensive treatment doubled the rate of job offers but had no identifable effect on international labor migration. Even the highest overseas job-search rate they induced (22%) falls far short of the share initially expressing interest in migrating (34%). They conclude that unilateral migration facilitation will at most induce a trickle, not a food, of additional emigration.

Geographic coverage

42 barangays from six municipalities in Sorsogon Province

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

Early in 2010, we randomly selected 42 barangays from six municipalities in Sorsogon Province in which to conduct the baseline survey. We collected a household roster from each barangay that included a list of households, and we used these to set barangay-specific target sample sizes proportional to population. We targeted approximately 5% of the total population from each barangay, or roughly 26%of households. We sorted households randomly and selected the first listed households to be our target. When a household could not be located or had no eligible members, we replaced it with the next household on the list.

From each household, interviewers screened the first member they met who had never worked abroad and was age 20-45. Subsequent to the baseline survey, we learned from recruitment agencies that most individuals over age 40 would not be eligible for overseas work, so we restricted our baseline sample to the 4,153 individuals age 20-40 we interviewed. Houses selected were typically far enough apart from each other that concerns about information spillovers are second order; to the extent that there were spillovers, our treatment estimates are lower bounds on the differential impact of more information. The passport assistance was only offered to the respondents themselves, and so it is not subject to such spillovers.

Mode of data collection

Face-to-face [f2f]

Research instrument

Attached

Response rate

We obtained measures of whether the respondent migrated abroad for work from full, proxy, or log surveys for 4,089 respondents, or 98.5% of our sample. Of those, 73% were surveys with the respondents themselves, 20% were proxy surveys, and 7% were log surveys. Excluding the log surveys, we have a 91% response rate for their full set of job search and migration outcome variables.

Abstract

This study aims at enlightening the various factors that affect their reintegration in Albania. The study conducted through a national level survey in September-October 2013 suggests that the return migration phenomenon has assumed significant size, particularly after 2009; therefore, the resolving of the problems and the emigrants’ reintegration are the challenges of the Albanian society. Hence, the civil society, the policy makers, the international organizations, the local and national administrative structures, the academic and university community will get hereby a useful tool to understand the problems of migration by contributing to an efficient approach for the reintegration of emigrants into the society. The specific objectives of the survey are:

• To profile return migration to Albania, push and pull factors, characteristics of returning migrants; • To collect information on migrants’ experiences and perceptions of reintegration in Albania; • To formulate several recommendations for further research on return migration as well as the provision of services that facilitate the reintegration of returnees.

Geographic coverage

National.

Analysis unit

• Individuals

Universe

The survey found out that a total of 133, 544 Albanian citizens of the age segment 18- above returned to Albania in the period 2009-2013.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size for a particular survey is determined by the accuracy required for the survey estimates for each domain, as well as by the resource and operational constraints. The accuracy of the survey results depends on both the sampling error, which can be measured through variance estimation, and the non-sampling error from all other sources, such as response and other measurement errors, coding and data entry errors. It is important to emphasize that INSTAT recognizes that the sample size of a particular survey is determined by the accuracy required for the national level estimates, as well influenced by logistical issues related to the organization and size of the teams, and the workload for survey administration and data collection. Considering all of these factors, calculations suggested that a sample size of 2000 individuals would give sufficient power to meet the study objectives. When multi-stage sampling is used, the design effect mostly measures the impact of the level of clustering on the sampling efficiency. The design effect depends on the number of sample individuals selected in each stratum. The sample size for

Sampling deviation

The study consisted in a cross-sectional population-based household survey conducted at a national level across each of the 12 prefectures in Albania. A stratified sample designed was used for selecting the individual for sampling. The primary sampling units (PSUs) selected at the first stage are the enumeration areas (EAs), which are small operational areas defined on maps for the 2011 Census enumeration. To control coverage errors, which make the sample less representative, the sampling frame must be of an optimum quality during all the stages of selections. At the first stage, the EA must cover all the areas inhabited by the population under study, without omission or duplication. The boundaries of the EA must be clearly defined and subject to easy identification in the field. SAS software was used at this stage to systematically select the sample of (EAs) with probability proportion to size (PPS) within each prefecture. The second stage of selection dealt with household lists from the selected EAs. The list of households enumerated in the 2011 Census for each sample EA was used as the sampling with equal probability. The third stage of selection was the individual selection in the pre-selected household. The advantages of this two-stage selection procedure are:

1. The selection procedure is simple to implement and reduces possible non-sampling errors in the selection process.
2. It is easy to locate the selected individuals, reducing non-sampling errors and non-response.
3. The interviewers interview only the individuals in the pre-selected households. No replacement of household was permitted, preventing survey bias. When developing the sample selection criteria, special attention was paid to define the study population and its characteristics. Based upon pre-established criteria an individual was considered eligible to be surveyed if he/she was
4. An Albanian citizen, who had migrated abroad and returned to Albania either voluntary or involuntary;
5. 18 years old and above.

The goal was to generate a sample of households that would allow for the production of statistically reliable estimates of the nature and extent of return migration to Albania and reintegration needs of returnees at the national level, and would allow for urban versus rural comparisons.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was structured along three main migratory stages: - Stage 1: Situation before leaving the country of origin; - Stage 2: Experience of migration lived in the main country of immigration; - Stage 3: Return to the country of origin – Postreturn conditions.

The survey was conducted through a structured questionnaire. In line with the objectives of the survey, the contents of the questionnaire were geared towards collecting the amount of necessary information on the following issues:

• Socio-economic conditions and socio-demographic characteristics of returnees prior to departure from Albania, during migration and upon return;
• Reasons for leaving the country and for returning to Albania;
• Experiences of integration in the last country of emigration and their influence on the decision to return to Albania;
• Process of return to Albania and prospect of stay; Links with the last country of immigration;
• Reintegration support and returnee reintegration experiences in Albania.

Response rate

The survey also found that the majority of responses (55%) indicate that employment opportunities should be allocated to enable smooth return and reintegration processes. Financial incentives (25%) were also perceived as important, as well as professional training programs (6%).

Sampling error estimates

The accuracy of the survey results depends on both the sampling error, which can be measured through variance estimation, and the non-sampling error from all other sources, such as response and other measurement errors, coding and data entry errors. It is important to emphasize that INSTAT recognizes that the sample size of a particular survey is determined by the accuracy required for the national level estimates, as well influenced by logistical issues related to the organization and size of the teams, and the workload for survey administration and data collection.

Tracking radar data and bird migration intensity data from Israel to Spain and from the Baltic Sea to the Sahara are available for analysis. All Data were collected by the ex-military Tracking-Radar-System, type “Superfledermaus” (see Bruderer et al. 1995). Data are stored as CSV-files and are downloadable.

Data available:

1. File Site.csv includes all site dependent data like geographical coordinates and period of measurements.

2. Track.csv contains data on flight path of birds, consisting of speed, altitude, direction, species as far as identified and more: One Dataset per track including average values of ground-, air-, wind-speeds and corresponding directions for track and heading, climbing rate, effective speed (measure for straightness) and in rarer temperature, humidity and pressure from soundings are included. Furthermore, for each flightpath any determination of measures that helps identifying species are given like wingbeat frequency and pattern, visually identifications and more.

3. Species.csv includes all identified species (or “species groups”) tracked. Since most tracks were recorded during nighttime, only 18’117 of 401’254 of the available tracks contain speciescodes, that include 212 different species or “species groups” (see table 3). Systematics follow the IOC World Bird List version 14.2 updated 2024-07-17.

4. VSUDensityH200.csv contains one dataset on “bird density” per 200 m height interval as objects (birds) pro km-3. The method VSU is described in Bruderer et al. (1995).

5. FBMTRH50.csv contains Migration Traffic Rates per 50 m height interval. The method is described in Schmaljohann et al. (2008).

Relationships between tables and description of data including data formats are shown in TableSystem.pdf.

China’s rapid urbanisation has induced large numbers of rural residents to migrate from their homes in the countryside to urban areas in search of higher wages. As a consequence, it is estimated that more than 60 million children in rural China are left behind and live with relatives, typically their paternal grandparents. These children are called Left Behind Children (LBCs). There are concerns about the potential negative effects of parental migration on the academic performance of the LBCs that could be due to the absence of parental care. However, it might also be that when a child’s parents work away from home, their remittances can increase the household’s income and provide more resources and that this can lead to better academic performance. Hence, the net impact of out-migration on the academic performance of LBCs is unclear. This paper examines changes in academic performance before and after the parents of students out-migrate. We draw on a panel dataset collected by the authors of more than 13,000 students at 130 rural primary schools in ethnic minority areas of rural China. Using difference-in-differences and propensity score matching approaches, our results indicate that parental migration has significant, positive impacts on the academic performance of LBCs (which we measure using standardised English test scores). Heterogeneous analysis using our data demonstrates that the positive impact on LBCs is greater for poorer performing students.

This dataset is derived from a nationally-representative sample of Montenegrins and used in the working paper "Towards a socio-psychological theory of migration". The data was collected by Montenegrin agency De Facto Consultancy in late September and early October 2024 using multistage random probability sample sampling approach and 1000 face-to-face interviews. The survey was designed using several sources—notably, the Swiss-Subsaharan Migration Network’s S-SAM Survey, the (Montenegrin surveys of the) European Social Survey, and the Eurobarometer (in the latter two cases allowing for triangulation). The survey includes four extensive batteries of questions, including measures of: (1) migration behaviour (aspirations, plans, preparations, history), desired destination countries, motivation, irregular willingness; (2) psychological predispositions including schema such as Schwartz’s Basic Human Values 21-item battery, the five-item Behavioural Inhibition Scale, the six-item Self-Efficacy scale, and single item indicators for social trust and risk aversion; (3) political and social attitudes and behaviours; (4) socio-demographic variables (age, gender, education, region, employment type, relationship).

Migratory birds typically separate energetically demanding parts of the annual cycle like breeding, moult, and migration with some species engaging in so-called moult-migration. Moult-migration is known to occur in starlings from the northern breeding populations, however, little is known about the dynamics of this phenomenon and the costs and benefits for the involved individuals. Here, using state-of-the-art multi-sensor geolocators, we gathered information about the annual cycles of 10 starlings from two breeding sites in Latvia. We used a novel analytical approach based on atmospheric pressure measurements to reveal that all but one of the tracked individuals migrated to wintering sites in the British Isles. Tracking data exposed two separate migration strategies – (1) departure from the breeding grounds in mid-June soon after chick fledging with long stationary periods at moulting sites approx. 900 km westward (n = 5 of 10); (2) residing in close vicinity of the breeding sites up until the end of October (n = 5 of 10). Accelerometer data revealed significantly higher activity budgets during moult for the individuals exhibiting moult-migration. Furthermore, birds that underwent moult-migration arrived at the breeding sites in the following year on average 10 days later and showed significantly higher activity levels during the pre-breeding period compared to birds without moult-migration. Activity tracking also showed that 67% of all migratory flights were performed during the night, contradicting previous assumptions of starlings being predominantly diurnal migrants. Maximum recorded flight altitudes reached 2500 m.a.s.l. and the longest uninterrupted flight lasted 22.5 hours. Our results highlight energetic trade-offs of moult-migration in starling, but their downstream consequences remain to be tested. Methods In 2020, we set up two new study sites in Latvia to study Starlings. One in eastern Latvia in Zeltaleja (57.19°N 26.86°E) consisting of 100 nest boxes and the second in western Latvia near Lake Engure (57.27°N 23.10°E) consisting of two plots ca. 8 km apart with 100 nest boxes each. The distance between the two study sites was 230 km. During the breeding season from mid-April until mid-June all nest boxes were monitored at least once every four days and adult birds and chicks were ringed. Onset of egg laying at both study sites was highly synchronized among individuals with 90% of all recorded broods (n = 181) started between 19 and 29 April. No second brood was recorded at either of the two study sites. In 2020, we fitted 70 adult breeders (35 at each site; 34 males, 36 females) with multi-sensor archival data loggers (model: GDL3-PAM, Swiss Ornithological Institute, Liechti et al. 2018), which were attached on the bird's back using nylon cord leg-loop harness. Adult birds were predominantly captured with traps inside the nest boxes. To avoid nest abandonment, the trapping and deployment of loggers were carried out during the late stages of the breeding between 17 and 26 May when the chicks were more than 10 days old. The geolocator including the harness on average weighed 1.90 ± 0.04 g, which represents less than 3% of the body mass of starling (80.4 ± 3.93 g, n = 107), falling under recommended ethical threshold (Barron et al. 2010, Brlík et al. 2020). The loggers accommodated sensors for ambient light intensity, atmospheric pressure, ambient temperature, and acceleration. The light sensor was equipped with a 7 mm long light guide and light intensity was measured every minute storing maximum values in 5 min intervals. Acceleration/activity was measured along the Z-axis for 3.2 seconds at 10 Hz frequency every 5 minutes. Atmospheric pressure and temperature recordings were set to 30-minute intervals (Briedis et al. 2020). In 2021, we acquired nine full tracks (5 males, 4 females) and two incomplete tracks (1 male, 1 female). One of the incomplete tracks (male) had recorded data up until the 10th of December, thus, including information about the autumn migration and the first half of the wintering period, while the others (female) barometric pressure data were unusable due to malfunction of the sensor. Overall recapture rate for birds with loggers was 15.7% (11/70); 20% (7/35) in Engure, and 11% (4/35) in Zeltaleja. To see whether loggers had any impact on return rates we used Chi-square test comparing return rates of logger birds with those of control groups (n = 18; birds undergoing the same handling procedure except fitting the loggers). Results showed no significant difference between the two groups (χ2 = 0.02, p = 0.88; males only: χ2 = 4.77, p = 0.21; females only: χ2 = 1.08, p = 0.84). For distinguishing between movement and stationary phases during the annual cycle, we used accelerometer and atmospheric pressure recordings. Because starlings use flapping flight for migration (Rayner et al. 2001), we used the flapping flight classification from the R-package ‘pamlr’ (Dhanjal-Adams et al. 2022) and set the duration threshold to 1 h (equals to 12 consecutive readings of flapping activity at 5 min recording intervals). Thus, activity measures that were classified as flapping and lasted for at least 1 h were regarded as migratory flights (Briedis et al. 2020). Using 1 h as the cut-off threshold for the identification of migratory flights may have left some shorter duration flights unnoticed. However, lowering the threshold increases the risk of misidentifying extended commutes to and from roosting sites or lengthy murmurations (King and Sumpter 2012) as migration. From the classified migratory flights, we could invertedly identify stopover periods in-between migration flights. Longer periods exceeding 45 days were regarded as residency sites. We considered long stationary phases during winter months (November-February) as the main wintering grounds. To derive geographic locations of stationary sites, we used atmospheric pressure as it is not expected to change rapidly when the bird is stationary (weather-related changes; Liechti et al. 2018, Sjöberg et al. 2018). Throughout the analyses, we followed the general procedure outlined in Nussbaumer et al. (2022a). The recorded pressure data were compared with surface-level atmospheric pressure data (ERA5 hourly surface-level pressure data) at 0.25x0.25-degree grid cells available at The Copernicus Climate Change Service (Hersbach et al. 2018). Based on pre-analyses of light data, we defined an area between 60°N, 45°N, 10°W and 30°E where all birds had resided throughout the annual cycle and further location estimates were rendered in this pre-defined area to save computational time. To avoid birds’ behaviour (e.g., flying, foraging, etc.) potentially influencing the recorded pressure data, only recordings during night (light recording = 0) and while the bird was not in motion (acceleration < 5 units on the arbitrary scale; max recorded value across all birds = 101) were used for location estimation. First, we excluded all the cells where recorded pressure data did not match the range of air pressure between lowest and highest elevation points within a cell at a given hour. To calculate this range, we combined remotely sensed elevation data at 90x90m resolution available from SRTM DEM Digital Elevation Database (Jarvis et al. 2008) and the ERA5 data. Second, to further refine possible locations we calculated the maximal distance starlings could have travelled from the previous stopover/residency site assuming flight duration as per flight classification analyses and maximum average ground speed of 110km h-1 (we deliberately chose a very high ground speed value to account for potential effects of strong tailwind support). Consequently, we excluded all cells outside the potential flight range. Third, we derived longitudinal boundaries of the stopover/residency sites from recorded light intensity data using R-package, ‘GeoLight’ (Lisovski et al. 2012). Longitude, in contrast to latitude, estimates are more reliable throughout the year as they are not influenced by calibration and equinox times (Lisovski et al. 2012). After integrating these steps, we ran a correlation analysis of pressure recorded on the geolocator and ERA5 data in all remaining grid cells. Higher correlation coefficient here indicates a higher likelihood that a bird was residing at a given grid cell. Finally, we checked residuals between the recorded pressure data from geolocators with the ERA5 data from the cell with the highest correlation coefficient. This procedure allowed for detection of changes in roosting sites and potential migratory flights that were shorter than 1 hour. If such flights were discovered, we split the stationary period accordingly and rerun the analyses (see section Labelling tracks from the user manual for GeoPressureR, Nussbaumer et al. 2022a, 2022b PREPRINT). Final maps illustrate grid cells with correlation coefficients within the 95th percentile (pressure data) of the potential residency/stopover area. Recorded light intensity data were used to identify sunrise and sunset times, which allowed us to distinguish between diurnal and nocturnal migratory flights. To better understand the migratory behaviour of starlings, this data was also used to compare departure and landing times to local sunrise and sunset. Also, individual migratory flight durations were compared with take-off times relative to local sunset. Further, for each individual, we calculated cumulative flight duration, migration distances, average ground speed and migration speed in summer/autumn and in spring. Migration distance was calculated as a great circle distance between the breeding site and the most likely location estimates across the annual cycle. Here, ‘migration speed’ (km day-1) describes speed at which a bird travelled from breeding site to the main wintering grounds and back, including stopover time. ‘Ground speed’ (km h-1) describes flight

Context

**This dataset shows the migration to and from New Zealand by country and citizenship from 1979 to 2016. **

Content

The columns in this dataset are:

• Measure: The signal type given in this row, one of: "Arrivals", "Departures", "Net"
• Country: Country from where people arrived into to New Zealand (for Measure = "Arrivals") or to where they left (for Measure = "Departures"). Contains special values "Not Stated" and "All countries" (grand total)
• Citizenship: Citizenship of the migrants, one of: "New Zealand Citizen", "Australian Citizen", "Total All Citizenships"
• Year: Year of the measurement
• Value: Number of migrants

Permanent and long-term arrivals include overseas migrants who arrive in New Zealand intending to stay for a period of 12 months or more (or permanently), plus New Zealand residents returning after an absence of 12 months or more. Permanent and long-term departures include New Zealand residents departing for an intended period of 12 months or more (or permanently), plus overseas visitors departing New Zealand after a stay of 12 months or more. For arrival series, the country of residence is the country where a person arriving in New Zealand last lived for 12 months or more (country of last permanent residence). For departure series, the country of residence is the country where a person departing New Zealand intends to live for the next 12 months or more (country of next permanent residence).

Acknowledgements

Curated data by figure.nz, original data from Stats NZ. Dataset licensed under Creative Commons 4.0 - CC BY 4.0.

Inspiration

A good challenge would be to explain New Zealand migration flows as a function of the economic performance of New Zealand or other countries (combine with other datasets). The data could be possibly linked up with other data sources to predict general migration to/from countries based on external factors.

Pervasive inbreeding is a major genetic threat of population fragmentation and can undermine the efficacy of population connectivity measures. Nevertheless, few studies have evaluated whether wildlife crossings can alleviate the frequency and length of genomic autozygous segments. Here, we provided a genomic inbreeding perspective on the potential effectiveness of mammal population defragmentation measures. We applied a SNP-genotyping case study on the ~2500 wild boar Sus scrofa population of Veluwe, The Netherlands, a 1000-km2 Natura 2000 protected area with many fences and roads but also, increasingly, fence openings and wildlife crossings. We combined a 20K genotyping assessment of genetic status and migration rate with a simulation that examined the potential for alleviation of isolation and inbreeding. We found that Veluwe wild boar subpopulations are significantly differentiated (FST-values of 0.02-0.13) and have low levels of gene flow. One noteworthy exception was the Central and Southeastern subpopulation, which were nearly panmictic and appeared to be effectively connected through a highway wildlife overpass. Estimated effective population sizes were at least 85 for the meta-population and ranged from 31 to 52 for the subpopulations. All subpopulations, including the two connected subpopulations, experienced substantial inbreeding, as evidenced through the occurrence of many long homozygous segments. Simulation output indicated that whereas one or few migrants per generation could undo genetic differentiation and boost effective population sizes rapidly, genomic inbreeding was only marginally reduced. The implication is that ostensibly successful connectivity restoration projects may fail to alleviate genomic inbreeding of fragmented mammal populations. We put forward that defragmentation projects should allow for (i) monitoring of levels of differentiation, migration and genomic inbreeding, (ii) anticipation of the inbreeding status of the meta-population, and, if inbreeding levels are high and/or haplotypes have become fixed, (iii) consideration of enhancing migration and gene flow among meta-populations, possibly through translocation.

Определение: Доля всех мигрантов, которые оказывают перераспределяющее воздействие на численность населения на данной территории. [Переведено с en: английского языка] Тематическая область: Демография (CELADE) [Переведено с en: английского языка] Область применения: Миграция [Переведено с en: английского языка] Единица измерения: Процент [Переведено с en: английского языка] Источник данных: ЭКЛАК / Экономическая комиссия для Латинской Америки и Карибского бассейна / CELADE - Отдел народонаселения ЭКЛАК, банк данных MIALC: https://celade.cepal.org/bdcelade/MIALC в хранилище микроданных переписи населения, доступном в CELADE в формате REDATAM. Обработано с использованием Redatam 7. [Переведено с es: испанского языка] Комментарии: Только чистая миграция оказывает влияние на пространственное перераспределение населения. Таким образом, сальдо суммируется, но с использованием их абсолютных значений, чтобы они не перекрывали друг друга. А чтобы рассчитать относительный показатель, мы делим его на валовую миграцию. Двойной подсчет случаев не корректируется, поскольку, когда он встречается как в знаменателе, так и в числителе, он аннулируется. Значение 100% означает максимальную или идеальную эффективность территориального перераспределения миграции. Каждый мигрант создает эффект перераспределения населения на эквивалентной ему территории, поскольку он не уравновешивается мигрантом в противоположном направлении. Значение 0 означает нулевую эффективность миграции, поскольку у каждого мигранта есть мигрант в противоположном направлении, который нейтрализует перераспределение. [Переведено с es: испанского языка] Последнее обновление: Sep 21 2023 4:40PM Организация-источник: Экономическая комиссия для Латинской Америки и Карибского бассейна [Переведено с en: английского языка] Definition: Proportion of all migrants that generate a redistributive impact of population in the territory. Thematic Area: Demographics (CELADE) Application Area: Migration Unit of Measurement: Percentage Data Source: ECLAC / Economic Commission for Latin America and the Caribbean / CELADE - Population Division of ECLAC, databank MIALC: https://celade.cepal.org/bdcelade/MIALC, on the census microdata repository available in CELADE in REDATAM format. Processed using Redatam 7. Comments: Only net migration produces a spatial redistributive effect of population. Therefore, the balances are accumulated; but by using their absolute values to prevent them from cancelling each other out. And in order to calculate a relative indicator we divide by gross migration. The double counting of cases is not corrected because when it occurs in both denominator and numerator it cancels out. A value of 100% means maximum or perfect territorial redistributive efficiency of migration. Each migrant generates a population redistribution effect in the territory equivalent to him/herself because it is not counterbalanced by a migrant in the opposite direction. A value of 0 means zero migration efficiency because each migrant has a migrant in the opposite direction that neutralizes the redistribution. Last Update: Sep 21 2023 4:40PM Source Organization: Economic Commission for Latin America and the Caribbean

The data presented in this data project were collected in the context of two H2020 research projects: ‘Enhanced migration measures from a multidimensional perspective’(HumMingBird) and ‘Crises as opportunities: Towards a level telling field on migration and a new narrative of successful integration’(OPPORTUNITIES). The current survey was fielded to investigate the dynamic interplay between media representations of different migrant groups and the governmental and societal (re)actions to immigration. With these data, we provide more insight into these societal reactions by investigating attitudes rooted in values and worldviews. Through an online survey, we collected quantitative data on attitudes towards: Immigrants, Refugees, Muslims, Hispanics, Venezuelans News Media Consumption Trust in News Media and Societal Institutions Frequency and Valence of Intergroup Contact Realistic and Symbolic Intergroup Threat Right-wing Authoritarianism Social Dominance Orientation Political Efficacy Personality Characteristics Perceived COVID-threat, and Socio-demographic Characteristics For the adult population aged 25 to 65 in seven European countries: Austria Belgium Germany Hungary Italy Spain Sweden And for ages ranged from 18 to 65 for: United States of America Colombia The survey in the United States and Colombia was identical to the one in the European countries, although a few extra questions regarding COVID-19 and some region-specific migrant groups (e.g. Venezuelans) were added. We collected the data in cooperation with Bilendi, a Belgian polling agency, and selected the methodology for its cost-effectiveness in cross-country research. Respondents received an e-mail asking them to participate in a survey without specifying the subject matter, which was essential to avoid priming. Three weeks of fieldwork in May and June of 2021 resulted in a dataset of 13,645 respondents (a little over 1500 per country). Sample weights are included in the dataset and can be applied to ensure that the sample is representative for gender and age in each country. The cooperation rate ranged between 12% and 31%, in line with similar online data collections.

Core Banking Migration Services Market Outlook

According to our latest research, the global core banking migration services market size reached USD 9.2 billion in 2024, demonstrating robust demand for advanced banking infrastructure modernization. The market is expected to expand at a CAGR of 13.5% from 2025 to 2033, with a projected market size of USD 28.4 billion by 2033. This impressive growth trajectory is primarily driven by the increasing need for seamless digital transformation, regulatory compliance, and the adoption of next-generation technologies in the banking sector. The evolution of customer expectations and the imperative for operational efficiency are fueling investments in core banking migration services worldwide.

A major growth factor for the core banking migration services market is the accelerating pace of digital transformation across global financial institutions. As banks strive to remain competitive in an era defined by fintech disruption and evolving customer preferences, they are prioritizing the modernization of their core systems. Legacy banking platforms, often decades old, pose significant challenges in terms of scalability, security, and agility. Migration to modern, cloud-enabled core banking systems enables banks to launch innovative products faster, enhance customer experience, and achieve cost efficiencies. This shift is further amplified by the increasing adoption of open banking frameworks, which require banks to integrate seamlessly with third-party providers and digital ecosystems. As such, the demand for comprehensive migration services encompassing data, application, and platform migration is witnessing a significant upswing.

Another critical driver is the stringent regulatory landscape and the growing emphasis on data security and compliance. Regulatory authorities across regions are mandating higher standards for data privacy, risk management, and anti-money laundering measures. Legacy systems often struggle to keep pace with these evolving requirements, leading banks to migrate to modern platforms that offer enhanced security features, real-time monitoring, and comprehensive compliance tools. Additionally, the rise of cybersecurity threats and the need for robust disaster recovery capabilities are compelling banks to invest in advanced migration services. These services ensure that data integrity, continuity, and regulatory compliance are maintained throughout the migration process, minimizing operational risks and safeguarding customer trust.

The proliferation of cloud computing and the availability of scalable, cost-effective cloud-based solutions are also catalyzing the growth of the core banking migration services market. Cloud adoption enables banks to achieve greater flexibility, reduce IT infrastructure costs, and accelerate time-to-market for new services. Migration services providers are increasingly offering tailored solutions that support hybrid and multi-cloud environments, allowing banks to optimize workloads and maintain regulatory compliance. The ability to leverage artificial intelligence, machine learning, and advanced analytics on cloud platforms further enhances the value proposition of core banking migration. As a result, financial institutions of all sizes, including small and medium enterprises, are embracing migration services to future-proof their operations and meet the demands of a rapidly evolving digital landscape.

From a regional perspective, Asia Pacific is emerging as a powerhouse in the core banking migration services market, driven by rapid economic growth, a burgeoning middle class, and government initiatives promoting digital banking. North America and Europe continue to lead in terms of technology adoption and regulatory compliance, while the Middle East & Africa and Latin America are witnessing increased investments in banking infrastructure modernization. The competitive landscape is characterized by the presence of global technology giants, specialized migration service providers, and a growing number of fintech startups offering innovative solutions. Strategic partnerships, mergers and acquisitions, and investments in research and development are shaping the future of the market, as players strive to capture a larger share of this dynamic and rapidly expanding industry.

"https://growthmarketreports.com/report/data-migration-testing-for-financial-services-market" target="_blank">Data Migration Testing for Financial Ser

Key performance indicators and success metrics for headless WordPress migrations

Spain Demographic Trends

Population, Migration, and Age Trends from 1955 to 2050

By [source]

About this dataset

This dataset provides an in-depth look into the demographic information for Spain. It includes trends in population, migration, and age from 1955 to 2050. This dataset can provide understanding into the growth of Spain which has been marked as one of the fastest-growth developing countries. It reveals important statistics such as population numbers, yearly change percentages, fertility rate figures, density of people per square kilometer and more across all ages over a considerable period of time. Furthermore, it also outlines aspects such World Population Total and Country’s Share of World Pop with each country’s global rank among other nations. It will be useful for those wanting to gain insight into specific populations numbers that shape the Spanish culture today

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How to use the dataset

This dataset provides comprehensive information about the demographic trends in Spain from 1955 to 2050, including population, migration, urbanization, age and fertility rates. This data can be utilized to gain a better understanding of population structure changes of Spain over time and helps answer some important questions such as: What is the overall trend in population growth? How has migration affected population change? How is the median age changing?

To make the most effective use of this dataset you should begin by exploring each column one by one. You can see an overview of each year's data using summary statistics like mean, median or mode which can help you identify any interesting trends that might exist among these metrics. Next investigate how each statistic has changed over time by creating a line graph for each of them. These visualizations will help you compare different variables side-by-side and better understand their relationships with one another. Finally, analyze all observations together to form your conclusions about demographic patterns in Spain from 1955 to 2050 and how they have impacted its overall population makeup

Research Ideas

• To calculate the rate of population growth over the years and predict future population levels in Spain.
• To analyze migration trends of people from abroad moving to Spain and compare it to those of Spanish citizens leaving or entering the country.
• To study age trends in Spain, including median age for both general population and specific regions within the country, as well as fertility rates/birth rates for each demographic group/region

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Population_of_Spain_Historical.csv | Column name | Description | |:---------------------------------|:------------------------------------------------------------------------------| | Year | Year of the data point. (Integer) | | Population | Total population of Spain in a given year. (Integer) | | Yearly% Change | Percentage change in population from the previous year. (Float) | | Yearly Change | Change in population from the previous year. (Integer) | | Migrants (net) | Net migration rate of Spain in a given year. (Integer) | | Median Age | Median age of the population in a given year. (Float) | | Fertiliy Rate | Fertility rate of citizens in a given year. (Float) | | Density(/km2) | Population density of Spain in a given year. (Float) | | Urban Pop | Percentage of population living in urban areas in a given year. (Float) | | Urban Population | Population living in urban areas in a given year. (Integer) | | Country's Share of World Pop | Percentage ...

The dataset contains records of terrestrial, aquatic and riparian birds (Aves) collected in the lower valley of River Sabor (NE Portugal), in the scope of the monitoring programs of the Baixo Sabor Hydroelectric Dams (AHBS), promoted by EDP – Energias de Portugal, S.A.. Specifically, the dataset relates to monitoring carried out to evaluate the effectiveness of measures implemented to mitigate the impacts of AHBS. In this case, the mitigation measures refer to #34;MC1 - Compensatory Habitat of Vilariça Stream#34;. The dataset includes the results of bird surveys made in the Vilariça stream from January 2013 to September 2014. Sampling was performed in selected points distributed along the stream, up to 5 km upstream of the mouth. The presence and identity of bird species were assessed by visual and/or acoustic observations, with binoculars. The records include taxa identified at the species or genus levels.