Enes Olgun and Furkan Baki Zaralıoğlu of Istanbul Medipol University set out to answer a deceptively simple question: what determines whether a country attracts internationally mobile talent? The literature offers six familiar answers — economic attractiveness, career opportunities, work–life balance and livability, institutional trust and governance quality, cultural compatibility, and migration policies and incentives. Each has its own explanatory tradition, from push–pull wage-differential accounts to human capital theory, from the quality-of-life and amenity literature to institutionalist accounts of governance, from cultural-distance studies to the policy and regulatory strand. What the literature has never agreed on is which of these is most decisive, and the two authors argue that the disagreement is not accidental. Because the determinants have been examined largely in isolation and treated as mutually independent, existing rankings cannot separate the factors that drive the system from those that merely register its outcomes — a distinction on which policy design depends.

The methodological core of the study is an integrated spherical fuzzy DEMATEL–AHP framework, a mouthful that hides a genuinely novel piece of decision mathematics. DEMATEL, the Decision-Making Trial and Evaluation Laboratory, recovers the causal structure among criteria: it asks which factors influence which, aggregates those judgments into a total-relation matrix, and classifies each criterion into a cause group (net influencer) or effect group (net recipient). AHP, the Analytic Hierarchy Process, elicits relative importance through pairwise comparisons. The innovation here is to run both within a spherical fuzzy environment, a generalization of fuzzy set theory that admits three independent degrees — membership, non-membership, and hesitancy — subject to a unit-sphere constraint. In plain terms, when a panelist says a factor has high influence, the mathematics records agreement, disagreement, and uncertainty simultaneously rather than collapsing them into a single number. The authors also introduce a defuzzification anchored at the No-Influence point of the linguistic scale, so that a unanimous judgment of no influence maps onto a zero entry, preserving the zero semantics of the classical crisp scale while retaining the representational flexibility that distinguishes spherical fuzzy sets from their intuitionistic predecessors.

Judgments were elicited from a purposively sampled panel of ten internationally mobile professionals working in academia, engineering, human-resource management, and medicine, each having completed a degree in one country, migrated, and worked in the destination country for at least three years. Panelists were based in six countries — Czechia, Germany, the Netherlands, Oman, Qatar, and the United States — and supplied thirty directed influence judgments and fifteen pairwise importance comparisons each. Crucially, the authors did not treat all experts as equally reliable. A distance-based expert weighting procedure derived stage-specific weights from consensus proximity, rewarding panelists whose judgments sat closer to the group consensus. Because the two stages elicit different relations — perceived influence and preferential importance — consensus alignment was assessed separately within each stage, preventing disagreement about causal influence from contaminating the aggregation of preferential importance.

The results deliver a finding that should unsettle any talent-attraction strategy built on a single ranking. In terms of stated importance, economic attractiveness ranks first with a weight of 0.209, followed by career opportunities (0.178), work–life balance (0.170), and institutional trust (0.169), with cultural compatibility (0.139) and migration policies (0.135) closing the ranking. But in terms of causal role, the ordering is almost inverted. Migration policies and incentives exert the largest net influence in the system, at +1.548, and institutional trust follows at +1.379. The three determinants the panel rates most important — economic attractiveness, career opportunities, and work–life balance — all belong to the effect group. Economic attractiveness combines the highest prominence in the system, 13.786, with a moderately negative net effect of −0.458: it is heavily involved in the causal structure, but predominantly as a recipient.

The strongest directed links in the total-relation matrix tell a coherent story. The four links that exceed the threshold by the widest margin all originate in institutional trust or migration policy: institutional trust into economic attractiveness (1.403), migration policy into career opportunities (1.396), migration policy into economic attractiveness (1.384), and institutional trust into career opportunities (1.377). The quality of institutions and the design of migration policy shape the economic, career, and livability conditions that internationally mobile professionals experience, rather than the reverse. This does not contradict the literature documenting that skilled migrants sort on earnings and wage differentials; it re-situates that evidence. Earnings differentials are likely to be the channel through which institutional and policy conditions become observable to a prospective migrant, which may explain why economic attractiveness dominates stated preferences. The economic advantage a destination offers is, in substantial part, a reflection of the institutional and regulatory environment that shapes it.

The authors are careful about what this causal language means. The DEMATEL indices summarize influence as perceived and reported by the panel; they are not effects identified from realized migration flows. What the cause–effect classification recovers is where a group of experienced and, in the majority, disciplinarily qualified observers locate the leverage in the system — a claim about a shared model of how the determinants act upon one another, not an estimate of how much a given intervention would move a given outcome. The recommendations should be read as an ordering of where to intervene first, not as predictions of the return to intervention. Within that scope, the policy implications are pointed. Institutional trust and governance quality occupies the most actionable position in the importance–influence map, combining above-average importance with the second-largest net influence and the highest total influence exerted on other criteria, 7.395. Measures directed at the rule of law, corruption control, administrative predictability, and the reliability of contract enforcement are not adjacent to talent policy but constitutive of it. Migration policy, meanwhile, presents the sharpest contrast: the panel assigns it the lowest stated importance, yet it exerts the largest net influence. The most plausible reading is that experts evaluate migration policy as a precondition rather than an attraction — visa regimes and residence rules are not what draw a skilled individual to a country, but they determine whether the other determinants can operate at all. Its low standing in stated preferences should be read as a measure of its invisibility to those it affects rather than of its inefficacy. Unlike institutional quality, migration policy is a fast-moving instrument that can be redesigned within a legislative cycle, though whether a redesign realizes the leverage the panel attributes to it depends on policy quality, administrative capacity, and political feasibility.

The robustness analysis is unusually thorough for a study of this kind. Replacing the consensus-based weights with uniform weights leaves the results essentially intact: the Spearman correlation between the two SF-AHP weight vectors is 0.943, with a single adjacent reversal involving the near-tied pair of work–life balance and institutional trust. On the influence side, the prominence ranking is identical and the cause–effect classification changes only for cultural compatibility, whose net effect moves from −0.015 to +0.104 — both within rounding distance of zero. Repeating the computations with a conservative anchoring constant changes no classification and no prominence rank. A leave-one-out analysis, recomputing the entire pipeline ten times with one panelist omitted each time, shows that economic attractiveness is ranked first in all ten runs, while institutional trust and migration policies constitute the cause group in all ten runs and migration policies carry the largest net influence in nine of them. The causal structure proves markedly more stable than the importance ranking. A further check partitions the panel by competence: five panelists hold disciplinary expertise in political science and five come from medicine, engineering, and human-resource management. Running the influence stage separately on each stratum returns the same cause group in both. The influence-based weights obtained through the DEMATEL-based Analytic Network Process also correlate strongly with the preference-based weights, at 0.943, so the reported ranking is reproducible from a derivation that does not pass through the pairwise preference judgments at all.

One qualification deserves attention. Agreement on the importance ranking itself is limited: Kendall’s W across the individual importance orderings equals 0.168 and does not reach conventional significance, while agreement in the influence stage is more than twice as high and significant at 0.398. The authors show that this heterogeneity is structured rather than diffuse — panelists resident in EU settlement regimes display substantial and significant agreement at W = 0.677, while the pooled figure results from combining internally coherent groups ordered differently. The aggregate should therefore be read as the center of a genuinely dispersed set of views, and the causal structure carries the more robust part of the message. The panel is purposive rather than representative, the analysis is framed at a general level without distinguishing destination contexts, and the cause–effect classification expresses net perceived influence rather than econometric identification. Replication with larger panels stratified so that destination regime and disciplinary background are crossed rather than aligned would sharpen the picture considerably.

What the study ultimately contributes is a framework that separates what matters from what moves the system, converting a ranking into a policy sequence. A study that measured importance alone would rank economic attractiveness first and stop; the two-stage design shows that intervening there addresses the symptom. The three determinants in the effect group — career opportunities, which absorb more influence than any other criterion at 7.211, work–life balance, and economic attractiveness — merit systematic monitoring rather than direct targeting, since they register whether interventions elsewhere in the system are working. Cultural compatibility, near-neutral in influence and last in prominence, is better understood as a buffer that conditions the cost of adaptation after the decision to move has been taken. For governments competing in the global talent market, the message is uncomfortable but actionable: the fastest way to improve the economic conditions that skilled migrants say they want is to strengthen the institutions and policies that make those conditions possible in the first place.

Subject of Research: Decision Science and Migration Policy

Article Title: Determinants of international talent attraction: an integrated spherical fuzzy DEMATEL–AHP framework with stage-specific expert weighting

Article References: Olgun, E., & Zaralioǧlu, F. B. (2026). Determinants of international talent attraction: an integrated spherical fuzzy DEMATEL–AHP framework with stage-specific expert weighting. Frontiers in Political Science, 8, Article 1959067. https://doi.org/10.3389/fpos.2026.1959067

Image Credits: AI Generated

DOI: 10.3389/fpos.2026.1959067

Keywords: talent attraction, skilled migration, spherical fuzzy sets, DEMATEL, AHP, multi-criteria decision making, institutional trust, migration policy, Determinants, international, talent, attraction

The research team, led by Vasim Ahamad of the Department of Migration and Urban Studies at the International Institute for Population Sciences in Mumbai, together with Rahul Rahul, Ram B. Bhagat, and T. V. Sekher, analyzed data from 64,711 respondents aged 45 and above who took part in the first wave of the Longitudinal Ageing Study in India, conducted in 2017 and 2018. LASI, as the survey is known, was carried out with guidance and ethical approval from the Indian Council of Medical Research and is designed to follow its participants over time, making its first wave an unusually rich snapshot of mid-life and old-age wellbeing across the country. Because the study used this existing secondary dataset, no new information was collected directly from respondents, and the analysis was approved as part of the original LASI protocol.

Two design choices shaped what the study could reveal. First, migration status was defined using each respondent’s place of last residence, a standard demographic measure that classifies people as migrants if their current home differs from where they previously lived. Second, perceived discrimination was measured with an everyday discrimination scale, an instrument widely used in social epidemiology that asks people how often they encounter slights such as being treated with less courtesy or respect than others, receiving poorer service, or feeling that others act as if they are not smart or as if they are afraid of them. The everyday discrimination approach captures the routine, cumulative texture of unfair treatment rather than dramatic single events, which researchers consider especially important because chronic low-level mistreatment is thought to wear down health over years and decades.

The headline numbers are striking. Fifty-seven percent of the older adults in the sample were internal migrants who were growing older at their destination rather than in the place where they had spent their earlier lives. After adjusting for a battery of socioeconomic and demographic factors in multivariable logistic regression models, migrants had significantly higher odds of reporting discrimination than non-migrants, with an adjusted odds ratio of 1.38 and a 95 percent confidence interval running from 1.28 to 1.48. In practical terms, even when older migrants and non-migrants were compared at similar levels of income, education, and other characteristics, the experience of having moved still raised the likelihood of perceiving discrimination by a meaningful margin. That statistical adjustment matters: it indicates the effect is not simply an artifact of migrants being poorer or less educated on average, though those disadvantages certainly compound the picture.

The study’s most unexpected result concerns gender. Male migrants faced markedly higher odds of reporting discrimination, with an adjusted odds ratio of 1.59 and a confidence interval of 1.44 to 1.77, compared with an adjusted odds ratio of 1.21 and a confidence interval of 1.08 to 1.36 among female migrants, each relative to non-migrants of the same comparison. This pattern runs counter to a large body of literature in which women report more discrimination than men, and it points to something specific about the male migrant experience in India. One plausible reading, consistent with the structure of Indian migration itself, is that men who move for work enter competitive labor markets, informal settlements, and public spaces where they interact daily with strangers, landlords, employers, and officials, while many women migrate primarily through marriage and become embedded more quickly in household and neighborhood networks. The study reports the pattern without overinterpreting its causes, but the gap between the two confidence intervals is wide enough to suggest a genuine difference rather than statistical noise.

The type of move mattered as much as the fact of moving. Inter-state migrants, who cross linguistic and cultural boundaries when they relocate, reported greater discrimination than intra-state migrants, who move within a state where language, food, and social norms are more familiar. Among migration streams, rural-to-urban migrants fared worse than those moving along other routes, a result that resonates with decades of qualitative research on how city dwellers view newcomers from the countryside. And the timing of the move shaped outcomes too: recent migrants reported a higher level of discrimination than long-settled migrants, suggesting that the sharpest social friction occurs in the years immediately after arrival, when a person is still visibly an outsider, and that discrimination may fade, though not disappear, as migrants put down roots over decades.

Why should perceived discrimination concern public health researchers and not only sociologists? A substantial international literature links the chronic experience of discrimination to elevated stress, higher blood pressure, worse mental health, and earlier biological ageing, mechanisms that operate through sustained activation of the body’s stress response systems. For older adults, whose physiological reserves are already diminishing, this cumulative burden can be particularly consequential. The authors of the new study frame their findings around the idea of dual vulnerability: older migrants in India face disadvantage on two fronts simultaneously, first because of their age in a society where ageing itself can bring diminished respect and exclusion, and second because of their migrant status, which marks them as outsiders in the communities where they now live. Each form of disadvantage reinforces the other, and the everyday discrimination scale used in the study cannot always separate them cleanly, which the researchers acknowledge implicitly by treating the two as intertwined dimensions of the same lived experience.

The scale of the phenomenon gives the findings national significance. Internal migration in India involves more people than international migration to the entire rest of the world combined, with census and survey estimates consistently placing the number of internal migrants in the hundreds of millions. As the country’s population ages, the cohort of people who migrated in their youth and are now entering their later decades will keep expanding, which means the population exposed to whatever social penalties migration carries is growing rapidly. The LASI sample, with more than sixty thousand respondents aged 45 and above, provides an unusually solid foundation for detecting these patterns, and the use of variance inflation factors to check for multicollinearity among the predictors reflects standard epidemiological care in the modeling. The adjusted odds ratios were estimated with confidence intervals that exclude the null value of one in every reported comparison, indicating that the associations are statistically robust at conventional thresholds.

The authors argue that their results underscore the need for appropriate policies and legal frameworks to address and prevent discrimination against older migrants in India. That recommendation arrives at a moment when the country is still building the institutional infrastructure to serve a mobile, ageing population. Migrants frequently lack local documentation, social networks, and familiarity with grievance mechanisms, and older migrants may additionally face barriers in accessing healthcare, pensions, and social protection schemes that are tied to place of residence. The study did not test specific interventions, so the policy implications remain inferential, but the empirical pattern it documents, a consistent elevation in reported discrimination across migration types, streams, and durations, gives policymakers a clear target: the transition period after a move, and the settings where rural-to-urban and inter-state migrants concentrate.

For a study of this kind, the limitations are worth keeping in view. The data come from a single cross-sectional wave, so the analysis captures associations at one point in time rather than changes over the life course, and perceived discrimination is, by definition, a self-reported measure shaped by expectations, memory, and willingness to disclose sensitive experiences. The place of last residence measure also cannot capture the full complexity of circular or repeat migration, which is common in India. Yet even with these caveats, the study makes a contribution that is hard to dismiss: it takes a question usually asked about young workers, namely whether moving within India exposes people to unfair treatment, and asks it about people in the second half of life, where the stakes for health and dignity are arguably higher. The answer it finds, that migrants who are growing old away from home are more likely to feel treated as lesser, turns a demographic statistic into a human problem, and gives researchers a starting point for the longitudinal work that LASI’s later waves will eventually make possible.

Subject of Research: Perceived discrimination among older internal migrants in India

Article Title: The influence of migration status on perceived discrimination among older adults in India: a nationwide cross-sectional study

Article References: Ahamad, V., Rahul, R., Bhagat, R. B., & Sekher, T. V. (2026). The influence of migration status on perceived discrimination among older adults in India: a nationwide cross-sectional study. BMC Public Health. https://doi.org/10.1186/s12889-026-29800-0

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29800-0

Keywords: internal migration, perceived discrimination, older adults, LASI, India, public health, ageing, rural-to-urban migration, inter-state migration, social epidemiology, migration policy, everyday discrimination scale