Employment probabilities of migrants and local residents in the labor market of the Republic of Tuva - Ledeneva, Victoria Yu.; Rakhmonov, Abubakr Kh. (The New Research of Tuva, 2026)
This article examines differences in employment odds between migrants and local residents in the Republic of Tuva (Russia) within a peripheral and economically constrained regional labor market. The empirical basis is individual-level microdata from the 2010 All-Russian Population Census (10% sample) harmonized in IPUMS International. The choice of these data is motivated by their status as the most recent comparable and publicly accessible microdata source that allows measurement of migrant status and key socio-demographic characteristics at the individual level, enabling statistically valid cross-group comparisons. In order to enhance contemporary relevance, the findings are interpreted in light of recent scholarship on migrant incorporation into the Russian labor market and descriptive administrative series on migration inflows to Tuva for 2018–2023, which indicate persistent structural constraints on employment and a predominantly work-oriented profile of mobility.A binary logistic regression model is applied to estimate the probability of being employed (versus unemployed) among migrants relative to local residents, controlling for age (including a quadratic term), sex, marital status and education (N = 580 economically active individuals aged 15–64). Results indicate near-parity: migrants exhibit comparable or slightly higher odds of employment than local residents (OR = 1.05; p < 0.001), although the substantive magnitude of this difference is small. At the same time, a stable gender gap is observed (women have lower employment odds), and education remains one of the strongest predictors of employment. These patterns are interpreted as evidence consistent with employment-oriented and selective migration into the region. They remain relevant for understanding integration mechanisms in a “compressed” labor market. The study is explicitly statistical (not field-based); future research should test gender selectivity more directly, examine interactions between sex and migrant status, as well as assess job quality and sectoral employment structure.
Keywords: migration; employment; Tuva; labor market; integration; logistic regression