Tag: Mobility

Intergenerational Occupational Mobility in Belarus

20221113 Intergenerational Mobility Belarus Image 01 representing Intergenerational occupational mobility

This brief presents an analysis of the magnitude of the intergenerational occupational mobility in Belarus, taking into account a differentiated gender effect. The analysis considers movements along the occupational scale for individuals with respect to their parents, both through an aggregate magnitude (using transition matrices and mobility rates) and in detail (using a multinomial logit model), using data from the 2017 Generations and Gender Survey for Belarus. The findings show, firstly, that the downward intergenerational changes of occupational status have a strong gender bias: downward mobility is higher for men than for women. Secondly, the probability of moving up the social ladder is higher for women than for men in Belarus. Additionally, the results verify the important role of education as a mechanism towards reaching a society with more equal opportunities. In particular, the effect is more intense for individuals with higher education.


Intergenerational social mobility is defined as the movement of individuals from the social class of the family in which they lived when they were young (the origin class) into their current class position (the destination class), where social class is determined by as decided by income, occupation, education etc. (Ritzer, 2007; Scott and Marshall, 2009).

One of the main results from the economic literature on intergenerational social mobility shows that the degree of social mobility depends on the characteristics of an individual’s family background. These characteristics include an individual’s choice to acquire human capital and corresponding type of education, innate and acquired abilities, gender differences, or the knowledge people acquire through lifelong learning or work experience (Behrman & Taubman, 1990; Dutta, Sefton & Weale, 1999).

However, such characteristics may encourage children to work in the same occupations as their parents, slowing down intergenerational change. Research on intergenerational mobility can help identify and remove barriers to mobility which could improve the effective distribution of human skills and talents, in turn increasing productivity and promoting competitiveness and economic growth.

This brief summarizes the results of the first research focused on intergenerational occupational mobility in Belarus (Mazol, 2022). The research attempts to obtain new empirical evidence on intergenerational social mobility in Belarus by examining the movements of individuals along the occupational scale in relation to their parents, while taking into account other relevant factors such as gender differences and educational background of the individuals. Two specific gender dimensions are introduced: on the one hand, this study analyzes whether mobility in occupational categories differs for men and women; on the other hand, it examines whether there is a difference in the transmission of occupational categories from fathers to sons in comparison to mothers to daughters.

Data and Methodology

The study makes use of data from the Generations and Gender Survey (GGS) conducted in Belarus in 2017 by the United Nations Population Fund (UNFPA) and the United Nations Children’s Fund (UNICEF) within the framework of the Generations and Gender Program of the United Nations Economic Commission for Europe. The survey provides information on a range of individual characteristics (age, gender, marital status, educational attainment, employment status, hours worked, wages earned, etc.) as well as household-level characteristics (household size and composition, religion, land ownership, location, asset ownership, etc.).

The research considers the subsample of respondents between 25-79 years old and utilizes the information on occupation of the respondent and his/her parents. In order to evaluate the intergenerational occupational mobility, occupations are ranked by their position in the occupational ladder according to the National Classification of Occupations, based on the International Standard Classification of Occupations (ISCO-08) This defines a ranking of occupations based on the performance area and qualification required to carry out the occupation, from armed forces occupations (ranking the highest), through  a manager, a professional, a technician or professional associate, a clerk, a sales worker, a skilled agricultural worker, a craft worker a plant and machine operator, ending with an elementary occupation ranking the lowest. The influence from the father’s/mother’s occupation on that of the son’s/daughter’s is then estimated.

The analysis is carried out partly by estimation of transition matrices and mobility rates, and partly by the use of a multinomial logit model that aims to analyze the impact of a set of covariates on intergenerational occupational mobility. The explanatory variables are: the highest degree of education an individual has achieved (educational attainment), gender, potential labor experience (calculated as the number of years an individual has regularly worked), status in the labor market (full-time or part-time), and region of residence. The choice of these independent variables relies on channels identified from relevant sociological and economic literature.

Figure 1. Intergenerational occupational transitions in percent, by gender lines

Source: Author’s estimates based on GGS.

The intergenerational transmission of occupational immobility is almost equal for men and women (31 percent and 30,1 percent respectively). Occupational upward mobility is far more common as compared to downward mobility. 39.7 percent of men, compared to their father’s, and 50.6 percent of women, compared to their mother’s, have better occupations. The gender differences may be explained by the high proportion of women with higher educational levels in Belarus.

The estimates of the marginal effects obtained by the multinomial logit model indicate that social occupational mobility in Belarus depends on personal and labor characteristics. Three possible states are considered in relation to father-son and mother-daughter gender lines: the individual experiences downward intergenerational occupational mobility as compared to their parent of the same gender (Y = 0); they remain in the same occupation (immobility) (Y = 1) or they experience upward intergenerational occupational mobility (Y = 2) (see Table 1).

Table 1. Estimates of the marginal effects corresponding to the multinomial logit model

Notes: Estimates reflect weighted data. Standard errors in square brackets. Significance: *** – 1% level, ** – 5% level, * – 10% level. OV – omitted variable. Source: Author’s estimates based on GGS.

As evident from Table 1, gender is an important determinant of intergenerational occupational mobility. In particular, the results show that women are more likely to move up the social ladder than their male counterparts, as men are 10 percentage points less likely to have upward occupational mobility than women with similar (on average) socio-economic characteristics, with all coefficients being statistically significant.

In terms of educational attainment, the findings show that, on the one hand, higher educational attainment has a positive and significant influence on upward occupational mobility, with the highest values displayed for higher education. The probability of moving up to the occupational ladder is around 27 percentage points higher for an individual within this educational group than for an individual with primary studies and similar (on average) socio-economic characteristics. On the other hand, higher education has a negative and significant influence on downward occupational mobility, indicating that the probability of moving down the occupational ladder is around 13 percentage points lower for a highly educated individual compared to an individual with primary education.

Considering human capital, there is a positive impact of potential labor experience on upward intergenerational occupational mobility. Specifically, the probability of moving up along the occupational ladder increases on average by about 0.3 percentage points for every additional year of labor experience.

Finally, the results show that full-time workers are more likely to move up the social ladder than their part-time counterparts. Full-time workers are about 12 percentage points more likely to experience upward occupational mobility and 11 percentage points less likely to face downward occupational mobility compared to their part-time working counterparts.


This brief summarizes the findings for the first study on intergenerational occupational mobility in Belarus.

Firstly, the findings indicate, from a gender perspective, that the probability of moving up the social ladder is higher for women than for men in Belarus.

Secondly, the research results verify the important role of education as a mechanism to reach a society with more equal opportunities. In particular, the effect is more intense for individuals with higher educational attainments.

Thirdly, potential labor experience positively influences the upward intergenerational occupational mobility. This may reveal an underlying effect from training (however an unobservable variable given the data provided by the GGS).

Lastly, the impact of employment status on intergenerational occupational mobility in Belarus depends on the stability of labor relations, where possessing a part-time job worsens one’s probability of accomplishing a social class advancement.


  • Behrman, J., and P. Taubman. (1990). The Intergenerational Correlation between Children’s Adult Earnings and Their Parents’ Income: Results from the Michigan Panel Survey of Income Dynamics. Review of Income and Wealth, 36(2), pp. 115-127.
  • Dutta, J., Sefton, J., and M. Weale. (1999). Education and Public Policy. Fiscal Studies, 20(4), pp. 351-386.
  • Mazol, A. (2022). Intergenerational Occupational Mobility: Evidence from Belarus. BEROC Working Paper Series, WP no. 79.
  • Ritzer, G. (2007). The Blackwell Encyclopedia of Sociology. Malden: Blackwell Publishing Ltd.
  • Scott, J., and G. Marshall. (2009). A Dictionary of Sociology. Oxford: Oxford University Press.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Why Do Scientists Move? The Mobility of Scientists

Image of a man walking inside the airport with his luggage representing a mobility of Scientists

This policy brief provides an overview of new evidence on the determinants of the mobility of scientists – high human capital workers who generate new ideas and expand the frontier of knowledge. New evidence from a large dataset of elite US life scientists shows that professional factors, including individual productivity and the quality of a scientist’s peer environment, matter for mobility. Strikingly, family structure also plays a significant role, with the likelihood of moving to decrease when a scientist’s children are in high school (14-17 years old). This suggests that even “star” scientists take into account more personal, family factors in their mobility decisions, likely due to the costs associated with disrupting their children’s social networks.

Workers often face an important decision during their career: should I move to a new city for a new job? Relocation is a complex decision that can involve numerous professional and personal factors, and some of these factors can constrain moves while others facilitate them. Relocating can, on one hand, mean significant transition costs in terms of uprooting one’s family and navigating a new city and workplace; on the other hand, it can open up new career opportunities and provide environments where one’s skills are put to better use.

Why should we care about whether and why workers move? Economic theory suggests that mobility is one channel through which worker productivity can be increased by improving the employer-employee “match”. Moreover, particularly for highly-skilled workers, the theory suggests that the mobility of workers can impact the productivity of their peers; if the human capital of the mobile worker “spills over” to their peers, then the peers left behind would experience a decline in productivity and those at the new destination would get a boost.

In light of this, understanding the mobility of scientists – high human capital workers who are generating new ideas and expanding the frontier of knowledge – is of particular importance when considering the potential role that mobility can play in increasing productivity and innovation, which are central to models of economic growth.

The Determinants of Mobility

While there is a growing literature trying to document how the mobility of scientists can impact their own productivity and the productivity of their peers (see e.g. Agrawal, McHale, & Oettl, 2014), a significant challenge is finding plausibly exogenous variation in both the timing and location choices of movers. In order to fully understand the impacts of mobility, we first need to know more about the determinant of mobility: why and when in their careers scientists move.

Several studies have examined the determinants of the mobility of scientists and inventors, but the literature has been hampered by lack of data that allow researchers to observe the relevant factors that may matter for mobility. These studies have tended to focus on professional factors for moves; especially how individual productivity measures, like a scientist’s number of publications, citations and patents, predict moves. Importantly, there has been less attention in this literature on the constraints to mobility, including more personal factors, like the role of children and family, and the quality of one’s peer environment.

The findings from these studies on the role of individual productivity for mobility is mixed with evidence pointing to a positive relationship (Zucker, Darby and Torero, 2002; Lenzi, 2009), and some evidence showing negative (Hoisl, 2007) and/or no effects (Crespi et al, 2007). One key professional factor that has remained underexplored in these studies is the quality of the peer environment, or how one’s colleagues can influence the choice of moving.

Moreover, very few studies have been able to examine non-professional factors such as the role of family and children. There is some evidence on family factors and inventor mobility from Sweden, where detailed data is widely available but within country mobility is relatively low (Ejermo and Ahlin, 2014). There is also some evidence from the sociology of science literature showing that children influence the scientific performance and mobility of scientists. Using data from the U.S. Census, Shauman and Xie (1996) find that children tend to constrain mobility; for women, children negatively impact mobility regardless of the age of the children, while for men, it is older, high school-age children that tend to constrain mobility. However, given that the study uses Census data, individual productivity measures are limited, which are needed to compare the effects for similarly accomplished scientists.

Evidence from Elite Life Scientists in the US

In Azoulay, Ganguli and Graff Zivin (2016) we examine the determinants of mobility of elite life scientists in the U.S. and for the first time, provide evidence on both professional and personal determinants of mobility. We use a unique panel dataset we compiled from the career histories of 10,004 elite life scientists to understand why and when scientists make decisions to move to new locations. We are able to observe the transitions scientists make between institutions, and focus on moves that are at least 50 miles apart (based on distance between the zip codes of the institutions) to increase the likelihood that a transition leads the scientists to change their place of residence.

The dataset includes individual productivity measures of publication counts and the U.S. National Institutes of Health (NIH) funding data. We also measure the quality of the peer environment at the scientists’ origin and destination locations using publication and funding counts of peers. We define peers as both collaborating and non-collaborating peers (those who are close in “idea space”), and those who are geographically close (less than 50 miles apart) and those who are distant (more than 50 miles apart). Finally, to examine the personal factors, for each scientist in our sample, we hand-collected information on their children, including each child’s year of birth.

Through regression analysis, we find that individual productivity is a positive predictor of moves, which is consistent with several other studies (Zucker, Darby and Torero, 2002; Coupé, Smeets & Warzynski, 2006; Lenzi, 2009; Ganguli, 2015). We also provide new evidence on additional professional factors that influence the propensity to move. For example, we find that obtaining recent NIH funding deters moves, perhaps as a result of transaction costs associated with transferring federally funded research between institutions. We also find that a scientists’ peer environment is a significant predictor of mobility, as scientists are less likely to move when the quality of the peer environment near their home institution is high and more likely to move when the quality of the peer environment at distant institutions is high.

Figure 1. Age of Children and Mobility: Age of Oldest Child


Figure 2. Age of Children and Mobility: Age of Youngest Child


Our most striking result is the role that family structure plays for mobility. We find a significant decrease in mobility when scientists’ children are of high school age. The likelihood of moving increases just before their oldest child enters high school, and again when their youngest child is beyond high school age. Figure 1 shows a notable spike in distant moves just before the oldest child in the household enters high school (11-14 years old), while Figure 2 shows a similar spike after the youngest child completes high school (18-20 years old). In both figures, the relationship between age of children and local (less than 50 miles) moves does not show a similar spike. This relationship between mobility and age of children persists in regressions that allow us to control for productivity measures and potential confounders.


This brief has discussed theory and evidence related to scientific mobility. New evidence from a large dataset of elite life scientists shows that while professional factors do matter for mobility, we also find that even “star” scientists take into account more personal, family factors in their mobility decisions, likely due to potential disruptions to the social networks of their children.

Given that there is still little evidence about what drives relocation decisions, it is important for further analysis of these issues, and our study raises several more questions for researchers to examine, many of which have important policy implications. For example, what is it about recent NIH grants that deter mobility – the terms of the grant contracts or costs of moving personnel and equipment? Regarding the family factors, we were unable to look at differences among female and male scientists, but an important question for further research is whether the age of children and other factors appear to affect women’s and men’s relocation decisions differently.


Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

The Relationship between Education and Migration. The Direct Impact of a Person’s Education on Migration

20140623 FREE Network Policy Brief featured image 01

This brief is based on a section from a large policy report, which investigates to what extent education directly influences major migration decisions. The results indicate that education does not have a clear and persistent effect on most of the migration decisions of Ukrainians — while in 2005-2008 education did not have any effect on the probability of migration at all, in 2010-2012 an inverse relation between qualification and probability of migration appeared. It has been observed that education is positively related to the probability of finding high profile positions, such as professionals, technicians or clerks. Still, the analysis of 2005–2008 data tends to support the “brain-waste”, or better to say, “skills-waste” hypothesis for white-collar Ukrainian migrants but not for blue-collar workers. In 2010-2012 the hypothesis doesn’t hold. *