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
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
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.
Recent economic research suggests that childhood environments in part determine success in life. So far, most of this evidence comes from rich countries. In a new paper, we use education data to measure intergenerational mobility across 26 African countries and find large differences across space. Results using data on migrants suggest that regions have causal effects on social mobility of Africans.
Why do people “make it” in life? Few of us can claim, as Robert Strauss, former U.S. ambassador to the Soviet Union and Russia, once quipped, that we were born in a log cabin we built ourselves. One chunk of individual success in climbing the social ladder is determined by our parents – be it through their genes (Sacerdote 2002, 2004), their parenting style (Doepke and Zilibotti 2017), or their income and connections. Another chunk is individual effort. Companies like Apple or Google were started in garages. That leaves our surroundings. Can the places we grow up in raise us up or pull us down? A growing body of research suggests that they can.
Growing evidence that “places matter” for individual mobility
At the forefront of these efforts, Chetty and Hendren (2018a, 2018b) have compared the incomes of American children to those of their parents. They link parents to kids through social security numbers in tax returns. Among families that moved, they find that children exposed to places with higher average social mobility for longer during childhood do better than children exposed to places with lower average mobility. Importantly, this holds when comparing the kids of parents with the same income and other observable characteristics, i.e. holding the “starting line” constant for everyone. Their findings have been reproduced for the U.S. (Chetty, Hendren, and Katz 2016), (Chyn 2018), Canada (Laliberte 2018), Australia (Deutscher 2018), and Denmark (Eriksen 2018). By contrast, studies identifying the causal effects of places on individual mobility in developing countries are still rare (a recent contribution by Asher, Novosad, and Rafkin (2018) on India is a notable exception).
New evidence from Africa
In a new paper (Alesina et al. 2018) we fill part of this gap by examining intergenerational mobility in Africa. After decades of stagnation, there is optimism about Africa’s future. Growth has returned (Young 2012), and some now see Africa as a continent of “1.2 billion opportunities” (Economist 2016). At the same time, anecdotal evidence suggests large inequalities, indicating that the recent aggregate gains may not be broadly shared, and that social mobility remains limited.
Measuring intergenerational mobility using education data
Measuring intergenerational mobility in Africa is difficult because of patchy data. Economists typically think of mobility in terms of income or wealth. In Africa, we lack tax records as well as administrative information linking children to parents. Instead we rely on censuses from 26 African countries and measure mobility using education data on children that share a household with their parents. [Card, Domnisoru, and Taylor 2018; Azam and Bhatt 2015; Narayan et al. 2018; Black and Devereux 2011 also study intergenerational mobility using education data.]
We measure upward mobility as the likelihood that kids of parents with less than primary education complete at least primary school. Similarly, we call an individual downwardly mobile if her parents have completed at least primary education and she has failed to do so. We compute these measures among children aged 14-18. This gives them enough time to complete primary school if they were ever going to do so. At the same time, most children at that age still live with their parents, which limits potential bias from co-residence selection.
Using education to measure social mobility has five advantages. First, education is a broad measure of living standards, reflecting not just income, but also aspirations and capabilities. Second, unlike income, much of which is informal and therefore under-reported in poor countries, schooling can be easily measured. Third, education, once completed, remains fixed and so intergenerational mobility can be assessed early in life. Fourth, “Mincerian returns” – how much extra income one more year of schooling commands in the labor market – seem to be especially high in Africa (Young 2012; Psacharopoulos 1994; Caselli, Ponticelli, and Rossi 2014), suggesting that education is a meaningful proxy of income. Finally, more schooling is correlated with many positive outcomes: household asset ownership, lower fertility, and even support for democracy. These correlations hold strongly comparing two individuals living in the same place, which means that education “quantity” is a useful stand-in-measure of living standards, even if the quality of schooling differs from place to place.
Main data patterns
The census data give us millions of individual observations to accurately measure intergenerational mobility over time (birth-cohorts) and in small geographic areas. First and most prominently, the descriptive analysis reveals differences in mobility both across and within countries. Figure 1 shows the geography of upward mobility across the 26 countries. Darker regions indicate places with lower mobility – children of illiterate parents are less likely to finish primary school.
Figure 1. Upward mobility across Africa
Source: Alesina et al. 2018
Country-differences are clearly important – South Africa is more mobile than Mozambique. Still, even within countries, there are vast differences as figure 2, which zooms in on Ghana, illustrates.
Figure 2. Upward mobility in Ghana
Source: (Alesina et al. 2018)
In some regions in Northern Ghana, average mobility is below .2 while it exceeds .8 in Accra, the capital. Second, while mobility does increase over time, these increases are modest and most pronounced in the most recent decades. This is still consistent with overall rising education, since average schooling in Africa was low until recently. Taking patterns one and two together, the persistent variation in mobility between places is more important than changes in mobility over time.
What accounts for differences in mobility across space? By far the strongest correlate of intergenerational mobility is the average literacy in the same place in the generation of the parents. This means that, comparing two individuals that grew up as children of illiterate parents in different regions, the individual that grew up in the region that has higher literacy in her parents’ generation has a greater chance of completing at least primary school. Several explanations might account for this pattern. Most simply, some regions have more schools than others, and can educate more individuals “per period”. One alternative story are peer effects: even though my parents are uneducated, I learn by example from the people around me that going to school is possible and desirable.
Beyond the correlation with initial education, we find that geography, colonial history, and at-independence development matter for intergenerational mobility. There are two important caveats to these results. First, pinning down the mechanism of why initial literacy and mobility are related remains a challenge. Second, these results represent correlations and not causally identified effects.
Causal effects of regions
To make causal inferences, we use data on families that have moved between two regions within a country in two ways. First, we compare siblings from migrant households, one child born in the origin of migration, the other in the destination. Figure 3 shows a (binned) scatter plot of the association between average birth-region upward mobility (computed among non-migrants) on the horizontal and individual likelihood of upward mobility on the vertical axis, conditional on household as well as birth-cohort effects. The slope indicates that kids born in a region with a ten percent higher mobility are 2.65 percent more likely to complete primary school compared to their siblings born in a different region with lower mobility.
Figure 3. Migrant vs non-migrant siblings
Source: (Alesina et al. 2018)
Second, we compare migrants that moved at different ages during childhood. Figure 4 plots the effects on individual outcomes of moving from a place of on average zero mobility to a place where all children of uneducated parents become educated against the age of the child at which the move occurred, once again comparing individuals within the same household. As intuition would suggest, earlier moves to better regions have larger positive effects than later moves, and effects turn insignificant towards the end of the period relevant for primary school.
For both empirical strategies, the sibling comparisons (enabled by household fixed effects) are crucial to separate treatment effects of regions from sorting whereby illiterate parents that may be more motivated/capable in educating their children move to regions with greater average opportunities.
Figure 4. Migration exposure effects
Source: (Alesina et al. 2018)
New research points to the importance of “places” in shaping individual social mobility. Complementing several recent works on developed economies, we document that opportunities for educational advancement vary widely within and across African countries. The strongest correlate of differences in mobility between places are differences in the initial education level in the generation of the parents, with more educated places showing higher mobility. Using information on migrants, we find that regions have a causal impact on individual outcomes. Taken together, our results suggest that initial conditions have persistent effects on the transmission of human capital between generations and that overall regional differences in human capital transmission in turn matter for who “makes it” in Africa.
- Alesina, Alberto, Sebastian Hohmann, Stelios Michalopoulos, and Elias Papaioannou. 2018. “Intergenerational Mobility in Africa.” Centre for Economic Policy Research Discussion Paper 13497 https://cepr.org/active/publications/discussion_papers/dp.php?dpno=13497
- Asher, Sam, Paul Novosad, and Charlie Rafkin. 2018. “Intergenerational Mobility in India: Estimates from New Methods and Administrative Data.” Mimeo, Dartmouth College.
- Azam, Mehtabul, and Vipul Bhatt. 2015. “Like Father, Like Son? Intergenerational Educational Mobility in India.” Demography 52 (6): 1929–59. https://doi.org/10.1007/s13524-015-0428-8.
- Black, Sandra E., and Paul J. Devereux. 2011. “Recent Developments in Intergenerational Mobility.” In Handbook of Labor Economics, 4B:1487–1541. Elsevier.
- Card, David, Ciprian Domnisoru, and Lowell Taylor. 2018. “The Intergenerational Transmission of Human Capital: Evidence from the Golden Age of Upward Mobility,” 102.
- Caselli, Francesco, Jacopo Ponticelli, and Federico Rossi. 2014. “A New Dataset on Mincerian Returns.” Unpublished.
- Chetty, Raj, and Nathaniel Hendren. 2018a. “The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects.” The Quarterly Journal of Economics 133 (3): 1107–62. https://doi.org/10.1093/qje/qjy007.
- ———. 2018b. “The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates.” The Quarterly Journal of Economics 133 (3): 1163–1228. https://doi.org/10.1093/qje/qjy006.
- Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment.” American Economic Review 106 (4): 855–902. https://doi.org/10.1257/aer.20150572.
- Chyn, Eric. 2018. “Moved to Opportunity: The Long-Run Effects of Public Housing Demolition on Children.” American Economic Review 108 (10): 3028–56. https://doi.org/10.1257/aer.20161352.
- Deutscher, Nathan. 2018. “Place, Jobs, Peers and the Teenage Years: Exposure Effects and Intergenerational Mobility.” Mimeo.
- Doepke, Matthias, and Fabrizio Zilibotti. 2017. “Parenting With Style: Altruism and Paternalism in Intergenerational Preference Transmission.” Econometrica 85 (5): 1331–71. https://doi.org/10.3982/ECTA14634.
- Economist, The. 2016. “1.2 Billion Opportunities.” The Economist.
- Eriksen, Jesper. 2018. “Finding the Land of Opportunity Intergenerational Mobility in Denmark.” Mimeo.
- Laliberte, Jean-William. 2018. “Long-Term Contextual Effects in Education: Schools and Neighborhoods.” Mimeo.
- Narayan, Ambar, Roy Van der Weide, Alexandru Cojocaru, Silvia Redaelli, Christoph Lakner, Daniel Gerszon Mahler, Rakesh Ramasubbaiah, and Stefan Thewissen. 2018. Fair Progress?: Economic Mobility Across Generations Around the World. World Bank Publications.
- Psacharopoulos, George. 1994. “Returns to Investment in Education: A Global Update.” World Development 22 (9): 1325–43. https://doi.org/10.1016/0305-750X(94)90007-8.
- Sacerdote, Bruce. 2002. “The Nature and Nurture of Economic Outcomes.” American Economic Review 92 (2): 344–48. https://doi.org/10.1257/000282802320191589.
- ———. 2004. “What Happens When We Randomly Assign Children to Families?” NBER Working Paper 10894. https://www.nber.org/papers/w10894.
- Young, Alwyn. 2012. “The African Growth Miracle.” Journal of Political Economy 120 (4): 696–739. https://doi.org/10.1086/668501.
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.
To understand the nature of income inequality one needs to know how persistent the inequality is across generations. The same inequality levels could conceal different intergenerational mobility. We utilize the Russian Longitudinal Monitoring Survey (RLMS-HSE) to find out how large intergenerational mobility in Russia is as measured by income, educational and occupational mobility. We find that although a sizeable upward intergenerational educational mobility, there is a pronounced occupational immobility and a low level of intergenerational income mobility. Indeed, the position of children in the income distribution is highly correlated with the income position of their parents, especially their mothers.
Sizeable and non-decreasing inequality in Russia poses a threat to social stability and long-term sustainability. Inequality in Russia has remained high throughout the transition period, and even slightly increased in the 2000s; the Gini inequality index rose from 0.397 in 2001 to 0.416 in 2014. The ratio of average incomes of the highest decile to those of the lowest decile also increased from 13.9 to 16 during this same period. This income gap is driven primarily by the gap between incomes of the top decile and all of the others: the top decile is estimated to have thirty percent of total monetary income in the economy. Furthermore, income inequality originates in earnings inequality: the top decile of wage earners gets thirty five percent of total wage earnings in the economy.
A key question is how persistent the inequality is, given that the same inequality levels could conceal different intergenerational mobility. In particular, social stability is challenged when income inequality is stable across generations, or put differently; there is little intergenerational mobility. Economic developments of the last 25 years seem to increase the risks of getting this problem in Russia.
Data and research methodology
We employ Russian Longitudinal Monitoring Survey (RLMS-HSE) to find out how large intergenerational mobility in Russia is as measured by income, educational and occupational mobility (Denisova and Kartseva, 2016). The RLMS-HSE questionnaires in 2006 and 2011 contain questions on dates of birth, education and occupation of the father and mother of the respondent when the respondent was 15 years old.
To study occupational and educational mobility, we use the subsample of respondents of 25-55 years old and utilize the information on education and occupation of the respondent and his/her parents. We then estimate whether the parental education level predicts the probability that children have a university degree, a secondary or a junior professional degree.
To study intergenerational occupational mobility, we estimate influence of parental occupation on the probability that the child works as a manager, a professional, a technician or professional associate, a clerk, a qualified worker or an unskilled worker.
To study the child-parent income correlation based on RLMS is trickier. There is a panel component in RLMS but it is not long enough to study intergenerational mobility directly since we for most cases are not able to observe both parents and children during their working ages. To overcome the problem we impute wages for parents. In particular, we choose respondents aged 25-35 (children) in 2006 (and 2011). We then identify respondents born in the period 1945-1961 (1945-1966 for children in 2001) (‘parents’) and use the labor market information for this group as of 1995 (2001 as robustness check) to impute parental wages. We estimate a wage equation (separately for males and females) on the sample of ‘parents’ and then use the estimated returns (coefficients) and the reported age and education of respondent’s mother and father to impute wages of respondent’s parents.
We follow Björklund and Jantti (1997) to estimate the child-parent correlation of earnings based on the equation:
delta= β0 + β1X+ β2 delta_father + β3 delta_mother + ε
where delta=log(wage/average wage in respective sample), X – age, education, settlement type, region. Standard errors are clustered on primary sampling unit.
Intergenerational educational mobility
Our analysis shows that the education of parents, high professional (university) and secondary professional in particular, is a major determinant of children’s education. Moreover, there are clear signs of upward educational mobility across generations for both males and females: the coefficients in the transition parent-child matrix are significantly higher above the diagonal (Table 1).
Table 1. Father-child education matrix
The probability to have a university degree is 2.4 percentage points higher if the mother’s education is at university level (as compared to secondary school), and 2.1 percentage points higher if the father’s degree is at university level (as compared to secondary school). A secondary professional degree of parents also increases the probability of a child getting a university degree by about 1 percentage point. The probability of having secondary professional degree decreases if the father or mother has a university degree.
Intergenerational correlation of occupations
There are signs of sizeable occupational rigidity between generations, especially for the top two occupational groups (managers and professionals). The probability that a child works in the same occupational group is the highest for parents-professionals: it is 40% for fathers-professionals and 35% for mothers-professionals. Surprisingly, it is also rather high for parents employed as skilled workers – about 20%. These patterns survive controlling for other variables.
The correlation of parent-child wages measured for 2006 data are presented in Table 2. The results point to the sizeable average intergenerational rigidity of relative wages: the wage elasticity of children’s wages with respect to parental wages is about 0.4. This is at the level of the intergenerational wage rigidity in the US (Solon 1999).
There is sizeable gender asymmetry in the rigidity: we observe a high and significant correlation of son-mother wages, but an insignificant correlation of son-father wages. There is no significant correlation of daughter-parents wages.
Table 2. Parent-child income correlations, 2006
Generational poverty stemming from low intergenerational income mobility is a threat for sustainable development in any country. The economic and social development in transition seems to increase the risks of having this problem in Russia. Our estimates show that although there is sizeable upward intergenerational educational mobility in Russia, there is a pronounced occupational immobility, and low level of intergenerational income mobility. Indeed, the position of children in the income distribution is highly correlated with the income position of their parents, especially mothers. These findings are worrisome signals important for the design of policies of sustainable development.
- Björklund, Anders; and Markus Jantti, 1997. “Intergenerational Income Mobility in Sweden Compared to the United States,” American Economic Review, 87(5), 1009–18.
- Denisova, Irina; and Marina Kartseva, 2016, “Intergenerational Mobility of Russian Households”, mimeo
- Solon, Gary, 1999. “Intergenerational Mobility on the Labor Market,” Chapter 29 in Handbook of Labor Economics, Vol.3 edited by O.Ashenfelter and D.Card , 1761-1800.