Tag: Education

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.

Higher Education and Research in times of War and Peace: Key Insights from the 2022 FREE Network Conference

20220925 Higher Education and Research Image 02

More than thirty years after the collapse of the Soviet Union, Europe is struck with war following the Russian aggression on Ukraine. Russia’s war on Ukraine entails lost human capital, both in actual lives lost and due to major disruptions to key functions of the society, such as education and research. In light of this, the FREE Network, together with the Centre for Economic Analysis (CenEA) and the Stockholm Institute of Transition Economics (SITE), hosted the public conference “Higher Education and Research in War and Peace“ in Warsaw on the 10th of September 2022. This policy brief is based on the presentations and panel discussions held during the conference.

The large-scale Russian invasion of Ukraine has disrupted an entire society, including the education system, with Ukrainian schools just recently partially welcoming back students to the classrooms for the first time since the 25th of February 2022. Closing schools has severe impacts on a population, as highlighted by the recent Covid-19 pandemic. The lockdown and closure of schools around the world following the virus have had and will continue to have massively negative consequences globally, with severe losses in human capital due to lost years of education. This is especially in countries where access to online education is limited or of poor quality. Inequalities also rise following the closure of schools and girls return to school in fewer numbers than their male counterparts. The disruption to the Ukrainian education system will result in lost human capital and lowered levels of knowledge among the population. The war has further restricted access to relevant information for many Ukrainians but also for Russians, making people susceptible to the increased Russian propaganda and misinformation about the war on Ukraine depicted within and outside of Russia.

In light of this, the FREE Network gathered representatives from its affiliated institutions and other relevant actors in the region to discuss the relevance and necessity of continued support for higher education and research within social sciences in Ukraine, and more broadly in Eastern Europe and post-Soviet countries. The conference and the overarching theme related back not only to the original ambition of the FREE Network, namely to support outstanding academia within economics and relate it to policy work but also to the current situation in Europe and the existing threat from Russia to this objective.

This brief will initially cover the work carried out by the Kyiv School of Economics (KSE) in response to the Russian aggression, followed by thoughts on Russia’s role in the evolution of knowledge and human capital in the region. The brief continues by covering the benefits and positive outcomes of investments into education and research and lastly concludes with reflections on the role of the FREE Network.

The Kyiv School of Economics’ Response to the Russian Aggression

The war on Ukraine put the spotlight on the importance of high-quality academic institutions as a safety net for the government to maintain vital functions to society. The Vice President for Policy Research at KSE, Nataliia Shapoval, gave a brief overview of how KSE’s work has changed since the Russian war on Ukraine and its implications. Shapoval initially painted a picture of the disruption to the Ukrainian society caused by the Russian aggression, explaining how KSE stepped up during the first months of the war, in some areas doing the work of ministries. While the government has mainly taken back some duties, the KSE is still providing policy advice in areas related to the effects of sanctions, estimates of damages, and food security among others. KSE is also highly active within the areas of education and health, working with Ukrainian schools through the KSE Charitable Foundation (KSE CF) to ensure students can safely return to the classrooms.

Another important aspect of the work carried out by KSE concerns spreading knowledge about and shedding light on the situation in Ukraine. Through the various networks, by talking to colleagues within academia but also to the media, KSE is trying to explain what has happened and is still happening in Ukraine. According to Shapoval, there is a need for delivering correct information and to keep attention fixed on the situation in Ukraine such that people are kept aware of what is going on in the region.

Shapoval also regularly returned to the role of education and research for the present and future Ukraine. According to Shapoval, avoiding brain drain and ensuring Ukrainians are equipped with the necessary knowledge is key to rebuilding a future Ukraine founded on well-functioning democratic institutions. To facilitate this, the KSE is offering two programs, Memory and Conflict Studies (a multidisciplinary field concerned with how the past can be understood and remembered, and how it might impact the present transformation of societies) and Urban Studies, both aimed at covering the future need for competence within these fields. Further mentioned by Shapoval is the fact that, due to the war, many Ukrainians have left the country and are being educated elsewhere. While this partially ensures intellectual human capital is not lost, these students must be kept anchored to Ukraine through networks to ensure they will return back to help rebuild Ukraine. This is especially important in order to counter the ongoing evolution in Russia.

Thoughts on the Role of Russia in the Region

While the recent developments in Ukraine have of course disrupted education and research in more severe and tangible ways, the situation for independent researchers in Russia has also deteriorated. Torbjörn Becker, Director of SITE, emphasized how several Russian colleagues in exile still collaborate with the FREE Network on policy work and research. Becker also further stressed how they will be paramount once Ukraine wins the war, as will the role of partnerships for a future transformation of the Russian society. Acknowledging that there are many Russians (especially amongst academics in exile) who oppose the war, Shapoval however stressed the disturbing fact that many Russians do seem to support the Russian aggression and that the role of Russia as a destructive force in the region cannot be understated. This was seconded by Tamara Sulukhia, Director of the International School of Economics at Tbilisi State University (ISET). Sulukhia argued that Russian politics slow down and disturbs the free states within the region, and hampers organizations and countries from moving in the right direction in regard to democracy, economic evolution and integration toward Europe. Both Shapoval and Sulukhia reminded the audience that even with a Ukrainian victory, and this in a war which is defining the future of democracy in the region, Russia will persist. Russia has proven time and again, by effectively occupying 23 percent of Georgia as of 2008, with the occupation of Crimea in 2014 and with the most recent war on Ukraine, to be a real military threat to post-Soviet countries. Even though Russia losing the war would shift the power dynamics in the region, the ever-present threat of Russia is not only of a military character. Russia also attempts to impact education, research and knowledge more generally by promoting a Soviet-style education and by altering reality through propaganda and false information.

While discussing the current situation of higher education within economics in Belarus, Dzmitry Kruk, Deputy Academic Director of the Belarusian Economic Research and Outreach Center (BEROC), regularly came back to the negative impacts from Russia on the quality of education and research. Where the western style education is free but also differential, Soviet-style education is centred around learning how to fulfil instructions, according to Kruk. The Belarusian educational system is anchored to Russia and as a result Belarusians today have what Kruk referred to as a “spoilt mental map”. The necessity of free education and research outside the Russian alternative (which is mainly published in Russian and with a post-Marxist view of the world) is vital in order to equip people with the tools to respond to the new types of dictatorship evident in the region. Young people within academia who have experienced freedom and have had the opportunity of thinking for themselves will also be vital on the future path toward democracy. Kruk’s opinions were furthered by Shapoval stating how education must and should counter the risk of brainwashing in the region and in the world as a whole. Shapoval argued the necessity of countering propaganda with the help not only of education but also the legislation of media and social media and enforcement of international laws in general. The necessity of ensuring new values for intellectuals and students in times to come is of paramount value and, according to Shapoval, as important to halting the Russian imperialist visions today as it was some thirty years ago. Shapoval further argued that the threat from Russia’s ambitions should be met not only with education and research but also through installing a sense of hope and prosperity among young people.

Investments into Education and Research as a Safeguard and Development Driver

While countries within the turbulent region differ, not least in regard to overall political ambitions and structure, in most of them investments into education and research have been paying off. KSE’s expertise allowed it to work closely with the Ukrainian government, standing strong in their fight against Russia. The impact from investments into education and research in the region is also evident in both Georgia and Latvia.

Sulukhia argued ISET to be, and to have been, a key contributor to human capital among Georgians as well as others in the Caucasus region. Sulukhia argued this to be especially important when under occupation, mentioning how Georgia has, since the occupation of the two regions of Abkhazia and South Ossetia, in all ways possible tried to ensure that the human capital of internally displaced people is not lost. ISET have ten folded its intake of students and is today providing world-class education in the Georgian language, effectively counteracting brain drain. Post-graduates are working in major institutions providing relevant knowledge and competence in key areas of not only the Georgian society but also other countries in the Caucasus. A similar picture was painted by Anders Paalzow, Rector at Stockholm School of Economics in Riga (SSE Riga). Paalzow specifically pointed out how the investments in education made in Latvia in the 1990s have truly paid off, with graduates having been absorbed into relevant parts of the Latvian society and the Baltics for decades.

Having previous students in key positions in society to ensure sound policy work (such as good fiscal and audit control of the countries in question etc.) is however not the only benefit of investing in education and research within the region. As emphasized by Sulukhia, institutes within the FREE Network and other networks alike are strategically vital in the sense that they ensure knowledge and evidence for policy makers and as they convey evidence-based messages for the general public. This is especially important in a time when the message of the developmental direction for the countries within the region has to be reinforced in order to stand against Russian misinformation and propaganda as well as voices questioning the benefits of European integration. Sulukhia emphasized how it is of importance that the relevance of education and research is rooted among the people and not only within academia to evade the risk of preaching to the choir. Vlad Mykhnenko, Fellow at St. Peter’s College at the University of Oxford, further argued it is necessary for academia to be much more policy oriented than what is the reality today. Researchers should comment on political events and public policy to ensure the outreach of knowledge and information, not just to help the public have a greater understanding of complex issues but also to help inform experts. According to Myhnenko, other researchers are keen on getting context-relevant knowledge and insights from economists working within the region.

The necessity of communicating the outcomes from investments within economics education and research and more broadly within social sciences was a recurring theme during the conference. Presenting the University’s engagement in various programs such as Erasmus+, Horizon Europe, The European Strategy for Universities etc., Professor Agnieszka Chłoń-Domińczak from the Warsaw School of Economics (WSE) outlined the importance of funding from the EU. Chłoń-Domińczak highlighted how EU support has enabled greater partnerships and internationalization and pointed out that while the transfer of knowledge and internationalization of students and researchers are of the essence, there is a need for also ensuring capacity building among other staff when building sound institutions. Internationalization through the exchange as a hedge against brain drain and as a means of improving the quality of academia was further emphasized by Michal Myck, Director of CenEA.

Chłoń-Domińczak, alongside Paalzow and the Swedish Ambassador to Poland, Stefan Gullgren, further argued the necessity to bridge between business and academia. This, especially as investments in social sciences, as compared to investments in natural sciences or technology cannot be commercialized. Additionally, the former havs payoffs in the long run which lowers investment incentives for firms making it even more crucial to communicate the large benefits to society of investments into the sphere. Ensuring consistent and continued support requires not only a good connection to businesses but also proper legal structures in place. As argued by Gullgren, the Swedish model with private businesses funding about 70 percent of research and education in Sweden, is made possible largely thanks to the fact that many investments are funnelled through foundations that are exempt from taxation when set up to finance research grants and education. Thus, one should consider not only business, academia and investors when thinking about future funding for research and education, but the legislative framework as well, especially in contexts such as the future rebuild of Ukraine.

As for how the benefits from investments into social sciences best are communicated, opinions shifted among participants throughout the day. On the one hand, Becker’s argument of being visible not only in traditional media but on social media alike was met by Shapoval, highlighting the need for a regulatory framework for both platforms. On the other hand, Myhnenko’s argument for more policy oriented and outreaching research was met by Kruk claiming there is a risk of researchers within economics deviating too far from research within the field. Kruk also addressed the argument of being available on social media by countering that in his view, researchers should refrain from work based on what generates clicks or reads.

The Relevance of the FREE Network in times of War

Considering the evidence brought forth during the conference by colleagues within the FREE Network, be it the suppression of BEROC in their efforts of founding a School of Economics in Belarus, the effects on the KSE from the war on Ukraine, or the rise of anti-European expressions in Georgia, the necessity of the network was at the end of the day perhaps clearer than ever. As highlighted by virtually all speakers during the conference, internationalization through networks such as the FREE Network fosters open minds, allows for improvements within all aspects of academia, and enables the exchange of thoughts, ideas and experiences. Although the heterogeneity of the region should not be overlooked and investments made in accordance with this, the similarities between the countries within the FREE Network outnumber the differences. The immediate threat from Russia must be met with knowledge and fact-based information as well as high-quality education and research being made available among the population in the region as a whole. To ensure a continued transition within the region, the risk of brain drain must be evaded through continuous support to the social sciences, as these have the power to truly transform nations.

Concluding Remarks

The FREE Network public conference in Warsaw was the first in-person conference since the outbreak of the Covid-19 pandemic. The benefits of meeting in person were however overshadowed by the ongoing Russian aggression on Ukraine and ultimately on democratic ideals, including those of independent academia. We hope to welcome all FREE Network institutes to next year’s conference in Kyiv, to further discuss how outstanding education and research can help rebuild a sovereign Ukraine.

List of Participants

  • Torbjörn Becker, Director of SITE
  • Agnieszka Chłoń-Domińczak, Professor at WSE
  • Stefan Gullgren, Swedish Ambassador to Poland
  • Dzmitry Kruk, Deputy Academic Director, BEROC
  • Michal Myck, Director of CenEA
  • Vlad Mykhnenko, Fellow, St. Peter’s College, University of Oxford
  • Anders Paalzow, Rector SSE Riga
  • Nataliia Shapoval, Vice President for Policy Research at KSE
  • Tamara Sulukhia, Director of ISET

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.

School Lockdown: Distance Learning Environment During the COVID-19 Outbreak

Image of an empty classroom representing distance learning environment during the COVID-19 outbreak

Students in Poland, as in many other countries, have been obliged to participate in distance learning as a result the COVID-19 pandemic and the lockdown of schools. Successful participation in this format of schooling requires some basic equipment (a computer with Internet connection) as well as adequate housing standards, in particular a separate room during online classes. Based on the data from the Household Budget Survey 2018, in this brief we take a closer look at the living conditions of schoolchildren in Polish households and their access to adequate infrastructure. Our findings indicate that in the case of 11.7 percent of households with schoolchildren aged 6-19 years housing conditions are insufficient for home schooling. Additionally, for about a quarter of households with schoolchildren distance learning can be a challenge due to inadequate technical equipment. These conditions vary significantly with household income and across urban and rural areas, which signals that prolonged distance learning in Poland is likely to exacerbate the influence of children’s socio-economic background on inequalities in education outcomes.


In connection with the coronavirus COVID-19 outbreak, Poland’s Minister of Education, in a Regulation introduced on the 20th March 2020, postponed the end date of the lockdown of Polish schools until the 10th April 2020. Also, the regulation requires that education be organized for school-age students during this period by means of distance learning channels and methods (Ministry of Education 2020a). It is the responsibility of the principal of every educational facility to make sure that such education is provided. Furthermore, a “Guide to Education” was developed by the Ministry of Education with information and instructions on distance learning for all interested parties, such as school principals, teachers, parents and students (Ministry of Education 2020b). Due to the restrictions on the movement of people during the state of epidemic in Poland, effective as of the 20th March 2020, electronic media (the Internet and, potentially, the telephone) should serve as the main channel of communication between teachers and students/ parents.

Thus, since the 25th March 2020, 4.6M students in Poland have been studying remotely, and any decisions on reopening schools or extending the lockdown depend on the course of development of the pandemic. Even at the time of “regular” access to schooling, the discrepancies in living conditions between students, in particular in terms of their housing conditions and household infrastructure, have a substantial impact on the overall quality of learning and educational outcomes (e.g. Author et al. 2019; Guryan et al. 2008), all the more so when students have to switch to distance learning. In the current situation, substandard housing conditions and lack of access to a computer or the Internet can make it difficult or outright impossible for many students to access education in the coming weeks. Fair and equitable assessment of students’ skills and knowledge may also be affected, as well as their future academic achievements, especially for the cohorts who are about to complete their Grade 8 in the primary school and those who are preparing for their secondary school graduation examination (Polish: Matura). For a student to be able to participate in distance learning activities and benefit from online learning materials, s(he) must have access to a computer terminal with an Internet connection at home. In addition, it seems that effective distance learning requires adequate housing standards, such as a separate room for studying. The “Guide to Education” says little about the importance of these infrastructure- and housing-related factors, merely recommending that a problem, if any, should be reported to the school, and an adequate solution should be implemented in consultation with the form master.

As argued in this Policy Brief, the unexpected need for schools to switch to a distance learning environment will underscore the magnitude of inequalities among households (HHs) in terms of their access to the infrastructure required for the students to benefit from distance learning opportunities and the living conditions in which such distance learning is supposed to proceed. The findings in this Policy Brief are based on the latest data from the 2018 Household Budget Survey (HBS), as made available by Statistics Poland (GUS). Notably, while HH status regarding computer equipment and Internet access may have improved since the time the survey was conducted, it can be assumed that the living conditions reflected in survey data are an accurate representation of the present-day status.

The first part of the Policy Brief presents the living conditions of the HHs with students aged 6-19, attending schools of all levels, according to the number of rooms in a house or apartment. The analyses presented in the second part of the Policy Brief are focused on HH infrastructure required for distance learning. According to HBS data, in 11.7 percent of HHs with students the number of rooms is equal to or lower than the number of students. A total of 833K students live in those HHs. During the state of epidemic, when the adult population is also committed to the lockdown and self-isolation, the living conditions may not be optimum for home schooling. According to the 2018 HBS data, in 7.1 percent of HHs with students there is no computer or other similar device with Internet access, and in 17.3 percent of HHs the total number of such devices in the HH is lower than the number of students living in the HH. That means that for more than 1.6M students distance learning may be a serious challenge for technical reasons. In that context, it should be noted that the shortage of computer equipment in HHs varies significantly with HH financial conditions and place of residence. As discussed in the Policy Brief, the highest percentage of the HHs with inadequate supply of the equipment necessary for distance learning is reported in the bottom half of the income distribution, and in the HHs in rural areas.

1. Living Conditions of Students in Poland

The living conditions in which students are expected to continue their education over the next few weeks can affect the outcomes of distance learning and their academic achievements. Students who share a single-room dwelling unit with other members of the HH will experience particularly harsh conditions, especially in view of the lockdown also applying to adults. There are over 130K such students throughout Poland (Table 1), with top percentages reported in large cities (4 percent of HHs with students; Figure 1). Many HHs living in a two-room dwelling unit or house include only one student, but there are 490K students in two-room dwelling units or houses who share the two rooms with their school-age siblings.

In rural areas such HHs represent only 5.7 percent of the total (Figure 1), but in cities with populations exceeding 100K the figure is 7.6 percent, which means that the affected student population is 174K and 140K, respectively (Table 1). Another piece of pertinent statistics: in many of the HHs in multi-room dwelling units or houses (i.e. with three or more rooms), the number of students is equal to or greater than the number of rooms. In cities with populations exceeding 100K the figure is 1.2 percent of HHs with students, while in rural areas this ratio is 2.5 percent, with 116K students affected.

As illustrated in Figure 2, housing conditions that can be described as not conducive to distance learning vary significantly with HH income. At the bottom end of the income distribution scale, among HHs with students, there are significantly more HHs in which the number of rooms may be inadequate in relation to the number of students living there. In every fifth HH from the second and third income decile group, each of the students living there may not have a separate room at their disposal; whereas in the group of top income HHs (from the tenth decile group) with students, this ratio is only 3.7 percent.

Table 1 Student count in the breakdown according to their living conditions and place of residence

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015.)

Figure 1 Count of rooms and students in households by place of residence

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015.)

Figure 2 Count of rooms and students in households by income decile group

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015).
Nota Bene: Income decile groups are ten groups covering 10 percent of the population each, from households with the lowest disposable income to the most affluent households, on the basis of the so-called equivalent income, i.e. taking into account the differences in the size of the household using the modified OECD equivalence scale.

2. Distance Learning Infrastructure in Households

To be able to use electronic educational materials available on the Internet; to participate in classes conducted by teachers on various online platforms; or even to send back homework assignments over the Internet; students need to have home access to a computer connected to the Internet (for simplicity, the term “computer” used in this Policy Brief means a computer or a similar device with Internet access).

According to 2018 HBS data, close to 330K students do not have home access to a computer connected to the Internet (Table 2). In the case of another 1.3M students, the number of such devices is lower than the number of students in the HH, so it may not be sufficient to satisfy the needs of all students undergoing parallel remote education in the HH. In other words, as many as 7.1 percent of HHs with students have no access to distance learning at all due to the lack of appropriate equipment, while for a further 17.3 percent of the HHs the shortage of relevant infrastructure may significantly impede distance learning efforts (Figure 3).

As shown in Figure 3, the challenge of inadequate infrastructure for distance learning is reported much more frequently in single parent HHs, as compared to couples with school-age children. Among students raised by a single parent, every tenth family does not have a computer with Internet access, and in every eighth family the number of such devices is insufficient for all the students living in the HH. Among married couples with children, 6.4 percent of families report no computer, and in 18.2 percent of families the number of computers is lower than the number of students in the HH.

Table 2 – Students with/without a computer with Internet access, by place of residence

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015).
Nota Bene: The values shown in the Table refer to computers with an Internet connection. The total number of students is slightly different from the value shown in Table 1, because 2018 HBS survey sample for HH infrastructure has been reduced.

Figure 3 Computers with Internet access in households with students, by place of residence and family type

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015). Nota Bene: Family types are listed within HH category.

Map 1 Computers with Internet access in student population, by region of the country

a) Student has no computer with Internet access at home

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015).

b) Student must share the computer with school-age siblings

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015).

According to HBS data, students living in rural areas may be particularly exposed to problems in using distance learning. Although the percentage of HHs with students that do not have a computer with Internet access in rural areas is similar to that reported for urban areas (regardless of the size of the city/town), there are visible discrepancies in the availability of a sufficient number of hardware items between different categories defined according to place of residence. In rural areas one in every five HHs reports that the number of computers in the HH is lower than the number of students, whereas in big cities (population above 100K) this issue is reported by 9.7 percent of the HH.

Inequalities in access to distance learning are also visible across Poland’s regions. As illustrated on Maps 1a and 1b, students from Lubuskie Voivodeship do not have access to a computer connected to the Internet (12.6 percent) or have to share a computer with school-age siblings (37.5 percent) much more often than students from other regions of the country. For comparison, 4.4 percent of the students from Zachodniopomorskie Voivodeship do not have a computer at home, and every fifth student does not have a computer for their personal use.

Significant differences in access to the infrastructure required for distance learning are also manifested in division by income deciles (Figure 4.) In the population of HHs with students, in the two bottom decile groups (i.e. among 20 percent of HHs with the lowest income), as many as one in ten HHs does not have a computer connected to the Internet, and another 20 percent plus cannot provide individual access to a computer for each of the school-age children. At the other end of income spectrum, only about 4.1 percent of HHs with students do not have a computer, and in the case of another 8.3 percent students do not have a computer for their personal use.

Figure 4 Computers with Internet access in households with students, by income decile group

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015). Nota Bene: Income decile groups are ten groups covering 10 percent of the population each, from households with the lowest disposable income to the most affluent households, on the basis of the so-called equivalent income, i.e. taking into account the differences in the size of the household using the modified OECD equivalence scale.


According to 2018 Household Budget Survey data, close to 330K students do not have home access to a computer connected to the Internet; and in the case of another 1 320K students the number of computers in the HH is lower than the number of students living in the HH. Under such circumstances, distance learning on a regular basis during the COVID-19 outbreak is either outright impossible or very difficult. Due to infrastructure shortages, distance learning is particularly difficult for students living in the HHs in rural areas (30 percent of all HHs with students), but the difficulties of this nature are also reported by students living in big cities (17.1 percent of HHs). Single parent families are affected by a lack of computer equipment more frequently than married couple families (11.2 percent vs 6.4 percent); and the situation varies to a large degree depending on HH income levels. While in the HHs with students grouped in the bottom decile as much as 33.9 percent do not have access to a computer or have a computer to share with their school-age siblings, in the HHs from the top decile group the corresponding percentage is almost three times lower.

The housing conditions in which Polish students follow the curriculum are an additional impediment to distance learning. More than 130K students live in one-room dwelling units, and nearly 700K live in multi-room units where the number of rooms is the same or lower than the number of students in the HH. In terms of the housing stock, access to an adequate number of rooms for effective distance learning also varies with income level. While in the bottom two decile groups the number of rooms in relation to the number of students is insufficient for 16.6 percent and 20.7 percent of the HHs, in the top two income deciles the corresponding ratio is as low as 4.5 percent and 3.7 percent.

The longer the duration of the distance learning regime, the greater the impact of inequalities in access to distance learning for students. It may take a particular toll on the cohorts which complete their final year of each stage of education. The inequalities will be compounded by differences in support in distance learning the students can receive from their parents or guardians. A population of 720K students live in single-parent HHs, and 380K of those single parents are economically active; and speaking of the population of students living together with both parents, there are 2.6M students in whose case both parents were economically active at the point of the pandemic outbreak. Even if some parents have now been forced to cut down on their professional responsibilities, others continue working – either at the workplace or from home.

For many reasons, students as well as their parents, guardians and teachers are looking forward to students’ return to schools – it will be a long-awaited sign that the epidemic situation has stabilized. Yet, this moment will be especially important for those students for whom distance learning was a particular challenge due to their living or infrastructure-related conditions. In an effort to reduce inequalities in access to distance learning, educational facilities in cooperation with local authorities, should extend special support to the students for whom distance learning is difficult due to objective causes. It seems that the first step should be to collect specific information about the distance learning environment available to students and, if necessary, to fill in the gaps in computer equipment and Internet access. Furthermore, if the epidemic allows, it seems purposeful to introduce, to a limited extent and with appropriate security measures, direct contact between students and teachers, especially where effective distance learning turns out to be difficult or impossible to implement.



This brief was originally published as a CenEA Commentary Paper of 28th March 2020 on www.cenea.org.pl. The analyses outlined in this brief make part of the microsimulation research program pursued by CenEA Foundation. The data used in the analysis is based on the 2018 Household Budget Survey, as provided by Poland Statistics (GUS). Poland Statistics (GUS) has no liability for the results presented in the brief or its conclusions. Conclusions presented in the brief are based on Authors’ calculations based on the SIMPL model.

CenEA is an independent research institute without any political affiliations, with main research focus on social and economic policy impact assessment, with a particular emphasis on Poland. CenEA was established by the Stockholm Institute of Transition Economics (SITE) and is a Polish partner of the FREE Network. CenEA’s research focuses on micro-level analyses, in particular in the field of labor market analysis, material conditions of households, and population ageing. CenEA is the Polish scientific partner of the EUROMOD international research project (European microsimulation model), and maintains its microsimulation model SIMPL. For more information, please visit www.cenea.org.pl.

This brief was prepared under the FROGEE project, with financial support from the Swedish International Development Cooperation Agency (Sida). Research in the FROGEE project contributes to the discussion of inequalities in the Central and Eastern Europe with a particular focus on the gender dimension. For more information, please visit www.freepolicybriefs.com. The views presented in the brief reflect the opinions of the Authors and do not necessarily represent the position of the FREE Network or Sida.

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 Learning Crisis: Combining Incentives and Inputs to Raise Student Achievement

20190527 The Learning Crisis

As school enrolment in low- and middle-income countries has increased substantially in the last couple of decades, attention has instead turned to the poor quality of education. This ”learning crisis” (UNESCO 2013) manifests itself in primary school students without basic skills in language and mathematics, and high school students being vastly outperformed by their peers in high-income countries (World Bank 2018). In this policy brief, I give a very brief background to the learning crisis and report on a research project we have implemented and evaluated in the Democratic Republic of Congo (DRC) with the aim of improving student learning in primary education. The intervention consisted of an incentivized program to stimulate more usage of existing textbooks for self-study, and the impact was evaluated through a randomized experiment (Falisse, Huysentruyt and Olofsgård 2019).

Education systems in many low- and middle-income countries fail to deliver actual learning at the level necessary for people and societies to thrive. According to leading international assessments of literacy and numeracy, the average student in low-income countries performs worse than 95 percent of the students in high-income countries. According to an assessment of second-grade students in India, more than 80 % could not read a single word from a short text or conduct two-digit subtraction. Students perform poorly also in some European middle-income countries; more than 75 % of students in Kosovo and the Republic of North Macedonia perform worse than the 25th percentile in the average OECD country (World Bank 2018). The reasons behind the learning crisis are of course many, ranging from poorly trained and absent teachers, lack of financial resources for infrastructure and learning material, malnutrition and lacking early childhood development, and sometimes weak demand.

Textbooks for Self-Study in the DRC

The learning crisis is particularly evident in fragile, low income countries. This is also where the major challenge to achieve the 2030 Sustainable Development Goal 4 of quality education to all lies (World Bank, 2018). Yet, very few interventions targeting student achievement have been evaluated in the most fragile countries of the world (Glewwe and Muralidharan 2016). This is a concern, since interventions that work in poor but stable environments may not be feasible or effective in even more resource constrained and violent environments (Burde and Linden 2013). In particular, there is an extra value in identifying interventions that are not only cost efficient, but also low cost in absolute terms and simple and transparent.

Projects focusing on school inputs have often yielded surprisingly disappointing results (Glewwe and Muralidharan 2016). One example is interventions focusing on textbook distribution despite belief in their effectiveness and investments from donors and governments (Glewwe, Kremer and Moulin 2009; Sabarwal et al. 2014). One major challenge with textbooks is that they for different reasons are often not used by teachers or pupils, and certainly not to their potential (e.g. Sabarwal et al. 2014). This raises the question of whether the potential of textbooks can be leveraged through incentives on their usage. A couple of recent papers have found that it is indeed the combination of inputs (including textbooks) and incentives that is critical to yield a significant impact on student test scores (Mbiti et al. 2019; Gilligan et al. 2018).

Following up on this idea we collaborated with the Dutch NGO Cordaid that is running a program in primary education in South Kivu, in eastern DRC, in 90 schools. We designed an intervention that encouraged 5th and 6th grade students from 45 randomly selected schools to regularly take home textbooks and use them for self-study. We used a mix of financial and non-financial incentives focused on the students, such as a public display of stars assigned to each student that brought math and French textbooks home and back in good condition, and an in-kind gift of pens and pencils for all students in classes regularly participating in the routine. We also offered participating schools a small flat compensation to compensate for lost and damaged books. The main goals of the intervention were to increase student achievement and to affect their aspirations for further study and more qualified careers.

To measure student achievement, we rely on self-conducted tests in the French language and math, but also high stakes national exam scores that determine eligibility to secondary education. Following the literature, we analyze test results using a model that assumes that baseline test scores capture student learning up to that point, so once this is controlled for end line results capture cleanly the added value of the intervention introduced. We also carefully address potential statistical problems due to slight unbalance between treatment and control groups, students from baseline not present at end line and poor compliance with the intervention in a small set of schools. The results are generally robust across different specifications of the details of the model.

We emphasize three main sets of results. First, we find that the students in the treatment schools (those selected to receive the books) scored significantly better than those in control schools on the French language tests. The estimated improvement was 1/3 of a standard deviation, which compares favourably with other interventions in developing countries targeting student test scores (Kremer et al. 2013). On the other hand, we found no significant impact on math scores. We cannot tell for sure why we observe this difference between French and math, but it should be noted that both textbooks were in French, suggesting that language could be learned from both books. It has also been suggested that math requires more supervision than language and that math is more ”vertical” in terms of skills progression while language is more ”horizontal”. That is, if students are far behind the curriculum in the textbook, they don’t have the necessary basic building blocks to understand the math problems. But for language, this matters less, as progress can be made in different areas more independently.

Secondly, students in treatment schools were more likely to sit and pass the national exam. This is important as this is a requirement for the continuation of schooling at a higher level. More qualified jobs, and jobs that require more French language skills, typically require at least secondary schooling. This is also consistent with the finding that students exposed to the intervention were more likely to aspire to non-manual jobs. Finally, the intervention was low cost and cost-efficient. In particular in fragile environments with very limited resources, this is essential. The intervention is also easy to implement and transparent and does not give raise to incentives to cheat as has been the case in some interventions linking incentives directly to student test performance.


The current key challenge in education policy in low- and middle-income countries is to improve student achievement while continuing the successful increase in enrolment despite often serious constraints in complementary inputs in the education production function. Financial resources for school infrastructure and material are limited, competent and motivated teachers are in short supply, and weak parental support and little early childhood development leaves children unprepared for sometimes too ambitious curricula. In such circumstances simple and low-cost interventions that make better use of existing resources are particularly valuable. In this project we designed and evaluated such an intervention, using incentives to stimulate more usage of existing textbooks, in a particularly challenging environment, Eastern DRC. We find a positive impact on French language skills and higher student aspirations as shown through greater participation in national exams required for continued education. On the other hand, we find no impact on math test scores. Serious sustainable improvement in student learning in a country like the DRC requires wholesale reforms to the education sector and substantially increased financial resources. Realistically, this is a long-run ambition. In the meanwhile, small low-cost interventions that match incentives with existing resources can significantly increase student achievement also in the short run.


  • Burde, Dana and Leigh L. Linden, 2013. “Bringing Education to Afghan Girls: A Randomized Controlled Trial of Village-Based Schools.” American Economic Journal: Applied Economics, 5(3), 27-40.
  • Falisse, Jean-Benoit, Marieke Huysentruyt and Anders Olofsgård, 2019. “Incentivizing Textbooks for Self-Study: Experimental Evidence on Student Learning from the Democratic Republic of Congo”, Working Paper.
  • Gilligan, Daniel O., Naureen Karachiwalla, Ibrahim Kasirye, Adrienne M. Lucas, Derek Neal, 2018. “Educator Incentives and Educational Triage in Rural Primary Schools.” NBER WP 24911.
  • Glewwe, Paul, Michael Kremer, and Sylvie Moulin, 2009. “Many Children Left Behind? Textbooks and Test Scores in Kenya.” American Economic Journal: Applied Economics, 1(1): 112-35.
  • Glewwe, Paul and Karthik Muralidharan, 2016. “Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications”, in Handbook of the Economics of Education, pp. 653-743. Elsevier.
  • Kremer, Michael, Conner Brannen, and Rachel Glennerster, 2013. “The Challenge of Education and
  • Learning in the Developing World.” Science 340, 297-300.
  • Mbiti, Isaac, Karthik Muralidharan, Mauricio Romero, Youdi Schipper, Constantine Manda, Rakesh Rajani, 2019. “Inputs, Incentives, and Complementarities in Education: Experimental Evidence from Tanzania.” NBER WP 24876.
  • Sabarwal, Shwetlena, David K. Evans, and Anastasia Marshak, 2014. “The permanent input hypothesis: the case of textbooks and (no) student learning in Sierra Leone”, Policy Research working paper, no. WPS 7021. Washington, DC: World Bank Group.
  • UNESCO, 2013. “The Global Learning Crisis: Why every child deserves a quality education”, UNESCO, Paris.
  • World Bank, 2018. “World Development Report 2018: Learning to Realize Education’s Promise”, Washington DC: World Bank.

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.

Development of Belarusian Higher Education Institutions Based on the Entrepreneurial University Framework

Photo Of People Doing Handshakes representing Belarusian higher education

In contrast to developed Western countries, higher education institutions (HEIs) in transition economies such as Belarus do not have the pretension to being key actors in cutting-edge innovation and in creating entrepreneurship capital. Rather, they tend to educate job seekers or knowledge workers, as well as to adapt, redevelop and disseminate existing knowledge and technologies. At the same time, policy makers in Belarus have realized that transformation of HEIs is needed to respond to the global challenges. In this regard, this policy brief discusses prerequisites and factors conditioning the development of entrepreneurial HEIs in Belarus.

Capitalizing on state-of-the-art academic research, as well as on the custom-made survey of Belarusian faculty members, the brief concludes that Belarusian policy makers need to create a supportive institutional environment before requiring from HEIs outcomes of the entrepreneurial mission. First-priority measures for the current stance are delineated.

Entrepreneurial University and University 3.0

As a productivity factor, entrepreneurial activities started appearing in economic growth models at the beginning of the twenty-first century (Wennekers & Thurik, 1999; Wong et al., 2005). Consequently, the role of HEIs broadened from educating labor force and knowledge creation to development of “entrepreneurial thinking, action and institutions” (Audretsch, 2014) – HEIs took on the third “entrepreneurial” mission.

Well-studied outcomes of this mission are new firms (academic spin-offs, spin-outs, student-led start-ups), patenting, licensing and the development of entrepreneurial culture and attitudes among graduates and academics.

The concept of an entrepreneurial HEI is multifaceted and is explored within different research streams: from knowledge transfer to entrepreneurship education and HEI management. Consequently, there is no consensus in the understanding of the term “entrepreneurial university” that can, for this policy brief, be broadly defined as a HEI that acts entrepreneurially and is a natural incubator, creating a supportive environment for the startup of businesses by faculty and students, promoting an entrepreneurial culture and attitude for the purpose of responding to challenges of the knowledge-based economy, and facilitating economic and social development.

Figure 1. Evolution of the HEIs’ missions

20190208 Development of Belarusian Higher Education Picture1

Source: Adapted from Guerrero & Urbano (2012)

Meanwhile, the concept of “University 3.0” –mostly corresponding to the concept of “Entrepreneurial university” and adopted from J.G. Wissema – started appearing in Russian publications, where the number ‘3’ corresponds to the three HEI missions or to the third generation of HEIs. A possible explanation of this renaming is that, on the one hand, in the post-Soviet context entrepreneurship per se still does not have a positive meaning in a broader society and it is not associated to HEIs. On the other hand, it was expected that such numbering makes the evolution visible. However, this led to speculation on this numbering and gave rise to publications on University 4.0 that should correspond somehow to Industry 4.0 – the current trend of automation and data exchange in manufacturing technologies.

Admittedly, the entrepreneurial mission of HEIs is not associated or equaled to start-ups and knowledge transfer any more, but is increasingly considered as a procedural framework for HEI’s and individual’s behavior.

Belarusian Context

Political, economic, social, technological and legal conditions determine the path and the speed of the evolution of HEIs as well as their contribution to national economies in different stages of economic development. Thus, in Belarus – an efficiency-driven economy, i.e., a country growing due to more efficient production processes and increased product quality (World Economic Forum, 2017), – HEIs are considered to contribute to economic development if they successfully fulfill teaching and research missions. While the outcomes of the third mission are supposed not to be relevant at this stage (Marozau et al., 2016).

However, trying to replicate the success of Western HEIs in the development of the entrepreneurial mission, the Ministry of Education of Belarus initiated the Experimental project on implementation of the “University 3.0” model aimed at the development of research, innovation and entrepreneurial infrastructure of HEIs for the creation of innovative products and commercialization of intellectual activities.

In general, Belarus has a state-dominated well-developed, by some estimates, oversaturated higher education sector that remains mostly rigid and unreformed since the Soviet times. Belarus outperformed all CIS and EU countries except Finland in terms of the number of students per 10,000 population in 2014 (Belstat, 2017) and according to the World Bank has one of highest enrollment rates in tertiary education of about 90%.

Belarusian students have quite high entrepreneurial potential in comparison to other countries participating in the Global University Entrepreneurial Spirit Students‘ Survey (GUESSS).  Thus, in five years after graduation, 56.8% intend to be entrepreneurs, while the global average level is 38,2% (Marozau and Apanasovich, 2016). However, curricula of most specialties majors provided by Belarusian HEIs are not supplemented with formal and experiential entrepreneurship education to equip students with entrepreneurial competencies. Innovative methodologies and entrepreneurial approaches to teaching as well as faculty entrepreneurial role models are rare. Moreover, all changes in degree syllabuses need state approval that makes HEIs less flexible and nimble. The situation is further complicated by the fact that supporting entrepreneurial activity has not been an important part of the HEI culture.

Methodological Approach

We conducted online and face-to-face surveys of 48 Belarusian HEI authorities and faculty members that were based on HEInnovate self-assessment tool widely used by policy makers and HEI authorities (see Marozau, 2018).

Overall, emails were sent out to a population of 284 pro-active and advanced representatives of the Belarusian academic community whose email addresses were available in the databases of BEROC and the Association of Business Education. We benefitted from open-ended questions included in the questionnaire to study how representatives of Belarusian HEIs perceived the Entrepreneurial university (University 3.0) concept as well as its conditioning factors and potential outcomes.

Main Findings

First of all, we revealed that the Belarusian academic community is not unanimous in understanding the concept “Entrepreneurial university”. According to the main emphasis provided by respondents, we got the following distribution of answers about what an entrepreneurial is: 12 respondents associated the concept with knowledge transfer and commercialization; 7 respondents stressed the interrelation of teaching, research and innovations; 5 respondents believed that the concept is about earning money; 1 respondent indicated that an entrepreneurial university means developing entrepreneurial competences.

These findings demonstrate the general misunderstanding or fragmented understanding of the phenomenon that may lead to a negative attitude from both HEI staff and policy makers and stress the importance of raising awareness and providing training at least for decision makers and spokesmen.

Figure 2 demonstrates the results of the assessment of Belarusian HEIs against the categories proposed by HEInnovate (1 – very low; 5 – very high).

Figure 2. Assessment of HEIs

20190208 Development of Belarusian Higher Education Figure 2

Source: Author’s own elaborations

We distinguished pairwise between (i) HEIs that participated in the Experimental project and those that did not: (ii) estimates of faculty members that were aware of the concept and those who were not.

Surprisingly, the representatives of HEIs that were left beyond the scope of the Experimental project and those who were aware of the concept perceived their HEIs more advanced in all the areas.

To understand this paradox, we used the chi-square test for independence to discover if there was a relationship between two categorical variables – awareness of the concept and employment at a HEI participating in the Experimental project. Surprisingly, no statistically significant relationship was identified evidencing that implementation of the Experimental project went without raising awareness and wider involvement of faculty.

The analyses of answers to open-ended questions showed that many environmental factors are not only unsupportive to the HEI entrepreneurial development but jeopardize the sustainability of the higher education system in general.


The main conclusions from the study are as follows:

  • Belarus has not reached the stage of institutional development to foster entrepreneurial HEIs and to expect outcomes of the entrepreneurial mission. To some extent, this explains the skepticism and misunderstanding of the concept of “Entrepreneurial university” (University 3.0).
  • The main omission of the Experimental project is that the education and training of HEI authorities and faculty are not defined as first-priority measures. Such policy initiatives need to be clear in their objectives, tools, benefits and outcomes as well as evidence-based and open for discussion.
  • Comprehensive initiatives in this sphere should be developed and implemented in close collaboration with the Ministry of Economy that is responsible for entrepreneurship, the business environment, entrepreneurial infrastructure as well as the State Committee for Science and Technology that is subordinated to the Council of Ministers and deals with the state policy in its sphere.

An important concern here is whether it is currently feasible to have the measures that are relevant and not-for-show rather than half-way initiatives and sticking plaster solutions despite the lack of funding, and absence of elaborate study in the field.

  • Since the weakest area of Belarusian HEIs according to the HEInnovate tool is the problem of ‘Measuring impact’, the state should reconsider short-term target indicators for HEIs such as export growth rate and workforce productivity growth rate to stimulate investments the entrepreneurial transformation. It is worth monitoring such indicators as number of start-ups/spin-offs founded by graduates/faculty members; number of patents, licenses, trademarks co-owned by a HEI, income from intellectual property; number of R&D projects funded by enterprises etc.  Alternatively, the Ministry of Education could adopt the ranking of entrepreneurial and inventive activity of universities used in Russia.
  • Development of entrepreneurship centers as organizational units at HEIs – ‘one-stop shops’ or ‘single front doors’ for students, faculty, businesses – could be an initial step towards both raising awareness and the integration and coordination of entrepreneurship-related activities within a HEI in order to increase their impact and visibility of these activities.


  • Audretsch, David B., 2014. “From the entrepreneurial university to the university for the entrepreneurial society.” The Journal of Technology Transfer 39(3), 313-321.
  • Belstat (2017). Education in the Republic of Belarus. Statistical book.
  • Guerrero, Maribel, and David Urbano, 2012. “The development of an entrepreneurial university.” The journal of technology transfer 37(1), 43-74.
  • Marozau, Radzivon, Maribel Guerrero, and David Urbano, 2016 “Impacts of universities in different stages of economic development.” Journal of the Knowledge Economy, 1-21.
  • Marozau, Radzivon and Vladimir Apanasovich, 2016. National GUESSS Report of the Republic of Belarus. http://www.guesssurvey.org/resources/nat_2016/GUESSS_Report_2016_Belarus.pdf
  • Radzivon Marozau, 2018. Modernization and development of Belarusian higher education institutions based on the entrepreneurial university framework. BEROC Policy Paper Series, PP no.63.
  • Wennekers, Sander, and Roy Thurik, 1999. “Linking entrepreneurship and economic growth.” Small business economics 13(1), 27-56.
  • World Economic Forum, 2017. “Global Competitiveness Report 2017-2018”, edited by Klaus Schwab.
  • Wong, Poh Kam, Yuen Ping Ho, and Erkko Autio, 2005. “Entrepreneurship, innovation and economic growth: Evidence from GEM data.” Small business economics 24(3) 335-350.

Acknowledgments: The author expresses gratitude to Prof. Maribel Guerrero from Newcastle Business School, Northumbria University for her valuable comments and reviews as well as to Yaraslau Kryvoi and Volha Hryniuk from the Ostrogorski Centre (Great Britain) for coordinating the research project that has resulted in this policy brief.

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.

Poland’s Road to “High Income Country” Status: Lessons Learnt – Not Only for Other Countries

20180311 Poland Road to High Income Country Status Image 01

In this brief we summarize and discuss results presented in a recent World Bank Report focused on Poland’s path from middle to high-income country status. In the period until 2015, Poland’s economic development distinguished itself by its stability and consistency of the implemented reform package, and its inclusive nature. Poland became classified as a high-income country after only 15 years from gaining a middle-income status. At the same time, income inequality remained stable and absolute poverty levels fell significantly. The World Bank Report offers lessons from and insights for Poland, which are discussed from the perspective of the policies implemented by the governments in the last two years.

Poland’s status in the World Bank nomenclature has recently been “upgraded” from being middle to high-income country. While this categorization is only a nominal change, it reflects the country’s economic development over the recent decades and is an important recognition of the success of a wide range of reforms implemented across a broad number of areas. Notably, Poland moved from the middle to high-income status in a period of less than 15 years.

In a book recently published by the World Bank, it is argued that the Polish experiences from the reform process can serve as valuable lessons for countries that are in the process of, or have just embarked upon major socio-economic reforms, as well as for those, who have fallen into the so-called middle-income trap and are looking for solutions to their stagnant economies. At the same time, in comparison to other established high-income countries, there are a number of insights that Poland’s policy makers ought to bear in mind in order to stay on course of the reform process and continued stable growth.

Looking at policies of the recent governments, however, one gets a strong impression that some important insights have been ignored. As rapid population aging looms over the horizon, the lack of necessary adjustments combined with the risks to stability of the political and economic environment might in the medium run have significant implications for Poland’s further development.

The big picture

The key feature of the Polish socio-economic policy approach, over the period covered by the World Bank analysis (i.e. up to 2015), was a unique consistency of a broad direction taken by subsequent administrations. This allowed the reform process to develop without major breaks or U-turns, which ensured the overall stability of the socio-economic environment and provided stable investment prospects. The World Bank highlights the key role of institutions, including rule of law, property rights, and democratic accountability of different levels of government. Basic market institutions, including the respect for rules on price and product regulations, corporate governance and market regulations, as well as foreign trade and investment, have played a crucial role. This framework allowed for continued improvement in the efficiency of resource allocation – including the allocation between sectors of the economy, as well as between and within enterprises.

Crucially, Poland prepared well and took full advantage of the integration with the European Union. The EU accession was first used as a common anchor for stability of the reform process, and after 2004, the European funds became an additional engine of growth. At the macro level, stability of the fiscal framework with limited deficits and public debt were combined with appropriate regulation and supervision of the financial sector, an independent central bank, and close links to global markets.

Shared prosperity

While the above points provided the basis for Poland’s economic development, the Report highlights another unique feature of Poland’s success, namely the degree to which the fruits of the process have been equally shared among different groups of society. The overall income inequality has remained relatively stable, with the Gini coefficient actually falling slightly between 2005 and 2014, from 0.351 to 0.343. Relative income poverty levels remained stable over this period (at about 20%), and the levels of absolute poverty fell significantly. For example, the proportion of the population living on less than $10 per day fell from 51.3% in 2005, to 29.6% in 2014. Growing incomes were primarily driven by increases in labor earnings, but employment growth – in particular among older age groups –also made a contribution. The government’s labor market policy also played a role with a rapid increase in the level of the national minimum wage (NMW), which grew by 65% in real terms between 2005 and 2015, i.e. almost twice as fast as the average wage. While there is evidence that the rapid growth in the NMW had negative effects on employment – in particular among temporary, young, and female workers, these have been relatively modest. Additionally, the tax and benefit policy has contributed to reduced inequality. It has been estimated that nearly half of the reduction in the Gini coefficient, over the period 2005–2014, resulted from reforms of the tax and benefit system (Myck and Najsztub, 2017).

It is clear that human capital was one of the cornerstones of Poland’s success in recent years. Developments on the labor market, such as a rapid productivity growth, were facilitated by a well-educated labor force, which could respond and adjust to the changing conditions and requirements. In this regard, Poland’s advantage in comparison to many other low and middle-income countries has been the relatively high level of spending on public education and healthcare, not only since the start of the economic transformation in the 1990s, but also before that. Indicators, such as the infant mortality rate, were low in Poland already in the 1980s, and have since further improved (see Figure 1). For a long time, public spending on education has been at levels comparable to those in established high-income countries (see Figure 2). Additionally, a series of reforms to the education system since 1990, have resulted in improvements in the quality and coverage of education. This, in turn, has lead to a rapid improvement of scores in language, mathematics, and science in the PISA study (Programme for International Student Assessment), in which Polish students recently outperformed those from many other OECD countries (OECD 2014). Importantly, the improvements in the education results have been found across the socio-economic spectrum, which further stresses the inclusive character of the changes that have taken place.

Figure 1. Infant mortality rate (per 1,000 live births), 1980 and 2014

Notes: Countries grouped in the following manner: red – middle-income countries; blue – new high-income countries; green – established high-income countries. Horizontal lines represent group averages. Source: World Bank (2017), Figure 5.16, based on World Development Indicators.

Figure 2. Government expenditure on education, percent of GDP, 1990

Source: World Bank (2017), Figure 5.11, see notes to Figure 1.

Insights for Poland

“As economies enter the high-income group, weakness in economic institutions such as the rule of law, property rights, and the quality of governance become increasingly important to sustain convergence.”

World Bank (2017)

While the Polish reform experience, over the period examined in the World Bank Report, offers important lessons for other countries aspiring to the high-income status, the authors point out that Poland’s continued development needs to rely on further improvements in a number of key areas. The following policy areas have been highlighted in the Report:

  • Working on more inclusive political and economic institutions and enhancing the rule of law with the focus on the judiciary;
  • Adjustments to fiscal policy in particular to deal with the consequences of population aging;
  • Increasing the domestic level of savings to facilitate large investment needs;
  • Supporting innovation through more intense competition and high quality research education;
  • Improving social assistance programs and access to high quality health and education for low income groups;
  • Increasing the progressivity of the tax system to support inclusive growth;
  • Adjusting migration policies to bring in skills and innovative ideas and compensate for the country’s aging workforce.

“Sustaining Poland’s record of high, stable growth will require adjustments to fiscal policy (…). Government will need to create the fiscal space to deal with the increasing pressures coming from aging, the inevitable decline of EC structural funds for investment, and a more uncertain global context.”

World Bank (2017)

Lessons, insights and recent policies

While several of the Law and Justice majority governments’ policies since 2015 have been well in line with the World Bank recommendations, there have also been a number of questionable policy areas. One major concern seems to relate to the broad background of reforms of the judiciary, which have drawn significant criticism of the European Commission and other international institutions. Implications of such major changes for economic growth are uncertain but potentially very damaging.

Another long-term concern arises from the new pension age reform. From the socio-economic perspective, rapid ageing of the population is one of the main challenges facing the country. Between 2015 and 2030, the number of people aged 65+ will grow from 6.1 million to 8.6 million, i.e. by over 40%. This will put significant strains on the country’s public finances due to increasing public-pension expenditures and growing costs of health and long-term care. These pressures will only be exacerbated by the current government’s decision to lower the statutory retirement age to 60 for women and 65 for men, from the target uniform age of 67 legislated in the reform of 2012. Given the contributions-defined nature of the Polish pension system, this will result in significantly lower levels of pensions, especially among women, and a substantial drain on public finances resulting from lower levels of contributions and taxes.

The generous family benefits of the Family 500+ Program – implemented in 2016 and which cost about 1.3% of the GDP – have also been criticized on a number of grounds. They have undoubtedly changed the financial conditions of numerous families and limited the extent of child poverty. At the same time, they contribute to maintaining low levels of female labor-force participation and there is so far little indication that they have significantly changed Poland’s very low fertility rate. It seems that while the program may have positive long-term consequences resulting from reduced poverty, it is unlikely to shift the demographic dynamics.

Uncertainty also surrounds the consequences of a haphazard major education reform, which is another trademark policy of the Law and Justice party. The reform re-introduced the 8+4 system in place of the post-1999 three-level educational arrangement (6+3+3). The new system takes the number of years of general education back from 9 to 8 years, and instead extends by one year the length of secondary schooling. While the potential effects of such a change are difficult to foresee, the 8+4 system may be in particular disadvantageous to children from rural areas, who are most likely to continue their education in their rural primary schools for the two extra years.

A number of steps taken by the government since late 2015, and in particular those related to the redistributive policies implemented in the last two years, seem to be consistent with the World Bank insights. On the other hand, the approach towards the reforms of the judiciary, the general approach to the rule of law, and the reforms of education and pension regulations, quite clearly appear to ignore not only the insights, but also the lessons resulting from Poland’s own experience of the recent decades. Given the challenge of rapid aging in the Polish population, there seems to be much gained from taking them seriously if the current and future administrations want to ensure Poland’s continued inclusive growth and to secure its status as an established high-income country.


This policy brief draws heavily on the World Bank (2017) Report: “Lessons from Poland, Insights for Poland: A sustainable and inclusive transition to high-income status” (co-authored by Michal Myck) and the accompanying Working Paper by Myck and Najsztub (2016). Views and opinions expressed in this brief are the sole responsibility of the author and are not endorsed by the World Bank or CenEA.


  • Myck, M., and M. Najsztub (2016) “Distributional Consequences of Tax and Benefit Policies in Poland: 2005–2014.” CenEA Microsimulation Report 02/16, Centre for Economic Analysis, Szczecin.
  • OECD (Organisation for Economic Co-operation and Development) (2014) PISA 2012 Results: What Students Know and Can Do—Student Performance in Mathematics, Reading and Science (Volume I: Revised edition, February 2014). Paris: OECD Publishing.
  • World Bank (2017) “Lessons from Poland, Insights for Poland: A sustainable and inclusive transition to high-income status”, The World Bank, Washington.

Intergenerational Mobility of Russian Households

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

Source: Authors’ calculations based on RLMS

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.

Income mobility

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

Source: Authors’ calculations based on RLMS


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.


Coming to Terms with the Past – Challenges for History Teaching in Russian Schools

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Russia is currently reforming its history teaching in basic schools. An original idea of producing one single textbook was abolished. Instead, three different textbook series for 6th to 10th graders, as well as teacher’s manuals and map books, have been written. They are all based on the same conceptual framework (kontseptsiia) and differ merely in pedagogical approaches. To facilitate teaching on topics in Russia’s millennium-long history where the professionals disagree on interpretations, a series of survey brochures are written on over twenty “difficult questions”. Contrary to the views by some observers in the West of a state-ordered streamlining of historical narratives, the new history textbooks offer the pupils an overview of how and why historians diverge in their interpretations and assessments of many events, personalities and transitions in the millennium-long Russian history.

How to educate the next generations on Russia’s past has been in the focus for more than a decade, and not only among the professional historians, history teachers and pedagogues. New textbook proposals have spurred debates in society at large.

After the breakdown of the Soviet educational system in history, one whole year passed when the subject of history even disappeared from the curriculum; the old textbooks on the 20th century in particular were recognized as full of myths; taboo subjects were omitted and propaganda clichés abundant. In lack of new textbooks, teachers were free to use materials from the vital press and journals of the glasnost period. Only a few years later, however, there was a plethora of newly written history textbooks, including translations such as the French historian Nicolas Werth’s on Russia in the 20th century (Werth 1992). These new textbooks were checked at the Ministry of Education and either authorized or recommended, depending on their pedagogical qualities. Among the pioneers of history schoolbooks should be mentioned Aleksandr Danilov and Liudmila Kosulina, whose works are printed in many new editions (see e.g. Danilov 2007).

In the 2010s, there were already a huge number of recommended and authorized textbooks on Russian history. Every schoolteacher in the Russian Federation had a plethora of handbooks with their accompanying pedagogical matters (‘blind history maps’, questionnaires, teacher’s manual, CD-ROMs, etc.) to choose from. Interpretations differed in these books. Children from two parallel schools in the same town could have read two contradictory presentations of a number of events, depending on the textbook author’s ideological framework.

In 2013, the president and the government approved the oft-repeated demand for ‘a unified, single history textbook for schools’ (edinyi uchebnik istorii). While president Putin had just hinted on what was wishful, the burdensome task to accomplish a sound standardization of history teaching fell on commissions in the academic community. The historians responsible for the new conceptual framework emphasize their striving towards de-politization of history (Chubarian 2013). The evolution of ‘history policy’ in neighboring states set a bad example, as parliaments, governments or even presidents legitimate the one and only correct historical facts or interpretations. The Council of Europe and OSSE use a similar ‘history policy’ adopting resolutions described as scientific accomplishments, not merely political attitudes. Institutes for the national memory, sometimes jointly with laws of the parliaments, dictate how historical personalities, events and political movements are to be characterized, and divergent presentations may be subject to judicial prosecution. Contrary to a widespread opinion in the West, Russian historians and politicians who are interested in history questions actually strive to avoid ‘history policy’ (Chubarian 2016).

Nonetheless, Russian parliamentarians have sometimes tried the same approach to counter the political use of past events. Examples can be quoted of how specific events during and after the First World War, as well as the Second World War 1939-1945 have been used to criticize the present-day Russian regime, its leaders or even its people (Miller & Lipman 2012) .

The process to achieve a new uniform history textbook was multifaceted. First, a ‘concept framework’ (kontseptsiia) was set up in a concise form. This included the main historical facts to be treated. It further enumerated tens of historical events, processes and changes that have been hotly debated. This framework was thereafter widened to become a ‘historical-cultural standard’ with detailed description of how each epoch in Russia’s millennium-long history would be presented in the new textbooks.

Russia decided to use a ‘linear system’ of history education’ (lineika), i.e., to teach chronologically from 5th to 10th class and use the 11th, final year for special courses. Six author groups and publishing houses participated in the contest for a set of new schoolbooks. Merely three of them were approved. Today, the first textbooks have appeared from the publishing companies Prosveshchenie (Enlightenment), Russkoe Slovo (Russian Word) and DROFA. The reformation will however be gradual as the older, authorized books can still be used. It will take at least until 2020 before these new history textbooks are the only standard ones.

Professional historians do not create history manuals for teachers, textbooks as well as auxiliary pedagogical matters in splendid isolation. Numerous seminars and colloquiums have been organized all over the country, where history teachers met with authors, discussed projects or shared their experiences from using pilot copies of the new books. Likewise, now that the first new textbook is used in schools, several hundred tutors will organize courses for teachers’ advancement and acquaintance with recent research.

The ‘conceptual framework’ was widely discussed in 2013–14 at teachers’ seminars all over Russia and at the First All-Russian congress of history teachers. The first new teacher’s manuals and textbooks have been presented by the authors at the Third history teachers’ congress held in Moscow in the first week of April 2016. Most sections at this congress concerned strict pedagogical and examination matters. For your humble servant, the most fascinating section at this congress was devoted to what has been termed as “difficult questions”.

Contrary to the views by some observers in the West of a state-ordered streamlining of historical narratives, with emphasis on the state and high politics level, these new textbooks give the teenage pupils a basic understanding on how and why historians diverge in their interpretations and assessments of many events, personalities and transitions in the millennium-long Russian history. Already at the first meetings with history teachers, over thirty such thorny historical riddles were mentioned. To give history teachers a better position, the publishing companies have engaged leading specialists to write up-to-date surveys of recent research and the present state of debates.

These surveys start with the century-long debates on the origins and character of the early medieval Russian state formation. How the rule of Ivan IV (Ivan the Terrible, Ivan Groznyi) has been evaluated is described in another survey. Similar debate surveys are due to appear on Peter the Great and other tsars.

Western observers of the Russian historical scene concentrate on how the country’s 20th century history is analyzed. These are also the matters that tend to divide the scholarly community as well as the general public in Russia. Consequently, teacher’s guidebooks on these topics are much in demand. They treat such complex questions as how the autocracy had progressed by the 1900s, what long-term processes and which events caused the downfall of the monarchy in 1917. Other surveys analyze Soviet nationality policies. The international situation in the 1930s and in the early phase of World War Two is carefully described. The Soviet Union during the Cold war is analyzed with references to the most recent findings in Russian and Western archives. Furthermore, the causes of the failure of Gorbachev’s perestroika and its effects are discussed in another survey.

In Russia, as elsewhere, anniversaries and centenaries tend to heighten an already eager public interest in history. For the approaching 100th anniversary of the Russian revolution, we likewise expect to find numerous collective monographs, encyclopedias, as well as re-printed memoirs and scores of unearthed archival documents in exhibitions. No doubt, however much professional historian complain of “the tyranny of jubilees” that divert from their chosen fields, the scholarly community in Russia will certainly take this opportunity to widen its research field to new aspects of the historical scenes during 1917.


  • Chubarian, Aleksandr O. (2013) “Istoriia ubivaet Ivana Groznogo” (History kills Ivan the Terrible), Rossiiskaia gazeta, 4 September.
  • Chubarian, Aleksandr O. (2016), “Voprosy k istorii” (Questions to History), Ogonëk, No 12, 28 March.
  • Danilov, A.A. & L.G. Kosulina (2007), Istoriia. Gosudarstvo i narody Rossii: 9 klass (History. The State and peoples of Russia. 9th class), Moscow.
  • Miller, A. & M. Lipman (2012), Istoricheskaia politika v XXI veke (History policy in the 21st century), Moscow.
  • Werth, Nicolas (1992), Istoriia sovetskogo gosudarstva 1900 – 1991, Moscow (translation of Histoire de l’Union Soviétique. De l’Empire russe à la CEI, 1900 – 1991, Paris 1991).

Highly Educated Women No Longer Have Fewer Kids

This policy brief summarizes evidence that the cross-sectional relationship between fertility and women’s education in the U.S. has recently become U-shaped. The number of hours women work has concurrently increased with their education. The theory that the authors advance is that raising children and home-making require parents’ time, which could be substituted by services such as childcare and housekeeping. By substituting their own time for market services to raise children and run their households, highly educated women are able to have more children and work longer hours. The authors find that the change in the relative cost of childcare accounts for the emergence of this new pattern.

In 2012, the European Commission published a special report on “women in decision making positions”, suggesting legislation to achieve balanced representation of women and men on company boards. Some countries such as Norway, France, Italy, Belgium and the Netherlands have already taken legal measures in that direction. Trends in women’s education give hope that such goals may be achieved as women are increasingly occupying more prestigious and demanding careers. Indeed, in today’s world, women have surpassed men in higher education in most developed countries (Goldin et al 2006; Hazan and Zoabi 2015a).

What are the consequences of this important development for fertility? Historically, highly educated women have had fewer kids than less educated women (see, for example, Jones and Tertilt 2008). This relationship is deep rooted in the economic and sociological literature to the extent that many theories have already been proposed to explain this relationship. Leading explanations rely on the difficulty to combine children and career (Mincer, 1963; Galor and Weil, 1996) and the quantity-quality tradeoff (Becker and Lewis, 1973; Galor and Weil, 2000; Hazan and Zoabi 2006). The shift in women’s education coupled with more demanding careers for women means that if the cross-sectional relationship between women’s education and fertility is stable over time, then future fertility rates will continue to decline from their already historically low levels.

In Hazan and Zoabi (2015b) we find, however, that the cross-sectional relationship between women’s education and fertility has changed from monotonically declining until the 1990s to a U-shaped pattern during the 2000s. This change is due to a substantial increase in fertility among women with advanced degrees who increased their fertility by 0.7 children, or by more than 50%. This is illustrated in Figure 1, which plots the cross-sectional relationship between fertility and women’s education in 1980 and during the period 2001-2011.

Figure 1: Fertility Rates by Women’s Education, 1980 and the 2000s.


What can explain the rise of fertility among highly educated women during the period that saw the largest increase in the labor supply of highly educated women? We argue that the rise in college premium increased the demand for child-care and housekeeping services by highly educated women and a rise in the supply for such services by low educated women. This ‘marketization’ weakened the tradeoff between career and family life and enabled highly educated women to pursue demanding career without giving up on their desired family size.

To establish the relationship between the rise in the college premium and fertility of highly educated women, we use data from the March CPS for the period 1983-2012. We estimate the average hourly wage in the “child day-care services” industry and allow it to vary by state and year. In addition, we compute the hourly wage of all women in the 25-50 year-old age group who reported a positive salary income. Figure 2 presents the fitted values of the average of this variable for each of our five educational groups. The figure shows that childcare has become relatively more expensive for women with less than a college degree but relatively cheaper for women with a college or an advanced degree.

Figure 2: Linear Prediction of the Log of the Ratio of Average Wage in the Childcare Industry to Average Wage in the Five Educational Groups 1983–2012


To utilize the micro data we estimate regression models where the dependent variable is a binary variable that takes the value of one if a woman, living in a specific state in a specific year gave birth during that year and zero otherwise. Our main explanatory variable is the labor cost in the child daycare industry divided by the own wage of that woman. We show that there is a highly statistically significant and economically large negative correlation between this measure of childcare cost and the probability of giving a birth. In our empirical analysis we find that this change in the relative cost can account for about one-third of the increase in the fertility of highly educated women. We use a battery of tests to show that this correlation is not driven by selection of women into the labor market, by the endogeneity of wages, or by specific years over the last three decades.

Figure 3: 2000s Actual and Hypothetical Fertility under the 1980s prices of Childcare


Figure 3 uses the estimates from the regression models described above and shows a hypothetical fertility for 2001-2011 under the 1983-1985 relative childcare cost. The figure shows that the hypothetical fertility curve is obtained by a clockwise rotation of the actual fertility curve around the group of women that has some college education.

Direct evidence on paid childcare services is consistent with this view. Figure 4 shows the average weekly paid childcare hours by all women aged 25-50 in 1990 and 2008. The figure has two salient features. First, in each of these years, paid childcare is increasing with women’s education. Secondly, between 1990 and 2008, paid childcare hours have stagnated for women with up to some college education but have sharply increased for highly educated women.

Figure 4: Paid Childcare Weekly Hours for Women aged 25-50.


We then rule out potentially other explanations. What if the increase in labor supply stems from women who did not give birth during that year? To address this concern we shows that the cross-sectional relationship between education and usual hours worked for the sub-sample of women age 15-50 who gave birth during the reference period exhibit the same positive correlation. Another concern might be that it is in fact the spouses who respond to a birth by lowering their labor supply enabling their wives to work more. Find that men who are married to highly educated women work more than men who are married to women with lower levels of education. Interestingly, fathers to newborns work more than husbands who do not have a newborn at home, regardless of the education of their wives. More importantly, usual hours worked by fathers to newborns monotonically increased with their wives’ education. Thus, the spouses of highly educated women are not the ones substituting in childcare for their working wives.

Another concern our model may raise is that marriage rates differ across different educational groups. If married women have higher fertility rates and if more educated women have higher marriage rates, more educated women’s higher fertility rates may not be caused by the availability of relatively cheaper childcare and housekeeping services, but rather simply by their higher marriage rates. We find that the fraction of women with advanced degrees who are currently married is somewhat lower than that of women with college degree.

Figure 5: Number of birth per 1,000 White Women in the US in Age Groups 35-39, 40-44 and 45-49: Women with Advanced Degrees (2001-2011) and Historical Rates.


A final potential explanation might be related to recent advancements in Assisted Reproductive Technology (ART) that enable women to combine long years in school without scarifying parenthood. We address this possibility in three ways. First, we show that historical levels of fertility rates among women above age 35 were higher than the levels during the 2000s (see Figure 5). This stands in contrast to the argument that highly educated women were not able to have higher fertility rates in the past due to medical reasons. Secondly, we note that ART accounts for less than 1% of births occurred during the 2000s. Finally, fifteen U.S. states have infertility insurance laws that provide coverage to infertile individuals. We compare fertility patterns by women’s education in these states to the rest of the country and find no difference in fertility rates during the 2000s between the two groups of states.

The results of this study have several implications. For public policy, it highlights potential benefits from pro-immigration policies. Unskilled immigrants can potentially have positive effect on fertility via an increase in the supply of cheap home production substitutes. For many developed countries that are facing aging and shrinking population this may be something to consider. It also has consequences for economic growth. Given the strong correlation between parents’ education and kids’ education, an increase in the relative representation of kids coming from highly educated families means that the next generation is going to be relatively more educated. These are good news for economic growth.


  • Gary S. Becker and Gregg H. Lewis. On the interaction between the quantity and quality of children. Journal of Political Economy, 81:S279–S288, 1973.
  • Oded Galor and David N. Weil. The gender gap, fertility, and growth. American Economic Review 86(3): 374–387, 1996.
  • Oded Galor and David N. Weil. Population, technology, and growth: From Malthusian stagnation to the demographic transition and beyond. American Economic Review 90(4): 806–828, 2000.
  • Claudia Goldin, Lawrence Katz, and Ilyana Kuziemko. The homecoming of American college women: A reversal of the college gender gap. Journal of Economic Perspectives 20(4): 133–156, 2006.
  • Moshe Hazan and Hosny Zoabi. Does longevity cause growth? A theoretical critique. Journal of Economic Growth, 11 (4), 363-376, 2006.
  • Moshe Hazan and Hosny Zoabi. Sons or Daughters? Endogenous Sex Preferences and the Reversal of the Gender Educational Gap. Journal of Demographic Economic, Vol 81, pp: 179-201, 2015a.
  • Moshe Hazan and Hosny Zoabi. Do highly educated women choose smaller families? Economic Journal, 125(587):1191–1226, 2015b.
  • Larry E. Jones and Michele Tertilt. An economic history of fertility in the u.s.: 1826-1960. In Peter Rupert, editor, Frontiers of Family Economics, pages 165 – 230. Emerald, 2008.
  • Jacob Mincer. Market prices, opportunity costs, and income effects. In Carl F. Christ, editor, Measurement in economics: Studies in mathematical economics and econometrics in memory of Yehuda Grunfeld. Stanford University Press, pages 67-82, 1963.

Education for the Poor


Authors: Lasha Lanchava and Zurab Abramishvili, ISET and CERGE-EI.

This brief summarizes the results of a study by Lanchava and Abramishvili (2015), which investigates the impact on university enrollment of an unconditional cash transfer in Georgia, designed to help households living below the subsistence level. The program, introduced in 2005, selects recipients based on a quantitative poverty threshold, which gives us the opportunity to measure the influence on university enrollment with an econometric regression discontinuity design. We use data on program recipients from the Social Service Agency of Georgia (SSA) and university admissions from the National Examination Center (NAEC) to create a single dataset and compare the enrollment rates of applicants who are just above and below the threshold. We find that being a program recipient significantly increases a student’s likelihood of university enrollment by as much as 1.4 percentage points (while the sample mean of university enrollment is 12.7%). We also find that the impact is stronger for males and the firstborn children in a family. Our analysis also shows that the effect is equally strong across different locations in the country. Our straightforward policy recommendation is that if a government is trying to increase enrollment in tertiary education, need-based university scholarships may prove to be an appropriate instrument.