Project: FREE policy paper

Household Exposure to Financial Risks: The First Wave of Impact From COVID-19 on the Economy

An areal image of households in suburbs representing household financial risk exposure to covid19

Since March 12, 2020, Poland has been under an increasing degree of quarantine due to the COVID-19 pandemic. The strict isolation-driven lockdown measures have implied significant restrictions to social interactions and economic activity. While the duration of this lockdown and the resulting overall scope of economic implications are highly uncertain at this point, in this brief we take a closer look at the possible extent of the first wave of economic consequences of the pandemic faced by Polish households. This is done by identifying sectors of the economy whose operation has been severely limited due to the lockdown, such as those involving travel, close interpersonal contact and public gatherings or those related to the retail trade. We find that about 17.2% of Polish households include members active in these sectors, and for 5.2% of households, the risk can be described as high due to the nature of the employment relationship. According to our estimates, 780K people (57% of whom are women) face a high risk of negative economic consequences as a result of the first direct wave of implications of the pandemic.


The full scale of the socio-economic impact of the COVID-19 outbreak is incalculable today, given the uncertainty of lockdown duration and the severity of the pandemic-driven slowdown in the international economy. Still, it is possible to analyze the direct implications of the lockdown, self-isolation and quarantine measures introduced over the last few weeks in an attempt to formulate a preliminary assessment of how the outbreak will affect households in economic terms. The priority challenge now is, of course, to contain the spread of the coronavirus, but as we identify the scale of potential economic consequences associated with the pandemic, we may help calibrate the safeguards that could protect households from the impact of the imminent economic slowdown.

In this commentary paper, based on the Household Budget Survey (HBS) data, the percentage of households (HHs) whose members are most at risk of losing their job or compromising their income due to the first wave of economic consequences of the pandemic is taken as a measure of the economic impact of the COVID-19 outbreak. The analysis looks into the population of people who are economically active (through employment or self-employment) in those sectors of the economy which are most exposed to the effects of the lockdown. We discuss the HHs with a particularly high risk of income deterioration in the breakdown according to the level of household income, the place of residence, and the family type. The first part of the paper presents a detailed description of the economic sectors which were considered to be particularly exposed to the risk associated with the first wave of economic consequences of the pandemic, together with risk level definitions. Analytical findings are presented in the second part of the paper.

Households at Risk of the Negative Impact of the First Wave of Economic Consequences of the COVID-19 Pandemic

The granularity of HBS data collected annually by Poland Statistics (GUS) is not sufficient for a very precise determination of the size of risk groups in terms of individual activity on the labor market, but the data can help identify the HHs whose members have been employed in the sectors of the national economy particularly affected by the pandemic, i.e. on the first line of exposure to its economic consequences. These are, in particular, economic sectors that involve frequent interpersonal contacts and large public gatherings: following the announcement of the state of epidemiological hazard in Poland on March 14th, 2020, serious restrictions have been imposed in those sectors in an effort to prevent the rapid spread of the coronavirus.

Pursuant to the Regulation of the Minister of Health of March 13th, 2020, on the announcement of the state of epidemiological hazard in the territory of the Republic of Poland, restrictions on doing business in the food industry, as well as in culture and entertainment, sport and recreation, hospitality and tourism have been imposed on a temporary basis (Ministry of Health 2020). The operation of large-size retail commerce facilities has also been restricted. In addition, self-isolation and social distancing result in significant decreases in the overall level of trade turnover. In view of the lockdown, we decided that the risk of economic slowdown also applies to the service sector and education (personal services included) for the purpose of this paper. The workforce from the above-mentioned sectors has been divided by type of employment contract, and those hired under a contract of employment (fixed-term or open-ended, regardless) have been ranked as less exposed to the risk of job loss or lower earnings, while all the others employed on civil law contracts (service contract, zero-hours contract, etc.) have been grouped under an elevated risk label. The elevated risk category includes all those who are self-employed in the above-mentioned sectors in Poland or abroad, regardless of whether they have employees onboard or not.

Exposure to Financial Risks in Families and Households

In accordance with the risk categories applicable to the economically active population, we can conclude that there are over 780 thousand members of the workforce (57 percent of them are women) who are particularly exposed to the negative economic consequences of the pandemic, as they work in the affected sectors of the economy on the basis of self-employment or contracts other than the contract of employment. In addition, 1.9 million people (70 percent of them are women) are employed in these sectors of the economy on contracts of employment. The status of the latter group is less precarious in the short term, but if the lockdown should continue in the long term, this population may also be affected.

The adverse impact of job loss or lower earnings will affect an entire household whose member works in a sector particularly affected by the crisis. Therefore, the risks below are presented in a breakdown by family type and by HH group aggregated according to the place of residence and income level. Moreover, the HHs were also grouped according to their members’ activity on the labor market, with analytical findings presented for all HHs and for the group of HHs with at least one economically active member in the HH.

The highest percentage of HHs whose members are particularly exposed to the negative consequences of the pandemic is reported in cities (Figure 1). For example, in cities with a population above 500,000, it is 6.6 percent of all HHs, and 9.1 percent of the HHs with at least one active member on the labor market. Additionally, in cities with a population count exceeding 500,000, 12.4 percent and 17.1 percent of the population, respectively, is employed in the affected sectors on the basis of an employment contract. In smaller cities/towns and in rural areas the percentage of HHs with the population most exposed to the crisis are slightly lower. In rural areas, it is 4.8 percent of all HHs and 6.4 percent of the HHs with at least one economically active member of the HH.

In terms of HH income levels, middle-income HHs demonstrate the highest percentage of those exposed to the negative consequences of the first wave of pandemic-driven impact on the economy (Figure 2). For example, in the 6th income decile group, in the population of HHs with at least one economically active member, 8.5 percent of HHs include a member who is economically active in an affected sector and working either on a self-employment basis or on a contract other than a contract of employment. Together with HH members who are economically active in those sectors on a contract of employment, the rate exceeds 30 percent.

Figure 1. Financial risk in the households in connection with the first wave of COVID-19 impact on the economy, by place of residence

Source: Authors’ compilation based on 2018 HBS data.
Nota Bene: Economically active HHs – households with at least one member of the household active on the labor market.

The percentage distribution of the HHs economically active in the affected sectors by family type is also uneven (Figure 3). In the group of families with at least one economically active member, the largest proportion of such HHs is reported in the group of single parents, with 31.5 percent working in the affected sectors and 6.6 percent in self-employment or on the basis of a contract other than the contract of employment. Similar percentages are reported for couples with children and at least one economically active HH member (24.2 percent and 7.8 percent, respectively.) Among working singles and couples with no dependent children, on average, one in five HHs has a HH member economically active in an affected sector. Of these HHs, 4.5 percent of the singles and 5.6 percent of the couples with no children are economically active in the affected sectors with contracts other than a contract of employment.

Figure 2. Financial risk in the households in connection with the first wave of COVID-19 impact on the economy, by income decile

Source: Authors’ compilation based on 2018 HBS data.
Nota Bene: Economically active HHs – households with at least one member of the household active on the labor market. 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.

Figure 3. Financial risk in the households in connection with the first wave of COVID-19 impact on the economy, by family type

Source: Authors’ compilation based on 2018 HBS data. Nota Bene: Economically active HHs – households with at least one member of the household active on the labor market. The following family types are distinguished: Singles – working age singles without dependent children; Single parents – working age single parents with dependent children; Couples without children – working age married couples without dependent children; Couples with children – working age married couples with dependent children.


Although our estimates of the percentage of families and households potentially exposed to the negative effects of the first wave of economic consequences of the COVID-19 pandemic do not necessarily imply that such a high share will actually be affected, the mere fact that so many families face the prospect of a deteriorating financial condition should stimulate a wide array of public policy support mechanisms. The economic support package called the “anti-crisis shield”, announced by the Government of Poland on March 18th, is a reaction to this challenge, though specific details of the announced version of the program have not been disclosed to date (Government announcement 2020). Still, the main focus of the package is on support for enterprises and entrepreneurs to help them continue business operation by postponing the due dates of business taxes and levies, and partially subsidizing employment of the workforce already on board. There is no doubt, however, that if the general economic slowdown continues for more than a few months, enterprises will be forced to start the layoffs and the self-employed will have to deregister. Therefore, the public finance system must be prepared to provide direct financial support to the households and offer a comprehensive benefit package to those who are laid off and to their families.

It is to be hoped that the economic consequences of the pandemic will be short-lived, and business activity will recover quite quickly to the pre-existing levels. For this to happen, first of all, we must keep the enterprises afloat, especially the small and medium-sized enterprises. Secondly, a fast economic reboot will be easier if the existing employment relations are preserved, even if the workload or the wages are curtailed. To that end, one solution would be to provide periodic financial support to employees in the affected sectors, even without formal termination of the contract between the employee and the employer. If the lockdown continues for more than two or three months, the financial support provided for in the “anti-crisis shield” package, representing 40 percent of the wage, may turn out to be inadequate to keep current employment levels intact.

If the pandemic-driven economic slowdown is prolonged – and there is no way this option can be ruled out today – it should be remembered that, apart from the sectors included in the analysis, the remaining sectors of the Polish economy will also be affected by the negative consequences of the recession; and the prolonged slowdown will eventually lead to a significant increase in unemployment rates. If that happens, households will need support through social transfers, both in the form of the unemployment benefit and benefits not related to a beneficiary’s track record in social security contributions paid, i.e. the housing benefit and social welfare benefits. With the expected substantial increase in public spending, the current policy of the state, focused primarily on universal public benefits, would have to be refocused on the transfers targeted at the most vulnerable households.


Ministry of Health (2020). Regulation of the Minister of Health of the Republic of Poland of the 13th March 2020 on the announcement of the state of epidemiological hazard in the territory of the Republic of Poland.

Government announcement (2020). “Anti-crisis Shield” will protect companies and employees from the consequences of coronavirus epidemics.


This brief was originally published as a CenEA Commentary Paper of 28th March 2020 on The analyses outlined in this brief make part of the microsimulation research program pursued by CenEA Foundation. The data used in the analyses 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.

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 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

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 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. 

Foreign Investors on the Investment Climate in Latvia

Railway bridge across river Daugava representing Foreign Investors on the Investment Climate in Latvia FREE Network Image 01

This brief summarizes the results of an annual study on the development of the investment climate in Latvia from the viewpoint of key foreign investors – companies that have made the decision to invest in the country and have been operating here for a considerable time period. The study was initiated in 2015 and aims to assess investors’ evaluation of the government policy initiatives to improve the investment climate in Latvia. It also aims to provide an in-depth exploration of the main challenges for and concerns of the foreign investors, both by identifying problems and offering solutions. The study draws on a survey/ mini case studies of the key foreign investors in Latvia. Our findings suggest that in recent years, some progress has been achieved on a number of dimensions that are crucial for the competitiveness of the investment climate in Latvia, such as the political efforts by the government of Latvia to improve the investment climate, the overall attitude to foreign investors, and labour efficiency. At the same time, foreign investors see little, if any, improvement with regards to other key areas, such as the availability of labour, the quality of education, the court system, corruption and the shadow economy.


The study on the development of the investment climate in Latvia from the viewpoint of key foreign investors in Latvia was first launched in 2015 by the Foreign Investors’ Council in Latvia (FICIL) in cooperation with the Stockholm School of Economics in Riga (SSE Riga). This study aims to foster evidence-based policy decisions and promote a favourable investment climate in Latvia by:

  • (i) Assessing how foreign investors evaluate the government’s efforts and current policy initiatives aimed towards improving the investment climate in Latvia, and
  • (ii) Providing an in-depth exploration of the main challenges and concerns for the foreign investors, both by identifying problems and offering solutions.

The study draws on a survey/mini case studies of the key foreign investors in Latvia. The first 2015 wave of the survey covered 28 key foreign investors in Latvia. Our panel has gradually expanded over time, reaching 47 participating companies in 2019. From September to early November 2019, we interviewed 47 senior executives representing companies that are key investors in Latvia. Altogether, these companies (including their subsidiaries) contribute to 23% of Latvia’s total tax revenue from foreign investors, 9% of the total profit and employ 11% of the total workforce employed by foreign investors in Latvia, where by foreign investors we mean companies with above a 145 000 EUR turnover and 50% foreign capital (data form Lursoft, 2018).

All interviews were conducted by FICIL board members. The guidelines for the interviews consist of the following key parts:

  • (i) Assessment of whether, according to foreign investors, the investment attractiveness of Latvia has improved during the past 12 months;
  • (ii) Assessment of the work of Latvian policy-makers in improving the investment climate during 2019;
  • (iii) Evaluation of progress in the major areas of concern identified by foreign investors in Latvia in 2015, including demography, access to labour, level of education and science, quality of business legislation, quality of the tax system, support from the government and communication with policy-makers, unethical or illegal behaviour on the part of entrepreneurs, unfair competition, uncertainty, the court system and the healthcare system in Latvia.

Furthermore, in the 2019 study we included questions related to some of the key issues discussed between foreign investors and policymakers during 2019, including the tax system, the stability of the financial sector and the quality of higher education and science in Latvia.

Investment Attractiveness of Latvia: Key Concerns of Foreign Investors in Latvia

The results of the 2019 study suggest that, even though the assessment of foreign investors with regards to the investment attractiveness of Latvia and the work of policy-makers to improve the investment climate in Latvia is still at the average level, it shows some positive tendencies. Namely, on a scale from 1 to 5, where ‘1’ means that there are no improvements at all, ‘3’ some positive improvements and ‘5’ significant improvements, the development of the investment climate in 2019 was evaluated as ‘2.6’ (‘2.5’ in 2018 and 2017). Furthermore, when asked to score the policy-makers’ efforts to improve the investment climate in Latvia, using a scale of 1-5, where ‘1’ and ‘2’ were fail and ‘5’ was excellent, investors responded with an average of ‘2.9’ in both the 2017 and 2018 studies, whereas in 2019, the score improved to ‘3.1’.

Foreign investors were also asked to evaluate whether there has been any progress within the key areas of concern as identified in 2015. The results of the most recent study suggest that the demographic situation, which in the long term reflects both the availability of labour and market size, is still among the key challenges for the foreign investors. Namely, on the scale from 1-5 (where an indicator value of 1 means that Latvia is not competitive and 5 means that Latvia is very competitive in this dimension), investors assessed the demographic situation of Latvia with only ‘1.5’ in 2019. Furthermore, as many as 35 (out of 47) foreign investors stated that they had not seen any progress in this area over the past 12 months. This lack of progress is, perhaps, not very surprising as demographic changes may take substantial time.

Another two key areas where investors would like to see more progress are the quality of education and science and the availability of labour. On a 5-point scale, the quality of education and science was evaluated with ‘2.7’ in 2019 (‘3.0’ in 2018, ‘3.1’ in 2017) and 30 out of the 47 investors interviewed have seen no progress in the development of education and science in Latvia over the past 12 months. The availability of labour was evaluated with ‘2.8’ in 2019 (‘2.7’ in 2018 and 2017); investors scored the availability of blue-collar labour with ‘2.4’ in 2019 (‘2.3’ in 2018, ‘2.5’ in 2017) and the availability of labour at management level with ‘3.1’ (‘3.0’ in 2018, ‘2.9’ in 2017). The majority, i.e. 39 of 47 investors have also seen no progress with regards to the access to labour during the past 12 months. In this context, however, it should be emphasised that the efficiency of labour is increasing in Latvia, according to foreign investors: in 2018, it was assessed with ‘2.9’, yet, in 2019, investors evaluated the efficiency of labour in Latvia with ‘3.4’ out of ‘5’.

The quality of health and social security as well as the quality of business legislation are yet another two indicators of the competitiveness of the investment climate in Latvia that have been evaluated around the average level of ‘3’. Further, 33 of 47 investors have seen no progress with regards to improvement of the healthcare system in Latvia over the past 12 months.

While the overall standard of living is evaluated rather positively at ‘3.8’ in 2019, there is still not much improvement in this indicator as compared to the previous three years. One encouraging result of the 2019 study is that according to foreign investors, the attitude towards foreign investors is gradually improving in Latvia: from ‘3.2’ and ‘3.1’ in 2016 and 2017 to ‘3.6’ in 2018 and reaching ‘3.7’ in 2019.

The foreign investors in Latvia who took part in the 2019 study also expressed an expert opinion with regards to whether there has been any progress during the previous 12 months in the other areas of concern. In this light, the perception of uncertainty should be highlighted. As many as 25 (out of 47 investors) have seen no progress in this area, 16 have seen partial progress and 6 stated that there has been progress in reducing uncertainty. The court system of Latvia is another area where many foreign investors have seen no progress, i.e. 22 said ‘no progress’, 23: ‘partial progress’ and only 1 that there has been progress in the development of the court system in Latvia.

Specific Issues: Tax System, Stability of the Financial System and Quality of Higher Education and Science

In the 2019 study, we also initiated an in-depth exploration related to three key issues of concern extensively discussed between foreign investors and Latvia’s government during the FICIL High Council 2019 spring meeting, and throughout the year 2019 in general. These are: (i) the tax system, (ii) the stability of the financial system, and (iii) the quality of higher education and science. Foreign investors were asked to comment on the current situation and progress over the past years, as well as to provide suggestions to the policymakers in order to improve the situation in the particular area.

(i) Tax system:

The most recent tax reform was implemented in 2018, and the newly elected government has announced that the next reform will take place in 2021. Therefore, this year we asked investors to evaluate the results of the previous tax reform in Latvia. We also asked investors to comment on whether the recent tax reform has brought any benefits to their company and the overall economy of Latvia. On average, foreign investors scored the results of the previous tax reform in Latvia with ‘3.1’, i.e. slightly above the average.

Overall, at least one part of the foreign investors who took part in the 2019 studies highlighted that the previous tax reform was a step ‘in the right direction’. In particular, the zero-rate on reinvested profit was highlighted by a large number of investors as a very positive improvement. In some cases, investors also praised the progressivity of labour tax rates. However, a number of foreign investors highlighted that the tax system has actually become more complex after the reform. Investors also expressed suggestions for further steps to improve the tax system in Latvia, and these are as follows:

Avoid uncertainty. Stability and predictability of the tax system is what the majority of the foreign investors wish to see. In essence, this means fewer changes to the tax system.

Simplify and explain. Investors highlight that paying taxes should be a “simple task” and easy to understand. According to the viewpoints of foreign investors, there is also the potential for improvement with regards to how the responsible organisations, such as the State Revenue Service, communicate changes in the tax system to the private sector.

(Continue) the shift from taxing labour to consumption. Some of the investors that took part in the 2019 studies see that the process has been initiated by the previous tax reform and recommend continuing in this direction.

(ii) Stability of the financial sector in Latvia.

On average, foreign investors evaluated the progress with regards to the effectiveness of combating economic and financial crime with 3.2, i.e. above average. We then asked foreign investors whether they have felt any negative effects on their companies with regards to the situations in the financial sector over the past 2 years. We received some positive opinions, yet the negative ones prevailed. Namely, foreign investors highlighted the reputation risks of Latvia that often impact upon the operation of their companies and create challenges when working with foreign banks.

(iii) Quality of university education and science in Latvia.

Here, foreign investors were asked to reflect upon whether they were aware of any activities that policymakers carried out during the past year to improve the situation. On a positive note, a number of investors mentioned the recent development of the University of Latvia and Riga Technical University’s campuses. Some investors also highlighted that the reform to change the governance model of higher education institutions, initiated by the Ministry of Education and Science, was a good step towards improving the quality of higher education and science in Latvia. However, we also received a number of negative opinions, such as “Nothing has been accomplished, just talking”.

When asked “What changes would you suggest to improve the quality of education and science in Latvia and why? How would this help the business environment, e.g. companies such as yours?”, foreign investors emphasised the following:

Higher education (and science) is too local, fragmented and outdated. In essence, investors pointed out that there are simply too many higher education institutions in Latvia, that they work with outdated methods and are afraid (with no good reason) to open up internationally – also by attracting top quality foreign staff.

Change the governance of higher education institutions in Latvia is another strong request from foreign investors in Latvia. Many investors believe that changes in the financing model should also follow.

Improved connection between education and science and the world of business was yet another important aspect which was highlighted during the 2019 interviews, and also strongly emphasised in the previous studies.

Further Investment Plans and Message to the Prime Minister

When asked whether they plan to increase their investments in Latvia, as many as 64% of the investors interviewed answered with ‘yes’ (in the 2018 study, 55% interviewed answered with ‘yes’), 25% said ‘no’ (35% in the 2018 study) and 11% answered that ‘it depends on the circumstances’ (10% in the 2018 study) or that they have not yet decided.

Finally, we invited foreign investors to send a message to the Prime Minister of Latvia: one paragraph on what should be done to improve the business climate in Latvia, from the viewpoint of a foreign investor. These messages closely parallel the other findings of the 2019 study, stressing a number of key concerns that foreign investors are still facing in Latvia: the situation with regards to demography, quality of education and science, availability of labour, challenges with corruption and the shadow economy as well as needs for improvements in the health care sector amongst others.


The findings of the 2019 study on the view of the key foreign investors of the investment climate in Latvia suggest that in recent years, some progress has been achieved on a number of dimensions, such as political effort to improve the investment climate, attitude towards foreign investors, and labour efficiency. At the same time, foreign investors see little, if any, improvement with regards to other key areas, such as the availability of labour, the quality of education, the court system, corruption and the shadow economy.

Our findings highlight the need to continue policy-makers’ efforts to improve the investment climate in Latvia and provide policymakers with better grounds for making informed policy decisions with respect to the entrepreneurship climate in Latvia. We also hope that our study will further facilitate constructive communication between foreign investors and the government of Latvia.


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.

Human Trafficking, Prostitution Legislation, and Data

An image of red light street with signs of striptease club representing human trafficking and prostitution legislation

This report is the compilation of exploratory research work conducted within a project led by Giancarlo Spagnolo (SITE and EIEF), together with Maria Perrotta Berlin (SITESSE) and Ina Ganguli (UMass Amherst). Whilst investigating the effects of asymmetric punishment in the regulation of prostitution, the interaction of markedly different legislations for this along the Franco-German border was of interest. In this report, we present gathered information and data regarding human trafficking and sex work in Germany. We begin by broadly outlining both topics and continue with presenting points that should be considered in future research. The results from a limited survey, where sex workers and counsellors were interviewed, are also presented.

Exploring the Interaction Between Sex Work Regulation and Human Trafficking along the Franco-German Border

In this report, we present data regarding human tracking and the forced prostitution with which it is often connected, as well as data regarding voluntary and consensual sex work. Though we present these topics alongside one another because of the many possible ways in which they may be connected, we do not make any correlating assumptions.

There are different claims regarding the existence of a correlation between sexual exploitation and prostitution policies. Working under the assumption that a correlation does exist, it is still unclear what it would look like (Sonnabend and Stadtmann, 2018). While some argue that legalising sex work leads to an increased social acceptance of the phenomenon and thereby also increased demand for voluntary sex work. It also makes it easier and cheaper for criminals to track people and find customers that, unwittingly or not, pay for sex from victims of human tracking for sexual exploitation (Sonnabend and Stadtmann, 2018; Cho et al., 2013; Jakobsson and Kotsadam, 2013).

On the other hand, restricting or criminalising sex work may make it less likely for such victims, as well as buyers of sexual services of any kind, to collaborate with police forces to report illicit activities related to human tracking (Bisschop et al., 2017; Cunningham and Shah, 2014). Thus, when sex work is criminalised, any that remains will move into the dark, and hence become much more difficult to control (Scoular, 2010).

Sonnabend and Stadtmann (2018) found that different empirical studies have given rather contradictory results. For instance, whilst Cho et al. (2013) found a positive correlation between legal prostitution and tracking flows comparing existing data from over 150 countries, they also acknowledged that this needed to be considered with caution due to the lack of consistent data on human tracking across countries.

On the other side of the spectrum, a report by the New Zealand Government (2008) and a study on human tracking in Europe (Hernandez and Rudolph, 2015) suggest that no links between the sex industry and human tracking can be made. Instead, Hernandez and Rudolph would argue that human tracking in Europe stems mostly within already existing migratory and refugee corridors and is more likely to happen where host countries have weaker institutions, higher general crime rates and more liberal border controls. Host countries’ rates of asylum seekers, however, do not seem to play a role in the extent of human tracking.

Sonnabend and Stadtmann (2018) also endeavoured to calculate the effects of the Nordic model on sex work. They found that the prohibition of sex work is likely to create more loopholes and worse conditions for voluntary prostitution, and thus conditions that facilitate sexual exploitation and human tracking. As can be deduced from this brief introduction, any pre-existing view on the interplay between sex work and human tracking can be easily reinforced as virtually every standpoint can find some support in research. Throughout this report, we attempt to present the information we have gathered bearing these disparate previous findings in mind.

This report consists of six sections. This brief introduction is followed by a section that elaborates on the connections that can be made between human tracking and prostitution legislation. Section 3 presents current issues regarding human tracking internationally, as well as a compilation of the available data for Germany. Section 4 focuses on Germany and the legislative and regulatory environment under which lawful sex work is carried out there. Thereafter, we present some findings from a limited field survey of actors within the prostitution scene along the Franco-German border. We round this report off with a conclusion, briefly summarising and discussing our findings.

Regulatory Efforts

Regardless of how sex work and human tracking may be related, the likelihood of discovering tracking victims can be affected by the policies in place surrounding prostitution. Opponents of laws restricting prostitution often argue that when sex trade is outlawed, even if only for one party (as in the case of the Nordic model) the activity does not cease to exist, but is simply moved into the black market. Getting a full grasp of the extent of human tracking within the already restricted environment of prostitution will be considerably more difficult than if it had been within a legalised setting that allowed for regulatory oversight. However, as will discussed in this report, getting the regulatory oversight right is a challenge and there are considerable legitimate criticisms of this liberal stance too.

Table 1 Varieties of Legislation

Table 1 Varieties of Legislation

Varieties of Legislation

Across Europe, we find different kinds of prostitution legislation. Countries have chosen to combat the negative elements often associated with sex trade in ways that differ greatly in mechanisms and outcomes. These can be grouped into four overarching groups, all working in somewhat different ways, presented in Table 1 above.

Historically, prostitution has been a highly sensitive issue. Traditional moral and religious values all over Europe have condemned extramarital sex of any kind. Laws have ranged from outlawing prostitution to barely touching the topic. This becomes apparent when looking at the four overarching types of legislation that we will cover in this section. Abolitionist and prohibitionist policies present the two dominant legal traditions.

The four groups do not only differ from one another, but there is great variation between countries of each respective group, perhaps with the exception of countries that have neo-abolitionist policies. For instance, while brothels are legal in Germany and the Netherlands, Latvia has chosen to regulate prostitution from an abolitionist standpoint by making sex work a licensed profession but largely does nothing more (Cabinet of Ministers (Latvia), 2008). In Turkey, prostitution was legalised already in 1923. In recent years, however, Turkey has been a lot more restrictive in issuing new licenses (Sussman, 2012).

Figure 1 Typical Historical Development of Legislation

Figure 1 Typical Historical Development of Legislation

The prohibitionist group differs less in terms of legislation and more when it comes to enforcement. The enforcement of these policies tends to be weak, with measures needed to ensure that no prostitution is carried out behind closed doors largely lacking. We find the greatest in-group variation amongst the abolitionist countries. This approach is also the one that is least easily defined. In a sense, it, therefore, becomes a catch-all term for countries that fall outside of the scope of the other three groups.

The reasons for the differences in legislation on this matter is that countries, for varying reasons, have chosen to prioritise different goals. This is obvious when looking at the mechanisms of each type in Table 1. What they all have in common, however, is that without diligent enforcement of the laws, illegal prostitution is likely to persist.

Project and Intentions

As part of a research project at the Stockholm Institute of Transition Economics (SITE) in which researchers Giancarlo Spagnolo (SITE), Maria Perrotta Berlin (SITE) and Ina Ganguli (UMass Amherst) are looking into the effects of asymmetric punishment in legislation, the neo-abolitionist ‘Nordic model’ for prostitution is of interest.

Though the focus is placed on Scandinavia, France came into the picture as similar legislation was adopted there in April 2016. As a result of the change in France, which presumably made it harder for people to purchase sexual services, it became interesting to see how potential consumers may take advantage of the open border to Germany, where prostitution is legal. Before the law changed, the exchange of sex for money had been legal, with some restrictions on solicitation. Running a brothel, pimping or paying for sex with a minor had, however, never been allowed. With the change towards asymmetric punishment, selling sex for money remained legal whereas purchasing it was now considered a crime.

This put the two most recently developed types of prostitution legislation, regulationst in Germany and neo-abolitionist in France, next to one another along the Franco-German border. The effects of the cross-border dynamics are what we, as research assistants to the team at SITE, tried to map. We assumed that the regulationist approach of Germany would enable us to more easily investigate the effects of the change in legislation in France across the border.

For this reason, we investigated how customers and working conditions, among other things, changed in areas of Germany neighbouring France. As our research progressed, this underlying assumption would prove to be less straightforward than we had thought originally. Though we cannot give a conclusive answer to how the change in regulation came to affect the market for sex in Germany, we gained insights and gathered information that we have compiled in this report.

The State of Human Tracking

. . . the recruitment, transportation, transfer, harbouring or receipt of persons, by means of the threat or use of force or other forms of coercion, of abduction, of fraud, of deception, of the abuse of power or of a position of vulnerability or of the giving or receiving of payments or benefits to achieve the consent of a person having control over another person, for the purpose of exploitation. Exploitation shall include, at a minimum, the exploitation of the prostitution of others or other forms of sexual exploitation, forced labour or services, slavery or practices similar to slavery, servitude or the removal of organs.


The international community has long been struggling with the prevalence of human tracking. Initially, under the heading of slavery, human tracking has, through multiple conventions over the past 150 years, been explicitly restricted and outlawed by most countries. Though what we regard today as human tracking and modern slavery might not resemble stereotypical ideas from the past, at its core, it is still very much the same. We consider a situation as human tracking when a person is involuntarily under the control of another and forced to commit acts against his or her own will.

As the definition above is read, we see that human tracking can encompass many different criminal acts. However, there are three basic elements that are needed to legally define a situation as an instance of human tracking: an act, a means, and a purpose of exploitation. Because of its often hidden nature, the full extent of human tracking is difficult to map. Nonetheless, many countries across the world acknowledge the seriousness of the issue and are making efforts to combat it. Policymakers are further aware that the fight against human tracking is wholly dependent on international cooperation given that perpetrators often exploit vulnerable people by removing them from their countries of origin.

Once again, when discussing sexual exploitation in tracking, it is inevitable that regulations regarding prostitution will have to be discussed. Though sexual exploitation in trafficking does not necessarily entail prostitution, it is a straightforward way for trackers to monetise an already illicit activity. However, this does not mean that tracking for sexual purposes will be the underlying cause for all sex work, or even related to it in general, especially in jurisdictions where it is permitted. The relationship between prostitution and human tracking is complex and multifaceted and is deserving of thoughtful analysis.

A Lack of Reliable Information

There is uncertainty surrounding the prevalence of human tracking. Due to its illicit nature, it is hard to grasp really how widespread it is. It is only possible, as with any other black market good or criminal activity, to observe the number of detected instances of tracking. Currently, 173 of 193 UN member states have ratified the Palermo protocol on tracking in persons (UN Treaties Collection, 2019). In 160 of those, human tracking has been explicitly criminalised, and the numbers we have to rely on come from those countries (Chatzis, 2018). In the period 2012-2014, the United Nations Oce on Drugs and Crime (2016), UNODC, reported a total of 63,251 victims worldwide. Compared to estimates ranging in the tens of millions from the ILO (2012) among others, the number of detected victims is minuscule (US State Department, 2017).

There is also an under-reporting of different kinds of tracking. Especially organ removal is yet to be sufficiently mapped (Chatzis, 2018). Using any currently existing data involves an inherent bias stemming from this under-reporting that should be kept in mind. There are, however, some obvious tendencies shown in the data available to us, that help us in understanding human tracking at large.

A clear majority of reported victims are women, though the share of men has increased over time and is substantial. Data from 2014 published by the UNODC (2016) puts men at 21 per cent of victims. Men are particularly under-represented among victims of sexual exploitation; women make up 97 per cent of those exploited for sexual purposes (Chatzis, 2018). Men are considerably more likely to be exploited as forced labour. Namely, 85 per cent of detected male victims were exploited for labour. Overall, the victims of forced labour are in 63 per cent of all cases male (Chatzis, 2018).

Reasons for Persistence

Regardless of many efforts to combat this problem, human tracking persists. According to Chatzis (2018), the reasons for this lie not only in the complex nature of the crime, but also (i) in the widespread use of the internet in facilitating it, (ii) in an international trend to deregulate labour markets, and (iii) in increased flows of migration. Especially in Europe, labour markets have seen a push from politicians asking for more flexibility after the most recent recession. Conflicts around the world, particularly in the Middle East, saw the nationalities of tracking victims mirror those of increased outward migration (Chatzis, 2018).

The sheer number of refugees over the most recent years, and the strain this has put on countries, agencies and organisations, has also added to a likely increase in the undetected cases of tracking. European countries have not been capable of effectively screening for likely victims of tracking (Chatzis, 2018). One could even claim that the incapability in understanding the mechanisms of tracking has caused some European countries to induce it, albeit inadvertently. For instance, the Italian government’s agreement with Libyan authorities to stem the flow of migrants across the Mediterranean has been reported to create slave-like conditions for predominantly African migrants in Libya (Kirchgaessner and Tondo, 2018). This was also reflected in our study, which will be further discussed below, counsellors working in Germany that we interviewed had personally met Nigerian women, whose experience as victims of sex tracking began in Libya.

Public Official Figures

Available data allow us to make some general statements concerning the types of tracking around the world. A majority of detected tracking victims globally have been victims of sexual exploitation. However, the most prevalent kind of tracking changes with geography. In Africa, victims are more commonly detected in circumstances defined as forced labour, rather than as sexual exploitation. However, this information comes with the caveat that it could instead be a reflection of what kind of tracking rapporteurs in different parts of the world are able to detect.

When looking at Europe, the most recent figures from 2016 reported by the UNODC show that the share of detected tracking victims that had suffered sexual exploitation is substantial, reportedly more than two-thirds of victims were tracked for sex (UNODC, 2018b). Similar numbers were reported for Western and Southern Europe, a region largely made up of the countries that were part of the Western Bloc, in the preceding report from the UNODC (2016). Throughout Western and Southern Europe, twelve (thirteen) countries reported the 12,226 (12,775) victims detected there come from all over the world in 2016 (2014), with citizenships in 124 (137) different countries.

There are, however, some clear trends that can be gleaned from the numbers. For Western and Southern Europe, victims more frequently come from outside the country they are detected in or nearby countries. A third of victims had their origin in Central and South-Eastern Europe, an area largely corresponding to countries of the former Eastern Bloc that have now become part of the European Union (UNODC, 2018b). This share was the same in 2014 (UNODC, 2016). The remainder is largely made up of victims from Africa and East Asia.

German Data

The Federal Criminal Police Oce (Bundeskriminalamt), BKA, is the government agency that compiles data for all sorts of criminal activities across the country. The BKA annually publishes a report on the state of human tracking in Germany, currently under the name of ‘Bundeslagebild Menschenhandel’. Again, when viewing these figures, we need to keep in mind that the numbers presented here only concern the detected instances of human tracking.

These reports, from 1999 until now, are available online at the BKA’s website. The most recent report for 2018 was made public in September 2019. Though the structure of the report differs slightly from year to year, there are some tables that are available throughout the period. For this report, we have primarily used the disclosure of nationalities of victims of human tracking for sexual exploitation. As could perhaps be expected, the vast majority of reported victims over the past 20 years come from Europe. Over time, we see a decreasing trend in the number of victims of human tracking. However, due to the relatively low levels of human tracking from non-European countries, this trend is really only noticeable for victims of European origin.

Figure 2 Non-European Victims by Continent (1999-2018)

Figure 2 Non-European Victims by Continent (1999-2018)

Figure 3 Victims of European and Unknown Backgrounds (1999-2018), incl. Germans from 2003

Figure 3 Victims of European and Unknown Backgrounds (1999-2018), incl. Germans from 2003

Ideally, we would want to disaggregate the data and look at individual countries instead of continents. However, the BKA does not publicly disclose the figures for all countries. What it does, though, is feature numbers for a selection of countries of origin each year. Though not explicitly stated, it is likely to be the most frequent origin countries for each year. In Table 2 below, we show which countries appear and when. An important change in the report is the inclusion of German victims from 2003 (BKA, 2003).

Table 2: Specified Origin Countries of Victims of Tra�cking 11 for Sexual Exploitation

Table 2: Specified Origin Countries of
Victims of Tracking 11
for Sexual Exploitation

Figure 4 Victims from Specified FSU Countries (1999-2005)

Figure 4 Victims from Specified FSU Countries (1999-2005)

Throughout the period and not subject to any major change, is that most victims of human tracking are citizens of Central and Eastern European countries. However, over the course of 18 years, there are some changes regarding which countries are more and less prominent.

At the turn of the millennium, countries of the former Soviet Union (FSU) constituted a majority of all non-German human tracking victims. This can be said despite having explicit data for only five of those countries, Russia, Ukraine, Belarus, Lithuania and Latvia, in the period 1999-2002. For 2003-2007, it is possible to observe a shift where the share and number of these countries decrease in conjunction with them being less and less mentioned in BKA’s reporting.

Bulgaria and Romania first appear in the 2001 report and the number of victims rises sharply until 2003. Since then, there have been fluctuations in the number of reported victims from these two countries, but they have overall been somewhat stable at a considerably higher level than before. Since 2008, they have made up more than 55 per cent of the European non-German victims, and the majority of all non-German nationalities for all but three years.

Figure 5 The Most Frequent Origin Countries 2018 (2001-2018)

Figure 5 The Most Frequent Origin Countries 2018 (2001-2018)

Figure 6 Total Number of Victims 1999-2018

Figure 6 Total Number of Victims 1999-2018

Prostitution in Germany

In the past 30 years, prostitution laws in Germany have undergone numerous changes. Not only German law is likely to have affected the prevalence of prostitution within the country, though. The expansion of the EU and domestic as well as EU-wide policies in relation to it, policies in neighbouring countries, and major geopolitical events might have all contributed to the current state of prostitution and human tracking in Germany.

However, the greatest change is arguably the 2001 law, the Prostitutionsgesetz (ProstG), which institutionalised prostitution in Germany, taking the exchange of sex for money from a legal grey area into a legally recognised occupation. In principle, this regulationist approach could bring illegal and criminal acts often related to the sex trade, such as human tracking, to the surface, thereby creating a safer prostitution market for both sex workers and consumers through the possibility of regulatory oversight. However, with time, polarised opinions have been raised about this policy. Some have praised the ProstG as a milestone for sex workers’ rights. Others have proclaimed that Germany has become an exploitative ‘battery cage’ (Conrad and Felden, 2018). There have been several previous investigations into the ways in which the ProstG has impacted the state of prostitution, as well as reports on human tracking in Germany reaching different conclusions (e.g. Tavella, 2008; Czarnecki et al., 2014; Gunderson, 2012; Kavemann and Steffan, 2013).

With time, it became clear that legalisation without regulation may be fertile soil for the uncontrolled growth of prostitution activities. For this reason, the German government enacted the Prostituiertenschutzgesetz (ProstSchG), or the ‘Prostitute Protection Act’, in July 2017. The act added a number of statutory requirements on sex workers, which we will cover shortly. Germany’s approach to prostitution represents an interesting case in the European context, where prostitution has typically been very differently conceptualised and thus, legally dealt with.

The Evolution of German Law

The most significant shift for Germany is arguably the recently mentioned ProstG, which created the occupational status for sex work in Germany. It was enacted after extensive debate. Sex workers had voiced their misgivings with the then-current legislation, where prostitution was not illegal but without a legitimate position in society. Brothels and sex workers were perceived to be prevented from achieving acceptable standards in their working conditions because of the regulations in place. From the early 1980s to the mid-90s, several debates on the topic were held in the Bundestag. Draft legislation was rejected in June 1998 by the governing centre-right CDU/FDP coalition. The following centre-left SPD/Greens government brought the proposals back to parliament, which then later passed them with support from all parties bar the CDU/CSU group on 20 December 2001. The law came into force on January 1, 2002 (Kavemann and Steffan, 2013).

With ProstG, sex work was set on an equal legal footing to any other kind of profession. Those practising it were now entitled to social insurance and were given the legal means to demand payment from customers (Kavemann and Steffan, 2013). However, there are geographical restrictions on prostitution, which vary between states. The 1974 Einfu ̈hrungsgesetz zum Strafgesetzbuch, EGStGB, contains one article (number 297), which is of particular relevance.

The EGStGB article gives states the right to restrict the areas and times in which prostitution is allowed through decrees. For municipalities (Gemeinde) with a population above 20,000 inhabitants, a part of the municipality can be set off-limits for prostitution, with the option to forbid it completely in municipalities with up to 50,000 inhabitants. This law has been used as a justification for instance in Baden-Wu ̈rttemberg and Saarland, where prostitution has been forbidden in municipalities with less than 35,000 inhabitants since 1979 and 1982 respectively. The law also allows for restrictions regarding which times of the day prostitution is permitted.

In October 2016, the Bundestag passed the ProstSchG, effective as of 1st July 2017. introducing new regulations on the trade of sex. To lawfully work as a prostitute, one would now have to register as such and thereafter carry a work ID. Registration requires valid ID documents, a health check-up and a yearly health examination to maintain the status. Furthermore, the registration needs to be renewed every two years. In order to protect sex workers’ right to anonymity, one may be registered under a pseudonym if requested.

Additional provisions in ProstSchG include barring registrations if there is evidence of the registration being induced by third parties, or when in the last six weeks of pregnancy. When registering, the responsible agency is required to inform sex workers of their rights and responsibilities, including what the ProstSchG entails, such as consultation opportunities in relation to health and pregnancy, and how to get help in emergencies. Additionally, all prostitution-related businesses, such as brothels and Laufhauser (establishments where sex workers rent rooms), need to register and get permits as well.

Lastly, the ProstSchG also introduced a statutory condom requirement during intercourse. Not following this requirement could result in a fine of up to 50,000 Euros. Though the law as a whole was welcomed by most states, especially because of the statutory permission requirement (Erlaubnispflicht), many of the other requirements are related to significant implementation costs.

In connection to the introduction of ProstSchG, the German Federal Statistical Oce, Destatis, was tasked with gathering statistics on several of the registration requirements now statutorily demanded (law ProstStatV, 2017) from state and local authorities. As of yet, there have been significant difficulties in gathering this data. At the time of writing this report, only ten of Germany’s sixteen states have provided any data. The data provided is also incomplete, with several Landkreise and even some major cities unable to successfully roll out the new legislation (Destatis, 2019). Hopefully, many of the current data issues will be mitigated in the future.

The Extent of Prostitution in Germany

Though a country with regulationist policies, Germany has little publicly available statistics concerning the state of prostitution in Germany. A central issue is and has long been the actual size of the sex trade market. One figure that is often referred to in many newspaper articles is that of 400,000 prostitutes. It has been circling around in the media for at least the past 15 years and we have not been able to identify the original source.

However, the estimates vary widely depending on the paper reporting the number, and there is generally no reference to how estimates were created or by whom. One extensive, though not exhaustive search by us found the total number of prostitutes in Germany over the past 20 years to reportedly range from a lower bound of 60,000 (Stephani, 2017) to an upper bound of 700,000 (Junge, 2001). The most recent official number of issued licenses (from 31st December 2017), however, are 1,350 across Germany (See Table 3 below)4 and a total of 7,000 having reported to relevant authorities (Destatis, 2019).

Today, prostitution is commonplace across Germany. In German and international media, the country is often referred to as one of the prostitution hubs of Europe. One figure that is commonly referred to in the media is that of 1.2 million transactions a day (Junge, 2001). Though again, this is only an estimate with unclear foundations. Three surveys conducted between 2012 and 2015, one by bi-weekly women’s magazine Brigitte and two by the German edition of Playboy, had German men responding to if and how often they buy sex. There were considerable differences in their results, indicating that between 10 and 88 per cent of German men had bought sex at some point (Crocoll, 2013; FOCUS Online, 2012; 2015).

Table 3 Number of Issued Licences by State

Table 3 Number of Issued Licences by State

By moving away from sex workers having the option to register as such on their social insurance and instead of making it mandatory to register to get a permit, the German government hopes to tackle the difficulties it has had in understanding the full extent of the prostitution market. In 2013, the Federal Employment Agency reported that the number of women registered as sex workers on their social insurance was 44 (Meyer and Nagel, 2013). Beyond doubt, this figure did not correspond to reality.

In a study included in the 2007 governmental evaluation of ProstG, 305 sex workers completed written interviews to explore some of the reasons why the number of registered sex workers was so low. Only 1 per cent of respondents had a formal employment contract as sex workers, while some had other professions outside of prostitution. A clear majority (roughly 70 per cent) said they were freelancing. Responses from brothel operators also indicated that sex workers were given the option to be registered as “employees of an artists’ agency or as a prostitute” (BMFSFJ, 2007). This demonstrates the failure to turn sex work into a profession like any other, which might be related to the common stigma associated with this profession, and likely spurred on the introduction of the ProstSchG measures.

Changes Outside of Germany

Table 4 Changes abroad likely to have impacted the German market for sex

Table 4 Changes abroad likely to have impacted the German market for sex

Apart from domestic reforms in regulation, the German market for sex is likely to have been affected by multiple outside factors since the enactment of the 2001 ProstG. Together with our initial point of interest, the French reform in 2016, we have listed some of the most prominent events in Table 4 above.

EU Enlargement

On 1 May 2004, the European Union gained ten new member states: Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia. Roughly two and a half years later, in 2007, Bulgaria and Romania also joined. In this period, the European Union saw the number of member states rise from 15 to 27 and its total population increased by roughly a quarter (Eurostat, 2018). All these countries were and still are, below the EU-28 GDP per capita average, together with only four ‘old’ members (Eurostat, 2017).

Since their accession, there has been a considerable movement of labour across Europe from these countries (European Commission, 2011). However, there were certain provisions in place for both accession groups, restricting the free movement of labour from those countries to Germany at first. Full freedom was not granted until seven years after joining (Andor, 2014; European Commission, 2018).


Following the annexation of Crimea by the Russian Federation and the subsequent war in Donbas, around 1.6 million people have been displaced according to the UN (2018). The majority of those are registered as internally displaced within Ukraine, but around 1 million people have sought asylum in neighbouring countries. In the period from 2011 to 2015, a report commissioned by the International Organisation for Migration (IOM) and written by the market research institute GfK estimated that the share of the population vulnerable to human tracking rose from 14 to 21 per cent, a 50 per cent increase (GfK, 2015). In 2017, that share had not changed (GfK, 2017).

At the same time, the number of registered tracked persons has followed a bit of a U-curve. The OSCE (2016) reported that the number was 380 in 2006, and in 2015 it was 111 and during the first ten months of 2016 only 96. The UNODC (2018a), however, presents slightly different numbers from the Ukrainian Ministry of Social Policy, totalling 83 in 2015. From 2014 to 2017 they report the number of victims rising from only 27 to 198. According to the UNODC, a majority of victims were tracked for labour exploitation, but the share of those exploited for sexual purposes increased from a few percents initially to around a quarter in 2017. The discrepancy in the numbers reported by the UNODC and the OSCE is an issue, though.

Additionally, the change in visa rules for Ukrainian (and Georgian) citizens in 2017 eased access to the Schengen area. If in possession of a biometric passport, Ukrainian citizens could now go to Schengen countries without a visa (European Commission, 2019). In a small survey, conducted in December 20175, we found through interviews with sex workers and counsellors in Germany that there has been a perceived increase in sex workers from Ukraine recently. Again, we need to acknowledge that the origin countries of sex workers do not necessarily indicate activities surrounding human tracking. There are, however, reasons to assume that with an increased migratory flow from Ukraine, human tracking activities may possibly take advantage of the same corridor.

Migration Crisis

As mentioned in the case of the war in eastern Ukraine, displaced people and refugees are exposed to an increased risk of being tracked (IOM, 2015). In armed conflict, fighting groups not uncommonly abduct and recruit men, women and children to be forcibly used as combatants, sexual and domestic slaves, forced labour or coerced into marriages (UNODC, 2016). Chatzis (2018) also stressed conflict as one of the main reasons for the persistence of tracking.

Though the number of reported cases of tracking from especially Syria initially went up following the outbreak of the Syrian Civil War, absolute levels remained relatively low (UNODC, 2016). As noted by Chatzis (2018) though, it is not safe to say that this is an accurate reflection of reality as the crisis created circumstances where screening for tracking was, and in part still remains, neglected.


On the 6th of April 2016, France implemented the ‘Nordic model’, designed to discourage buyers of prostitution and ease pressures on prostitutes. Those caught buying sex could face up to 3500 Euros in fines for repeat offences and fines of up to 1500 Euros for first-time offences (McPartland, 2016). Before the law changed, France would have been categorised as a country with abolitionist legislation. It had in place restrictions on pimping, brothels, and solicitation, while the passive solicitation of customers had been banned in 2003 (Chrisafis, 2012). However, with regard to the fundamental act of paying and being paid for sex, there was no statutory ban (RFI, 2015).

EU Directive

The 2011 EU Directive ‘on preventing and combating tracking in human beings and protecting its victims’ (Directive 2011/36/EU) was agreed by the Council to homogenise the varying national regulations of this cross-country problem. It replaced a 2002 Council Framework Decision. For Germany, it included new penal provisions, which would ease the implementation against trackers and tracking (CBSS, 2016).

In the directive, member states were requested to transpose it by April 6th, 2013. By that date, Germany was the sole member state not to have transposed the directive into law (European Parliament, 2016). Not until roughly three years later in July 2016 did the Bundestag pass a bill proposed by the federal government to turn it into law. Even after the decision, implementation would wait until 1st July 2017.

Though Germany today has the legislative tools to combat tracking, critics lament the lenient application of laws. Only 26 per cent of the trackers convicted receive prison sentences, leaving potential trackers less than deterred (US State Department, 2017).

Data Gathering Mission

As any publicly available data on prostitution in Germany has been fraught with issues, the research team at SITE decided to get in touch with those most familiar with the subject: sex workers, brothel owners, as well as people working closely with sex workers and potential victims of human tracking, this last group henceforth referred to as counsellors. These counsellors worked in the public sector and for non-profit organisations as either health care professionals or social workers. By reaching out to all three groups, we intended to get closer to finding out the answers to the questions at the core of this effort: What were the effects on sex work and human tracking in the German regulationist environment caused by a change from abolitionist to neo-abolitionist legislation in France? More specifically, would sex buyers become more likely to go from France to Germany than they had been before to circumvent the risk of sanctions?


To uncover the dynamics of the prostitution market in Germany, we deployed a mixed-method approach to create a holistic picture of the sex work market at the German border (Plowright, 2011), combining quantitative and qualitative methods. Specifically, we held semi-structured interviews with counsellors working with sex workers and victims of human tracking for the purpose of sexual exploitation active in the region along the border. This allowed the interviewees to share relevant information. Through our interactions, we could also gain vital knowledge of how to improve our plan for approaching and surveying sex workers.

One crucial constraint when interacting with sex workers in the field was time. As we could not offer any financial incentives, we developed a questionnaire (see Appendix) that would be able to capture the information we were searching for, without taking up too much of their time. Questions ranged from asking the sex workers about the extent to which they felt safe, liked or disliked their working conditions, to what the perceived national backgrounds of their clients were. The questionnaire aimed particularly at asking about possible changes in the nationalities they served, their profits, and prices they could charge over the preceding two years.

Most questions did not mention the law change in France or the possible difference in the number of French customers, although it was asked at the end of the survey in case this had not been mentioned by the interviewees. With the help of our counsellor contacts, we created a questionnaire that could be answered quickly, within 5-10 minutes from when we first approached them. It enabled us to gather mostly quantitative data with some minor scope for open answers, allowing some data of a qualitative nature as well.

When working with a sensitive subject such as sex work, it is crucial to respect the personal integrity of everyone involved. This meant that we had to ensure the sex workers’ anonymity, not record the conversations and even limit the sharing of raw data between researchers. One counsellor emphasised that many sex workers typically hide their occupation from their wider social circle, often even close family, due to the stigma and fear of social exclusion.

There is also a tendency for many sex workers to be reluctant about reporting the sometimes precarious conditions they live or work under, due to the stigma they face. Our efforts tried to mitigate this issue, committing ourselves to not share information that could identify them afterwards even within the research team. Of course, this did not apply to the experts we interviewed. These latter interviews were recorded and transcribed to ensure the completeness of the information provided.


Geographically, we endeavoured to interview sex workers and brothel owners as close to the French border as possible. Specifically, this meant that the interviews we carried with these actors were in the cities of Saarbrucken, Saarlouis, Offenburg, Trier, and Freiburg and the village Rilchingen- Hanweiler outside of Saarbrucken. In order to get a more general understanding of sex work and human tracking across Germany, as well as more border-specific insights, we primarily interviewed counsellors exposed to the German-French border dynamics, based in Kehl, Strassbourg, Heilbronn, Freiburg, and Trier, but also some farther away in Berlin and Dortmund.

When approaching sex workers in the field, we learned quickly the truth of what counsellors had already warned us of. Out of 44 sex workers, only 17 accepted to be interviewed when approached. The survey was conducted in October and November 2017, generally late at night and either in a street setting or in a brothel. More than half of the sex workers (around 60%) had at that point been working in the sector for more than 1.5 years and almost a quarter for more than 8 years. Three-quarters of the sample was judged to have a good of comprehension of the questions, whereas language proved to be a considerable problem with the remainder of our interviewees.

Table 5 Nationalities of surveyed sex workers

Table 5 Nationalities of surveyed sex workers

Nine semi-structured interviews with counsellors working directly with either sex workers and/or victims of human tracking working along the Franco-German border were conducted. These interviews took place in person or over the phone. In both cases, they were recorded and later transcribed. These experts were working, as previously mentioned, either in publicly run health institutions that directed their services specifically towards the needs of sex workers or at NGOs aiming to support sex workers or victims of human tracking in various concerns.

In contrast to the mostly quantitative data obtained from the interviews with sex workers, the interviews with counsellors were of a qualitative nature and allowed a more nuanced understanding of the complexities surrounding sex work and sexual exploitation across the German-French border.

Limitations and Difficulties

Initially, our plan had been to reach out to sex workers through health organisations that counsel them on a regular basis. However, we faced major obstacles to that. These organisations work for a long time to establish trust between them and this vulnerable and often stigmatised part of society. For this reason, they were very hesitant to arrange any contact between us and the sex workers they counsel. Research and health experts have repeatedly shown how hard it is to detect whether or not a sex worker is the victim of human tracking. It often takes social workers a long time of counselling until a sex worker decides to open up about the reasons that led them to pursue sex work.

For this reason, the options of gathering data were few, and sex workers had to be approached during their working time in brothels or street prostitution areas. This, in turn, caused several difficulties.

First, it highlighted the lack of resources at our disposal. Approaching subjects in the field is one of the most costly methods, particularly when dealing with a hidden population such as this. Whatever information we would be able to gather would not necessarily provide a representative sample of answers.

Second, whether in the context of a brothel or on the street, sex workers usually have a limited time in which they actually earn money. In most cases, street prostitution is not only limited to certain streets, but also to certain times of the day. For instance, in Saarbrucken, it is only allowed between 10 pm and 6 am. As for brothels, sex workers generally have to pay high rents for a limited number of hours there. Considering that we could not offer financial compensation for the interviews, many women felt disturbed by the request, even if it took only 10 minutes, as it could mean the loss of a potential client.

Third, most women working as sex workers often hide their occupation from friends and family members. When working, they generally use other names, thereby enacting the role of their sex worker persona. This makes it harder from a research perspective to ask questions that might touch upon their more private experiences outside their role as a sex worker. For instance, the probability of finding a robust indicator of their real-life satisfaction or health may be rather low.

Fourth, when visiting brothels, there was another complication that emerged for the researcher- in-field. Most people who go to brothels want to be guaranteed privacy. This, however, can be disturbed if it becomes clear that an observer who is not part of the milieu is in the brothel. For this reason, most attempts to talk to prostitutes within brothels failed.

Lastly, a major limitation in the data gathering process was the language barrier between us and many sex workers. The lacking language skills were an important indicator to understand that these women were less likely to have spent a long time in Germany. However, it also made it difficult to ensure that they understood the questions posed in the survey. In some cases, sex workers with better German skills translated on behalf of their colleagues, which in turn may have caused some inaccuracies.


On sex work conditions and the impact of changes in prostitution law in France
By surveying sex workers, we could not find any evidence for our working hypothesis. The change of the French prostitution law did not seem to increase the number of French people coming to Germany to buy sex. From the interviews with sex workers that we carried out, all but one assessed the number to be the same, while the last one, based in Offenburg, stated that there had been an increase in tourism from France. Neither had there been any noticeable difference in the number of customers from any of the countries listed in our questionnaire: the US, the UK, France, Italy, Spain, and Russia.

A possible reason for this seemingly minimal change in the influx of French customers was suggested by a health expert working both in France and in Germany with sex workers. According to her, there had been very few cases of prosecution of prostitution customers despite in France the law (Kehl/Straßburg BE0009). This can be disputed though, as the Coalition Against Prostitution (2017) reported that 937 sex buyers were arrested in the first year following the implementation of the law, rising to 4000 by early 2019 according to the Fondation Scelles (2019). Where most of these arrests have happened is not disclosed though. If there are geographical differences across France, it could mean that there are regions in France, possibly bordering Germany, where it may still be safe enough for sex buyers not feeling the need to go elsewhere. Should that be the case, then it could be a reason why no increase in the German sex work market can be detected.

Asked about more general changes taking place since 2016, sex workers and health experts reported that the economic conditions for prostitutes had worsened considerably. Five out of the 17 sex workers, based in different cities, reported that prices for their services had dropped. According to them, this was related to two recent developments within the market. Firstly, more and more women from Eastern European countries such as Bulgaria and Romania were working as sex workers. Considering the comparatively low salaries in these countries, these sex workers tend to be more willing to compete by lowering their prices. Secondly, according to sex workers, customers claim to have less money and thus bargain more.

From the questions posed on our questionnaire, we are unable to clearly identify whether the prices had been falling since 2016 or if it is a continuation of a more long-term trend. Although several women indicated that the decline in prices was recent, those who did had only worked for less than five years.

When responding to questions regarding health and overall life satisfaction, half of the interviewed sex workers claimed that they felt completely safe while working, against almost a quarter that did not. Looking more closely at the ten sex workers that said they had been working in milieu for at least five years, just three of them claimed to feel safe. Therefore, it seems that sex workers who have worked longer in the milieu feel less confident about their safety.

Overall, the sex workers who worked predominantly in the context of street prostitution reported feeling less safe, and also a lower overall life satisfaction, than those in brothels. When asked about their personal health, no sex worker reported to be unwell, but six of them refused to answer this question.

Counsellor Inputs

Migratory flows from Eastern Europe
Many of the counsellors we interviewed seemed to agree that the total number of active sex workers did not necessarily differ since the implementation of the ProstG in 2002. However, those who had been working in the field for many years said they had noticed the proportion of foreign-born prostitutes rising significantly with the implementation of the 2001 law. All but two of the seven counsellors we interviewed estimated that the share of non-German sex workers was above 50 per cent. Those of the other view still believed it to be above a quarter. An earlier policy report by the Institute for East and Southeast European Studies from August 2015 confirmed this belief (Petrungaro and Selezneva, 2015). The fact that most of our counsellors found prostitutes to be majority non-German corresponds with earlier findings that showed this share for female sex workers rising from 52% in 1999 to 63% in 2008 (TAMPEP, 2008).

What they did all agree on, however, was that Eastern Europe was the most common background of foreign sex workers, which is also the case in the data we gathered. More specifically, women from Bulgaria and Romania form a majority within the group of sex workers in Germany who recently took up this profession. Women from Asian countries, such as Thailand, as well as from Latin America were observed to be the second-largest group of people working as sex workers by counsellors. Though, they were far fewer than their Eastern European colleagues. For example, in Strasbourg the most frequent nationality among sex workers was Bulgarian (Straßburg/Kehl, BE0004). Around Saarbrucken, we got to talk predominantly to Romanian and Bulgarian sex workers.

Our survey of sex workers indicated that the high share of Bulgarian and Romanian sex workers was to be found especially in street prostitution contexts. In brothels, we found a more diverse ethnic composition (Saarbrucken, BE0003). Some counsellors, for instance, one in Kehl, claimed that this shift towards more and more women from Bulgaria and Romania is strongly correlated with the incorporation of Eastern European countries in the EU. For sex workers, this meant that they were particularly vulnerable in the transition phase that new member states had to go through after becoming an EU member but before being allowed unrestricted freedom of movement, as they were not eligible for the protections in the 2002 German law (Kehl/Straßburg, BE0009).

According to a counsellor working with sex workers in Trier, the socio-economic background of sex workers from Romania and Bulgaria varies. Some of the women are students in their home countries and thus highly qualified in the labour market. They often work in rather high-priced brothels. At the same time, there are also women who are living under very poor conditions (Trier, BE0008).

A further finding from our interviews was in contrast to a common belief that sex workers migrate to Germany. Counsellors reported that it is very common for sex workers to have a permanent base in their origin country and only travel to Germany, as well as Austria and Switzerland, in order to earn a living and support their families for shorter periods (Trier, BE0008). There are some women, however, who do not have the know-how to organise their own trips, and thus, stay at one place for longer and end up settling down in Germany (Trier, BE0008). Often, the families and close acquaintances back home know little about the occupation of the women.

From the interviews, we got to understand more about the mobility of sex workers. Sex workers that come to Germany commonly also travel throughout the country to pursue work. This makes them different from domestic sex workers, who are usually based in one place and do not travel around far away to pursue work. According to a social worker in Dortmund (BE0006), the police refers to the high mobility of foreign prostitutes within German borders as ‘prostitution tourism.’

Our survey showed a certain (albeit weak) relationship between sex workers’ country of origin and the number of years they had been working in Germany. Of the 17 respondents, those who stated to have worked in Germany for less than 3 years were exclusively Romanian and Bulgarian nationals. The over-representation of Romanian nationals also strengthens the view that it is among the most dominant origin countries in the current German prostitution market. For Bulgaria, our survey did not have a similar over-representation.

In Saarbrucken, counsellors reported that with the change of the visa-rules for Ukraine, more and more Ukranian women started working as sex workers in and surrounding Saarbrucken. However, according to one counsellor, none of them showed indications of being victims of human tracking (BE0003). In the tracking data from the BKA, there was an uptick in the number of Ukrainian victims in 2016, 22 compared to 2 for 2015. However, for the most recent report, Ukraine did not feature and we are therefore unable to say what the situation is like now.

Origin Countries of Victims
Of the information that we could gather from our interviews, there were some things that stood out to us. Repeatedly, Eastern Europe and West Africa were mentioned as the primary origins of tracking victims. Particularly one country was mentioned repeatedly: Nigeria.

One of our counsellors in Saarbrucken noted that women from Nigeria were more likely to be victims of human tracking. To get from Nigeria to Germany is generally rather difficult and they are therefore more exposed to be exploited by trackers (Saarbrucken, BE0003). In Freiburg we met with another counsellor who also emphasised the plight of Nigerian women, going so far as to say that since the refugee crisis, human tracking patterns have changed and Nigerian sex workers in Germany are mostly always victims of human tracking (Freiburg, BE0007). A counsellor working in Kehl and Strassbourg also noted how the refugee crisis has been used to bring vulnerable women from Nigeria to Germany. Summarising, it seems to be the case that the poorer the country, the more likely it is that the woman you encounter in this milieu is a victim of human tracking (Kehl/Straßburg BE0009).

In Dortmund, one counsellor noted that West African (and Nigerian) women have often been tracked elsewhere too (Dortmund, BE0006). A fellow counsellor working in Kehl and Strassbourg gave a similar account. Probably throughout the refugee crisis, women from non-EU states have been particularly over-represented. This does not mean that there are not any EU-citizens who are victims of human tracking, but right now, the biggest stream of human tracking victims are Nigerian women, who usually apply for asylum in Germany. They have come from Nigeria, often through Libya, then to Italy, and further on to other EU states. Once they reach Germany, some seem to attempt to save themselves from further exploitation, but sometimes they remain under the control of their trackers (Kehl/Straßburg BE0009).

In Heilbronn, one expert was keen to also discuss the German victims of tracking, who according to BKA statistics made up around a quarter of all victims. These usually fall prey to a partner who uses the ‘lover boy method’ making a woman, or usually young girl, fall in love with him and later forcing her into prostitution (Heilbronn, BE0002).

Changing characteristics of human tracking in Germany over time
In Saarbrucken, one counsellor also noted how the nature of human tracking has changed over the past 10–15 years, with trackers becoming more and more subtle. Where they before were much more violent towards their victims, they seem to have adopted strategies in order to hide the signs of violence and tracking better and instead apply more psychological violence. According to this person, it is also increasingly common for a family member or partner to be involved in the tracking. Overall, the lines drawn between prostitution and human tracking appear to become increasingly blurry and hard to detect. It makes it more difficult for victims to identify themselves as such, and in turn complicates legal processes as it is sometimes hard to prove whether a person working as a prostitute did it voluntarily or under coercion (Saarbrucken, BE0003).

As for the trend in human tracking, there appear to be differing opinions. In Heilbronn, one counsellor reported the number of cases rising. However, it is difficult to say if this is a reflection of an actual increase or of heightened awareness among the general public with the influx of West Africans (Heilbronn, BE0002). In Trier, however, the situation was perceived to have improved slightly, with one counsellor (BE0008) noting that stricter laws seem to lower the number of cases of human tracking.


Though interlinked, it is important that prostitution and human tracking for sexual exploitation are not associated by default. There are many sex workers that have practised their profession free from any undue influence by a third party. However, there is undoubtedly an abundance of evidence showing that this is not always the case. For the past few decades, however, policymakers have been attempting novel legislative approaches to create a clear delineation between these two phenomena.

The initiative behind this report was the interest in legislation introducing asymmetric punishment. As has been described above, the Nordic model’s approach in which only the party purchasing sex is committing an offence applies a similar sort of mechanism to the market for prostitution. Though this topic has been covered before, it still represents an area of rather novel research. Generating broad-brush findings applicable to all settings is unlikely, but findings that are relevant and robust for a specific time and place might be obtainable with the right methodology.

The introduction of new regulations regarding prostitution in France was therefore of great interest, as the proximity to other countries, especially Germany, where sex work has been a legal profession since 2002, could mitigate the issues on finding good data. The outset of our research efforts was a belief, now in hindsight perhaps rather naive, that data would be more readily available in the German regulationist institutional setting.

As was later discovered, data availability was an issue there too. Recent legislative efforts, namely the 2017 ProstSchG, might amend the most pressing concerns. However, it is more than likely that outright criminal activities are being perpetuated within the scope of a regulated market for sex. Finding methods that accurately track the effects of this law is an area of crucial research. In futures studies on this topic, remaining aware of unrelated events and changes that might possibly affect this policy is important. For larger countries, differences within a country also need to be considered. In the case of Germany, the federal structure allows Bundeslander to adopt slightly different policies.

We were not able to identify robust indicators that suggest a changing inflow of customers from France to Germany after the 2016 neo-abolitionist law in France. Assuming that the law has not been implemented and enforced sufficiently, this may also raise a question that affects the scope of this report. For instance, one may ask whether the ‘Nordic model’ of prostitution policy is easily implemented in different countries disregarding the cultural, institutional and social characteristics that originally brought it about and if it is reasonable to expect similar outcomes in each setting. Investigating which characteristics are important would improve any future changes of this kind elsewhere.

We can, however, confidently argue for additional research on cross-country sex work, as well as on the working conditions and financial situations those operating within the milieu face. Though we were unable to establish robust evidence on the interplay between sex work and human tracking, we were repeatedly told that general flows of migrant workers temporary working in Germany, mostly from Eastern Europe, affect conditions within the milieu. In this regard, sex work differs little from other business sectors that report similar concerns. This issue was particularly significant for street workers, especially since they already (and possibly because of this) reported to feel much less safe than those working in brothels.

On a practical level, our attempt to survey sex workers taught us just how difficult it is to gain the trust needed to obtain information from sex workers. Since prostitutes are only identifiable while they are working, remaining cautious of not being perceived as contributing to the experienced stigma sex workers face is imperative. For those not working in brothels and Laufhauser, there are generally also rather strict restrictions in place for when and where they may operate. Regardless of setting though, there tend to be time pressures whilst at work, meaning many approached to answer questions will decline that request. Building a dataset of any significant size will, therefore, require significant time and resources.

Having spent significant time working on this project, we have few clear-cut answers to give. Prostitution and human tracking may be intertwined. However, how they correlate and the causal relations between them remains a perplexing matter. By talking to people working in and around the milieu and improving the availability of data, the general understanding can be greatly improved. For instance, through the interviews we conducted in late 2017 with counsellors providing support to sex workers in Germany, the uptick in 2018 of detected Nigerian victims of human tracking for sexual exploitation was in part foreseen. This highlights the need for better and more data, as well as research covering sex work and human tracking so that both topics can be addressed appropriately and more effectively in the future.


  • [Andor 2014] Andor, Laszlo: End of restrictions on the free movement of workers from Bulgaria and Romania. Version: January 2014
  • [Bisschop et al. 2017] Bisschop, Paul; Kastoryano, Stephen; Klaauw, Bas van d.: Street Prostitution Zones and Crime. In: American Economic Journal: Economic Policy 9 (2017), Novem- ber, Nr. 4, 28–63. – DOI 10.1257/pol.20150299. – ISSN 1945–7731
  • [BKA ] BKA: Bundeslagebilder Menschenhandel. AktuelleInformationen/StatistikenLagebilder/Lagebilder/Menschenhandel/ menschenhandel_node.html
  • [BMFSFJ 2007] BMFSFJ: Bericht der Bundesregierung zu den Auswirkungen des Gesetzes zur … Version:January2007. zu-den-auswirkungen-des-gesetzes-zur-regelung-der-rechtsverhaeltnisse-der- prostituierten–prostitutionsgesetz—prostg-/80766. 2007. – Report
  • [Cabinet of Ministers (Latvia) 2008] Cabinet of Ministers (Latvia): Regulations Regarding Restriction of Prostitution. Version: 2008
  • [CAP 2017] CAP: 937 sex buyer arrests: read how France has effectively shifted the burden in one year. Version:2017. how-france-has-effectively-shifted-the-burden-in-one-year?e=[UNIQID]
  • [CBSS 2016] CBSS: Human Tracking – Baltic Sea Region Round-Up 2016. Version:2016. Region-Round-Up-2016.pdf. 2016. – Report
  • [Chatzis 2018] Chatzis, Ilias: Human tracking – an overview. http://play.quickchannel. com/qc/play/ability543/26439/mainshow.asp?id=1tkisd. Version: April2018
  • [Cho et al. 2013] Cho, Seo-Young; Dreher, Axel; Neumayer, Eric: Does Legalized Prostitution Increase Human Tracking? In: World Development 41 (2013), January, 67–82. http://dx. – DOI 10.1016/j.worlddev.2012.05.023. – ISSN 0305750X
  • [Chrisafis 2012] Chrisafis, Angelique: How prostitution became France’s hottest social issue. In: The Guardian (2012), September. prostitution-france-hottest-social-issue. – ISSN 0261–3077
  • [Conrad and Felden 2018] Conrad, Naomi; Felden, Esther: Inside the ’battery cage’: Prostitution in Germany. In: Deutsche Welle (2018), June. battery-cage-prostitution-in-germany/a-44350106
  • [Crocoll 2013] Crocoll, Sophie: Und keiner will’s gewesen sein. In: Su ̈ (2013), September. keiner-will-s-gewesen-sein-1.1782896
  • [Cunningham and Shah 2014] Cunningham, Scott; Shah, Manisha: Decriminalizing Indoor Prostitution: Implications for Sexual Violence and Public Health / National Bureau of Economic Research. Version: July 2014. 2014 (20281). – Working Paper
  • [Czarnecki et al. 2014] Czarnecki, Dorothea; Engels, Henny; Kavemann, Barbara; Schenk, Wiltrud; Steffan, Elfriede; Tu ̈rnau, Dorothee: Prostitution in Germany – A Comprehensive Analysis of Complex Challenges. (2014). 2014/11/ProstitutioninGermanyEN_main.pdf
  • [Destatis 2019] Destatis: Prostituiertenschutzgesetz: Verwaltungsvorg ̈ange noch im 32
    Prostituiertenschutz/prostituiertenschutzgesetz.html. Version:2019
  • [European Commission 2011] European Commission: Frequently asked questions: The end of transitional arrangements for the free movement of workers on 30 April 2011. https://europa.
    eu/rapid/press-release_MEMO-11-259_en.htm. Version: April2011
  • [European Commission 2018] European Commission: Enlargement – transitional provisions. Version:2018
  • [European Commission 2019] European Commission: Visa liberalisation with Moldova, Ukraine and Georgia. Version: 2019.
  • [European Parliament 2016] European Parliament: Plenarsitzungsdokument A8- 0144/2016. REPORT+A8-2016-0144+0+DOC+PDF+V0//DE. Version:2016
  • [Eurostat 2017] Eurostat: Volume indices of GDP and AIC per capita, 2016 (EU-28=100). 2ChartVolume_indices_of_GDP_and_AIC_per_capita,_2016_(EU-28%3D100).png.
    Version: 2017
  • [Eurostat 2018] Eurostat: Population on 1 January. Version:2018
  • [FOCUS Online 2012] FOCUS Online: Jeder zehnte deutsche Mann geht ins Bordell. In: FOCUS Online (2012), October. maennern-33-prozent-geben-geld-fuer-sex-aus_aid_834202.html
  • [FOCUS Online 2015] FOCUS Online: Jeder vierte deutsche Mann hat schon fur Sex bezahlt. In: FOCUS Online (2015), January. playboy-umfrage-jeder-vierte-zahlt-fuer-sex_id_4398001.html
  • [Fondation Scelles 2019] Fondation Scelles: The abolition of prostitution, a French reality: a 3-year assessment of the 2016-444 Law. the-abolition-of-prostitution-a-french-reality-a-3-year-assessment-of-the- 2016-444-law. Version: April2019
  • [GfK 2015] GfK: Human tracking survey: Ukraine. files/pres_gfk_iom2015_ukraine_eng_fin_3_2.pdf. Version: June2015
  • [GfK 2017] GfK: Survey on Migration and Human Tracking in Ukraine, 2017. 2017
  • [Gunderson 2012] Gunderson, Constance: Human Tracking: The Tracking of Women in Northern Germany for the Purpose of Sexual Exploitation: Systemic Overview of Community Based Responses and Challenges. LIT Verlag Mu ̈nster, 2012. – ISBN 978–3–643–90263–4
  • [Hernandez and Rudolph 2015] Hernandez, Diego; Rudolph, Alexandra: Modern day slavery: What drives human tracking in Europe? In: European Journal of Political Economy 38 (2015), Nr. C, 118–139.
  • [Hubbard et al. 2008] Hubbard, Phil; Matthews, Roger; Scoular, Jane: Regulating sex work in the EU: prostitute women and the new spaces of exclusion. In: Gender, Place & Culture 15 (2008), April, Nr. 2, 137–152. – DOI
    10.1080/09663690701863232. – ISSN 0966–369X
  • [ILO 2012] ILO: 21 million people are now victims of forced labour, ILO says. Version: June 2012.
  • [IOM 2015] IOM: Addressing Human Tracking and Exploitation in Times of Crisis- Evidence and Recommendations for Further Action to Protect Vulnerable and Mobile Populations.
    exploitation-times-crisis-evidence-and-recommendations-0. Version: December2015
  • [Jakobsson and Kotsadam 2013] Jakobsson, Niklas; Kotsadam, Andreas: The law and economics of international sex slavery: prostitution laws and tracking for sexual exploitation. In: European Journal of Law and Economics 35 (2013), February, Nr. 1, 87–107.
    1007/s10657-011-9232-0. – DOI 10.1007/s10657–011–9232–0. – ISSN 0929–1261, 1572–9990
  • [Junge 2001] Junge, B (signature B.: Prostitution: 1,2 Millionen M ̈anner am Tag. In: Der Tagesspiegel (2001), May.
  • [Kavemann and Steffan 2013] Kavemann, Barbara; Steffan, Elfriede: Zehn Jahre Prostitutionsgesetz und die Kontroverse um die Auswirkungen | APuZ / Bundeszentrale fur politische Bil- dung. Version: 2013. und-die-kontroverse-um-die-auswirkungen. 2013 (9/2013). – Report
  • [Kirchgaessner and Tondo 2018] Kirchgaessner, Stephanie ; Tondo, Lorenzo: Italy’s deal with Libya to ’pull back’ migrants faces legal challenge. In: The Guardian (2018), May. back-migrants-faces-legal-challenge-human-rights-violations. – ISSN 0261–3077
  • [McPartland 2016] McPartland, Ben: Paying for sex in France: New law has been ’catastrophic’. clients-changed-anything. Version: September2016
  • [Meyer and Nagel 2013] Meyer, S.; Nagel, L.-M.: 44 Huren in Deutschland – oziell – WELT. In: Welt am Sonntag (2013), November. 44-Huren-in-Deutschland-offiziell.html
  • [New Zealand Government 2008] New Zealand Government: Report of the Prostitution Law Review Committee on the Operation of the Prostitution Reform Act 2003 / Ministry of Jus- tice. Version: May 2008. 10/report-of-the-nz-prostitution-law-committee-2008.pdf. Wellington, New Zealand, May 2008. – Report. – ISBN 978-0-478-29052-7
  • [OSCE 2016] OSCE: #LiveFree: OSCE Project Co-ordinator warns Ukrainians of human trafficking risks. Version: December 2016
  • [Petrungaro and Selezneva 2015] Petrungaro, Stefano; Selezneva, Ekaterina: Rights of Sex Workers in Germany: Shifting Focus from the Locals to the Migrants from Eastern and Southeastern Europe? In: IOC Policy Issues (2015), Nr. 8, S. 7
  • [Plowright 2011] Plowright, David: Using Mixed Methods: Frameworks for an Integrated Methodology. Los Angeles: SAGE, 2011 – ISBN 978–1–84860–108–6
  • [RFI 2015] RFI: French Senate tears up controversial prostitution law. france/20150331-french-senate-overturns-controversial-law-against-prostitution. Version: March 2015
  • [Scoular 2010] Scoular, Jane: What’s Law Got To Do With it? How and Why Law Matters in the Regulation of Sex Work. In: Journal of Law and Society 37 (2010), Nr. 1, 12–39. http:// – DOI 10.1111/j.1467–6478.2010.00493.x. – ISSN 1467–6478
  • [Sonnabend and Stadtmann 2018] Sonnabend, Hendrik; Stadtmann, Georg: Good intentions and unintended evil? Adverse effects of criminalizing clients in paid sex markets with voluntary and involuntary prostitution / European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics. Version: 2018. paper/zbweuvwdp/400.htm. 2018 (400). – Discussion Paper
  • [Stephani 2017] Stephani, Ilan: Jeder, der arbeitet, verkauft seinen K ̈orper. In: Die Zeit (2017), December. sexarbeit-gesellschaft. – ISSN 0044–2070
  • [Sussman 2012] Sussman, Anna: Turkey: The Brothel Next Door. https://pulitzercenter. org/reporting/turkey-brothel-next-door. Version: May2012
  • [TAMPEP 2008] TAMPEP: TAMPEP National Mapping Reports (Germany). Version:2008. deliverable_t8_annex_10_d_national_reports_mapping.pdf. 2008 (9). – Report. – 108–122 S.
  • [Tavella 2008] Tavella, Anne M.: Sex Tracking and the 2006 World Cup in Germany: Concerns, Actions and Implications for Future International Sporting Events. In: Northwestern Journal of International Human Rights 6 (2008), Nr. 1, 23.
  • [UN 2018] UN: Humanitarian response. system/humanitarian-response. Version:2018
  • [UN Treaties Collection 2019] UN Treaties Collection: 12 a Protocol to Prevent, Suppress and Punish Tracking in Persons, Especially Women and Children, supplementing the United Nations Convention against Transnational Organized Crime. ViewDetails.aspx?src=TREATY&mtdsg_no=XVIII-12-a&chapter=18&lang=en. Version:2019
  • [UNODC 2016] UNODC: Global Report on Tracking in Persons 2016. United Nations Pubns, 2016. – ISBN 978–92–1–130339–1. – OCLC: 1045553483
  • [UNODC 2018a] UNODC: Country Profiles – Eastern Europe and Central Asia. Version:2018. EASTERN_EUROPE_AND_CENTRAL_ASIA.pdf. 2018. – Report
  • [UNODC 2018b] UNODC: Global report on tracking in persons 2018. 2018. – ISBN 978–92–1– 130361–2. – OCLC: 1102388905
  • [US State Department 2017] US State Department: Tracking in Persons Report. Version: 2017. 2017. – Report

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.

How Should Policymakers Use Gender Equality Indexes?

We look at the development of gender inequality in transition countries through the lens of the Gender Inequality Index (GII), which aims to capture overall gender inequality. Extending the measure back to 1990, we look at the development of the overall index as well as that of its components. We show that, even though gender inequality in transition countries for the most part has decreased since 1990, once overall development is taken in account these countries appear to fare better in 1990 than today. We also caution against relying exclusively on composite indexes to understand patterns of gender inequality. While the desire of policy makers to get one number that captures gender inequality development is understandable, weak correlations of the GII with other indexes (over years when multiple gender inequality indexes exist) as well as across sub-indexes suggests that such an approach has limitations. Finally, we emphasize the need to understand levels as well as trends and underlying mechanisms to better inform policy to improve gender equality.

On Measuring Progress

When studying economic development, or any issue really, one faces the challenge not only of finding the right way to identify and measure what are often complex changes, but also of communicating the bottom line efficiently. This naturally leads to the search for a single metric according to which we can rank progress and follow it over time. In the realm of economic development the standard measure is GDP growth. But, of course, focusing only on GDP leaves out many important dimensions of development, such as health and education.[1] In an attempt to capture these dimensions, while still arriving at a single number that measures development, the Human Development Index (HDI) was developed in the late 1980s. Since then, a number of alternative indexes capturing additional aspects of human wellbeing have been suggested; see the report by the “Commission on the Measurement of Economic Performance and Social Progress” (Stiglitz, Sen and Fitoussi, 2009).

Just as for overall development, there is great interest in single measures that capture the gender dimension of this development. Over the past decades a number of such “gender equality indexes” have been developed by international organizations such as the UNDP, the EIGE (European Institute for Gender Equality) and the WEF (World Economic Forum), to name a few.

These measures receive a lot of attention and in particular the reporting of country rankings tends to have an influence on political and policy discussions. The various indexes proposed differ in what dimensions they include (as will be explained below) and, much as a consequence of this, in the time periods they can cover. In some cases (as will also be shown below) it is possible to extend the time coverage of the indexes, but most of the times it is hard to recover the underlying data.

In this brief we summarize what the most popular indexes tell us about the development of gender equality in transition countries, contrasting these to Western European countries.[2] Whenever we have been able to find the underlying data, we also add to publicly available measures by extending indexes back to early 1990s. We then comment on the development of gender equality in transition countries and, perhaps most importantly, on why an indexes-based analysis should be interpreted with some care.

Gender Equality Before 1990

As has often been pointed out, the Soviet Union and many of the countries in Eastern and Central Europe were, at least in some dimensions, forerunners in terms of promoting gender equality (e.g., Brainerd, 2000; Pollert, 2003; Campa and Serafinelli, 2018). This was mainly due to the high participation of women in the labor market as well as the (official) universal access to basic health care and education.

However, some scholars have suggested that not all aspects of gender equality were as advanced in the countries in the Soviet Union and in Central and Eastern Europe (Einhorn, 1993; Wolchik and Meyer, 1985). Even though women were highly integrated in the labor market, they were also still expected to take care of child rearing and house work (UNICEF, 1999). The gender pay gap and gender segregation in the labor market was also similar to levels found in OECD countries. In addition, despite the high number of women in representative positions in communist party politics, women were rarely found in positions of real power in the political sphere (Pollert, 2003).

Generally speaking, while the communist regimes succeeded in promoting women’s access to the labor market and tertiary education, they failed to eliminate patriarchy (LaFont, 2001). Such a dichotomy gives rise to a broad set of questions regarding gender equality in transition countries as well as the measurement of gender equality in this context. What happened to gender equality, in relation to economic growth, during the transition, when new governments often broke with the tradition of promoting women’s employment and education? Did gender equality enhanced by communism leave a legacy or did underlying patriarchic values characterizing many of the communist societies come to dominate? How should we regard developments of indexes that try to weight several components within a context, such as that of transition countries, where these components may move in different directions from each other, given the dichotomy characterizing gender relations?

The Different Indexes

There are several different indexes that are often quoted in policy discussions. Two important measures are the Gender Development Index (GDI) and the Gender Inequality Index (GII), both calculated by the UNDP and reported annually in the Human Development Report (HDR). A third, more recent index that has received increasing attention is the World Economic Forum’s global Gender Gap Index (GGI), which is published in the yearly Gender Gap Report. These three can serve as illustrations of what gender equality indexes typically try to capture.

The Gender Development Index (GDI) essentially measures gender differences in the Human Development Index (HDI). The HDI in turn aims to capture achievements in three basic dimensions of human development: health (measured by life expectancy), knowledge (measured by expected and mean years of schooling) and living standards (measured by GNI per capita). The GDI then basically tries to assess the relative performance in these three dimensions for men and women respectively. If health (or education, or income)  in the population on average goes up, this improves the HDI. But to the extent that the improvements are felt differently by men and women, this will show in the GDI. There are several potential problems with the measurement of this index, especially when it comes to dividing GNI per capita between men and women (see e.g. Dijkstra and Hanmer, 2000); on the other hand, the index offers a transparent way to connect gender inequality to the HDI measure.

The other UNDP measure, the Gender Inequality Index (GII), was reported for the first time in the 2010 Human Development Report. It was created to address some of the perceived shortcomings of its forerunner, the Gender Empowerment Index (GEM) which had been introduced together with the GDI in 1995 (see e.g., Klasen and Schuler, 2011 for problems with GDI as well as GEM). The GII measures gender inequalities in three dimensions of human development: 1) reproductive health, measured by maternal mortality and adolescent birth rates; 2) empowerment, measured by representation in parliament and secondary education among adults; and 3) economic status, measured by labor force participation. As with the GDI, the areas of health, education, and economic empowerment are present, but the index also considers some aspects of health that are more directly relevant for women, and includes a component trying to capture political participation. The economic measure of labor force participation is also somewhat easier to interpret (and measure) than GNI divided between men and women. As for the GDI, GII country-values from 1995 are available on the UNDP website.  Conveniently for our purpose, most of the underlying data that the index is based on are also made available from the UNDP for the years 1990, 1995, 2000, 2005, and every year between 2010 and 2015, with the only exception of female seat share in parliaments in 1990[3]. We downloaded the latter from the World Bank indicators database[4]. We also added information on the share of women in the 1990 Polish Parliament, from the Inter-Parliamentary Union[5], and on the share of women in the 1990 Georgian “Supreme Council,” from Beacháin Stefańczak and Connolly (2015).

A third more recently developed index is the Global Gender Gap Index. This covers areas of political empowerment, health and survival, economic participation and educational attainment, as measured using 14 different variables. An indicator is available for each of the sub areas covered, which are then weighted together in an overall indicator of the gender gap. The Global Gender Gap Index is clearly more detailed and provides a more nuanced picture of existing gender gaps compared to the GDI or the GII. But this amount of detail also comes with potential costs; it is more difficult to interpret the overall index as there are more underlying components that may change simultaneously, and it is also more difficult to reconstruct the index back in time.

What Does the GII Index Tell Us About Gender Equality in Transition Economies?

Among the above mentioned indexes, we focus on the GII here. Extending this measure when possible allows us to study gender inequality starting from 1990 for a limited set of countries (we expand the sample of countries when looking at different dimensions of the GII separately below)[6]. Figure 1 reports values for the index in box plots, which show the index median, maximum, minimum, 75th and 25th percentile for two groups of countries: transition countries and Western-European countries. When interpreting Figure 1, recall that higher GII values imply more inequality.

Figure 1. The Gender Inequality Index in transition countries and Western Europe, 1990-2015

Source: Own calculations based mainly on UNDP data.

Figure 1 shows that based on the GII, median gender inequality is larger in transition countries than in Western Europe and has been so throughout the entire period since 1990. In both regions the index shows a decreasing trend, after an initial increase in 1995 in the transition countries. Below we will show that this is mainly due to a drop in female representation in national parliaments. The variance of the index scores has declined over time in Western Europe, while it remained mostly unchanged in the transition countries[7].

This first piece of evidence from the data is somewhat at odds with the common notion that transition countries enjoy relatively low level of gender inequality. However, two qualifications are in order here. First, transition and Western European countries are generally at different levels of development. Figure 2 displays the country groups performance in relation to their level of human development. This is done by measuring the difference between their GII ranking and their HDI ranking among all the countries with non-missing GII values in the years considered. The larger the difference, the worse the group performance in terms of gender inequality in relation to its level of development.

Figure 2. Difference between Gender Inequality Index ranking and Human Development Index ranking in transition countries and Western Europe, 1990-2015

Source: Own calculations based mainly on UNDP data.

The trends of transition countries and Western Europe are now opposite. In the former group, in 1990 the median standing in terms of gender equality was better than that in human development; this difference appears to have narrowed over time, and it is close to zero in 2015. Western European countries have instead improved their gender equality in relation to their level of overall human development over the period studied. Put differently, the gains in human development made by former socialist countries since the transition have not translated into comparable gains in gender equality as measured by the GII index.

Second, it is also important to emphasize that, as noted above, according to several scholars the socialist push in favor of gender equality was directed only to certain spheres of women’s lives, namely their economic empowerment. This suggests that a composite index can mask important contrasting patterns among its components.

In Figures 3 to 5 we document that different variables indeed paint quite diverging pictures of gender inequality in transition countries.

Figure 3. Development of adolescent births and maternal mortality, 1990-2015

Figure 4. Development of secondary education and share of women in parliament, 1990-2015.

Figure 5. Labor force participation, 1990-2015

Source: Own calculations based mainly on UNDP data.

In each figure we display box-plots for the three areas covered by the GII: health (measured by teenage births and maternal mortality), empowerment (measured by secondary education and share of women in Parliament) and labor force participation. Looking at the different variables separately also allows us to increase the number of countries significantly, since for many countries only the seat share of women in parliaments is missing in 1990.

As the figures show transition countries in 1990 displayed relatively low levels of gender inequality in labor force participation and secondary education. Over the last 25 years, they have kept improving the latter, while the former has stalled, resulting in Western European countries displaying a higher median level of gender equality in labor force participation for the first time around 2010. Reproductive health, while improving since the transition, is still far from converging to Western European standards. Finally, political representation appears to be responsible for the increase in inequality immediately after the transition that we have noted in Figure 1. While it is hard to compare the meaning of representation in the context of 1990 totalitarianisms to that of the democratic regimes emerged later, during the regime change women de facto lost descriptive representation, which was sometime guaranteed in socialist times by gender quotas (Ostrovska, 1994).

In conclusion, breaking down the GII by its components shows that, while Western European countries have invariantly improved their levels of gender equality since 1990, the trend in transition countries depends on the measure one looks at: women maintained but did not improve their relative status in the labor force, they gained more equality in education and especially in terms of reproductive health, and lost descriptive political representation.

What Does the GII Index (And Other Indexes) Not Tell Us?

The conclusion in the previous paragraph raises the question of which other areas of progress, stagnation or deterioration in gender equality in transition countries that might be overlooked in the GII index. Above, we have summarized two more indexes, the GDI and the Gender Gap Index, which focus on additional dimensions of gender inequality but are more limited in terms of time availability. For the time over which there is overlap between the available indexes, the correlation between the GII index and the GDI and the Gender Gap Index respectively, is roughly 0.60. Interestingly, such correlation is higher in the sample of western European countries (0.64 and 0.68 respectively); when the sample is limited to transition countries, the correlations are down to 0.40 and 0.50 respectively.

Several factors might account for the differences across indexes. Unlike the GII, both the GDI and the Gender Gap Index, for instance, include measures of income inequality. On the other hand, the GDI, as pointed out above, does not account for issues related to reproductive health and political representation. The Gender Gap Index is the only one to include, among the health measures, sex-ratios (typically defined as the ratio of male live births for every 100 female births). This turns out to be especially important for some of the transition countries: in the most recent Gender Gap Report, Georgia, Armenia and Azerbaijan remain among the worst-performing countries globally on the Health and Survival sub-index, due to some of the highest male-to-female sex ratios at birth in the world, just below China’s. This goes hand in hand with very high scores in terms of gender equality in enrolment in tertiary education, for which each of these countries ranks first (at par with a few other countries), having completely closed the gender gap. In fact, women are more likely to be enrolled in tertiary education than men.

The relatively low correlation among the different indexes for the group of transition countries also deserves special attention, because it might be a direct consequence of the peculiar history of women’s rights and empowerment in the region. Since some dimensions of gender equality were fostered through a top-down approach, rather than as the result of demands and needs expressed by an organized society, it is more likely that over the last thirty years elements of modernization coexisted with more traditional forms of gender inequality.

Finally, it is worth pointing out that none of the above indexes accounts for important dimensions of gender inequality such as,: gender violence, division of chores in the household, political representation at the local level, and the presence of women in STEM’s professions (where the largest job creation might happen over the next couple of decades). Once more, some of these measures might be particularly relevant for transition countries. Just to mention one example, gender violence is an urgent issue in a few of the countries in the area[8]. A case in point in this respect is Moldova: in 2017, the country ranked 30th out of 144 countries in the Gender Gap Index. Its rank for the sub-index called “Economic Opportunity and Participation” was 11[9]. The country performs especially well in terms of economic opportunity and participation because women not only participate in the labor market in almost equal rates as men, but they are also relatively fairly represented in professions traditionally less feminized elsewhere, such as “professional and technical workers” and “legislators, senior officials and managers.” At the same time, gender violence appears quite prevailing in Moldova: according to the UN, in 2014 “lifetime prevalence of psychological violence” in Moldova was of 60%. Official country statistics also report that the percentage of ever-partnered women aged 15-65 years experiencing intimate partner physical or sexual violence at least once in their lifetime in 2011 was 46%[10].

While limited in scope, the example above illustrates how some of the available indexes might not capture some important drivers of gender inequality in the region.


In this policy brief, we have reviewed some of the available gender inequality indexes that are commonly used in policy discussion as well as in policy-making.

We have then discussed gender inequality in transition countries focusing on one of these indexes, the Gender Inequality Index, whose span we have extended to the beginning of the transition period. Our analysis has highlighted some points to be mindful of when using comprehensive indexes to discuss gender inequality, especially in transition countries:

  • It can be fruitful to analyze gender inequality indexes in relation to levels of development. Some issues related to gender inequality, such as maternal mortality, are potentially addressed with a comprehensive strategy aimed at overall development. Conversely, other drivers of gender inequality, such as women’s political empowerment or gender violence, might require more targeted policy interventions, since they do not necessary go hand in hand with overall development.
  • While comprehensive indexes can be useful in terms of effective communication, it is often difficult to compress all the potential forms that gender inequality can take into a single index, especially over time. This is due to both conceptual issues and data limitations. Moreover, even when this is done, a comprehensive index can overshadow important sources of gender inequality if it is composed of sub-indexes that move in opposite directions.
  • The previous point can be especially relevant in the context of transition countries, which historically experienced a top-down approach to gender equality, the results of which in the long-term appear to be major advancements in some dimensions of women’s empowerment and contemporary potential backlash in other dimensions. In the context of transition countries, for instance, it has been argued that low levels of female representation in political institutions can be the result of women’s large participation to the labor market while division of roles in the household remained traditional. In the words of anthropologist Suzanne LaFont, “Women have been and continue to be overworked, and their lives have been over-politicized, the combination of which has led to apathy and/or the unwillingness to enter the male dominated sphere of politics. Many post-communist women view participation in politics as just one more burden.”[11] In such a context, average values of an index on gender equality might mask high achievements in economic empowerment coexisting with lack of political representation.
  • Identifying policies to address gender inequality in transition countries might be especially difficult because, depending on the dimension that one focuses on, the challenge at hand is different: in terms of education and employment, the policy goal appears to be maintaining current levels of equality or increasing them from relatively high initial points; the type of policies to do so are likely different than those used in Western European countries in the last 30 years, where the challenge was rather how to increase equality from relatively much lower levels. Conversely, in other dimensions the challenge is how to make major leaps forward, which move transition countries closer to Western European standards: this is the case for sex-ratios, for instance, and reproductive health more in general. The importance of initial levels and trends for policy implications also showcases how crucial it is to acquire more historical knowledge of policies, institutions, and statistics.

Overall, policy discussions and policy-making should go beyond mere descriptions of what indexes and related international comparisons tell us about gender inequality. A better knowledge and understanding of all of the drivers of gender inequality, of their historical evolution, and of their connections both with overall development and among them, is crucial to give sound policy recommendations.


  • Beacháin Stefańczak, K.Ó. and Connolly, E.(2015),  ‘Gender and political representation in the de facto states of the Caucasus: women and parliamentary elections in Abkhazia’. Caucasus Survey, 3(3), pp.258-268.
  • Brainerd, E. (2000), ‘Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union’, Industrial and Labor Relations Review, 54 (1), pp. 138-162.
  • Campa, P. and Serafinelli, M. (2018), ’Politico-economic Regimes and Attitudes: Female Workers under State-socialism’, Review of Economics and Statistics, Forthcoming.
  • Dijkstra, A. and L. Hanmer (2000), ‘Measuring socio-economic gender inequality: towards an alternative for UNDP’s Gender-related Development Index’, Feminist Economics, Vol. 6, No. 2, pp. 41-75.
  • Einhorn, B. (1993), Cinderella goes to market: citizenship, gender, and women’s movements in East Central Europe, London: Verso.
  • Klasen, S. and Schuler, D. (2011) Reforming the Gender-Related Development Index and the Gender Empowerment Measure: Implementing Some Specific Proposals. Feminist Economics. (1) 1 – 30
  • LaFont, Suzanne (2001), ‘One step forward, two steps back: women in the post-communist states.’ Communist and post-communist studies 34(2), pp. 203-220.
  • Ostrovska, I. (1994). Women and politics in Latvia. Women’s Studies International Forum 2, 301–303.
  • Pollert, A. (2003), ‘Women, work and equal opportunities in post-Communist transition’, Work, Employment and Society, Volume 17(2), pp. 331-357.
  • Stiglitz, Joseph, Amartya Sen, and Jean-Paul Fitoussi (2009). `The measurement of economic performance and social progress revisited.’ Reflections and overview. Commission on the Measurement of Economic Performance and Social Progress, Paris.
  • Tur-Prats, Anna (2018). Unemployment and Intimate-Partner Violence:  Gender-Identity Approach. GSE Working Paper No. 1564
  • Unicef. Women in transition. 1999.
  • UN. The World’s Women 2015.
  • Wolchik, S. L. and Meyer, A.G. (1985), Women, State and Party in Eastern Europe, Durham, NC: Duke University Press.


  • [1] In contrast to a common perception, economists are generally well-aware of the limitations of GDP as a measure of welfare. In fact, the reference manual of national accounts, the SNA 2008, makes this explicit in stating that there is “no claim that GDP should be taken as a measure of welfare and indeed there are several conventions in the SNA that argue against the welfare interpretation of the accounts”.
  • [2] By “transition countries,” we refer to all countries that were part of the Soviet Union plus the Central and Eastern European countries that were heavily influenced by the Soviet Union before 1990 (not including Albania and former Yugoslavia). Starting from this, we – as will be made clear below – sometimes limit the set of countries further depending on data availability.
  • [3]
  • [4]
  • [5]
  • [6] For Western Europe these countries are: Austria, Belgium, Cyprus, Denmark, Finland, France, Greece, Iceland, Italy, Luxembourg, Malta, Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland. The transition countries are: Armenia, Bulgaria, Georgia, Hungary, Poland, Romania, Russian Federation.
  • [7] The outlier among Western countries is Malta.
  • [8] While explaining the sources of gender violence in the region is beyond the scope of this report, incidentally we notice that, according to recent research, female economic empowerment in a context where patriarchal values are dominant might backfire against women in the form of increased gender violence. See Tur-Prats, 2018.
  • [9]
  • [10] UNFPA (2015). Combatting Violence against Women and Girls in Eastern Europe and Central Asia.
  • [11] LaFont, Suzanne (2001). One Step Forward, Two Steps Back: Women in the Post-Communist States. Communist and Post-Communist Studies, Vol. 34, pp 208.

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.

Whistleblower Protections but no Rewards: EU Commission Proposes a New Directive

20180626 Whistleblower Protections Image

On the 17th of April 2018 the European Commission adopted a package of measures to increase protections for whistleblowers (European Commission Newsroom, 2018). This is good news, as whistleblower protection in Europe has been uneven and in some member states non-existent. Transparency International (2013) rated a disappointing four European countries as having adequate or extensive protection. In a report by Wolfe et al (2014), several European countries, including Germany, France and Italy, were judged to have inadequate laws with respect to several aspects of whistleblower protection, although France and Italy recently improved them considerably. Corruption, fraud of various types, and related forms of economic crime are widespread almost everywhere in the world (See e.g. Dyck et al 2014, and Global Crime Survey 2016). Criminal organizations such as drug cartels, have become increasingly sophisticated and their ability to use the international financial markets has made it ever more difficult for law enforcement agencies to discover them with more traditional law enforcement tools (see e.g. Radu 2016 for an overview). Incentivizing whistleblowers through protection and rewards can prove effective at getting information on these hard-to-detect crimes. Whistleblower protection is central for ensuring democratic values such as freedom of speech and fair elections, and recent cases also suggest that it may be central for protecting investigative journalists and their sources.

The Need for Protection and Possibly Rewards

On February 26th 2018 Ján Kuciak, a Slovakian journalist, was murdered in his home for investigating political connections to organized crime in the heart of Europe (Washington Post, 2018); Daphne Caruana Galizia was killed on 16th of October 2017 by a car bomb while she had been writing about corruption in Malta in connection with the Panama papers (Financial Times, 2018a); Maria Efimova, an employee at a private bank that claimed that her employer had illegally moved funds for Maltese politicians, is under an arrest warrant from Malta and Cyprus on seemingly unrelated charges (The Guardian, 2018); and Hervé Falciani, who blew the whistle on the bank he was working for in Switzerland that helped clients evade billions of dollars in taxes, was arrested in April in Spain after an arrest warrant issued by Switzerland on March 19th, though he has now been released on bail (Financial Times, 2018b).

While some EU countries recently improved whistleblower protection, some seem to be heading in the opposite direction. An extreme example is Germany. A provision packed into the German Data Retention Framework of 2015 allows for prison sentences of up to 3 years for the handling of “stolen data”, and journalists are no longer protected against search and seizure (European Digital Rights, 2017). This provision was included despite Germany’s problems with underreporting of corporate crime.  On the need of whistleblowing in the country, consider Volkswagen’s emissions scandal in 2015 when the public learned that the company had installed defeat devices in millions of diesel cars to ‘cheat’ on environmental emissions standards and increase pollution all over the world. The response of management was to blame a set of “rouge engineers” (Congressional Hearing, 2015), while we now know that power points on how to circumvent U.S. emissions tests by a top technology executive circulated within the company as early as 2006 (New York Times, 2016). Excess diesel emissions were associated 38 000 premature deaths in 2015 (Anenberg et al, 2017), implying that whistleblowing could have saved thousands of lives, yet the wrongdoing went on for close to a decade without anyone blowing the whistle. Cheating on emissions tests also turned out to be an industry wide phenomenon.

Germany also has a history of treating whistleblowers poorly. Consider for example the case where a German nurse brought a complaint to her employer in December of 2004 over poor treatment of patients, and she was fired in January 2005. Her employer cited repeated illness as the reason for being fired, the nurse claimed that it was retaliation for speaking out about poor conditions. The nurse then filed a complaint in German Labor Court which was dismissed in August 2005. She then brought the claim to the European Convention of Human Rights, alleging that her right to expression under article 10 of the European Convention of Human Rights had been violated by her employer. She won that case in 2011, and Germany was ordered to pay the nurse 10 000 Euro in non-pecuniary damages, and 5000 for costs and expenses (Heinish V Germany 2011).

Large firms do not appear to be doing better. Even after the Siemens scandal in 2008, when the company was discovered pursuing a long-term, extensive and systematic strategy of bribing foreign governments and purchasing agencies, and promises about a drastic change in corporate governance. Recent cases suggest that the corporate culture at Siemens has not improved. Meng-Lin Liu, a compliance officer at Siemens China, brought attention to alleged kickbacks paid in connection with equipment sales to army hospitals in China to the chief financial officer for healthcare in China. He was fired after reporting internally and filed a claim alleging violations of the Foreign Corrupt Practices Act. Siemens lawyers argued that since he was no longer an employee, he was not entitled to protection under Dodd-Franks definition of “whistleblower” (Forbes, 2014).

The situation in such an important European country like Germany suggests that protection applying across all member states is needed, and the experience of other countries further suggest that protection may not be enough. In the UK, the country recognized to have some of the best protections in the EU (Wolfe 2014, Transparency International 2013), whistleblowers are still experiencing pushback. The recent case of Jes Staley, Barclays Bank’s CEO is enlightening. He ordered his security team to unveil the identity of an uncomfortable whistleblower, going so far as to request video footage of the person who bought the postage for the letter. Yet, the Financial Conduct Authority and the Prudential Regulation Authority (FiCA & PRA) decided to just fine him £642 000 – a small fraction of his pay package that year (Reuters, 2018). Cases like this suggest that the US Congress was right in pushing for rewards. The mild sanctions established by the UK regulators sent a loud and clear message to prospective whistleblowers: even in the UK, where protection was judged as high in the above-mentioned reports, a CEO that violates the law trying to uncover someone reporting his potential mismanagement (probably not to give him a premium), will just have to pay a mild fine, if he is caught of course!

In the following we review the new proposal for whistleblower protections and argue that evidence from the US suggests that financial incentives for whistleblowers may still be needed to ensure an adequate level of reporting. We then consider objections to monetary rewards which are praised by regulators in the US, while EU agencies remains hesitant. Finally, we conclude with suggestions on how to improve the European legislation.

The EU Proposed Directive Versus US Developments

The new Directive includes mandatory establishment of internal reporting channels for firms with more than 50 employees that should allow for anonymous claims (Article 5). It includes prohibition against a wide range of retaliation (Article 14); and the burden of proof is reversed in case of alleged retaliation (Article 15). Who counts as a whistleblower under the Directive is defined widely to encompass subcontractors, trainees, and people associated with a wrongdoing firm in a “work-related context” (Article 2).

The Directive is bound to improve the situation for whistleblowers given the current uneven protection. It bears similarities with the US Sarbanes-Oxley act of 2002 (SOX), but it goes beyond SOX in that it applies more broadly. Since SOX, the legal debate in the US has increasingly focused on rewards to whistleblowers as protections alone are often insufficient to ensure an adequate level of reporting.

After the financial crisis, the US concluded in the Dodd-Frank Act of 2008 that protections were insufficient, and that above and beyond protections, Dodd-Frank allows for rewards to whistleblowers who report wrongdoings in securities trading where the sanction against the wrongdoing party exceeds 1$ million.

The use of rewards was not unfamiliar to the US before Dodd-Frank. They had formerly concluded that in the tax area, whistleblowers who report tax evasion should be eligible for rewards through the Tax Relief and Health Care Act of 2006 which established the Internal Revenue Service “Office of the Whistleblower”. Although previously to 2006 whistleblowers could receive rewards at the IRS, this was entirely up to the agency’s discretion.

In the procurement area, whistleblowers are also eligible for rewards in the US under the False Claims Act (FCA) enacted in 1863. The commitment to rewards was reaffirmed in 1986 when revisions to the act reinvigorated the whistleblower or “qui tam” provisions of act (for an overview of reward programs, see Nyreröd & Spagnolo 2017).

Despite being regarded as having some of the best whistleblower protections in the world (see e.g. Wolfe et al 2014), the US did not settle for protections alone in key regulatory areas. The new EU directive does not address rewards at all which is unfortunate given their law enforcement potential if they are coupled with independent and competent judicial institutions.

Although the US experiment with whistleblower rewards is working, the only EU institution to evaluate reward policies to our knowledge is the UK’s PRA & FiCA on the request of the UK parliament. Their assessment concludes strongly against rewards, yet they do not provide any evidence to back up their negative assessment and make claims that later evidence has refuted. In the following, we review the concerns raised by critics of reward programs, primarily the PRA & FiCA.

Evidence on the Effectiveness of Rewards

Under reward programs in the U.S whistleblowers can receive a percentage of the fine imposed on the wrongdoing firm or person. The range is usually between 15-30% of the sanctions against the firm, and of the money recovered. The exact reward percentage within the range is determined by how central the whistleblowers information was to unearth and sanction the wrongdoing.

One fundamental concern with rewards is their cost effectiveness. Some argue that they can come with a costly government structure and that they attract a lot of meritless claims by opportunist employees, which increase the administrative costs (PRA & FiCA 2014, Ebersole 2011).

On the other hand, many argue that they can be a cost-effective tool in an age when governments are looking for austere economic policies (Engstrom 2014). Some argue that they are at least as efficient as classical “command and control” methods of enforcement, such as selecting random persons or firms for audit. We evaluate cost-effectiveness with respect to three important effects: deterrence, increased quality of claims, and increase quantity of claims.

A significant part of determining cost-effectiveness is the extent to which whistleblowing has any significant deterrence effects on future misbehavior. Johannesen & Stolper (2017) found that whistleblowing had deterrence effects in the off-shore banking sector. They studied the stock market reaction before and after the whistleblower Heinrich Kieber leaked important tax document from the Liechtenstein based LGT Bank. They found abnormal stock returns in the period after the leak, and the market value of banks known to derive some of their revenues from offshore activities decreased.

Wilde (2017) also provide evidence that whistleblowing deters financial misreporting and tax aggressiveness. Using a dataset of retaliation complaints filed with OSHA between 2003 through 2010 on violations of paragraph 806 which outlaw’s retaliation against employees who provide evidence of fraud, he found that firms subject to whistleblower allegations exhibited decreases in financial misreporting and tax aggressiveness.

As for experimental evidence, Abbink and Wu (2017) conducted laboratory experiments studying collusive bribery, corruption, and the effects of whistleblower rewards on deterrence. They find that amnesty for whistleblowers and rewards strongly deter illegal transactions in a one-shot setting, but in repeated interaction the deterrence effect is limited. Their results support a reward mechanism, especially for petty forms of bribery (which are more like one-shot games).

Bigoni et al (2012) conducted laboratory experiments on leniency policies and rewards as tools to fight cartel formation. They find that rewards financed by the fines imposed on the other cartel participants had a strong effect on average price (returning it to a competitive level). In the model setting, this implies that rewards have a deterring and desisting effect on cartel formation.

Another central question is whether rewards increase the quality and quantity of claims. PRA & FiCA (2014) writes that “There is as yet no empirical evidence of incentives leading to an increase in the number or quality of disclosures received by regulators” (PRA & FiCA 2014, p.2).

As for increased quality, there is evidence suggesting that this claim is untrue. Dyck et al (2010) compared whistleblowing in the health care sector where rewards are available through the FCA with non-healthcare sectors where they are not. They found that 41% of fraud cases are detected by employees in the healthcare sector. That number was only 14% for other sectors, a difference highly statistically significant (at the 1% level) despite a small sample size (Dyck et al 2010, p. 2247).

More recently, Call et al (2017) examined empirically the link between whistleblowing and (i) penalties, (ii) prison sentences, and (iii) duration of regulatory enforcement actions for financial misrepresentation. They found that whistleblowers’ involvement in financial misrepresentation enforcement actions was correlated with higher monetary sanctions for the wrongdoing firm and increased jail time for culpable executives. They also found that enforcement proceedings began quicker, and further that whistleblower involvement increased the likelihood that criminal sanctions were imposed by 8.58%, and that criminal sanctions were imposed against the targeted wrongdoer increased by 6.64%.

Another highly contested point is the relation between the quantity and quality of claims and regulatory effectiveness. Some argue that rewards may attract a lot of meritless claims by employees who are either malicious or hope to reap some reward (PRA & FiCA 2014, Ebersole 2011). This does seem to have been the case with some reward programs, but not to the extent many opponents of rewards argue, and this effect does not render rewards a futile or ineffective policy approach, see Nyreröd & Spagnolo (2017) for a thorough discussion.

There are, however, valuable lessons to be learned from the quantity of claims received and the percentage of claims determined to have merit from, for example, the IRS Whistleblower Office. At the IRS there has been a significant backlog of claims, and an exceedingly small number of claimants receive rewards. The IRS program, under 7623(a), does not have a threshold for claims to be considered, and the vast majority of claims fall under 7623(a). These are lessons for optimal design, but not an insurmountable obstacle for effective reward programs. One way around this problem is to have a threshold for claims to be considered. Another is the FCA model, where persons pursue litigation on their own if the Department of Justice declines to join, thereby taking on the risks and costs of losing in court.

Concerns over administrative burden and costly government structures are not salient enough to warrant a rejection of reward policies, as benefit in deterrence and quality outweigh the administrative costs of reviewing even large quantities of incorrect claims.

Entrapment and Malicious Claims

Another central concern has been that “Some market participants might seek to ‘entrap’ others into, for example, an insider dealing conspiracy, to blow the whistle and benefit financially”, FiCA & PRA (2014).

There are presently good ways of preventing this issue, which does not seem to have been salient in the U.S. experience with these policies. Regarding the FCA, for example, when the relator (whistleblower) initiated or planned the wrongdoing, courts can reduce the reward below 15% as they see fit (False Claims Act, 31 U.S.C. §3730 (d) (3)). The IRS has similar restrictions that in cases where the whistleblower planned and initiated the tax evasion, they may considerably reduce or deny any reward. If the whistleblower is convicted of criminal conduct related to the suit, then they should deny her any reward (Internal Revenue Code, 26 U.S.C §7623 (b) (3)).

These restrictions on reward payouts is probably the reason why, judging from the reports by the U.S agencies, entrapment has not emerged as a salient issue in the US experience with the various programs. As for evidence, the National Whistleblower Center (2014) claims they did not find a single case of entrapment in over 10 000 cases in which the planner and initiator of the wrongdoing received an award. Of course this does not exclude the possibility that a poorly run European agency/regulator might mismanage the whistleblower program to the point where this indeed becomes an issue; a sufficiently incompetent administration can generate problems even with the most robust and effective tools.

A related concern is that financial incentives could encourage employees to submit fraudulent claims, e.g. to “fabricate claims of wrongdoing for personal profit” (Howse & Daniels 1995, p.540, see also Rose 2014, p.1283). A similar concern is that: “Financial incentives might lead to more approaches from opportunists and uninformed parties passing on speculative rumors or public information. The reputation of innocent parties could be unfairly damaged as a result” (PRA & FiCA 2014, see also Vega 2012, p.510). There is also the fear that opportunistic whistleblowers will force “corporations into financial settlements in order to avoid the adverse reputational and related effects caused by highly public, albeit ill-founded, accusations” (Howse & Daniels 1995, p.526/27).

Although evidence on this is hard to find, judging from the reports of agencies, fraudulent and malicious has not been a significant issue. This is probably because fraudulent reporting is a crime, and a whistleblower who report fraudulent information exposes him or herself to a legal fight with the falsely accused employer and to sanctions against perjury and defamation. Indeed, in the case of the IRS, the information is submitted under penalty of perjury (Internal Revenue Code, 26 U.S.C §7623 (b)(6)(C)), which is also the case of the SEC if the whistleblower is represented by an attorney (Exchange Act, U.S.C 78u-6(h)). In the case of the FCA, should the whistleblower lie to the court, he risks felony charges punishable by up to five years in jail for perjury, and the possibly of being convicted of other crimes related to lying under oath. Further, the FCA has a reverse fee-shift for obviously frivolous claims (Engstrom 2016, p.10).

Whether fraudulent claims are a concern for the efficacy of a whistleblower reward program depends to a large extent on the precision of the court system. Buccirossi et al. (2017) analyze this concern within a formal economic model. They show that fraudulent reports are entirely irrelevant for countries with sufficiently precise/competent court systems, provided strong sanctions against perjury, defamation and lying under oath are there to balance the incentives generated by large bounties. Where the judicial system makes a lot of mistakes, instead, this may not be sufficient for the scheme to have crime deterrence effects, which may make it preferable not to introduce large rewards for whistleblowers.


Some suggest that the European hesitation over improving whistleblower protection and considering rewards may have partially historical roots, as both Nazi Germany and Soviet Russia relied heavily on citizens reporting on one another (Givati 2016, p.26.). But the lack of voices speaking out against what the Nazi’s were doing should suggest the opposite, and it is not clear how these parallels should be drawn when we are talking about rewarding whistleblowers in the financial offices of private corporations.

It is also the case that most valuable information to law enforcement is often in the hands of higher-ups in the organization, those who have more to lose in the case of whistleblowing (Engstrom 2016), and for whom protections would be an insufficient compensation relative to their current position and salary. The blunt tool that is horizontal protection for whistleblowers who report violations of EU law could be coupled with precise tools such as rewards for violations of specific EU laws whose undermining can be particularly detrimental to financial stability or the environment.

If European countries and their regulatory and law enforcement institutions are not capable of having an open and honest debate, competently based on the available evidence from rigorous research and from previous experiences in other countries, then they would hardly be able to competently design and properly administer a system of rewards for whistleblowers. As argued in Buccirossi et al. (2017), in countries with weak institutions high powered tools like whistleblower rewards should better be avoided, as in the hand of incompetent law makers and corrupt regulators they would likely produce more damage than good.


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

Remaining Challenges for Faster Growth in CESEE

20180205 Remaining Challenges for Faster Growth in CESEE Featured Image 02

Between 1995 and 2016, per capita GDP levels in Europe have converged, as countries that had lower income levels in 1995 on average have seen faster growth rates between 1995 and 2016 (Figure 1).

Figure 1

GDP per capita in 1995 and its change, 1995-16

Income differentials between CESEE and Germany have narrowed significantly during this time. If we look at CESEE as a whole, in 1995 GDP per capita of CESEE was only a third of Germany. By 2016 it has increased to almost half. If we look at individual countries, all countries in CESEE have seen faster GDP growth than in Germany, but there have been important cross-country differences. For example, growth has been relatively rapid in the EU New Member States and very slow in Ukraine.

Nevertheless, CESEE is still much poorer than Germany. The richest country in CESEE – Slovenia – has the income level per capita Germany had in 1990 (Figure 2). Poland is as rich as Germany was in the late 1970s. And Ukraine, which in early transition had similar level of income to Poland, is now as rich as Germany was in the early 1950s.

Figure 2

GDP per capita in Germany

CESEE is poorer both because labor productivity is lower and a smaller share of the population works. GDP per capita is the product of GDP per worker and the employment to population rate:

GDP per worker and the employment to population rate

In 2015, labor productivity in CESEE was still well below that in Germany and the Netherlands (Figure 3, x-axis). Employment rates were also lower, but those differences were less pronounced (Figure 3, y-axis).

Figure 3

Labor productivity and employment to total population ration, 2015

Differences in employment rates are, however, more pronounced if we take into account that in CESEE a higher share of the population is of working age. The employment to population rate is the product of the employment to working age population [1] rate:

Employment to population rate

The share of the working age population in CESEE is relatively high (Figure 4), although it is now declining. The employment to working age ratios in CESEE are well below those in Germany (Figure 5); only the Baltics come close.

Figure 4

Population ages 15-64

Figure 5

Employment rate

It will be challenging to further increase the employment to total population rate, given the impact of aging and the already relatively low level of unemployment. The decline of the working age population will accelerate in the next decade (Figure 6) as the baby-boom generation is retiring; in a number of countries the working age population is set to decline by more than 1 percent annually. [2] If the share of the working age population that works remains constant, the share of the employment to total population rate will fall sharply. At the same time, the unemployment rate in many countries is already close to pre-crisis lows (Figure 7). It will therefore be key to increase labor force participation rates, which in most countries are still below those of Germany, particularly those of women (Figure 8).

Figure 6

Working age (15-64) population growth

Figure 7

Unemployment rate

Figure 8

Labor force participation rate, 2015

A higher capital stock may be even more important than raising the employment rate. There is a strong correlation between the level of capital stock per capita and GDP per capita (Figure 9, left panel). The relationship between the employment rate and GDP per capita is much weaker (Figure 9, right panel).  Further convergence of CESEE will thus require capital deepening. As of 2015, the capital stock per capita in CESEE region is on average only a quarter of that in Germany.

Figure 9

Capital stock per capita and GDP per capita

Figure 10

Net capital stock per worker growth

Figure 11

Investment to GDP ratio, 2015

Figure 12

National saving ratio, 2015

Unfortunately, the growth of the capital stock per capita has slowed (Figure 10), which reflects the decline in investment rates. Investment rates are low compared with other emerging market countries (Figure 11). Saving rates are low too (Figure 12), which suggests that a rebound of investment could lead to a re-emergence of high current account deficits, unless savings increases as well. Yet it may be challenging to boost saving. With labor markets tightening, wages shares are likely to increase, which is likely to reduce corporate profits. Indeed, in a number of countries this is already happening (Figure 13). Household savings are difficult to influence. Boosting public savings would help, yet even though unemployment rates are falling, few countries plan a meaningful fiscal tightening (Figure 14).

Figure 13

Change in wage share of income and corporate saving, 2013-16

Figure 14

Change in unemployment rate and structural balance

TFP growth has slowed as well. TFP growth has recovered somewhat in recent years, but it is still much slower than in the pre-crisis years (Figure 15). The TFP slowdown might be a result of both the decrease of productivity in main trading partners and unfinished post-crisis adjustment.

The IMF’s CESEE Regional Economic Issues have identified several factors that might restrain productivity and investment. The May 2016 and November 2016 IMF CESEE Regional Economic Issues [3] analyzed several areas where reforms are needed in CESEE, and recommended to improve institutions to boost productivity. The May 2016 REI suggested the largest efficiency gains might come from increasing protection of property rights, upgrading legal systems and other government services. In this context, the November 2016 REI discussed the need to improve public investment management and tax administration. Given the large gaps in infrastructure and capital stock to Western Europe, improving the efficiency of public investment by improving its allocation and the implementation of frameworks and procedures could boost potential growth significantly. Regarding tax administration, reducing compliance gaps, would help improve tax collection, which could generate more fiscal revenues and allow for higher public investment.

Figure 15

Total factor productivity growth

In short, further catch-up is possible but challenging. Labor force participation could be further increased, which would also help to offset declining share of working age population. A slowdown or even reversal of net emigration would also contribute. The capital stock is relatively low, and higher investment is needed especially in infrastructure, but raising the saving rate will be a challenge. Since the crisis the TFP has slowed considerably, and re-igniting TFP growth will be crucial for boosting growth. For all this, improving the quality of institutions and legal frameworks will help.

Bas Bakker is the IMF’s Senior Resident Representative for Central and Eastern Europe; Marta Korczak and Krzysztof Krogulski are economists in the IMF’s regional office for Central and Eastern Europe in Warsaw. The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF or IMF policy. Comments by [Jorg Decressin] on an earlier version are gratefully acknowledged.

[1] The working age population is the population ages between 15 and 64.

[2] In many countries, demographics pressures have been exacerbated by the net emigration. A reduction in emigration, or even reversal, would also help. See IMF Staff Discussion Note “Emigration and Its Economic Impact on Eastern Europe” available at

[3] In many countries, demographics pressures have been exacerbated by the net emigration. A reduction in emigration, or even reversal, would also help. See IMF Staff Discussion Note “Emigration and Its Economic Impact on Eastern Europe” available at

Economic Gender Equality Issues in Transition Economies

20170129 Economic Gender Equality Issues in Transition Economies Featured Image 12 | 8 size 01

Until a couple of decades ago, gender was almost a non-topic within development economics.[1] But in the 1990s research gradually showed that gender inequality could have substantial impact on macroeconomic outcomes. At the same time it became clear that women and men were hit differently by economic shocks.[2] These insights triggered an unprecedented focus on gender both in research and at the policy level – see Duflo (2012) for a brilliant overview with a developing country focus. The largest collective action process in history targeted at reducing world poverty, the Millennium development goals, focused on gender inequalities in several dimensions when enacted in year 2000.[3]

In the so-called transition economies, economic gender issues came on the agenda in the late 1990s as it became evident that the transition process had affected men and women differently – see e.g. Dijkstra (1997) – and that these growing gender inequalities had important humanitarian and economic costs. For instance, in many transition economies men’s mortality skyrocketed in the 1990s while the gender wage gap rapidly increased.[4] In particular, Pastore and Verashchagina (2011) show that the gender wage gap in Belarus doubled during the decade from 1996 to 2006, partly as a result of women’s increased segregation into low-wage industries.

From a gender perspective, the Soviet model had focused on full employment for both men and women, but without aspiring to dismantle traditional gender roles. Women therefore tended to work full time alongside with men, while remaining primary caretakers of children and household. The differences in gender equality were, however, significant across the Eastern and Central European countries already before the transition process started. It is thus essential to carry out country-specific analysis of gender equality so as to fully account for context-specific institutional, economic and cultural aspects.

This paper aims to provide a short overview of research on economic gender inequality that might be of particular relevance to transition economies. Given the extensive literature on gender inequality on the one hand and transition economies on the other, this report hopes to serve as an introduction and therefore provides extensive references to the literature to ease further reading.

The structure of the paper is as follows. Section 2 presents the efficiency gains associated with gender equality; while the subsequent section examines education from a gender perspective. Section 4 reports on the research on gender differences in the labour market, while the following section exposes how gender stereotypes lead to less competent politicians, missing women, etc., while stereotypes at the same times can be changed quickly. The report ends with an overview of current research and policy relevant questions for transition economies.

Research based on economic gender equality

Had gender equality been a universally accepted goal, no further arguments would have been needed to promote it. In this report, the presumption is that men and women are equally worthy of human rights and civil liberties. Given conflicting policy goals, scarce resources and a lack of women decision-makers, more knowledge about the economic gains associated with gender equality is needed. Furthermore, research on the economic impact of gender inequality might not only provide arguments for promoting gender equality, but can also ease the formulation of actual policies by suggesting mechanisms through which gender equality and economic development are linked.

Economists’ argument for gender equality

From an economic point of view, the main argument to strive for gender equality is that men and women on average have the same cognitive and non-cognitive abilities. Few scientists would today question the statement that the differences within genders with respect to abilities are larger than the differences across genders. In other words, men and women are in terms of innate productive capacities more similar than men among men and women among women are. As long as we define our productive capacity only in terms of brains, most would also agree on the productive equality of men and women. But brawn is often raised as a divisive trait that makes men on average more productive than women. Galor & Weil (1996) even posits that there is no reason for women to enter the formal labour market as long as brawn is more important than brains in production as an explanation as to why women were not on the formal labour market in big numbers until the event of industrialization. Albeit seductive, this line of argument has several fundamental flaws.

First of all, no formal labour market existed before the industrial revolution. In agrarian economies everyone works – men, women and children – but are seldom paid with a monetary salary and have no formal contract regulating pay and work hours. With industrialization men came to constitute the majority of the workforce early on as a consequence of women being the main caretakers, and hence not being able to work far from home once they became mothers (until the children themselves were old enough to work). Moreover, social norms prescribing women to stay at home further impeded mothers to work during certain historical phases. Ultimately, there are few occupations – historically and especially now – that were too brawn-intensive for women.  Rather social norms assigned occupations according to one of the genders and occupation-specific technologies developed accordingly. As a first step in the overview on the mechanisms of economic gender inequality, follows in the next section an exposition on its relation to economic development.

Engendering economic development

Two flagship reports from the World Bank (2001, 2012A) were exclusively dedicated to the role of women in economic development.[5] The point of departure for both reports was the strong correlation between any measure of gender equality and economic development (measured for instance as GDP per capita). While it is clear that gender equality in education and formal labour force participation enhance economic growth – see e.g. Klasen (1999) and Klasen and Lamanna (2009) – it is also clear that sustained economic growth generates a new demand for women’s human capital and indirectly promotes gender equality. From a policy perspective the direction of causality is not unimportant in the short and medium run. In the very long run it is unlikely that a high-income economy can flourish without utilizing the female half of the country’s productive capacity.

Recent research – as Bandiera and Natraj (2013) and Cuberes and Teignier (2014) – indicate that the methodological problems are such that it is challenging to draw policy conclusions on the link between gender equality and economic development based on cross-country studies, and that country-specific analyses are needed to be able to formulate precise policy conclusions.

In the transition economies, gender equality varies greatly along with economic standard. There are clearly efficiency gains to be made by increasing gender equality, but each country needs to perform an analysis of which factors are most crucial to improve. For instance, Hsieh, Hurst, Jones och Klenow (2016) calculates that 15-20 per cent of GDP per capita growth during the period 1960 to 2008 can be attributed to the increased efficiency in the allocation of talent in the American economy. This increase in efficiency is mainly explained by the improved allocation of women’s talents according to Hsieh, Hurst, Jones och Klenow (2016). In a closely related study, Cuberes och Teignier (2016), it is estimated that the OECD’s GDP per capita is 15 per cent lower at present compared to a situation without gender segregation on the labour market and where equally many women and men become entrepreneurs.

In the following, the main gender differences that are central for gender equality and economic efficiency (and thereby growth) are discussed. Out of these, it has been viewed as a first priority to assure that girls and boys both get primary and possibly secondary and tertiary education. Secondly, from an economic standpoint, women’s activity on the formal labour market is essential for sustained economic development. Thirdly, gender norms and their relevance for a wide spectrum of economic (and political) issues are discussed.

Men and women’s education

At the beginning of the 1990s, there were few gender differences in terms of level of education and the labour force was highly educated in most transition economies, although there are considerable regional differences. Gender segregation in terms of field of study was relatively low and gender differences in math performance small. While in most transition countries there has been a feminization of higher education  – in line with the trend in most countries in the world – in other transition economies the increase in economic gender inequalities post 1991 has led to a widening of the gender gaps in both primary and secondary schooling.[6]

While it is debated – see for instance Breierova and Duflo (2004) – that girls’ education is more important than boys’ education for economic growth, it is uncontested that a gender gap in basic education harms future possibilities of a gender equal labour market and economic gender equality in a broad sense.

On a more positive note, the general math-intensity of education in transition countries is still associated with a relatively small gender gap in math performance. In some countries, girls even have a relative advantage in math relative to boys according to Unicef (2013). This becomes of special interest, since recent research has pointed to the importance of math-intensive higher secondary studies for future labour market outcomes – see Buser, Niederle and Oosterbeek (2014). This research also suggests that young women in the Netherlands (and in other European countries) are disadvantaged by their lack of math and science interests. More generally, there is an extensive literature on the existence of stereotype threat of women in mathematics, implying that especially the most talented women shy away from mathematics due to the fear of being found lacking in terms of mathematical performance – see e.g. Spencer, Steele and Quinn (1999).[7]

In most developed countries, math-intensive sciences, engineering and computer science are heavily male-dominated fields of higher education, maybe partly as a consequence of the predominant norm of math being a “male” subject. Thus, there is ample scope to promote women in IT and technology (by more research and explicit policy) in transition economies, where the preconditions for women entering these fields are generally more advantageous. At present Mexico and Greece have the largest share of women graduates in computing (around 40 per cent) according to OECD (2014). Transition countries have the potential to reach similar levels.

Women and men in the labour market

In this section, the overall findings regarding women’s labour force participation (and how it relates to economic development) and the gender wage gap are reviewed. Gender segregation on the labour market is only briefly discussed, but the following section reviews some evidence on vertical segregation. (Gender segregation varies across cultural and technological context and thus requires a more in-depth analysis.)

Development and women’s labour force participation

Women’s labour force participation has been shown to be sensitive to production technology. Research indicates that married women’s labour force participation is U-shaped of over the industrialization process – as first documented in Goldin (1994) and in Mammen and Paxson (2000) in a developing country-context. The line of arguments goes as follows. Before industrialization, most economies had a limited formal labour market. This does not imply that men and women do not work, but rather that they work in self-subsidence farming, or in the informal labour market. As economies develop, the labour force participation of married women tends to decrease for two main reasons. As production moves out of the homes, it becomes more difficult for women to combine work and the care for children. While in agricultural economies, children simply follow the mother when she works, this becomes unfeasible as production occurs in factories and under regulated conditions both because it is practically difficult to find someone to mind the children but also socially unacceptable often for a woman to leave home and children. Moreover, as economies develop there is a strong income effect, which makes it economically possible for married women not to work. Therefore, there is a decline in married women’s labour force participation as an industrialization process occurs. As the economy continues to develop the substitution effect comes into play. By this time, both men and women are more educated and eventually the family’s loss of well-educated married women’s salary becomes notable. Therefore, as the return on education increases with industrialization, the labour force participation of married women increases.

Women’s labour force participation in general has been shown to be sensitive to the introduction of new technology and new medicines. Greenwood, Seshadri and Yorukoglu (2005) indicate that the washing machine and the vacuum cleaner made home production less time-consuming, thereby freeing up time for women to dedicate more time to formal labour market work. Moreover, Goldin and Katz (2002) and Bailey (2006) show how the introduction of the Pill made it possible for women to control and plan their fertility and thereby made labour market work more feasible. Furthermore, Albanesi and Olivetti (2016) suggest that medical progress that led to improved maternal health in the US during the period 1930-1960 positively affected women’s labour force participation. Even though technological breakthroughs might come at a specific point in time, Fogli and Veldkamp (2011) has shown that it takes time for a change in social norms to occur. More precisely, their research shows how women’s labour market entry is closely related to the spread of information from working to non-working women at the local level.

Summing up, while it is clear that there is an overall tendency of women’s labour force participation increasing as a country develops into an industrialized economy with a well-developed service sector, this development is far from automatic or linear. Therefor it is important to identify country-specific conditions, technologies and norms that might enhance or hinder women to enter the labour force.

Gender wage gap

A persistent overall gender wage gap is often mistakenly interpreted as a prime indicator of women being discriminated against in the labour market. While a gender wage gap within a specific occupation in a sector might suggest the existence of discrimination, the overall wage gap is often more of an indication of gender segregation on the labour market or of low female labour force participation.

Even though a large gender wage gap is not synonymous with gender discrimination, it is associated with economic inefficiency. By simulating a theoretical growth model of the American economy, Cavalcanti and Tavares (2016) calculate that GDP per capita in the US would be 17 per cent higher if the US would have the same (relatively low) gender wage that Sweden has.

At an international level the trends in the gender wage gap appears to be related to several differences between men and women on the labour market. One correlation in international cross-country comparisons – that for long puzzled researchers – is that countries with high female employment rates tend to have higher gender wage gaps than countries with a lower female employment rate. The expectation would, if anything, be the reversed: in countries with a high share of women in formal employment, women are more emancipated and thus do not accept a considerable gender wage gap. But Olivetti and Petrongolo (2008) convincingly show that more than half of this cross-country correlation is due to selection. In countries with a high gender employment gap, such as southern Europe and Ireland, there is a selection of high-skilled women into the labour market resulting in a relatively high average wage for women, and thus in a comparatively low gender wage gap. Another potential mechanism explaining why the gender wage gap is smaller in for instance Scandinavia than in the UK and the US would be that the overall wage distribution is more compressed and thereby the gender wage gap is mechanically smaller – see Blau and Kahn (2003).

Even in countries with small gender employment gaps, women on aggregate tend to work fewer hours on the formal labour market. Recent research in Olivetti and Petrongolo (2016) suggests that for industrialized countries it is the growth in the service sector that drives the number of hours women are working. It is further shown that half of the variation in female working hours across industrialized countries is explained by the share of the service sector.

But even as men and women work to the same extent and the same hours, in most countries occupational gender segregation on the labour market is widespread. Horizontal segregation signifies that men and women tend to work within different occupations and even sectors, while the vertical segregation implies that women to a less extent than men tend to be managers. In the next section we will examine some of the costs related to vertical gender segregation.

Gender stereotypes, political quotas and missing women

For a long time, women were underrepresented in politics around the world. This constituted a democratic problem since it implied that half of the constituency in a country was not represented politically. Therefore, quotas for women at different levels in politics have been introduced around the world with considerable success. Pande and Ford (2011) review the evidence on the Indian case, where quotas have been shown not only to increase the representation of women but also to dismantle the negative stereotypes towards female politicians – see Beaman et al (2009). As suggested in Besley et al (2017), the introduction of gender quotas in politics can considerable also improves the quality of politicians. With an exceptionally rich dataset, Besley et al (2017) show that the voluntary quota, implying that every second candidate to the local elections in Sweden in the mid 1990s was a female politician, increased the average competence of politicians. This was achieved by the quota allowing for competent women to be elected and by less competent male politicians not being re-elected.

Even though quotas to increase the share of women on corporate boards are more controversial – despite several European countries having implemented them (see European Commission, 2015)– there is ample evidence that the social norm envisioning the leader/executive to be a man further cements vertical gender segregation – see e.g. Babcock and Laschever (2003) and Reuben et al (2012). Changing leadership norms is indeed a most important measure for increasing economic efficiency at the firm and societal level. Sekkat, Szafarz and Tojerow (2015) investigate which governance characteristics at the firm level are most likely to yield a female CEO in a vast sample of developing countries and find that a female dominant shareholder as well as the firm being foreign-owned are most conducive to women at the corporate top.

Generally, gender norms are known to be persistent and difficult to change. But there are examples where stereotypes change quickly, such as when the introduction of cable television to remote rural villages in South India almost instantly wiped out the traditional son preference with the introduction of more modern gender norms – see Jensen and Oster (2009). Unfortunately, son preferences can also be intensified due to worsening economic conditions, as for instance happened in South Caucuses after the breakup of the USSR. Georgia, Azerbaijan and Armenia all experienced a significant decline in fertility after 1990 and a sharp increase in the de facto son preference, measured as of the average share of boys to girls at birth. Research – see Das Gupta (2015), Dudwick (2015), and Ebenstein (2014) – suggest that this is the outcome of a combination of factors that all concurred to emphasize sons’ larger economic capability in helping their parents economically. In times of economic crises, increased availability of ultrasound technology and abortion together with having fewer children per family, the traditional preference for sons, at least temporarily, peaked to Chinese levels (after the One-Child policy).

Economic gender analysis in transition economics

In the following, the need for sex-disaggregated data and country-specific research are discussed, as well as recent policy work on gender equality.


The prerequisite for well-informed research and policy is data availability. At the international level an impressive effort has been made during the last decades to create sex-disaggregated data, and there are now many gender databases as, for instance, the World Bank’s Gender data portal ( While there are surveys such as the Life in Transition Survey (LiTS,, Demographic and Health Services (DHS, and others being made, there is still a lack of gender-disaggregated data in most transition economies.

The national Statistics Bureau should have the mission of collecting and reporting sex-disaggregated data. Moreover, it is excellent if all interesting gender statistics regularly are published in an overview report to increase accessibility both for the general public but also for policy-makers. In Sweden, Statistics Sweden biannually since 1984 publishes “Women and Men in Sweden – Facts and Figures” (, a much appreciated publication. Since 1989, the Swedish government publishes, in an Appendix to its annual Autumn Budget, an overview of the “Economic Allocation of Resources between Men and Women”, where both past policy and current statistics are presented. Initially, the intention was to in this way guarantee the production of sex-disaggregated statistics that was necessary for the formulation of gender-sensitive economic policies.

An even more ambitious step would be to create longitudinal micro-datasets where individuals are followed in terms of family, education, work, health and other characteristics so as to be able to fully evaluate the effect of economic policy.

Country-specific research

Gender-specific analysis of labour market conditions and economic outcomes exist for several countries, see e.g. Khitarishvili (2016). However, there is a vast array of dimensions and mechanisms within the field of research about economic gender equality in need of further investigation, particularly incorporating deep knowledge about country-specific economic circumstances.

As discussed in Section 2, the correlation between gender equality and economic development is generally strong but the direction of causality is unclear. There is therefore scope to analyse the precise nature of the gender inequality within each transition economy with respect to the driving forces of economic growth. Are there, for example, any differences in accumulation of human capital at young age between men and women? Are women able to capitalize on their human capital in the labour market? Are there regulations in place impeding women to work in certain sectors and how is the availability of childcare? Is male mortality higher than female mortality – as has been the case in some transition countries in recent years?

In Section 3 about gender inequality in human capital, there are several dimensions that need country-specific contextualization. Higher education has generally undergone a feminization during recent decades in many transition economies, but not in all. To map such trends, it is essential both to analyse whether the economy capitalizes on women’s newly gained human capital and to study why men are becoming less present in higher education. Moreover, by field of study, transition economies have been exceptionally gender equal in math from an international perspective. One could try to exploit such an advantage by channelling women into programming and IT. This could provide transition economies with a considerable comparative advantage by them using their talent pool better than most countries.

Regarding gender inequality in the labour market, there are a number of interesting research projects that must be pursued at the country level as exemplified in Section 5. For instance, in Moldova there is only a tiny gender gap in labour force participation. While this can pass as an indication of a gender equal labour market, in reality it masks a highly (horizontally and vertically) gender segregated labour market, which might also be one explanation of Moldova’s elevated rates of human trafficking – see further World Bank (2014).


Gender inequality has been perceived as one of the most important dimension to both investigate and address by part of the international organizations working with development assistance. Three major policy areas can be identified, beyond the policy initiatives addressing basic health, violence against women and trafficking: a) the labour market; b) norms; and c) political representation. Regarding gender inequalities in the labour market, the trend is now for a deeper analysis attempting to identify the mechanisms at work in the labour market – see for instance Morton et al (2014).

The policy work on social norms is innovative and often uses surveys and interviews to map gender-specific stereotypes and expectations in order to provide a background and explanation for the wide gender differences in economic outcomes. World Bank (2012B) constitutes such an example, where gender norms are contextualized and at the same time put into a cross-country perspective. Here the attempts of involving men by at least mapping their attitudes are well on their way.

Lastly, there is a considerable amount of policy work – hand in hand with the extensive research on the topic – on women’s low degree of political representation. Introducing quotas for women in parliament is not enough to assure women’s political representation as overly evident in the report by the European Commission on the topic (European Commission, 2015). Further policy work is of the essence to support and ease the implementation of quotas and other measures to assure women’s political representation actually improves.

Concluding remarks

This report touches upon main gender issues in transition economies with a focus on economic dimensions, but essential human rights issues as equal access to health care and legislation, and policies against trafficking are, of course, presupposed. Ultimately gender equality is not a women’s issue. But women are the most engaged so far and efforts must continue to involve men and make them active stakeholders.

Even with the best intentions, it remains crucial to formulate actions on the basis of research. Given that economic resources for policy interventions are limited and that we strive for having policy-impact, continuous effort has to be made to let research inform policy on how to best use available resources.


Albanesi, S., and C. Olivetti (2016). “Gender Roles and Medical Progress”. Journal of Political Economy 124(3): 650-695.

Alesina, Alberto, Giuliano Paola and Nathan Nunn (2013). “On the Origins of Gender Roles: Women and the Plough”, Quarterly Journal of Economics 128(2): 469-530.

Babcock, Linda and Sara Laschever (2003). Women Don’t Ask: Negotiation and the Gender Divide. Princeton University Press, Princeton.

Baden, Sally (1993). “The Impact of Recession and Structural Adjustment on Women’s Work in Selected Developing Countries”. Institute of Development Studies, University of Sussex, BRIDGE Report 15.

Bailey, Martha J. (2006). “More Power to the Pill: The Impact of Contraceptive Freedom on Women’s Lifecycle Labor Supply”. Quarterly Journal of Economics 121(1): 289-320. Uppdatering “Erratum and Addendum,”, September 2009.

Beaman, Lori, Chattopadhyay, Raghabendra, Duflo, Esther, Pande, Rohini and Petia Topalova (2009). “Powerful Women: Does Exposure Reduce Prejudice?”. Quarterly Journal of Economics 124: 1497-1540.

Bandiera, Oriana and Ashwini Natraj (2013). “Does Gender Inequality Hinder Development and Economic Growth? Evidence and Policy Implications”. World Bank Research Observer 28(2): 2-21.

Becker, Gary S., Hubbard, William H. J. and Kevin M. Murphy (2010). “The Market for College Graduates and the Worldwide Boom in Higher Education of Women”. American Economic Review 100(2): 229-33.

Besley, Tim, Folke, Olle, Persson, Torsten and Johanna Rickne (2017). “Gender Quotas and the Crisis of the Mediocre Man: Theory and Evidence from Sweden”. American Economic Review 107(8): 2204-42.

Bhattacharya, Jay, Gathmann, Christina and Grant Miller (2013). “The Gorbachev Anti-Alcohol Campaign and Russia’s Mortality Crisis”. American Economic Journal: Applied Economics 5(2): 232-60.

Blau, Francine and Lawrence M. Kahn, 2003. “Understanding International Differences in the Gender Pay Gap”. Journal of Labor Economics 21: 106-144.

Boserup, Ester (1970). Woman’s Role in Economic Development. London: George Allen & Unwin.

Breierova, Lucia and Esther Duflo (2004). “The Impact of Education on Fertility and Child Mortality: Do Fathers Really Matter Less Than Mothers?”. NBER Working paper 10513.

Buser, Thomas, Niederle, Muriel and Hessel Oosterbeek (2014). “Gender, Competitiveness, and Career Choices”. Quarterly Journal of Economics 129(3): 1409-1447.

Cavalcanti, T. och J. Tavares, 2016. “The Output Cost of Gender Discrimination: A Model-Based Macroeconomic Estimate”. Economic Journal 126: 109–134.

Cuberes, David and Marc Teignier (2014). “Gender Inequality and Economic Growth: a critical review”. Journal of International Development 26: 260–276.

Cuberes, D. och M. Teignier, 2016. “Aggregate Effects of Gender Gaps in the Labor Market: A Quantitative Estimate”. Journal of Human Capital 10(1): 1-32.

Das Gupta, Monica (2015). “’Missing Girls’ in the South Caucasus Countries: Trends, Possible Causes, and Policy Options”. Policy Research Working Paper 7236. Washington, D.C.: World Bank Group.

Djikstra, Geske, A. (1997). “Women in Central and Eastern Europe: A Labour Market Transition” in Djikstra, Geske and Janneke Plantega (eds.). Gender and Economics. London: Routledge.

Dudwick, Nora (2015). “Missing Women in the South Caucasus: Local Perceptions and Proposed Solutions”. Washington, DC: World Bank Group.

Duflo, Ester (2012). “Women Empowerment and Economic Development”. Journal of Economic Perspectives 50(4): 1051-1079.

Ebenstein, Avraham (2014). “Patrilocality and Missing Women”. Mimeo, Hebrew University of Jerusalem.

Elborgh-Woytek, Katrin et al. (2013). “Women, Work, and the Economy: Macroeconomic Gains from Gender Equity”. IMF Staff Discussion Notes No. 13/10.

European Commission (2015). “Gender Balance on Corporate Boards Europe is Cracking the Glass Ceiling”.

European Commission (2015). “Women in Power and Decision-Making in the Eastern Partnership Countries”.

Fogli, Alessandra and Laura Veldkamp (2011). “Nature or Nurture? Learning and the Geography of Female Labor Force Participation”. Econometrica 79: 1103–1138.

Galor, Oded and David N. Weil (1996). “The Gender Gap, Fertility, and Growth“. American Economic Review 86(3): 374-38.

Goldin, C., 1994. “The U-shaped Female Labor Force Function in Economic Devlopment and Economic History”. NBER Working Paper 4707.

Goldin, Claudia and Larry F. Katz (2002). ”The Power of the Pill: Oral Contraceptives and Women’s Career and Marriage Decisions”. Journal of Political Economy 110(4): 730-770.

Greenwood, J. Seshadri, A. and M. Yorukoglu (2005). ”Engines of Liberation”. Review od Economic Studies 72(1): 109-133.

Hsieh, C.-T., Jones, C. I., Hurst, E. och P. J. Klenow (2016). ”The Allocation of Talent and U.S. Economic Growth”. Mimeo. Older version in NBER Working paper 18693.

Jensen, Robert and Emily Oster (2009). “The Power of TV: Cable Television and Women’s Status in India”. Quarterly Journal of Economics 124: 1057-94.

Kabeer, Naila (2003). Gender Mainstreaming in Poverty Eradication and the Millennium Development Goals – A handbook for policy-makers and other stakeholders. International Development Research Centre, Ottawa.

Kazandjian, Romina, Kolovich, Lisa, Kochhar, Kalpana and Monique Newiak (2016). “Gender Equality and Economic Diversification”. IMF Working Paper 16/140.

Khitarishvili, Tamar (2016). “Gender Dimensions of Inequality in the Countries of Central Asia, South Caucasus and Western CIS”. Levy Economics Institute Working Paper 858.

Klasen, Stephan (1999). “Does Gender Inequality Reduce Growth and Development? Evidence from Cross-Country Regressions”. Background paper for Engendering Development, World Bank, Washington DC.

Klasen, Stephan and Francesca Lamanna (2009). “The Impact of Gender Inequality in Education and Employment on Growth: New Evidence for a Panel of Countries”. Feminist Economics 15(3): 91-132.

Mammen, K. and C. Paxson (2000). “Women’s Work and Economic Development”. Journal of Economic Perspectives 14: 141-164.

Morton, Matthew, Klugman, Jeni, Hanmer, Lucia and Dorothe Singer (2014). Gender at Work : A Companion to the World Development Report on Jobs. Washington, DC: World Bank Group.

OECD (2015). Education at a Glance 2015: OECD Indicators. OECD Publishing, Paris.

Olivetti, Claudia and Barbara Petrongolo (2008). “Unequal Pay or Unequal Employment? A Cross-Country Analysis of Gender Gaps”. Journal of Labor Economics 26: 621-654.

Olivetti, C. and B. Petrongolo (2016). “The Evolution of Gender Gaps in Industrialized Countries”. forthcoming Annual Review of Economics.

Pande, Rohini and Deanna Ford (2011). “Gender Quotas and Female Leadership: A Review”. Background Paper for the World Development Report on Gender

Pastore, Francesco and Alina Verashchagina (2011). “When Does Transition Increase the Gender Wage Gap?”. Economics of Transition 19(2): 333-369.

Reuben, Ernesto, Rey-Biel, Pedro, Sapienza, Paola and Luigi Zingales (2012). “The Emergence of Male Leadership in Competitive Environments”. Journal of Economic Behavior and Organization 83(1): 111–117.

Sekkat, Khalid, Szafarz, Ariane and Ilan Tojerow (2015). “Women at the Top in Developing Countries: Evidence from Firm-Level Data”. IZA Discussion paper 9537.

Spencer, Steven J., Steele, Claude M. and Diane M. Quinn (1999). “Stereotype Threat and Women’s Math Performance“. Journal of Experimental Social Psychology 35: 4–28.

UNICEF (2013). Equity in Learning? A Comparative Analysis of the PISA 2009 Results in Central and Eastern Europe and The Commonwealth of Independent States. Geneva: United Nations Children’s Fund.

World Bank (2001). Engendering Development – Through Gender Equality in Rights, Resources and Voice. Washington, DC.

World Bank (2012A). World Development Report 2012: Gender Equality and Development. Washington, DC.

World Bank (2012B). On Norms and Agency Conversations about Gender Equality with Women and Men in 20 Countries. Washington, DC.

World Bank (2014). “Moldova: Gender Disparities in Endowments and Access to Economic Opportunities”. Report 76077-MD, Washington, DC.

[1] The exception was the seminal Boserup (1970).

[2] See for instance Baden (1993).

[3] See Kabeer (2003) for an overview of research in development economics and policy experience relevant to the achievement of the Millennium Development Goals from the perspective of gender equality.

[4] Research – see Bhattacharya, Gathmann and Miller (2013) – however suggests that it might have been changing alcohol policy rather than transition per se that caused the sudden increase in mortality.

[5] The IMF has published a number of reports recently, such as Elborgh-Woytek et al (2013) and Kazandjian, Kolovich, Kochhar and Newiak (2016).

[6] See for instance, Becker et al (2010) and OECD (2015).

[7] Stereotype threat is defined as when an individual perceives to be ”at risk of confirming, as a self-characteristic, a negative stereotype about one’s social group” in the seminal paper by Steele and Aronson (1995).

Avoiding Corruption and Tax Evasion in Belarus’ Construction Sector

20171119 Avoiding Corruption and Tax Evasion in Belarus Image 01

This brief summarizes our research on the problem of corruption and tax evasion in the construction sector in Belarus. We conducted a survey of construction companies, asking them to estimate the extent of different dimensions of tax evasion and corruption within the sector. The results show the most problematic directions in the sphere. Based on international experiences, we develop recommendations of how to reduce corruption and tax evasion in construction of Belarus.

Shadow economy and the construction sector

The problem of a shadow economy is real for many countries in the world. Many countries try to minimize the level of this illegal activity. But it is very difficult to liquidate tax evasion or envelope wages fully.

In Belarus there is a lot of discussion about corruption and tax evasion limitation. The country ranked 79th in the Corruption Perception Index 2016. The situation in Belarus is much better then in Russia or Kazakhstan, but worse than in Sweden, Finland and Switzerland.

There is lack of systematically updated knowledge about the situation with corruption and tax evasion in the different economic spheres of Belarus. At the same time, there are sectors, which are more prone to develop a shadow economy. One of them is the construction sector. Multilevel chains of relations between contractors and subcontractors, numerous suppliers, and complicated procedures for facility acceptance create possibilities for illegal schemes.

Construction plays an important role in national production. In 2016, the construction sector corresponded to more than 6% of Belarusian GDP. In 2014, this indicator was above 10%. The decline can be explained by a reduction of preferential lending in housing construction and a recession in the economy. Despite the reduction in the share of GDP, around 8% of the total labor force works in construction. More than 90% of the legal entities in the sphere are presented by privately owned enterprises [8].

Taking into account the importance of construction it is necessary to emphasize that reducing the size of the shadow economy could create a better business environment, reduce companies’ expenditures for resolving issues in informal ways, and increase budgetary revenues.

In this brief we present a short summary of our research “Problems of corruption and tax evasion in construction sector in Belarus”, which is forthcoming in the International Journal Entrepreneurship and Sustainability Issues. The project was made in the framework of the project “Corporate engagement in fighting corruption and tax evasion”, financed by the Nordic Council of Ministries.


In order to understand the main issues and challenges in construction sector, we surveyed 50 Belarusian construction companies. We took 20 companies from Minsk and its surrounding region, and 6 organizations from each Belarusian region (Brest, Grodno, Vitebsk, Gomel, and Mogilev). The survey was based on the method used in Putnins and Sauka (2016). This method includes a questionnaire, which helps understanding the actual situation with the shadow economy in the sector. The questions of the survey were divided into three parts.

The first part included neutral questions about economic characteristics of the company, such as number of employees, profit level, the year of establishment, wage levels, and form of ownership.

The second part include more sensitive questions, but which can help us understanding the most problematic issues concerning to corruption and tax evasion. These questions concern such indicators as the level of underreported business income, the degree of underreported number of employees, the percentage of revenue that firms pay in unofficial payments to ‘get things done’, and main barriers to business development. In order to make the answers easier for participants, all the questions deal with the situation in the sector as a whole, and not the company in particular.

The third part of questions concerns the situation in public procurement, and includes the perception of main problems in the sphere.

Survey results

The first part of the survey shows that there has been a decline in many of the economic indicators during the last two years. This may be one factor stimulating the sector’s development of informal activities. Indeed the results of the second part of survey demonstrate that level of shadow economy has significant dimensions. More then 60% of the respondents agree that some firms in the sector received hidden income. More than 50% of the interviewed companies believe that some organizations in the construction sector hire part of their employees unofficially. Wages in “envelopes” is also a problem for the construction companies.

Unregistered firms are a big threat to having a well-developed construction sector. More than 60% of the interviewed companies agree with the existence of unregistered companies. Such non-official organizations create unfair competition in the sector and decrease the level of budget revenues. Many of the unregistered companies work in the sphere of home improvements and renovations.

Figure 1. Estimation of the approximate level of hidden salaries (“wages in the envelopes”) in construction industry

Notes: X-axis is the percentage of respondents that agree with the statement. Source: Results of the survey

The survey results allow us to conclude that the state budget loses part of its corporate income taxes, taxes on wages and social contributions due to the existence of hidden incomes, wages in envelopes, and unregistered companies and employees.

The last, but not the least, question in the second part of the survey was about main obstacles and barriers for operating in the construction sphere. Most of the respondents underlined three groups of barriers. One of them is the administrative challenge, including high level of taxation, inconsequent business legislation, and attitude of the government towards business in general. The second barrier includes economic problems such as lack of funds for business investments, payment behavior of clients, low product or service demand from customers, low access to credits, and inflation. The third group of problems in the construction sector is related to the shadow economy. A large part of the enterprises experiences a problem of high competition from illegal business and corruption. At the same time, a positive thing is that the majority of respondents does not consider crime and racketeering as a threat for the sector.

Figure 2. Estimation of approximate share of unregistered firms production in the total output in construction industry

Notes: The X-axis is the percentage of respondents that agree with the statement. Source: Results of the survey

In the third part of the survey, companies were asked about their participation in public procurement tenders. About 42% of all respondents did not have this experience over the past two years. One of the questions was about competition among construction companies. About 40% of all respondents underlined that they have lost at least one public tender because of unfair competition. Given that only 58% of the companies participated in tenders, we can conclude that unfair competition is a widespread problem for the majority of public procurement auction participants. Imperfect legislation is another problem for the companies. 46% of all respondents believe that the quality of legislation in the sphere is unsatisfactory. Only 12% of the companies did not see any problems in the national legislation.

At the end of the interview, companies were asked to list three main problems in the sphere of public procurement. The answers are shown in Figure 3.

Figure 3. Main problems that companies face when participating in public procurement tenders

Notes: The X-axis is the percentage of respondents that agree with the statement. Source: Results of the survey

The most common answer was corruption. Unfair competition and nepotism were also quite common problems in the public procurement sphere. Among administrative barriers, companies emphasized the complexity of documentation preparation and imperfect legislation. Important economic problems were inflation and unequal conditions for public and private enterprises.

International experiences and recommendations in fighting corruption and tax evasion in the construction sector

Corruption and tax evasion can be stimulated by different factors. One of the main preconditions of the shadow economy in the Belarusian construction sector is inconsistent and frequently changing legislation. For example, public procurements are regulated by the Presidential Decree (Ukaz) on procurement of goods (works, services) in construction. However, this regulation document expires at the end of 2018. Before 2017, such operations were regulated by several legislative acts. Developing understandable and sustainable legislation, which creates clear rules for participants of the market, is very important to increase transparency and openness of the market [11; 12; 13; 15; 18].

Another problem concerns the relations of contractors and sub-contractors. In many cases negotiations between parties are closed and non-transparent. So, it is very difficult to estimate the effectiveness of costs and proper use of funds.

Modern E-Government system adoption can support increased transparency between contractors and sub-contractors, as well as improve the quality of state services. One of the directions in this sphere is the transition towards full electronic document management. [3; 4; 6].

Another risk is related to public procurement procedure. Direct communications between public tender participants and organizers create possibilities for unfair competition. There is substantial international evidence showing that full digitalization of the process would improve the transparency of the public procurement procedure [3; 4; 21]. For example, good reference points for implementation of such digitalization can be the Georgian or Ukrainian experiences of electronic tenders. These two countries have relatively similar institutional environment and heritage as Belarus.

The problem of tax evasion is often related with payments in cash. Such transactions are less transparent and visible for authorities. According to national legislation operations between legal entities should be in cashless form. But there are exceptions to the rule [20]. In this regards the level of tax evasion would be decreased if payments in cash will be minimized.

Another concern is the efficiency of the public procurement procedures. During public procurement auctions in construction, price plays the most important role. The share of “Bid Price” criterion in total volume of all criteria can be up to 50%. The project with the lowest price has the best chance to win the tender. This is not always reasonable. Moreover, some companies hire disabled people that allow them to obtain preferential treatment in the public procurement procedure – for example, apply special correction indicators to the final price. In many cases it is better to install more expensive but high efficiency (more qualitative or ecological) equipment instead of buying cheap but low quality ones. Of course, even in EU legislation, the cost or price of projects is a very important criterion. But then it is often defined as a price-quality ratio. In this regards, the quality of the project can be estimated from the environmental, qualitative or social side [12; 19].

One more issue according to survey results is the problem of unregistered labor force in construction. It can be partly resolved by ID card implementation for all workers and employers in construction sector. In Finland, for example, all workers in construction must have such cards during workdays. Tax authorities can check the availability of the cards at any time [17].


Our survey of Belarusian construction companies confirmed wide exposure of the sector to tax evasion and corruption. The majority of the respondents agreed that some companies hire unregistered workers, pay wages in envelopes, or have hidden income. The most common answer to the main problems in the public procurement sphere was corruption. Based on international experience and national peculiarities, it is advisable to propose the following measures to reduce corruption and tax evasion in construction sector:

  1. Adoption of sustainable legislation.
  2. E-Government system development.
  3. Modernization of the electronic tender system to require no direct contacts between organizers and tender participants.
  4. Reduction of the possibilities of making payments in cash.
  5. Implementation of a price-quality ratio as one of the main criteria for choosing the winner of tenders.
  6. Introduction of ID cards for all employees and employers in the construction sector.

These and other measures are likely to significantly improve the business environment in the construction sector.


[1] Anderson, E. 2013. Municipal “Best Practices”: Preventing Fraud, Bribery and Corruption, International Centre for Criminal Law Reform and Criminal Justice Policy. Available on the Internet:

[2] Fazekas, M., Toth, I.J., King, L.P. 2013. Corruption manual for beginners: “Corruption techniques” in public procurement with examples from Hungary, Working Paper series: CRCB-WP/2013:01 Version 2.0, Budapest, Hungary. Available on the Internet:

[3] Krasny, A. 2014. Georgia E-Government. Available on the Internet:

[4] Luzgina, A. International experience of the e-Government System development/ A. Luzgina //Journal of the Belarusian State University. Economics. – Minsk, 2017. – P.76-83.

[5] Luzgina, A., Laukkanen E., Larjavaara I., Viavode I., Volberts J. ,Corporate engagement in fighting corruption and tax evasion in construction sector”, forthcoming in “Entrepreneurship and sustainability issues”

[6] Naumov, A. 2014. Georgia E-experience for Belarus. Available on the Internet:

[7] Official website of Transparency International. Available on the Internet:

[8] Official website of Belarusian National Statistical Committee. Available on the Internet:

[9] Official website of the European Commission. Available on the Internet:

[10] On procurements of goods (works, services) [Electronic source] // Decree of the President of the Republic of Belarus/ 20.10.2016 # 380. Rus.: О закупках товаров (работ, услуг) при строительстве, Указ Президента Республики Беларусь от 20.10.2016, №380. – Mode of access:

[11] On public procurements of goods [Electronic source] // Law of the Republic of Belarus/ 13.07.2012, # 419-З. Rus.: О государственных закупках товаров, работ услуг Закон Республики Беларусь от 13 июля 2012 г. № 419-З. – Mode of access:

[12] On organization and conduct of the procurement of goods (works, services) procedures and settlements between customer and contractor in facilities construction [Electronic source] // Resolution of the Council of Ministers of the Republic of Belarus / 31.12.2014, # 88.: Rus: Об организации и проведении процедур закупок товаров (работ, услуг) и расчетах между заказчиком и подрядчиком при строительстве объектов, Постановление Совета Министров Республики Беларусь №88 от 31.12.2014. – Mode of access:

[13] Putnis, J.T., Sauka, A. 2016. Shadow economy index for the Baltic countries 2009 – 2016. The Center for Sustainable Business at SSE Riga. – 47 p.

[14] Pelipas, I., Tochitskaya, I. 2016. Problems of corruption in the assessments of small and medium enterprises. Available on the Internet:

[15] Procurement in construction, what has been changed since January 1, 2017. Available on the Internet:

[16] Preventing corruption in public procurements. 2016. OECD Publishing. Available on the Internet:

[17] Briganti, F., Machalska, M., Steinmeyer, Heinz-Dietrich, Buelen, W. 2015. Social Identity cards in the European construction industry, edited by Buelen W. Available on the Internet:

[18] Zaiats, D. 2015. The authorities will strengthen the fight against the shadow economy [Electronic resource]. – Mode of access:

[19] On public procurement and repealing Directive 2004/18/EC [Electronic resource]// Directive 2014/24/EU of the European Parliament and of the Council / 26 Februay 2014.  – Mode of access:

[20] On making amendments and alterations to Instruction on the procedure of conducting cash transactions and the procedure of the cash settlement in Belarusian rubles on the territory of the Republic of Belarus // Resolution of the National Bank of the Republic of Belarus / 31.03.2014. #199. Rus: – О внесении дополнений и изменений в Инструкцию о порядке ведения кассовых операций и порядке расчетов наличными денежными средствами в белорусских рублях на территории Республики Беларусь. Mode of access:

[21] Prozorro [Electronic source]. – Mode of access: https: //

Cross-Country Differences in Convergence in CESEE

An image of cars travelling up and down the highway next to tall buildings representing convergence in CESEE

Since 1989, there have been large differences in the convergence of the income levels of the former communist countries in CESEE with those in the US. Most Central European countries have seen a sharp rise in relative incomes, but many countries in former Yugoslavia and the CIS have not—indeed, some countries, including Moldova and Serbia, are now poorer than they were in 1989 (Figure 1).

Figure 1. Transition outcomes

01 Figure Transition outcomes. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: Total Economy Database and IMF staff calculations.

Figure 2. GDP level in Poland and Ukraine

02 Figure GDP level in Poland and Ukraine. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: Total Economy Database and IMF staff calculations.

The difference between Ukraine and Poland is particularly stark. In 1989, both had similar income levels, but Poland is now more than three times as rich (Figure 2). As a result, cross-country income differences in CESEE remain large. In 1989, the Czech Republic, Russia, Slovenia and Croatia had the highest income per capita in 1989, about 4 times as high as in Albania and Moldova, the poorest in the group. Twenty-six years later, the differences are even larger. GDP per capita in Slovenia is 6 times as high as in Moldova (Figure 3).

Figure 3. Cross-country income differences

03 Figure. Cross-country income differences. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: Total Economy Database and IMF staff calculations.

 What Explains Convergence Differences?

These differences in convergence do not seem to reflect data problems. True, GDP statistics in 1989 were not very good. It is hard to measure value added when prices are not quite right. Moreover, GDP at that time was probably not a good indicator or of consumer welfare. Much of what was produced was not wanted by consumers (e.g. military expenditures) and/or of low quality. Nevertheless, these issues apply to all post-communist countries in the regions—it is not clear that some countries suffered from data problems more than others.

Indeed, more direct measures of economic activity also suggest large initial output falls and large cross-country differences. Between 1990 and 1995 electricity consumption per capita fell by almost 40 percent in Ukraine and Moldova. By then electricity consumption in Poland had nearly recovered to the 1990 level (Figure 4).

Figure 4. An alternative measure of decline in economic activity

04 Figure. Alternative measure of decline in economic activity. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: IFA Statistics and IMF staff calculations.

Instead, several factors seem to have a played a role:

  • The speed of transition to a market economy
  • War and conflicts
  • Boom-busts
  • EU Membership
  • Whether transition has been completed

Countries that reformed early had a shorter and shallower post-transition recession. The lower the EBRD transition index in 1995 (i.e., the less the economy was reformed), the sharper the output decline between the beginning of the transition and 1995 (Figure 5).

Figure 5. Market reforms and post-transition recession

05 Figure. Market reforms and post-transition recession. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: Total Economy Database and IMF staff calculations.

Why was this? In late 1989, a fierce debate broke out over what came to be called gradualism versus shock therapy. Many gradualists argued that the structural flaws of the economy would frustrate attempts at liberalization, and therefore that reforms should be implemented in a gradual, sequenced way. But for others—including key figures such as Leszek Balcerowicz in Poland—understanding the nature of the problem meant the opposite: reform was a seamless web that could only succeed if all the changes happened together, because liberal prices, improved governance, and a stable economic and financial environment were needed to reinforce one another; little could be achieved with a partial reform. The evidence from the past 25 years has vindicated the seamless web theory of transition. There is no doubt that some reforms took much longer than anticipated, including privatization, both of banks and companies. But it seems clear that the countries that made sweeping changes, and that kept at reform and stabilization have done well.[2] Countries that followed a more gradual path suffered from the decline of the old industries and did not get the boost from the growth of new firms. And in some countries bouts of macroeconomic instability repeatedly undermined reforms and sapped political momentum.

Weaker growth in the early transition years was not compensated by faster growth later. Countries, where output declines were deeper in early 1990s, did not see more rapid growth in subsequent years (Figure 6).

Figure 6. Permanent output losses in the early transition

06 Figure. Permanent output loses in early transition. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: Total Economy Database and IMF staff calculations.

Wars and conflicts also played an important role. It is striking that the five countries with the lowest growth all had a war or serious conflict between 1990 and 2015 (Figure 7).

Figure 7. Wars and conflicts impact on long-term growth

07 Figure. Wars and conflicts impact on long-term growth. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: Total Economy Database and IMF staff calculations.

Avoiding boom-busts helped boost longer-term growth. Steady growth rates seem to be more conducive to higher long term growth than booms followed by busts. Between 2002 and 2008, Romania had capital inflows fueled boom and grew much faster than Poland, but thereafter it suffered a deep bust, and between 2002 and 2015, Poland has grown faster (Figure 8).

Figure 8. The hare and the tortoise

08 Figure. The hare and the tortoise. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: Total Economy Database and IMF staff calculations.

EU accession was a powerful catalyst for reforms and upgrading of institutional frameworks. CESEE countries that joined the EU were required to bring their regulations and institutions up to Western European standards. There is a striking difference in the level of EBRD transition indicators between EU countries and non-EU countries (Figure 9).

Figure 9. EU accession as a reform catalyst

09 Figure. EU accession as reform catalyst. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: EBRD and IMF staff calculations.

Thus, prospects of EU Membership have led to more reforms and, as a consequence, to stronger growth (Figure 10).

Figure 10. Market reforms and changes in income levels

10 Figure. Market reforms and changes in income levels. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: EBRD, Total Economy Database and IMF staff calculations.

Countries that upgraded their institutions to EU standards saw a decline in cross-country income differences. Countries that joined the EU in 2000s show clear pattern of convergence. The difference between Bulgaria and Slovenia has narrowed by 15 percent of Slovenia’s GDP since the former begun EU accession negotiations in 2000 (Figure 11, right panel). Similarly, a group of candidate and potential candidate countries, including Croatia (which joined the EU only in 2013) have converged as well (Figure 11, left panel).

Figure 11. Convergence within CESEE regions

11 Figure. Convergence within CESEE regions. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: Total Economy Database and IMF staff calculations. Note: The EU has recognized Bosnia and Herzegovina as potential EU candidate countries.

By contrast, there was no convergence among the European CIS-countries. Russia, the richest of CIS countries grew by only 0.6 percent annually since 1989, while output per capita declined in Moldova and Ukraine. Only Belarus achieved growth rates comparable to non-CIS countries, but its largely unreformed economy may have approached the limits of the current extensive growth model (Figure 12).

Figure 12. Convergence in the European CIS region

12 Figure. Convergence in European CIS region. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: Total Economy Database and IMF staff calculations.

Countries that have a more completed transition are richer. There is a strong correlation between progress in market reforms and a country’s income level (Figure 13).

Figure 13. Market reforms and income level

13 Figure. Market reforms and income level. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: EBRD, Total Economy Database and IMF staff calculations.

Similarly, richer countries have a more vibrant private sector (Figure 14).

Figure 14. Market reforms and private sector share in the economy

14 Figure. Market reforms and private sector share in the economy. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: EBRD, Total Economy Database and IMF staff calculations.

Correlation does of course not mean causality but is it telling that there is no highly reformed poor country.

Convergence Post-2009 Crisis

Post-2009, catch-up has slowed down. Pre-crisis, convergence was rapid and widespread. In some countries, the GDP per capita gap to the US narrowed by more than 12 percentage points in 2003-08. Since 2010 only two-thirds of countries in the region have continued to catch-up with the US, while Ukraine and Slovenia saw a widening of income differences (Figure 15). And if we include the 2009 crisis, which was deeper in CESEE than in Western Europe, convergence has been even less.

Figure 15. Convergence pace pre- and post-crisis

15 Figure. Convergence pace pre- and post-crisis. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: WEO database and IMF staff calculations.

More recently, there have also been large differences across regions: while the CIS was in recession, the non-CIS countries doing much better.

  • The CIS countries suffered from falling commodity prices, and from the impact of sanction on Russia.
  • By contrast, the non-CIS countries saw a gradual acceleration of GDP growth, on the back of a pick-up of domestic demand in the euro area. Labor markets in many EU New Member States (NMS) are tightening rapidly, and unemployment is quickly approaching pre-crisis lows, though GDP growth rates are well below those in the pre-crisis years.

How can we boost Convergence going forward?[3]

GDP per capita is the product of GDP per worker (labor productivity) and the share of the population that works (the employment rate):

15.2 Formula calculation

Low GDP per capita can thus be the result of both low labor productivity and a low employment rate. In CESEE, both factors play a role:

  • In most CESEE countries, the employment rate is below that in Western Europe (Figure 18). Low employment rates are a particular problem in SEE and some CIS countries.
  • The labor productivity gap with Western Europe is still large, even though it has declined in the past twenty years.

Figure 16. Big differences in growth among regions

16 Figure. Big differences in growth among regions. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: WEO database and IMF staff calculations.

Figure 17. Labor markets in EU new member states

Figure 17. Labor markets in EU new member states. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: Eurostat.

Figure 18. Labor utilization and productivity

18 Figure. Labor utilization and productivity. Cross-Country Differences in Convergence in CESEE. FREE Policy paper

Source: Total Economy Database, UN population statistics and IMF staff calculations.

To raise labor productivity more investment is needed.  The capital stock per worker in a typical CESEE economy is only about a third of that in advanced Europe. Domestic saving rare are too low in most the region; policies should, therefore, focus on institutional reforms that reduce inefficiencies and increase returns on private investment and savings.

Boosting total factor productivity (TFP) is important as well. CESEE countries have to address structural and institutional obstacles that prevent efficient use of available technologies or lead to an inefficient allocation of resources. The recent IMF CESEE report suggests the largest efficiency gains are likely to come from improving the quality of institutions (protection of property rights, legal systems, and healthcare); increasing the affordability of financial services (especially for small but productive firms), and improving government efficiency.


Since the fall of communism, there have been large differences in the convergence of income levels with the US among CESEE countries. Much of these differences reflect differences in policies. Countries that reformed more and earlier saw faster growth than countries that reformed less or later. Macro-stability also helped, and countries that avoided boom-busts tended to grow faster.

Continued convergence will require a higher investment, higher TFP, and higher employment rates. The capital stock per worker is still below that in Western Europe. Higher investment rates will require higher saving rates, lest large current account deficits emerge anew. Addressing structural and institutional obstacles would also help convergence, as it will support higher labor force participation and allow for a more efficient allocation of resources.

Notes and References

  • [1] Bas B. Bakker is the Senior Resident Representative and Krzysztof Krogulski an economist in the IMF’s Regional Office for Central and Eastern Europe in Warsaw. The views expressed in this paper are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
  • [2]This is not to say that the rapid and seamless approach was without problems, notably large losses of output and high unemployment in the short run. Thus, reform will always have to worry about the social safety net and, under some circumstances, may benefit from external assistance, which is where the IMF and others can come in.
  • [3]The IMF addressed this question in depth in the spring 2016 issue of “CESEE Regional Economic Issues.”

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