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3 comments

• Raf Uzar

  Unfortunately, history paints a grim picture of Hungarian-Slovakian relations. I wonder if the EU will really do anything to help…
  Raf
  http://uzar.wordpress.com/
  http://newzar.wordpress.com/

  # 2008-11-22 at 12:37 pm

  Reply to this
• Judit

  I think it is a political issue, unfortunatelly.

  There was a living chain formed by Slovak and Hungarian people hand in hand from Komarom to Komarno (neighbour boarder cities) to show to the politicians and to the world that they live in peace.
  _______________________________________
  thalweg in navigable waterways.Convention relating to the Settlement ofsigned at Prague on 14 November 1928 with ratifications exchanged at Budapest onVienna Award of 2 November 1938, this was annulledMoscow Agreement signed

  The Nagymaros-Gabcikovo barrage dispute:

  Since 1 January 1993 when the Czech and Slovak Federal Republic was divided, the boundary line has

  become the Hungarian-Slovak international boundary. One major issue has continued to dominate the

  management of the international boundary since this change: the continuing dispute over the Nagymaros-

  Gabcikovo dam, and the alignement of the Danube river.

  The Nagymaros-Gabcikovo dam scheme was designed as a joint Czechoslovak-Hungarian project to dam

  the river Danube in order to generate hydroelectric power, and to improve communications by constructing

  navigation channels past the dam. Such a project was first discussed by the two governments in the early

  1960s, however a formal agreement was not signed until September 1977 in Budapest, entitled the

  on the Construction and Operation of the System of Waterworks of Gabcikovo-Nagymaros

  1978). One major problem with the scheme is that it will change the course of the river Danube, and

  therefore the relationship between the river and the international boundary as delimited by the Treaty of

  Trianon and the 1947 Treaty of Peace.

  During the late 1970s and early 1980s progress on the scheme was slow, mainly for financial reasons. In

  1981 negotiations were started to consider a postponement of the scheme, and in October 1983 a Protocol

  was signed by the two governments in Prague, by which it was agreed to postpone the scheme for five

  years. Though construction was started in the mid-1980s with considerable Austrian financial backing, there

  was opposition to the scheme particularly in Hungary, where doubts were expressed by environmental

  groups about the impact of the scheme on the Danube. There was also increasing concern over the huge

  cost. However in Slovakia the government gave strong backing to the scheme, not least because it would

  dramatically increase power generation capacity in the region, something Slovakia needed desperately.

  On 13 May 1989 the Hungarian government announced the unilateral suspension of construction of the dam

  at Nagymaros, and this was approved by the Hungarian Parliament on 2 June 1989. The Czechoslovak

  government opposed the Hungarian position in diplomatic correspondence in July and August 1989,

  however in November 1989 the Hungarian side terminated contracts with Austrian companies related to the

  construction of the scheme.

  Relations between the two governments regarding the dam project deteriorated, and in May 1990 the new

  Hungarian government published its general political programme in which it announced that it considered

  the scheme to be a mistake, and would initiate negotiations with the Czechoslovak government to change

  the scheme. Discussions were held between the Hungarian and Czechoslovak governments during 1990 and

  early 1991 on various proposals for the future of the scheme. On 22 April 1991 an intergovernmental

  meeting was held in Budapest at which the Hungarian government argued for a complete termination of the

  1977 Treaty. The Czechoslovak government rejected this, and little progress was made at a second

  intergovernmental meeting held in Bratislava on 15 July 1991.

  At a subsequent meeting in Budapest held in December 1991 the two sides agreed to establish a joint expert

  committee to review the scheme. However this did not prove successful. The Czechoslovak side argued that

  it could not suspend construction, and the Hungarian side demanded that the river should not be diverted by

  the scheme until full agreement had been reached.

  Treaty(ratified in June

  Articles Section 68

  IBRU Boundary and Security Bulletin July 1993 ©

  69 Articles Section

  IBRU Boundary and Security Bulletin July 1993 ©

  At a meeting in February 1992 in Prague between representatives of the two sides no agreement was

  reached over the construction of the dam. Hungary wanted construction to be stopped and a new agreement

  between the two countries to be drafted. However the Czechoslovak side rejected this, arguing that the

  1977 Treaty gave sufficient scope for any changes which experts might propose.

  On 24 March 1992 the Hungarian Parliament voted to terminate the 1977 Treaty unilaterally. The official

  statement of the Parlaiment, together with a diplomatic note from the Hungarian government was presented

  to the Czechoslovak government on 19 May 1992.

  Disagreement between the two states continued during 1992. As well as a dispute over the financial

  implications for stopping the project, there was strong disagreement over the environmental impacts of the

  scheme, and the change in the flow of the Danube along the border region between the two states. One of

  Hungary's main concerns was that the scheme would change the course of the river Danube, thereby

  changing the relationship of the river and the international boundary line as defined by the Treaty of

  Trianon. Hungary argued that any movement of the river channel would violate Hungarian territory, as well

  as the Treaty of Trianon and the 1947 Treaty of Peace. Hungary was also concerned to prevent the

  Czechoslovak authorities from instituting a temporary water management scheme which would involve a

  partial redirection of the waters of the Danube, and so possibly lead to the

  the

  Hungary's Application to the International Court of Justice (ICJ) against the Czech and Slovak Federal

  Republic over the projected diversion of the Danube submitted on 23 October 1992. In its Application

  Hungary invited the Czech and Slovak Federal Republic to accept the jurisdiction of the ICJ.

  The Government of the Czech and Slovak Federal Republic did not submit its consent to the jurisdiction of

  the ICJ before the Czech and Slovak Federal Republic was dissolved on 31 December 1992. After 1

  January 1993 the international boundary divided Hungary and the new republic of Slovakia. Negotiations

  were held between the Governments of Hungary and Slovakia in early 1993 regarding a new Application to

  the ICJ.

  In March 1993 the European Parliament in Strasbourg considered the issue, and on 12 March 1993 it passed

  a resolution calling on Hungary and Slovakia to take the dispute to the ICJ immediately, and to leave the

  decision regarding the future of the dam project to the ICJ. On 31 March 1993 the Hungarian Foreign

  Ministry issued a press statement accepting the European Community proposal, and on 7 April 1993 the

  Foreign Ministers of Hungary and Slovakia signed an agreement to take the dispute to the ICJ, and to accept

  the decision of the ICJ as binding.

  To date no new formal Application had been made to the ICJ by either government. It is therefore not

  known what the formal nature of the dispute is, or will be, and there are no indications as to when both sides

  might submit the dispute to the ICJ. Though it is reassuring that two states in the region are prepared to

  take a boundary dispute to the ICJ, when so many other disputes have led to open conflict, this

  disagreement is likely to continue for some considerable time.

  de facto permanent movement ofthalweg of the Danube (see map 2). Argument between the two states continued, culminating in

  * Paralegal associate, Clifford Chance Partnership, London.

  # 2008-12-16 at 22:29 pm

  Reply to this

   

   

  Articles Section 66

  IBRU Boundary and Security Bulletin July 1993 ©

   

  The International Boundary Between Hungary And Slovakia: The

  Nagymaros-Gabcikovo Dispute

   

  Greg Englefield*

   

  Central and Eastern Europe has witnessed a dramatic growth in boundary conflicts and disputes in the past

  three years, with attention focussed particularly on inter-ethnic (or inter-national) conflict over territory -

  the most obvious case being the wars in Croatia and Bosnia-Hercegovina. One exception to this trend is the

  dispute between Hungary and Slovakia (Czechoslovakia prior to 1 January 1993) over the Nagymaros-

  Gabcikovo dam (see map 1), and the international boundary along the river Danube. Of especial interest is

  the fact that both sides have now agreed to take the dispute to the International Court of Justice in The

  Hague for resolution, a welcome change from so many other international disputes in the region.

   

  Delimitation History:

   

  The present Hungarian-Slovak international boundary stems from the treaties and acts ending World Wars I

  and II. No treaty prior to 1918 is of significance for the boundary alignment since the region was controlled

  by the Austro-Hungarian empire and was previously part of the Hungarian Kingdom.

  The Treaty of Peace between the Allied and Associated Powers and Hungary and Protocol and Declaration

  signed at Trianon on 4 June 1920 included a detailed delimitation of the boundary (Article 27, paragraph 4).

  Article 29 of the treaty created a boundary commission to demarcate the boundary on land. Where the

  boundary followed a waterway, according to Article 30 of the treaty, the boundary should follow the

  median line in non-navigable waterways and the

  A Czechoslovak-Hungarian Boundary Commission worked from 1921 to 1925 on the demarcation of the

  boundary. Maps of the boundary were incorporated into the

  Questions arising out of the Delimitation of the Frontier between the Kingdom of Hungary and the

  Czechoslovak Republic

  2 December 1930. The 1928 Convention provides detailed descriptions of the management of the

  boundary.

  Though the boundary was altered temporarily by the

  by the Treaty of Peace signed on 10 February 1947 by Hungary and the Allied powers which came into

  effect on 15 September 1947. The boundary had been reduced in length however by the transfer of

  5,500 square miles of Ruthenia from Czechoslovakia to the Soviet Union by the

  on 29 June 1945 by Czechoslovakia and the Soviet Union. This shortened the Hungarian-Czechoslovak

  boundary by about 50 miles, though it did not alter the alignment of the boundary line - now between

  Hungary and Ukraine.

  One minor change to the prewar boundary was made by the 1947 Treaty of Peace in the Antonienhof sector,

  close to the Austria-Czechoslovakia-Hungary tripoint.

  67 Articles Section

  IBRU Boundary and Security Bulletin July 1993 ©

• Stan R

  I have left Czechoslovakia (Slovak part) 40 years ego, even then this nonsense was going on. I like the idea of people holding hands across what used to be border. I believe both sides are part of EU now. I think guys you should elect new people to your local parliament and throw out those 19th century nationalists.

  Hungarians the Atilla the Hun is long gone, Slovaks the Janosic was make believe character. After 1000 years of inter marriage and sorry to say “rape” they are not pure Slovaks or Hungarians.

  # 2009-01-12 at 17:02 pm

  Reply to this
  __________________________________________________
  

  1

  Human Development Report, Slovakia and Hungary

  Roma, poverty, and social exclusion in Eastern Slovakia and

  Northern Hungary

  Working Paper

  Draft

  standards. The content does not necessarily represent the opinion or position of the

  UN Development Programme

  has not undergone the review and language proof-reading according to official

  Contents:

  I. Introduction

  II.

  Poverty and Inequality

  II.1 Poverty indicators

  II. 2 Expenditures overview

  III. Demographics and Education

  III.1 Demographic structure of the population

  III.2 Educational attainment of the population

  IV. Housing, Living Conditions and Health

  V. Labor Market Situation

  V.1 Labor market overview

  V.2 Unemployment profile

  V.3 Employment profile

  VI. Income and Earnings

  VII. Mobility

  VII.1 Internal migrations

  VII.2 External/international migrations

  VIII. Conclusion

  2

  I. Introduction

  The challenges facing the regions of Eastern Slovakia and Northern Hungary include

  ending poverty and overcoming social exclusion of marginalized segments of

  population. The Roma population represents the group which is amongst the most

  vulnerable, that means among those who are threatened by social exclusion and

  poverty. Many researches identified Roma in Slovakia as to be ‘at-risk group’ which

  social exclusion had a long-term character and which “despite improvements of their

  situation in the period of socialism, … ranked for a long-time among the poorest

  because in their case the key factors in the reproduction of poverty – unequal life

  opportunities- were not eradicated” (Kusa 1997). In Hungary, Roma has been

  identified most likely to be long-term unemployed through an analysis of a Hungarian

  households being “…in the lowest income decile, of whom nearly half were poor…”

  (Andorka, Speder 1996, Kemeny, Janky Lengyel 2005). Roma as an ethnic group are

  mentioned explicitly in political documents and action plans of the Slovak republic,

  for eradication of poverty and exclusion. For example National Action Plan on Social

  Inclusion 2004-2006 identifies groups within the population which are the most

  vulnerable to poverty as “long term unemployed … migrants, people with disabilities

  included people living in Roma communities”. Also regional documents pay attention

  to these issues. According to the Development Plan of Eastern Slovakia, one of the

  strategic objectives is “to increase the employment rate as well as the employability of

  the marginalized groups of society” Within the priorities of social area there are

  formulated specific aims such as i) to improve labour market access conditions for

  the marginalized groups of inhabitants including the members of the Roma

  community, so that the percentage of those in the total number of registered

  unemployment drops by 15 percent until 2010 and ii) to improve qualification and

  skills of the marginalized groups of the society. Similar attention is paid to the

  integration of marginalized groups and the Roma population in Program of Economic

  and Social Development of Presov self-governing region for 2007- 2013.

  In Hungary the North Hungary Operational Programme 2007-2013 identifies Roma

  also as the vulnerable and “the chances for life of the population in micro-regions

  inhabited by major Roma population is worsened by these areas mostly lacking

  towns, being areas covered by small villages which get depopulated” (NORDA,

  2006).

  The socio-economic conditions and the development challenges in the three different

  regions of Northern Hungary and Eastern Slovakia are sufficiently similar (see

  Chapter 2) that is why some common problems could be treated together.

  3

  The actual sample frequencies in the 2004 Vulnerable Groups Survey (VGS),

  designed and administered with the help of UNDP, are also in comparable magnitudes

  for the three regions of interest (Table 4.1). The survey used is rich in information

  concerning Roma and proximity non-Roma populations, and characterize the situation

  of the most vulnerable to poverty residents in those regions. Based on the VGS,

  indicators for Roma in Northern Hungary, Kosicky and Presovsky regions are

  presented in the following sections. As a substantial part of the survey sample was

  collected in the North region of Hungary and the Eastern regions of Slovakia, the

  computed indicators are based on statistically significant large sub samples, and are

  thus themselves statistically significant. As control group, the VGS provides data on

  non-Roma populations living in close proximity to Roma. Their sub sample is of

  much smaller size is therefore not statistically significant comparison group.

  Table 4.1 Vulnerable group survey sample sizes

  Individuals Households

  Hungary 4,140 1,005

  Roma in North Region 1,411 276

  Slovakia 4,973 1,075

  Roma in Kosicky Region 1,399 248

  Roma in Presovsky Region 1,095 192

  Source: UNDP Vulnerable Groups Survey 2004.

  It is important to bear in mind that the data collected in the VGS reflect only

  vulnerable groups. These data are therefore not representative of two countries as a

  whole. The survey targeted only areas where estimated Roma population shares were

  equal or greater than national averages. Majority populations in the survey are

  likewise representative of communities living in closer proximity to Roma

  communities. The majority-in-proximity sampled may share some of their Roma

  neighbors’ vulnerability determinants, and thus may be more vulnerable to poverty

  than national averages. Because of the interest in a small regional sub sample, the

  number of non-Roma observations is too small to provide meaningful information.

  Another note to mention is that the surveys differ somewhat between the two

  countries, so it is not possible to merge two datasets. Whenever the questions are not

  the same, the Slovak and Hungarian regions were studied independently. In several

  subsections, Hungary and Slovakia are represented in separate tables. However,

  wherever possible, the tables compare jointly the similarities and differences of those

  regions and the Roma populations living there. Ideally the Roma indicators are

  compared to certain national benchmarks whenever available.

  The regional indicators covered in this Chapter are grouped in the following sections:

  1) poverty and inequality; 2) demographics and education; 3) housing and living

  conditions, 4) labor market situation, 5) income and earnings; and 6) mobility. Data

  are compiled from National Statistical Office of Hungary (HCSO) and the Statistical

  Office of the Slovak Republic (SOSR) for indicators on national and regional level,

  and from UNDP’s Vulnerable Groups Survey (VGS, 2004) for data on Roma

  populations. Other specific sources used in this report are mentioned in footnotes.

  4

  II.

  Poverty and Inequality

  On the whole, in the new EU member states, Slovakia and Hungary, poverty remains

  substantially lower than that of other, poorer, countries in Eastern Europe. However,

  even in these more prosperous countries, significant poverty pockets persist within

  some segments of the population. The unemployed, the poorly educated, rural

  populations and children are more likely to be poor. Roma minority populations

  represent one of the main poverty groups. They are both poorer than other population

  groups and they are more likely to fall into poverty and remain poor.

  Poverty is a multidimensional phenomenon that goes well beyond low income or lack

  of material consumption. In the case of Roma, poverty is particularly multifaceted.

  Many Roma are deprived of the resources necessary for adequate living conditions, as

  well as access to opportunities and channels for participation. These problems are

  often interconnected.

  II.1 Poverty indicators

  In this section, the incidence and depth of poverty and the extent of inequality among

  Roma across the three regions is assessed and contrasted with that of the whole

  countries. Indicators on poverty and inequality are not yet widely produced by the

  national statistical offices of Hungary and Slovakia. Therefore, the various indicators

  which are available from international sources such as the UNDP, the World Bank

  and the European Commission were used. All sources report slightly different

  indicators or different values due to different base years and various datasets used for

  calculation. Overall, world wide Hungary and Slovakia rank very closely with

  Hungary showing slightly better indicators than the Slovak Republic.

  Table 4.2 EUROSTATS poverty indicators

  Hungary Slovakia

  At-risk-of-poverty rate before social transfers 17% 28%

  At-risk-of-poverty after social transfers 12% 21%

  Relative at-risk-of-poverty gap 20% 39%

  Inequality of income distribution (income quintile share ratio) 3.3 5.8

  Dispersion of regional employment rates 9.4 9

  Early school leavers 12.6% 7.1%

  Long term unemployment rate (12 months or more) 2.7% 11.8%

  Very long term unemployment rate (24 months or more) 1.3% 8.2%

  Children aged 0 – 17 living in jobless households 13.2% 12.8%

  People aged 18 - 59 living in jobless households 11.9% 10.8%

  Source: Eurostats (See Box 1.1 in Annex for definitions)

  Note: Indicators for Hungary are based on 2003 calculations, while for Slovakia, they are based on

  2004.

  EUROSTATS’ indicators are broadly defined beyond the material consumption and

  resources (Table 4.2). At-risk-of-poverty before social transfers is 17% in Hungary

  and 28% in Slovakia. The system of social transfers in those countries manage to

  attenuate poverty but the indicators still remain relatively high – at-risk-of-poverty

  5

  after social transfer falls down to 12 % in Hungary and down to 21% in Slovakia.

  The relative at-risk-of-poverty gap is almost twice lower in Hungary signifying higher

  difference between individuals at the poverty threshold and those below it suggesting

  existence of pockets of extreme poverty. The other measure of inequality, income

  quintile share ratio, is almost twice higher in Slovakia, which suggests that there is a

  higher general inequality in the population, with rich people holding higher shares of

  the overall income. The shares of children and adults living in jobless households are

  very similar between the two countries. Lastly, the indicators related to employment

  differ substantially which reflect major structural differences between the labor

  markets in Hungary and Slovakia. Long term unemployment is more than 4 times

  higher in Slovakia than it is in Hungary.

  Table 4.3 UNDP poverty indicators

  Hungary Slovakia

  Probability at birth of not surviving to age 60 (% of cohort)

  (2000 – 2005) 18.3% 14.9%

  Long term unemployment (% of labor force) (2003) 2.5% 10.7%

  Population below poverty income line (%)

  50% of median income (1999 – 2000) 6.7% 7.0%

  $4.00/day (1996 – 1999) <1% 8%

  Share of income or consumption (%)

  Poorest 10% 4.0% 3.1%

  Poorest 20% 9.5% 8.8%

  Richest 20% 36.5% 34.8%

  Richest 10% 22.2% 20.9%

  Inequality measures

  Richest 10% to poorest 10% 5.5 6.7

  Richest 20% to poorest 20% 3.8 4.0

  Gini coefficient 26.9 25.8

  Source: UNDP HDR 2005, tables 4 and 15, pp 230 and 270.

  Notes: The source of poverty line of $4.00/day did not specified whether in PPP terms. The share of

  income and inequality measures for Hungary are computed based on a consumption survey in 2002,

  while for Slovakia these indicators are computed based on income survey in 1996.

  The slightly different indicators released by UNDP depict similar situation between

  the two countries (Table 4.3). Hungary is ranked 35

  42

  of median income Hungary and Slovakia do not diverge too much, with about 7% of

  the population. At the threshold of $4.00/day, however, the population below the

  poverty income line is less than 1% in Hungary while it is close to 8% for Slovakia.

  This again implies that in Slovakia some pockets of acute poverty are contributing to

  the overall higher poverty rates. Nevertheless, the Gini coefficients to the two

  countries do not reflect major differences in the overall inequality distributions. In

  Slovakia the share of income of poorest 10% is only 3.1% vs. 4.0% in Hungary and

  the share of income of the poorest 20% is only 8.8% vs. 9.5% in Hungary, which

  suggests somewhat deeper impoverishment. On the other end, the richest 20% of

  Hungary hold among them 36.5% of the national income vs. 34.8% in Slovakia and

  6

  the richest 10% of Hungary hold 22.2% of the national income vs. 20.9% in Slovakia

  which suggest that the top quintiles in Hungary are richer relative to the top quintiles

  of the Slovak distribution. However, the overall inequality, measured by Gini index,

  is very similar between the two countries, which imply that large middle classes keep

  the inequality levels down overall.

  The World Bank uses two poverty lines, an absolute and a relative, to compare

  poverty across countries: US$2.15 purchasing power parity (PPP

  and $PPP 4.30 per capita per day. Extreme poverty threshold, at $PPP 2.15/day,

  which is most often used for low-income countries, is practically nonexistent in

  Hungary (Table 4.4). However, at the more appropriate level of $PPP 4.30/day, the

  relative poverty rate in Hungary reaches 12%. Contribution to poverty seems equally

  from rural and urban areas, excluding the affluent capital, which is attracting the most

  highly educated and where better employment opportunities are available. It has been

  shown in the literature as well as confirmed by the data, that location has significant

  effects on income and welfare and thus on poverty.

  Table 4.4 Hungarian poverty indicators according to the World Bank

  $PPP 2.15/day $PPP 4.30/day

  th in the world and Slovakia isnd within the High Human Development group. According the poverty line of 50%1) per capita per day

  Poverty indices

  Poverty rate 0 12

  Poverty dept 0 2

  Poverty severity 0 1

  Contribution to poverty

  Rural 35% 46%

  Urban 65% 54%

  Capital 13% 11%

  Other urban 52% 42%

  Inequality indices

  Gini coefficient (per capita) 24.96

  Share of the lowest 20% 10%

  Source: Growth, Poverty and Inequality, World Bank, 2005.

  Data analyzed in this Chapter reveal a worrying picture of poverty among Roma in

  those regions with about two thirds of Roma households living in poverty there. In

  contrast, only a share of about 9-12% of the whole countries lives in poverty. Roma

  poverty there is also deeper – the shortfall from the poverty line of average Roma

  households in poverty is bigger than that of the whole countries, making it more

  difficult to get out of poverty (Table 4.5). The calculations using vulnerable

  households’ data confirm the World Bank finding that in Hungary the fraction of

  people living with $PPP 2.15 or less per day is quite small even among Roma in the

  poorer North region, only 4%. Poverty in Hungary, seems to be clustered around the

  upper threshold of $PPP 4.30/day. In Slovakia, however, 2% of the whole population

  is below the $PPP 2.15/day threshold, which suggests of the presence of extreme

  poverty there, in addition to 9% of relatively poor population. Focusing on Roma, the

  data shows a worrying picture of poverty in those regions: about two thirds of Roma

  1

  The adjustment to PPP accounts for differences in price levels across countries.

  7

  are living in relative poverty. In North Hungary, as well as in Kosicky and Presovsky

  regions, more than 60% live below relative poverty line of $PPP 4.30/day. Moreover,

  28% of Roma in East Slovakia live with barely $PPP 2.15/day which represents much

  higher concentration of extremely poor (in comparison with North Hungarian Roma).

  The Gini index for Hungary is 24.96 while the one calculated only for North Roma is

  26.10. Both numbers are of very similar magnitudes, which indicate that the overall

  low inequality in the Hungarian society is preserved even among the most vulnerable

  groups. The overall Gini index for Slovakia is 25.80, and for Roma alone in Eastern

  region – 26.75 in Kosicky and 29.31 in Presovsky regions. Those figures suggest that

  even among Roma, expenditure inequality remains similar to the national average or

  slightly above.

  The numbers provided by the international sources show how official poverty

  statistics do not always capture the status of the poorest minorities – Roma fall out of

  the scope of typical household budget and labor force surveys which are used for the

  computation of those indicators.

  Table 4.5 Poverty rates from Vulnerable Groups Survey

  Hungary Slovakia

  Roma in

  North

  Hungary

  Roma in

  Kosicky

  region

  Roma in

  Presovsky

  region

  At $PPP 2.15/day 0% 2% 4% 28% 28%

  At $PPP 4.30/day 12% 9% 61% 62% 58%

  Gini index 24.96 25.80 26.13 26.75 29.37

  Source: Growth, Poverty and Inequality, World Bank, 2005,Slovak Republic: Living Standards,

  Employment, and Labour Market Study, World Bank 2002, UNDP Vulnerable Groups Survey 2004.

  The composition of poverty at the threshold of $PPP 2.15/day shows 46% children

  below the age of 16 and 56% adults in Hungary (Table 4.6). The VGS confirms the

  characteristics similar to the Hungarian national averages. The number of children in

  a household has been shown to have a strong negative relationship with welfare

  (Revenga, Ringold and Tracy, 2002). As Roma households have on average more

  children than the rest of the countries, the number of children could be a contributing

  factor to the higher incidence of poverty among Roma households. Children of Roma

  families in the three regions are very vulnerable to poverty with 39-46% among them

  living below $PPP 2.15/day. According the number of children the families with

  children constitute 60-86% of the poor families.

  The survey data also exhibits significant benefits of education for Roma who seek to

  escape poverty. The huge problem of education is seen among Roma with 80-93% of

  the poor with at most primary education. However, those with secondary or more are

  barely 2-3% among the extreme poor. This points out to the importance of education,

  and particularly, the lack of it, as a possible determinant of poverty. Another

  observation is secondary special education contributing to poverty with as much as

  28% of the poor. These numbers imply that completing secondary school has in fact

  adverse effects as it is inadequately adapted for the needs of pupils with disability and

  handicaps. Moreover, the Roma children face higher likelihood to end up in special

  classes which in turn leads to higher vulnerability to absolute poverty.

  8

  Table 4.6 Composition of poverty at $PPP 2.15/day

  Hungary

  Roma in North

  Hungary

  Roma in

  Kosicky region

  Roma in

  Presovsky region

  Age

  0 – 16 years 46% 43% 46% 39%

  17 – 65 years 56% 57% 53% 59%

  65 + years 2% 0% 1% 2%

  Number of children in HH

  No children 19% 14% 31% 40%

  One or two children 24% 29% 60% 46%

  Three 57% 57% 9% 14%

  Education

  Incomplete primary 35% 14% 34% 45%

  Completed primary 33% 79% 46% 39%

  Secondary 5% 0% 2% 0%

  Secondary special 28% 7% 18% 16%

  Tertiary 0% 0% 0% 0%

  Source: Growth, Poverty and Inequality, World Bank, 2005, UNDP Vulnerable Groups Survey 2004.

  Additionally, for Slovakia the commonly agreed indicators for poverty and social

  exclusion adopted by the members of the European Union were computed (Table

  4.7). The most common indicator, at-risk-of-poverty rate after social transfers,

  reaches 88% for Roma in the Kosicky region, while it is only 21% for Slovakia as a

  whole. Seven out of every eight Roma individuals are living below the poverty line

  of 60% of national median income. The relative at-risk-of-poverty risk gap, which

  measures the actual income gap of those below the poverty line, suggest that Roma

  are receiving less than half of that poverty threshold income level. In other words, on

  average, the poor Roma in these regions are clustered under the threshold. The last

  two indicators measuring population living in jobless households are quite alarming –

  as high as 93% of all Roma children live in jobless households in Kosicky region in

  comparison of 13% for the country as a whole. For adults, that share varies between

  67% and 85% vs. 11% for Slovakia. Living in a jobless environment contributes to

  low incentives to invest in one’s education and the lack of role models within the

  families could be detrimental for the upbringing of young Roma in Eastern Slovakia.

  Table 4.7 Slovak poverty indicators according to the EUROSTATS

  Slovakia

  Roma in

  Kosicky region

  Roma in

  Presovsky region

  At-risk-of-poverty rate after social

  transfers

  21% 88% 85%

  Relative at-risk-of-poverty gap 39% 49% 43%

  Children aged 0 - 17 living in

  jobless households

  13% 93% 86%

  People aged 18 - 59 living in

  jobless households

  11% 85% 79%

  9

  Source: Eurostats (see Box 1.1 in Annex for definitions), UNDP Vulnerable Groups Survey 2004.

  II. 2 Expenditures overview

  Another way how to assess the welfare is to measure the consumption in terms of

  expenditure. In some cases, such measures are considered better indicator of welfare

  than poverty rates based on income, as it permits a direct assessment of the ability of a

  household to meet its basic needs. Furthermore, studying expenditures allows for

  particular spending habits to emerge which are consequence of efforts to cope with

  insufficient resources. Therefore, in this section the analysis of expenditures is

  included as well.

  Note that households with similar expenditures but of different size and composition

  will have different individual allocation. Large families are more likely to have

  greater expenses but not necessarily per capita. Because poverty measures are very

  sensitive to household composition, the calculations are based on per equivalent adult

  expenditures

  the household).

  Annual household expenditures are used as the main measure of household welfare.

  Table 4.8 shows average annual per capita expenditures, in Euros PPP for crosscountry

  comparison, by main categories. Overall, the average annual expenditures of

  a Roma household in the North Region are 56% of the national Hungarian average.

  For Roma in Eastern Slovakia, the situation is even worse, with average annual

  expenditure of only about 40% of that of an average Slovak household. As a

  consequence of higher incidence of poverty, Roma have lower average expenditure

  than the national averages, and devote a higher proportion of total expenditures to

  food purchases and a lower proportion to education, health care and consumer

  durables.

  Table 4.8 Average annual per capita expenditures (Euros PPP)

  Hungary Slovakia

  Roma in

  North

  Hungary

  Roma in

  Kosicky

  region

  Roma in

  Presovsky

  region

  Food 950 868 923 530 567

  Housing maintenance 673 695 358 210 200

  Transport 681 305 45 50 53

  Clothing / Shoes 204 246 207 88 98

  Medicine / Healthcare 231 69 104 60 50

  Domestic products 206 171 109 82 74

  Cigarettes / Alcohol 202 102 191 87 90

  Entertainment 293 252 12 2 2

  Total 3,442 2,709 1,934 1,108 1,135

  2 (where expenditures are adjusted for both the size and composition of

  Source: Statistical Yearbook of Hungary 2003, table 5.11, p.128, Statistical Yearbook of the Slovak

  Republic (table VI.1-2.), UNDP Vulnerable Groups Survey 2004.

  Note: Annual amounts are converted in Euro PPP using exchange rates from OECD and ECB.

  2

  for any children below the age of 15 years old.

  OECD equivalence scale uses weight 1 for head of household, 0.5 for any additional adults and 0.3

  10

  The main expenditure category is food, and average spending seems slightly lower in

  Slovakia than in Hungary reflecting structural differences in food prices. Roma

  households in the North which are spending on average only 3% less than the national

  average. For this reason, it could be asserted that Roma households in North Hungary

  do not suffer malnutrition. Nevertheless, as their total incomes are far smaller, their

  food share becomes much larger. On the other hand, Roma in Kosicky and Presovsky

  regions are consuming about a third less than the Slovak averages and their food

  expenditures are 61-65% of the national average, which suggests that Roma

  households consume smaller amounts of (as well as cheaper) food. Such average

  magnitudes may be concealing cases of malnutrition.

  The next two largest expenditures, in Hungary, are on housing maintenance and

  transportation. Housing maintenance, which includes rent and utilities, exhibits a

  considerable difference. Roma households in the North region spend on average EUR

  PPP 358 vs. 673 nationwide, only about half of the Hungarian average. For Roma in

  Kosicky and Presovsky regions, the amount they spend on housing falls even lower to

  29% of national average. Part of these differences may be explained by their, often,

  large outstanding payments in housing, electricity and water (see Figure 4.1). Another

  explanation lies within their homeownership characteristics (see section 3). As for

  transportation costs, Hungarian households spend, EUR PPP 681 annually while on

  the other extreme, an average Roma household in the North region spends a mere 7%

  of that amount. Roma in Kosicky and Presovsky regions spend similar amounts. One

  important explanation is the (lack of) purchases of vehicles, fuel, and accessories,

  which are made mainly by middle- to upper-income families. Indeed, in the survey,

  only 1% of all Hungarian vulnerable households report owing a car.

  The other categories exhibit regional differences as well. When it comes to expenses

  on clothes and shoes, alcohol and cigarettes Roma in the North region spend as much

  as national averages, and about half as much on medicine and health care and

  domestic products. Roma in Eastern Slovakia seem to spend more on medicine and

  healthcare, about 80% and alcohol and cigarettes, about 87% relative to Slovak

  national averages. As for clothing and shoes and domestic products they spend about

  38% and 45% of the national averages. More notably, the actual amounts per capita

  and shares in total expenditures are much higher for cigarettes and alcohol than those

  for medicine and healthcare, which has a direct implication on their poorer health.

  Figure 4.1 Expenditure shares

  28%

  32%

  48%

  48%

  50%

  20%

  26%

  18%

  19%

  18%

  20%

  11%

  2%

  4%

  5%

  6%

  9%

  11%

  8%

  9%

  7%

  3%

  5%

  5%

  4%

  6%

  6%

  6%

  7%

  7%

  6%

  4%

  10%

  8%

  8%

  0% 20% 40% 60% 80% 100%

  Hungary

  Slovakia

  Roma in North

  Hungary

  Roma in Kosicky

  region

  Roma in Presov

  region

  Source: UNDP Vulnerable Groups Survey 2004.

  11

  Figure 4.1 presents the shares of the main expenditure categories. Looking more

  closely at average expenditure shares for different categories, spending on food (a

  commonly used proxy for welfare) clearly stands out. Expenditures on food weigh

  more heavily on Roma household in all of the three regions. Next, by importance, are

  the categories of housing maintenance, clothing and shoes, cigarettes and alcohol.

  When those households become better-off, it should be expected their expenditure

  shares in transport, medicine and healthcare, and entertainment to reach the national

  averages.

  Expenditure patterns show the poverty status of Roma households, with high shares of

  expenditures on food. But they also outline the contours of the poverty cycle Roma

  are caught in: the smallest shares of their household budgets devoted to education

  make it more difficult for young Roma to escape poverty. They spend similarly low

  shares on health care and consumer durables. Moreover, given the larger numbers of

  children in Roma households, these low shares of expenditures on health and

  education underscore the Roma communities’ vulnerability and thus contribute to

  their perpetual poverty trap and state dependence.

  Many households who are vulnerable to poverty face great difficulties in covering

  their utilities bills, such as water, electricity, and heating and they cumulate unpaid

  bills for several months. The average outstanding utility bills sometimes amount to

  more than 175% of their total monthly expenditures. National sources do not provide

  information on such indebtedness; however it seems that Roma households are in

  more critical situation concerning outstanding payments for water, electricity and

  housing-related payments. The magnitudes often make prospects for breaking this

  circle of outstanding payments unrealistic. The severity of the problem is also

  confirmed by the low share of expenditures on housing and utilities Roma households

  have, seen from the previous section. Many Roma households spend less on housing

  and utilities simply because they cannot afford paying regularly their utility bills –

  despite the threat of being cut off from the electricity or other utilities supply.

  Table 4.9 Outstanding utility bills

  Roma in

  North Hungary

  Roma in

  Kosicky region

  Roma in

  Presovsky region

  Fraction of households in arrears

  Water 19% 9% 3%

  Electricity 14% 15% 7%

  Other overhead 11% 8% 4%

  Food debts n.a. 8% 2%

  Average amounts per capita in arrears (Euros PPP)

  Water 114 212 98

  Electricity 80 154 190

  Other overhead 350 291 195

  Food debts n.a. 103 44

  Source: UNDP Vulnerable Groups Survey 2004.

  12

  The fraction of households in arrears are the highest for Roma in the North, 19%,

  mostly in water, then electricity and lastly in housing. For Roma in Eastern Slovak

  regions, electricity represents the highest burden. According to the actual average

  amounts though other overhead costs, such as heating, telephone, etc. are the highest

  for Roma in North Hungary reaching on average EUR PPP 350 per capita (Table 4.9).

  Surprisingly, the fractions of households who are in arrears among Roma in Eastern

  Slovakia appear to be lower than those in North Hungary, but their average amounts

  (as shares of household income) are relatively much larger.

  Figure 4.2 outlines the severity of indebtedness of households as a share of average

  total monthly expenditures. Roma in all three regions rely on debt with Roma across

  the border in Slovakia, particularly in Kosicky region being most indebted in all

  categories. Share of housing liabilities reach up to 175% indebtedness for Roma in

  the Kosicky region, followed by 130% water and 112% electricity arrears. As shares

  of monthly expenditures, overhead costs such as heating and telephone bills, remain

  the main burden to Roma families. In the case of Slovakia, the survey asked

  additional question on food indebtedness, which occurs rarely but represents a

  substantial share of total household monthly expenditures which points to the few

  cases of extreme poverty facing risks of malnutrition.

  Figure 4.2 Outstanding payments as share of monthly household

  expenditures

  39

  130

  70

  42

  112

  97

  69

  175

  136

  0

  63

  35

  0 50 100 150 200

  Roma in North

  Hungary

  Roma in Kosicky

  Roma in Presov

  Water Electricity Housing Food debts

  Source: UNDP Vulnerable Groups Survey 2004.

  There are three additional categories of expenditures, which are not included in the

  monthly break down usually reported by national sources. In the VGS, households

  were asked about their annual expenditures on consumer durables, healthcare and

  education. These are fair indicators of individual well being, beyond satisfying the

  subsistence needs while providing families with means for sustainable development.

  Unfortunately, comparing these expenses with national averages is quite problematic

  as these three general questions on expenditures do not correspond to the

  classifications used in the national household budget surveys due to incompatibility in

  methodology. One thing is however clear from the data, few Roma households

  spend too little on consumer durables – this was also confirmed by our previous

  analysis on possession of usual household appliances. For example, the Hungarian

  average expenditure amount on consumer durables is about EUR PPP 288, which

  includes purchases on motor vehicles. For Roma in the North, who are spending

  positive amount, the average expenditure is 85% of that national mean, even though

  most of the households do not possess a car, not even other common household

  appliances. A second observation is the very lower expenditures on education

  supplies (such as text books and other school supplies) and its negative implications

  13

  for successful completion of grades and obtaining diplomas. Lastly, Roma, in all

  three regions spend much lower amounts on health care, medicine and doctor visits

  with vulnerable Roma households in the North spend 65% of the national averages.

  Two additional questions from the Hungarian survey (those are missing for Slovakia),

  were asked based on a qualitative self-definition of households. From those who had

  children in school, 43% of the Roma in the North region said that they were not able

  to pay the cost of education for their children. When asked whether there were

  schools within reach where the children could continue their studies, only 2% claim

  that there are no schools within reach. This clearly indicates that poverty induced

  financial difficulties constitute significant obstacles to education which could in turn

  lead to a poverty trap for young Roma.

  III. Demographics and Education

  Other determinants of poverty, according to UNDP publication (2006), include

  number of children in the household, education and skill level of the household head.

  Therefore Section III. focuses on demographics and education of Roma in North

  Hungary, Kosicky and Presovsky regions.

  III.1 Demographic structure of the population

  Roma birth rates have historically been high across Eastern Europe, as they reflect the

  Roma demographic characteristics and reproduction behaviors. Roma families have

  traditionally been large as they place highest value in having children. However,

  Roma demographics are not just about population growth: they have substantial labor

  force and other socio-economic consequences for the current and next generations.

  Table 4.10

  Hungary

  North

  region

  Roma in North

  Hungary

  Distribution

  Children ( 0 – 14 years) 17% 16% 43%

  Younger adults (15 – 39 years) 34% 36% 38%

  Older adults (40 – 59 years) 27% 27% 15%

  Elderly (60 + years ) 22% 21% 4%

  Average age n.a. n.a. 22.3

  Ratios

  Children and elderly per 100 adults 59 62 88

  Children per 100 adults 25 27 81

  Elderly per 100 adults 34 35 7

  Number of elderly per 100 children 137 128 8

  Number of household members

  1 26% 25% 4%

  2 29% 29% 12%

  14

  3 20% 20% 13%

  4 + 25% 26% 71%

  Average household size 2.6 n.a. 6.5

  Source: Hungarian Micro-Census 2005, UNDP Vulnerable Groups Survey 2004.

  The demographic structure in the North region is not very different from national

  averages (Table 4.10). However, focusing at the Roma populations in the North, the

  composition changes dramatically in favor of the young – 40% are children under the

  age of 15 and only 6 are elderly of 60 years or more. The number of elderly per 100

  children is 137 for Hungary and 128 for the North region alone. In the case of Roma

  in the North, the picture becomes even younger. The ratio of elderly per 100 children

  is a mere 8 which confirms their much higher average fertility rates and shorter life

  expectancies. This is important to keep in mind when forecasting Roma future labor

  force.

  Table 4.11 Demographic indicators

  Slovakia East Slovakia

  Roma in East

  Slovakia

  Distributions

  0 – 14 years 19% 22% 42%

  Male, 15 – 59 years 33% 32% 27%

  Female, 15 – 54 years 30% 30% 26%

  Male, 60 + years 6% 6% 2%

  Female, 55 + years 12% 11% 3%

  Average age 37.1 n.a. 22.4

  Number of household members

  1 n.a. n.a. 3%

  2 n.a. n.a. 10%

  3 n.a. n.a. 9%

  4 + n.a. n.a. 78%

  Average household size n.a. n.a. 5.7

  Source: Slovak Population census 2001, UNDP Vulnerable Groups Survey 2004

  .

  In the same way, the distribution of households by size in the North region does not

  differ too much from the country as a whole – slightly lower fraction of single

  member households and slightly higher fraction of households with four or more

  members. For the vulnerable groups, the situation is very distinctive – individuals

  rarely live alone but rather in large households, with on average 6 or more members.

  In table 4.11, the age distributions is computed for the entire Eastern region (Kosicky

  and Presovsky together), in order to match the indicators from the Population census.

  Similar to the case of Hungary, Slovakia’s poorer eastern region is populated by

  younger than average people who live in much bigger households. One direct

  implication with respect to budgetary consequences is that fewer Roma rely on the

  state for pension benefits while a majority depends on social assistance for child and

  unemployment benefits. In both countries, similar to the EU as a whole, the majority

  populations are aging and the sustainability of the national pension schemes for future

  15

  generations depends on high current fertility. Roma minorities seem a budgetary

  burden today but in the future they may be the principal contributors to the welfare

  systems in order to support the elderly majority populations. The average age in

  Slovakia is 37 years old while among Roma in East Slovakia and North Hungary it is

  only 22.

  III.2 Educational attainment of the population

  Table 4.12 presents the educational attainment of the population, which is the most

  important determinant for assessing the human capital level of the labor force and

  their prospects of working their way out of poverty. Data on educational attainment

  of the entire Slovak population is not published by the census and therefore Roma

  data is compared to the educational attainment of Hungary.

  Education in both countries is mandatory for young children and adolescents – in

  Hungary up to the age of 18, while in Slovakia it is compulsory for the first 9 years

  regardless of actual age, or up to about the age of 16. For this reason, in both

  countries, even among Roma in the three poorer regions, most of the population has

  completed at least some primary school – about 94% of all Roma in Eastern Slovakia

  vs. only 63% of Roma in North Hungary. The fraction of highly educated individuals

  drops sharply after primary school with about 12-17% of Roma completing at least

  secondary school and almost no one with university qualifications.

  The education attainment is shown in the literature to be perfectly correlated with

  potential earning power of the individuals. The lower educational attainment suggest

  that even if all Roma find employment, their labor income will still reflect their lower

  skills and thus will confine them in a low-income subgroups of the population.

  Having few professional qualifications and diplomas makes it more difficult for them

  to move up the professional lather even if they cumulate several years of experience

  and makes them less preferred (for employment or promotion) than other individuals

  who hold various certifications and degrees.

  Table 4.12 Educational attainment of population

  Hungary

  North

  region

  Roma in

  North

  Hungary

  Roma in

  Kosicky

  region

  Roma in

  Presovsky

  region

  No schooling

  (10 + years)

  0.7% 0.8% 11.0% 3.6% 3.1%

  Completed at least primary

  school (15 + years)

  88.8% 86.2% 63.1% 94.3% 94.0%

  Completed at least secondary

  school (18 + years)

  38.2% 33.2% 17.1% 15.9% 12.4%

  Completed tertiary education

  (25 + years)

  12.6% 9.3% 1.1% 0.0% 0.0%

  Source: Hungarian Population Census 2001, UNDP Vulnerable Groups Survey 2004

  The problems directly related to lower educational attainment, such as poor

  employability and low income generating potential, are likely to persist in the near

  future due to the dire situation of the Roma children and adolescents today.

  Aggregate enrollment rates in 2004 compared to the national indicators show the clear

  16

  phenomena of poverty trap. First, enrollment rates in primary school are close to

  100% for Roma in North Hungary and are as high as 119% for Roma in Presovsky

  region reflecting mandatory schooling which unfortunately does not guarantee

  successful level completion. Secondly, sharp drop in enrollment rates occurs in

  secondary schools and at university level. In Hungary and Slovakia where 94-97% of

  all children at the age to be in secondary school are actually attending only merely 14-

  18% of the corresponding Roma are attending high schools. This disturbing fact

  should be preoccupying the authorities. As for tertiary level, there are practically no

  Roma university students in any of three regions. The main channel for poverty

  alleviation through accumulation of human capital seems ineffective and thus can

  maintain parts of the population in poverty for generations ahead. The low

  enrollment rates in higher education reflect low adaptation of this population to the

  requirements of a changing labor market and destines many of them rely on the state

  welfare or may force them to enter a grey sector in order to provide for their

  subsistence(see Table 4.13).

  Table 4.13 School enrollment in 2004

  Hungary Slovakia

  Roma in

  North

  Hungary

  Roma in

  Kosicky

  region

  Roma in

  Presovsky

  region

  Primary school 98% 99% 98% 117% 119%

  Secondary school 97% 94% 14% 16% 18%

  Tertiary school 60% 36% 2% 1% 1%

  Source: World Development Indicators, UNDP Vulnerable Groups Survey 2004

  Note

  enrolled in particular level.

  : Rates are computed as total number of pupils enrolled over the number of children of age to be

  The high enrollment rates in primary school, mentioned above, are partly explained

  by mandatory schooling in both Slovakia and Hungary. On the other hand, among

  Roma children in Eastern Slovakia enrollment rates are boosted much above 100%

  which may be due to older children returning to school in order to complete their

  primary degree. The recent social reform took place in Slovakia in 2004 in order to

  better motivate this finding. One of the aspects of the reform consisted in cutting

  child care benefits almost by half while introducing stipends for low-income children

  conditional on attending school as incentives to increase their enrollment and

  eventually their educational attainment. The presence of conditional transfers for

  poor families’ parents in addition to free lunches and book supplies provided at

  school, partly explains the particularly high enrollment rates among Roma children in

  Slovakia for primary education. Even though the reform provides financial incentives

  for secondary and tertiary schools as well, their enrollment remains low as it may be

  too early to account for any positive impact.

  In both countries, attending secondary schools and especially universities requires

  successful completion of primary school. High drop out rates, interruptions from

  school for long periods of time and not fulfilling all requirements for obtaining final

  diploma are all clear causes (in addition to the obvious financial ones) for Roma

  children not to continue their higher educations. Rather than low “Roma value of

  education” main explanation about the enrollment rates lies within objective grounds.

  Various systematic forms of direct and indirect discrimination and excessive isolation

  in schools nowadays contribute for Roma parents’ lower incentives to send their

  17

  children to school. One in three Roma children, attending school, are not in the

  correct grade suggests that they are unable to complete the school curriculum, are

  often repeating grades and are stigmatized as “losers” which implies poor prospects of

  these children upon reaching working age. One in four Roma children is set apart in a

  special school for disabilities with little prospect to acquire the minimum necessary

  for continuing at secondary or university level.

  The low level of qualification of Roma in terms of lower level of completed schooling

  has direct implication of their employability. Their low qualification does not depend

  alone on lack of motivation but is largely caused by the shortage of funds,

  discrimination as well as inadequate adaptation of the school system to meet their

  particular needs. Low enrollment rates and low educational attainment thus leave

  individuals unprepared for the labor market, especially in an economy where no low

  skill jobs are being created, and highlight the budgetary consequences in supporting

  the welfare of these groups for the future. Public policy should address these

  systemic factors that reduce access to education can therefore reduce discrimination

  (for example providing Roma children with adequate preparatory pre-school classes

  could dramatically increase their chances of coping with the school material and thus

  decrease the probability of dropping out or ending up in a special school). The same

  applies for other factors like improving the Roma families’ income opportunities,

  which would result in decreased involvement of children in the household economy

  and income generation and would improve their ability to prepare for school (UNDP

  2003).

  IV. Housing, Living Conditions and Health

  Material deprivation, pitiable housing and living conditions as well as poor health are

  correlates of poverty.

  Housing and living conditions have direct implications on consumers’ behavior and

  on construction industry prospects, among others. In growing economies, as

  households’ income increases, a direct impact is observed in home improvements and

  rising living standards. Given that Roma families have a low starting point, as a

  consequence of well-targeted public policies, much progress is to be expected in this

  area.

  The questions on housing from VGS were designed to match the information

  provided by latest Censuses on general dwelling characteristics in both Hungary and

  Slovakia (Tables 4.14 and 4.15) and provides with a good baseline comparison.

  Question on “features of household’s living conditions” shows that 34% of Roma

  households in North Hungary live in old houses in relatively good condition, and 36%

  live in dilapidated homes or slums. Only about 13% live new houses in good

  condition and 11% of them live in flats. Moreover, from those who lived in

  dilapidated houses or slums, most have lived in the same settlements 15 years ago,

  which clearly underlines how permanent those poor living conditions have been for

  these Roma households. Similarly, Roma in Eastern Slovakia live in poor conditions

  with 18% of Roma families living in shacks, i.e. dwellings, which were not initially

  built for housing and 7 % live in wooden houses, 59% are living in brick houses and

  16% are living in a flat in an apartment building.

  18

  The sometimes unclear property ownership, as a consequence, prevents the

  improvements of the housing conditions – individuals are unable to maintain and

  invest in buildings and local infrastructure. Those who do not own their land/home

  are limited in their ability to make needed improvements to their homes. On average,

  95% of Hungarian households are direct owners of their home or through a relative.

  In the North region as a whole, that fraction remains similar but it falls to 69% for

  Roma there. By opposition, the Hungarian census reports 3% of property owned by

  municipality or local government, while in the VGS close to 30% for Roma in the

  North report living in a “property of the self-government/state”. This adds to the

  insecurity and discomfort as well as to the precarious living conditions of many Roma

  families. The transition process has created problems with the legal status of Roma

  housing, in part because poverty rights were often not clearly defined during

  communism. Some Roma were evicted from state-owned apartments when housing

  subsidies were withdrawn, properties were privatized, or returned to prior owners.

  Many Roma are living illegally in dwellings, either because they had no choice but to

  squat or because the property rights on their buildings were transferred following the

  transition (OSCE 2000). In other cases, poor Roma have intentionally become

  squatters. These developments have seriously limited access to social services, as

  residency and ID papers are frequently required for social assistance benefits, health

  care, and education.

  Table 4.14

  Hungary

  North

  region Roma in North Hungary

  Total surface of dwelling

  Less than 29 2% 2% 17%

  30 – 49 15% 12% 30%

  50 – 79 42% 42% 29%

  80 + 41% 44% 23%

  Mean size 78 m

  2 80 m2 60 m2

  Average sq. meter per capita 31 m

  2 31 m2 14 m2

  Number of rooms

  1 11% 9% 40%

  2 41% 42% 39%

  3 32% 34% 19%

  4 + 16% 15% 2%

  Source: Statistical Yearbook of Hungary, 2003, table 6.1, Hungarian Population Census 2001, UNDP

  Vulnerable Groups Survey 2004.

  Mean size of homes, again, does not differ very much between the national and

  regional averages but for the vulnerable groups it decreases from 80 to 60 square

  meters in the North region. In addition, after controlling for bigger household sizes,

  the average square meter per capita is less than half for Roma in the North in

  comparison to national averages, only 14 vs. 31 square meters. This suggests that the

  often large Roma families live in twice as small dwellings than the average

  Hungarian. The survey data show that Roma living quarters are smaller than others,

  have larger households, and are consequently more crowded. Almost 80% of all

  19

  Roma household in the North regions live in one or two rooms vs. only about 50%

  nationwide, while 71% of them have more than four household members vs. only

  25% for Hungary as a whole.

  Assessing the level of comfort, in all indicators – access to piped water, flush toilet,

  piped gas and sewer systems - the Roma families are far behind the Northern regional

  and national averages. Lack of water, gas, electricity, and public services, such as

  waste collection, contribute to the deterioration of many Roma neighborhoods and to

  the emergence of ghettos. Another consequence of unclear property ownership is

  prevention to legal access to water and electricity. Houses are often constructed with

  makeshift materials and they do not comply with basic construction and safety

  standards. Lack of garbage collection also seriously affects living conditions and

  creates health problems for residents. Additionally, lack of garbage collection

  perpetuates negative stereotypes about poor hygiene among Roma. Many families

  lack access to running water, only 55% of Roma in the North region. As a

  consequence, poor health conditions are linked to the inadequacy of the water supply

  – only 45% have flush toilet or bathroom inside their home. Many Roma complained

  that their drinking water was contaminated and caused diseases among their children.

  In Hungary as a whole 91% of the population reports having access to sewer system

  and 89% have bathroom at home. For Roma in the poorer North region, only 33%

  have access to sewer system and 45% have bathroom at home.

  Table 4.15

  Slovakia Kosicky Presovsky

  Roma in

  Kosicky

  region

  Roma in

  Presovsky

  region

  Housing features (averages)

  Living space (m

  Rooms 3.2 3.4 3.3 3 2.5

  (m

  People/room 1 1.1 1 2.8 3.1

  2) 56.1 59.1 57.9 51.9 52.32/person) 17.5 15.9 17.3 13.2 11.7

  Share of households

  Central heating 76% 75% 77% 3% 4%

  Bathroom or shower 93% 92% 93% 34% 34%

  Automatic washing

  machine

  61% 61% 58% 10% 16%

  Personal computer 12% 10% 12% 2% 1%

  Car 39% 40% 39% 9% 11%

  Source: SOSR Population census 2001, UNDP Vulnerable Groups Survey 2004.

  Similar is the depiction of the living conditions of low-income Roma minorities in

  Eastern Slovakia. The average living space and the number of rooms per household

  are slightly smaller for Roma in Kosicky and Presovsky regions in comparison with

  the country as a whole. However, due to their bigger family size, the space per person

  decreases to as low as 11.7 square meters per person for a Roma in Presovsky region

  while the number of people per room there is three times the national average. The

  degree of poverty is seen very clearly among the ownership of basic amenities: only

  20

  4% of Roma households in Eastern Slovakia report having central heating vs. 76% in

  the entire country, 34% with bathroom or shower inside their home vs. 93% for

  Slovakia.

  According to home ownership, only about 10% of all Roma households in Slovakia

  there are living in municipal or state owned dwellings. Table 4.16 also shows access

  to three additional facilities, in line with the Millennium Development Goals. In

  terms of access to piped water and piped gas, and availability of flush toilets inside

  their homes, Roma in Eastern Slovakia seem equally deprived as Roma in Northern

  Hungary and intervention in that area is essential in order to prevent major health

  risks.

  Table 4.16

  Hungary

  North

  region

  Roma in

  North

  Hungary

  Roma in

  Kosicky

  region

  Roma in

  Presovsky

  region

  Ownership

  Property of a

  private person

  95% 96% 69% 90% 89%

  Municipal/state

  ownership

  4% 3% 30% 9% 10%

  Other 1% 1% 2% 1% 1%

  Facilities

  Piped water 89% 86% 55% 50% 49%

  Piped gas 69% 65% 17% 17% 14%

  Flush toilet 86% 80% 45% 33% 35%

  Source: UNDP Vulnerable Groups Survey 2004.

  Ownership of consumer durables (Table 4.17) among Roma household give details

  about their daily household chores – the lack of common household appliances

  constrains Roma women and girls to spend their time in traditional housework and

  childrearing. Ownership of car is quite unusual and only a few of Roma households

  possess a personal computer or telephone lines, both in North Hungary and in Eastern

  Slovakia, which limits further their likelihood in finding and keeping employment, as

  well as it restrains the learning tools available for their children.

  Table 4.17 Consumer durable goods stock (% of reporting households)

  Hungary Roma in North Hungary

  Car 44% 11%

  Refrigerator 75% 81%

  Washing machine 70% 80%

  Television 96% 93%

  Personal computer 18% 3%

  Mobile phone 71% 37%

  Source: Hungarian Statistical Yearbook 2003 (table 5.12), UNDP Vulnerable Groups Survey 2004.

  Poverty in Roma settlements is closely linked to regional economic conditions,

  proximity of the settlement to jobs or public services, and the degree of ethnic

  integration or segregation of the settlement. From the survey it seems that Roma live

  21

  in much higher concentration with other Roma. Some of the explanation is purely

  economic as Roma rely much more on other family members and relatives for all

  sorts of needs – from help in case of emergency to financial assistance, thus proximity

  is more efficient for such cooperation. Non-Roma populations, on the other hand,

  have greater access and rely more heavily on official providers for such services –

  banks, hospitals, etc. Other explanations may include religious differences and

  language barriers, but these are not obvious from the vulnerable group survey. Roma

  religious identification is very similar to the Non-Roma, so this may not be an

  important determinant for their concentration in settlements. As for language barriers,

  only very few households use primary language as Romany/Gipsy there are no

  significant language barriers apparent from our survey either.

  Another important component of welfare is health status. Roma have generally worse

  health than non Roma and is attributed to unhealthy lifestyle factors including poor

  diets and smoking and poor housing conditions. The prevalence of transmissible

  diseases associated with poor living conditions is found higher among them,

  especially in more isolated settlements and are associated with overcrowding at home,

  unsafe water supply, lack of waste disposal and proximity to environmentally

  contaminated areas – all characteristics of Roma living conditions. Other factors

  linked to deteriorating health status are insufficient clothing and high alcohol

  consumption and inability to purchase common medicine supplies, as we saw in the

  expenditure section.

  V. Labor Market Situation

  Weak labor market competitiveness and the effects of discrimination are widely

  perceived as major causes of the poverty and exclusion experienced by Roma.

  Indisputably, employment is the principal source of the income needed to escape

  poverty. Yet, Roma are facing inadequate employment opportunities for various

  objective and subjective reasons. Among Roma low-skilled jobs predominate and are

  associated with low incomes, poor job quality and weak social and employment

  protection. Those differences with national averages in unemployment and the type

  of employment influence the sources and level of Roma income. Roma have limited

  opportunities to reenter the workforce, so unemployment rates, and particularly longterm

  unemployment, for Roma are often exceptionally high.

  Roma have historically had connections to traditional occupations. But few of these

  connections still exist and development policies to revive them seem unsuccessful and

  undesirable. During communism, Roma were most frequently employed in

  manufacturing industries, because of their lower education. During the post-transition

  period, they were the first to be laid off and then they faced restricted employment

  opportunities.

  The most important sources of measuring unemployment are national labor force and

  household surveys. These surveys ask about economic activity in general and can

  reflect both informal and formal employment. On the other hand, in the vulnerable

  group survey questions pertaining to labor market differ somewhat between Hungary

  and Slovakia. In the case of Slovakia, the questions match the country’s labor force

  survey very closely, while not for the case of Hungary. In this report are presented in

  22

  the same tables both countries, the regions of interest and the Roma populations there

  in particular. The actual comparison between the two countries should be done with

  caution.

  A main conclusion and policy recommendation is to modify the currently welfare

  system to promote job creation for Roma rather than provide them with financial aid

  alone, as this weakens them and makes them more dependent on social assistance,

  decreases their self-respect and motivation.

  V.1 Labor market overview

  The Table 4.18 presents the global overview of the population by economic activity

  and the most common ratios. In Hungary 51% of the population is employed vs. 40%

  in Slovakia. The situation of Roma in the North region of Hungary sharply falls to

  only about 17% being employed and is comparable to Roma in the Eastern regions of

  Slovakia, 17-20%. On the other hand the fraction of unemployed individuals in the

  population is 3% in Hungary and 9% in Slovakia. Unemployed individuals among

  Roma in Eastern Slovakia remain the same range, 6-8%, while that fraction reaches

  45% of the Roma population in North Hungary. This is partly explained with the

  differences in definition of unemployment and the methodology used in the survey.

  Most of those unemployed Roma in North Hungary are actually economically

  inactive as they may have been without a job for a very long time. Indeed, the

  fraction of economically inactive population is about 75% for Roma in Eastern

  Slovakia much higher than the national average of 51%.

  Table 4.18 Labor market overview

  Hungary Slovakia

  North

  region Kosicky Presovsky

  Roma in

  North

  Hungary

  Roma

  in

  Kosicky

  Roma

  in

  Presov

  Distribution by economic activity

  Employed 51% 40% 45% 37% 39%

  17% 20% 17%

  Unempl. 3% 9% 5% 11% 10% 45% 6% 8%

  Econom.

  inactive 46% 51% 50% 52% 52% 38% 74% 75%

  Ratios

  Unempl.

  rate 6% 17% 10% 22% 20%

  73% 23% 30%

  Empl.

  rate 51% 40% 45% 37% 39% 17% 20% 17%

  Particip.

  rate 54% 49% 50% 48% 48% 62% 26% 25%

  Source: Statistical Yearbook of Hungary 2003, tables 4.2 and 4.19, pp 86 and 107, Statistical yearbook

  of SR 2004, regional data 2003, UNDP Vulnerable Groups Survey 2004.

  The most common ratios additionally clarify the labor market overview. Again there

  are large differences between Roma from North Hungary and East Slovakia which are

  due to methodology used. The unemployment rates among Roma in Kosicky and

  Presovsky regions range between 23 and 30%, which may be lower than what is

  usually estimated and is much lower than that of North Hungarian Roma. These

  23

  unemployment ratios remain quite high in comparison with the national averages –

  6% for Hungary and 17% for Slovakia. Some of the explanation lays in the fact that

  many people are not counted as unemployed but as economically inactive. Many

  Roma lack regular jobs and high unemployment rates may indicate involvement in the

  informal sector. In addition, many people are classified as working/employed

  because they have worked on a farm owned or rented by a member of their

  household, either working on land or other farm tasks, or have cared for livestock

  belonging to a household member (this is about half of all who are working). As can

  be seen in the income section later, those individuals have no labor earnings even

  though they are counted as working.

  In previous studies, Roma unemployment rates were often projected to be close to

  100%. The dataset provided in vulnerable groups’ survey allows for the

  unemployment rates of Roma in Eastern Slovakia to be pinned down quite precisely.

  It reveals to be much lower than usually expected Roma unemployment rate. That is

  not something to rejoice about but it clarifies more on the actual problem – Roma are

  not simply unemployed, they have exited the labor force. Instead the analysis and

  policy recommendation needs to focus on the low employment and participation rates.

  Employment rate among Roma in Kosicky and Presovsky regions are as low as 17%

  and their participation rate are less than half in comparison with the national average.

  Participation rate for Roma in North Hungary is 62% which is fueled by high

  unemployment; however, the employment rate is only 17%.

  One explanation for Roma low employment rate is their demographics – Roma are

  mostly young with more than 40% of them younger than 15 years old, who are

  supposed to be in school. The other crucial explanation is that unemployed Roma

  have exited the labor force and are discouraged to search for jobs, therefore,

  development policies need to focus on increasing their employability and creating

  incentives for individuals to re-enter the labor force. Current government policies, as

  for example in Slovakia, are already targeting the low employment rate and have

  implemented social reforms in 2004 by introducing lower personal income tax and

  more active labor market policies. Results of these are still to be measured in the near

  future. Another issue is whether such policies are enough to address the systematic

  socio-economic problems of Roma minorities.

  Undeniably, education remains the major factor for improving ones labor market

  outcomes. In Eastern Slovakia, Roma employment rates increase by education

  reaching 100% for the very few university graduates (Table 4.19). Education affects

  positively also participation rates. Not surprisingly, unemployment rates fall steadily

  with more schooling confirming the returns on education. The unemployment rate for

  Roma in Presovsky region with only basic education is 41% but it drops down to 13%

  for those with secondary education and 0% for tertiary. The above mentioned effects

  of education on labor market ratios are preserved for Roma in Northern Hungary.

  24

  Table 4.19 Economic activity ratios by education

  Roma in

  North Hungary

  Roma in

  Kosicky region

  Roma in

  Presovsky region

  Unemployment rate

  Primary 79% 28% 41%

  Secondary 46% 19% 13%

  Tertiary 0% 0% 0%

  Employment rate

  Primary 14% 30% 24%

  Secondary 45% 50% 54%

  Tertiary 50% 100% 100%

  Participation rate

  Primary 65% 42% 41%

  Secondary 83% 62% 62%

  Tertiary 50% 100% 100%

  Source: UNDP Vulnerable Groups Survey 2004.

  Of course, education here is too broadly defined and many subtle differences

  distinguish Roma from non-Roma for a given school level. The actual acquired

  qualifications and diploma as well as the particular fields of schooling are what

  matters on the labor market. Cumulated skills and years of uninterrupted working

  experience, as well sound health, are drivers for successful market outcomes. Other

  factors which might account for lower performance of Roma on the labor market

  include lack of information among them of available employment opportunities, lack

  of physical access to suitable positions, and discrimination against Roma workers.

  The existing barriers to employment and spectrum of available occupations explain

  some of the difference in wages for Roma with equivalent level of education.

  The second most important determinant of labor market outcomes is age as a proxy

  for cumulated experience (Table 4.20). In the framework of usual life cycle, labor

  outcomes, such as employment and participation rates, exhibit a hump shape function

  with age – increasing until the peak of mature age and then slowing decreasing

  towards retirement age. In case of Roma, both employment and participation seem to

  increase up to about 30-39 age group, which corresponds to the prime age for

  physically intensive occupations, and then gradually decrease. However, age alone

  does not absorb much of the problems due to a weak start of their working life with

  very poor initial human capital.

  Unemployment rates by age show a serious problem among most young, 15-29 age

  group as those individuals are not attending school, are therefore lacking advanced

  skills and have not cumulated yet much experience. There are also incidences from

  the datasets of children working at the age of 15 which limits their opportunities to

  cumulate additional skills and knowledge because of not attending school. They are

  particularly vulnerable to poverty for the reason that as they grow older, being

  unemployed and not attending school will leave them with too few options to compete

  25

  on the labor market. Unemployment rates for 15-29 years old Roma in Kosicky and

  Presovsky regions vary between 23 and 36% but reaches 80% for Roma in North

  Hungary. For Roma between 30 and 39 years old the situation seems to worsen.

  Roma are more likely to be laid off and to find themselves in between jobs. Adding

  to this fact, their lack of skills and scarce experience makes it even more difficult to

  be employed with age. This is a direct consequence of their lower school enrollment

  and attainment coupled with not being able to find jobs corresponding to their

  minimum skills.

  Table 4.20 Economic activity ratios by age

  Roma in

  North Hungary

  Roma in

  Kosicky region

  Roma in

  Presovsky region

  Unemployment rate

  15 – 29 years 80% 23% 36%

  30 – 39 years 67% 38% 37%

  40 – 49 years 68% 16% 25%

  50 + years 61% 14% 18%

  Employment rate

  15 – 29 years 12% 32% 21%

  30 – 39 years 26% 30% 34%

  40 – 49 years 20% 40% 36%

  50 + years 13% 34% 34%

  Participation rate

  15 – 29 years 63% 42% 32%

  30 – 39 years 78% 48% 54%

  40 – 49 years 63% 47% 48%

  50 + years 34% 39% 42%

  Source: UNDP Vulnerable Groups Survey 2004.

  V.2 Unemployment profile

  According to the International Labor Organization (ILO) definition, in order to be

  considered unemployed, a person must be: a) without work; b) willing and able to

  work; and c) actively seeking work. The survey data used for Hungary reflect a

  broader definition of unemployment, in which discouraged workers are treated as

  unemployed. The so defined unemployment rates are far higher among Roma than in

  the whole countries (more than twice as high as for Roma in Eastern Slovakia). This

  is partly due to the survey design but also reveals distinct phenomena: some people

  consider themselves as unemployed even though they have recently worked while

  others do not define themselves as unemployed but would be so classified according

  to the criteria used here. Since, for many, unemployment is associated with the

  absence of a regular job, those involved in informal or irregular employment may

  define themselves as unemployed even though they may be engaged in some sort of

  work – which is captured in the case of Roma in Slovakia. Regardless of the

  overestimated unemployment in North Hungary, East Slovakia numbers still remain

  relatively high.

  26

  Next the profile of the unemployed will be detailed in order to understand better their

  characteristics and to address their vulnerabilities. Both countries report the main

  distributional characteristics of the unemployed by duration, age and educational

  attainment. In Hungary, the unemployed are more or less equally distributed between

  short and long term unemployed, 24% for 1-3 months and 37% for more than a year.

  In Slovakia, as the unemployment rate is a little higher, also the distribution is a little

  skewed, with 61% of all unemployed for a year or more. In that respect, the

  distribution of unemployed Roma in the three regions is similar to that of Slovakia as

  a whole with 67-73% of them being without a regular employment for a year or more.

  20% of all unemployed Roma in North Hungary have never had a job in their life

  (Table 4.4).

  Table 4.21 Unemployment profile

  Hungary Slovakia

  Roma in

  North Hungary

  Roma in

  Kosicky

  Roma in

  Presovsky

  Duration

  1 – 3 months 24% 14% 10% 6% 9%

  4 – 6 months 16% 9% 6% 5% 5%

  7 – 12 months 23% 15% 14% 16% 19%

  1 year or +

  50%

  Never worked

  37% 61%

  20%

  73% 67%

  Age groups

  15 – 29 years n.a. 41%

  52% 43% 46%

  30 – 39 years n.a. 23% 25% 36% 28%

  40 – 49 years n.a. 23% 16% 12% 18%

  50 – 59 years n.a. 13% 7% 8% 8%

  Highest educational attainment

  Primary (incomplete) 3%

  36%

  Primary (completed) 30%

  23%

  52%

  75% 88%

  Secondary 61% 73% 12% 25% 12%

  Tertiary 6% 4% 1% 0% 0%

  Source: Statistical Yearbook of Hungary 2003, tables 4.11 and 4.12, p.94, Statistical yearbook of the

  SR, 2004, p237, UNDP Vulnerable Groups Survey 2004.

  Similarly by age, Roma in Eastern Slovakia are matching the age distribution of

  average unemployed Slovak with 43-46% between the age of 15 and 29. North Roma

  show particularly high concentration of unemployed adolescents, both boys and girls,

  representing more than half of all unemployed Roma there. These young individuals

  do not attend school and also are not working for wages. In summary, overall, the

  unemployed Roma in all three regions, are very young, more than 3/4 are less than 39

  years old. Main explanation is the demographic composition in that area – more than

  80% of the population there is less than 39 years old with poor education.

  27

  Weak educational background is often cited as a key contributing factor to the high

  levels of Roma unemployment. By distribution across education level, in Hungary as

  a whole about 33% of the unemployed have at most primary education and 61% of

  the unemployed have at least some secondary education. The main explanation of this

  relatively educated unemployment is the fact that the educational attainment of the

  Hungarian population as a whole is relatively high. While in Hungary as a whole, the

  unemployed remain relatively educated, the most vulnerable in the North region, as

  well as in Eastern Slovakia, 88% of them have at most primary education, which

  make them very unappealing for employers to hire and thus more likely to remain on

  the dole longer. Similarly, in Slovakia, only 23% of the unemployed have primary

  while this rate reaches 88% for unemployed Roma in Presovsky region.

  V.3 Employment profile

  As the issues with unemployment definitions have been established, it seems more

  appropriate to focus analysis and policies on the employment figures. Moreover, the

  employment numbers are comparable across the three regions of interest.

  Roma who are employed are most commonly working in industry and services (Table

  4.22). Interestingly enough, their predominant sector varies with the regions. For

  example in Kosicky region most employed Roma work in the services industry and

  secondly in agriculture and forestry, which is not traditionally typical for Roma

  populations. This is most likely due to the more available jobs in services and the

  scarce employment opportunities for them in manufacturing and mining there. On the

  other hand, over 53% of all working Roma are employed in manufacturing and

  construction in North Hungary. This is consistent with pre-transition employment

  patterns, when Roma were most frequently employed in low-skilled jobs in industry,

  including manufacturing and mining. Lastly, Presovsky region shows the least

  concentration of Roma in services.

  These different sector concentrations lead to a hopeful interpretation that Roma

  employment responds to the regional industry distributions and are not confined to be

  in a single sector where they are most vulnerable to structural changes. However,

  relative to the national Hungarian and Slovak averages they are still underrepresented

  in the services. Fewer Roma reported working in education, social services and

  public administration. The low levels of Roma employment in these areas may limit

  access to these services, including education and health care, as there are few Roma

  who can bridge cultural barriers and facilitate interactions between Roma and non-

  Roma service providers. Also for all three regions, the share of Roma working land is

  much higher than the national averages, which has clear implications for their wages.

  Given these considerations and their low skills, it is less likely that the sector

  distribution will change much unless more unskilled jobs are created in the services in

  the short term and improving access and accumulation of education in the medium

  term.

  In immediate consequence of choice of sector is the relative wage income. The

  higher than average concentration of Roma workers in manufacturing and

  construction, in agriculture and forestry, and in the same time their underrepresentation

  in the driving services sector results in lower monthly gross earnings.

  In Hungary as a whole, for example, average monthly gross earnings in

  28

  manufacturing, mining and electricity were 7% lower than national average; in

  construction 32% lower; services are reported by subgroups separately (retail,

  banking, education, etc.) almost all of them above national average.

  Besides the sectors of activity, the types of employment influence the sources and

  level of Roma income. Only 2% of all employed Roma in North Hungary work as

  private entrepreneur vs. 10% in the country as a whole, which reflects some major

  structural differences in access to information, funding and markets. Access to local

  capital may be crucial explanation for Roma self-employment and entrepreneurial

  activities. Microfinance has grown rapidly with the transition helping households

  absorb structural shocks and increase self-employment (Forster et al. 2003). At the

  other extreme, the share of Roma private entrepreneurs in Eastern Slovakia is 21%

  which is double the national average there. This could be a consequence of recent

  beneficial policies promoting small- and medium-sized enterprises there. A

  consistent monitoring and overtime analysis is needed in order to assess the medium

  term implications and whether those policies address permanently Roma problems.

  Moreover, in the case of self-employment considerable benefits may appear only after

  a certain period of time. Encouraging Roma to be self-employed and create jobs for

  themselves is one way to provide with channels for poverty alleviation.

  Also many Roma reported working in jobs without a permanent contract. Roma were

  more commonly employed in jobs with either no contract at all, or contracts of less

  than one year. This likely reflects the large share of Roma who are active in the

  informal sector. Even among Roma who reported that they were not employed,

  nearly twice as many Roma claimed to be doing some kind of side work. These

  patterns indicate a higher level of vulnerability of Roma in the labor market.

  Table 4.22 Employment by sectors and status

  Hungary Slovakia

  North

  region

  Roma in

  North

  Hungary

  Roma

  in

  Kosicky

  region

  Roma in

  Presovsky

  region

  Distribution by industries

  Agriculture and

  forestry

  5% 6% 4% 10% 39% 35%

  Manufacturing

  and

  construction

  31% 38% 36% 53% 18% 37%

  Services

  industries

  64% 56% 61% 37% 44% 29%

  Employment status

  Employee 82% 90% 86% 97% 79% 79%

  Private

  entrepreneur

  10% 10% 9%

  2% 21% 21%

  Other 7% 0% 5% 1% 0% 0%

  Source: Hungarian Micro-Census 2005, Statistical yearbook of the SR, 2003, UNDP Vulnerable

  Groups Survey 2004.

  29

  The general demographic trends of Roma populations affect their employment profile,

  as seen in the age groups breakdown – in Hungary and the North as a whole, a quarter

  is less than 29 years old and 30% of the employed in their “prime age”, i.e. 40 – 49

  years old (Table 4.23). For Roma in the North region, the age distribution is more

  skewed to towards the young. Employed there are slightly younger, on average, with

  35% below 29 and only 20% in the 40 – 49 years range. Among employed Roma in

  Eastern Slovakia, there is similar concentration of young workers, 45% of all

  employed Roma in Kosicky region are less than 29.

  Distribution by education of employed individuals: as with the unemployment profile,

  the distribution of employed across education level in Hungary and the North as a

  whole is very similar to the population profile, with 61-65% of employed individuals

  with at least secondary education. This share is only about 35% among the Roma in

  the North. By contrast, 1% nation wide of all employed has less than 8 years of

  schooling, while this share is up to 15% among Roma in the North, which makes

  them exposed to high risks of losing their job. Furthermore, there is an immediate

  implication of the education level on the wages those people earn. Under the

  assumption that earned wages correspond to educational level in terms of acquired

  human capital or skill level, it is expected that the Roma average labor income should

  be much lower than that of the national/regional average. Indeed, the average

  monthly gross earning in the North region is 13% less than the Hungarian average.

  Table 4.23 Employment profile

  Hungary Slovakia

  North

  region

  Roma in

  North

  Hungary

  Roma

  in

  Kosicky

  Roma

  in

  Presovsky

  Age groups

  15 – 29 years 27% 26% 25% 35% 45% 36%

  30 – 39 years 25% 27% 26% 33% 18% 22%

  40 – 49 years 30% 31% 32% 20% 20% 24%

  50 – 59 years 17% 16% 16% 12% 12% 15%

  60 + years 2% 1% 1% 1% 4% 3%

  Highest educational attainment

  Primary

  (incomplete)

  1% 1% 15%

  Primary

  (completed)

  20%

  5%

  18% 47%

  65% 61%

  Secondary 61% 80% 65% 35% 35% 39%

  Tertiary 18% 14% 16% 4% 0% 1%

  Source: Hungarian Population Census 2001, Statistical yearbook of the SR, 2003 p220, UNDP

  Vulnerable Groups Survey 2004.

  Overall, in the three regions of interest, due to Roma lower educational attainment,

  61-65% of the employed have at most primary education and consequently their

  earning potential will be lower in comparison with the national averages. However,

  educational attainment of the employed Roma shows some positive effects – the

  distribution is not just mimicking the Roma populations as a whole. More than a third

  of the employed have completed at least secondary school while among Roma as a

  whole it was about half of those rates. In other words, educated people are twice as

  30

  likely to be employed. In the long run a development priority must be improving

  Roma educational attainment in order to close their income gap.

  VI. Income and Earnings

  This section concentrates on the actual sources of income and earnings and points out

  the similarities and differences between Roma in three regions of interest. For

  comparison purposes all amounts are computed in 2004 Euros PPP. Unfortunately,

  national and regional numbers on income are not widely distributed by the statistical

  offices. HSCO and SOSR are reporting only aggregate income, social benefits, and

  transfers for the countries as a whole but not on individual level. In summary, the

  specific social welfare systems in place in Hungary and Slovakia are perfectly

  reflected in the social assistance profile of Roma in the three regions. Inter-country

  differences in eligibility reflect contrasting program design and eligibility criteria

  across the two countries. The 2004 social reform in Slovakia, for example, is an

  important factor for some results that were found.

  Social protection benefits are important sources of income for all households in the

  three regions. For Roma, state transfers comprised about half of total household

  income. Because of their different characteristics, Roma and non-Roma tend to rely

  on different benefits (UNDP, 2006). Roma are more frequently eligible for, and

  receive, child allowances, unemployment benefits and social assistance benefits.

  These benefits are typically linked to larger family sizes, and greater likelihood of

  being poor and unemployed. In contrast, pensions are less important for Roma,

  because they are less likely to have a contribution history and be eligible for benefits

  and due to the smaller share of elderly within the Roma population.

  The distributions of households by number of earners, individuals aged 15 years old

  or more who were employed in the previous month, in all three regions depict an

  alarming picture (Table 4.24). Even though the definitions of active earner differ

  between the two countries the trends there coincide – most vulnerable households do

  rely on the state. Among all Roma households from North Hungary 63% have no

  adult earners. In Kosicky region as many as 89% of all Roma households rely on

  other than labor sources. The high concentration of households with no active earners

  is in line with the morose unemployment situation of Roma minorities depicted

  previously. These households consist of unemployed or economically inactive

  individuals and consequently rely on income from other sources such as social

  assistance for unemployment, childcare and pensions. Between 4 and 9% of Roma

  households consist of two adults earning income.

  As expected, the average annual household income increases with the number of

  earners. An immediate implication for policy is to create incentives for more people

  to become active earners in order for their family income (and wealth) to increase. As

  for the magnitudes, Roma in Eastern Slovakia seem to receive half of the amount

  Roma in North Hungary both on a household and on an individual level. This

  disparity reflects major structural differences between the two countries and do not

  contradict our analysis so far. Another explanation for the much lower amounts in

  Slovakia is related to the methodology used – due to the strict definition of

  31

  employment used. Indeed, in Slovakia many of the employed Roma have not earned

  any strictly positive labor earnings such as wages, salaries or other payment either in

  cash or in other form for their work. Lastly, some of the differences in amounts may

  be due to the latest reform in Slovakia which resulted in cuts in benefits and

  redefinition of eligibility.

  Table 4.24 Distribution and average income by earners (Euros PPP)

  Roma in North

  Hungary

  Roma in

  Presovsky region

  Roma in Kosicky

  region

  Distribution of households

  No earners 63% 83% 89%

  One earner 28% 11% 7%

  Two or more

  earners

  9% 6% 4%

  Average annual income per household

  No earners 5,620 2,886 3,006

  One earner 7,806 6,700 7,165

  Two or more

  earners

  10,317 9,291 10,266

  Average annual income per capita

  No earners 1,332 591 644

  One earner 1,609 1,172 1,577

  Two or more

  earners

  2,360 1,682 2,313

  Source: UNDP Vulnerable Groups Survey 2004.

  Note: In Slovakia, all labor earnings are reported on individual basis while social income is reported

  by the person entitled to it for all other members

  income levels of each household member.

  . Total household income is computed by summing the

  The three main components of total household income include earned income from

  work, income from social welfare programs, and income from other sources (such as

  begging, fortune telling, etc). Unlike general beliefs, it is clear from the collected

  data that the latter source is of insignificant magnitudes. Given the low share of

  earners, one main source remains for Roma – social assistance. The average amount

  of social assistance benefits should decrease with number of earners – these family

  members have less need for supplementary resources. Indeed, the average annual

  social income per household and per capita is decreasing with the number of earners

  in the household (Table 4.25). However, even when there are two adults earning in

  the household, since their total income is below the socially accepted minimum,

  almost all households are eligible for some financial help from the state.

  32

  Table 4.25 Average annual social income (Euros PPP)

  Roma in North

  Hungary

  Roma in Kosicky

  region

  Roma in Presovsky

  region

  Average annual social income per household

  No earners 5,508 3,006 2,886

  One earners 4,299 3,229 3,010

  Two or more

  earners

  2,885 2,080 1,562

  Average annual social income per capita

  No earners 1,290 644 591

  One earners 817 644 502

  Two or more

  earners

  504 384 299

  Source: UNDP Vulnerable Groups Survey 2004.

  A more interesting question is to disentangle the importance of the various social

  benefits. From the survey data, the following principal sources of revenue are

  distinguished – labor earnings, childcare support and unemployment benefits (Table

  4.26). A marginal source of income, which includes money received from interest,

  capital, from gambling, heritage and presents, aids and private pensions, represents,

  on average, less than a 1% of total income, and it can be ignored. Roma in all three

  regions seem to earn very similar amounts from working and exhibit the similar

  increases with number of earners. This can be explained with the fact that Roma in

  those regions seem to have similar low skilled jobs and are earning minimum wages.

  The child benefits are also in comparable magnitudes except that in Hungary the

  benefits increase more steeply with the number of children. For Slovakia, the much

  more gradual increase is explained by the recent reform. Main component of the

  reform was to modify the simple incentive to have more children into one where

  parents invest more in their children’s education

  practically inexistent for Roma in Eastern Slovakia, due to the latest reform which

  imposed much stricter restrictions for contributions and benefits. As most Roma there

  are long-term unemployed and have not contributed (enough or at all) to the social

  funds, they become therefore ineligible to receive the newly specified unemployment

  benefits, as the data shows.

  3. Lastly, unemployment benefits are

  3

  school stipends to children of low-income families, conditional on attainment.

  2004 Reform in Slovakia consisted in a cut the existing child benefits and the implementation of

  33

  Table 4.26 Average annual income sources (Euros PPP)

  Roma in North

  Hungary

  Roma in

  Kosicky region

  Roma in

  Presovsky region

  Average annual labor income per household

  One earner 3,476 3,936 3,690

  Two or more earners 6,666 8,186 7,729

  Average annual family benefits per household

  One child 1,237 1,108 1,290

  Two children 2,587 1,755 1,971

  Three children 3,850 1,904 2,324

  Four or more children 5,779 3,283 3,160

  Average annual job related benefits per household

  One unemployed 696 0 29

  Two or more

  unemployed

  1,588 0 0

  Source: UNDP Vulnerable Groups Survey 2004.

  Table 4.27 shows shares of households receiving positive labor income, childcare

  assistance or unemployment benefits. Probably more Roma households are

  beneficiaries even if the amounts are on average lower. Indeed, only 39% of all

  households receive positive labor earning among Roma in the North, which explains

  partly why 55 and 77% of all households there receive respectively some

  unemployment benefits and childcare support. As in Hungary the share of

  households receiving positive labor income is very low, reaching an alarming low 9

  percent for the Roma households in Kosicky region. As for the family benefits, the

  Roma in the East of Slovakia are very similar to the Roma in Northern Hungary with

  77 percent. The main difference between two countries comes from the job related

  unemployment benefits, where in Slovakia barely 3 - 4 percent of all Roma families

  in the East report receiving those benefits. This particular low share of households is

  most likely due to the recent social reform.

  Table 4.27 Shares of households receiving benefits

  Roma in

  North Hungary

  Roma in

  Kosicky region

  Roma in

  Presovsky region

  Shares of households receiving labor income

  One earners 95% 89% 73%

  Two or more earners 96% 60% 91%

  Shares of households receiving family benefits

  One child 91% 97% 90%

  Two children 100% 100% 88%

  Three children 100% 89% 100%

  Four or more 97% 99% 100%

  Shares of households receiving job related benefits

  One unemployed 46% 0% 4%

  Two or more

  unemployed

  79% 0% 0%

  34

  Source: UNDP Vulnerable Groups Survey 2004.

  So far the analysis studied the total household income. The survey provided also

  information on total individual income. Table 4.28 examines the average annual

  individual incomes by gender, age groups and education. First observation is that

  Roma women in North Hungary have on average higher income – they are principal

  receiver of childcare support (which on average is more than the supplements for

  unemployment). In the case of Roma women in Eastern Slovakia, they receive

  slightly less than Roma men there, partly due to the above mentioned change in social

  contributions. Roma women are traditionally more involved in childrearing and other

  housework that are not reflected in monitored income. They usually earn less by

  working but they qualify for more social benefits and consequently the gender gap is

  minimized. Across age groups, individual income peaks between 30 and 39 for Roma

  in the North Hungary and in Presovsky regions. For Roma in Kosicky region,

  revenues seem to increase with age up to 50 - 59. After 60, with arrival of retirement

  pensions, individual income increases a bit in North Hungary but not in Eastern

  Slovakia. Most uniform is the increase of individual income by highest educational

  attainment across the three regions suggesting better accumulation of skills and

  experience.

  Table 4.28 Average individual income profile (Euros PPP)

  Roma in North

  Hungary

  Roma in

  Kosicky

  Roma in

  Presovsky

  Average annual individual income by gender

  Males 2,510 2,615 2,634

  Females 3,344 2,252 2,087

  Average annual individual income by age groups

  15 – 29 years 2,884 1,933 1,972

  30 – 39 years 3,598 2,471 2,872

  40 – 49 years 2,577 2,742 2,682

  50 – 59 years 2,324 3,009 2,765

  60 + years 3,079 2,901 2,779

  Average annual individual income by highest educational attainment

  Primary (incomplete) 2,818 2,319 2,402

  Primary (completed) 2,873 2,184 2,379

  Vocational 3,499 3,580

  Secondary

  3,407

  3,965 3,475

  Tertiary 4,669 9,113 2,782

  Source: UNDP Vulnerable Groups Survey 2004.

  In conclusion, Roma household rely heavily on social welfare. A major policy

  recommendation implication is to create incentives for such households to go without

  social welfare as their burden will become unsustainable for the future governments

  and taxpayers. The reform in Slovakia is targeting to break the cycle of state

  dependence by cutting benefits and defining more strict eligibility criteria on one side

  and on the other side by allowing for creation of more employment opportunities. In

  35

  both countries, extensive active labor market policies and programs are set in place to

  accompany such transitions form vulnerability to poverty toward prosperity. Without

  robust monitoring and accountability it is hard to measure short term impact and to

  predict long term impact on Roma vulnerability.

  VII. Mobility

  Another important aspect of the socio-economic background is the mobility patterns

  of people in border regions and their motives. During transition years, it is likely to

  see increased mobility of households, seeking better employment opportunities. Most

  likely seasonal workers or even permanent residents have moved from more

  economically depressed areas across one country as well as across the border.

  VII.1 Internal migrations

  In the literature, internal migration has found to be one of the reasons for the current

  high segregation of Roma in the cities. Previous research in Hungary traced down the

  migration of Roma from the countryside during the economic crisis at the end of the

  1980s. Faced with growing unemployment, many Roma moved to Budapest in search

  of better opportunities. Over time, due to declining living conditions and poor access

  to municipal services, conditions in these neighborhoods severely deteriorated.

  Common side effects associated with slums appeared, including drug addiction and

  rising crime (Ladanyi, 1993). The continuing deterioration of living conditions and

  employment opportunities has probably led to continued rural-urban migration.

  On the opposite, during 2000-2003, internal migration in Hungary as a whole has

  been characterized by a net negative migration to Budapest and other towns and a

  positive net migration to villages. According to HCSO, less than 5% of the population

  moved in 2003

  well as in other towns declined after 2001. In contrast, between 2001 and 2004, the

  population in villages has been increasing steadily due to adverse demographic trends

  and internal migration. The HCSO reports also the directions of migrations in 2003,

  and there too it seems to be some sort of revival of appeal towards villages. This

  trend is not observed among Roma populations, which predominantly live in

  segregated areas in the cities.

  The HCSO distinguishes between permanent and temporary internal migration.

  Permanent migrants have given up their place of residence – similar to the migration

  questions in the UNDP survey, while temporary migrants keep their permanent

  dwellings. The permanent alone or permanent and temporary migrations together,

  show the trends described above. If we look at temporary migration alone, only in

  2003, we see positive migration towards Budapest and other towns and negative

  towards villages.

  According to VGS, 90% of all Roma households in North Hungary have never moved

  and are living in the same settlement as 15 years ago. Thus, the overall mobility of

  vulnerable households for the last 15 years is about 10%, twice the national average.

  That makes the vulnerable Roma twice more likely to move, most of them moving

  4. After a net increase during the 1990’s, the population in Budapest as

  4

  Statistical yearbook of Hungary, 2003, tables 3.14 – 16, p. 49

  36

  from countryside to town rather than the other way around, contrary to national

  trends. About half of those who have moved in the last 15 years have moved from the

  country probably in search for more jobs in the towns. A third has moved to the

  countryside probably for the purpose of working land.

  However, the question on motivation reveals a different story. The major cause for

  changing location for Roma is family reasons, 55%, while 15% responded for

  economic reasons and only 2% for security reasons or felt forced to move. These

  answers may seem surprising given their dire economic situation but it is very

  representative for Roma cultural differences. Family values are considered of utmost

  importance for Roma minorities and should be incorporated when designing

  development programs.

  In the survey, mobility questions differ between the two countries with additional

  information provided in Slovakia’s vulnerable group survey. About 15% of all

  respondents in Eastern Slovakia have lived in other municipality than the current one.

  This fact exhibits a relatively high mobility, i.e. one out of every six individuals has

  lived elsewhere for sometime. Out of all those who have lived elsewhere, 12% have

  lived in another municipality 3 or more months (i.e. 2% of all respondents), which is

  surprisingly low. Even though it seems that the vulnerable groups move substantially

  more, they do not stay away from home for long periods of time. That could suggest

  two phenomena: 1) that the high mobility is only for short periods of time and those

  moving individuals return home without settling down in other municipalities; 2) or a

  more or less “nomadic” pattern where individuals move from one location to another

  after less than 3 months. However, given the limitations of the survey, we could not

  measure the intensity of these mobility patterns, i.e. how many moves were made in a

  lifetime or how often they were initiated.

  Slovak Roma give the following most frequent reasons for migration, with equal

  weight: (i) marriage, (ii) family reasons and (iii) moved with family, answers very

  similar to those of Roma from the Hungarian survey. Roma decisions to change

  location are mainly motivated by founding a new family or following a family

  member. Moreover, due to relatively large size of households, this explains the high

  number of family members following the household head.

  The fact that the more than two-thirds move for reasons related to family is not so

  unanticipated, considering that our sample comes from the poorest subpopulation in

  Slovakia and Hungary. In richer industrialized countries, the individuals who move

  for better careers or education are likely to be from the upper quintiles. Therefore, a

  more important aspect to study is the indirect motivation of poorer individuals to

  move – what are the implications of those who moved on their socio-economic status.

  High rates of subjective unemployment and the importance of income-generating

  activities conducted within extended families suggest that behind “family reasons” for

  migration there could be indirect economic reasons. Indeed, we could assert that

  those who have moved are performing better on the labor market with higher

  employment and participation rates and lower unemployment rate in comparison with

  those individuals who have always lived in the same municipality. The higher

  mobility is beneficial for making contacts, acquiring skills and experience among

  others. Therefore, even though the main reasons were stated as family formation,

  those mobility choices have led to improved economic situation. This is suggesting

  37

  that the matchmaking is also driver for better outcomes in the labor market. If that is

  the case, even though there are cultural Roma specifics, measures to improve their

  employability rather than control for early marriage may help better alleviate them

  from poverty.

  VII.2 External/international migrations

  Unlike common expectations, there is no evidence of significant migration patterns of

  Roma households and individuals between North Hungary and Eastern Slovakia.

  Fractions of people who identify with the bordering nationalities’ cultural values and

  traditions and Roma, on national and regional level, are reported in table 4.29. North

  Hungary has about five times the national average of Roma and twice that of Slovaks,

  including those who are Hungarian citizens. According the Hungarian statistical

  yearbook, the largest influx of foreigners, recorded between 1997 and 2002, has been

  from Romania. On the other hand, the VGS shows no Hungarian Roma have

  emigrated from another country. Unlike common expectation, there is no significant

  migration of Roma households from Eastern Slovakia into North Hungary. This

  suggests that perhaps the international mobility is generated mostly by middle- and

  upper-income families who can afford it, but not among the most vulnerable to

  poverty groups.

  Table 4.29 Fractions of population

  Hungary

  North

  region

  Slovakia

  Kosicky

  region

  Presovsky

  region

  Slovakian 0.27% 0.5%

  - - -

  Romanian 0.09% 0.05%

  Roma 1.33% 5.36% 1.67% 3.89% 4.01%

  Ukrainian 0.05% 0.06% 0.2% 0.27% 0.86%

  Hungarian

  Czech

  - - -- - 9.68% 11.15% 0.1%- - 0.83% 0.65% 0.48%

  Source: Hungarian Population Census 2001, Slovak Population Census 2001.

  Similarly, in Kosicky and Presovsky regions in Slovakia, the share of Roma is three

  times higher than the national average. The fraction of Hungarian population in

  Kosicky region is higher than national average, while the Ukrainian minorities are 4

  times more in Presovsky region. Some of the ethnic Hungarians living in Slovakia

  may have moved to Hungary and it would be helpful to study their characteristics.

  Unfortunately, the Hungarian dataset from VGS does not show any international

  migration among the vulnerable groups. There seems to be no noteworthy cross

  border Roma migration between Slovakia to Hungary. Again, this may suggest that

  moving, traveling and commuting are expensive and if any, it is generated by middleand

  upper-income individuals and families.

  As for international migration among vulnerable Roma households on the Slovak

  side, 90% of all the international migrants among the VGS arrive from the Czech

  Republic, with Germany and Ukraine following with about 3% each. Once again,

  there is no particular movement between Hungary and Slovakia from the VGS. The

  38

  top four reasons for those who moved from abroad remain the same as the reasons for

  internal migration: family reasons (33%), return to hometown/ village (19%),

  marriage and moved with family (14% each). The employment situation of those who

  have moved from abroad is again better than that of those who have always lived in

  the same municipality.

  VIII. Conclusion

  This Chapter generates a detailed cross-section description of the current situation of

  Roma minorities. However it remains only a static, one time evaluation and cannot

  portray the latest dynamics among those vulnerable groups. In most aspects of life,

  Roma minorities in Eastern Slovakia and in Northern Hungary bear similarities and

  are particularly vulnerable to poverty in comparison with national and regional

  averages. There seem to be enough room to explore those cross-border characteristics

  to elaborate adequate social development policies.

  Prior to implementing new policies, it is vital to increase the monitoring of existing

  social policies in place in order to assess their effectiveness. In both countries, in

  Slovakia as well as in Hungary, various programs targeting low-income families and

  individuals are designed to alleviate them from poverty. However, their

  accountability is almost impossible to assess. (For example, in 1992 “Zero Grade

  Classes” were implemented in Slovakia, a free one-year pre-school program which

  was designed to prepare children for basic school and mainly targeted districts of high

  Roma populations. Fifteen years later, no evaluation study has been performed on the

  program while the problem is aggravated.) Before any new social programs are

  designed and more taxpayers money is spent, a robust evaluation of the current

  system is required by increasing data collection on many levels – in schools on

  performance of children of low-income families, in labor bureaus on addressing

  adequately Roma needs by current active labor market policies, in social welfare

  points on housing, living and health conditions. National statistical offices have

  already a range of monthly and annual surveys that could be extended to Roma. This

  data should be collected and progress over time should be estimated using wide range

  of tools available in the literature.

  39

  References

  Hungarian Statistical Yearbook, 2003

  Statistical Yearbook of the Slovak Republic, 2004

  World Bank (2002), Slovak Republic: Living Standards, Employment and Labor

  Market Study

  World Bank (2002), Poverty and Ethnicity: A Cross-Country Study of Roma Poverty

  in Central Europe

  World Bank (2002), Poverty and Welfare of Roma in the Slovak Republic

  World Bank (2005), Roma in an Expanding Europe, Breaking the Poverty Cycle

  World Bank (2005), The Quest for Equitable Growth in Slovak Republic

  World Bank (2005), Growth, Poverty and Inequality, Eastern Europe and the Former

  Soviet Union

  UNDP (2003), Avoiding the Dependency Trap

  UNDP (2005), Human Development Report

  UNDP (2006), At risk: Roma and the Displaced in Southeast Europe

  40

  ANNEX

  Box 1.1: Definitions of EUROSTATS poverty indicators

  At-risk-of-poverty rate.

  below 60% of the national median income. Equivalized median income is defined as

  the households’ total disposable income divided by the size and composition of the

  household, and is attributed to each household member.

  Share of persons with an equivalized disposable income

  Relative at-risk-of-poverty gap.

  of persons below the at-risk-of-poverty threshold and the threshold itself, expressed as

  a percentage of the at-risk-of-poverty threshold.

  Difference between the median equivalized income

  Income quintile ratio.

  population with the highest income (top quintile) to that received by the 20% of the

  country’s population with the lowest income (lowest quintile). Income must be

  understood as equilvalized disposable income.

  Ratio of total income received by the 20% of the country’s

  Dispersion of regional unemployment rates.

  unemployment rates.

  Coefficient of variation of regional

  Early school leavers.

  education and have not received education or training in the four weeks preceding the

  survey.

  Share of persons aged 18 to 24 who have only lower secondary

  Population living in jobless households.

  living in jobless households expressed as a share of all children. Proportion of all

  people aged 18–59 years who live in jobless households as a proportion of all people

  in the same age group. Students aged 18–24 years who live in households composed

  solely of students are not counted in neither numerator not denominator.

  Proportion of children (aged 0–17 years)

  Box 1.2: Definitions of World Bank poverty indicators

  Poverty rate.

  Measure the fraction of individuals below the poverty line.

  Poverty debt.

  line. This measure takes into account how far the poor, on average, are below the

  poverty line.

  The average poverty debt is a measured as a percentage of the poverty

  Poverty severity.

  deeper into poverty than others.

  This measure takes into account whether some of the poor are

  Gini index.

  inequality.
  link:http://europeandcis.undp.org/uploads/public1/files/Working%20paper%20Eastern%20Slovakia%20Northern%20Hungary%20Roma%20Poverty%20and%20Social%20Exclusion.pdf
  __________________________________________

  A measure in the interval [0; 1] where 0 is perfect equality and 1 is worst