Report No. 20645-HU Hungary Long-Term Poverty, Social Protection, and the Labor Market (In Two Volumes) Volume 1: Main Report April 2001 Poverty Reduction and Economic Management (ECSPE) Europe and Central Asia Region Document of the World Bank Currency equivalents (as of 16 April 2001) Currency unit = Hungarian forint I forint = $0.003 $1 = 262 forint Weights and measures Metric system Fiscal year January 1 - December 31 Abbreviations and acronyms CSO Central Statistical Office (Hungary) HHP Hungarian Household Panel (survey) ILO International Labour Organization NGO nongovernmental organization TARKI Social Research Centre Hungary) Vice President Johannes Linn (ECAVP) Country Director Roger Grawe (ECC07) Sector Director Pradeep Mitra (ECSPE) Team Leader Jeanine Braithwaite (ECSPE) This report was managed by Jeanine Braithwaite (ECSPE), with contributions from Robert Ackland (Australian National University), Tunde Buzetzky (ECCHU), Paulette Castel (ECS:PE), Zoltan Fabian (TARKI), Anita Papp (ECSPE), Jan Rutkowski (ECSHD), Zsolt Speder (Budapest University of Economic Sciences), Peter Sziv6s (TARKI), and Istvan T6th (TARKI). Christina Bogyo (ECSPE) contributed a review of the Hungarian-language literature. Paul Holtz edited, the manuscript, Usha Rani Khanna (ECSPE) edited the copy, and Doreen J. Duff (ECSPE) was responsible for document processing. Comments were provided by Christine Jones and Philip O'Keefe (peer reviewers) and by Pedro Alba, Tunde Buzetzky, Hafez Ghanem, Roger Grawe, Laurens Hoppenbrouwer, Erzo Luttmer, Kate McCollum, Anita Papp, Carlos Silva-Jaugeri, and Debbie Wetzel. From the Hungarian side, comments were provided by Zsuzsa Ferge, Julia Szalai, Balizs Kremer, Ildik6 Ekes,. Istvan Gyorgy T6th,. Zsolt Speder, Zoltan Fabian, Peter Sziv6s, Zsuzsa Laczk6, Zsolt Zadori, Janos Zolnay, Eva Konig, Gyorgy Mezei, and Janos Ladanyi. In Memorium Anita Papp Economist, World Bank Budapest Office 1969-2000 A 'SS A terrific colleague, a gifted and bright mind, a courageous and loving heart. Sadly missed by her friends, coworkers, and family. CONTENTS Foreword ....................................i Executive Summary .................................... ii Background .................................... ii Hungary's Long-Term Poor ................................... .111 Social Protection System and the Long-Term Poor ............. .. .................... iv 1. Trends in Poverty .1 2. Profile of the Long-term Income Poor .4 Location .7 Labor Market Links .12 Disabled Members .12 Children .12 Single Parents .13 Single Elderly Women .13 Primary-only Education .14 Roma Ethnicity .14 3. The Social Protection System .23 Pension System .23 Health Care .23 Disability Benefits .24 Unemployment Benefits .24 Active Labor Market Programs .26 Family and Child Benefits .27 Proposed Changes in Family and Child Benefits .28 Social Assistance Programs .33 Spending Trends .35 Poverty Impact of Social Protection Benefits .37 Targeting of Social Assistance .38 4. The Labor Market and Poverty .42 Trends in Unemployment .43 Features of Long-term Unemployment .46 Wage Developments .49 5. Policy Considerations AND further research .55 Reintegrating the Long-term Poor .55 Roma .59 Making Decentralization More Equitable .61 Further Research .65 List of Tables Table 1.1 Macroeconomic trends and household income, 1992-97 . .............................1 Table 1.2 Poverty trends, 1992-97 ..............................................................................................2 Table 1.3 Households receiving certain types of income, 1992-98 . . 3 Table 2.1 Incidence of and hazard functions for income poverty, 1992-97 . . 7 Table 2.2 Correlates of income poverty (percent) ..8 Table 2.2 Correlates of income poverty (percent) (continued) .....................................................9 Table 2.3 Composition of income poverty (percent) ................................................................. 10 Table 2.3 Composition of income poverty (percent) (continued) ........................................ 11...... 11 Table 2.4 Correlates of individual income poverty (percent) .............................................. ...... 14 Table 2.4 Correlates of individual income poverty (percent) (Continued) ........................... ...... 15 Table 2.5 Composition of individual income poverty ......................................................... ...... 15 Table 2.5 Composition of individual income poverty (Continued) ...................................... ...... 16 Table 2.6 Probit regression on the probability of being long-term poor .............................. ...... 20 Table 2.7 Ordinary least squares regression on current welfare .......................................... ...... 21 Table 3.1 European Union: Duration of Unemployment Benefits ....................................... ...... 25 Table 3.2 Distribution of unemployment benefits, 1992-98 ............................................... ...... 26 Table 3.3 Spending on active and passive labor market measures, 1992-97 ....................... ...... 26 Table 3.4 Spending on family and child benefits, 1994-2000 ............................................. ...... 28 Table 3.5 European Union: Age Limit of Child Allowances ..................................................... 29 Table 3.6 Tax deduction for children under the personal income tax, 1999-2000 ...................... 31 Table 3.7 The family allowance, 1999-2000 . .................................................................. 31 Table 3.8 Municipal social benefits, 1997 ................................................................. 34 Table 3.9 Characteristics of cash social benefits, 1992-96 .................................................. ..... 36 Table 3.10 Poverty rates with different equivalence scales, welfare benefits, and poverty thresholds, 1995/96 ................................................................. 38 Table 3.11 Social transfers 1997 ................................................................. 39 Table 3.12 Average number of ex ante poor helped per I million forint of social transfers.. ..... 40 Table 3.13 Poverty alleviation impact of social transfers ........................................................... 40 Table 3.14 A targeting simulation: cutting 36,000 forint a year from social transfer of recip ent households ................................................................... 41 Table 4.1 Labor market activity and forms of inactivity, 1992-98 ....................................... ..... 42 Table 4.2 Labor taxes, 1995-99 ................................................................. 43 Table 4.3 Unemployment rate dynamics, 1992-97 ................................................................. 43 Table 4.4 Unemployment rates by region, 1992-98 ................................................................. 45 Table 4.5 Incidence of relative poverty by individual employment status, 1992-97 ................. 47 Table 4.6 Incidence of relative poverty by household employment status, 1992-97 ............. .... 48 Table 4.7 Labor market transition rates, 1992-97 ................................................................. 49 Table 4.8 Monthly gross and net average earnings, inflation, and real earnings, 1989-98 ..... .... 50 Table 4.9 Minimum wage and real increase of minimum wage, 1990-98 ............................. .... 51 Table 4.10 Minimum wage and subsistence minimum, 1989 to 1999 ................................... .... 51 Table 4.11 Public sector wages, 1992-98 ................................................................. 52 List of Figures Figure 2.1 Multivariate correspondence analysis on ethnicity and poverty status .................. .... 22 Figure 3.1 Benefits as a percentage of net average wages, 1989-96 ..................................... .... 37 List of Boxes Box 2.1 Hungary's homeless ..................................................................4 Box 2.2 Poverty reduction NGOs ..................................................................S5 Box 2.3 Hungary: Voices Of The Poor ................................................................. 5 Box 2.4 Poverty in a Roma village ................................................................. 16 Box 2.5 The Roma in Hungary ............................................................ 18 Box 3.1 Social protection benefits in Ozd and Ferencvaros ....................................................... 35 Box 4.1 Declining female participation rates ............................................................ 44 Box 5.1 France's Revenu Minimum d'Insertion-a reinsertion system for the long-term unemployed ............................................................ 57 Box 5.2 Views on Reinsertion Programs for the Long-Term Poor ............................................. 58 Box 5.3 Views on Policy Towards the Roma ........................... ................................. 60 Box 5.4 Views on Decentralization ............................................................. 62 Box 5.5 Discretion In Local Social Policy ............................................................ 63 Box 5.6 New Public Work Scheme ............................................................ 64 FOREWORD 1. Poverty in Hungary has been studied extensively by researchers both within and outside the country. Drawing on this coverage and on the lively debate on poverty in the country (Szalai 1998), this report studies Hungary's long-term poor-defined as people who were poor in four or more years of a six-year (1992-97) panel survey. The objectives are to develop a profile of the long-terrn poor (as defined by income), to compare and contrast that profile with other data, to consider the efficacy of social transfers, and to analyze poverty and the labor market. 2. Recent major works were produced for this study by Szivos and T6th (1998), (presented in Annex 4) and Speder (1998), (presented in Annex 5), while the United Nations Children's Fund commissioned a major investigation into the status of children and the elderly (Galasi 1998). The problem of child poverty in Hungary was first highlighted by Andorka (1989, 1990; see also Andorka and Speder 1996). Previous work by the World Bank includes a Poverty Assessment (World Bank 1996), background papers for the Poverty Assessment (Grootaert 1997), a participatory study (Moser and McIlwaine 1997), and a major study on targeting (van de Walle, Ravallion, and Gautam 1994). (Sziv6s 1994 and Andorka, Speder, and T6th 1995 also provided background papers for the Poverty Assessment.) In addition, Hungary's Social Research Centre, TARKI, has produced a series of working papers on poverty and social policy based on its surveys (Forster, Sziv6s, and T6th 1998; Sipos and T6th 1998). 3. The main source of data for this study is a longitudinal panel survey, the Hungarian Household Panel, conducted by TARKI and the Budapest University of Economic Sciences (see Annex 1). A survey of household incomes and expenditures conducted by the Central Statistical Office was also used in the analysis. 4. The team which produced the report is grateful for the collaboration of the Hungarian Government, and in particular, the Ministry of Social and Family Affairs and the Central Statistical Office. The team also recognizes TARKI for its participation in the report. We are also grateful to the group of Hungarian experts who provided their comments during a seminar held in Budapest on May 15, 2000. Finally, our thanks to the local social welfare offices of Ozd, Pisko, and Ferenzvaros (Budapest) and to their mayors. i EXECUTIVE SUMMARY BACKGROUND Poverty in Hungary has been studied extensively by researchers both within and outside the country. Previous work by the World Bank includes a Poverty Assessment, a participatory study where the poor were studied by sociological and anthropological techniques, and a major study on targeting of benefits to the poor.. The problem of child poverty in Hungary was first highlighted by a Hungarian scholar, and the United Nations Children's Fund commissioned a major investigation into the status of children and the elderly. Hungary's Social Research Centre, TARKI, has produced a series of working papers on poverty and social policy based on its surveys. These studies reveal similar trends in poverty and inequality. Absolute poverty (measured by equivalent income below the minimum pension) was very low in 1992-97, topping out at less than 5 percent of the population in 1996. Poverty depth and severity were even lower. Hungary is a country with low income inequality. Inequality was more or less the same (with minor fluctuations) from 1992 to 1997. Poverty and social exclusion tend to be high among the poorly educated, those living in rural areas, and those with weak links to the labor market. In addition, poverty rates are higher among households with three or more children, a single parent, or a single elderly female. Ethnicity is also important: Roma households are far more vulnerable to poverty than any other group. This report focuses on the long-term poor. This concern was highlighted in the previous World Bank Poverty Assessment as well as in other studies. Poverty in Hungary does not resemble other transition economies where poverty is shallow and economic growth is expected to lift many of the poor out of poverty. Poverty in Hungary is much more like entrenched poverty in OECD countries, where concerns have been raised about the emergence of a permanent underclass of poor. For these reasons, the objectives of this report are to develop a profile of the long-term poor, to describe the social safety net, and investigate the linkages between long-term poverty, the social safety net, and the labor market. This study defines Hungary's long-term poor as people who were poor in four or more years of a six-year (1992-1997) panel survey, the Hungarian Household Panel, conducted by TARKI and the Budapest University of Economic Sciences. A survey of household incomes and expenditures conducted by the Central Statistical Office was also used in the analysis. The primary poverty line used to identify the long-term poor is 50% of mean equivalent income. Equivalence is used to capture economies of scale in consumption, whereby additional members of the household require less additional income to maintain the household's standard of living. ii Income is used instead of consumption because the latter appears to be systematically under- reported; this finding is consistent with the household budget survey of the Central Statistical Office, but is unusual as compared to other similar economies. HUNGARY'S LONG-TERM POOR In Hungary, 7.5 percent of the population are classified as long-term poor by this report, meaning that they were poor 4 or more years during the six-year period studied. As in many developed countries, long-term poverty in Hungary has an important -thnic dimension. One-third of the long-term poor in Hungary are found to be of the Roma ethnicity, yet the Roma account for only 4-5 percent of the general population. The long-term poverly rate for Roma households is 53 percent, against the average rate of 7.5 percent for the population. Multivariate analysis, including regressions and factor analysis, demonstrate that even when controlling for factors such as family size, low level of education and unemployment, the Roma are still disproportionately represented among the long-term poor. This points to the existence of social exclusion of the Roma which precludes many of them from finding well-paying work that would lift them out of poverty. These findings about the Roma's long-term poverty are corroborated by sociological and anecdotal evidence. Besides Roma ethnicity, long-term poverty is concentrated among the most disadvantaged groups of the population, and these groups are essentially the same as those found in long-terrn poverty in the OECD countries. Specific groups at risk are single-parent households, single elderly females, households with three or more children, households headed by someone with primary-only education, households with disabled members, and rural households living in micro-communities. The existence of the long-term poor presents significant challenges for social policy, since they have emerged in Hungary despite an extensive social safety net. Further, the long-term pocr are not likely to be lifted out of poverty by the resumption of growth alone. Approximately thiree- quarters of the long-term poor are out of the workforce (either transfer recipients or have withdrawn from the labor market), and therefore unlikely to obtain the new jobs that will be created by a resumption in growth. Additionally, many of the long-term poor are living in micro-communities in rural areas, which are often the last to benefit from growth. The low educational attainment of the long-term poor also make them less likely to be able to take advantage of new opportunities brought by growth. Finally, one-third of the long-term poor face social exclusion and discrimination because of their ethnicity which will prevent them from benefiting from growth. 111 SOCIAL PROTECTION SYSTEM AND THE LONG-TERM POOR The social safety net in Hungary consists of seven basic elements: old-age pensions, public health care, disability benefits, unemployment benefits, active labor market programs, family support, and child protection. Most of these programs were quite complex, with varying eligibility requirements, payment levels, and duration of benefits. The central government is usually responsible for universal benefits, local governments for targeted (means-tested) benefits. In addition, local governments operate locally defined programs. The system is further complicated by the complex financing arrangements between the central and local governments. The social protection system touches almost every Hungarian household-in 1997, 86 percent of Hungarian households received at least one social transfer. The system functions reasonably well, and the four main social transfers-family benefits, unemployment benefits, pensions, social assistance-are effective at reducing overall poverty. But the system does not lift the long-term poor out of poverty, because many of the long-term poor have slipped outside of the boundaries of the social safety net. During the period studied (1992-1997), a key aspect of the social protection system was protection against unemployment. In this period, unemployment insurance and allowance was paid for three years, followed by "regular" social assistance as long as the recipient "cooperated" with the local social assistance authorities. In practice, as many as 100,000 people dropped out of this safety net and were no longer in receipt of regular social assistance. This finding suggests that the social safety net did not protect all the needy. Further, evidence suggests that the majority of these dropouts were Roma. Several destitute Roma households visited in the course of preparing the report were surviving through a combination of infrequent informal earnings by the household head, and the child allowances, not means-tested social assistance. There is a risk that decentralization will worsen the situation of the long-term poor by resulting in poor areas that have less resources for poverty alleviation than richer areas. Micro- communities in particular face the dual challenge of high concentrations of poverty and limited resources. The Labor Market and Long-Term Poverty The interaction between poverty and the labor market is a definite challenge for public policy. There is an obvious connection between poverty and an income loss resulting from unemployment. Long-term poverty is sharply higher in households with one or more unemployed members. The report finds that 14 percent of households with one unemployed member, and 22 percent for households with two unemployed members, are long term poor, as compared with 7.5 percent of households in the general population. The share of those unemployed for more than one year almost tripled between 1992 and 1996. According to official labor force survey data, roughly one in two unemployed persons goes without a job for more than one year. In the Hungarian Household Panel survey the mean iv duration of unemployment increased from less than 10 months in 1992 to 16-18 months in 1997. Moreover, long-term unemployment has been increasingly concentrated among a small group of workers. Ten percent of the unemployed account for more than one-third of unemployrnent duration, up from one-quarter in 1992. This group of hardcore unemployed has limited ,zhances of finding new work-and likely faces a higher risk of chronic poverty. Conclusions This report has documented the emergence of a group of long-term poor in Hungary. While growth will continue to be necessary to create well-paying jobs that would enable people to escape poverty, the long-term poor are not likely to benefit from growth since they are detached from the labor market, socially excluded, and in many cases, facing discrimination which keeps them from reintegrating into the labor market. The long-termn poor in Hungary are comprised of several distinct social groups: the homeless, rural population particularly those living in micro- communities, unemployed or withdrawn from the labor market, households with more than three children, single parent families, single elderly females, and the Roma. A third of the long-term poor are of Roma ethnicity, even though this group is only approximately 5 percent of the Hungarian population. The analysis of the labor market in this report confirns the link between poverty and low-pay, as well as the negative consequences of withdrawal from the labor market. The connection between long-term unemployment and long-term poverty is demonstrated. One of the clearest messages of this report was that the Roma need good-paying jobs first and foremost. Many Roma villages are characterized by a cycle of dependency on state tra-isfers. Poor Roma households survive by a combination of either unemployment insurance or regular social assistance and family allowances, but these amounts are too low to lift the household out of poverty. Local authorities try to cycle recipients into unemployment insurance receipt by providing public works which enables the recipient to re-qualify for centrally-fir anced unemployment insurance in preference to locally-provided regular social assistance. However, cycling from unemployment insurance to public works back to unemployment insurance does nothing to reintegrate the applicant into the labor market and more or less dooms the recipient to further dependency. Reinsertion programs are needed to break this cycle of dependency. In the medium term, it is clear that more emphasis on providing high-quality general (not occupational) education to the Roma is needed. The best jobs in the Hungarian labor market are for those with the skills provided by higher education, yet very few Roma complete generalized secondary education. In Hungary many people have fallen out of the safety net of three years of unemployment insurance plus regular social assistance and have permanently withdrawn from the labor market. The reasons for dropping out are not clear and are an object for future research. The number of such drop-outs is estimated to be 100,000 and the majority of them are Roma. A special outreach program to identify these dropouts and reinsert them at least into the regular social assistance programs is needed. Therefore, it is recommended to extend the intent of the 1993 Social Act to encompass reinsertion programs for those who have dropped out and do not receive v regular social assistance. The Hungarian program of cooperation should be formalized with a reinsertion contract that addresses an expanded range of programs such as provided in France. Hungary's social protection policies must address three main challenges: reinserting the long- term poor, the special problems of the Roma, and decentralization. Hungary's long-term poor are cut off from the labor market, making reinsertion into productive society difficult. The Roma are socially excluded and require even more effort to integrate them into the labor market. Thus, a strategy to meet the needs of the long-term poor cannot rely exclusively on the current social protection system. Steps must be taken to reintegrate the long-term poor into productive economic activity, to address the particular problems of the Roma, and to provide social protection equitably across Hungary's many municipalities. These challenges for Hungary's social protection system are complicated by decentralization, which means that the poor are treated differently depending on where they live and what local social protection programs are available. This is a major disadvantage of decentralization: it leads to unequal treatment of the poor, with less financing available where social programs are most needed-in poor regions. There are several areas in which policy could be better informed by further research. Further research is clearly called for to understand the Roma culture and the interaction between Roma sociology, majority prejudice, and poverty. These interactions are extremely complex and vary depending on the subgroup of Roma involved. Better quantitative data for poverty analysis such as undertaken in this report, with over-sampling of Roma so as to have enough observations for statistical inference is also important. If more quantatitive data on the Roma were available, further multivariate regression analysis could be undertaken. In particular, panel regressions allowing for fixed and random effects could shed light on the dynamics of Roma ethnicity and long-term poverty. Interaction terms could be added to the standard multivariate analysis presented here, to capture the interface between education and Roma ethnicity more fully. vi 1. TRENDS IN POVERTY 1.1 Hungary experienced a severe recession in 1991 that continued, somewhat abated, through 1992-93. GDP contracted nearly 12 percent in 1991, and further contractions occurred in 1992- 93 (Table 1.1). Although macroeconomic indicators rebounded in the mid-1990s, private consumption and household income did not. 1.2 Hungary is a country with low income inequality (Milanovic 1998). Inequality has slightly increased (with minor fluctuations) across the study period as shown by the Gini coefficients 1 in Table 1.1.2.. There was also a jump in 1998/1999 when the coefficient reached 34%. Table 1.1 Macroeconomic trends and household income, 1992-97 (percent unless otherwise noted) Indicator 1992 1993 1994 1995 1996 1997 Real GDP growth -3.1 -0.6 2.9 1.5 1.3 4.6 Real private consumption growth -0.7 0.4 0.5 -5.5 -2.4 3.7 Unemployment (official) 9.6 11.9 10.7 10.1 9.9 9.2 Inflation 23.0 22.5 18.8 28.2 23.6 18.3 Equivalent monthly household income (1992 =100) 100 89 88 75 66 73 Per capita annual household income (1992 = 100) 100 91 91 82 74 74 Gini coefficient 27.75 29.47 31.62 30.85 30.85 32.00 Source: Ministry of Finance; World Bank 1999a, Szivos and Toth 1998. Note: Gini coefficients in this table show the concentration of non-zero equivalent incomes of households (e=0.73) from Szivos and Toth 1998 (Annex 4). 1.3 The steady decline in real incomes was not offset by dramatic declines in inequality and thus caused an increase in absolute poverty. Two poverty lines are used to examine trends in poverty: the subsistence minimum (with values for 1994) and the minimum pension. The welfare measure used is equivalent income (see Annex 1 for details on the equivalency scale used), and all poverty measures are calculated at the (weighted) individual level. 1.4 Four measures of poverty have been calculated (Table 1.2). The incidence of poverty, or headcount index (PO), is the percentage of individuals who are poor (that is, live in households with monthly equivalent income below the poverty line). The headcount index is the most widely used poverty measure, but it does not provide any information on the extent to which the welfare of individuals falls below the poverty line. The depth of poverty is measured by the poverty gap index (PI), which measures the average shortfall in equivalent income, expressed as a percentage l The Gini coefficient is a measure of inequality that varies from 0 (perfect equality) to 100 (perfect inequality). Distributions with lower Gini coefficients are more equal. 2 Alternative Gini coefficients are presented in Annex 4 for non-zero equivalent incomes of households (e=0.73) These are somewhat different than the Gini coefficients presented in Table 1.1 because of small differences in equivalence assumption and income definition used. 1 of the poverty line.3 The poverty gap index is not sensitive to the distribution of welfare among poor households; if a household just below the poverty line were to make a transfer to a much poorer household, there would be no change in the poverty gap index. The Foster-Greer- Thorbecke index (P2) measures the severity of poverty, putting more weight on the welfare- levels of very poor households relative to households with equivalent income near the poverty line.4 The final measure is the poverty gap, which is the average shortfall in equivalent income expressed as a percentage of the poverty line, calculated only over poor people. Table 1.2 Poverty trends, 1992-97 Indicator 1992 1993 1994 1995 1996 1997 Mean equivalent income 18,453 19,628 21,991 23,993 26,746 35,30) (forint per equivalent person per month)a Subsistence minimum 8,873 11,183 13,300b 11,915 14,083 18,574 (forint per person per month) Poverty measures (percent) Headcount index (P0) 9.7 14.5 20.3 9.4 14.7 17.3 Poverty gap index (PI) 2.1 3.2 4.4 2.1 3.4 4.: Foster-Greer-Thorbecke index (P2) 0.9 1.2 1.5 0.7 1.2 l.61 Poverty gap 21.5 21.8 21.8 22.0 23.1 23. Minimum pension 5,700 6,400 7,480 8,400 9,600 11,50C (forint per person per month) Poverty measures (percent) Headcount index (P0) 1.9 2.4 2.5 2.6 4.5 3.4 Poverty gap index (PI) 0.7 0.6 0.5 0.6 0.9 0.9 Foster-Greer-Thorbecke index (P2) 0.4 0.2 0.2 0.2 0.3 0.3 Poverty gap 38.0 24.4 20.0 22.8 19.2 26.5 a. Calculated for households, not individuals, from the TARKI HHP data base. b. In 1994 a new methodology for calculating the subsistence minimum came into use, so for that year there are two subsistence minimum values. The second value is 9,785 forint per person per month, with a headcount index of 6.7, a poverty gap index of 1.4, a Foster-Greer-Thorbecke index of 0.5, and a poverty gap of 20.5. 1.5 Hungary's Central Statistical Office calculates a subsistence minimum based on a food basket differentiated by age. But this subsistence minimum is not used as a numeraire for social transfers and is not an official poverty line. When social transfers are referenced to a peverty standard, the minimum pension is typically used. But for the main analysis of long-term poverty presented in Chapter 2, the minimum pension is too low-meaning there would be toc few observations for analyzing the long-term poor. Thus this report adopted a relative poverty line to define the long-term poor. 3Note that the poverty gap index is calculated over all individuals (poor and nonpoor), with nonpoor individua s having a zero shortfall. 4 The first three poverty measures can be calculated using the following formula: p = I _(z -_Yi) N -Lzi where a = 0, 1, or 2, N = number of individuals, q = number of poor individuals, z = poverty line, andY, = consumption of i'th individual below the poverty line. 2 1.6 As measured by equivalent income below the minimum pension, absolute poverty was very low in 1992-97, topping out at less than 5 percent of the population in 1996 (see Table 1.2). The 1996 Poverty Assessment (World Bank 1996) found different numerical results when using the minimum pension as the poverty line because that study was based on a different data set, the Central Statistical Office (CSO) Household Budget Survey. This report's main findings are based on the TARKI panel, which was determined to capture more income and a greater range of incomes than the household budget survey (Andorka, Ferge, and T6th. 1998).. The 1996 Poverty Assessment found 8 percent of the population below the minimum pension poverty line in 1993 while our results are less-only 3 percent of the population. However, the poverty profiles of the two studies are substantially the same, with both studies identifying similar groups at risk of deep and persistent poverty (see Chapter Two). The only major difference in the profiles is that we are able to quantify the Roma as a major group among the long-term poor, while the 1996 Poverty Assessment could not since the CSO Household Budget Survey used did not include information on ethnicity, although the 1996 report did include the suggestion that poverty was much higher than average for the Roma. 1.7 Absolute poverty according to the subsistence minimum was much higher than when measured by the minimum pension, reaching 20 percent according to one definition of the 1994 subsistence minimum. Because the subsistence minimum methodology was changed in 1994, it is difficult to assess trends across the entire penrod. Still, absolute poverty increased considerably after 1994, paralleling the declines in real household income shown in Table 1.1. 1.8 The increase in absolute poverty and decline in real household income have been accompanied by relatively constant inequality and a decline in the share of earned (market) income in household income (Table 1.3). Changes in labor market status over the past 10 years determined the eaming possibilities of vanrous social groups. For example, the earnings of the permanently employed increased much more than the eamings of the temporarily employed. Table 1.3 Households receiving certain types of income, 1992-98 (percent) Type of income 1 991/92 1992/93 1993/94 1994/95 1995/96 1996/97 199 7/98 Market income 83.9 80.0 82.4 78.6 80.1 79.5 72.1 Old age pension 38.3 39.9 41.4 37.8 37.2 40.3 42.0 Disability pension 10.0 11.7 12.0 12.5 13.5 14.4 15.8 Other pension 8.7 7.5 9.2 8.2 8.0 7.6 8.6 Child care fee 5.8 6.0 5.0 4.4 4.4 4.6 2.0 Child care benefits 4.9 6.0 5.7 4.5 5.3 7.6 6.4 Unemployment benefits 9.8 13.5 13.9 8.8 8.3 9.2 8.9 Sick pay 11.6 11.9 11.7 10.9 10.9 9.0 7.9 Income support for long-term Unemployed 0.8 3.1 3.4 4.8 4.8 5.1 4.8 Farnily allowance 33.1 33.0 32.6 34.5 34.1 32.0 22.4 Social assistance 8.5 10.2 9.9 8.6 9.9 7.4 9.0 Source: Szivos and T6th 1998. 3 2. PROFILE OF THE LONG-TERM INCOME POOR 2.1 The baseline information in this chapter is the nominal income data generated by Hungary's Social Research Center (TARKI) from the Hungarian Household Panel (HHP) survey. The survey consisted of six rounds in 1992-97. Other sources of survey information are available, but panel data are required to assess long-term poverty-and the HHP is the longest and only widely available panel. 2.2 Household data omit a significant portion of the very poor-the homeless and the institutionalized. Hungary's homeless population ranges from 20,000-30,000, with 10,000- 20,000 in Budapest alone (Box 2.1). A number of non-governmental organizations (NGOs) provide night shelters and other services for the homeless (Box 2.2). Though not included in surveys, the homeless should be included in the general conception of the poorest. However, to provide poverty analysis and quantitative description of the poor and their characteristics, it is necessary to rely on household data. This should not preclude an understanding of poverty in non-income dimensions, and the concept of social exclusion is particularly germane for the homeless and the Roma. Box 2.3 presents some of the voices of the poor. Box 2.1 Hungary's homeless Homelessness became an issue in Hungary in the winter of 1989, when the homeless held several demonstrations in Budapest. But latent or hidden homelessness existed in the years before the transition, when potentially homeless people lived in worker hostels and residential state institutions. The disappearance of jobs and worker hostels during the transition and the closing or downsizing of psychiatric institutions forced institutionalized and transient people to live on the street. Other factors contributing to an increase in homelessness included young people who dropped out of state-run residential education institutes, an increase in alcoholism and drug abuse, and marital and family breakups. (When families break up, it can be difficult for the people leaving to find accommodations due to low incomes and high sublet costs.) The 1993 Social Act obliged local governments to provide shelters for the homeless. Any settlement with more than 20,000 inhabitants must provide at least a daytime shelter, while a settlement with more then 30,000 inhabitants must establish an overnight shelter or a temporary hostel for the homeless. NGOs-especially the Maltese Charity Service, the Shelter Foundation, and the Red Cross- play an important role in caring for the homeless. Local governments often subcontract NGOs to provide services to the homeless. These organizations receive a per capita subsidy based on the number of people they care for, though the subsidy does not fully cover their expenses. In the case of the Maltese Charity Service, the normative subsidy covers 60 percent of the expenses incurred. The difference is covered by donations and funds acquired through tenders. The number of homeless in Hungary is estimated at 20,000-30,000, of which 10,000-20,000 live in Budapest. The institutional system maintains 7,000 beds for the homeless, 3,500 of them in Budapest. In the capital 20-25 NGOs care for the homeless, providing night shelters and temporary accommodations, nursing care, health care, information, meals, aid, and personal hygiene facilities. Source: Center for Social Development: Information Yearbook II, 1997. 4 Box 2.2 Poverty reduction NGOs There are almost 50,000 NGOs in Hungary. In 1997, 1,500 of these organizations dealt exlplicitly with supporting the poor and mitigating indigence. In their work these poverty reduction NGOs support disadvantaged individuals and families, the unemployed, refugees, and the homeless. They cperate temporary accommodations and charity food services, give legal and lifestyle advice to people in crisis, and provide aid and support for those facing difficulties. Most NGOs helping the poor are located in Budapest and in county centers. In 1997 the income of poverty NGOs exceeded 19 billion forint, of which almost 9 billion forint was state subsidies, 5.5 billion forint was private donations, and 4.5 billion forint was other revenu ~. The subsidies and donations included the sums awarded in tenders, while the other revenue came from services provided and membership fees. Most state support is a subsidy for programs with specific objectives. In addition to financial donations, volunteer activity is significant. In 1997 an averge of 128,499 hours of volunteer activity was performed each month. NGOs help the poor by providing in-kind and financial benefits as well as political representation for disadvantaged groups. In 1997 poverty NGOs provided nearly 10 billion forint in seTvices and financial aid. Source: Interview with G. Meszaros (CSO). Box 2.3 Hungary: Voices Of The Poor Quotes from letters sent to the National Association of Large Families "... I turned to the local government for help but they refused and told me that I wasn't eligible for any kind of subsidy, that I shouldn't think that Uncle State is a milk-cow. With special permission I sent my smallest child to the kindergarten at the age of 2 so I could go to work in a sewing workshop. The company failed to secure continuous orders, so I lost my job. They stopped paying the regular child education allowance because the high income we had: HUTF 31,700. I went back to the local government but they kicked me out, saying that I d&dn't need to live in a luxury flat. Since than I have not gone back to the local government and will never go back, either. We received the regular child protection benefit with a 2-month delay because it is something you have to apply for and I didn't know that. If we loose this flat we will hav. no chance ever for the rest of our lives to have another one. I am aware that this could lead to loosing my children which is the same for me as being sentenced to death..." "... I asked for subsidy but I didn't get it. Instead, the mayor offered me work to replacc the subsidy. So I had to work off the temporary assistance. I hoed in the woods, painted the bus ,top and fences. Whatever it was, I did it. I worked all through the summer because I needed the money badly. While I was working, my 10-year old daughter looked after the smaller ones. School has started, and I can't work any more, because there is no one else to look after the babies. My husband can't find any work either. The pay that was registered as tempo -ary assistance has stopped." 5 "... the local government, that we have here, doesn't understand people in trouble. I am a 1- mother of three, the five of us live in a two-room flat. We did some construction back in 1987 and the situation came to the point where I am unable to pay the monthly installments from one month's pay. My eldest daughter goes to the three-year vocational school in Tiszakecske, she studies to become a seamstress. We would like her to finish school very much, if for nothing else, because we don't have minority self-government here that could support us. I would like to have a job because one wage is not enough, but they do not see a person as a person here, what they see is the color of your skin if you are not white you are trampled. The answer the local government gave me was that it is not their business and I should not expect them to provide for my children." Source: Ferge, Zsuzsa SAPRI Report, 1st Working Group "The reform of public sector involvement in social provisions", November 1999. 2.3 A peculiarity of the HHP data-one shared, though to a lesser extent, by the household budget surveys of the Central Statistical Office (CSO)-is that incomes are much higher than consumption (Annex 1). Consumption is reported money expenditures plus the imputed value of food produced on private plots. Normally, in countries with extensive informal sectors and less familiarity with survey interviews, income is sharply underreported and consumption is much higher. Hungary's informal sector accounts for 15-20 percent of GDP (Sik 1995; CSO 1998). 2.4 Given that anomaly, and in recognition of the use of income data in the large literature analyzing Hungarian poverty, it was decided to base this study's analysis on HHP income data. A consumption-based poverty profile is presented in Annex 2, while an alternative CSO data source is examined in Annex 3. 2.5 The primary poverty line used in this chapter is 50 percent of mean equivalent income. Equivalence is used to capture economies of scale in consumption, whereby additional household members require less additional income to maintain the household's standard of living (see Annex 1) and Szivos and T6th 1998, (Annex 3). The size elasticity (e, or theta 0) used averaged around 0.64. Households are considered long-term if they were poor four or more times during the panel period (1992-97). 2.6 During the panel period about 7.5 percent of Hungarians lived in households that experienced poverty four or more times (Table 2.1). Households that were not poor faced a 28 percent risk of falling into poverty. Households that were poor once or more faced a significant risk of persistent poverty-as demonstrated by the hazard functions. 6 Table 2.1 Incidence of and hazard functions for income poverty, 1992-97 Hazardfunction. Hazardfunction. Percentage of Percentage chance Percentage chance Number of times poor population of exiting poverty ofpersisting in poverty Never 72.1 72.1 27.9 Once 11.0 39.3 60.7 Twice 6.0 35.3 64.7 Three times 3.5 31.8 68.2 Four times 2.8 37.3 62.7 Five times 2.7 57.4 42.6 Always poor (six times) 2.0 ... ... Memorandum item Long-term poor (poor four or more times) 7.5 ... ... Note: Hazard function for once poor is the probability of being poor only once (11.0) divided by the probabi lity of being poor once or more (sum of 11.0, 6.0, 3.5, 2.8, 2.7, and 2.0) for percentage chance of exiting poverty. Exit may not be pennanent. Other states constructed similarly. Chance of persisting in poverty equals 100 minius exit probability. Poverty line is 50 percent of mean equivalent income (see Annex 1). 2.7 Households with the following characteristics contained most of the long-term poor when poverty is measured against equivalent household income: * Rural location. - Weak links to the labor market-including because of unemployment. * Disabled members. - Children-especially three or more. - Single parent. * Single elderly female. - Primary-only education. * Roma ethnicity. 2.8 This poverty profile is essentially the same as the one identified in the previous Poverty Assessment (World Bank 1996), which singled out four groups at significant risk of extreme poverty: the long-termn unemployed, those with irregular connections to the labor market, women on extended leave from jobs to care for young children, and single elderly female pensioners. Most of the groups identified as poor in the 1996 report are also correlates of 'ong- tern poverty in this analysis, as noted below for each category considered. Location 2.9 Location plays a major role in the opportunities available to households and so affects poverty rates (Table 2.2). The rate of the never-poor is highest and of the long-terrn poor lowest in Budapest. The next most advantageous location is another major city. Rural villages havy the lowest rate of never-poor and the highest rate of long-term poor. 7 2.10 Nearly 60 percent of Hungary's long-term poor live in villages. Only 4 percent live in Budapest, and 9 percent live in other major cities (Table 2.3). Just 40 percent of Hungarians live in villages; 18 percent are in Budapest, and 12 percent are in other major cities. Table 2.2 Correlates of income poverty (percent) Number of times poor Four or more (long-term Indicator Never One Two Three poor) Total Average 72.1 11.0 6.0 3.5 7.5 100.0 Location Village 64.4 12.6 7.1 5.0 10.9 100.0 Town 72.9 10.4 6.0 3.4 7.2 100.0 Major city other than Budapest 71.6 12.2 8.8 1.8 5.6 100.0 Budapest 87.7 7.7 1.7 1.2 1.7 100.0 Household head laborforce status Employed 82.0 10.0 4.1 2.3 1.7 100.0 Pensioner (all types) 66.8 13.4 7.0 2.3 10.6 100.0 Self-ernployed 83.2 6.7 7.5 2.6 0.0 100.0 Out of the labor force 34.0 12.2 7.3 7.6 38.9 100.0 Unemployed 34.0 10.4 14.8 17.2 23.6 100.0 Number of members receiving unemployment benefits Zero 76.8 10.3 4.9 2.0 6.0 100.0 One 49.6 15.0 11.7 9.8 13.9 100.0 Two 43.4 9.1 9.1 16.2 22.2 100.0 Number of members receiving disability pension Zero 74.2 10.1 5.5 3.3 6.8 100.0 One 61.7 15.3 9.5 4.5 9.0 100.0 Two or more 74.2 16.1 0.0 3.2 6.5 100.0 Household typology I No children, no elderly 80.1 8.8 5.4 1.6 4.1 100.0 Children, no elderly 63.2 13.1 7.4 5.5 10.9 100.0 Elderly, no children 80.1 8.4 5.0 2.1 4.3 100.0 Both children and elderly 82.9 10.2 2.4 0.0 4.5 100.0 Household typology 2 Single parent with child(ren) 51.4 20.8 6.7 4.3 16.9 100.0 Other household with child(ren) 67.9 11.8 6.5 4.6 9.1 100.0 Single elderly male 70.0 22.5 2.5 2.5 2.5 100.0 Single elderly fernale 46.4 17.7 10.4 6.8 18.8 100.0 Other household without children 84.0 7.3 4.7 1.4 2.7 100.0 Gender of household head Male 73.2 10.7 5.6 3.5 7.0 100.0 Female 66.7 12.4 7.9 3.2 9.8 100.0 Access to land No 68.4 11.3 7.1 3.7 9.5 100.0 Yes 85.4 9.6 2.2 2.4 0.4 100.0 (continued) 8 Table 2.2 Correlates of income poverty (percent) (continued) Number of times poor Four or more (long- Indicator Never One Two Three term poor) Total Owns color television No 44.0 14.5 9.9 7.3 24.3 100.0 Yes 79.5 10.1 4.9 2.4 3.1 101.0 Owns automobile No 60.8 14.5 7.7 4.8 12.2 100.0 Yes 86.0 6.6 3.9 1.9 1.7 10l).0 Education of household head Primary 51.4 14.0 9.1 6.3 19.2 100.0 Vocational 72.7 12.7 6.7 3.7 4.3 100.0 Secondary 87.5 8.0 3.6 0.8 0.1 100.0 Higher 96.0 3.5 0.5 0.0 0.0 100.0 Age of household head 18-24 64.6 10.9 7.5 5.4 11.0 10(0.0 25-29 54.7 16.5 10.7 12.9 5.2 100'.0 30-39 72.3 11.4 5.2 2.7 8.3 100'.0 40-49 72.6 8.4 7.7 3.4 7.9 1001'.0 50-59 77.1 11.2 2.5 2.3 6.8 100.0 60-64 79.9 12.2 5.8 0.7 1.4 10( .0 65-69 75.5 14.4 3.7 0.5 6.0 100.0 70 and over 68.2 10.6 6.6 3.8 10.9 10l(.0 Number of elderly Zero 67.3 12.1 6.9 4.5 9.2 10(:.0 One 74.6 12.0 5.7 2.0 5.6 10(.0 Two or more 90.7 4.0 2.2 0.8 2.3 10(1.0 Number of children under 18 Zero 80.1 8.6 5.2 1.9 4.2 10C.0 One 69.1 13.1 6.9 2.0 8.9 10(.0 Two 76.3 11.0 5.5 3.1 4.1 10(.0 Three or more 45.4 15.1 7.9 10.5 21.2 10(.0 Note: Totals do not always sum to 100 because of rounding. Household variables were weighted by sampling welights and household size to generate population estimates. Source: Calculated from the Hungarian Household Panel (HHP) survey. 2.11 The 1996 Bank Poverty Assessment found that although poverty was lower in Budapest, location did not play a significant role in poverty when family size and other demographic factors were controlled for in regression analysis. While that may have been the case, location can be thought of as a short-cut toward the various demographic factors and is a sirnple geographic indicator that can inform policy. Other studies of poverty in Hungary agree that location is a major correlate of long-term poverty, particularly Speder (1998), Galasi (1998). 9 Table 2.3 Composition of income poverty (percent) Number of times poor Four or more (long-term Indicator Never One Two Three poor) Average Location Village 36.0 46.2 47.6 58.4 58.6 40.3 Town 29.9 27.9 29.9 28.9 28.5 29.6 MajorcityotherthanBudapest 11.7 13.0 17.4 6.0 8.8 11.8 Budapest 22.4 12.8 5.2 6.6 4.1 18.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 Household head laborforce status Employed 55.9 44.9 33.3 33.1 11.1 49.2 Pensioner (all types) 29.1 38.6 36.5 21.5 44.3 31.5 Self-employed 10.0 5.4 10.8 6.7 0.0 8.7 Out of the labor force 2.6 6.1 6.6 12.3 28.3 5.5 Unemployed 2.5 5.0 12.8 26.4 16.3 5.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of members receiving unemployment benefits Zero 88.6 78.1 68.2 48.2 66.5 83.2 One 10.2 20.2 28.7 42.2 27.4 14.8 Two 1.2 1.7 3.1 9.6 6.1 2.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of members receiving disability pension Zero 85.3 76.5 76.4 79.0 75.4 82.9 One 12.7 20.6 23.6 19.2 17.7 14.8 Two or more 2.0 2.8 0.0 1.8 6.9 2.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 Household typology I No children, no elderly 17.5 12.7 14.2 7.2 8.6 15.8 Children, no elderly 43.0 58.7 60.4 77.2 71.0 49.1 Elderly, no children 28.4 19.7 21.5 15.6 14.6 25.6 Both children and elderly 11.0 8.9 3.8 0.0 5.8 9.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 Household typology 2 Single parent with child(ren) 3.8 10.0 5.9 6.6 11.9 5.3 Other household with child(ren) 50.3 57.6 58.3 70.7 64.8 53.4 Single elderly male 0.8 1.7 0.3 0.6 0.3 0.8 Single elderly female 2.6 6.4 6.9 7.8 10.0 4.0 Other household without children 42.6 24.2 28.5 14.4 13.0 36.5 Total 100.0 100.0 100.0 100.0 100.0 100.0 Gender of household head Male 83.4 79.8 76.4 83.2 76.5 82.0 Female 16.6 20.2 23.6 16.8 23.5 18.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 (continued) 10 Table 2.3 Composition of income poverty (percent) (continued) Number of times poor Four or more (long-term Indicator Never One Two Three poor) Average Access to land No 74.3 81.0 92.0 84.8 98.9 78.3 Yes 25.7 19.0 8.0 15.2 1.1 21.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 Owns color television No 12.8 27.6 34.7 44.3 67.7 20.9 Yes 87.2 72.4 65.3 55.7 32.3 79.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 Owns automobile No 46.8 73.3 70.9 76.0 90.1 55.4 Yes 53.2 26.2 29.1 24.0 9.9 44.6 Total 100.0 100.0 100.0 100.0 100.0 [00.0 Education of household head Primary 22.5 40.5 47.9 58.3 80.3 31.5 Vocational 34.5 39.9 38.2 36.8 19.4 34.3 Secondary 25.7 15.5 12.8 4.9 0.3 21.2 Higher 17.3 4.2 1.0 0.0 0.0 13.0 Total 100.0 100.0 100.0 100.0 100.0 [00.0 Age of household head 18-24 2.7 3.0 3.8 4.8 4.4 3.0 25-29 4.9 9.7 11.5 24.1 4.4 6.4 30-39 26.4 27.5 23.0 20.5 29.1 26.3 40-49 28.8 21.8 36.9 28.3 30.2 28.6 50-59 18.0 17.3 7.0 11.4 15.2 16.8 60-64 6.8 6.8 5.9 1.2 1.1 6.1 65-69 4.7 5.9 2.8 0.6 3.6 4.5 70 and over 7.8 8.0 9.1 9.0 11.9 8.3 Total 100.0 100.0 100.0 100.0 100.0 .00.0 Number of elderly Zero 60.5 71.3 74.4 84.4 79.6 64.8 One 22.5 23.8 20.8 12.6 16.3 21.7 Two or more 17.0 4.9 4.8 3.0 4.1 13.5 Total 100.0 100.0 100.0 100.0 100.0 1.00.0 Number of children under 18 Zero 45.9 32.3 36.0 22.3 23.2 41.3 One 18.5 23.1 22.1 11.4 22.9 19.3 Two 26.6 25.1 23.2 22.9 13.8 25.2 Three ormore 8.9 19.5 18.7 43.4 40.1 14.2 Total 100.0 100.0 100.0 100.0 100.0 00.0 Note: Totals do not always sum to 100 because of rounding. Household variables were weighted by sampling weights and household size to generate population estimates.. Source: Calculated from the Hungarian Household Panel (HHP) survey. 11 Labor Market Links 2.12 Households with employed heads had the lowest rate of long-term poverty-less than 2 percent, while the average for all households was 7.5 percent (see Table 2.2). Pensioner households (covering all types of pensions-old age, disability, widow's benefits) were slightly more long-term poor than average, but long-term poverty was highest among households where the head was out of the labor force (39 percent) or unemployed (24 percent). No household headed by a self-employed person was long-term poor, though a few such households were poor a few times during the period. (The labor market is analyzed in detail in Chapter 4 and Annex 6.) 2.13 There is an obvious connection between poverty and an income loss resulting from unemployment (though household heads out of the labor force seem to lose income at a faster rate than the unemployed). Long-term poverty is sharply higher in households with one or more unemployed members. The 7.5 percent of households living in long-term poverty increases to 14 percent for households with one unemployed member, and to 22 percent for households with two unemployed members (see Table 2.2). 2.14 The main link between unemployment and long-term poverty is the duration of the unemployment. In the second quarter of 1996, 55 percent of the unemployed had been looking for a job for more than a year (Micklewright 1999). Hungary's unemployment benefits consist of three years of unemployment benefits followed by regular social assistance. The main reason people left unemployment insurance rolls was to exit not to a job but to regular social assistance (Micklewright and Nagy 1997, Micklewright 1999). 2.15 Weak labor market links were also identified as a correlate of both poverty and severe poverty in the Poverty Assessment (World Bank 1996). That study found that poverty was deeper if the household head was unemployed, and much higher if the unemployed head did not receive unemployment insurance. Disabled Members 2.16 Limited access to labor income also plays a role in the disadvantaged position of the disabled.5 Disability rates in Hungary seem high-about 15 percent of the population lives in households with one disabled member, and another 2 percent lives in households with two disabled members (see Table 2.3). Such households face a higher risk of long-term poverty, with 18 percent of the long-term poor in households with one disabled member. Long-term poverty is much lower among households with two or more disabled members (see Table 2.2), but since only 2 percent of the population lives in such households, this finding is probably not representative. Children 2.17 Long-term poverty increases noticeably with the addition of the first child. Households with no children have a long-term poverty rate of 4 percent, below the overall household average of 7.5 percent (see Table 2.2). But households with one child have a long-term poverty rate of 9 5Disability was not considered in the 1996 Poverty Assessment. 12 percent, and more than 21 percent of households with three or more children are among the long- term poor. The rate of increase in long-term poverty is not monotonic with children: households with two children have a long-term poverty rate of 4 percent. It is difficult to account for this discontinuity because it does not reflect a small number problem-25 percent of Hun-arian households have 2 children, and 14 percent have three or more. Using a slightly differer.t size elasticity (see Annex 1), Speder (1998) and Andorka (1989, 1990) also found that children faced a higher risk of poverty. 2.18 Galasi (1998) found that Hungarian children were at significant risk for poverty and long- term poverty, and that their position in the income distribution had worsened markedly relative to that of those over the pension age. Children accounted for 32-38 percent of the population in the two lowest deciles of the income distribution, while the share of the elderly fell from 15 percent in 1992 to 7 percent in 1996. Similarly, Galasi (1998) found that 2 percent of the elderly were poor each year during 1992-96, but that as many as 12 percent of children were poo^ each year. The Poverty Assessment (World Bank 1996) found that poverty was more pervasi ve for children than the population at large, and that the poverty rate rose steadily with the numier of children. Single Parents 2.19 Only about 5 percent of Hungarians live in households headed by a single parent (see Table 2.3). But 17 percent of such households-more than twice the national average are among the long-term poor (see Table 2.2). Among long-term poor households, 12 percent are single parent. Speder (1998) also found higher poverty among single-parent households. 2.20 Most single-parent households are headed by females. Female headship is associated with a higher rate of long-term poverty-10 percent for females compared with 7 percent for males (see Table 2.2).6 Female headship is also widespread, accounting for about one-fil'th of households (see Table 2.3). These findings on single parents and female headship were quite similar to the results in the 1996 Poverty Assessment. Single Elderly Women 2.21 The elderly (defined as people beyond the retirement age of 60 for men and 55 for women) generally do not face a significant risk of being long-term poor until age 65. The rate of long- term poverty declines with the age of the household head until age 65, and the presence of elderly members in a household is associated with lower risks of long-term poverty (see Table 2.2). But gender and household siructure matter. Single elderly women face a 19 percent chance of long-term poverty-nearly eight times the rate faced by single elderly men (2.5). Single elderly women account for 10 percent of the population in long-term poor households, blit this figure is only 0.3 percent for single elderly men (see Table 2.3). In addition, the 8 percent of Hungarians in households with heads age 70 and above face an I 1 percent rate of long-term poverty. Given the demographic advantages of elderly women, most of the elderly living past 70 6Headship was defined based on the presence or absence of active-age and pension-age males. If there was ari active-age or pension-age male member, the household was defined as male-headed. 13 are female. Single elderly females were identified as at risk for severe poverty in the previous study (World Bank 1996). Primary-only Education 2.22 Low education is a significant correlate of long-term poverty, while higher education is a virtual guarantee against long-term poverty. Households whose heads have only a primary education run a 19 percent chance of being among the long-term poor-while households whose heads have a university education have no chance of being among the long-term poor (see Table 2.2). About 32 percent of households are headed by someone with only a primary education (see Table 2.3). Among households headed by someone with higher education, 96 percent were never poor and the remaining 4 percent were poor only once or twice over the panel period. These findings are broadly in line with the 1996 Poverty Assessment which found a strong inverse relationship between poverty and the education of the household head. Roma Ethnicity 2.23 Several studies have demonstrated the disadvantaged position of the Roma in Hungary (Kemeny, Havas, and Kertesi 1994; Havas, Kertesi, and Kemeny 1995) and their occupational and locational segregation (Ladanyi 1989, 1993 ). Without quantification, the 1996 Poverty Assessment noted that Roma had much higher poverty than average, due to their poor educational attainment and weak links to the labor market. In this study, Roma ethnicity is one of the strongest correlates of long-term income poverty. Nearly 53 percent of Roma households are in long-term poverty, compared with 7.5 percent for the general population (Table 2.4). The Roma accounted for about 4 percent of the sample (and for 5 percent of Hungary's population) but for a stunning 33 percent of the long-term poor (Table 2.5). The small number of Roma in the sample may make the results less robust. Table 2.4 Correlates of individual income poverty (percent) Number of times poor Four or more (long- Never One Two Three term poor) Total Population estimate 72.1 11.0 6.0 3.5 7.5 100.0 Reported individuals 74.7 10.6 5.6 3.0 6.2 100.0 Age 6-9 64.5 14.3 5.9 6.2 9.1 100.0 10-14 62.9 12.4 6.8 4.7 13.2 100.0 15-17 62.0 15.2 2.5 5.1 15.2 100.0 18-24 76.9 8.9 5.0 2.3 6.9 100.0 25-29 71.2 10.3 7.1 5.3 6.0 100.0 30-39 73.6 12.6 5.0 2.9 6.0 100.0 40-49 74.6 9.7 7.3 2.3 6.1 100.0 50-59 76.3 10.5 4.7 2.6 6.0 100.0 60-64 84.0 9.9 3.4 1.5 1.1 100.0 65-69 80.0 7.1 5.7 1.4 5.7 100.0 (Continued) 14 Table 2.4 Correlates of individual income poverty (percent) (Continued) 70+ 66.9 12.2 6.0 4.8 10.1 100.0 Gender Male 73.6 10.8 5.7 2.9 7.0 100.0 Female 71.9 11.3 5.7 3.8 7.4 100.0 Ethnicity Non-Roma 77.3 10.7 5.3 2.7 4.0 100.0 Roma 26.8 1.8 8.0 10.7 52.7 100.0 Education No schooling 27.8 0.0 5.6 0.0 66.7 100.0 Grades 1-3 64.0 4.0 12.0 4.0 16.0 100.0 Grades 4-5 62.8 8.1 5.8 5.8 17.4 100.0 Grades 6-7 60.6 10.4 7.6 2.8 18.7 100.0 Grade 8 64.3 15.3 6.9 5.0 8.6 100.0 Vo-tech secondary 72.9 12.6 7.0 3.7 3.8 100.0 Secondary completed 86.2 7.6 4.3 1.0 0.9 100.0 College completed 96.2 3.8 0.0 0.0 0.0 100.0 University completed 98.8 0.6 0.6 0.0 0.0 100.0 Note: Individual-level data are weighted by individual longitudinal weights and status in 1997. Not all individuals reported. Source: Calculated from the Hungarian Household Panel (HHP) survey. Table 2.5 Composition of individual income poverty (percent) Number of times poor Four or more (long- Indicator Never One Two Three term poor) Totai Age 6-9 6.6 9.6 7.7 13.7 9.4 7.5 10-14 8.0 10.3 11.1 12.9 16.8 9.2 15-17 1.6 2.6 0.9 2.9 4.0 1.9 18-24 7.8 5.9 6.4 5.0 7.0 7.4 25-29 6.7 6.3 8.5 10.8 5.7 6.8 30-39 17.3 19.3 15.0 14.4 14.1 17.0 40-49 16.0 13.6 20.1 10.8 13.1 15.6 50-59 13.7 12.3 10.7 10.1 10.7 13.0 60-64 7.4 5.7 3.8 2.9 1.0 6.4 65-69 5.6 3.3 5.1 2.2 4.0 5.1 70+ 9.3 11.2 10.7 14.4 14.1 10.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 Gender Male 46.4 44.7 46.2 38.8 44.6 45.8 Female 53.6 55.3 53.8 61.2 55.4 54.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 (Continued) 15 Table 2.5 Composition of individual income poverty (Continued) Ethnicity Non-Roma 98.7 99.4 94.6 87.2 67.0 96.4 Roma 1.3 0.6 5.4 12.8 33.0 3.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 Education No schooling 0.2 0.0 0.5 0.0 5.8 0.5 Grades 1-3 0.6 0.3 1.6 1.0 1.9 0.7 Grades 4-5 2.2 2.0 2.6 5.1 7.2 2.6 Grades 6-7 7.9 9.7 13.2 9.2 29.5 9.8 Grade 8 23.0 38.9 32.8 45.9 37.2 26.8 Vo-tech secondary 23.6 29.0 30.2 30.6 15.0 24.2 Secondary completed 27.9 17.6 18.5 8.2 3.4 24.2 College completed 8.1 2.3 0.0 0.0 0.0 6.3 University completed 6.5 0.3 0.5 0.0 0.0 4.9 Total 100.0 100.0 100.0 100.0 100.0 100.0 Note: Individual-level data weighted by individual longitudinal weights and status in 1997. Not all individuals reported. Source: Calculated from the Hungarian Household Panel (HHP) survey. 2.24 High long-term poverty among the Roma reflects the interaction between their low education attainment and consequent lack of employment opportunities, and their geographic segregation in urban ghettos and microcommunities ( 2.4). In 1971 only about one-third of the Roma age 20-29 had completed the eight years of general schooling, but by 1993 nearly three- quarters had. But the economy changed in the intervening years, and in recent years eight years of general education-even combined with skilled worker training school-has not been enough to find a job (Havas, Kertesi, and Kemeny 1995). Box 2.4 Poverty in a Roma village Researchers visited the Roma village of Pisko, in southwestern Hungary. Of the 330 inhabitants, about 95 percent are Roma. The average family has four or five children, and the village has 114 children under 18. Only 10 adults have full-time jobs; 56 receive unemployment supplementary income and 45 receive regular social assistance. Several Roma homes were visited, and the poorest had dirt floors and no running water. Poor Roma homes were severely overcrowded-one home was a single room occupied by two beds and nine people (two parents with seven children). The mayor of Pisko noted that applications to the government housing program had been successful, and that three new houses would be built for the worst-off, but these funds had not yet been made available a year after the village was visited. The village has its own school for grades 1-4 and has been able to attract and retain a committed and skilled teacher. After grade 4 children are bused to a nearby larger cormmunity, Vajszlo. 2.25 After completing eight years of general education, Hungarians can continue to study in skilled worker training school, vocational secondary school, or general secondary school. Only the skilled worker training schools had significant Roma enrollment by 1993, and these suffered from outdated curriculums that did not respond to labor market demands. Havas, Kertesi, and Kemeny (1995) found that it was almost impossible to find a job with only a certificate from a skilled worker training school, that workers who had not completed eight years of general 16 education were in the worst position in the labor market, and that many older Roma in this category had lost their jobs. 2.26 In 1993 only 3 percent of Roma children continued on to general secondary school, while nationally nearly 50 percent of children finishing eight years of general schooling continul d on. Moreover, the Roma's dropout rate from secondary school is 40 percent compared with 14 percent among the general population (Havas, Kertesi, and Kemeny 1995). About 1 perc.-nt of Roma age 30-64 were higher education graduates in 1993, but this dipped to 0.6 percent for those age 25-29. 2.27 The lack of general secondary and higher education has made the Roma extr-mely uncompetitive in Hungary's labor market. In 1971 there was no significant difference between employment rates for Roma and non-Roma men. But by 1993 the employment rate for non- Roma men was twice that of Roma men, and nearly 65 percent of previously employed Roma men were unemployed or had dropped out of the labor force (Havas, Kertesi, and Kemeny L995). The reduction in the Roma's labor market participation began in the mid-1980s, and mor - than 40 percent of those not active in the labor market in 1993 had lost their jobs before the e nd of 1990, when the Employment Act (which provided for unemployment insurance) was enactc d. 2.28 Labor force participation among Roma women was well below the national average even in 1971, reflecting cultural factors (the traditional Roma lifestyle) and labor market realities (few jobs for unskilled female workers). In 1993 only about 18 percent of Roma women were employed, compared with a national average of 63 percent (Havas, Kertesi, and Kemeny 1 (J95). 2.29 In 1971, 65 percent of the Roma lived in traditional, segregated colonies with extremely overcrowded housing, typically lacking running water and electricity. By 1993, because of government housing and slum-clearance programs, only 14 percent of the Roma lived in such colonies (Havas, Kertesi, and Kemeny 1995). This does not mean that the Roma had be come integrated, as their continued residential segregation in Budapest demonstrated (Ladanyi 1989, 1993). Indeed, 60 percent of the Roma lived in segregated areas or former colonies (Ilavas, Kertesi, and Kemeny 1995). 2.30 The 1993 survey found that housing conditions (except overcrowding) had improved considerably since 1971. Only 10 percent of Roma homes had dirt floors, only 2 percent acked electricity, and less than 1 percent were without individual latrines. In addition, in only 5 percent of Roma homes did members have to carry water from a distance of more than 100 ineters (Havas, Kertesi, and Kemeny 1995). Moreover, the Roma are not a homogeneous group- -there are three major subgroups in Hungary ((Romungro, Vlach, and Bayash), and an emrirging tradition of self-government (Box 2.5). 2.31 The findings on the Roma are so significant that it is important to see whether they hold when controlling for other factors. For example, is it long-term unemployment or Roma etlmicity that determines long-term poverty, given that the two coexist? Several multivariate m -thods were used to disentangle these effects. The first involved two types of regression analysi:;.7 The first is a probit analysis, where the dependent variable is a binary variable indicating whether a 7All the multivariate methods are conducted on weighted household data, not individual-level data. 17 household is long-term poor (Table 2.6). Probit analysis may be preferable to levels regression- where the dependent variable is the log of equivalent welfare-when constructing a poverty Box 2.5 The Roma in Hungary A representative national research study on the Roma completed in the winter of 1993/94 seems to be the most comprehensive, reliable, and comparable with a similar study performed in 1971 (Kertesi and Kezdi 1998). The surveys considered people Roma if they were identified as such by non-Roma "experts." In 1993/94 the Roma population was 450,000-500,000-about 5 percent of Hungary's population and a 40 percent increase since 1971. Demographic calculations suggest that in 20 years the Roma will account for 7 percent of Hungary's population. One out of every eight babies bom in Hungary is of Roma origin (Kemeny and Havas 1996). Roma live all over the country, though they are unevenly distributed, and regional proportions change due to interregional migration. Most of Hungary's Roma speak Hungarian, not Roma. About 40 percent of the Roma live in towns (9 percent in Budapest) and 60 percent in villages. In 1971 about three-quarters of the Roma age 20-30 were illiterate, but by 1993/94 three-quarters of the same age group had finished elementary school. Although the education levels of the Roma have improved considerably, the distance between them and the majority population have increased. The reason? Secondary school education: only 1-2 percent of the Roma over 25 have finished secondary school. In 1971 the difference in employment between working-age Roma and non-Roma men was negligible, but by 1993/94 the difference had become quite significant. Just 29 percent of working-age Roma men are employed, while the national figure is 64 percent. Unemployment in northem and eastem regions places a disproportionate burden on the Roma, because 44 percent of the Roma live in those regions (Zadori and Puporka 1999). (continued) The 1993 Act on the Rights of the National and Ethnic Minorities instituted a system of minority self-govemment. On issues of cultural autonomy, minority self-govemments may exercise their right of consent and expression of opinion toward central and local govemment agencies responsible for performing relevant tasks. Local govemments are responsible for providing conditions needed for corporate and administrative management and the regular operation of local minority self-govemments. A local minority self-govemment is established if voters elect the number of representatives prescribed by the law, or if more than 30 percent of the representatives of a local government decide to establish a minority self-govemment. In 1998, 775 Roma self-govemments were formed. The National Roma Self- Govemment was then elected by the representatives of the minority self-govemments. profile if there is concern that measurement error in the welfare measure is correlated with some of the explanatory variables in the model (Grootaert and Braithwaite 1998). For example, the elderly may have difficulty with accurate reporting, and households with self-employment income may try to hide it to avoid taxation. Such correlation could lead to bias in the welfare equation estimated by ordinary least squares. 2.32 From the probit regression it is apparent that Roma ethnicity is associated with a highly significant increase in the probability of a household being long-term poor, even after allowing for other household characteristics. A household headed by a Roma has a 12.6 percentage point higher probability of being long-term poor relative to a comparable household headed by a non- Roma. This may not appear to be a large increase in the risk of long-term poverty, but the only other characteristic with a bigger impact is the household head being out of the labor force. Because poverty status was derived using a measure of equivalent income, the finding that the 18 Roma have a higher risk of long-term poverty is not influenced by the fact that they tend to have larger households.8 8 An alternative way of controlling for household size would have been to use per capita income to derive po verty status, then to have included household size or composition variables on the right-hand side in the probit regression. 19 Table 2.6 Probit regression on the probability of being long-term poor Change in probability of Dependent variable: household long- being long-term term poor/not long-term poor Coefficient P-value poor Location Village -0.006 0.971 0.000 Major city other than Budapest 0.177 0.479 0.005 Budapest -0.538 0.054 -0.010 Household head labor force status Pensioner 0.810 0.002 0.026 Out of the labor force 1.618 0.000 0.205 Unemployed 0.985 0.003 0.072 Two or more members unemployed -0.444 0.327 -0.007 Household typology Single parent with child(ren) 0.605 0.076 0.029 Other household with child(ren) 0.617 0.010 0.021 Single elderly male -0.260 0.649 -0.005 Single elderly female 0.325 0.191 0.011 Female household head 0.452 0.062 0.015 Has access to land -0.882 0.005 -0.014 Owns color television -0.493 0.002 -0.018 Owns automobile -0.198 0.426 -0.005 Education of household head Primary 0.330 0.096 0.009 Secondary -1.123 0.025 -0.017 Higher -0.666 0.302 -0.010 Two or more elderly members -0.151 0.597 -0.003 Three or more children -0.417 0.211 -0.007 Roma ethnicity 1.290 0.000 0.126 Constant -2.256 0.000 Note: Omitted categories are: located in town, household head employed, household typology: other household with children, and household head has vocational education. 20 2.33 For completeness, the second regression presented is ordinary least squares, where the dependent variable is the log of equivalent income in 1997 (Table 2.7). In this regression, however, the factors influencing current welfare are being modeled, not long-tern poverty status. It is apparent that Roma ethnicity has a significant negative effect on equivalent income, even after controlling for other characteristics. A Roma-headed household is estimated to have an equivalent income 26 percent lower than a comparable household headed by a non-Roma. Table 2.7 Ordinary least squares regression on current welfare Dependent variable: log of 1997 equivalent income Coefficient P-value Location Village 0.028 0.342 Major city other than Budapest 0.051 0.192 Budapest 0.250 0.000 Household head labor force status Pensioner -0.193 0.000 Out of the labor force -0.503 0.000 Unemployed -0.338 0.000 Two or more members unemployed -0.086 0.402 Household typology Single parent with child(ren) -0.245 0.000 Other household with child(ren) -0.215 0.000 Single elderly male -0.108 0.162 Single elderly female -0.088 0.079 Female household head -0.136 0.000 Has access to land 0.096 0.002 Owns color television 0.120 0.000 Owns automobile 0.141 0.000 Education of household head Primary -0.047 0.157 Secondary 0.160 0.000 Higher 0.368 0.000 Two or more elderly members -0.008 0.827 Three or more children -0.094 0.072 Roma ethnicity -0.256 0.000 Constant 10.266 0.000 21 Note: Omitted categories are: located in town, household head employed, household typology: other household with children, household head has vocational education. 2.34 Factor analysis led to the same conclusion as regression analysis: even when controlling for other factors, Roma ethnicity is an important determinant of long-term poverty. Factor analysis is based on the assumption that a number of general, unobservable factors cause relations between observed variables. Attributes that "load" onto constructed factors are considered significant (see Annex 8 for more details). 2.35 Long-term poverty status loads relatively strongly onto both the first and second retained factors (of the three factors, the factor loadings are highest for the first and second). Roma ethnicity also loads strongly onto the second factor-evidence of a positive correlation between Roma ethnicity and long-term poverty as well as onto the first factors. The other household characteristic shown in the probit analysis to have a large positive impact on long-term poverty status was the household head not being in the labor force. The factor analysis supports this result because this characteristic also loads positively onto the second factor, though not as strongly as Roma ethnicity, which loaded onto both the first and second factors. 2.36 The final multivariate technique used is multivariate correspondence analysis, which generates a number of different plots (see Annex 8). The plot of most interest contrasts ethnicity (among other characteristics) with household poverty status (Figure 2.1). Long-term poverty status and Roma ethnicity both load (highly) positively onto the horizontal axis, and so can be said to define the positive section of the horizontal axis. Thus the results from the multivariate correspondence analysis support the findings from the other two multivariate methods. That is, Roma ethnicity and long-term poverty status are highly correlated, even after controlling for other household characteristics. Figure 2.1 Multivariate correspondence analysis on ethnicity and poverty status >=2 elderly children U not P°pfion I oma A .2 -0.1 <=1 elderly0 1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 * poor It p . -0.2 - * h poor -0.3- - >=3 children . - - - -- - _ _._._ -1.5 -1 22 3. THE SOCIAL PROTECTION SYSTEM 3.1 Hungary's social protection system consists of social assistance programs and labor-related social insurance benefits such as pensions and unemployment benefits. The seven basic elements of the social safety net are old-age pensions, public health care, disability benefits, unemployment benefits, active labor market programs, family support, and child protection. Insurance and assistance elements are not particularly distinct, and in practice it is difficult to separate them. For example, unemployment benefits run for three years and are followed by means-tested social assistance if the applicant meets eligibility criteria and is still unemployed.9 Maternity and child allowances are even more complex. The central government is usually responsible for universal benefits, local governments for targeted (means-tested) benefits. In addition, local governments operate locally defined programs. The system is further complicated by the complex financing arrangements between the central and local governments. Pension System 3.2 In 1997 Hungary replaced its pay-as-you-go public pension system with a fully funded scheme (Palacios and Rocha 1997). The current multipillar system consists of a pay-as-you-go public pension pillar, a mandatory privately funded pillar, and a voluntary private pillar. In addition, a sharp increase in the ratio of pensioners to employed workers (from 46 percent in 1990 to 75 percent in 1995) and a continuous decline in the ratio of pensions to average earned income (from 64 percent to 57 percent) resulted in comprehensive reform of the pay-as-you-go pillar. The retirement age was raised from 60 to 62 for men and from 55 to 60 for women. The minimum service period required to receive a full pension was increased to 40 years. And indexation rules for pensions have been modified. If the retirement age and service period requirements are not met, the pay-as-you-go pension is proportionately smaller than it would be for a comparable wage history. 3.3 A supplement to pension reform, an old-age allowance, was established in 1998. With the stricter pay-as-you-go pension rules, the social security system had to support people who did not qualify for such pensions or whose income did not cover their subsistence. People above the retirement age are eligible for the old-age allowance if their monthly per capita net income (including the income of their spouse) is less than 80 percent of the minimum pension, or less than 95 percent for single-member households. The central and local governments share the responsibility of caring for the elderly-local governments pay the old-age allowance but are reimbursed up to 70 percent by the central government. Health Care 3.4 Every Hungarian who pays the health care contribution, or on whose behalf the contribution is paid, has access to free health care. People without insurance can visit health facilities, but services are provided only for a fee. Local governments can grant low-income individuals the right to access public health care for free even if they do not pay the contribution. In addition, municipalities can issue public health cards to subsidize medication costs for low- income families. 9 In 2000, the Government changed its unemployment insurance and allowance policy. See below for details. 23 Disability Benefits 3.5 Regulations and financing for disability pensions tightened in the second half of the 1990s. Today only people whose health status has deteriorated by 100 percent qualify for long-term disability pensions, up from 67 percent in the first part of the decade. Until the mid- [990s, however, many people opted for disability benefits rather than unemployment insurance- because eligibility for a disability pension, once met, is almost never revoked. By 1999 there were about 360,000 under-retirement-age disabled. 3.6 Long-term disability benefits are financed from the pay-as-you-go pension pillar. All others below retirement age whose health status has deteriorated by less than 100 percent (but more than 67 percent) are considered to have only a temporary deterioration in health status. The Health Fund is responsible for financing their benefits and for reevaluating their disability -laims on a regular basis. As a result the Health Fund has a financial interest in dealing with new and renewed claims more strictly. Human resources for reevaluating the temporarily disabled have been strengthened significantly in recent years. Unemployment Benefits 3.7 Hungary's unemployment insurance protects workers-defined as those who have been employed at least 90 days in the previous four months-against unemployment. The maximum benefit was 65 percent of the average income for the past four years spent in employment (and a minimum of 90 percent of the minimum wage). The benefit, provided for a maximum of one year, was paid by the central government from an unemployment contribution collected by the Labor Fund. 10 3.8 Once unemployment insurance expires, the unemployed became eligible for a long-term (up to two years) unemployment benefit worth up to 80 percent of the minimum pension, if the per capita income in her household is below that level. (If a household has other sources of income, the benefit supplements them up to 80 percent of the minimum pension.) The benefit was awarded by local governments and cofinanced by the central and local governments Once their eligibility for the long-term unemployment benefit expired, the unemployed became eligible for regular social assistance subject to means-testing, provided and financed by municipalities. Alternatively, they participated in public work programs, which make thom re- make them re-eligible for unemployment insurance and the long-term unemployment benefit if they work at least 90 days. 3.9 The government has madesubstantial changes to unemployment benefits. The duration of unemployment insurance is reduced from 12 to 9 months-which is on the low side for EU countries (Table 3.1). A minimum of 200 working days are nowrequired to access unemployment insurance. Five days spent working will earn one day of insurance; hence the minimum requirement ensures 40 days of coverage (as opposed to the previous three montlis). 1° In 1999 employers paid a 4.2 percent and employees a 1.5 percent unemployment contribution on the gross wage. 24 Table 3.1 European Union: Duration of Unemployment Benefits Duration of Unemployment Benefits Belgium Unlimited Denmark Two periods, one of two years and a second one of three years duration. Recipient required to participate in anti-unemployment measures. Germany Insurance-proportional to periods of compulsory insurance coverage, varying from 6 months (1 year coverage) to 32 months (5 years coverage). Assistance is unlimited (granted for a maximum of one year but renewable). Greece Ranges from 5 to 12 months duration depending on employment ranging from 125 to 250 days. Spain Normally 6 months, but possible extensions up to 18 months or beyond for special cases such as workers aged 52 and over. France Insurance-from a minimum of 4 months to a maximum of 60 months. Long- term unemployment benefit given in 6 month increments indefinitely. Ireland Insurance-390 days. Assistance-unlimited. Italy 180 days. Luxembourg 365 calendar days during a reference period of 24 months. 182 extra calendar days for those "particularly difficult" to place. Netherlands General benefit-6 months. Extended benefits range from 9 months to 5 years depending on employment duration (from at least five years employment to more than 40 years). Follow-up benefits-2 years. Austria Insurance-depends on insurance duration and age, ranging from 20 weeks to 52 weeks. Can be extended to 156 or 209 weeks if beneficiary participates in special training measures. Assistance-unlimited. Portugal General benefit-proportional to age (25-55 and older) varies from 12 to 30 months. Assistance-proportional to age (25-45 and older) varies from 12-30 months. Finland 500 calendar days. Assistance-unlimited. Sweden 300 days if aged under 57, otherwise 450 days. United Kingdom 182 days in "any" job seeking period. Source MISSOC 1999. 3.10 In May 2000 the two-year long-term unemployment benefit were abolished, leaving local public work schemes to protect the long-term unemployed. In abolishing the long-term unemployment benefit, Hungary has taken a step that is uncommon among EU countries (Table 3.1), which often have long-lasting unemployment assistance programs. Those who are eligible for the long-term unemployment benefit, or whose entitlement will be approved by 30 April 2000, will receive the benefit for two years under the old conditions. Those who are terminated from the benefit system and do not have access to local public work schemes can apply for local social assistance. 3.11 Under the previous syste, each month about 14,000 people experienced their entitlement to unemployment insurance expiring. About 10,000 of these people were eligible for the long- term unemployment benefit; the rest have exhausted their two-year eligibility for the benefit and must apply for local social assistance. Under the new system all these people are expected to enroll in local public work schemes. This means that by the end of 2000 the schemes would employ 150,000-160,000 people (60,000-80,000 from the unemployment insurance system, 25 80,000-100,000 from the long-term unemployment benefit system)-and by the end of 2001, 200,000-250,000 people. 3.12 The number of unemployed receiving the long-term unemployment benefit has been growing, while those receiving unemployment insurance has fallen (Table 3.2). This shift has reduced compensation under the benefit, and the number of people searching for work and receiving no support has increased. Table 3.2 Distribution of unemployment benefits, 1992-98 (percentage of unemployed) Unemployment Long-term Year insurance unemployment benefit No benefit 1992 72 6 22 1993 52 22 26 1994 37 40 23 1995 40 39 21 1996 29 44 26 1997 29 42 29 1998 35 39 26 Source: Labor Market Information January 1999. Active Labor Market Programs 3.13 Until 1993 Hungary spent a lot supporting the unemployed with active (training, public work programs, wage subsidies) and passive (unemployment insurance, long-term unemployment benefit) measures. Hungary introduced such programs before other transition economies, and their scale rapidly reached levels comparable to those in OECD countries. But fiscal constraints led to the tightening of benefits, and spending fell from 2.9 percent of GDP in 1992 to 1.4 percent in 1996 (Table 3.3). Table 3.3 Spending on active and passive labor market measures, 1992-97 (percentage of GDP) Year 1992 1993 1994 1995 1996 1997 Active 0.61 0.66 0.61 0.43 0.37 0.45 Passive 2.29 2.28 1.53 1.21 1.04 Total 2.90 2.94 2.14 1.64 1.41 Source. Ministry of Social Affairs. 3.14 About one-third of unemployed workers find a new job in less than a year; about one-tenth of those remaining enter an active labor market program. In 1999 the budget for active programs totaled 30 billion forint, of which 27 billion forint was decentralized to counties. About one-third of these resources are spent on administrative costs, half on subsidized employment, and the rest on training programs. Counties in western Hungary prefer training programs, while those in the east prefer public work schemes. 3.15 Training programs focus on those who are more likely to get a job-the young and the well educated. Public work programs promote community works, public education, public health, and social assistance services. These schemes are usually small scale and organized by local governnents, though any employer is allowed to manage them. The central government 26 supports these employment schemes with grants, with preference given to programs that employ the long-term unemployed. In general, allocations for public work schemes have been limited more by a lack of feasible proposals than by a lack of funds. 3.16 Public work programs are the most common tool for keeping people in the work-related benefit system, covering about one-quarter of the participants in active schemes. Many municipalities prefer to keep employees in public work schemes as long as possible in order to put them back on centrally funded unemployment insurance. (One year of employment entitles a worker to three months of unemployment insurance.) Localities also try to keep the same people employed because they have acquired skills in past programs. Both factors undermine the central government's goal of widespread coverage for active work schemes. 3.17 Local public work schemes rarely ensure a return to the labor market-just 5 percent of the participants in such programs find sustainable employment (World Bank 1999a). The reasons? Labor costs are high, and participants in public work schemes have traditionally been members of disadvantaged groups, with primary education or less. Moreover, public work schemes usually do not provide vocational skills. 3.18 Public work schemes are also criticized for their high costs. In 1999 the central government spent about 8.6 billion forint on such schemes-enough to finance 110,000 people for 3.5 months of work. Because local governments have to cover part of the costs, total public resources spent on the schemes are 10-30 percent higher. Financing the long-term unemployment benefit for the same number of people with would cost about half as much. But while public work programs may be inefficient and costly, they are the only reasonable alternative to living on an allowance for the most vulnerable members of society. 3.19 In 1996 several central public work schemes were launched for the growing number of long-term unemployed in certain regions. Central public work schemes are usually tied to major government infrastructure projects, environmental protection efforts, or development projects for depressed regions. Since 1996 central schemes have employed 50,000-55,000 people for four to six months. The maximum salary obtainable in these programs is the average wage in a given county, though wages differ by participants' education level. About 5 billion forint was spent on central public work schemes in 1998, but this dropped to 1.7 billion forint in 1999, and hence, the number of participants fell by more than half. Only the registered unemployed are allowed to participate in such programs. 3.20 In 1999 central public work schemes were combined with 60 hours of personal development training for 30 percent of the employees. This training teaches skills for preparing job applications and carrying out interviews. Of the 2,500 people who received this training, about 400 were offered stable employment in 1999. Family and Child Benefits 3.21 Partly due to worsening demographics, family support and child raising, education, and protection are of special importance for social policy. Eligibility conditions for child and family- related benefits were modified several times in the 1990s. Benefits were made universal in 1990, 27 became subject to means testing in 1996, and were made universal again in December 1998 (Annex 8). 3.22 In addition to a pregnancy benefit (paid from the fourth month of pregnancy until de'ivery) and a one-time birth allowance (paid per birth), there are three types of maternity benefits 'Table 3.4). In the past the GYED, available only to those with a history of contributions, reimbursed 65-75 percent of a mother's wages for the first two years of a child's life. The GYED, abclished in 1996, was reestablished in January 2000 for mothers who have paid social security contributions for at least 180 days in the previous two years, in an amount equal to 70 percent of the mother's wages (with a cap of twice the minimum wage). The GYES pays an amount equivalent to the minimum pension for the first three years of a child's life to mothers who do not have a work history (and so do not qualify for the GYED). The GYET (also equal to the minimum pension) pays families with three or more children aged 3-8.1' Table 3.4 Spending on family and child benefits, 1994-2000 (billions of forint) Type of benefit 1994 1995 1996 1997 1998 1999 2900 Family allowance 110.6 100.7 95.6 106.1 120.2 133.8 13:.8 Pregnancy benefit 8.3 8.9 8.3 6.0 7.2 7.8 - . One-time birth allowance 1.4 1.8 1.1 2.3 2.7 GYED 18.8 20.4 22.3 13.0 1.1 3(.3 GYES 10.3 11.3 14.2 27.0 38.6 47.7 2 .0 GYET 3.4 5.4 6.6 8.7 11.0 11.2 12.9 Regular child protection benefit 25.7 31.5 3(.0 Tax deduction for children 36.2 4&.8 Motherhood allowance 2.1 1.1 Total 151.3 148.8 149.3 162.6 204.9 270.5 30(.1 Source: Ministry of Social Affairs. 3.23 The family allowance is universal until the age of 6, when it becomes a schooling allowance payable until age 16 (20 for children who remain in full-time study). Low-income families receive an additional child protection benefit. Child care and child raising are also supported through the personal income tax system, with parents able to take tax deductions 3.24 New forms of child assistance were established in 1997. These programs are the responsibility of local governments, with limited cofinancing by the central government Four main types of supports are offered: cash benefits, in-kind allowances, personal care (including institutional care), and temporary care. Annex Table 7.2 summarizes eligibility conditions, amounts and length of benefits, and financing responsibilities. Proposed Changes in Family and Child Benefits 3.25 Family and child benefits include the family allowance, pregnancy benefit, one-time birth allowance, GYES, GYED, GYET, schooling allowance, and regular child protection benefit. The family allowance and child protection benefit are considered the income of the child. The 1 l The GYET is a defined amount paid per family as long as the family has three or more children and at least one child is aged from 3 up to 8. 28 pregnancy benefit, one-time birth allowance, GYES, GYED, and GYET (depending on eligibility) are considered the income of parents caring full-time for their child(ren). Universal child allowances are standard in the European Union countries (Table 3.5). Table 3.5 European Union: Age Limit of Child Allowances Age Limit Belgium 1 8 Denmark 1 8 Germany 1 8 Greece 1 8 Spain 1 8 France 19 Ireland 16 Italy 1 8 Luxembourg 18 Netherlands 1 7 Austria 19 Portugal 16 Finland 1 7 Sweden 16 United 16 Kingdom Source: MISSOC 1999. 3.26 The general government budget for 2000 made several major changes to family benefits. The GYED has been reintroduced. The GYET is no longer the responsibility of local governments, but is paid from the central budget through county offices of the Health Insurance Fund. There is a 30 percent increase in the tax deduction for children. The family allowance stayed the same in nominal terms. And all other family benefits increased in line with the 8 percent increase in the minimum pension. The GYED 3.27 The insurance-based child care benefit (GYED), abolished in 1996, was reestablished on I January 2000. The GYED is available for the first two years of a child's life to parents who were paid social security contributions for at least 180 days in the previous two years. The GYED can be paid to either parent (but not both) who can demonstrate that the parent takes care of the child full-time. The benefit amount is 70 percent of previous wages-but no more than twice the minimum wage-and subject to income tax. The amount of the benefit will not change during the eligibility period. If a beneficiary wants to make pension contributions, the period of the GYED is considered part of the service period. The employer's pension contribution is then paid by the central budget. 3.28 Parents are ineligible for the GYED if they received the pregnancy benefit, the GYES, or the medical treatment fee for the same child. Parents earning income or receiving any benefits defined in the Act on Social Assistance are also ineligible for the GYED. 3.29 About 100,000 people receive the new GYED. The average gross amount of the benefit is 33,000 forint a month. Hence the direct budget costs of the benefit will be about 36.3 billion 29 forint. In addition, the central budget contributes approximately 7.3 billion forint in pension contributions for the beneficiaries, while beneficiaries make 3.3 billion forint in pension contributions. Furthermore, beneficiaries are expected to pay 3-7 billion forint in taxes ' in the benefit. Recipients of the GYED no longer apply to the GYES system as they could ly;ve in 1996-2000, generating 17-18 billion forint in savings. Finally, a 1.0-1.5 billion forint drop in sick pay, paid for sick leave taken for child care, was expected. Thus the net budget costs of reintroducing the GYED was estimated to bel 5 billion forint in 2000.12 The GYET 3.30 The GYET is for parents who raise three or more children in their own households, as long as at least one of the children is aged 3-8 and the family income is below the ceiling. The bznefit is equal to the minimum pension and is independent of the total number of children raisesd. In recent years the GYET was paid by local governments from an earmarked fund. But this approach caused confusion for applicants as well as misuse of funds because the GYES and family allowance were administered by the county offices of the Health Insurance Fun]. To reduce confusion and ease administrative burdens, as of 2000 the county offices will administer the GYET as well. Tax deduction for children 3.31 In 1999 the personal income tax system introduced a tax deduction for each ch ld in recognition of the fact that raising children is costly. Because tax declarations for 1999 are not yet available, it is not known which types of families-with what income level-benefited from the tax deduction. 3.32 In 2000 the tax deduction for children increased 30 percent (Table 3.6). The tax dedt.ction is available to one parent with taxable income for children who are eligible for the filmily allowance (see below). In addition, a new feature has been added: if the recipient parent does not have enough income to use the full tax deduction, the other parent can deduct the difference from his or her taxes. 3.33 For example, if two parents with three children each earn 50,000 forint a month, nr ither can fully use the tax deduction. That is because the monthly personal income tax owed for that salary is 6,333 forint, while the tax deduction for three children is 9,000 forint. Under the new system one of the parents can deduct the total tax due (6,333 forint), while the other can dzduct the difference (2,667 forint)-making the total deduction 9,000 forint. The tax deduction for children is expected to cost the central government 48 billion forint in 2000, up from 39 billion forint in 1999. 12 Total net costs: (36.3 + 7.3) billion - [(3.3+7) - (17+1.5)] billion. 30 Table 3.6 Tax deduction for children under the personal income tax, 1999-2000 (forint per child per month) 1999 2000 Change (percent) Parent with one or two child(ren) 1,700 2,200 500 Parent with three or more children 2,300 3,000 700 Parent with disabled child 2,600 3,400 800 Source: Ministry of Finance. Family allowance 3.34 In December 1998 the family allowance was once again made available to every family with children. About 2.3 million Hungarian children are eligible for the benefit, but in 1999 it was claimed by just 2.1 million children. This is all the more puzzling when one considers that in 1997-98, when the family allowance was means-tested, the number of claimants was also around 2.1 million. The Ministry of Finance expected at least 100,000 more claims in 1999. 3.35 In recent years the family allowance has accounted for the largest share of family and child benefits (see Table 3.4). The budget for 2000, however, kept spending on the family allowance at 133.8 billion forint-the same as in 1999. (Keeping the family allowance constant in real terms would have cost another 9-10 billion forint.) Given that the budget assumes a 6-7 percent increase in prices, the family allowance deteriorated in real terms (Table 3.7). Table 3.7 The family allowance, 1999-2000 (forint per child per month) Type offamily Benefit Parent in farnily with one child 3,800 Single parent with one child 4,500 Parent in family with two children 4,700 Single parent with two children 5,400 Parent with three or more children 5,900 Single parent with three children 6,300 Parent with disabled child 7,500 Source: 1998 Act on Farnily Support. 3.36 According to the draft budget, the lack of increase in the family allowance will be compensated through the personal income tax system (see above). Opponents agreed that the real decline in the value of the family allowance would be partly compensated by the 9 billion forint increase in the tax deduction for children-but argued that the two systems do not benefit the same recipients. The key difference is that the tax deduction is favorable only to families who have income-and only income above a certain level. (Opponents also argue that the new system mixes social policy and tax policy, making the objectives of the tax system less clear.) 3.37 Many poor people do not have sufficient income to benefit from the tax deduction for children. In July 1998 a full-scale survey of family allowance recipients found that 39 percent of single-parent families and 23 percent of two-parent families in the lowest income decile do not have any earned income. Instead they lived on the GYES and family allowance (OEP 1998). 31 Otherfamily benefits 3.38 As noted, families without income are expected to be the losers from the stagnant family allowance because they will not be able to use the tax deduction for children. They will, however, be partly compensated by the regular child protection benefit. This income-tested benefit is available to children under 25 if the family's net per capita monthly income is below the minimum pension. The benefit, which is at least 20 percent of the minimum pension, is paid by local governments from non-earmarked central government normative transfers. 3.39 In 1999 the regular child protection benefit was paid to about 900,000 children, and the Ministry of Finance expected the same number of recipients in 2000. The ministry considered this a generous benefit because the monthly amount-3,070 forint in 1999, 3,320 forint in 2000-is more than the standard tax deduction for children. (The minimum pension was 16,600 forint in 2000.) Because the regular child protection benefit is linked to the minimum pension, the budget appropriation for it automatically rose from 33 billion forint in 1999 to 36 billion forint in 2000 (because of the 8 percent increase in the minimum pension). 3.40 Other family benefits (GYES, GYET, one-time birth allowance) are linked o the minimum pension and will also increase in line with it. The pregnancy benefit is linked to past income; hence rose according to the increase in income levels in 1999. 3.41 The new system of family benefits ensures universal access, extends generous benefits to people with earned income, and acknowledges the family as the main provider of chilcl care. While there is no actual information on outcomes and on whether the poor are as well prctected as in 1995-98, certain changes have improved the poor's access to benefits-particularly to the family allowance. 3.42 Universal access is a guiding principle. In passing the Act on Family Support in Dec -mber 1998, the government reinstated universal access to family benefits. That move had several dimensions. First, allocating family benefits on the basis of income testing was complicated for government agencies and eligible citizens. Although means testing seems crucial for fair benefit systems, it leads to misallocation when income declarations are false on a large scale---as in Hungary. Only a combined income and wealth declaration could have provided appropriate information about the means available to potential recipients. But such a declaratioin was politically impossible to introduce and in practice would be difficult and cumbersome to implement. 3.43 Second, the main argument for universal coverage is that it minimizes exclusion, not that it benefits the upper deciles. Third, in a country with a shrinking and aging population, younger generations and children should be a high priority. Accordingly, the benefit system has to cover all children. In addition, the government has to acknowledge that raising children is costly and that families with children bear more financial burdens than those without. Most important, universal benefits ensure that all poor families with children will qualify-greatly reducing errors of exclusion. 3.44 Families with earned income receive considerable benefits. The new benefit svstem strongly supports those with employment income (including the self-employed). The s;stem provides higher child care benefits (through the GYED) for parents who have been employed 32 and paid insurance in the two years before the birth of a child. In addition, the new system offers a tax deduction for children that benefits those with taxable income. Unlike the system in place until 1998, the new system does not just focus on the poor-it also tries to provide roughly the same family benefits to "average" families. 3.45 Thus the average family-one with two children, one parent earning income, one parent on child care leave, and a per capita income above the minimum pension-is supposed to receive a number of family benefits. Each child is eligible for a family allowance (or schooling allowance, depending on the age of the child). The parent on child care leave is eligible for the GYED if the parent worked before the child was born, or for the GYES if she had less than two years of insurance payments. In addition, the working parent is eligible for a tax deduction for each child. (And, if the parent's income is not high enough to use the full deduction, the other parent can deduct the difference from his or her taxes.) 3.46 A poor family with two children and no working parents, with per capita income below the minimum pension, is also eligible for benefits. Each child is eligible for the family allowance, the parent on child care leave is eligible for the GYES, and the family is eligible for the regular child protection benefit for each child. Total benefits are higher for the average family if the parent on child care leave receives the GYED. Otherwise, benefits are higher for the poor family, because the regular child protection benefit is larger than the tax deduction. 3.47 The new system does not make the poor worse off. According to the Ministry of Finance, the new family benefit system, with its tax deduction scheme, does not disfavor the poor despite their lack of earned income. That is because the benefit available only to the poor, the regular child protection benefit, automatically changes with the minimum pension. Only one group may not receive proper support under this scheme: families whose per capita income is above the minimum pension but not by enough to use the entire tax deduction for children. 3.48 For example, in a single-parent family with two disabled children and a monthly salary of 50,000 forint, the per capita monthly income is above the minimum pension. The parent would be eligible for a tax deduction of 6,800 forint but could not use the full amount because the monthly tax is only 6,333 forint. According to the Ministry of Finance such cases are rare, because if a taxpayer cannot fully use the tax deduction, the family is likely to have access to the regular child protection benefit. Detailed information on the distribution of income in the lower ranges would be needed to assess how many families fall into these rare cases. Social Assistance Programs 3.49 Beyond the main elements of the social safety net-pensions, health care, unemployment insurance, universal family support programs-means-tested social assistance programs are the responsibility of local governments. Local governments are legally obliged to provide social assistance based on the principle that individual needs are best identified and monitored locally. Central financial support for social assistance is revised every year in the annual budget laws. In addition, local governments have the authority to institute additional local programs covered 100 percent from their own budgets (Table 3.8). 33 Table 3.8 Municipal social benefits, 1997 Expenditure Type Recipients (thousands) (millions offorint) Total 2,287 38,391 Nationally mandated, locally financed benefits Regular social assistance 27 2,691 Medical treatment fee 24 2,634 Housing maintenance 296 3,698 Temporary assistance 1,064 6,131 Funeral benefit 81 702 Public burial 5 171 Nationally mandated benefits with mixed (central and local) financing Income supplement to unemployed 187 20,841 Regular child protection benefit 400 1,080 Locally mandated, locally financed benefits Other social benefits 203 443 Source: Ministry of Social Affairs. 3.50 The three main forms of social assistance are cash benefits, in-kind benefits, and peisonal care (see Annex Table 7.2). Cash benefits include the GYET, old-age allowance, long-term unemployment benefit, regular social assistance, housing support, medical treatmenl fee, temporary assistance, and funeral benefits. In-kind benefits include public burial, health insurance, and benefits that might contribute to schooling, heating, eating, or access to public utilities. Personal care involves basic and institutional care. Basic personal care programs a-e for public food, home care, and family support. Institutional care involves special homes, rehabilitation institutes, day-care homes, and temporary homes. 3.51 About 70 percent of local government current spending is financed through norm ative grants-central government transfers allocated to local governments based on in-kind indic ators (Fox 1998). The so-called social normative, for example, is calculated according to the number of elderly, unemployed, and children at a given settlement, and the primary school normative on the basis of the number of primary school pupils. In general, normatives do not cover the total costs of a service. This approach forces local governments to contribute financial resources and so share responsibility for service provision. Local governments can use these central trarsfers according to local priorities. For example, education normatives can be spent for social assistance programs and vice versa depending on the preferences of the citizens of a given settlement. 3.52 As noted, Hungary's 3,200 municipal govermments are free to establish their own social assistance benefits. For example, Ozd, a small town in northeast Hungary, offers 9 main kin Is of social protection benefits, while the Ferencvaros district in Budapest offers 20 kinds (Box 3. t). 34 Box 3.1 Social protection benefits in Ozd and Ferencvaros Ozd is a small town with 46,000 residents in northeast Hungary. Unemployment is a major problem in the region-in 1998 unemployment in Ozd and 20 surrounding settlements was about 22 percent. Most of the unemployed have been out of work for more than two years, and nearly 2,000 of the 5,637 registered unemployed do not receive unemployment insurance or the long-term unemployment benefit. Ozd provides nine basic social protection benefits financed from its own resources: a child allowance, one-time assistance (which, despite its name, can be given repeatedly), a housing maintenance allowance, funeral benefits, public health cards, nursing care, meal subsidies, other school benefits, and assistance for school supplies. These benefits are nationally mandated, but the need for them is locally identified and they are financed partly or exclusively from the local budget. The local budget also covers some or all of the costs of regular child protection benefits, regular social assistance, transitional help, and allowances for those who stay home to care for a sick or disabled family member. Ferencvaros is one of Budapest's poorer districts, but it offers a variety of nationally and locally mandated social protection. These include an income supplement for the long-term unemployed, regular social assistance, a regular child protection benefit, an old-age allowance, nursing care, temporary assistance, a temporary child protection benefit, funeral benefits, public burial, a housing maintenance allowance, transport assistance for the disabled, public health cards, a regular child education allowance, a temporary child education allowance, meal subsidies, assistance for school supplies assistance, military assistance, back-rent subsidies, a Christmas subsidy, and a vehicle purchasing subsidy for the disabled. Spending Trends 3.53 During 1992-96 nominal spending on the four main social benefits-family benefits, unemployment benefits, pensions, and social assistance-rose 70 percent (Table 3.9). Spending on pensions doubled, while spending on family benefits and unemployment benefits hardly changed. Developments in spending were influenced by changes in the number of benefit recipients as well as by changes in the average value of benefits. The portion of households receiving pensions rose by just a few percentage points and indexing was in place, so this benefit lost the least of its real value. By contrast, the portion of the population receiving unemployment benefits fell, and the average payment dropped nearly 60 percent. The real value of family allowances also suffered a significant decline. 35 Table 3.9 Characteristics of cash social benefits, 1992-96 Type 1992 1993 1994 1995 1996 Billions offorint Family allowances 92 109 111 102 96 Unemployment benefits 48 59 52 50 47 Pensions 315 393 477 553 634 Social assistance 18 22 25 29 33 Total (of above four) 474 583 665 734 810 Total income 2,051 2,351 2,889 3,560 4,366 Nominal change, 1992 = 100 Family allowances 100 119 120 111 104 Unemployment benefits 100 122 108 104 98 Pensions 100 125 152 176 201 Social assistance 100 122 136 159 182 Total(ofabovefour) 100 123 141 155 171 Total income 100 115 141 174 213 Inflation 100 123 146 187 231 Real change, 1992 = 100 Family allowances 100 97 83 59 45 Unemployment benefits 100 100 74 56 42 Pensions 100 102 104 94 87 Social assistance 100 99 93 85 79 Total (ofabove four) 100 101 97 83 74 Total income 100 94 97 93 92 Share in household income (percent) Family allowances 4.5 4.6 3.8 2.9 2.2 Unemployment benefits 2.4 2.5 1.8 1.4 1.1 Pensions 15.4 16.7 16.5 15.5 14.5 Social assistance 0.9 0.9 0.9 0.8 0.8 Total (ofabove four) 23.1 24.8 23.0 20.6 18.6 Source: TARKI Social Policy Database. 3.54 Because disposable income increased faster than benefits, the share of benefits in income decreased-from 23 percent in 1992 to 19 percent in 1996 (see Table 3.5). Family allowances and unemployment benefits lost the most value. Pensions, which were of much g:-eater importance in terms of household income, fell as well. Social assistance stayed about the same. 3.55 Severe budget constraints led to dramatic erosion of some benefits, especially family and child benefits (Figure 3.1). Between 1990 and 1994 child allowances lost half their real v,alue. Eligibility rules did not change for the GYED or the GYES, but both benefits were eroded. The introduction of a benefit ceiling for the GYED in 1992 accelerated this erosion. 36 Figure 3.1 Benefits as a percentage of net average wages, 1989-96 70,0 60,0 .-....~...- family allowance 50,0 = minimum wage 40,0 30°0!- - -minimum pension 20,0 - . . average old age pension 0,0 1 9 1 9 i 9 1 9 1 9 1 9 19 89 90 91 92 93 94 95 96 Note: June data or yearly average. Family allowance is for a two-child, two-adult household. Source: TARKI Social Policy Database. 3.56 These trends changed the structure of social transfers even though no radical reforms were implemented in the first half of the 1990s. Family benefits remained mostly universal until 1995-96, but their distribution became more pro-poor. Social spending was rising until 1991-92, then began falling as a percentage of GDP. Poverty Impact of Social Protection Benefits 3.57 Do welfare benefits reduce poverty? To answer that question, we examined poverty indexes based on various poverty thresholds when welfare benefits are included in total incomes. We then calculated the indexes when various benefits are removed, leaving the thresholds unchanged (Table 3.10). 3.58 The second column of Table 3.10 shows the percentage of those whose monthly income is less than the given poverty threshold. Different equivalence scales give different poverty rates because the poverty rate is sensitive to the equivalence scale used. 37 Table 3.10 Poverty rates with different equivalence scales, welfare benefits, and poverty thresholds, 1995/96 (percent) Without Equivalence Without family unemployment Without Without sociL I scale Total income allowances benefits pensions assistance e= Poverty threshold: 50 percent of average income 0.5 15.0 20.6 16.3 31.2 15.3 0.73 15.3 20.5 16.5 32.8 15.7 1 18.0 22.7 19.8 36.4 18.4 e= Poverty threshold: 50 percent of median income 0.5 8.8 14.7 10.6 25.8 9.4 0.73 9.6 15.3 11.1 26.5 9.7 1 12.7 18.0 14.3 29.9 13.3 e= Poverty threshold: Upper limit of bottom quintile 0.5 20.0 25.6 21.1 40.0 20.7 0.73 20.0 24.9 21.6 41.6 20.6 1 20.0 25.6 22.1 43.1 20.4 Source: Szivos and T6th 1998. 3.59 Of the four benefits investigated, the removal of unemployment benefits and social assistance would have the least dramatic effect-reflecting the minor importance and good targeting of these two benefits. The very small increase in poverty that would have characterized the withdrawal of social assistance is surprising and should be investigated further. 3.60 It can also be concluded that, in a certain sense, the poverty-reducing effects of family allowances and pensions work against each other. The fact that these two benefits are the two biggest items in the social benefits system certainly plays a major role in this. A decision on one of these benefits always has an effect on the other because they are in competition for the funds relating to the maintenance of benefits. Targeting of Social Assistance 3.61 In a recent assessment of social assistance in six transition economies, Braithwvaite, Grootaert and Milanovic (1998) found the Hungarian system to be both efficient (with a large percentage of social assistance received by the poorest 10 percent of households) and effective (in terms of the percentage of the poverty gap covered for the poorest 10 percent). Yet benefits did not appear to be well targeted-24 percent of households received social assistance, and 23 percent of the nonpoor received some social assistance. Previous work has also found that most social assistance (other than family allowances) was not well-targeted and failed to move mnany recipients out of poverty (van de Walle, Ravallion, and Gautam 1994; various studies by TARKI). 3.62 To assess the targeting of cash transfers, we first used household survey data fror-a the Central Statistical Office to analyze trends in benefit coverage and amounts (see also Anne Kes 4 and 9). Six components of the social safety net were considered: pensions, child care aid 38 (GYES), many-child allowance (GYET), family allowances, social assistance, and unemployment benefits. In 1997 social transfers accounted for 28 percent of gross income, and 86 percent of households received at least one transfer (Table 3.11). Among recipient households, transfers averaged 281,000 forint a year. Table 3.11 Social transfers 1997 Share of gross Share of households Average amount received by Type of transfer income (percent) receiving (percent) recipient households (forint a year) Pensions 21.8 55.4 337,863 Child care aid (GYES) 0.9 7.6 104,186 Many-child allowance 0.2 2.0 106,952 (GYET) Family allowances 3.3 35.5 80,094 Social assistance 0.3 6.1 36,437 Unemployment benefits 1.5 12.0 106,489 Total 28.0 85.6 280,999 Source: Calculated from Central Statistical Office database. 3.63 Pensions were the largest transfer in 1997, accounting for 22 percent of gross income. Pensions were received by 56 percent of households, with the average recipient household receiving 337,863 forint-88 percent more than in 1993 (Grootaert 1997). But because the consumer price index more than doubled during this period, the real value of pensions fell. The real value of most other social transfers-family allowances, unemployment benefits, many- child allowance-fell as well. Child care aid is the only benefit considered here that increased in real value between 1993 and 1997. In 1997, 6 percent of households received social assistance. 3.64 The effectiveness of the social safety net can be gauged by looking at the number of poor people helped per forint spent on social transfers. Here what is being calculated is the poverty impact of a transfer program after normalizing for the size of the program. Per I million forint spent, many-child allowance reaches the most poor people-27.9 (Table 3.12). Many-child allowance also has the largest impact on poverty, lifting 8.3 people out of poverty per 1 million forint spent. This poverty impact reflects the fact that the amount of many-child allowance transferred per person is quite high. Social assistance reaches 25.7 people per 1 million forint spent, while all other programs reach fewer than 19 people. The pension system reaches the fewest poor people per I million forint spent (4.4), but this is to be expected given that it transfers the largest amount (Table 3.11) and is not designed to alleviate poverty. 39 Table 3.12 Average number of ex ante poor helped per 1 million forint of social transfers Average number of pO07 Average number ofpoor Average number ofpoor recipients lifted out of Type of transfer people recipients poverty Pensions 5.5 4.4 3.7 Nonpension transfers 12.1 11.4 5.6 Child care aid (GYES) 55.6 18.5 6.9 Many-child allowance (GYET) 162.0 27.9 8.3 Family allowance 17.7 15.8 5.9 Social assistance 135.1 25.7 6.2 Unemployment benefits 34.9 17.0 5.8 Average 5.4 5.4 4.0 Source: Calculated from Central Statistical Office database. 3.65 The effectiveness of the social safety net can also be assessed by looking at the impact of each transfer on different poverty measures. First consider the share of ex ante poor recipient households moved above the poverty line as a result of the transfer (second column of T'able 3.13). Pensions are the largest component of the safety net and so contribute the most to keeping people out of poverty: 81 percent of households who receive pensions are lifted above the poverty line. The second best poverty alleviation effect (31 percent) is achieved by the family allowance. Social assistance has the second lowest poverty alleviation impact (5 percent)- mainly because of the small amounts of money per recipient household. Social assislance benefits will have to be increased-and targeting improved-if they are to play a significant role in reducing poverty in Hungary. Table 3.13 Poverty alleviation impact of social transfers (percent) Share of ex ante poor Social transfers recipient households lifted received by poor above poverty line as a households as a Individual ex Individual er ante Type of transfer result of social transfer share of poverty gap ante poverty rate poverty ap Pensions 80.5 418.4 37.6 65 7 Nonpension transfers 47.8 601.9 23.5 38 9 Child care aid (GYES) 14.4 83.1 14.7 24 4 Many-child allowance 3.5 41.0 13.2 23 1 (GYET) Family allowance 31.2 295.0 18.7 28 5 Social assistance 4.5 27.2 13.0 23 0 Unemployment benefits 21.8 155.6 15.2 28 1 Total 83.3 1,020.3 47.9 67 7 Source: Calculated from Central Statistical Office database. 3.66 The social safety net can significantly improve the living standards of recipient households even when it does not lift them above the poverty line. The third column of Table 3.10 shows the transfers received by poor households as a percentage of the poverty gap. Total social tranisfers received by the poor are 1,020 percent of the poverty gap. Hence, without transfers, the pc,verty gap would be more than 10 times larger. 40 3.67 A final method for measuring the effectiveness of social assistance is to consider the impact of targeting on different poverty measures. Using per capita consumption as the welfare measure, the individual poverty rate is 12.5 percent and the individual poverty gap (defined as the average shortfall of expenditures of poor persons, as a percentage of the poverty line) is 21.8 percent. The last two columns of Table 3.13 show the ex ante poverty measures-what the poverty measures would be if transfers were not received. The social safety net has a large impact on the poverty measures: the poverty rate would be 48 percent and the poverty gap 68 percent in its absence. 3.68 Two targeting simulations were considered to assess possible adjustments to Hungary's social benefits. The first removed the transfer program that gives the smallest benefit per recipient household. From Table 3.11, this program is social assistance, which in 1997 delivered just 36,437 forint to recipient households. The poverty impact of removing this program is shown in Table 3.13-the individual poverty rate would increase slightly to 13 percent and the poverty gap to 23 percent. 3.69 The second simulation deducted a fixed amount-36,000 forint-from the expenditure per recipient household. (If the amount of benefit received is less than 36,000 forint, the amount of the benefit was deducted.) The impact of this change is presented in Table 3.14. Table 3.14 A targeting simulation: cutting 36,000 forint a year from social transfer of recipient households Type of transfer Individual headcount Individual poverty gap Pensions 14.0 22.9 Child care aid (GYES) 13.2 22.1 Many-child allowance (GYET) 12.7 22.2 Family allowance 14.9 23.3 Social assistance 12.8 22.0 Unemployment benefits 13.5 22.9 Source: Calculated from Central Statistical Office database. 41 4. THE LABOR MARKET AND POVERTY 4.1 Hungary's labor market has been transformed by the transition to a market economy. External and policy shocks-especially price liberalization and changes in ownership resulting from privatization-forced many firms out of business, resulting in mass layoffs. Employment fell much more dramatically than production. The number of jobs dropped about 30 percent between 1990 and 1996, was stable in 1997, and started to grow slightly only in 1998. 4.2 Many laid-off workers-especially those above 50-withdrew from the labor market, opting for early retirement or becoming disability pensioners. Though unemployment peaked in 1993, employment started to grow only in 1998 (Table 4.1). Unemployment fell after 1993 because of slower inflows into measured unemployment and high outflows to inactivity. Overall, the ratio of economically inactive to active citizens grew 30 percent between 1992 and 1997. School enrollments increased significantly during this period, significantly reducing youth unemployment. Partly because of high labor costs (Table 4.2), employment fell even in years when industrial output started to recover. Table 4.1 Labor market activity and forms of inactivity, 1992-98 (thousands of people) Group 1992 1993 1994 1995 1996 1997 1998 Economically active population 4,527 4,346 4,203 4,095 4,048 3,995 4,011 Employed (incl. conscripts) 4,083 3,827 3,752 3,679 3,648 3,646 3,698 Unemployed 444 519 451 417 400 349 313 Economically inactive population 3,202 3,417 3,577 3,724 3,760 3,805 3,745 GYES and GYED recipients 250 250 241 280 297 292 291 Students 548 565 578 590 605 607 616 Disability pensioners 331 348 365 380 397 415 354 Pensioners (below 74) 1,320 1,359 1,389 1,419 1,546 1,443 1,477 Survivor pensioners 268 261 254 248 242 234 220 Early retirement 35 32 30 31 32 36 58 Other (working abroad, unknown) 451 602 721 777 641 778 729 Total 15-74 7,729 7,763 7,780 7,820 7,808 7,800 7,756 Source: Labor Research Institute 1998 and staff estimates 42 Table 4.2 Labor taxes, 1995-99 (a) (percentage of gross wage) Contributor 1995 1996 1997 1998 1999 Employer 50.0 48.5 48.5 48.3 42.8 Pension 24.5 24.5 24.0 24.0 22.0 Health 19.5 18.0 15.0 15.0 11.0 Flat health tax a 3.5 3.8 5.3 Labor Market Fund 4.2 4.2 4.2 4.0 3.0 Wage guarantee contribution 0.3 0.3 0.3 0.3 0.3 Wage guarantee contribution 0.3 0.3 0.3 -- Vocational training 1.5 1.5 1.5 1.5 1.5 contribution Employee 11.5 11.5 11.5 11.5 12.5 Pension 6.0 6.0 6.0 7.0 8.0 Multipillar Second pillar 6.0 6.0 Pay as you go 1.0 2.0 Health 4.0 4.0 4.0 3.0 3.0 Labor Market Fund 1.5 1.5 1.5 1.5 1.5 Total 61.5 60.0 60.0 59.8 55.3 a. Expressed as a percentage of the payroll tax base. Source: Ministry of Finance; Ministry of Labor. Trends in Unemployment 4.3 Declining employment caused a jump in the number of unemployed. While there was officially no unemployment in Hungary until 1989, hidden unemployment became transparent at once, and economic shocks further increased the number of unemployed. After a sharp increase at the outset of transition, unemployment remained roughly stable during 1994-97. According to official data, in 1997 unemployment was 9.2 percent, about the same as in 1992 (Table 4.3). Using a comparable definition of unemployment (a lack of any paid job), the Hungarian Household Panel yields a somewhat higher figure of 11.3 percent in 1997, 1 percentage point higher than in 1992. Unemployment is higher still-around 12 percent-if casual jobs are excluded and instead only those who have a regular job are considered. Table 4.3 Unemployment rate dynamics, 1992-97 (percent) Unemployment definition 1992 1993 1994 1995 1996 1997 Official rate 9.6 11.9 10.7 10.1 9.9 9.2 Lack of any paid job 10.1 11.8 11.5 10.4 10.8 11.3 Lack of a regular job 11.0 12.6 12.2 11.8 11.8 12.3 Note: Data are weighted. Source: Labor Force Survey; Hungarian Household Panel survey; World Bank staff estimates. 4.4 Since 1990 education has been a determining factor in employment. People who have not completed the eight years of primary education seem to be the biggest losers from stnLctural changes in the labor market-with unemployment rates 10 times higher than among people with higher education (World Bank 1999a). Since 1997, when the Hungarian economy started to grow significantly, the demand for people with higher education has increased (Ferenczi 1999). 43 4.5 Unlike in other socialist countries, in Hungary far more men than women are unemployed. There are several reasons. First, women's enrollments in higher education doubled between 1986 and 1996, from 9 percent to 17 percent (World Bank 1999a). As a result women are either inactive as students (Box 4.1) or well-placed in the labor market. Second, women traditionally work in the public sector, which in the 1990s provided more job security than the private sector. Box 4.1 Declining female participation rates Female participation rates-the ratio of employed and unemployed women to the working-age population-have historically been high in former socialist countries. In 1992 the average participation rate for women age 15-59 was 53 percent in European OECD countries, compared with 62 percent in Hungary. Hungary's female participation rate was even higher, 66 percent, for women age 15-54. Hungary's female participation rates fell significantly in the 1990s, however-dropping 10 percentage points for women age 15-54 by 1997. (Participation rates recovered slightly in 1998, but changes from one year to the next are often driven by changes in social insurance rather than by trends in labor markets.) The decline has occurred during a period when the number of working-age women remained almost constant at about 2.9 million. Changes in the relative size of age groups explain less than 1 percent of the decline-while changes in participation rates by age group explain 98 percent. In other words, a large number of women left the labor market. Among women age 15-24 the increase in inactivity is partly due to a jump in female enrollments in secondary and higher education. High education levels have shielded many women from structural changes in the labor market. As a result female unemployment was low in the 1990s. A conservative turn in society may also explain part of the increase in inactivity, with more women deciding to become housewives. A recent survey found that some women stay at home because socialist institutional child care services have disappeared and families cannot afford private care. Other women do not feel that the salaries in the part-time jobs they have been offered are worth taking. In their views the costs of commuting and of organizing care for their children would have used up eamed incomes. The sample in this study was not representative, however, so it is not possible to generalize its findings (Soltesz and Laczko 1999). 4.6 Unemployment has hit young people harder than other age cohorts: unemployment for the youngest cohort is four times that for the middle-aged cohort (World Bank 1999a). The number of school leavers who could not enter the labor market, and started their active career unemployed, was quite high in 1990-98: unemployment among 15-19-year-olds was 27-33 percent and among 20-24-year-olds was 14-16 percent. These high rates seem to be due to the youngest generation's lack of experience and to demographics: the generation entering the labor market in the 1990s was larger than the previous and subsequent generations (Ministry of Labor 1998). 4.7 Regional differences were high and enduring in 1990-98 (Table 4.4). High-unemployment regions (eastern and northern Hungary as well as southern Trans-Danubia) experience large inflows to the stock of unemployed and low outflows. Regional differences are linked more to a region's initial conditions (ethnic composition, land quality, location, sector composition) than to the shock of transition (K6l11 and Fazekas 1998). Hence high-unemployment regions are not only those where a single industry was the main employer before the transition (Borsod-Abauj- Zemplen County), but also agricultural regions with poor infrastructure and land, located far from prosperous areas (Szabolcs-Szatmar). Similarly, unemployment is highest in regions with 44 large portions of semiskilled, poorly educated people. And because of the heritage of these regions, the chances of finding new employment are the lowest there. Worker mobi ity is severely restricted by high costs, including major differences in housing prices among regions, a lack of rental markets, and high transportation costs (World Bank 1 999a). Table 4.4 Unemployment rates by region, 1992-98 (percent) 1992 1993 1994 1995 1996 1997 1998 Budapest 6.7 9.4 8.9 7.2 8.4 7.0 5.5 Central region Pest county 8.8 10.5 8.3 7.5 7.5 6.6 5.9 Western Danubia 7.2 8.8 7.7 6.9 7.0 5.9 6.2 Gyor-Sopron 7.3 8.9 7.4 6.4 6.7 6.2 5.1 Vas 6.5 7.0 4.9 5.7 5.5 4.2 5.5 Zala 7.7 10.5 10.7 8.6 8.9 7.4 7.9 Northern Danubia 11.6 12.5 10.6 10.9 10.6 8.1 6.3 Fejer 9.7 12.5 9.7 9.8 8.8 8.4 7.1 Komarom-Esztergom 13.1 13.2 10.5 12.3 13.3 9.7 6.5 Veszprem 12.0 11.7 11.6 10.7 9.6 6.3 5.4 Southern Danubia 9.8 12.6 11.8 11.9 9.5 9.9 9.4 Baranya 8.0 12.0 11.5 11.9 7.8 9.0 8.5 Somogy 10.6 14.6 12.4 12.1 9.7 10.7 10.3 Tolna 10.7 11.2 11.6 11.8 11.1 10.1 9.5 Northern Hungary 14.2 15.5 14.9 16.0 15.2 13.3 11.4 Borsod-Abauj-Zemplen 13.3 15.4 15.5 16.2 15.6 15.3 13.8 Heves 14.5 14.1 13.0 13.2 14.2 11.3 9.7 Nograd 14.9 17.1 16.1 18.7 15.7 13.2 10.8 North Great Plain 12.4 14.5 13.5 13.7 13.0 11.9 11.1 Jasz-Nagykun-Szolnok 13.1 13.9 12.0 14.5 13.3 11.2 11.8 Hajdu-Bihar 11.3 14.6 14.2 12.8 13.3 11.6 9.7 Szabolcs-Szatmar-Bereg 12.7 15.1 14.3 13.8 12.5 12.8 11.8 South Great Plain 9.9 12.0 10.3 9.3 8.3 7.3 7.1 Bacs-Kiskun 12.1 14.1 12.3 9.0 9.2 7.6 7.8 Csongrad 7.8 11.9 9.4 8.9 6.4 6.4 5.4 Bekes 9.9 10.0 9.3 10.0 9.4 7.9 8.1 Source: Central Statistical Office: Labor Force Survey 1992-97 time series. Due to data constraints, regional values -have been approximated by simple averages of county data. 4.8 Unemployment also has an ethnic dimension (Abraham and Kertesi 1998). The Roma have traditionally lived in poorer regions with fewer employment opportunities. In addition, the Roma tend to be poorly educated. As a result the size of the Roma population proved to be the key factor in explaining unemployment differences among regions in the early 1 990s. Unemployment in regions with large Roma populations peaked at 35 percent in 1993. Discrimination also contributed to high unemployment among the Roma. While poorly educated people were generally hit hard by the transition, in the early 1990s the Roma were the first to be fired in favor of poorly educated non-Roma employees. But as education became more important in the mid-1990s, unemployment increased among poorly educated non-Roma as well (Abraham and Kertesi 1998). 45 Features of Long-term Unemployment 4.9 Unemployment has fallen since 1993, and its composition has changed significantly. The share of those unemployed for more than one year almost tripled between 1992 and 1996. According to official labor force survey data, roughly one in two unemployed persons goes without a job for more than one year."3 In the Hungarian Household Panel survey the mean duration of unemployment increased from less than 10 months in 1992 to 16-18 months in 1997. Moreover, long-term unemployment has been increasingly concentrated among a small group of workers. Ten percent of the unemployed account for more than one-third"4 of unemployment duration, up from one-quarter in 1992. This group of hardcore unemployed has limited chances of finding new work-and so likely faces a higher risk of chronic poverty. 4.10 Most frequently unemployed persons are men (71 percent) age 25-49 (78 percent) with primary or lower education (49 percent) living in rural areas (55 percent). Among the occasionally unemployed, women, younger and older workers, workers with vocational and secondary education, and those living in large cities are much more common than among the frequently unemployed. 4.11 An unemployed individual faces an extremely high risk of relative poverty: every second unemployed person is relatively poor. This risk is as much as five times higher than that faced by an employed person. Relative poverty is defined here as being in the bottom fifth of the equivalent income distribution, not as having less than 50 percent of mean equivalent income. This different definition of poverty for labor market analysis is necessitated by the fact that there were too few observations combining unemployment and poverty status using the under 50 percent of mean equivalent income definition. 4.12 The negative impact of unemployment on relative poverty is much stronger in Hungary than in some other transition economies. For example, in Bulgaria most of the unemployed are not poor, and the lack of jobs has a much weaker impact on poverty. 4.13 Relative poverty among unemployed women is similar to that among unemployed men. Unemployed younger and older workers are less likely to be relatively poor than prime age workers, reflecting the fact that prime age workers are usually primary earners and support larger families. Younger workers tend to be secondary earners, and older workers are less likely to be supporting children. A person employed throughout 1992-97 had an 11 percent chance of being poor for a short period, a 9 percent chance of being poor for about half of the period, and less than a 2 percent chance of being poor for most of the period (Table 4.5). Thus employment does not protect against short-term relative poverty, but it almost eliminates chronic relative poverty. At 31 percent, the incidence of chronic relative poverty among the frequently unemployed is twice as high as among the one-time unemployed, and more than 15 times as high as among the always employed. 13 Hungarian Household Panel data seem to underestimate long-term unemployment. The likely reasons for this are small sample size and non-random sample attrition, whereby the long-term unemployed leave the sample at a higher rate than other persons. 14 This is a lower-bound estimate for reasons indicated in the previous footnote. 46 Table 4.5 Incidence of relative poverty by individual employment status, 1992-97 (percent) Never Short-term Medium-term Long-ter n relatively relative poverty relative poverty relative po' erty N poor (I year) (2-3 years) (4-6 yea. s) Frequency of unemployment Never 1,943 65.8 12.0 11.8 10.1 Once 206 48.2 15.4 21.3 15.1 More than once 186 33.4 14.8 21.2 30.7 Frequency of employment Never 859 25.1 12.3 13.0 22.7 Seldom (1-3 years) 392 48.6 12.6 20.8 18.1 Usually (4-5 years) 384 58.2 16.4 16.8 8. 5 Always 700 78.7 10.9 8.6 1.3 Note: Relative poverty is defined as the bottom fifth of the equivalent income distribution. Unemployment i~ defined as the lack of a regular job. The sample consists of workers who were continuously observed over thi e six years. Source: Hungarian Household Panel survey; World Bank staff estimates. 4.14 Few of the frequently unemployed are able to avoid relative poverty altogether. Two out of every three long-term unemployed have experienced at least temporary relative poverty. But even a one-time experience with unemployment is likely to lead to at least temporary relative poverty: one out of two one-time unemployed was poor at some point. The duration of poverty increases with the duration of joblessness. Most of the relatively poor who were long-term unemployed lived in prolonged or permanent poverty (that is, they were in the bottom quintile of the equivalent income distribution from two to six times in 1992-97). Most of the relatively poor who were unemployed once were poor only temporarily (in the bottom quintile one to three times). By contrast, most of the relatively poor who were continuously employed were pc or for only a brief time (in the bottom quintile once). 4.15 The conclusion is simple but meaningful: unemployment multiplies the risk of relative poverty. The risk is higher the longer unemployment lasts. And the longer unemployment lasts, the higher is the probability of prolonged relative poverty. 4.16 How does the duration of unemployment affect the risk and duration of poverty? Table 4.6 presents longitudinal data on relative poverty incidence and duration by household employment status. To provide a backdrop for further analysis, consider a family where one or more members were employed in each of the six years during 1992-97. Two-thirds of the family d'Id not experience poverty. The third who did tended to be temporarily relatively poor-that is, fo- three years at most. Long-term relative poverty among such families is rare but not insignificant', about 5 percent of people from continuously employed families were chronically poor. Th is the problem of the working poor exists in Hungary. 47 Table 4.6 Incidence of relative poverty by household employment status, 1992-97 (percent) Never Short-term Medium-term Long-term relatively relative poverty relative poverty relative poverty N poor (I year) (2-3 years) (4-6 years) Frequency of unemploymenta Never 826 67.0 12.4 12.5 8.2 Once 187 47.5 13.2 25.0 14.3 More than once 229 34.8 14.9 21.7 28.6 Frequency of employmentb Never 350 35.8 12.6 12.4 39.3 Seldom(1-3 years) 134 30.3 12.7 20.2 36.7 Usually (4-5 years) 151 32.2 9.5 28.9 29.5 Always 607 65.0 14.2 16.0 4.8 Note: Data are weighted. Relative poverty. is defined as the bottom fifth of the equivalent income distribution. Unemployment is defined as the lack of a regular job. The sample consists of workers who were continuously observed over the six years. a. One or more household member unemployed (lacking a regular job). b. One or more household member employed (having a regular job). Source: Hungarian Household Panel survey; World Bank staff calculations. 4.17 A one-time experience of unemployment by one or more household members considerably increases the risk of the family being relatively poor. More than half of the people from families that suffered short-term unemployment experienced poverty. Relative poverty for this group tends to last for two to three years. 4.18 The incidence and duration of poverty increase dramatically among families that experience long-term or recurrent unemployment. Two out of three people from families suffering from prolonged unemployment experienced poverty, and poverty tended to be long- term. Nearly 30 percent of people from families suffering from long-term or recurrent unemployment lived in chronic poverty-yet more evidence that a group of hardcore unemployed poor has emerged. This group is small, but its existence points to the problem of social exclusion and the incipient danger of the emergence of an underclass. 4.19 A person who is unemployed or comes from a family hit by unemployment is likely to be poor. In this sense unemployment is an important cause of poverty. It is thus important to assess the likelihood of losing a job and not being able to find a new one, as well as the likelihood of escaping joblessness. Those who do not have a job are likely to end up in poverty; those who find a new one increase their chances of avoiding it. 4.20 Table 4.7 presents estimated probabilities of entry into and exit from unemployment for selected years. The probability of losing a job within a year ranged from 3.7 percent in 1994-95 to 5.4 percent in 1996-97. In addition, about 3 percent of people who had been out of the labor force launched an unsuccessful job search, filling the ranks of the unemployed. By international standards, the inflow into unemployment is not high in Hungary. A more important source of the unemployment problem-one common to nearly all transition economies-is low outflow from unemployment. 48 Table 4.7 Labor market transition rates, 1992-97 (percent) 1992-93 1994-95 1996-97 Entry into unemployment From employrnent 5.1 3.7 5.4 From inactivity 3.5 3.1 2.6 Exit from unemployment To employment 30.3 33.7 30.2 To inactivity 24.5 23.2 29.6 Stay in unemployment 45.2 43.1 40.2 Note: Data are weighted. Unemployment is defined as the lack of a regular job. The transition rate is the number of people who moved from labor force state X (say, employment) to labor force state Y (say, unemploymnt) as a percentage of all people in state X in the baseline year. Source: Hungarian Household Panel survey; World Bank staff calculations. 4.21 The probability that an unemployed person will find a job within a year in Hungary is 3- 33 percent-about half the probability in the United States and two-thirds of those it high- unemployment European countries (Boeri 1998). Correspondingly, most unemployed in Hungary either stay unemployed for a year or more (40-45 percent) or become discourap ed and withdraw from the labor force (23-30 percent). Thus the chances of escaping unemploymcnt and finding a job are relatively low in Hungary (though much higher than in slowly reforming transition economies such as Bulgaria). The implication of modest inflows into unemployment and low outflows is that policies to reduce unemployment-and thus poverty-should fccus on improving chances for finding a job, rather than on preventing layoffs and thus slowing industrial restructuring. 4.22 Labor market history strongly affects the probability of losing a job. Workers who have been unemployed in the past are much more likely to lose a job than workers who have not. In the former case the risk of losing a job is 12-14 percent, while in the latter it is less than 4 percent. This implies that a group of workers in Hungary is susceptible to recurrent unemployment, and thus has a weak attachment to the labor market. Wage Developments 4.23 Real wages started to decline in Hungary before the transition. In 1989, wher,. wage liberalization measures were introduced after decades of regulated wages, the price-wage gap started to grow immediately (Galasi and Kertesi 1996). While real wages grew in some years, 1994, 1998, these increases were inadequate to offset declines (especially those in 1990-91 and 1995-96). Between 1988 and 1996 real wages dropped 26 percent (Table 4.8). Real wage declines in the early l990s were associated with the contraction of economic activity and loss of external markets. The declines in 1995-96 were policy related: the stabilization package of 1995 although allowing nominal public wages to increase to some degree, this increase did not keep pace with inflation, so real wage values eroded.. 4.24 The 1994 increase in real wages seemed to be the result of several factors. First, it was an election year, so the government's wage policy was looser than originally planned. Second, the composition of the workforce had changed considerably, and the weight of better-paid non manual workers started to increase (Vaughan-Whitehead 1998). An upward trend in real wages in 1997-98 was partly the result of economic growth and the disinflation that had started already 49 in 1996. In general, recovery in real wages seems to lag behind output recovery by one year (World Bank 1999a). Table 4.8 Monthly gross and net average earnings, inflation, and real earnings, 1989-98 Average earnings offull-time employees Consumer price Index of net real index earnings Gross Net Gross Net Year (forintper month) (previous year= 100) Previous Year 1989 = 100 = 100 1989 10,571 8,165 118 117 117 100 1990 13,446 10,108 129 122 129 94 1991 17,934 12,948 130 126 135 88 1992 22,294 15,628 125 121 123 86 1993 27,173 18,397 122 118 123 83 1994 33,939 23,424 125 127 119 89 1995 38,900 25,891 117 113 128 78 1996 46,837 30,262 120 117 124 74 1997 57,282 37,555 122 124 118 77 1998 68,738 44,691 118 119 114 81 a. 1989-91 covers all organizations with legal entity; 1992-94 covers just 20 + organizations; and 1995-98 covers 10 + organizations. Net earnings data are calculated by the upper limnit of social insurance rate. Source. Employment and rate of earnings from 1993 to 1995 (CSO 1996); Major labor processes, QI to IV 1996, 1997, Statistical Yearbook of the CSO 4.25 In 1989-98 a well-functioning tripartite wage negotiation mechanism aimed at establishing a uniform annual minimum wage (Ministry of Labor 1998) and proposing an economically acceptable average wage for the entrepreneurial sector (Vaughan-Whitehead 1998). In practice, however, paid wages could be below the official minimum wage for two reasons. First, the Interest Reconciliation Council allowed delays in introducing the increased wage for branches facing major difficulties. Second, in industries with piece-rate or payment-by-result schemes, workers who could not meet the criteria were not paid the minimum wage. Such schemes were particularly common in industries employing female manual workers. 4.26 The movements in the real value of the minimum wage and in that of the average wage are presented in Table 4.9. Minimum wage increased in the first two years of the period in real terms, then started to decline in the first half of the 1990s. Net average income decreased from 1989 and fell by more than 25% until 1996. The turning point for both the minimum wage and the net average income came in 1997, where the real increases were 3 and 5 percent respectively. In this period the wage policy of the government aimed at preventing the minimum wage from decreasing further relative to the average income. This goal however, due to the higher increase in nominal income, was not attained. The relationship between the minimum wage and subsistence minimum is described by the data in Table 4.10 and Chart 4.1. It should be noted that the methodology for calculating the subsistence minimum was changed in 1994. Values based on the new methodology are lower than those based on the old calculations. 50 Table 4.9 Minimum wage and real increase of minimum wage, 1990-98 Real charge in net nminimum Monthly average net w ge Real charge in average net w age minimum wage pre wg (HUF) wag prev. 1988=100 prev. year=100 . 1988=11)0 1989 3294 104.3 104.3 99.7 99.7 1990 4515 106.4 111.0 94.3 94.0 1991 5989 98.2 109.0 93.0 87.4 1992 7122 96.7 105.4 98:6 86.2 1993 7847 90.0 94.8 96.1 82.8 1994 9178 98.5 93.4 107.2 88.8 1995 10671 90.7 84.7 87.8 78.0 1996 12376 93.9 79.6 95.0 74.1 1997 15045 102.8 81.8 104.9 77.7 1998 17258 100.4 82.1 103.6 80.5 Source. Ministry of Economic Affairs, 1999. Table 4.10 Minimum wage and subsistence minimum, 1989 to 1999 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Minimum wage Gross 3658 5017 6700 8000 8917 10375 12062 14308 17000 19500 22500 Net 3294 4515 5989 7122 7847 9178 10671 12376 15045 17258 17888 Subsistence Minimum with two active 4059 5349 7147 8162 11183 13023 9785 11915 14083 17189 19287 21286 with I active 5642 .. .. .. .. 18036 13496 16435 19425 23709 26603 29360 Net minimum wage/subsistence minimum (%) with two active 81.2 84.4 83.8 82.7 70.2 70.5 93.8 89.6 87.9 87.5 89.5 84.0 with I active 58.4 .. .. .. 50.9 68.0 64.9 63.7 63.5 64.9 Source: CSO Chart 4.1: Minimum wage per average income ratio (%) 50 40 -uw- Net 30 20 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 Source: Ministry of Economic Affairs, 51 4.27 Estimates based on employer surveys show that few people are paid at or below the minimum wage-about 3-4 percent in 1991 and 2 percent in 1995-97. This is due to two factors. First, even the lowest-wage public servants earn more than the minimum wage. Second, the once-widespread practice among small enterprises (fewer than 10 employees) of paying the minimum wage and compensating employees in other ways became less common when fringe benefits started being taxed the same way as earnings. 4.28 Although wage differences started increasing in the 1980s, income inequalities were more compressed at the start of the transition. The second highest income decile (the ninth) earned 3.13 times more than the lowest in 1989; this number grew to 4.32 by 1997 (Ministry of Economic Affairs 1999). In the 1980s there were already significant occupational and industrial differences, but in the 1990s differences by age and sector becamne more pronounced. For example, earnings in comparable jobs in the public sector fell about 30 percent behind those of the private sector every year since 1992, reflecting fiscal tightening (Vaughan-Whitehead 1998). In the 1990s, the renumeration of higher educational and professional training qualifications has significantly improved which resulted in the increase of wage differences between blue-collar and white-collar employees. In the public sector, where white-collar employees account for a greater proportion, wages could not keep pace with the wage increase in the entrepreneurial sector. (Public wages are 30-35% lower than entrepreneurial sector wages in similar positions). In addition, excess employment in certain public services-especially health and education- pushes down individual wages (Bokros and Dethier 1998). 4.29 Relative to the private sector, wage increases in the public sector lagged behind in 1992-98 (Ministry of Labor 1998). Within the public sector, different employees experienced different outcomes. Wages of civil servants, lawyers, and judges exceeded the national average over the entire period, while those of education and health workers lagged behind (Table 4.11). In 1995, for example, judges earned 70 percent more than the national average, while teachers earned only 55 percent of the average. Table 4.11 Public sector wages, 1992-98 (private sector average =100) 1992 1993 1994 1995 1996 1997 1998 Public sector 94 71 72 71 58 66 64 Education 85 70 61 60 45 54.2 53 Health 91 86 71 67 59 68.1 65 Source: Ministry of Economic Affairs. 4.30 On average, wages in the private sector have been continuously above those in the public sector. Some sectors have done better than others, however. Retail trade, for example, was hit harder by economic restructuring than was any other sector; agriculture (including forestry) has also lagged behind. Manufacturing and construction wages are close to the average. And wages have grown rapidly in electricity, telecommunications, and other services (such as banking and financial services), especially among people with higher education. 4.31 According to World Bank (1999a), labor productivity in manufacturing grew 121 percent in 1992-97. Labor productivity rose particularly fast in companies with at least 10 percent 52 foreign ownership. High wage increases among skilled physical labor seems to be the result of improved productivity. 4.32 Wage disparities vary by enterprise size. Companies with more than 300 employees tend to pay wages close to or slightly above the national average. Companies with fewer than 20 employees fall about one quarter below the national average. 4.33 Regional unemployment differences are reflected in regional wage differences. Exc uding the central part of the country (Budapest and its surroundings), wages were about 7 percent lower in the most backward regions than in the best-earning region (Fazekas, Kertesi, and Ko6116 1997). When Budapest is included, this difference is 21 percent. 4.34 Returns to education (skills) started to increase in 1986. That year, wages of people with higher education were about 60 percent higher than those who had completed eight years of schooling; by 1996 these differentials had doubled. According to Galasi (cited in Kerte,i and K6116 1998), in 1990-94 returns to education increased the most for those with higher education. Those with secondary education were also better off. Returns to education in 1994-98 show similar patterns (Kertesi and K6116 1999). 4.35 Two groups suffered the largest drop in earnings: older workers and poorly educated workers. Young and university-educated workers suffered relatively little, with earnings fal ling by less than 7 percent. Workers who were highly paid in 1992 also lost big, while poorly paid workers improved their absolute and thus also their relative earnings position. This pattern of earnings mobility, whereby the rich lose and the poor gain, countervailed the tendency toward increasing earnigs inequality. 4.36 There is no clear pattern of wage growth by occupation. Manual workers experienced only slightly larger wage losses than nonmanual workers. Managerial and professional workers saw their earnings fall 10 percent, unskilled workers 12 percent-a small difference over five years. Surprisingly, skilled manual workers were hit the hardest; their wages fell 14 percent. 4.37 Low-paid workers are a natural focus of poverty analysis, because low pay is associatld with poverty. As indicated, the working poor account for a non-negligible part of poverty in Hungary. Defining low pay as less than two-thirds of median earnings, 14.1 percent of workers received low pay in 1997. That falls in the middle of the range for OECD countries. 4.38 Gender differences in wage growth are not large, although women have improved the r earnings position relative to men (World Bank 1999a). Nevertheless, a low-paid worker is more like to be a woman, either young (under 24) or older (over 50), with only primary or lower education or (less often) basic vocational training, with an unskilled manual job and living i ri a village. About one-third of unskilled and poorly educated workers are low-paid. Accordingly, the incidence of low pay among unskilled and poorly educated workers is more than twice the average. 4.39 While a relatively small fraction of workers are low-paid in Hungary at any given moment, more than one-third were low-paid in 1992-97.15 This means that the risk of low pay is much IS For the sake of comparability with other OECD countries, in the ensuing analysis low pay is defined here as 53 higher than in most OECD countries, where the portion of low-paid workers over six years is generally less than 25 percent-and often less than 20 percent. 4.40 In Hungary low-paid jobs are more often a permanent trap than a stepping stone to higher earnings. Three points deserve to be emphasized. First, an extremely large portion of workers who were low-paid in 1992 were no longer in full-time employment in 1997. Most had withdrawn from the labor force or became unemployed. Second, 54 percent of low-paid workers are still in the bottom income quintile after five years. This compares unfavorably with other OECD countries (except the United States), where less than 50 percent of workers cannot escape low pay within five years-more evidence of a relatively large group of permanently low-paid workers in Hungary. Third, most Hungarian workers who escape low pay move to the second quintile; "long-distance" earnings mobility is more limited in Hungary than in other OECD countries. For example, in Hungary only 19 percent of low-paid workers managed to move to the third, fourth, or fifth income quintiles. In the United Kingdom 27 percent did so. Thus in Hungary low-paid workers tend to stay trapped in relatively low-paid jobs. bottom quintile of monthly earnings. 54 5. POLICY CONSIDERATIONS AND FURTHER RESEARCH 5.1 Hungary's social protection policies must address three main challenges: reinserting the long-term poor, the special problems of the Roma, and decentralization. Further research on certain topics will assist in the process of policy formation. Hungary's long-term poor are cut off from the labor market, making reinsertion into productive society difficult. The Roma are socially excluded and require even more effort to integrate them into the labor market. These challenges are complicated by Hungary's decentralized social protection system, which means that the poor are treated differently depending on where they live and what local social protection programs are available. This is a major disadvantage of decentralization: it leads to unequal treatment of the poor, with less financing available where social programs are most needed-in poor regions. 5.2 Policy options for these three key areas (reinsertion, the Roma, and decentralization) were discussed with a group of Hungarian experts gathered in Budapest on May 15, 2000.16 Their views are summarized in each section. The final section of the chapter lays out possible areas for future research. Reintegrating the Long-term Poor 5.3 A comprehensive poverty reduction strategy for Hungary cannot rely exclusively on economic growth. While growth will continue to be necessary to create well-paying jobs that would enable people to escape poverty, still there is a group of long-term poor that are not likely to benefit from growth since they are detached from the labor market, socially excluded, and in many cases, facing discrimination which keeps them from reintegrating into the labor market. Neither can a poverty reduction strategy rely exclusively on the current social protection system to deal with those who are "stuck" outside from benefiting from growth. The system functions reasonably well, and the four main social transfers-family benefits, unemployment benefits, pensions, social assistance-reduce overall poverty. But the system does not lift the long-term poor out of poverty. 5.4 The socialprotection system works reasonably well to protect the poor, but this has been achieved primarily through universal benefits-which means that Hungary is spending more on social protection than would be required if benefits were targeted to the poor. These findings-that the social protection system is not targeted to the poor-echo those of Grootaert (1997) and Braithwaite, Grootaert, and Milanovic (2000) as well as the World Bank's poverty assessment (World Bank 1996). The social protection system touches almost every Hungarian household-in 1997, 86 percent of Hungarian households received at least one social transfer. By offering social assistance as a benefit of last resort, Hungary is taking an approach similar to that in EU countries, all of which but one exception have some sort of a minimum income guarantee. 16 The group included Ms. Zsuzsa Ferge , Ms. Julia Szalai , Mr. Balizs Kremer , Ms. Ildik6 Ekes, Mr. Istvan Gy6rgy T6th, Mr. Zoltan Fabian, Mr. Peter Sziv6s, Ms. Zsuzsa Laczk6, Mr. Zsolt Zadori, Mr. Janos Zolnay, Ms. Eva Konig, Mr. Gy6rgy Mezei and Mr. Janos Ladanyi. 55 5.5 The amounts that households receive for social assistance are low-too low to lift most households out of poverty. The long-term poor include the homeless, some of the rural population (particularly those living in micro-communities), workers who have lost their jobs or withdrawn from the labor market, households with three or more children, single-parent families, and single elderly females. Some of these households receive social benefits, including; family allowances, maternity benefits (GYES, GYET), and unemployment benefits. However, social assistance benefit in Hungary appears to be smaller than in many EU countries. As a result Braithwaite, Grootaert, and Milanovic (2000) have termed social assistance in Hungary "irrelevant." Better targeting of other benefits would free up resources that could be used to boost social assistance. Better targeting is not a new idea-it was raised in World Bank~ (1996) and in Grootaert (1997). Grootaert and Braithwaite (1998) and Braithwaite, Grootawrt, and Milanovic (2000) recommend a targeting mechanism based on a combination of indicalors that does not impose an excessive administrative burden. 5.6 For some groups with limited earnings potential, such as rural single elderly women, increasing social benefits could alleviate poverty. However, for other groups, such Is able- bodied Roma, this move could create a trap of poverty and dependency. For such long-term poor, a better solution would be to devise ways to reintegrate them with productive society. Cycling from unemployment insurance to public work programs back to unempl.oyment insurance does nothing to move participants back into the labor market, and more or less dooms them to further dependency. Reinsertion programs are needed to break this cycle. 5.7 The United States is reforming its welfare system to require recipients to work---with a mandatory cutoff of benefits after five years. Critics of the U.S. reform expect the withdrawal of benefits to expand the ranks of the poor, but most agree that something must be done to reintegrate the U.S. poor with the labor market. The best reforms have taken a comprchensive approach, teaching welfare applicants basic job skills and providing child care and transportation assistance. 5.8 France has considerable experience with reinsertion programs for the long-term unemployed. Its system provides a minimum income subject to a reinsertion contract that goes beyond job search assistance and training to include basic life skills such as literacy, treatment of drug and alcohol addiction, training for parents, guidance on applying for family allowances and driver's licenses, and so on (Box 5.1). The system is designed to reinsert recipients into work or society at large (see Annex 10). Introduced in 1988, the reinsertion contract and minimum income program were extended in 1992 to include free health care and housing benefits (Evans, Paugman, and Prelis 1995). 56 Box 5.1 France's Revenu Minimum d'Insertion-a reinsertion system for the long-term unemployed France's reinsertion system combines a safety net payment with active labor market and other social reinsertion programs. The goal of the program, besides providing a minimum income, is to give beneficiaries the incentives and tools to find a job. In some cases the search for employment first requires specific actions to help beneficiaries reintegrate with their social network or overcome nonwork-related limitations. The social reinsertion part of the program provides health and housing support to all beneficiaries as well as specific measures depending on individual needs. The program's mean-tested minimum income is based on the recipient household's composition, income, level of other social allowances, and access to free housing. In 1996 the average benefit was equal to 36 percent of the minimum wage net of social contributions. The program is open to all legal residents of France 25 years and older-and to younger residents if they have dependent children. The central government finances the safety net payments and their administrative costs. Departments (France's semiregional jurisdictions) finance the reinsertion programs. Department spending on the reinsertion programs must be at least 20 percent of what the central government spent on benefits in the corresponding area in the previous year. In 1997 the program cost 43.5 billion francs, of which 77 percent was centrally financed and 23 percent was locally financed. Besides financing health care, resources for the reinsertion programs are used for: . Professional reinsertion-subsidized jobs in enterprises and public or nonprofit organizations, financing to create enterprises (41 percent). * Social reinsertion-fighting illiteracy, refreshing knowledge, higher education, support for accessing public services and benefits, social or individual help such as family budget planning (27 percent). * Housing problems-higher financial support than stipulated by the law ( 11 percent). * Health status-prevention, financial support for health spending not covered by insurance, fighting alcoholism (4 percent). Every beneficiary must be offered a reinsertion contract within three months of receiving the benefits and receive an individual follow-up. In return, the recipient must provide a quarterly income report. In most cases the contract, negotiated with local social workers, lays out a strategy for re- employment. In cases where successful professional reinsertion occurs, benefits are reduced over 9 to 12 months to ease the tradeoff between getting a remunerated job and losing the subsidy. About half of the beneficiaries who get jobs work in enterprises. One-third work in unsubsidized occupations. The only indicators available to gauge the success of the reinsertion program relate to the number of exits. But exits are not a good measure of the success of social reinsertion. An exit may occur if a person receives another type of allocation such as for a handicap, single motherhood, or retirement. The exit may be interpreted as a success, but the person may still be experiencing hardship. In any case, exit from the program is low-in 1997 about 30 percent of beneficiaries left the system for at least three months. Young and better-educated beneficiaries are more likely to be able to find a nonsubsidized job. Women are more likely to exit the system because they are eligible for other benefits. By contrast, illiterate or unhealthy participants have little chance of success. 5.9 Hungary voiced a similar intent in the 1993 Social Act, which stipulates that individuals receiving regular social assistance must "cooperate" with social workers. Quite a few individuals 57 prefer not to cooperate, however, and so must rely on informal activities and transfers from friends and family. 5.10 Special outreach is needed to identify dropouts and reinsert them into at least the regular social assistance programs. Many Hungarians have withdrawn from the labor market after falling through the safety net of three years of unemployment benefits plus regular social assistance. It is recommended that the 1993 Social Act be extended to fill this need. An obvious first step would be outreach to the dropouts to register for regular social assistance. Beyond this, there is little consensus in Hungary about how to reintegrate the long-term poor (Box 5.2). Box 5.2 Views on Reinsertion Programs for the Long-Term Poor Hungarian experts have differing views on how to solve long-term poverty in Hungary. Th- major difference in approach is the question of whether social benefits should create the basis of ooverty reduction programs or if these are dead-ends and massive reinsertion programs requiring participation and activity are better. On the one hand, the social policy of 'Work instead of allowance', that is, reinsertion programs which require some form of cooperation from the poor seem acceptable to all the experts. That is because experience shows that social networks, links to the labor market, skills, and ability to communicate are crucial factors in helping people out from poverty. Reinsertion programs might help the poor to maintain or even acquire some of these skills and to develop their social capital. On the other hand, views differ in terms of expectations from reinsertion programs. Those wvho are opposed to launch reinsertion programs on a large scale argue that social policy should guarantee some minimum income to the poor. Implementation of reinsertion programs in Hungary has shown since 1996 that the principle of work instead of benefits have been turned into 'benefit for work'. People participating in public works did not obtain eamed income but were granted social benefits. This practice is rot only unfair to the poor but also non-constitutional. Experience shows that public work prograTris were particularly successful in r-gions where the economy grew, but less relevant in depressed areas of the country, or in small villages. In addition, , there is a risk that provisions to the poor will become subject to the capacity of local govemments to establish sufficient work programs. Another group of experts claims that the purpose of reinsertion programs is 'to save the ciildren' of long-term poor. There are two kinds of active programs at the local level with a focus on the long-term poor, local public works and mental programs providing basic self-management skills. Although public works are usually very costly, - three times as many people could be served if funds are allocated in forms of allowances -, and less than five percent of participants finds stable employment, still publ-c work programs provide the only long-term solutions. That is because reproduction of poverty could be diminished only through ensuring that healthy family pattems are inherited where the adults have daily activity and live on earned incomes. Implementation of public work programs may differ from seitlement to settlement, nevertheless, rural localities seem to have acquired more knowledge in managing active programs recently, so that the management constraint might not be so significant as some argue. 5.11 Active labor market policies could complement social protection interventions. The govermment has two main public work and training initiatives: public work programs administered and financed primarily by county labor offices, and prograrns managed by the Ministry of Social Assistance. Public work programs managed by the ministry are t'pically larger projects such as construction or road projects or social service programs. 58 5.12 Regional development programs should be considered as well as programs to help people migrate from small villages to places where they can find work. In many villages the local economy does not offer any well-paid employment, and recipients may not be able to afford transportation to other areas. The government has already taken action in this regard, with three regional development programs focused on areas dominated by unemployed Roma, but more action is required. 5.13 The Government is also considering to introduce a new initiative to combine public work programs with training for basic job skills. The program involves four days of public work and one day of basic training each week. Training would focus on basic skills because specific occupational training has been largely ineffective in returning the long-term poor to the labor market. 5.14 The Government should consider private sector involvement in reinsertion programs. For example, the World Bank supported a Youth Training Project in Hungary that involved the creation of curriculum and institutional development for post-secondary job-oriented training and secondary vocational orientation expanding the number of secondary vocational schools utilizing the curriculum developed under the Human Resources Project. Through the vehicle of the National Vocational Training Council and direct solicitation of views, labor market demand was a key input in design. Roma 5.15 The Roma present a particular challenge for social policy in Hungary. This report has documented that the Roma are more likely to be poor simply because of their ethnicity (controlling for other poverty correlates like household size). This points to the existence of systematic discrimination and bias against Roma which precludes many of them from finding well-paying work that would lift them out of poverty. But what should be done to end the discrimination and integrate the Roma into the labor market? There is no easy answer to this question. The Government faces fundamental challenges towards reintegrating the Roma into Hungarian society. The two most important opportunities -well-paying jobs and education--are also the most difficult to achieve. 5.16 Most of the dropouts from the social safety net are Roma. Many Roma villagers are totally dependent on state transfers, surviving on unemployment insurance or regular social assistance and family benefits. But these amounts are too small to lift households out of poverty. In addition, many Hungarians have withdrawn from the labor market after falling through the safety net of three years of unemployment benefits plus regular social assistance. Some experts favor special programs targeted towards the Roma, but others wam against this approach, suggesting that it could lead to further marginalization, exclusion, and discrimination against the Roma (Box 5.3). 59 Box 5.3 Views on Policy Towards the Roma Experts have markedly different approaches to policy measures regarding the Roma popu ation. While some argue for 'blind programs' treating the poor and the Roma the same way, others argle for special Roma programs. No consensus was reached. Those who oppose positively discriminating Roma programs claim that special programs evoke negative memories in the current society and public administration owing to their only partial success during the 1970s and 1980s. Due to these memories there is a risk that Roma programs would be turned into Roma registries and would stigmatize the Roma instead of successfully integrating them. Some experts considered it worthwhile to treat the Roma as a specific ethnic group so that they wou d get special treatment along the pnrnciples of supporting ethnic minorities in Hungary. Their autonomy, financial support, and programs should be strengthened just like in case of other minorities. In addlition, there are ample opportunities to improve the current situation of the Roma even without distincetive programs. On the one hand, the social integration of the Roma could be more successful if the public administration, from local levels to the courts, treats them equally. On the other hand, better target ng of benefits to them, as well as mechanisms to ensure that benefits mandated by the legislation are actually granted to the Roma could diminish poverty among them. Experts working with groups experiencing discrimination have argued that the liberal approach to treat all long-term poor equally is fundamentally wrong. The Roma do not start from the same situat: on as other poor social groups or minorities. They do not only have to overcome factors of poverty but react to wide range of discriminative practices in daily life. As a consequence, special Roma programs are nceded which actively countervail discrimination. These programs might not need to stigmatize. It is possiAle to design active programs which implicitly prioritize the Roma. Particular attention and support shoL Id be directed to schemes designed by Roma self-governments and other Roma associations. In addition, it is worth considering programs ensuring better education and socialization of younger generations (i-f the Roma, in the form of special education institutes and training of teachers with Roma origin. 5.17 The government of Hungary has some experience with programs targeted to the Roma One such program is a public works program for improving Roma settlements. The program benefits participants by providing work and local communities by improving infrastructure. Another program is the agricultural land program, which gives poor families plots of land and teaches them how to produce food. This program has helped create subsistence farmers but has not created stable employment or entrepreneurs. 5.18 In the medium term more emphasis should be placed on providing high-quality general (not occupational) education to the Roma. The best jobs in Hungary go to those with the skills provided by higher education, yet very few Roma complete generalized secondary education. The government is considering a version of the U.S. Head Start program consisting of special preschool for Roma children and special vocational-technical training for young Roma. For both Roma and non-Roma children, the Open Society Foundation has supported the "Step-by-'Step" initiative, modeled after the US Head Start program. The government recently signed an agreement with PHARE-the European Union program for Central and Eastern Europe--to provide new kinds of vocational-technical training to the Roma. However, the emphasis in the future should be on getting more Roma to complete generalized secondary education, since ihese general skills are what the labor market demands. 60 5.19 Education has an important role to play in reducing discrimination against Roma if multicultural approaches are used throughout the school system, and not just in the Ghandi School in Pecs, Hungary, which targets the Roma. The addition of Roma teaching assistants could provide a link between Roma communities and school and help to keep more Roma children in school. Making Decentralization More Equitable 5.20 Hungary's social assistance system is highly decentralized. Within the framework of the Act on Social Assistance, each of the country's 3,200 or so localities has a social decree specifying the social assistance it provides. Social assistance is financed by earmarked funds, and normative transfers from the central budget and from local budgets (see Chapter 3). But the normative transfers are not earmarked-and so can be reallocated from one area (such as social assistance) to another (such as education). Some localities reallocate as much as 90 percent of the social normative to other uses, while others spend 40 percent more on social programs than the normative specifies, covering the difference from the local budget (Konig 1998). 5.21 The poor are treated differently depending on where they live and what local social protection programs are available. Decentralization is often advocated because local authorities have access to information not available to the center and so presumably can make better decisions about allocating funds among local needs and priorities. But decentralization has a disadvantage as well and may lead to unequal treatment of the poor, with less financing available just where social programs are most needed-in poorer regions. Views vary widely on the desirability of decentralization in the provision of social assistance (Box 5.4). In other World Bank research on decentralization in Hungary, central financing for social assistance was recommended (Wetzel 1999) precisely so that a minimum standard could be attained in all regions of Hungary. 61 Box 5.4 Views on Decentralization Experts had substantially different views on whether social protection of the poor is a t-ype of public service that should be decentralized--that is--whether management and funding of poverty re lucing policies could be based on local governments. Experts supporting decentralized service provision to the poor argued that decentralization is an important policy priority in itself for ensuring that allocating mechanisms are close enough to citizens. Hence, whatever mispractices may exists in implementing social policy at the local level, caring tor the poor should remain a local responsibility. Re-centralization may cause more harm than good. Inslead, it should be ensured through monitoring that localities comply with the principle of the law on Social Assitance and that their local social decrees contain guarantees for the poor. Substantial programs, helping large number of people, should be cofinanced by the central govemment in order to ensure that local interest does not divert funds away from the poor. Others have argued for re-centralizing social protection of the poorest segment of the population. They claimed that the Act on Social Assistance allows too much discretion in implementing social policy at the local level. As a consequence, social services are unevenly provided across the country. Local priorities and/or political bargains often divert funds from the poor to the non-poor so that the leakages might be substantial in some localities. There are no guarantees in the current system that sufficient care will be afforded to the poor. In addition, the capacity of localities to manage active programs liffers significantly, so that the place of residence seem to determine opportunities to participate in such programs. For those supporting centralization, the central government best represents the interest of its poor citizens and should earmark funding for social purposes. 5.22 Hungary's decentralized social protection system means that poor people receive more benefits in certain regions than in others. For example, Ozd, in the poor eastern region of Hungary, provides nine local social protection benefits. But the Ferencvaros district of Budapest offers 20 different local benefits (see Box 3.1). The current regulations lead wide scope for local discretion in the granting of benefits (Box 5.5). 62 Box 5.5 Discretion In Local Social Policy The inherent contradiction of local social policy stems from the fact that municipalities carry out tasks prescribed by law, financed partially out of earmarked resources, (that is, out of state resources that cannot be used for other purposes); partially out of non-restricted normative state resources; and, finally, their own resources. At the same time, the Acts specifying the service provision responsibilities of the municipalities -Act on Social Assitance, Act on Housing, Act on the Protection of Children, Act on Minorities - are the legal framework within which local politicians are able to decide freely on the local regulation of social responsibilities, the way of providing benefits and utilization of the greater part of the resources. Citizens also should be aware of their social rights guaranteed by law, but they also should realize the fact that municipalities have great freedom in interpreting these rights and can provide these benefits only if they have the available resources. The municipalities have several tools to determine what kind of assistance they are going to provide: * They have the authority to decide the eligibility of the individual to certain benefits such as regular child education allowance, housing maintenance allowance, nursing care. * They can impose restriction on or make eligibility requirements extremely difficult to fulfill; this is the case when it comes to provide income supplement for long-term unemployment; housing maintenance allowance, temporary assistance; or regular social assistance. * They can restrict the amount of allowance by defining either the threshold for giving allowance, or the maximum amount of allowance that can be paid, or the number of allowances that can be given in a year. * They may also decide to swap a given cash allowance to an in kind allowance. For instance, to the expenses of the regular child protection benefits they pay the part of the costs of school meals that otherwise would be payable by the parents. It also happens that they do not give the temporary assistance granted to the beneficiary, but to a social worker who then buys the food or clothing deemed necessary for the recipient of the allowance. * They may decide to finance a certain type of allowance to the expenses of another type of allowance. This is a general practice in the case of child welfare benefits. The municipalities do not pay the regular child education allowance or the regular child protection benefit to the family, instead they cover the municipality's part of the meal subsidy and the subsidy given for schoolbooks out of that amount. Temporary assistance has a key role among municipal social benefits. According to the data of the CSO, the number of families receiving temporary assistance and the real value of the assistance is shrinking, however, the largest group of the recipients of allowances receive cash assistance under this title. The applicants receive occasionally low amounts of assistance, which do not improve their financial position substantially. The municipalities have the greatest freedom in creating the regulation when setting the conditions of temporary assistance. The conditions set locally are often bordering on illegality; they violate fundamental legal principles considered untouchable in other areas of life. Consider the example from the social regulation of Nagybajom (3427 inhabitants) specifying that no temporary assistance can be given to anyone spending more than a specified amount of time in pubs or around slot machines. The regulation specifies the maximum allowed period of time: someone may spend in pubs as four hours a week to avoid any sanction (reduction or elimination of benefit) by the municipality. Furthermore, the regulation even specifies that participation at some events organized by the local authorities does not affect this allowed weekly time limit. Source: Zolnay (1999): 63 5.23 If alleviating poverty is a priority for the central government, the tools for doing so should be centrally mandated to ensure uniformity across the country-and should receive central financing to ensure equal access to services. Conditional matching grants are needed to reduce horizontal inequality. The central government would provide such grants, conditioned on local spending. For social assistance the central government could set a minimum benefit to bz spent by localities and could transfer grants that would have to be matched by local spending (instead of the unconditional normative transfers now used). The grants could be differentiated according to a region's overall poverty or poverty gap, with poorer regions receiving larger grants. But such grants would erode local autonomy and require major legislative amendments--and so would be opposed by many or most localities. Thus conditional matching grants may not be politically feasible, even though this form of financing is consistent with the idea of providing a minimum standard in all areas of Hungary. 5.24 It is generally agreed that social protection policy should promote employmenit over allowances. An unemployment support system guided by local governments has advantages and disadvantages. Nevertheless, local administrations have proven themselves effect ive at identifying those in need, and so should play a key role in implementing social policies. Box 5.6 New Public Work Scheme Horizontal inequity is likely to be aggravated by the Government's proposed changes in the unemployment benefits system, which extensively makes use of local management capacity. This however varies substantially across municipalities. The new scheme reduced the total time of receiving unemployment related benefits fromn three years to 9 months, and after nine months, required beneficiaries to work in local public works sch,zmes at least for 30 working days prior to receiving further cash benefits. The scheme is based on the pinciple that the government shall provide work instead of allowance, and aims at mobilizing the large stock of long-term unemployed (approximately 200,000 people).The central government reimburses daily HUF 1,500/person for the working days plus 75 % of the regular social benefit which covers the direct costs of the employment while the administrative costs of organizing employment opportunities, preliminary medical check-up workers, equipment to work with, transportation costs. should be covered by the local government own resources. To those not enrolled in local public work programs but eligible fir cash benefits due to their low per capita family income localities have to provide regular social assistance These extra costs are also reimbursed up to 75 percent by the central government. The system has been operated since May 1, 2000, and the time elapsed is too short for ev- luating its advantages and drawbacks. The first experience shows that small villages, where long-term unemployment is highest, lack administrative capacity and potential work places for public work szhemes that would employ all or most of their large unemployed population. Cooperation among neighboring municipalities is crucial to create schemes of adequate size and to ensure effective administration-yet the system does not promote joint activities. Larger localities have outsourced the administrative iasks to newly established public purpose associations which in some cases provide services for neiglhboring localities too. The system has several weaknesses. First, local public work schemes hardly ensure re-entry to the labor market-only 5 percent of participants find permanent jobs. This happens because the s,zhemes rarely provide new skills. And despite recent economic growth and improvements in the labor market, the number of long-term registered unemployed has held steady at 200,000-250,000 people. Most have been written off by the labor market because they are poorly educated and lack marketable skills. To keep these people employed, localities will have to create permanent public work schemes on a large scale. 64 Second, it is unclear how the new system will affect other elements of the social safety net (disability pensions, family allowances, maternity benefits). In recent years local authorities tried to get those who were terminated from the long-term unemployment benefit-and hence should have received social assistance from the local budget-to cycle through local public work programs so that they would become eligible for (partly) centrally financed benefits (World Bank 1999a). Such moral hazard will continue under the new system of unemployment benefits. Some of the long-term unemployed will likely show up in universal benefit systems (such as the GYES), in targeted but centrally financed systems (such as the GYET), or in other parts of the social safety net (such as disability pensions). The potential budget implications have not been evaluated. Third, differences in local abilities, preferences, and budget levels mean that social assistance will differ greatly across the country, jeopardizing a national strategy for poverty alleviation. Each year about one-third of the registered unemployed-100,000 people-have not been eligible for any kind of unemployment benefit and have instead depended on local social assistance (especially the regular social allowance). With the new system, in the absence of sufficient local public work schemes, many of those who received the long-term unemployment benefit now need local social assistance.. Unfortunately, social assistance in and of itself is not sufficient to lift recipients out of poverty, nor is the existing system well-targeted to reach the poor. Further Research 5.25 There are several areas in which policy could be better informed by further research. Reasons why an estimated 100,000 people dropped out of the social safety net are unclear as is how such dropouts have managed to survive. Research should be undertaken to improve the estimate of dropouts and critically, to understand why they decided to drop out. 5.26 Further research is clearly called for to understand the Roma culture and the interaction between Roma sociology, majority prejudice, and poverty. These interactions are extremely complex (Fonseca 1995) and vary depending on the subgroup of Roma involved. Anthropological and sociological approaches would be necessary to elicit better understanding of Roma culture. Qualitative and participatory approaches would be necessary to get at these sensitive issues. 5.27 Better quantitative data for poverty analysis such as undertaken in this report, with over- sampling of Roma so as to have enough observations for statistical inference is also important. The Ford Foundation recently financed a quantitative survey on poverty and ethnicity for six transition countries including Hungary,'7 and researchers are beginning to work with the data, with results expected within two years. Sources of quantitative data on the Roma could be expanded by matching the TARKI panel data set used here with the cross-sectional data collected by the Central Statistical Office, to impute ethnicity into the cross-sectional data and have recourse to a greater number of observations. 5.28 If more quantatitive data on the Roma were available, further multivariate regression analysis could be undertaken. 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