POLICY RESEARCH WORKING PAPER 2328 Social fransfers and Social In Latvia, only I .5 per-cent of households receive social Assistance assistance, which for those households represents 20 . . . . . ~~~~~~~~~~~percent of income. The An Empirical Ana.lysis Using Latvian preto noe h allocation of social assistance Household Survey Data is unequal. Urban households outside the capital (Riga) and Branko Milanovic those headed by male adults are systematically 'discriminated against.' Because social assistance is locally financed, poor households in different parts of the country are treated unequally. The World Bank Development Research G-roup Poverty and Human Resources U April 2000 l1 POLIcy REsEARCH WORKINCG PAPER 2328 Summary findings Milanovic assesses the performance of Latvia's system of households (only 1.5 percent of all households receive it) social transfers, in three ways: but among those that do receive it, it represents a First, he analyzes the incidence (who receives transfers) relatively high share (20 percent) of income. Households of pensions, family allowances, unemployment benefits, that are systematically "discriminated against" in the and social assistance. Per capita analysis shows pensions allocation of social assistance are urban households living tending to be pro-rich and families allowances pro-poor outside the capital (Riga) and those headed by male (a finding typical in poverty analyses). Introducing an adults. equivalence scalc alters the results and shows all Third, he looks at the regional allocation of social individual cash transfers performing about the same: assistance. The results confirm earlier findings of large mildly pro-poor. horizontal inequalities - that people with the same Next, he examines the performance of social income from different parts of the country are treated assistance, which is, by definition, directed to the poor. unequally, because the existing system is based on local He shows that Latvia's current system is concentrated - financing of social assistance. meaning that social assistance is disbursed to few This paper - a product of Poverty and Human Resources, Development Research Group - is part of the Latvia Poverty Assistance Report (February 2000). Copies of this paper are available free from the World Bank, 1818 H Street, NW, Washington, DC 20433. Please contactPatricia Sader, room MC3-556, telephone 202-473-3902, fax202-522-1153, email address psaderCa4orldbank.org. Policy Research Working Papers are also posted on the Web at wwff.worldbank.org/ research/workingpapers. The author may be contacted at bmilanovic@worldbank.org. April 2000. (30 pages) The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, eveis if the presentations are less than fully polished. The papers caorr the namnes of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Produced by the Policy Research Dissemination Center Comments welcome SOCIAL TRANSFERS AND SOCIAL ASSISTANCE: AN EMPIRICAL ANALYSIS USING LATVIAN HOUSEHOLD SURVEY DATA Branko Milanovic' Key words: social assistance; social transfers; Latvia; transition economies. JEL classification: 132; 138; P35. I The paper is part of the Latvia Poverty Assistance Report (February 2000). The views expressed in the paper are author's own and do not necessarily reflect those of the World Bank. The aithor can be contacted at bmilanovic@worldbank.org. INTRODUCTION This paper analyzes the performance of Latvia's systemn of social transfers, using the 1997-98 Household budget surveys data (Annex gives more information on the survey). Section 1 describes the incidence of various social transfers (how much are they focused on the poor?). Section 2 presents an empirical study of Latvia's social assistance (who receives social assistance and how much?) Section 3 compares Latvia's results in poverty alleviation to those of five other transition countries. Section 4 attempts to explain what factors account for the fact that some poor households are systematically excluded from social assistance. Section 5 looks at regional inequalities in delivery of social assistance: a problem which has plagued Latvia authorities since Independence and which is exacerbated by the fact that the full authority for social assistance is vested into municipalities. 1. INCIDENCE OF SOCIAL TRANSFERS Household butlget surveys include six types of social transfers: old-age pensions, other pensions, family allowances, social assistance, unemployment benefits, and other social tranisfers (scholarships, sickness benefits, funeral grant). Total social transfers account for aboult 28 percent of gross income with the two types of pensions accounting for 24.5 percent of gross income. Non-pension social transfers are small: family benefits represent 2.3 percent of gross income,2 unemployment benefits l/2 percent of gross income, and social assistance only 0.3 percent of gross income. The incidence of social transfers therefore strongly depends on what is the incidence of pensions. However, since pensions are a "special" social transfer in the sense that they have an income- smoothing function and may be regarded as deferred wages, our interest in how the social transfers system performs will be focused mainly on the smaller transfers like unemployment benefits and social assistance whose prinmary function, in principle, should be to help the poor households. 3 Table 4.1a shows the distribution of social transfers across deciles of income distribution. All social transfers combined are, in absolate terms, pro-rich: their amounts tend to increase as level of income goes up. For example, the bottom ten percent of population receive only 31/2 percent of all transfers; the lop two deciles receive each more than 11 percent of all transfers. However, the pro-rich bias is due to the role of pensions. If we deduct pensions, the bottom decile receives 13 pe:rcent of transfers, and the top two deciles about 10 percent each. The distribution of non-pension transfers is slightly pro- poor (in absolute terms), as the poorer households (according to income per capita) receive slightly more than the rich. But that result in turn is driven by family benefits. 2 Family benefits inclide maternity, family allowance, child care allowance, and birth grant. 3 This holds by definition for a non-contributory transfers like social assistance. It is slightly different for unemployment benefits which are paid in respect of people who have, at least norninally, contributed (its function is thus an insurance one). 2 Since family benefits are not income-tested 4 and since children are disproportionately represented among the poor, 5 the distribution of family benefits is pro-poor. As the values of the concentration coefficients in Table 1 show, family benefits is the most progressive transfer. The two transfers in which we are particularly interested - unemployment benefits and social assistance-decrease in importance as one moves from the poorest toward the middle-class, and then, surprisingly, increase. Thus, the two bottom deciles of income distribution together receive only 11 percent cf social assistance, and 20 percent of unemployment benefits. The top decile receives 20 percent of social assistance and 10 percent of unemployment benefits. Social assistance displays a very high positive concentration coefficients of +23, indicating that it is strongly pro- rich. The concentration coefficient of unemployment benefits is not significantly different from zero (+3) suggesting an almost flat distribution across income groups. The incidence of social transfers changes when individuals are ranked according to their household per capita expenditures: with the exception of unemployment benefits and other transfers, they all become more targeted on the poor. This represents a true improvement if we hold that expenditures rather than incomes better express the actual level of welfare (both because expenditures reflect welfare, and because income may be underreported). As Table 4.1b shows, the bottom two deciles now receive 20 percent of social assistance, and 26 percent of unemployment benefits. The concentration coefficient of social assistance also improves: it declines from +23 to +5.2, still remaining slightly pro-rich. Family allowances and unemployment benefits remain the only two pro-poor social transfers (note the negative concentration coefficients). 4In 1998, a fanily benefit, paid in respect of all children under 18, was 4 1/2 lats per month. There are 10.3 percent income-poor households; however, they comprise 29 percent of all children. 3 Table 4.1a: Distribution of income sources across deciles of population (ranked by per capita income) Concent. Share in total Decile I Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Decile 10 Cff.i gross income Coeffcient (%) Wages f 2.22 4.40 6.31 6.50 6.56 7.48 9.98 11.78 17.04 27.73 36.9 58.1 Self employment (non-I agro) 1.42 2.09 3.55 3.96 5.18 2.76 5.82 8.91 14.21 52.10 74.7 i.9 Self employment(agro) 0.46 1.56 2.00 2.33 2.73 2.91 3.43 7.54 ;2.43 64.62 UV.1 I., Home consumption 5.90 8.13 7.95 7.34 7 14 16.90 181 72 Old-age pens 1.90 3.60 4.57 9.98 14.41 16.61 13.68 13.89 11.41 9.97 18.2 20.2 Otherpens 3.53 5.50 6.78 8.58 9.90 10.63 10.91 14.55 12.45 17.18 21.8 4.3 Family allowances 15.44 13.77 13.47 10.40 8.99 6.90 7.17 7.74 9.10 7.01 -14.8 2.3 Social assistance 6.60 5.07 8.40 6.64 9.88 6.96 11.28 11.45 13.59 20.13 23.0 0.3 UEB 12.60 8.39 13.63 10.24 4.57 11.16 13.98 5.22 9.62 10.61 -3.0 0.5 Other social 2.63 3.64 4.59 11.53 1.98 8.49 5.33 8.94 21.10 31.78 41.8 0.2 Priv. Cash transfers 2.53 3.06 4.25 3.50 4.15 5.20 8.44 10.63 15.94 42.31 52.1 3.1 Priv. Transfers in kind 4.62 6.29 6.84 6.67 6.14 6.94 11.02 12.50 14.56 24.42 28.8 4.7 Other income 5.28 7.66 8.13 7.83 10.27 8.24 13.44 12.39 10.57 16.19 15.6 1.2 Soc.sec. tax 2.36 4.73 7.03 6.92 7.03 8.45 10.33 12.16 16.68 24.33 3.7 PIT 1.48 3.16 .53 6. 25 8.18 !n3 12 si 18.04 28.25 7.3 Othertax 9.63 5.19 6.59 7.19 7.34 7.18 5.16 12.88 13.78 25.07 0.5 Gross income 2.79 4.60 5.95 6.98 7.87 8.91 10.16 11.92 14.89 25.93 32.8* 100 Taxes 2.13 3.75 6.06 6.48 6.54 8.22 10.14 12.42 17.41 26.85 37.2 11.5 Disposable income 2.87 4.69 5.94 7.04 8.03 8.99 10.17 11.86 14.60 25.82 88.5 Total transfers 3.52 4.84 5.85 9.78 12.95 14.63 12.64 13.26 11.43 11.10 15.9 27.8 Non-pension transfers 13.52 11.61 12.57 10.14 7.93 7.69 8.49 7.70 10.25 10.10 -6.5 3.3 SA + UEB 10.61 7.29 11.90 9.05 6.32 9.77 13.08 7.28 10.93 13.76 5.6 0.8 Note: Each row sums to 100. SA=social assistance. UEB=unemployment benefits. *=Gini coefficient. 4 Table 4.1b: Distribution of income sources across deciles of population (ranked by per capita expenditure) Decile I Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Decile 10 Concent. Coefficients Wages 3.31 4.81 6.42 7.11 8.03 8.18 10.10 12.49 15.04 24.51 31.1 Self employment (non- 2.44 2.54 4.08 1.91 7.10 10.08 12.75 5.88 18.43 34.79 19.5 agro) Self employment (agro) 2.68 7.24 8.92 9.28 9.30 10.12 6.09 24.53 7.26 14.59 46.3 Home consumption 6.04 9.38 9.53 9.00 9.38 11.18 10.38 10.71 11.94 12.47 8.9 Old-age pens 5.11 6.58 7.59 8.91 11.54 12.65 12.93 12.89 11.80 9.99 11.7 Other pens 5.64 7.09 7.83 9.52 8.58 11.36 10.99 10.97 13.61 14.41 15.3 Family allowances 15.49 13.87 12.54 10.66 9.76 8.96 7.78 7.78 7.62 5.53 -16.4 Social assistance 10.67 9.17 8.58 14.40 6.36 6.04 4.79 19.42 7.49 13.08 5.2 UEB 10.98 14.99 7.05 11.28 5.06 7.05 14.28 6.63 10.24 12.43 -0.9 Other social 6.03 4.27 0.98 9.57 7.14 3.84 3.61 10.74 17.61 36.20 42.2 Priv. Cash transfers 3.89 3.19 4.25 4.69 3.90 6.00 10.92 12.76 18.23 32.17 44.0 Priv. Transfers in kind 3.78 5.09 5.58 6.43 7.57 7.51 9.53 11.39 15.78 27.34 33.6 Other income 5.67 5.96 9.82 7.61 9.37 9.69 9.18 13.25 13.07 16.38 17.0 Soc.sec. tax 2.52 4.58 6.33 6.68 8.02 8.31 10.63 13.02 16.12 23.80 PIT 1.96 3.68 5.29 5.85 7.66 7.73 10.21 13.33 17.22 27.05 Other tax 4.37 4.20 8.92 6.76 7.80 7.37 9.42 16.23 14.08 20.86 Gross income 4.21 5.86 7.08 7.73 8.74 9.39 10.38 12.94 13.65 20.02 22.8 Taxes 2.25 3.99 5.78 6.16 7.78 7.90 10.31 13.36 16.73 25.73 36.1 Disposable income 4.44 6.08 7.23 7.91 8.85 9.57 10.39 12.89 13.29 19.36 Totaltransfers 6.21 7.43 7.99 9.25 10.73 11.92 12.09 12.10 11.71 10.57 9.9 Non-pension transfers 13.81 13.11 10.65 11.00 8.58 8.11 8.35 8.70 8.62 9.07 -8.7 SA+UEB 10.88 13.07 7.56 12.31 5.49 6.72 11.14 10.86 9.33 12.65 1.1 Note: Each row sums to 100. SA=social assistance. UEB=unemployment benefits. 5 Table 4.1c: Distribution of income sources across deciles of population (ranked by equivalent adult expenditure; theta=0.8) Decile I Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Decile 10 Wages 3.15 4.53 5.69 6.84 7.73 7.82 10.09 13.01 15.96 25.17 31.7 Selfemplovment(non- 2.76 2.76 3.26 2.79 3.72 11.69 13.69 7.48 17.19 34.66 47.7 agro) Self;np1loyment(agro) 2.69 6.07 5.78 1105 8.92 9.04 8.83 8.52 23.49 15.62 24.7 Home. consutmption 1 5.62 9.00 9.24 9.19 8.59 11.21 12.06 10.93 12.05 12.11 8.6 Old-age pens 6.74 8.00 9.91 10.91 10.96 13.32 11.73 11.10 9.74 7.59 2.7 Other pens 6.73 6.77 8.21 9.96 9.33 10.46 12.07 11.30 11.73 13.45 11.3 Family allowances 12.79 13.12 10.89 10.79 10.06 8.59 9.83 7.87 8.07 7.99 -10.7 Social assistance 10.36 11.05 11.47 7.62 10.35 3.07 20.87 6.10 6.95 12.18 -1.1 UEB 12.04 11.86 10.00 7.00 12.43 6.99 7.54 8.58 10.43 13.13 -2.9 Other social 4.35 5.96 0.00 8.00 6.75 5.08 3.75 15.53 13.93 36.66 42.2 Priv. Cashtransfers 4.05 3.37 4.75 4.78 5.71 5.81 11.23 12.94 17.54 29.81 39.6 Priv.Transfersinkind 4.11 5.19 5.71 5.95 7.86 7.58 10.10 11.91 14.65 26.93 31.2 Other income 5.44 5.68 10.11 9.46 9.28 9.34 9.12 14.20 11.78 15.59 14.7 Soc.sec. tax 2.28 4.23 5.78 6.43 7.69 7.66 10.57 14.20 17.02 24.13 33.3 PIT 1.77 3.50 4.72 5.73 7.14 7.24 10.45 14.01 18.38 27.05 38.7 Othertax 4.10 4.15 5.82 8.97 9.19 7.30 8.79 15.41 15.53 20.74 26.1 Gross income 4.41 5.84 6.90 8.08 8.48 9.23 10.59 11.90 14.68 19.89 21.6 Taxes 2.04 3.76 5.11 6.10 7.41 7.37 10.42 14.14 17.82 25.83 36.4 Disposable income 4.68 6.08 7.10 8.31 8.61 9.45 10.61 11.64 14.31 19.20 19.6 Total transfers 7.36 8.32 9.67 10.63 10.63 12.22 11.58 10.80 9.93 8.88 3.0 Non-pension transfers 11.98 12.33 10.15 9.76 10.27 7.68 9.98 8.30 8.71 10.85 -5.8 SA + UEB 11.48 11.59 10.49 7.20 11.74 5.69 11.95 7.76 9.27 12.82 -2.3 Note: Each row sums to 100. Expenditure per equivalent adult calculated as total expenditure divided by (household size)^0.8, where 0=0.8 is a coefficient of economies of scale. SA=social assistance. UEB=unemployment benefits. 6 But we still assume absence of economies of scale. If we now make a further assumption that the correct ranking criterion is equivalent expenditures and use (rather conservatively) a coefficient for economies of scale of 0=0.8, we get the results in Table 4.1c. Now, the percentage of poor individuals (calculated using the semi-official line of Lats 28 per person per month) is only 11.3, and they (or more exactly the bottom decile) receive 7.4 percent of total social transfers, almost 13 percent of family allowances (less than with per capita rankings that which to place families with children in lower income groups), 10 percent of social assistance, 12 percent of unemployment benefits etc. The targeting of all social transfers except family benefits improves. The concentration coefficient of social assistance now becomes mildly pro-poor (-1.1). The most remarkable change takes place in targeting of old-age pensions and family benefits. When households are ranked by their per capita income or expenditures, family allowances appear strongly pro-poor, old-age pensions strongly pro-rich. This is the usual finding in many poverty assessments. To a large extent, it stems from the fact that in any per capita ranking large households are generally found poor, and since large households are often households with many children in respect of whom family benefits are paid, family benefits appear well targeted. The opposite is true for pensions paid in respect of the elderly who typically live in small households; using per capita measurements the latter are often ranked as relatively well-off. But when we adj ust for both economies of size and lower cost of children (using 0 of 0.8 and 0.6 as in Figure 4.1), the rankings of large and small households change, and consequently does targeting of pensions and family benefits. Now, at 0=0.8 pensions seem to be an almost flat benefit (concentration coefficient close to 0), while family allowances turn out to be much less progressive (Figure 4.1). With a sharper allowance for economies of scales (0=0.6), targeting of the two transfers becomes the same. 7 Figure 4.1: Concentration coefficients of old-age penrsions and family benefits using different ranking criteria I~~ ~~~~~~~~~~~~~ ~ ~ ~~. .... ... ... ..... ........ .. ..... .... ------ ---- -... ------- --. ...... ... .... ......... ... ..... ............... ..... . . ...... ..... ...... .......... .... ................ .. . .. ........... T X 'n ' 'Family all.j oPensions -20 -15 -10 -5 0 5 10 15 20 Concentration coefficient Note: Positive values of thie concentration coefficient imply that the transfer is pro-rich; negative, that it is pro-poor. A flat transfer (same across income spectrum) will have a concentration coefficienit equal to 0. We can rmake two conclusions. First, even leaving aside pensions, the distribution of social transfers is almost flat: it does not seem to be targeted on the poor. For example, the bottom decile of the population receives 10.6 percent of combined unemployment benefits and social assistance when people are ranked by household per capita income; that share goes to 10.9 percent when ranked by expenditure per capita, and finally to 11.5 when adijustment for economies of scale, is introduced. Second, the targeting of the key presumed pro-poor transfers (unemployment benefits and social assistance) improves if individuals are rcanked by their household per capita expenditures rather than by per capita incomes. It imiproves further when we use a relatively mild adjuistmirent for economies of scale. However-, the, overall improvement in targeting i.s slight, and the performance of the key pro-poor transfers is disappointing: both social assistance and unemployment benefits are either neutral or slightly pro-rich. The only consistently pro-poor transfer is family allowances. 8 4.2 AN EMPIRICAL ANALYSIS OF LATVIA'S SOCIAL ASSISTANCE SYSTEM6 Eligibility for social assistance. Because there is no official poverty line in Latvia, we had to base our analysis on poverty incidence on some quasi- or semi-official poverty lines which have been in use. We take a poverty line of 28 LVL per person (at. October 1997 prices) in order to run the poverty profile analysis. That line is equal. to 1/2 of the official Minimum Crisis basket, and is close to 3/4 of the food component of MCB. 7 We further assume that a household would, in principle, qualify for social assistance if its monthly per capita expenditures were less than 28 LVL ($48 or $PPP90 at the time of the survey.8 Definition of social assistance. There are difference in coverage of social assistance between the Household Budget Surveys and the official definition of what constitutes social assistance. The HBS definition is more restrictive. As Table 4.2 illustrates, the difference resides in non-coverage of benefits in kind (housing benefits, social care benefits and rehabilitation) by the HBS. This is, of course, a common feature of most household surveys since they seldom include in-kind benefits like orphanage, kindergartens etc. Housing benefits are similarly difficult to cover because most of them are paid directly by municipalities to housing authorities. However, as far as cash benefits provided by local authorities, the HBS questionnaire covers all categories: it includes cash housing benefit, health (care) benefit, low-income family cash benefit and other benefits 9 The poor. 14.6 percent of households would thus qualify for social assistance: 6.4 percent of household are both income- and expenditure-poor (called hard-core poor). 10 8.2 percent of households are only expenditure-poor and 3.9 percent only income-poor (see Table 4.3). 6 A very detailed discussion of social assistance (e.g. eligibility, allocation rules, role of municipal authorities, intra- regional allocation of funds etc.) was recently provided by Goldman.(1998). 7 This amount, in turn, is also close to the minimum pension (LVL 30) which is used as the poverty line in Pola:nd and Hungary. 8 According to the 1995 Welfare Law, the minimum nation-wide poverty line is 26 lats per month. However, the local authorities can set higher poverty lines, and most usually do (e.g. in 1999, the eligibility threshold in Riga was :35 lats). Thus, the eligibility threshold of 28 lats assumed here is a good approximation to the actual policy. A person's eligibility for social assistance (status of a "low- income person") lasts for three months and is then reviewed. 9 There is one small difference: we have included funeral benefits as "other benefits", while the official classification treats them as social assistance. II The correlation between expenditure-poor (POORX) and income-poor (POORY) is only 0.47; the correlation between per capita income and expenditures is a relatively low 0.40. 9 Tabl.e 4.2: Differences between the official and HBS definition of social assistance Name of benefit Explanation Official Included definition in the HBS definition 1. Social benefits (paid by Local Authorities; based on tlhe 1995 Welfare Law) Low-income family benefit Maximum payment 2]. lats. Yes Yes In cash and in-kind Housing benefit In cash, or in kind (paid Yes Yes (only directly to utilities or in cash) __________ municipal housing aulhoritieos) Care benefit If elderly or child carc: needed. Yes Yes Only in cash Funeral benefit Yes No (other benefits) Additional benefits Yes Yes 2. Social care benefits For the elderly, childrzn Yes No (kindergartens, orphanages) etc. All in kind. Paid out of central and LA funds. 3. Rehabilitation benefit = = Yes No Table 4.3: Latvia: Who receives social assistance? Total Eligible for assistance N-ot eligible for assistance (expenditure-poor) (non-poor) All households 1l00 14.6 85.4 Receiving SA 1.5 0.35 1.15 Income-poor 10.3 6.4 3.9 Receiving SA 100 23.1 76.9 Receiving assistance Not receiving assistance All poor 1100 2.0 98.0 Hard core poor 100 0.0 100 All non poor 11)0 1.4 98.6 Note: Hard-core poor are defined as both income- and expenditure-poor. SA=social assistance. 10 Figure 4.2: Latvia: Percentage of households recipients of social assistance by level of welfare (expenditure per capita) 6 0 4 CoO0 0 0 0 0 0 0 0 0. > WU CC)0 0 CoC) CD 0 c 0 0 0 1.5 00 0 0 - o EfEoo OCD X o O0 oo 0o o Oo o cC) al oo 0c c co 03 15 40 60 80 100 acc. to tothhx_p Who are the recipients of social assistance? Figure 4.2 illustrates where along the income distribution spectrum are the recipients of social assistance.11 A mere 1.5 percent of households report receiving social assistance (see the line drawn at y=1.5). Among the poor, the share of social assistance recipients is 2 percent; among the non- poor, 1.4 percent (Table 4.3). Figure 4.2 displays the already noted lack of targeting of social assistance: the percentage of recipients (or differently put, the probability of receipt of social assistance) is about constant up to the 80th percentile. What is most extraordinary is that not a single hard-core poor household of which there are 6.4 percent receives social assistance. All households who are to the right of the x=1 5 line are non-poor, and should not in principle receive social assistance. Everything to the right of that line is therefore "leakage." In terms of the number of recipients, 76.9 percent of them are not qulalified (see Table 4.3). In terms of money amounts, 76.7 percent of social assistance is "leakage" (Table 4.4). How much do the recipients get? Figure 4.3 shows the average amounts of social assistance in lats (LVL). On average, a recipient household would receive $45 (or 26 LVL; see the horizontal line in Figure 4.3) per month, with both poor and non-poor households receiving about the same. There is therefore not much difference between the poor and non-poor either in terms of access to social assistance or the amounts they " Social assistance is the assistance provided by municipalities. It includes housing benefit, health cash benefit, low- income family cash benefit and other benefits. 11 receive. The amounts are, as we have just seen, about the same. As for the probability of receiving social assistance it is only marginally higher among the poor (2 percent) than among the non-poor (1.4 percent). Figure 4.3: Latviia: Amounts of received social assistance (in LVL per recipient household per month) by level of welfare (expendilture per capita) 0 Q 100- 0- ,4) 0._ 0~~~~~~~~~~~~ 100~~ I_ 0 60 15100 0 0 0 0 acc. to tothhx_p IIow muelh of the poverty gap is closed by solcial iassistance? Total monthly expenditure'-based pcove3^ty gap calculated from the Survey is'LVL 32,500 as compared to total monthly expenditu]res of slightly over LVL I million, or income of LVL 1.1 million (Table 4.4). Thuis, to close the entire poverty gap one Avould need to transfer to the poor and to the poor only 3.1 percent of total population expendlitures. This is a relatively large poverty gal) a reflection of a rather high poverty line we use. Total disbursed social assistance (in thev sarnple) amounted to LVL 2,882 or less than 1/10 of the poverty gap. However, only 23 percent of that amount was paid to the poor, therefore "covering" only 2 percent of the poverty gap. 12 Note that almost noile of the poverty gap among the bottom 5 percent of the population is covered (Figure 4A4). How much of total expenditures is financed by social assistance? Social assistance "paid" for about 0.3 percent of total population expenditures. Among the poor, the ratio is 0.8 percent, among the non-poor, 0.28 perceent. Devspite these low overall 12 The average social assistance-to-poverty gap ratio calculated across households is 4.6 percent. The difference stems from the fact that smatll poverty gaps -as we move right toward less poor !iouseholds-are "covered" more fully than the very large poverty gaps (see Figure 4.4). 12 Table 4.4: Latvia: social assistance: reduction of the poverty gap and "leakage" Total The poor The non poor Amounts in lats p.m. (from Survey) Social assistance 2,882 671 2,2L 1 (100) (23.3) (76.7) Expenditures 1,009,696 83,152 926,544 Expenditure of those with SA>O 14,694 1,984 12,710 Income 1,100,363 131,203 969,1 60 Poverty gap 32,551 32,551 Social assistance as percentage of. Expenditures 0.29 0.81 0.24 Expenditure of those with SA>O 19.6 33.8 17.4 Income 0.26 0.51 0.23 Poverty gap 8.9 2.0 Social assistance per recipient 26.2 ($45) 25.4 ($44) 26.4 ($46) household (LVL/ $ p.m.) Expenditure per capita of those with $81 $36 $98 social assistance ($ p.m.) a/ Memo: Expenditure per capita (overall $92 $39 $126 average $ p.m.) a/ Average HH size (overall average) 2.36 3.13 2.23 Notes: In October 1997 prices. Exchange rate: LVL 0.58=$1. p.m. = per month. SA=social assistance. HH=household. a/ Mean across households. amounts, the importance of social assistance for the recipient households was substantial: it covered one-third of expenditures of poor households and 17 percent of the non-poor (Table 4.4). The big difference between the share of social assistance in overall expenditures (0.3 percent) and in the expenditures of recipients (almost 20 percent) indicates that social assistance was distributed in relatively large chunks and to a few people. And indeed, the average recipient household received almost $45 as against an average unemployment benefit of $60 pm 3, or average wage of slightly over $200 pm. As we shall in the next Section, Latvia's social assistance can be considered "concentrated." The poor who do not receive social assistance. 98 percent of the poor received no social assistance. The percentage of the excluded (the poor who do not receive social assistance) does not vary with welfare as one moves toward the less poor the percentage of exclusion stays about the same (see Figure 4.6). 13 Both calculated from the Survey. There are 2.6 percent of all households who are receiving unemployment benefits. 13 Figure 4.4: Latvia: Social assistance received as percentage ofthe poverty gap by level of welfare (expenditure per capita) 10 o 0~~~~ 0.~~~~~~~~ (9 0~~~~~~~~~~~~ 0 0 o 0 0 10 15 acC. to tothhx_p Note: Mean calculated across households. 4.3 PERFORMAIANCE OF LATVIA'S SOCIAL ASSISTANCE: COMPARISON WITH OTHER TRANSITON COUNTRIES Features of ithe system. Using the approach from Braithwaite, Grootaert and Milanovic (2000), vve compare Latvia's social assistance to the social assistance systems of five transition countries (Bulgaria, Hungary, Estonia, Poland and Russia). We note first an exceptionally rnodest level of social assistance. Fewer households (1.5 percent) receive social assistance in Latvia than in any of the other five countries (Table 4.5). Social assistance finances less of household expenditures (0.29 percent) than in any country save Bulgaria. If we compare Estonia and Latvia, whose systems are, as we shall' see below, similar, the percentage of households receiving assistance is almost two times as large in Estonia, and the importance of assistance in relation to population expenditures is greater, But while Latvia's social assistance is extremely modest in its size, .,it is concentrated: those w]ho receive social assistance, get in Latvia (in dollar terms) more than elsewhere, except in Poland. Further, social assistance covers almost 20 percent of recipient householdls expenditures, again a proportion higher than in any other country except Poland. 14 Figure 4.5: Latvia: Social assistance as percentage of expenditures by level of welfare 0 0 0 0 0 0 CL 0~~~~~~~~~~ co 0 0 oO cP 0 /o 0 03 /0 0 3 0 / \0- 0 0 0 0/0~0 0c 000 0 0 000 0 0o0 ~ 0 000 0 0cb oo0 00 ow ocoo 00 0 coo0O)coc 0 15 100 ac.. to tothhx_p Note: Mean calculated across households. Performance of the system. How does Latvia's system perform compared to other countries? In order to make this comparison meaningful, we cannot base it on different poverty lines: the very fact that a country might have a low or a high poverty line (compared to its mean expenditures) will influence the calculated efficiency of the system. For example, if the poverty line is very low, the "eligible" population will be small, many poor may receive social assistance ("the error of exclusion" will also be small), and much of the poverty gap may be eliminated (thus showing high effectiveness too). The country may seem to perform very well but most of it may be due to a very austere poverty line which severely limits eligibility for assistance. If the poverty line were raised, both the error of exclusion and the coverage of the poverty gap may decline, but in reality the poor would be better off. Therefore, in order to compare diifferent countries, we need to assume that the objective of the social assistance system iin each country is the same. As in Braithwaite, Grootaert and Milanovic (2000), we assume that the poor in each country are the bottom ten percent of the population14 and that the objective of social assistance is to help them. The success of the social assistance system is then measured by how much of the (pre-assistance) poverty gap of the bottom decile is eliminated (effectiveness), and how much of disbursed social assistance is received by them (efficiency). 14 Ranked according to expenditures per capita. 15 Figure 4.6: Latvia: Failure to deliver social assistance (percentage of the poor who do not receive social assistgnce) 100 C, 0 _~~ _ eo-o-oe. o .- Q _ 0 0 0 90 70 0 15 acc. to tothhxp Consider lines 6 and 7 (Table 4.5), and Table 4.6. Latvia's results are poor. Less than 15 percent of social assistance is received by the poorest decile, a proportion inferior to that of any country except Russia. Since social assistance is badly targeted, and total amount of spending is small, it is not surprising that social assistance covers only 2.9 percent of the poverty gap of the bottom decile the smallest proportion of all countries except for Bulgaria. Table 4.6 cornplements these results with several additional statistics. We define relative effectiveness as the ratio between effectiveness, and social assistance shown as percentage of total expenditures. Here again, Latvia performs worse than all countries except Russia. The. correlation between social assistance and household percentile, and the social assistance concentration coefficient, both of which we expect to be negative, are, on the contrary, positive, indicating an absence of a focus on the poor. Similar results obtain only in Russia, which according to all indicators of performance scores the worst. One of the objectives in Braithwaite, Grootaert, Milanovic (2000) analysis was to determine the type of social assistance system exhibited by a country. It was done using three indicators: the level of the poverty line (compared to mean country expenditures), percentage of recipients of social assistance, and the importance of social assistance for the recipient households. The characteristics of Latvia's system are similar to those of Poland and Estonia: small percentage of recipients, but high importance of social assistance for those who get it (Table 4.7). This implies that Latvia's social assistance is concentrated, although its focus on the bottom decile is wieak (Table 4.8). 16 Table 4.5: Characteristics and performance of social assistance systems I Bulgaria Estonia Latvia Poland Russia Hungary System characteristics (1) % of HHs 2.55 2.7 1.5 3.7 13.0 24.4 receiving SA (2) SA as % of 0.11 0.38 0.29 0.74 0.45 1.1 expenditures (3) SA per 10 33 45 54 5 17 recipient HH ($ pm) (4) SA as % of 4.1 14.8 19.6 22.1 3.5 4.7 expend. Of recipients HHs (5) Eligibility 28 39 53 77 65 55 threshold as % of mean per capita expenditures System performance (6) % of SA 22.3 34.7 14.8 20.5 8.2 27.2 received by the lowest decile (7) SA to the 1.3 7.0 2.9 9.4 3.3 28.8 bottom decile as % of the poverty gap a/ Overall expenditures and distribution (8) Poverty gap 1.9 2.1 1.5 1.6 1.0 1.1 of the lowest decile as % of all expenditures (9) Memo: 83 (67) 74 (71) 107 (113) 93 (99) 47 (32) 134 (128) Overall expenditure (income) per capita in $ pm b/ (10) Gini 28.6 (31.4) 30.7 (35.4) 34.1 (33.5) 27.4 (29.1) 40.1 (44.5) 22.8 (21.8) coefficient of expenditures (income) per capita (individual based) _ a/ Poverty gap of the lowest decile. The poverty gap is expenditure-based (after social assistance). bf Household-weighted. Note: Countries ranked from left to right according to the percentage of households who are receiving social assistance. SA=social assistance. HH=household. Source: all countries but Latvia from Braithwaite, Grootaert and Milanovic (2000). 17 Table 4.6: Comparing the performance of the social assistance systems = = Hungary Estcnia Poland Bulgaria Latvia Russia Efficiency: /c of SA received by the 27.2 34.7 20.5 22.3 14.8 8.2 lowest decile Effectiveness: SA as % of the poverty 28.8 7.0 9.4 1.3 2.9 3.3 gap of the lovvest decile Relative effectiveness 26.2 18.3 12.6 11.4 10.7 7.3 Correlation btw. SAPC and perc_h -0.13 -0.04 -0.06 -0.03 +0.01 +0.03 Concentration coefficient a/ -25.8 -16.2 -19.8 -13.8 +5.2 +8.2 Note: Relative effectiveness is calculated as the ratio between effecti 'eness, and social assistance as percentage of total expenditures. Countries are ranked from left to right according to relative effectiveness. SA=social assistance. SAPC=social assistance per capita. perc_h=percentiles of households formed according the household per capita expenditures. Table 4.7: Characteristics of the systems Poland Bulgaria Hungary Estonia Latvia Russia Poverty line High Low High Low High High Percentage of recipients Low Low High Low Low High Importance of SA for High Low Lc,w High High Low recipients Type of system HLH LLL HHL LLH HLH HHL H/L=level of poverty line: high/low (over/under 50 percent of average expenditures). H/L=many or few receive SA (under/over 10 percent of the population). H/L=Social assistance (SA) is irnportant (high) or not (low) (under/over 10 percent of recipients' expenditures). Table 4.8: Taxonomy of social assistance: concentrated, dispersed, and irrelevant Importance of social assistance Number of recipients SA relatively important for SA relatively unimportant for recipients recipients Low number of recipients Poland Bulgaria Estonia Latvia [IRRELEVANT] [CONCENTRATED] High number of recipients Hungary Russia _________ ______ _______________ __ [DISPERSED] 18 In conclusion, Latvia's social assistance is: * very modest as the overall amounts disbursed and number of households who benefit from it are small; * however, for the recipients, social assistance represents an important source of income. The system is therefore concentrated, a feature it shares with social assistance in Poland and Estonia. * But while the system is concentrated, it is not focused on the poor, and its relative effectiveness is worse than in all countries considered here except Russia. 4.4 WHY SOME POOR HOUSEHOLDS DO NOT RECEIVE SOCIAL ASSISTANCE? We have seen that the percentage of the poor who are not receiving social assistance ("error of exclusion") is about 98 percent. Can we explain who and why among the poor is "denied" social assistance? In other words, are there identifiable household characteristics that account for household's exclusion? Is it the fact that they live in rural areas, own durables (e.g. a car or a productive asset), have an able-bodied male living in the household, or have small families? Finding out what these characteristics are should give us a better grasp on the performance of the system. For example, if single mothers are systematically discriminated, that probably means that the system is operating worse than if households with able-bodied male (who might work informally) are systematically excluded. Also, it should allow us to look more carefully for the causes of exclusion. For example, if urban areas are systematically discriminated, is it because there are no social assistance offices in the cities or because the offices are understaffed, or perhaps because the allocation of central funds is biased against urban areas? Methodology.'5 We want to estimate econometrically what household characteristics are associated with errors of exclusion. We cannot estimate such regressions simply across all households because for the non-poor we cannot, by definition, observe errors of exclusion. We deal with a censored sample. Differently, to run the regressions across the poor households only would yield biased estimates because people are not poor or non-poor randomly. There are distinct characteristics which are often associated with poverty. If that is the case, then, running the regression across the sub sample of the poor would be tantamount to disregarding information from the entire sample, thus yielding biased estimates. For example, we might find when running the regression across the poor only that the failure to deliver social assistance is strongly related to living in villages (peasants do not get much social assistance). But it could also be that living in a village is a strong determinant.of poverty and once we take it into account, none of the discrimination against peasants per se remains. The same exogenous variable in our example (living in a village) explains both the poverty status and the error of exclusion. We need to distinguish between the two. To do so, we rin a selection model where households first "select" to be in or out of poverty (the so-called "screening" equation). This is a probit regression because the dependent variable takes the value of either 1 or 0 depending on whether the household is respectively poo:r or 5 This section (Methodology) is reprinted from Braithwaite, Grootaert, Milanovic (1999; Chapter 111). 19 non-poor. Then, in the second regression, we identify factors that --for the poor households explain their exclusion from social assistance controlling now for the factors that make people more likely to be poor. We have, in essence, to face two importani econometric problems: the use of limited dependent variable (binary variable in the first equation), and the selection bias (people "select" to be poor non-randomly). The first problem renders OLS estimators even asymptotically biased; the second problem also makes them biased. We address the selection issue by using the Heckman correction (or Hleckman selection model); we address the limited dependent variable problem bv applying the maximum likelihood (ML) estimation. We are thus able to obtain unbiased and asymptotically efficient estimators. 16 More formally, we observe an error of exclusion only if the household is poor, that is if 1SX1 + Uj > 0 where xl is a vector of household characteristics, Pl=a vector of and ul=a normally-distributed random error term. At the same time, there is another equation explaining the exclusion error: FAILURE = ,82x2 + au2 where X2 is a vector of household characteristics, f32=a vector of coefficients, u2=a normally-distributed random error term potentially correlated with the first error term (ul) if u0. The two vectors of household characteristics (xi and x2) must have at least one different variable in order for the two equations to be identified. Our first ("selection" into poverty) regression is: (1) I)POOR, fct (HHSIZE, DEDUI, DEDU2, D]_DU3, AGE, AGE2, PRODUCA, I)HOUISE, SHRWAGEY, DSEX, DLOC1, DLOC2, DLFS1, DLFS2) where binaLry (0-1) variables are prefixed by a D standing for dummy variable, and all variables are household-based, DPOOR = poverty status (poor=1), HHSIZE = household size, DEDUL = dunmy for primary education or less (of household head), DEDU2 = dummy for secondary (general) educlation of household head, DEDU3= dummy for secondary vocational or technical education of household head (omitted variable=university education), 16 Since we have a limited dependent variable OLS estimators would be biased. We thus need to use ML methods. This is an improvement over the usual, and until recently more comrnon, Heckman two-stage estimation which solved the problem of selection bias but, by not using maximum likelihood estimation, still yielded inefficient (even if consistent) estimates. Until recently, using Heckman correction with ML methods was computationally prohibitive. 20 AGE = age of the household head, PRODUCA = ownership of productive assets, DHOUSE = dummy for tenancy status (vs. home ownership), SHRWAGEY = share of wage income in total household income (to proxy linkage with labor market), DSEX = dummy for female-headed household, DLOCI = dummy for other cities, DLOC2 = dummy for rural (omitted variable=capital city), DLFS1 = dummy if household head is unemployed, and DLFS2 = dummy if household head is inactive (omitted variable=employed). Our second ("error of exclusion") regression is: (2) FAILURE fct (HHSIZE, DEDUI, DEDU2, DEDU3, AGE, AGE2, DURABLA, PRODUCA, DHOUSE, DSEX, DLOC1, DLOC2, DLFS1, DLFS2) where all variables are the same except FAILURE = 1 if a household is poor and has received no social assistance. If household is poor and has received social assistance FAILURE =0; for all non-poor households, FAILURE is unobserved, and DURABLA = index of ownership of consumer durables (a new RHS variable), While SHWAGEY is dropped for identification purposes. The rationale is that linkage with the fornal labor market (reflected in high value of SHWAGEY) irnight explain whether the household is poor or not poor, but not whether it is discriminated in the allocation of social assistance. DURABLA is a composite index of durables ownership. It is obtained by assigning to the ownership of each consumer durable good a value of 1 and then summing up the score (e.g. if a household owns a TV eaLd a refrigerator it would score 2). Due to the potentially important role that family composition and ownership of durables might have when deciding whether or not to deliver social assistance (as in means- testing), we experiment with different formulations of the regressions. In one set, HHSIZE is replaced by the family composition variables: number of the unemployed in the household (UNEMPLN), number of children (CHILDN) and number of male adults (MADULTN). In the second set, ownership of specific durables, e.g. ownership of a car; black and white TV only, refrigerator, personal computer etc. are introduced in the equation instead of the composite durables index. The equation with houseihold composition (instead of size) and ownership of individual durables, for example; will look like: (3) FAILURE = fct (UNEMPLN, CHILDN, MADULTN, DEDUI, DEDU2, DEDU3, AGE, AGE2, DCAR, DTV, DPC, DREFRIGERATOR, DMICRO, DSTEREO, DMOTOR, PRODUCA, DHOUSE, DSEX, DLOCI, DLOC2, LFS1, DLFS2) 21 where the variables in bold show the ownership of various consumer durables. Finally, because of the difference in regiona]. approach to the delivery of social assistance, we replace location variables (DLOC 1 and DLOC2) with four regional variables dummy variables (DREG1=Riga region, DREG2=Kurzeme, DREG3=Vitzeme, DREG4=Zemgale, and omitted regional variable Latgale). The results. Table 4.9 and Table 4.10 show the results of the errors-of-exclusions regression. Eight regressions are run combining the following three formulations: (i) number of hcusehold members, or household composition, (ii) location or region, and (iii) index of durables owned or individual durable gcods.'7 Note first the variables which are not significarLt. Level of education and sex of the household head, or his/her age are not found to makce more or less likely the receipt of social assistance in any of the eight equations. Simnilarly, owning a house or being a tenant, or having, owvn business do not seem to matter. Moreover, the regional variables which we find significant both as determinants of poverty and unemployment are not significant here. Location, however, is. Table 4.9 and Table 4.10 show that urban households, both those living in Riga and outside of Riga, are -after controlling for all other characteristics-miore likely to be excluded. The obverse of this is, of course, that rural households seem to be giveni preference in the allocation of social assistance. 19 Greater nunmber of male adults (above one) is another characteristic correlated with likelihood of being denied social assistance. It seems that social assistance offices consider such farmilies better able to find alternative means of sustenance. One might recall that until 1968 when the Supreme Court struclk it out, a similar rule of "man in the house" was used by the US welfare offices to deny social assistance to households with able-bodied males (see Levitan, 1990, p. 51). In one formulation, having unemployed head makes household less likely to receive social assistance. The result may be driven by the fact that the while very modest unemploymenit benefits may keep the family below the poverty line, the very receipt of the benefit renders ithe family de facto ineligible for social assistance. 20 Out of 974 households who have, unemployed members (and out of wahich 302 are poor), only 21 are in receipt of social assistance. 17 As the "error of exclusion" equation is modified (e.g. by including household composition instead of household size), so is, in order to maintain the conditions for the exact identification, tie first equation. Is We cannot distinguish between rentors of public and private flats. 19 Using a poverty module attached to the 1998 HBS, the self-reported rejection rate (people who applied for social assistance but were refised) was 19 percent in urban, and 10 percent in rural areas (see Gassman and Neubourg 1999, p. 45). 20 Replacement rate of unemployment benefits ranges from 50 perceni. for those with 1-5 years of service, reaching 65 percent for those with more than 25 years of service. The average unemnployment benefit received is some 30 percent of the average wage, while the per capita poverty line is about IA of the average wage. Thus, a four-member household with one unemployed rnember, one member employed at less than 70 percent of the average wage, and two children will fall under the poverty line. 22 Ownership of durable goods, whether measured as an index, or as individual durables, does not appear to have an impact except for the ownership of refrigerator which makes the household more likely to benefit from social assistance. 21 It is unclear why this should be the case. Table 4.9: Explaining error of exclusion: Regressions with the index of durables With three areas With five regions With HH size With HH composition With HH size With HH comnoosition Poor who are "discriminated" in favor Poor who are Urban outside of Riga* Male adults* Large households Male adults "discriminated" Urban outside Riga Unemployed head* against k significant No No Note: The first column under each country gives the results for the regression which uses household size as explanatory variable; the second column gives the results using household composition. HH=household. All coefficients significant at 1% level unless otherwise * noted. Table 4.10: Explaining error of exclusion: Regressions with the individual durables With three areas With five regions With HH size With HH composition With HH size With HH composition Poor who are Ownership of Ownership of "discriminated" refrigerator* refrigerator* in favor Poor who are Riga* Male adults* Large households Male adults* "discriminated" Urban outside of Riga Riga* against Urban outside of Riga X significant_ _ _ Note: The first column under each country gives the results for the regression which uses household size as explanatc ry variable; the second column gives the results using household composition. HH=household. All coefficients significant at 1% level , unless otherwise * noted. In conclusion, we find that disbursement of social assistance displays a bias in favor of rural areas, although no regional bias could have been detected. 'Social assistance offices also tend to deny assistance to households with more than one adult male or headed by an unemployed person. Other than for the rural inhabitants, no other types of households, such as those headed by females, or by the elderly, or by the more educated, are found to be "discriminated in favor." Of all consumer durables, refrigerator is by far the most commonly owned: 91 percent of all households,, and 81 percent of poor households, own it. 23 4.5 REGIOPiAL INEQUALITY IN DISTRIBUTION OF SOCIAL ASSISTANCE Regional inequality in the allocation of social assistance has repeatedly been raised as an issue (see Goldiman 1998; World Bank, 1995). This is also the problem of which the Government has been aware. Regional inequities essentially stem from the way that the system of social assistance is organized and furnded. Like in most countries, social assistance in 'Latvia is administered at the local level. But, in addition, the funding of social assistance is to some extent localized. Local governments receive block grants from the Center throuigh the Equalization and also raise their own funds. They are free to allocate both centrally-provided ands local funds for amy use, depending on what they see as being a priority. Spending on education, health and public services thus competes with spending on1 social assistance. In principle, this approach is reasonable. First, Equalization fund should ensure that poorer local governmnents receive more funds than the rich, thus ensuring regional equity. Second, because local governments should know best what are local needs, the freedom to allocate money (that is, not to have earmarked uses) may be desirable. However, both points can also be questioned. First, Equalization fund may not ensure horizontal (that is, regional) equity. Second, even if Equalization fund achievecl this objective with respect to total secial spending, it may not achieve it with respect to social assistance. The poor often lack political power to "force" local governments to spend more on social assistance. Thus, reliance, in part, on local funding for social spending, plus lack of poor's political clout in the allocation of spending, implies that there are serious dangers of horizontal inequity: individuals with the same characteristics (e.g. low income level) may be treated differently depending on what part of the country they live in. We shall try to check this hypothesis using two approaches. In the first, we use Household Budget cdata to obtain an estimate of territorlial distribution of the need for social assistanice (approximated by the number of the poor) and its actual distribution. In the second approach., we use very desegregated data on social assistance from more than 500 local governments to contrast them with some demrographic characteristics of the population. This is, of course, far from perfect because demographic characteristics do not imply "need". Unfortunately, we cannot contrast the allocation of social assistance at the local level with poverty at the same level-as ideally we would like because HBS data are not representative at that level (and indeed are not even presented), and poverty headcount cannot be calculated. By conibining five regional (Riga, Kurzeme, 'Vidzeme, Zemgale, and Latgale) and three local (large cities, small cities, rural areas) classifications from Household Budget Surveys, plus Riga city, we obtain sixteen regional units. One of them is empty (Vitgale large city), so we are left with 15 regional HBS units (see Table 4.11). For each of them we calculate from -IBS, poverty headcount, and the disbursed poverty assistance per capita (Table 4.11). One can then obtain disbursed social assistance per poor person, that is social assistance per unit of "needs." Latvia-wide, social assistance spending was about 1 lat per month per poor person. 22 However, eight regions are severely "under- provisioned": social assistance per poor person is less than 50 percent of Latvia-wide 22 Note that this is an average of social assistance disbursed and number of the poor. Since not all social assistance is disbursed to the roor only, a poor person will not receive on average I lat per month. 24 Table 4.11: Poverty headcounts and allocation of social assistance by regions (based on HBS data and HBS regional units) (1) (2) (3) Poverty headcounts Social assistance per Social assistance per (in %) person (lats per poor person (lats per month) month) (2): (1) Riga city 10.8 0.066 0.61 Rig areca Lagcity (Jurm~ala 31.0000 Small citi,es 20. i0.0l3 0.06 Rural areas 16.3 1.211 7.45 Kurzeme Large citv (Liepaja) 14.7 0.240 1.64 S5Mal cities228002.5 Rural areas 3 Zemgale Large city (Jelgava) 15.3 0.077 0.50 Small cities 19.0 0.289 1.52 Rural areas 24.1 0.545 2.26 Latgale Large cities (Daugavpils, 23.4 0.121 0.52 Rezekne) Small, cities 25, 0.930 0J, Total Latvia 19.4 0.191 0.98 level. The "underprovisioned regions" are: in the Riga area, Jurmala and small cities; in the Kurzeme, Vidzeme and Latgale areas, small cities and rural areas. On the other h.and, rural areas around Riga, rural areas in Zemgale, or the city of Liepaja receive between 11/2 and 7 times as much as Latvia-wide average. We can conclude not only thal: the distribution seems to be uneven but also that a generalization based on location (rural vs. urban areas) is not a strong predictor of what areas do not receive sufficient social assistance. While, for example, small cities and rural areas in Kurzeme, Vidzeme and Latgale disburse inadequate amounts of social assistance, rural areas in Zemgale and around Riga receive far more than their "needs" seem to be. The geographic map of Latvia enclosed below shows that the underprovisioned areas include all Eastern districts (rajons), and several (four) in the West of the country. The region in the Center of the country, around Riga and in the South, is better off in terms of received social assistance.23 23 White-shaded districts receive social assistance per poor person that is less than 50% of country-wide average. The districts where the major (republican) cities are, are "allocated" to the group to which the city belongs (e.g. spending levels in Liepaja, see Table 4.t 1, determine the shading of the Liepaja district). The "unallocated" districts straddle two 25 Latviat: Map of regional distribution of social assistance per poor person z, SocialAssistance by Region Latvia Administrative Divisions ~~~~~~~~~~~~~~(Lats per poor person per year) - Inernticoal bKondary - Prvince (rajons) boundary * tatroraI capiti more than 1.0 * Province ~~~~~~~~~ ~~ ' 2 '~~~~~' ~unallocated The undentsed city n,ames a,, mtanravifties that h,iwe status equal to that caraons, o 25 50 oCbretts o ~~~~~~~Gulff ~~~ R~~~~ ~of Riga 2425 F26 ~ 221 Balti' c Sea I E12] 1IAIZK~RAWKLES R~AJONS 10 JELGAVAS RAJONS 19 REZEKNES RAJONS 2 ALDKSNES RAJOIiS I1 IKRASLAVAS RAJONS 20 RilGAS RAJONS 3 RALVU RIJONS 12 KULDFIGAS RAJONS 21 SALOUS RAJONS 4 BAUSKAS RAJONS 13 UEPkJAS RAJONS 22 TAX.SU RAJONS 5 CSU RAJONS 14 LIMBAU RAJONS 23 TUKUMA RAJONS 6-OAUGAVPiLS RAJONS 15 LUDZAS RAJONS 24 VAUKAS RAJONS 7DO20ELES RAJONS 16 MADONAS RAJONS 25 VALMIERAS RAJONS B GULBENES RAJONS 17 OGRES RAJONS 26 VENTSPILS RAJONS 9 JEKABPILS RAJO1NS 18 PREIJU RAJONS Omn 6026200 176Ot -296 regionis with different importance of social assistance; we thus could not precisely determine their correct level of social assistance received. 26 The HBS regions need to be mapped into the local government (LG) level at which social assistance is administered and funded. This presents somewhat of a problem because in a few cases, a given district can "belong" to two traditional regions used in the Household survey. At the level of towns and rural parishes (pagasts), however, there are no such problems: each pagast can be mapped into one of 15 HBS regions. Annex 2 shows the detailed "mapping" with cells belonging to the under-provisioned areas shaded. In total, we find that only one large city (Jurmala) receives insufficient social assistance; 70 cities and towns, and 338 pagasts (see Table 4.13 top panel). Iotal population living in the underprovisioned areas amounts to 934,000 people or 38 percent of Latvia's population. Out of these people, 876 thousands live in towns and rural areas (72 percent of all population living in towns and villages), and 60,000 in Jurmala (the sole underprovisioned republican city). The conclusion regarding what are the underprovisioned regions according to the HBS data can be contrasted with what are the underprovisioned regions using information obtained from the Ministry of Social Welfare. The Ministry has provided the Bank mission with the very detailed data on the 1998 total allocation of social assistance by almost 560 cities, towns and pagasts. Social assistance is defined more broadly than in the HBS (see Table 4.2). Broadly defined social assistance includes (1) cash and in- kind transfers disbursed in accordance with the Law on Social Assistance, namely general low-income support, cash and in-kind payment for rents and utilities, for wood and coal, for the care of children and the aged, and funeral allowance, and (2) other social care benefits like free food, free medical help (hospitals, drugs etc.), and support for children and family (free textbooks, kindergartens, school transport etc.). In Table 4.12, we use two definitions of social assistance: spending based on the Law on Social Assistance (point 1 above) and total spending (sum of points 1 and 2), and express it both per capita and per person of non-working age. First, total social assistance (broad concept) is twice as large as narrow social assistance (disbursed in accordance with the Social Assistance Law): average per capita spending on broad social assistance was Lats 4.7 per year vs. about Lat 2.3 for the narrow concept. Social assistance transfers thus represent about 0.6 percent of population income calculated from HBS. The narrow concept of social assistance accounts for 0.3 percent of total income, a percentage which is exactly the same as obtained from Household surveys (see Table 4.1a above). Second, inequality in distribution of social assistance is substantial. Whatever concept of social assistance or recipient used, the Gini coefficients is high. It ranges between 48 and 57. A note of caution is in order here. Even under the theoretical hypothesis of perfect targeting, inequality would still be present, and possibly high, because the poor are not evenly distributed across the country. Moreover, we do not know if the existing high Gini is high because the poor areas are well targeted or because most of the money is disbursed to the rich areas. Therefore, the Gini coefficient simply shows high inequality in the allocation. It says nothing whether that inequality is "justified" or not. 27 Table 4.12: Distribution of social assistance across local governments in 1998 Social assistance (narrow concept) Social assistance (broad concept) Per capita Per non- Per capita Per non- working person working person Mean (lats p.a.) 2.29 4.86 4.70 9.94 Standard deviation (lats 4.57 9.23 7.58 15.41 p.a.) Coefficient of variation 1.99 1.90 1.61 1.55 Gini coefficient 56.8 53.4 51.7 48.3 Local govt's with lowest Balgales Balgales Balgales Balgales disbursements Blontu Blontu Rojas Rojas Pavilostas Pavilosta Kraslava Kraslava Remtes Remtes Dobele Dobele Rojas Rojas Aizkraukle Berzaunes Local govt's with highest Valmieras Dobeles Balvu Balvu disbursements Dobeles Valmieras Dobeles Dobeles Aizkraukles Aizkraukles Aizkraukles Aizkraukles Balvu Balvu Valmieras Valmieras Kraslavas Kraslavas Kraslavas Kraslavas Source: Data provided by the Ministry of Welfare. Note: Total of 553 local governments. Third, inequality in distribution of social assistance decreases as we use a broader concept of social assistance and move from per capita to per non-working person approach. As can be seen in Table 4.12, the Gini coefficient for broad social assistance per non-working person is 48.3, but for narrow social assistance per capita it is almost 57. The same regularity is observable for the coefficien:i of variation. Ideally, if we had HBS-derived data on poverty headcounts by 553 cities and pagasts we could compare spending per poor person across all 553 local governments. But, as explained above, the most detailed picture of poverty that we can obtain from HBS is at the level of 15 HBS regions. We thus have to resort to a palliative solution. We compute the per capita spending of (broadly defined) social assistance across all local governments, and define as underprovisioned the local governments that spend less than 50 percent of the country-wide per capita average. Ideally, such underprovisioned areas should correspQnd to the underprovisioned areas obtained from the HBS, and discussed in para 4.42-4.45. Table 4.13 shows the correspondence between the two classifications. The calculations based on the Ministry of Welfare data show that only 131 rather than 408 local governiments can be considered underprovisioned. About 80 percent of these 131 local governments (104 to be exact), however, are also underprovisioned according to the HBS data. It seems that the use of the Ministry data gives us the "hard core" of the 28 underprovisioned areas. In terms of the population living in the underprovisioned areas, the calculations based on the Ministry of Welfare data give some 337,000 people or about 28 percent of total population living in towns and rural areas. This is much less than 876,000 people based on the HBS results. However, again, more 80 percent of people defined as underprovisioned according to the Ministry of Welfare are also underprovisioned according to the HBS data. Table 4.13: Comparison of underprovisioned areas according to the HBS data and Ministry of Welfare INumber of LG's According to HBs i__________________ IUnderprovisioned Satisfactory Total Acc. to Underprov. 104 27 131 Ministry Satisfact. 304 118 422 of Welfare Total 408 145 553 Population According to HBs Underprovisioned Satisfactory Total Acc. to Underprov 281062 56099 337161 Ministry Satisfact. 594983 289816 884799 of Welfare lTotal 876045 f 345915 1221960 Note: "Underprovisioned" areas according to HBS are defined as all areas where social assistance disbursed per estimated poor person is less than 50% of country-wide average. "Underprovisioned" areas according to the Ministry of Welfzre are defined as all areas where "broad" social assistance per capita is less than 50% of country-wide average. We conclude that the use of a relatively rough indicator of regional allocation of social assistance-broad concept of social assistance divided by the number of inhabitants-shows that (1) there is a great diversity between the local governments vvith the Gini coefficient only slightly below 50, (2) about one-fourth of LG's (131 out of 553) comprising 27 percent of the population living in towns and rural areas are underprovisioned, and (3) thus identified underprovisioned areas represent the lower bound, or the "hard core" of underprovisioned areas. The implication is that the use o f the more readily available Ministry data will allow us to avoid Type II error (we are unlikely to misclassify a rich area as underprovisioned), but will not protect us from the Type l of error-a fair number of underprovisioned areas may be missed out. 29 REFERENCE', Braithwaite .leanine, Christiaan Grootaert and Branko Milanovic (2000), Poverty and Social Assistance in Transition Economies, London: St. Martin's Press. Gassman, Franziska and Chris de Neubourg (1999), "Coping with Little Means in Latvia: Quantitative AnaLlysis of Qualitative Statements", UNDP Riga, mimeo, June 30 Policy Research Working Paper Series Contact Title Author Date for paper WPS2302 Why Liberalization Alone Has Not Klaus Deininger March 2000 M. Fernandez Improved Agricultural Productivity Pedro Olinto 33766 in Zambia: The Role of Asset Ownership and Working Capital Constraints WPS2303 Malaria and Growth F. Desmond McCarthly March 2000 H. 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