Report No. 21506-MOR Kingdom of Morocco Poverty Update (In Two Volumes) Volume II: Annexes March 30, 2001 Middle East and North Africa Human Development Sector (MNSHD) Document of the U'rid Bank CURRENCY EQUIVALENTS Unit of Currency = Moroccan Dirhams (DM) Period Average Exchange Rates (DM per US dollar) 1996 1997 1998 1999 2000 2001* 8.72 9.52 9.60 9.80 10.63 10.73 * Rate for 2001 is average January 1- April 20 FISCAL YEAR January 1 - December 31 ACRONYMS AND ABBREVIATIONS AMB Associations Musulmanes de Bienfaisance (Muslim Charity Associations) BAJ Barnamaj Aoulaouiyat Ijtimaiya (Social Priorities Program) BTP Bdtiments Travaux Publics CET Centres d'Education et du Travail (Education and Work Centers) CC Chantiers Collectivite' CFP Centres de Formation Professionnelle (Vocational Training Centers) CIMR Caisse Interprofessionnelle Marocaine de Retraite (Moroccan Interprofessional Pension Fund) CMR Caisse Marocaine de Retraite (Moroccan Pension Fund) CNOPS Caisse Nationale des Organismes de Prevoyance Sociale (Social Security Provisions Office) CNSS Caisse Nationale de Securite Sociale (Social Security Office) COSEF Royal Commission on Education Reform CPI Consumer Price Index CSE Centres Socio-educatifs (Socio-educational Centers) EN Entraide Nationale (Welfare Program) FNBT Farine de Blg Tendre (Low grade flour) GDP Gross Domestic Product LFPR Labor Force Participation Rates LMI Low Middle Income LSMS Living Standards Measurement Survey MEN Ministry of National Education MENA Middle East and North Africa MEST Ministry of Higher Education (complete name) MLD Mean Logarithmic Deviation MOH Ministry of Health NGO Non Governmental Organization OCP Office Cherifien des Phosphates (Sherif Phosphates Office) ODEP Office d'Exploitation des Ports (Port Operations Office) OFPPT Office de la Formation Professionnelle et de la Promotion du Travail (Vocational Training Office) ONCF Office National des Chemins de Fer (National Railways Office) ONE Office National de l 'Electricite (National Electric Office) PAGER Rural Portable Water Project WFP World Food Program (Programme Alimentaire Mondial - PAM). PN Promotion Nationale (Public Works) PPP Purchasing Power Parity RCAR Regime Collectif d'Allocations de Retraite (Retirement Benefits Collective Plan) REER Real Effective Exchange Rate SMAG Agricultural Minimum Wage SMIG Industrial Minimum Wage ULC Unit Labor Cost UNDP United Nations Development Program Vice President: Jean-Louis Sarbib Country Director: Christian Delvoie Sector Director: Jacques Baudouy Task Team Leader: Setareh Razmara ACKNOWLEDGEMENTS This study has been prepared by a team of several people and is based on the findings of a mission in February 2000. It was written by Setareh Razmara (Task team leader and principal author) and Giovanni Vecchi (Consultant), under the supervision of Zafiris Tzannatos (SP sector manager). Inputs were provided by Daniel Dulitzky (pension system), Karim El Aynaoui (agriculture policy and macro background), Guillermo Hakim and Sophal Ear (labor markets) and Furio Rosati (child labor and transfers). Dominique van de Walle and Martin Ravallion were the principal advisors for the poverty analysis. Bahjat Achikbache (MNSHD) supervised the preparation and the carrying out of the 1998/99 LSMS survey and facilitated the collaboration with the Statistical Office. The poverty lines update and the calculation of poverty incidence were prepared by the Moroccan team (Mr. Douidich, Living Conditions Observatory) and technicians from the statistical Office (Mr. Douidich and Mr. Bennani) visited headquarters in October 1999 to discuss a preliminary poverty profile with the Bank (Olivier Dupriez and Stefano Paternostro (AFfM3). Valuable comments and suggestions were received from Willem van Eeghen, Paolo Zacchia, Linda Likar, Maryse Pierre-Louis, Nicole Klingen, Regina Bendokat, Eluned Roberts-Schweitzer, Mark Thomas, Jeffrey Waite, Sonia Hammam, Pedro Alba, Douglas Lister. Peer reviewers were Peter Lanjouw (DECRG) and Tamar Manuelyan Atinc (EASPR). Ms. Emma Etori was responsible for formatting the paper. Special thanks to Moroccan authorities for their support and active collaboration. Particularly Mr. Cherkaoui (Director of the Statistical Office) supported the preparation of the poverty analysis and Mr. Abzad (in charge of Household Surveys) and Mr. Douidich (Living Conditions Observatory) and their team kindly provided assistance and information from the LSMS data. KINGDOM OF MOROCCO POVERTY UPDATE TABLE OF CONTENTS ANNEXES A. Poverty Measurement and Analysis B. Child Labor C. Effect of Transfers on Poverty D. Incidence of Public Expenditures in Education and Health E. Pension System F. Social Assistance Programs Statistical Annex ANNEX A Page 1 of 12 ANNEX A POVERTY MEASUREMENT AND ANALYSIS This annex discusses (i) the methodology used for updating the 1998/99 poverty lines; (ii) the incidence, depth and severity of poverty during the period of 1990-98; (iii) econornies of scale; (iv) statistical tests on significance of inequality changes; (v) future prospects for poverty reduction; and (vi) options for improving the statistical base and future poverty monitoring. 1. Methodolo2gy used for Updatinp 1998/99 Poverty Lines The aim of this section is to illustrate the choices made for calculating the poverty lines for 1998/99. The guiding method in setting poverty lines is the one described in Ravallion (1993).' According to this method, poverty lines are made of two components: (i) a food poverty line, giving the cost of a bundle of goods attaining a pre-deternmined minimum food energy requirement, and (ii) an allowance for basic non-food goods.2 The precise definition of both components usually varies from country to country and is also debated within any one country. Food poverty line. The food poverty line for 1998/99 was estimated by updating the 1990/91 poverty line using the Consumer Price Index (CPI). The 1990/91 food poverty line was based on a bundle of goods that (on the basis of the 1984/85 survey, which included food quantities) yields the average food energy requirement for Morocco estimated to be 2,000 calories per person per day (2,400 for an adult). In 1984/85, the bundle of goods was chosen to accord with food spending patterns of the second poorest quintile, which was found to yield (almost exactly) the mean food energy requirement. The availability of the CPI for both urban and rural areas allowed to account for differences in the cost of living between the two areas. Updated food poverty lines for 1998/99 are shown in Table 1. Table 1: The food poverty lines . : ,:::, ' - - ! ~~~~~~~~~. --:... ......:... : Food CPI for 1990/91 116.30 115.10 Food CPI for 1998/99 158.20 149.90 Actualization factor 1.36 1.30 Food poverty line for 1990/91 (current DH) 1442 DH 1442 DH Food poverty line for 1998/99 (current DH) 1962 DH 1878 DH Note: The CPls in the table are the aritmetic mean of monthly CPIs for the period covered by the surveys Source: Statistical Office, 1990/91 and 1998199 LSMS data Having set the food poverty line, a non-food component was added to obtain an overall poverty line that incorporated both food and non-food needs. In order to make an allowance for the non-food component, this report estimated both a lower and a upper poverty line, which represent a lower and a upper bound, respectively, for the "true" poverty line. 3 1 See Ravallion, M., Poverty Comparisons. Harwood Academic Publishers, 1993. 2 See Direction de la Statistique (2000), Approche Pratique de la Pauvrete. Document interne, September 1999 and Direction de la Statistique (1999), Series des ICV mensuels. 3 See Ravallion, M., Poverty Lines in Theory and Practice. LSMS Working, 1998. ANNEX A Page 2 of 12 Lower Povert' line. The lower poverty line is defined by considering those households whose total expenditure is just enough to reach the food poverty line. Anything that these households spend on non- food goods can be considered a minimum allowance for basic non-food goods, since the households gave up basic food needs. By adding such amount to the food poverty line one obtains the lower poverty line. This definition was implemented with the 1998/99 LSMS data available for Morocco. The lower poverty line was estimated using the following food-share demand system: (1) w = a+,8log(x/z, )+ E. where w denotes the budget shares for food, x is the total household per capita expenditure, Zf is the food poverty line, a and f are real parameters, and E is the error term with standard properties. From (1) it follows that oc represents the food budget share when x= Zf . Thus, the lower poverty line z1 can be defined as a scaled up version of the food poverty line: (2) z1 = Zf + (1 - a)zf = (2 - a)zf. The lower poverty lines are obtained by estimating (1) and (2) on the 1998/99 LSMS data (see Table 2). The table also shows the alternative estimates of the lower poverty lines, obtained by actualizing the 1990/91 poverty lines, thereby avoiding the estimation of the food share demand system. By applying the CPIs to the 1990/91 poverty lines (bottom part of Table 2) gives the actualized lower poverty lines for urban and rural areas for 1998/99. Those estimates result reasonably close to the estimated lower poverty lines shown in the top part of Table 2. Table 2: The lower poverty lines Dependent variable: w (equation (1), see text) 0.5316 0.6406 (0 0048) (0 0046) -0.0625 -0.0582 (0.0031) (0.0046) F-test 410 83 162.76 Adjusted R2 0.1211 0.0699 zf 1,962 DH 1,878 DH (2-a) 1 4682 1.3594 (*) z1 2,881 DH 2,553 DH 1990/91 z2 2,027 DH 1,963 DH 1990/91 CPI 114.1 114.5 1998/99 CPI 153.5 147.7 Actualization factor 1.3453 1.2900 (**) z1 2,727 DH 2,532 DH Notes: The Table shows the results obtained by applying ordinary least squares to equation (1) in the text. Standard errors are in parentheses (*) Estimat¢ed lower poverty lines. (**) Actualized lower poverty lines. Source: Statistical Office, 1990/91 and 1998/99 LSMS data Upper poverty line. A more generous allowance for non-food spending was estimated by considering those households whose food expenditure is equal to the food poverty line. The level of non- food spending found amongst those who actually reach the food poverty line (rather than those who can merely afford to do so, if they cut all non-food spending) provides a maximum allowance for basic non- food needs. A good first approximation can be obtained using the following formula: (3) Zc = Za /3 are the = (ar + e )/(s + d). where cc and ,B are the parameters of the demand system (l). ANNEX A Page 3 of 12 The upper poverty lines for urban and rural areas in 1998/99 are calculated based on two methods: (i) by updating the 1990191 upper poverty lines using the CPI, and (ii) by estimating equation (3), i.e. re- estimating the food share regression (1) on 1998/99 data. The results are quite similar. (see Table 3) Table 3: The upper poverty lines Urban Rnral W= (a÷+ )/(1÷f). 0.5004 0.6184 Zf 1,962 DH 1,878 DH (*) z, 3,922 DH 3,037 DH 1990/91 Z5 2,725 DH 2,439 DH Actualization factor 1.3453 1.2900 (**) zu 3,666 DH 3,146 DH Note: The Table shows the results obtained by estimating equation (3) in the text on the 1998/99 LSMS data (') Estimated upper poverty lines (**) Actualized upper poverty lines Source: Statistical Office, 1990/91 and 1998/99 LSMS data 2. Incidence, depth and severity of poverty during the 1990-98 period Poverty Incidence. People whose expenditures are inferior to the poverty line are considered poor. In view of the arbitrary quality associated with the choice of poverty lines, we suggest that different poverty lines be used to ensure a sounder analysis. For that purpose three poverty lines have been designed: (i) a food poverty line which represents the cost of a basket of goods which satisfy a minimum of basic nutritional needs; (ii) a lower poverty line which makes an allowance for non food goods; and (iii) an upper poverty line making a more generous allowance for non-food goods ( see Table 4). These poverty lines identify three categories of poor: the "extreme poor" (i.e. people who are below the food poverty line), the "very poor" (i.e. those who are below the lower poverty line), and the "poor" (i.e. those who are below the upper poverty line). The main reason for using three poverty lines is to allow for comparisons over time and across regions. The upper poverty line (indicated as (ii) in Table 4) was used in the report analysis in order to establish valid comparisons with previous estimates on poverty in Morocco for 1990/91 as well as with estimates from other countries. Table 4: Poverty lines (DHlperson/year) for 1990-91 and 1998-99 1990-91 1998-99 Food Low Upper Food Low Upper Urban (i) 1442 2106 2725 1962 2727 3597 (ii) 2027 2674 2881 3922 Rural (i) 1442 2042 2439 1878 2532 3075 (ii) 1963 2384 2553 3037 National (iii) 1442 2070 2495 1888 2652 3337 Salaries per year (DH) SMIG 14,980 19,920 SMAG 7,770 10,340 Salary for civil 60,000 service . Note: (i) estimated by applying the CPI to the poverty lines (ii) estimated by applying the food component of the CPI to the food poverty line and by re-estimating the food demand model to calculate the allowance for non-food goods Source: Statistical Office, 1990/91 and 1998/99 LSMS data ANNEX A Page 4 of 12 During the last decade, both the incidence of poverty and the economic vulnerability of the population have increased.4 In 1998/99 the incidence of poverty at the national level was estirmated to be 19,0 percent (one out of five Moroccans fall below the poverty line). The number of poor is estimated at 5,3 million compared to 3,4 million in 1990/91 (13,1 percent of the population) and 5,7 million (21,1 percent) in 1984/85 (see Table 5) Table 5: Poverty incidence for 1990-91 and 1998-99 Urban Rural National Urban Rural National Urban Rural I National ;~~ ~ i; -; - -7:-- -; -0 l- t -%>00:... Extreme Poor 0.5 1.9 1.2 0.5 1 6.6 3.3 b 0% 247% 175% VeryPoor 2.8 107 7.0 4.2 16.5 9.8 50% 54% _ 40% Poor 7.6 180 13.1 12.0 | 27.2 19.0 58% 51% 45% --it.-;.-..-- T: 4. _ . , - - , F PO Iwg n S i 0 ; Extreme Poor 59 2551 314 78 844 923 32% 231% 1194% Very poor 336 1,455 1,791 633 2,119 2,752 88% 45% _54% Poor 912 2,448 3,360 1,811 3,496 5,307 98% 43% 58% <~~~~~~~~~4me .f Po.. or:-t: Si U-~~ : ; 0¢;.~XdudID XEcnomii~Vlsea) , ,-ouaixs). . Highly 1,476 4,476 5,952 3,371 5,467 8,838 128% 22% 48% Vulnerable (25%>PL) Vulnerable 2,312 6,640 8,952 5,034 7,122 12,156 118% 7.3% 35% (50%>PL) I Less 3,746 9,146 12,892 7,796 9,377 17,173 108% 2.5% -33% Vulnerable (100%>PL- |:Total007| ;- - 12,00 l3-('-3 25i0 15,051 12,9 . t77 25% 77 -5 8.75% 7 S Popuation C - - -0 - .......---.- -;i- : :;0 : - :- XQ0 -;X Source: Statistical Office and World Bank staff estimates based on 1990/91 & 1998/99 LSMS data. In Morocco, poverty largely remains a rural phenomenon: in 1998/99 more than a quarter of the population living in the rural area was poor, compared to one tenth in the urban area. Although the rural population represents 46% of the total population, 66% of the poor live in the rural area (compared to 73% in 1990/91). This percentage increases to 77 percent for the very poor and to more than 90 percent for the extremely poor. An analysis of the population living below the food poverty line shows that the disparities between the rural and urban areas are striking: while the number of "very poor" in the urban area is estimated to be 78.000 persons, about 800.000 "extreme poor" live in the rural areas (compared to 255.000 in 1990/91). These findings confirm the rural characteristic of poverty which was already noted by both 1990/91 and 1984/85 surveys. They also confirm that extreme poverty has almost tripled in rural areas while poverty has practically doubled in the urban area. The number of "Economically vulnerable', has also increased during the 1990s. Durinrg 1990-99, the number of vulnerable has increased dramatically (on average, by 30%). In 1998/99, taising the poverty line by 25% increases the incidence of poverty to 32% of the population (about 8.8 million poor) and if the poverty line is raised by 50%, poverty incidence increases up to 43% (about 12 million poor). Finally, if the poverty line is doubled the incidence of poverty becomes pervasive, reaching 61% of the population at the national level. Thus, in 1998/99, there are from 9 to 17 million individuals who are likely to slip into poverty during episodes of economic and social shocks and can be considered "economnically vulnerable" (i.e., droughts, illness, lost of job, old age, etc.). While in 1990/91, there was from 6 to 13 million of "economically vulnerable" (between 23% and 50% of the population) (Table 5). 4 Increase in poverty can not be attributed to climatic conditions which were comparable during the two years of the surveys. Correlation between agriculture and poverty has been analyzed in more detail in Chapter III of the report. ANNEX A Page 5 of 12 Depth and severity ofpoverty during the period of 1990-98. Nevertheless, the above-mentioned poverty headcount index does not provide any information on the distance separating the poor's expenditures from the poverty line (meaning the poverty level of the poor) and on the inequality of expenditures among the poor (meaning whether a poor person becomes poorer or richer has no impact on the headcount index). Therefore, other measures of poverty are calculated: (i) the poverty gap index indicates the depth of poverty, i.e., the amount of expenditures that would be needed to raise every poor person up to the level of the poverty line, thereby eliminating poverty; and (ii) the poverty severity index reflects the changes in inequality among the poor by weighting the poor according to their distance from the poverty line, e.g., an increase in the severity index indicates that the distribution of expenditures below the poverty line (inequality between the poor) has worsened. Table 6: Depth and severity of poverty for 1990-91 and 1998-99 1990-91 1998-99 Urban Rural National Urban Rural National Volumetric index (%) Extremely poor 0,07 0,22 0,15 0,06 1,29 0,63 Very poor 0,43 1,79 1,15 0,64 3,88 2,13 Poor 1,47 3,80 2,7 2,49 6,68 4,42 Depth of poverty index (%) Extremely poor 0,02 0,04 0,03 0,00 0,41 0,19 Very poor 0,13 0,46 0,31 0,16 1,38 0,72 Poor 0,44 1,15 0,82 0,79 2,51 1,58 Source: Statistical Office, and World Bank estimates based on 1990/91 and 1998/99 LSMS data. Both the depth and the severity of poverty have unambiguously increased during the period of 1990-98, particularly in the rural area (see Table 6). In 1998-99 the estimated cost of raising the consumption level of every poor person to the (upper) poverty line was 4,4% of the cost of giving every person, regardless of how poor, a transfer equal to the poverty line (compared with 2,7% in 1990/91).5 In other words, a perfect targeting would allow for sizable gains. For the severity of poverty, the estimates indicate a deterioration of the distribution of welfare among the poor: between 1990/91 and 1998/99 the index has almost doubled, both at the national level and in the urban and rural areas, meaning that among the poor the distribution of income has become more unequal. 3. Econoniies of Scale A robustness test has been performed to assess the potential relevance of economies of scale in consumption. The hypothesis tested is that large households may have a distinct advantage over smaller ones as they can benefit from sharing commodities or purchasing products in bulk which may be cheaper. Note that economies of scale are independent from the age structure of the household and thus quite distinct from adult equivalency scales which derive from differing needs of different household members. There is no single agreed methodology for the estimation of economies of scale. Thus to assess their importance we chose a value of theta of 0.75. This derives from the transformation of household expenditures (E) in per capita terms as follows: Epc=E/(n0) 5 Poverty gap provides also important information to policymakers because it indicates the potential gains by targeting poverty alleviation programs vis-&-vis sharing out transfers to every poor by an amount equal to the poverty line. ANNEX A Page 6 of 12 where n is the household size and 0 is the scale parameter. If 0 is equal to 1 no economies o0 scale are assumed; while for values of 0 approaching 0 the higher is the assumed effect of economies of scale. Thus, our choice of 0.75 allows for a relatively small presence of economies of scale. o no economies of scale + economies of scale=0.75 1 0 1 12 Household Size Source: Statistical Office, and World Bank estimates based on 1998/99 LSMS data To investigate the relevance of economies of scale in Morocco we ftrst choose a poverty line that generates the same national poverty rate as if we were to use the unadjusted data. Once we have identified the subset of poor and non poor households in both sets of data we compute the poverty risk per household size and compare the adjusted results to the non-adjusted ones. The results are presented in graph 1. As expected adjusting for economies of scale has a flattening impact on the poverty/household size curve. While large households are still more likely to be poor the difference between larger and smaller households is smaller. To pursue this robustness tests further, it may be useful to investigate the effects of equivalence scales on poverty outcomes. To this end it is recommended that Morocco develop a country specific equivalence scale. 4. Statistical Test on Sienificance of Ineaualitv Chanees Between 1990/91 and 1998/99, the same pattern of inequality is found in both urban and rural areas, although the former is slightly more unequal than the latter. The data on the distribution of expenditure shares across population deciles are consistent with both the Gini coefficient and the mean logarithmic deviation (MLD): during the 1990s, the Gini coefficient is stagnated at arourd 39%. Unambiguously, relative inequality hardly changed during the 1990-98period, and in fact, neither the levels of inequality at the national level, nor the inequality within each area changed, nor the inrequality within each area changed (see Table 7). ANNEX A Page 7 of 12 Table 7: Cumulative distribution of expenditures in urban and rural areas by decile (in %) Decile Urban Rural National Urban Rural National 1 2.6 3.6 2.7 2.9 3.3 2.6 2 6.6 8.5 6.6 7.0 8.1 6.5 3 11.7 14.5 11.4 12.1 14.0 11.3 4 18.2 21.2 17.0 18.0 20.7 17.1 5 25.1 28.9 23.8 24.9 28.5 23.9 6 33.8 37.5 32.3 33.0 37.5 31.9 7 43.3 47.7 41.7 42.6 47.9 41.4 8 55.1 59.7 53.8 54.5 60.3 53.2 9 70.8 74.9 69.2 70.2 75.7 68.8 10 100.0 100.0 100.0 100.0 100.0 100.0 Nominal Mean expenditure 9,224 4,623 6,780 10,157 5,087 7,826 (DR/person/year) 1991 Mean expenditure 9.224 4,623 6,780 7,543 3,942 5,890 (Dl/person/year) Gini Coefficiet .37.7 31.2 39 3 37.7 31.6 39.5 Gini Coefficieint (1.04) (0.66) (0.72) (0 58) (0.57) (0 47) Notes: Deciles refer to households' per capita expenditure Bootstrap standard errors in parentheses Source: Statistical Office, 1990/91 and 1998/99 LSMS data To check whether the observed trend in the Gini indices are significant a statistical test has been carried out. These results confirm that inequality did not change over the 1990s. The null hypothesis of the equality of Gini indices for 1998/99 and 1990/91 was tested using the asymptotically standard normal statistic T = (G, - GJ )/ se(G, )2 + se(G )2 , where G, and se(G ) are the values of the Gini index and of its standard error in year t (t=1998/99, s=1990/91). If the estimated value of T has an absolute value less than 1.96 (2.58) then the difference in the Gini between the two dates cannot be considered statistically significant at the 5% (1%) level using a two-tailed test. The test was carried out with respect to inequality changes both at the national level and within regions (Table 8). Table 8: Significance Tests for Gini Indices for 1990.1998 T-statistics National 0.15 Urban 0.04 Rural 0.37 Source: World Bank staff estimates based on 1990/91 and 1998/99 LSMS data Table 8 shows that the changes in Gini indices between 1998 and 1990 are not statistically significant for every (conservative) confidence level, thereby supporting the argument that inequality did not change over the 1990s. However the estimates of inequality measures which are more sensitive to changes occurring in the left tail of the expenditure distribution point to the presence of non negligible redistributive effects among the poor. At the national level, during 1990 and 1998, the Gini coefficient increased by 0.5%, whereas the Atkinson measures, which is more sensitive to changes in inequality among the poor, increased by 0.9%, 2.4% and 5.4% for the coefficient of inequality aversion set equal to 1, 2, and 4 respectively. Thus, if more weight is given to the poor, the increase in aggregate inequality during the 1990s confirms worsening of inequality among the poor. ANNEX A Page 8 of 12 To check the robustness of this claim we have used Atkinson's measures of inequality where E the inequality aversion parameter, has been set higher than 2 (i.e. when using measures of inequality which care more about the poor than the Gini does), then the changes in inequality are statistically significant (Table 9). Table 9: Atkinson Indices for 1990/91 and 1998/99 Inequality Urban Rural National Urban Rural National 0.3634 0.2543 0.3695 0.3446 0 2727 0.3780 Atkinson (e=2) (0.0137) (0.0084) (0.0072) (0.0077) (0.0077) (0.0064) Atkinson (e=3) 0.4786 0.3387 0.4688 0.4415 0.3731 0.4868 (0.0154) (0.0100) (0.0072) (0.0088) (0.0103) (0.0077) Atkinson (&=4) 0.5682 0.4069 0.5410 0.5140 0 4561 0.5697 (0.0168) (0.0113) (0.0072) (0.0098) (0.0126) (0.0091) Atkinson (e=5) 0 6382 0.4643 0 5967 0.5698 0.5230 0.6333 (O 0186) (0.0133) (0.0072) (0.0105) (0.0140) (0.0101) Source: World Bank staff estimates based on 1990/91 and 1998/99 LSMS data The more positive e.O (E is usually referred to as the 'inequality aversion parameter') is, the more sensitive the Atkinson index is to income differences at the bottom of the distribution (Table 10j . TablelO: Signiricance Tests for Atkinson Indices 1990-1998. T-statistics 1 _(=2) (e=3) (£=4) (e=S) National 0.88 1.71 I 2.47 2.95 Urban -1.20 -2.09 -2.79 -3.20 Rural 1.6 2.40 GI.9 .0 Source: World Bank staff estimates bases on 1990/91 and 1998/99 LSMS data. The null hypothesis of the equality of Atkinson indices for 1998/99 and 1990/91 was tested using the asymptotically standard normal statistic T = (A: - A6 )/ se(Aj ) + se(Ae )2 , where A: and se(A4) are the values of the Atkinson index with parameter £ and of its bootstrap standard error in year t (t=1998/99, s=1990/91). Robustness of poverty changes: As far as poverty comparisons are concerned, the same methodology can be used to check the robustness of poverty incidence changes during the 1990s. To Frove that poverty in Morocco actually worsened, we need to prove that poverty changes are statistically significant (Table 11). Table 11: Significance Tests for Headcount Ratios 1990-1998 T-statistics National 4.72 Urban 6.69 Rural 7.08 Source: World Bank staff estimates based on 1990/t91 and 1998)99 LSMS data The null hypothesis of the equality of headcount ratios for 1998/99 and :1990/91 was tested using the asymptotically standard normal statistic T = (H, - H, )/se, where H, is the value of the headcount index in year t (t=1998/99, s=1990/91), and se = V~H(1 - H)(nj'i + nj, -I, where n, is the sample size in ANNEX A Page 9 of 12 year t and H = (n,H, + n,H, )H(n1 + n, ) Being that the calculated T for the headcount ratio are larger than critical values corresponding to standard confidence levels, we can conclude that the observed changes in the incidence of poverty are statistically significant. 5. Future Prospects for Poverty Reduction High Elasticities of Poverty with Respect to Growth. The lack of economic growth and the worsening of inequality are among the key forces behind the increase in poverty during the last decade. Calculation of elasticities can show the potential for future reductions in poverty in Morocco.6 According to table 12, all poverty measures are found to respond elastically to higher mean consumption (holding the Lorenz curve constant).7 For a given poverty line and area, the growth elasticity is highest for the distributionally sensitive measures of poverty and lowest for the headcount ratio. As far as the latter is concerned, however, it is found that 1998/99 elasticities are even higher than those for 1990/91 (2.7 and 2.9 for urban and rural sectors, respectively), thereby making the potential future reduction the incidence of poverty depending heavily on Morocco's ability to promote economic growth. Table 12: Elasticities of Poverty to Growth for 1998/99. Elasticities Poverty measure Urban Rural National Headcount Index 3.2 2.5 2 7 Poverty Gap 3.8 3.1 3.3 Severity Index 4.3 3.3 3.6 Note: The table shows the elasticities of poverty with respect to mean expenditure on consumption Source: World Bank staff estimates based on the 1998/99 LSMS data. Given the magnitude of the elasticities of poverty with respect to growth, a small rate of growth in a short period of time would also have a large impact on poverty reduction, particularly so in urban areas. It should be noted, however, that the same increase in mean expenditure would have very different impact on poverty, depending on the associated movement in income inequality. Because an increase in the latter would deteriorate the poverty measure, it is important for Moroccan authorities not only to promote economic growth but also to foster progressive social policies to insure that inequality will be reduced. Pro-poor growth policy. The experience of Morocco over the 1990s suggests that growth in consumption expenditure was associated with minor changes in the distribution of expenditure across households. According to LSMS data, the growth rate in real per capita expenditures fell by 13% from 1991 to 1998, while the national expenditure inequality changed by a negligible percentage (the Gini coefficient changed by less than 1% during the decade). Assuming that a distributional-neutral pattem of growth will continue in the future, we can investigate how future growth may change poverty incidence in Morocco. In order to support the argument that as far as poverty is concerned what matters is not only the aggregate growth rate, but also the composition of growth, we have assumed three simulations to show the poverty results when growth is biased towards a specific sector of the economic: (i) distributional-neutral pattern of growth; (ii) pro-rural growth; (iii) pro-agricultural growth. For each simulation two scenarios are considered: 6 See Ravallion and Huppi, 1991). 7 Some results from the previous section (on factors contributing to poverty increase) can be used to estimate the elasticity of poverty to any future distributionally neutral growth in mean consumption. Although one should be cautious in drawing policy implications from this analysis, mainly because distributionally neutral growth does not imply growth with distributionally neutral policies ANNEX A Page 10 of 12 a "low-case" where per capita household expenditure growth rate is set equal to 1% per year, and a "high: case" where it is assumed a more optimnistic growth rate of 2.5% (approximately equivalent to 6%Yo annual GDP growth rate). The results suggest that the prospects for poverty reduction through economic growth in Morocco are quite promising. Overall, the results show that in Morocco, expenditure growth rates have a large impact on poverty: under the "high-case" scenario, the headcount index would decrease at a remarkable rate of 8.2% per year. Taking into account the rate of growth of the population (according to projections, around 1.6% per year), the number of poor would fall by 6.6% per year. For instance, in 2005 there would be a poverty incidence of 12%, with the number of poor being around 3.6 million. For the "low-case" scenario, the decrease in the incidence of poverty would be equal to 3.5% per year, implying that in 2005 the poor would still number approximately 4.5 million, i.e. about 15% of the total population. Similar results are obtained for the poverty gap index and the severity index (see Table 13). Table 13: Simulated Effects of Alternative Economic Growth Scenarios on Poverty Rate of growth in per capita Rate of reduction in national poverty expenditure index (% per year) Growth scenario (% per year) Headcount Poverty Severity Index Ga _ Index Distributioa-newtirgrowth Low case 1.0 3.5 3.2 3.5 High case 2.5 8.2 7.8 8.5 Pro-rural growth Low case 1.0 7.0 6.6 7.6 High case 2.5 12.7 15.3 17.4 Peu-a-ri growth Low case 1.0 5.3 6.9 7.8 High case 2.5 13.4 15.5 16.9 Source: World Bank Staff estimates based on the 1998/99 LSMS data. However, since the poor are mainly located in rural areas a sectoral biased growth toward the rural population would have a much higher impact on poverty. For the "pro-rural growth" simulation, assumptions are as follows: (i) per capita expenditure of households living in urban sectors does not change, and (ii) the aggregate growth rates of 1% ("low case") and 2.5% ("high case") are entirely due to the growth in expenditure of households belonging to the rural sector. Such a pattern of growth, biased towards the rural households, would reduce the incidence of poverty at impressive rates under both the low- and high-scenario. For instance, mean per capita expenditure growing at 2.5% per year would reduce the headcount index by 12.7% per year: even taking into account the population growth, such a estimate implies that it would take less than 6 years to halve the number of the poor at the national level. The poverty gap would fall at 15.3% per year, implying about a 2-3% per year drop in the poverty gap itself. For the "pro-agricultural sector growth" simulation, the results show that when the growth benefits households with the main breadwinner employed in agriculture, then the impact on the incidence of poverty is impressive. For instance, the "high-case" with mean expenditure growing at 2.5% per year would see the headcount index falling at 13.4% per year. But given that prospects for the agriculture sector growth are not very rosy this promoting a "pro-agricultural sector growth" seems to be a difficult task. Finally, in case that agriculture and rural sectors are not growing, a sectoral biased growth toward the urban population and focus on industry, services or construction would have a much lower impact on poverty. ANNEX A Page 11 of 12 Let us begin from the "low case" scenario. A growth rate at national level of 1% per year is consistent with different sectoral patterns of growth. For instance, the aggregate growth rate can be entirely due to growth in the service sector and zero growth in all other sectors of the economy (for the national growth rate to grow at 1% per year, the service sector has to grow at 2.5% per year). Under this hypothesis, the impact of growth on poverty is shown by the simulation called "Service-biased growth" (see Table 14). The "Industry-biased growth" simulation shows an even lower impact on poverty: pro- industry policies would have the lowest impact on poverty. On the other hand, when the growth benefits households with the main breadwinner employed in BTP, the impact on poverty is much higher. Nevertheless all scenarios show a much lower impact on poverty than when the breadwinner is employed in agriculture sector. Table 14: Simulated Effects of Alternative Economic Growth Sectoral Patterns Rate of growth in per capita Rate of reduction in national poverty index (% expenditure per year) Growth scenario (% per year) Headcount Poverty Gap Severity Index PvryGp Index - L SERVICE-biasd growt7 Low case 1.0 1.9 1.6 1.6 Hihcase 2.5 4.1 3.7 3.8 - NDUSTRY-biased growt h - Low case 1.0 1.2 1.2 1.3 High case 2.5 2.0 2.2 2.5 BTP-biase groth- Low case 1.0 4.2 4.7 5.5 High case 2 5 6.8 8.0 9.3 Source: World Bank staff estimates based on the 1998199 LSMS data. 6. Imvroviny the Statistical Base and Future Poverty Monitoriny Based on the 1998/1999 Living Standard Measurement Survey (LSMS) data, this chapter has provided (i) an update poverty profile for Morocco in 1998-99, and (ii) an assessment of both the nature and the extent of poverty changes in Morocco over the years 1990-1998. Although comparisons between poverty measures over time are plagued with both conceptual and practical problems the results discussed above appear to be robust to standard sensitivity analysis. Looking ahead, the recommendations for improving poverty monitoring are as follows: * Survey data. To assist poverty choices, it is important to have timely information as well as high- quality data collected for poverty analysis. As far as the collection of new data is concerned, it is worth repeating two recommendations which were put forward by a previous report, namely (i) an annual collection of household level core consumption data could be carried out to closely monitor household consumption and basic social indicators, (ii) a full LSMS type survey could be implemented every five years. In addition, introduction of household panel surveys with two years interval would also be useful instrument to evaluate existing poverty policies. Although Moroccos - LSMS is well managed, there are margins to improve both the information collected through the questionnaire and its utilization. Particularly, more information is required on: (a) access to social services, including safety net programs, (b) sources of household incomes (wage, transfers, government programs, etc.), (c) labor market conditions and individual earnings, and (d) spatial data on prices. Moreover, in order to make sure that (i) the relevant issues are covered by the survey and (ii) the information collected would answer to the policy needs of the technical ministries, the questionnaire should be consulted with sectoral Ministries (i.e., Health, Education, Agriculture, Social Development, etc.). Finally, the data could be accompanied by exhaustive documentation to be made available to the users both in electronic format and in hard-copy. Particularly, in order to assist ANNEX A Page 12 of 12 analysts in dealing with the raw data the questionnaire could be usefully complemented with a technical-oriented document providing detailed description of the variables collected (labels, codes, format, range of admissible values, etc.). * Equivalence scales. Morocco has not developed a country specific equivalence scale. For this reason this report, in conmnon with previous reports, has used per capita expenditure as measure of welfare. Yet, differences in needs between individuals and differences in household compositions do exist and do affect individual welfare. Thus, some action should be taken to take into account the role of economies of scale. Although it is unlikely that allowing for economies of scale would alter the trend of poverty8 the use of equivalence scales would provide a powerful tool to improve the identification of the poor, thereby allowing better-targeted policy choices.9 * Permanent poverty committee. To strengthen the analytical capacity for poverty analysis a "permanent group of experts" with representatives of (a) all relevant ministries (education. health, agriculture, social development), (b) Moroccans analysts and data collectors (Direction de la Statistique and Observatoire of Living Standards ), and (c) nongovernmental users (academics, and selected international analysts concerned with poverty) could be set up. This group could coordinate poverty-related policies and priorities by exploiting the synergies implied by (i) a stable working group and (ii) the sharing of data, methodologies and information. For the trend to be reversed large changes in household demographics should take place between the dates that are being compared. In the absence of such demographic changes, changes in poverty (as opposed to levels) are unlikely to be affected by the use of per adult equivalent expenditures rather then per capita expenditure, as the differencing would cancel out the scale effect on the levels 9 To illustrate, consider two households with 8 members each: assume that household A is made by 2 adults and 6 ,,hildren, whereas in household B there are 6 adults and 2 children. Assume also that both households have a total expenditure amounting to DH 8,000. In terms of per capita expenditure household A and B fare equally. Yet, most likely it would be agreed that they deeply differ in their needs. In particular, household A is likely to have higher needs than household B, due to its higher dependency rate. According to most systems of equivalence scales, the adult-equivalent household expenditure would rank the two households so as to be consistent with the above conclusion. ANNEX B Page 1 of 15 ANNEX B CHILD LABOR Although poverty has been increasing during the last decade, child labor has been declining mainly as a result of the increasing school attendance. According to LSMS surveys, in 1998 about 64% of the children aged 7 to 15 were attending school, compared to about 58% in 1991. In parallel, in 1998 about 16% of the children were only working and about 18% were reported as doing nothing, compared to 18% and 25% respectively in 1991. Since the LSMS surveys does not record household works, most of the children reported as doing nothing, especially if female, are likely to be performing household chores. In addition, the LSMS does not allow to report the number of unemployed under the age of 15. Finally, only a negligible number of children both work and attend school but this group has increased: 0.2% in 1991 compared to 1,4% in 1998, particularly among boys (see Table 1). Table 1: Child Work and Enrolment Rate by Sex 1991 1998 Boys Girls Boys Girls School and work 0 2 0. 2.2 0 6 School only 68 3 48.1 69.6 57 9 Work only 18.1 15.9 16.3 15.7 Neither school nor work 13.2 35.8 11.2 25.0 Source Stafistical Office, 1990/91 and 1998/99 LSMS data. Dualism between rural and urban areas and gender gaps are also reflected in school attendance and child labor. Enrollment rates increase in 1998 was coupled with a reduction of the gender differential in enrollment of about 12 points. Particularly the increase in male school attendance (72% in 1998 compared to 68% in 1991) was associated mainly with a reduction of the number of working children (16% in 1998 compared to 18% in 1991), while for females, the enrollment increase (59% in 1998 compared to 48% in 1991) was coupled mainly by a reduction in the number of "idle" children (27% in 1998 compared to 36% in 1991). The gender differential in enrollment is relatively small in urban areas, while it grows to almost 20 points in rural ones: in 1998 about 57% of boys were attending school in rural areas compared to 38% of girls. The increase in total school enrollment during 1991-1998 has been largely due to the increase in enrollment by the rural female (38% in 1998 compared to 24% in 1991), while only a mnarginal increase in enrollment has been observed in urban areas and for male in rural areas (about 56%) during the same period. Finally most of the increase in the rural female enrollment rate was associated with a reduction of the number children performing household chores (36% in 1998 compared to 50% in 1991). The reduction in share of working children is mainly concentrated in non-poor households although school attendance increased in both poor and non-poor household: in 1998, 69% of poor urban children and 39% of poor rural children were enrolled compared to 71% and 34% respectively in 1991 (see Table 2). Based on the LSMS data, the increase in school attendance was higher for the "middle class" (third and fourth income quintiles) while the reduction in child labor was the smallest among the poor. In fact the share of poor children attending neither the school nor working has been slightly increasing in urban areas from 21% in 1991 to 23% in 1998. However the decrease in child labor seems rnainly due to increase in school availability which in turn might have compensated the negative effect of poverty increase during the period. In any case, the policy or changes that have lead to child labor reduction do not appear to have been especially targeted to the poor. ANNEX B Page 2 of 15 Table 2: Child Work and Enrollment for Poor and Noni-Poor (in %) 1998 1991 Urban Rural Urban Rural Poor Non-poor Poor Non-Poor Poor Non-Dpoor Poor Non. Poor School 69.4 87.2 36.9 49.8 70 7 84.1 34.3 4 3.2 only _ Work 9.5 5.4 25.6 25.6 8.0 4.2 22.4 26.5 only Neither 21.0 7.2 35.8 21.8 21.3 11.6 43.2 2.9 school nor work _ _ _ Source Statistical Office, 1990/91 and 1998/99 LSMS data Although at the national level many children tend to leave school without entering the seccnd cycle of primary education, the dropouts among girls and particularly in rural areas is the highest. In 1998, at the national level, about 18% of the male children and about 19% of girls by the age ol 12 are participating in the labor force, and at age 15, the participation rate increases to 43% for boys and almost 32% for girls. Particularly in rural areas we observe that by the age of 15, less than 8% of the riural girls are in school (compared to about 24% for rural boys) and almost 48% perform household chores (compared to 8% for rural boys). Most of the children classified either as 'working only' or 'combining school with work' do perform their activities within the family farm or business (see Table 2). As a consequence, they are likely to work in an environment that is more friendly and less harmful than that experienced by children working for non-family members. However, many of these children, especzially female, do nrct attend school and this is likely to strongly limit their possibility of development and social promotion.' On the basis of 1990/91 and 1998/99 LSMS data we have performed an econometric analysis and some policy simulations, aimed at investigating the determinants of child labor in Morocco The main findings and policy implications can be summarized as follows.2 Main findings * Age is important for male activities in both urban and rural areas. The probability of attending, school decreases with the age of the child. On the other hand, for females we hardly observe any significant age impact on the choice between school and work, while the probability of doing household:l chores increases with age. * Income, as proxied by per capita household expenditures, appears to have a significant influer ce only on the decision to be "idle" with respect to going to school. The decision to send a child to work does not appear to be influenced by a change in income. For a comprehensive analysis, see Direction de la Statistique - Royaume du Maroc (2000), Les moins de 18 ans au Royaume du Maroc, forthcoming. 2 To interpret the behavior of the households in deciding the activities of children, an intertemporal model of household's utility maximization has been used. Based on this factors which affect the household decision are the endogeneity of fertility, the transfers among successive generations and the intertemporal allocation of resouirces. See for more details Rosati and Tzannatos (2000). ANNEX B Page 3 of 15 * Family structure influences the decision of the household differently depending on the region of residence. In urban areas, the household structure impact is most likely caused by an income effect. Holding total household size constant, girls are less likely to attend school, while the probability that they will work or perform household chores is higher in households that have a large number of pre- school children. For boys, the number of school-age siblings increases the probability of working, but has no impact on those who are "idle". School-age children have a lower earning capacity than adults, hence, it is likely that in households where relatively more school-age children are present, some of them might be sent to work (remember that we are holding constant the total number of household members). The different effect of the household structure on boys and girls reflects, most likely, the gender specialization within the household. * In rural areas, where most people are farmers, the effects of the household structure are more linked to the returns to work in the agricultural sector. For a given level of fixed factors (land and capital), the productivity of child labor decreases with the number of adults present in the household. This is reflected by the negative effect that this variable has on the probability of working or being "idle". On the other hand, the number of siblings tends to reduce the probability of attending school. Gender differences are also rather strong. The presence of siblings increases the probability of work for males, while it leaves unaffected the probability of being idle. For females, who are more likely to be involved in household chores, the number of siblings increases the likelihood for both "work only" and "neither school nor work" outcomes. Moreover, for females the effect of the presence of pre- school siblings is stronger than that of school-age siblings. * The education of the parents also affects household choices regarding the children's use of time. In urban areas, the more years of formal education for the father, the lower the probability of male children working, and of female children working only or being idle. The education of the mother does not appear to have any significant effect in urban areas. In rural areas, the more years of formal education for the father, the higher the probability of attending school. Formal education of the mother is not significant. However, the dummy variable of religious education has a significant negative effect on the probability of girls working or performing household chores. Hence, the religious education of the mother appears to especially benefit the girls in the household. In a country where the gender issue is so relevant for child labor, this finding has interesting policy implications. - School availability does not influence household choices in urban areas. On the other hand, in rural communities the availability of schools appears particularly relevant. The probability of both working and being idle decreases if a school is present and the more grades the school has. Policy implications * In the case of Morocco, increasing school accessibility in rural areas appears to be one of the most effective policies for reducing child labor. For example, our simulations show that if a primary school were available in every Village (Duar), school attendance would rise by 6 percentage points. * A reduction in child labor will most likely be associated with a reduction in fertility. This will reduce the demand for household chores, and increase female school attendance. In conclusion, increased access to school is likely not only to reduce child labor, but also to increase female school attendance more than male attendance, thus reducing the gender differential. * The growth of the economy is not likely to induce large changes in child labor. We have simulated the effect of a generalized increase in income of 10%. The effect on child labor is small. (While policies promoting growth are likely to reduce child labor in the medium to long run, the irnpact is likely to be small in the short run. ANNEX B Page 4 of 15 * Income redistribution is a slightly more effective policy instrument. We have simulated the -ffect of a tax of 10% on the upper income quintile, which yield was redistributed to the bottom income quintile. As a result, enrolment increases by about 1 percentage point. * The education of the parents is important. But promoting education for mothers is more iniportant in order to reduce the gender gap. * Policies that reduce the burden of household chores (such as water availability in the village or, better, at the school, etc.) are likely to increase school attendance as a large number of girls in rural areas do not attend school because they perform household chores. ANNEX B Page 5 of 15 Table 3 Child Work and Enrolment Rate 1990/91 1998/99 _ Percent Percent School and work 0.2 1.4 School only 58.2 64.4 Work only 17.5 16.1 Neither school nor work 24.6 18.1 Table 4 Child Work and Enrolment Rate by Sex 1990/91 1998/99 Boys Girls Boys Girls School and work 0.2 0.1 1.1 0.4 School only 68.3 48.1 72 2 59.3 Work only 18.1 15.9 13.9 13.5 Neither school nor work _ 13.2 35.8 12.7 26.7 Table 5 Child Work and Enrolment Rate by Area and Poverty Status Using Lower Poverty Unes (2881 DH in Urban and 2553 DR in Rural Areas) 1998/99 Urban Rural Below Above Below Above School only 67.8 85.6 32.9 48.7 Work only 7.9 5.9 27.0 25.2 Neither school nor work 23.9 8.4 38.3 23.3 Table 6 Child Work and Enrolment Rate by Area and Poverty Status Using Higher Poverty lines (3922 DH in Urban and 3037 DR in Rural Areas) 1998/99 Urban Rural Below Above Below Above School only 69.4 87.2 36.4 49.8 Work onLy 9.5 5.4 25.6 25.5 Neither school nor work 21.0 7.2 35.8 21.8 Table 7 Child Work and Enrolment Rate by Area and Poverty Status Using Higher Poverty lines (2674 DH in Urban and 2384 DH in Rural Areas) 1990/91 Urban Rural Below Above Below Above School only 70 7 84.1 34.3 43.2 Work only 8.0 4.2 22.4 26.5 Neither school nor work 21.3 11.6 43.2 29.9 Source. Statistical Office, 1990191 and 1998/99 LSMS data' ANNEX B Page 6 of 15 Table 8 Child Work and Enrolment Rate by Expenditure Quintile 19910/91 1 2 3 4 5 School only 40.2 49.2 51.7 69.8 80.1 Work only 21.2 21.8 17.7 14.4 10.2 Neither school nor 38.5 28.8 30.4 15.7 9.6 work _ _ _ _ _ _ _ Table 9 Child Work and Enrolment Rate by Expenditure Quintile 1998199 1 2 3 4 S School only 45.0 58.8 69.9 78.0 85.32 Work only 22.5 19.0 14.7 10.5 8.3 Neither school nor 30.9 21.4 13.6 9.3 5.4 Table 10 Child Work and Enrolment Rate by Sex and Area 1990/91 Urban Rural Boys Girls BOYS Girls School and work 0.1 0.2 0.2 School only 86.3 79.3 56.3 25.7 Work only 4.9 4.3 27.1 24.2 Neither school nor work 8.6 16.3 16.5 49.9 Table 11 CHILD WORK AND ENROLMENT RATE by Sex and Area 1998/99 Urban Rural Boys Girls Boys Girls School and work 0.4 3.9 1.1 School only 87.5 81.5 54.1 36.8 Work only 7.3 4 7 25 0 26.2 Neither school nor work 5.2 13.8 17.0 35.8 Table 12 Child Work and Enrolment Rate by Age (1990/91 - Boys) Age School only School and Work only Neither school nor work w ork__ _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ _ _ _ _ _ 7 75.0 0.0 3.0 22.0 8 75.6 0.0 6.9 17.3 9 82 5 0.0 7.5 10.0 10 77 9 0.4 11.1 107 11 70.4 0.4 17.7 11.4 12 69.0 00 195 116 13 60 7 0.8 25.9 12 5 14 54.7 0.0 31.7 13.6 15 48.3 0.0 41.2 10.5 Source: Statistical Office, 1990/91 and 1998/99 LSMS data. ANNEX B Page 7 of 15 Table 13 Child Work and Enrolment Rate by Age (1990/91 - Female) Age School only School and Work only Neither school nor work work 7 51.4 00 5.5 43.1 8 59.6 0.0 64 34.0 9 56.9 0.0 10.5 32.6 10 51.3 0 0 16.9 31.6 11 523 04 16.1 31.2 12 44.2 0.4 23.5 31.9 13 44.6 0.0 157 39.7 14 39.5 0.4 25 2 34.9 15 31.1 0.0 23.8 45.1 Table 14 Child Work and Enrollment Rate by Age (1998/99 - Male) Age School only School and Work only Neither school nor work work _ 7 76.3 0.9 3.3 19.5 8 80.1 1.0 6.1 12.8 9 81.5 3.2 5.8 9.5 10 78.9 2.7 8.5 9.9 11 75.6 3.5 13.5 7.4 12 73.5 3.3 15 1 8.1 13 61.2 1.8 24.7 12.3 14 54,2 2.8 27.0 16.0 15 49.8 0.5 43.5 6.2 Table 15 Child Work and Enrollment Rate by Age (1998/99 - Female) Age School only School and Work only Neither school nor work work 7 69.3 0.0 3.9 26.8 8 75.0 0.9 6.5 17.7 9 69.8 1.9 9.7 18.6 10 66.0 0.0 13.6 20.4 11 62.2 0.3 13.7 23.8 12 55.0 0.9 19.1 25.0 13 52.8 0.5 17.6 29.1 14 46.7 0.9 24.2 28.2 15 31.7 0.0 32.5 35.8 Source: Statistical Office, 1990/91 and 1998/99 LSMS data. ANNEX B Page 8 of 15 Table 16 Child Work and Enrollment Rate by Age and Area (1990/91 - Urban Male) Age School only Work only Neither school nor work 7 912 8.8 8 88.8 11 2 9 95.5 4.5 10 94.4 5.6 11 87.9 5.5 6.6 12 89.6 3 8 6.6 13 78.0 9.2 112 14 75.0 10.0 15.0 15 74.5 16.7 8.8 Table 17 Child Work and Enrollment Rate by Age (1990/91 - Urban Female) Age School only Work only Neither school nor work 7 83.5 16.5 8 91.2 0.9 7.8 9 89.6 0.9 9.4 10 8261 1.9 16.0 11 82.5 3.5 14.0 12 84.1 425 11.4 13 7481 731 18.7 14 69.7 8.2 22.1 15 60.8 10.3 28.9 Table 18 Child Work and Enrollment Rate by Age and Area (1990/91 - Rural Male) Age School only Work only Neither school nor ________ ________________w ork 7 62.9 5.2 31.8 8 67.4 11.3 21.3 9 71.3 13.9 14.7 1 0 67.8 17.8 13.8 1 1 59.6 25.3 14.4 12 57.2 28.3 14.4 1 3 48.2 37.6 13.5 14 42.4 44 8 12.7 15 28.7 59.6 11.8 Source Statistical Office, 1990/91 and 1998199 LSMS data ANNEX B Page 9 of 15 Table 19 Child Work and Enrollment Rate by Age (1990/91 - Rural Female) Age School only Work only Neither school nor work 7 31.6 8.9 59.5 8 37.8 10.1 52.0 9 35.4 16.7 47.8 10 30.1 27.4 42.5 11 29.6 25 6 44 8 12 22.7 33.7 42.9 13 19.2 23.1 57.7 14 13.9 39.6 45.8 15 6.2 35.2 58.6 Table 20 Child Work and Enrollment Rate by Age and Area (1998/99 - Urban Male) Age School only Work only Neither school nor _______ ~~~~~~~work 7 92.2 0.5 7.3 8 95.9 4.2 9 95.0 4.4 10 95 6 0.6 4.1 11 91.1 2.9 5.6 12 91 2 5.2 2.6 13 82.1 11.4 6.5 14 71.9 19.2 7.7 15 72.0 22.7 4.8 Table 21 Child Work and Enrollment Rate by Age (1998/99 - Urban Female) Age School only Work only Neither school nor _______ ____ ____________w ork 7 85.7 14 3 8 97.3 2.7 9 95.9 0.6 2.7 10 92.2 1.4 6 4 1 1 85.1 1.6 13 3 12 77.7 4 1 18.2 13 78.1 3.6 18.4 14 71.4 8.1 20.5 15 57.1 20.4 22.5 Source: Statistical Office, 1990/91 and 1998/99 LSMS data ANNEX B Page 10 of 15 Table 22 Child Work and Enrollment Rate by Age and Area (199899 - Rural Male) Age School only Work orly Neither school nor work 7 60.4 6.1 31 8 8 66.8 11.3 20.2 9 70.1 10.4 13.7 10 65.0 15.1 14 8 11 60.7 23.7 9.2 12 55.1 25.4 13.8 13 43.6 35.8 17.3 14 35.2 35.5 24.9 15 23.5 68.2 8.0 Table 23 Child Work and EnroDlment Rate by Age (1998/99 - Rural Female) Age School only Work only Neither school nor work 7 55.0 7.4 37.6 8 54.6 12.5 31.2 9 49.7 16.7 30.7 10 43.6 24.0 32.4 11 44.9 22.9 31.7 12 32.4 34.0 32.0 13 24.0 33.4 41.5 14 16.3 44.0 37.7 15 8.2 43.63 48.2 Table 24 Child Work - Position in Employment 1990/91 = TOTAL MALE FEMALE Wage workers 11.1 9.8 12.7 Family help 84.1 82.4 85.9 Apprentices 3.7 6.3 .8 1998/99 _ TOTAL MALE FEMALE_ Wage workers 13.9 14.4 13.4 Family help 73.6 68.9 75.2 Apprentices 7.9 10.3 5.0 Source: Statistical Office, 1990/91 and 1998/99 LSMS data ANNEX B Page 11 of 15 Table 25 Multinonial Logit Estimates - 1998/99 Reference group: School Only (Male) Urban Rural Work Only Neither school nor Work Only Neither school nor work work coefficient z value coefficient z value coefficient z value coefficien z value _______________________________ ~~It age 4.78 2.52 -1.04 -2.41 -0.46 -1.44 -1.72 -6.00 age2 -0 16 -2.21 0.05 2.83 0.04 2.72 0.08 6.29 Idep_pc -0.90 -2.98 -0.97 -3.88 0.07 0.46 -0.97 -5.99 hsize 0.06 0.86 -0.05 -0.75 -0.15 -4.22 -0.06 -1.65 sibO_6 0.16 0.91 0.01 0.08 0.34 4.50 0.04 0.58 sib7_15 0.31 2 32 -0.02 -0.14 024 3.00 0.07 0.92 tuition 0.00 -0.48 0.00 -0.38 0.00 -0 93 0.00 2 16 yedu_f -0 14 -2.22 -0 04 -1.02 -0.22 -4.47 -0.14 -3.27 yedu_m -0.27 -1 73 -0.03 -0.55 -0.25 -1.00 0.12 0.89 edur_j -0.29 -0 94 -0.01 -0.05 -0.11 -0.67 0.29 1.80 edur_m -0.24 -0.22 -0.11 -0.19 -1.18 -1.48 -0.11 -0.19 school -0.15 -0.91 -0.16 -1.28 -0 56 -5.74 -0.50 -5 15 const -30.06 -2.36 11.31 3.52 -0.22 -0.10 16.13 8.01 Number of obs = 1213 Number of obs = 1363 LR chi2(24) = 219.49 LR chi2(24) = 355.02 Prob > chi2 0 Prob > chi2 = 0 ______________ Pseudo R2 = 0.19 Pseudo R2 = 0.1323 Note: The dependent variable takes on the values 1 - "work only" and 2 = "neither school nor work"; the reference group is "school only" Independent variables are defined as follows: (i) age: age of the child; (ii) age2: age squared; (iii) ldep_pc: natural logarithm of per capita household expenditure; (iv) hsize: household size; (v) sibO_6: number of siblings aged 0 to 6; (vi) sib7_15: number of siblings aged 7 to 15; (vii) tuition: tuition costs; (viii) yedu_f: years of schooling of the father; (ix) yedu_m: years of schooling of the mother; (x) edur_f father attended religious education; (xi) edur_m: mother attended religious education; (xii) school: presence of school; (xiii) const: constant term Source.- Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data. ANNEX B Page 12 of 15 Table 26 Multinomial Logit Estimates - 1998/99 Reference group: School Only (Female) Urban Rural Work Only Neither school nor Work Only Neither school nor work work coefficient z value coefficien z value coefficient z value coefficien z value t _ t age 0.37 0.23 -0.31 -0.83 0.07 0.21 -1.02 -3.67 age2 0.02 0.30 0.03 1.77 0.02 1.24 0.06 4.79 ldep_pc 1.20 3.59 -0.97 -5.13 -0.08 -0.46 -0.60 -4.19 hsize -0.08 -0.85 -0.02 -0.49 -0.18 -4.67 -0.15 -4.45 sibO 6 0.49 2.16 0.30 2.93 0.67 7.58 0.42 5.48 sib7_15 -0.15 -0.71 0.13 1.39 0.19 2.15 0.15 1.93 tuition 0.00 0.77 0.00 -0.48 0.00 -1.37 0.00 1.49 yedu_f -0.14 -2.35 -0.08 -2.69 -0.22 -4.83 -0.11 -3.33 yedu_m 0.07 1.09 0.04 1.14 -0.06 -0.47 -0.24 -1.55 edur_f -0.19 -0.48 -0.36 -1.84 -0.18 -0.97 0.17 1.09 edur m -0.66 -1.06 -0.31 -0.82 -1.10 -1.46 -1.38 -2.11 school 0.09 0.45 -0.19 -1.91 -0.84 -7.15 -0.77 -7.35 const -21.47 -2.03 6.61 2.47 -1.20 -0.53 10.07 5.42 Number of obs = 1145 Number of obs = 1342 LR chi2(24) = 277.74 LR chi2(24) = 419.42 Prob> chi2 = 0 Prob>chi2 = 0 _ Pseudo R2 = 0.2027 Pseudo R2 = 0.1449 Note: The dependent variable takes on the values I = "work only" and 2 = "neither school nor work"; the reference group is 'school only". Independent variables are defined as follows: (i) age: age of the child; (ii) age2: age squared; (iii) Idep_pc: natural logarithm of per capita household expenditure; (iv) hsize: household size; (v) sib0O6: number of siblings aged 0 to 6; (vi) sib7_15: number of siblings aged 7 to 15; (vii) tuition: tuition costs; (viii) yeduj_ years of schooling of the father; (ix) yedu_m: years of schooling of the mother; (x) edurjf: f ither attended religious education; (xi) edur_m: mother attended religious education; (xii) school: presence of school; (xiii) const: constant term Source: Statistical Office, and World Bank staff estimates based on 1998199 LSMS data Table 27 Effects of an Increase in School Availability in Rural Area - 1998/99 Observed Primar School in ever Duar Boys Girls Boys Girls School only 56.9 37.5 62.7 44.1 Work only 22.3 23.4 19.9 21.0 Neither school nor work 18.8 38.4 17.4 34.4 Source; Statistical Office, 1998/99 LSMS data ANNEX B Page 13 of 15 Table 28 Effects of an Increase in School Availability in Rural Area - 1998/99 (By Age) _____________ Observed Primary School in every Duar Age BoYs Girls Boys Girls 7 61.4 54.7 66.0 63.2 8 68.5 56.5 72.7 61.5 9 72.5 51.7 76.3 59.0 10 68.9 45.1 75.2 55.2 11 63.5 44.9 72.4 48.5 12 58.8 31.9 64.6 37.6 13 46.1 23.9 57.1 27.8 14 38.1 16.6 43.5 19.4 15 26.3 8.4 28.8 11.9 Source. Stafistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 29 Effects of Policy Interventions Urban Rural Boys Girls IBo s Girls a) Observed School only 87.5 81.5 56.9 37.5 Work only 5.6 3.5 22.3 23.4 Neither school nor work 6.6 14.9 _ 18.8 38.4 b) Decrease in fertility School only 86.6 81.1 59.2 42.4 Work only 4.7 3.1 20.0 19.8 Neither school nor work 8.6 15.7 20.1 37.7 c) Income Redistribution School only 87.8 _ 81.9 | 39.0 | 38.5 Work only 5.4 3.4 23.2 24.0 Neither school nor work 6.04 14.3 36.0 37.1 d) Income Growth School only 88.4 82.2 57.8 38.3 Work only 5.3 3.8 22.8 23.9 Neither school nor work 61 37.3 17.4 37.2 Source- Statistical Office, and World Bank staff estinmates based on 1990/91 and 1998/99 LSMS data ANNEX B Page 14 of 15 Enrollment Rate 1991 90.s. 80 ..... 0 [ | .E... 70 E 6 50i M * 2 * l _ |l | Female? 30 8 9 1 1 14 Age Enrollment Rate 1998 700_ 60 _ o50 30G9 Female 1 0 7 8 9 10 11 12 13 14 15 Age ANNEX B Page 15 of 15 Enrollment Rate - Rural Female 6 30 | r l < .. , l..*--. -. ....... 50 40 . 7 8 9 10 11 12 13 14 15 Age Enrollment Rate 1998 - Rural 9 0 - -------- - - 40 20 NO _ _ g 0 . ... ... .... ... .......... . 7 8 9 10 11 12 13 14 15 Age ANNEX C Page l of 10 ANNEX C EFFECT OF TRANSFERS ON POVERTY Transfers represent an important component of all household's income in Morocco, and particularly the family system does perform a set of functions similar to those performed by a public social security system. According to the 1998/99 LSMS, about half of the households in Morocco are engaged in a lively activity of private transfers: about 49% of the household receive private transfers I and about 40% of them give transfers. Transfersfrom abroad are received by a substantially smaller number of households (about 14%), and public transfers are received by about 10% of the household and they are mainly targeted to urban areas. As share of expenditures the amount of the transfers is far from negligible.2 Although public transfers are received by fewer households, they are the largest as share of total household expenditures (23% of total expenditures). Private transfers average about 12% of the expenditures, while transfers received from abroad are smaller (about 4% of the expenditures). Table 1 Percent of Household giving and receiving transfers T sfers E enditure Quintile _I II I IV V Total Receiving Private transfers 49.17 44.7 47 0 49.8 56.8 49.3 Receiving Public transfers 5.35 5.0 10.3 14.7 17.9 10.2 Receiving both private & 1.85 2.5 4.3 7.2 11.3 5.5 public transfers _ _ _ __ _ __ _ Giving transfers 28.04 34.2 37.7 44.3 58.4 40.0 Source: Statistical Office; and World Bank staff estimates based on 1998/99 LSMS data Who receives the transfers. Transfers given are much smaller in size that transfers received and households are net receivers (excluding transfers from abroad). Overall middle aged households are normally net givers, while younger and older households are net receivers.3 The probability of receiving a private transfer is correlated with the age of the household heads. Both private transfers with domestic origin and transfers receivedcfrom abroad are especially targeted to young and old head of households. While public transfers, which are mainly the pension, are particularly targeted to old head of household. Transfers given are less related with age, even if younger households are more likely to give than older ones. Private transfers correspond to about 15% of the expenditures of young and old households heads, while public transfers coffespond to about 25% of old household heads' expenditures. The probability of receiving private transfer is roughly the same in both rural (about 46% of households) and urban areas (50% of households). In urban areas, however, households are more likely to receive transfers from abroad (about 15% versus 10% in rural areas) or public transfers I The definition of private transfers (given and received) exclude those originating from Fettra or Zackat. 2 Given lack of information on income, the ratio of transfers received to total expenditures has been used to evaluate the size of the transfers. Although expenditures are not the best reference measure due to savings and consumption smoothing, this problem should be less relevant for the relatively poorer household whose propensity to consume is likely to be close to one and who are likely to be rationed in the credit market. However, for richer household the ratio of transfers received (or given) to expenditures may underestimate or overestimate the actual weight of transfers depending on whether they are saving or dissaving. 3 The available information refers only to transfers of money and goods, therefore other transfers, such as personal services and time transfers (i.e, services) are not taken into account In fact poorer households are more likely to transfer time in the form of services rather than goods or money ANNEX C Page 2 of 10 (20% versus 8% in rural areas). But there are no significant differences in the amount received or given associated with the area of residence of the households. The probability of receiving a private transfer is higher for households belonging to the lowest and to the highest income quintile while public transfers are more likely to be received by households belonging middle and higher income quintiles. Households who receive both private and public transfers are also mainly concentrated in middle and higher income groups. On the ot1± er hand the probability of making a transfer is positively correlated with income (see Table 1). Table 2 Amount of private transfers as per cent of household expenditures Expenditure quintile _~~~ ~~~ _1 II IV v Private Transfers . . _ Amount received 16.40 11.39 12.81 11.07 _ 8.55 Amount received from abroad 2.26 2.90 4.88 4.75 _ 4.35 Amount given 2.02 1.76 2.52 2.37 3.41 Public Transfers 23.55 24.92 28.61 25.20 _ 17.95 Source: Statistical Office, and World Bank staff estimates based on 1990/91 and 1998/99 LSMS data. Both private and public transfers are progressive meaning their share declines with income (see Table 2) and lack of these transfers would increase inequality. However private transfers are better targeted towards poorer households and have a larger redistributive impact (reducing inequality) than public transfers. For private transfers, poorer household, first quintile, receive almost twice as much as the higher expenditure groups. In absence of private transfers the inequality of income distribution (as measured by the Aktinson index) would increase by 14% (see Table 3). The same pattem is confirmed by the Gini index. For public transfers, the amount of transfer is however higher for the middle income groups. Public transfers also are progressive and tend to reduce income inequality, but to a much smaller extent than private transfers. They reduce income inequality of about 10% according to the Aktinson index, but have a negligible impact according to the Gini concentration index (see Table 3). Transfers from abroad are regressive because higher expenditure groups receive a higher amount of transfers. They do not appear to be targeted to the less well off, and are relevant especially for urban household. These transfers also do not appear as an instrument that generates income redistribution. The redistributive effect of both Private and Public transfers is, however, different according to the area of residence. Private transfers appear to be a more important instrument for income redistribution and poverty alleviation than public transfers, with the exception of the very poor in urban areas. In urban areas private transfers reduce income inequality by about 30 per cent. VVile in rural areas the impact is smaller, about 10 per cent (Table 4). The impact of public transfers, which are mainly pensions (about 70 per cent), are mainly concentrated in urban areas, where they reduce income concentration of about 12 per cent, but the impact in rural areas is negligible (Table 4). ANNEX C Page 3 of 10 Table 3 Effects of public and private transfers on income distribution Households' Expenditure (Per Capita) Decile Total Net of wrivate Net of Rub1ic Net of al transfers transfers transfers 1 2061 1979 2042 1961 2 3032 2924 2971 2863 3 3779 3605 3623 3448 4 4530 4325 4417 4213 5 5339 5102 4912 4675 6 6282 6001 5753 5473 7 7519 7108 7162 6751 8 9252 8768 8590 8105 9 12252 11658 11464 10869 10 24214 23556 22904 22246 Total 7826.08 7502.72 7383.81 7060.45 Gini 0.39468 0.40469 0.39847 0.40808 Atkzinson (e=2) 0.37795 0 43552 0.39442 0 44754 Per cent Change in 15.3 4 3 18.4 the Aktinson Index Atkinson Indices. A(e) The larger e>0 (the 'inequality aversion parameter'= 0.5, 1, 2) is, the more sensitive A(e) is to income differences at the bottom of the distribution Source: Statistical Office; and World Baik staff estimates based on 1998/99 LSMS data Table 4 Effects of public and private transfers on income distribution: By area of residence Households' Expenditure (Per Capita) URBAN RURAL Decile Total Net of Net of Net of all Total Net of Net of pubjic Net of all private public trransfers private transfers transfers transfers transfers transfers 1 2965 2837 2697 2569 1690 1622 1688 1620 2 4220 4003 4057 3840 2464 2401 2437 2374 3 5135 4876 4483 4225 2953 2846 2916 2809 4 6010 5813 5187 4989 3424 3263 3399 3238 5 6995 6806 6677 6488 3937 3782 3879 3724 6 8280 7679 7630 7029 4559 4347 4502 4291 7 9781 9286 9009 8514 5282 5078 5135 4931 8 12098 11473 11156 10531 6296 5907 6150 5761 9 15910 15215 14686 13991 7834 7370 7561 7097 10 30219 29517 28990 28289 12477 12092 11899 11513 Total 10157 9746 9453 9043 5088 4867 4953 4732 Gini 0.37741 0 38929 0.38784 0.39922 0 31569 0.32373 0.31459 0.32220 Atkinson (e=2) 0.34456 0.45115 0.38844 0.48052 0.27273 0.30026 0.27799 0.30345 Per cent Change intheAktinson 30.9 12.7 39.5 10.1 1.9 11.3 Index Atkinson Indices. A(e) The larger e>0 (the 'inequality aversion pararneter'= 0.5, 1, 2) is, the more sensitive A(e) is to income differences at the bottom of the distribution Source: Statistical Office; and World Bank staff estimates based on 1998/99 LSMS data ANNEX C Page 4 of 10 Overall public and private transfers have a substantial effect on both the incidence and depth of poverty. At national level, lack of transfers would increase the incidence of poverty from 19% to 24.7% and would more than double the poverty gap (from 4.4% to 10.8%). Therefore the poor would become poorer. Private transfers have a larger impact on the incidence of poverty thar public transfers: private transfers reduce the number of poor households by about 3 per cent point:,, while public transfers reduce the number of poor households by 2 per cent point. But public transfers have a larger impact on the poverty gap than private transfers: lack of public transfers would increase the poverty gap from 4.4% to 9.8%, while lack of private transfers would raise the poverty gap to 6.5%. In urban areas both public and private transfers have a similar impact on poverty, reducing the number of poor households by about 3 per cent. In rural areas, the impact of private transfers on poverty is larger than that of public one. But in urban areas Public transfers have a larger effect on the poverty gap (see Table 5). Although Public transfers have a smaller redistributive impact than private transfers, they are more effective in alleviating the condition of the very poor in the urban areas. It appears that the part of public transfers not made up of pensions, is well targeted towards the very poor in urban areas. ANNEX C Page 5 of 10 Table 5 Effects of public and private transfers on poverty: by area of residence Poverty Index Total Net of private transfers Net of DUblic transfers Net of all transfers National Head count 18.9 22.3 21.4 24 7 Poverty gap 4.4 6.5 8 7 10.8 Urban Head count 12 15.2 15 18 7 Poverty gap 2.5 4.6 9.8 12.1 Rural Head Count 27.2 30.5 28.4 31.7 Poverty gap 6.7 8.6 7.4 9.3 Table 6 Public and Private Transfers Percent of Household Receiving Private transfers 49.3 Of which from abroad 14.4 Receiving Public transfers 10.2 Giving Transfers 40.0 Table 7 Private Transfers. Percent of household RECEIVING GIVING No yes no 34.20% 17.70% yes 25.14% 22.96% Table 8 Percent of Household giving and receiving transfers by age class AGE CLASS 0-30 30-40 - 40-50 50-60 60-70 >70 Receiving Private 53.46 46.99 43.08 41.70 51.29 61.88 transfers . _ Of which from abroad 13.20 12.36 11.84 11.13 14.26 15.02 Receiving Public 3.96 8.12 11.12 14.06 28.81 29.37 transfers Giving 41.91 42.49 42.58 37.75 40.20 37.00 Table 9 Amount of private transfers as per cent of household 's expenditures .__ ___ __ ___ ___ __ ___ ___ __ ___ ___ __ ___ _ M ean PRIVATE TRANSFERS Amount received 11.97 Amount received from abroad 3.84 Amnount given 1.48 PuBLIc TRAmnS 23.12 Source: Statistical Office; and World Bank staff estimates based on 1998199 LSMS data ANNEX C Page 6 of 10 Table 10 Amount of transfers (as per cent of household's expenditures) by age class Age S tlass 0-30 30-40 40-50 50-60 60-70 >70 PRIVATE TRANSFERS ___ _ _ ___________ Amount received 15.22 11.64 9.67 10.86 12.72 14.26 Amount received from 4.20 3.64 3.81 3 29 4.36 3 95 abroad _ ____.______ XAmount given 3.84 3.34 2.64 1.72 2.02 1.92 PUBLIC TRANSFERS 18.74 10.94 19.03 24.83 27.12 25.42 Table 11 Percent of Household giving and receiving by area of residence Urban Rural Receiving Private transfers 50.7 47.3 Of which from abroad 16.7 11.39 Receiving Public transfers 14.3 4.7 Giving 38.3 42.4 Table 12 Private Transfers. Percent of household receiving and giving by area URBAN Receiving No Yes No 32.78% 17.94% Yes 27.75% 21.53% Rural Receiving Giving Yes ________________________ N o Y es No 36.17% 17.36% Yes 21.54% 24.93% Table 13 Amount of private transfers as per cent of household expenditures by area VARUBLES AREA OF RESiDENCE Urban Rural PRIVATE TRANsFERs Amount received 11.80 12.22 Amount received from 4.44 2.97 abroad Amount given 2.96 2.94 PUBLIc TRANSFERS 23.84 20.65 Source; Statistical Office; and World Bank staff estirnates based on 1998/99 LSMS data. ANNEX C Page 7 of 10 Table 14 Amount of transfers as per cent of household's expenditures: by age class and area of residence URBAN Variables Age Class 0-30 30-40 40-50 50-60 60-70 >70 Amount received 15.61 12.10 9.63 10.46 12.88 11.69 Amount given 4.85 __ 4.10 3.05 1.93 1.65 1.58 RURAL Variables ~ ~ ~ ~ ~ ___________________Agze Class ________________ Variables _ 0-30 3040 40-50 50-60 60-70 >70 Amount received 14.75 10.87 9.72 11.43 12.51 16.77 Amount given 2.22 2.01 1.94 _ 1.49 2.35 2.16 Table 15 Percent of Household giving and receiving: by expenditure quintile Mean by expenditure quintile I RL1BLESIII. IV V Receiving Private transfers 49 17 44.15 44.73 47.17 53.70 Receiving Public transfers 5.35 8.3 14.03 21.05 26.70 Giving 28.04 33.82 37.81 44.15 59.16 Table 16 Percent of Household giving and receiving: by expenditure quintile and age class Age Receiving Giving classes Expenditure quintie Expenditure quintiles _ _ I H II _III_ IV V I II I IV | v 0-30 45.36 56.06 48.15 67.35 62.16 25.77 45.45 42.59 57.14 56.76 30-40 44.24 39.53 42.42 54.49 60.71 29.37 35.66 41.13 44.94 68.88 40-50 42.58 44.49 41.21 42.39 46.74 30.14 34.60 34.24 48.87 61.86 50-60 44.24 37.50 42.37 40.29 46.25 26.06 31.00 33.33 38.83 54.17 60-70 52.21 47.62 55.65 47.22 58.94 27.43 33.33 41.13 40.56 j 54.97 >70 68.03 57.32 53.25 56.47 71.25 24.59 29.27 46.75 37.65 53.75 Table 17 Amount of private transfers as per cent of household expenditures: by expenditure quintile ExPEND1TURE QUNTrILE I if III IV V PRIVATE TRANSFERS I______I___ _ I ______ __II ___ I Amountreceived 16.40 11.39 12.81 11.07 8.55 Amount received fron 2.26 2.90 4.88 4.75 4.35 abroad__ _ _ _ _ _ _ __ _ _ _ _ _ _ __ _ _ _ _ _ _ __ _ _ _ _ _ _ AmouNTGLvEN 2.02 1.76 2.52 2.37 3.41 PUBLUC TRANSMES 23.55 24.92 1 28.61 1 25.20 17.95 Source: Statistical Office; and World Bank staff estimates based on 1998/99 LSMS data. ANNEX C Page 8 of 10 Table 18 Amount of private transfers as per cent of household expenditures: by expenditure quintDle and age class Age Amount received Amount given classes Expenditure quintile Expenditure quintile I 11 III IV v I [I III Iv V 0-30 17.45 13.95 13.38 20.03 8.94 1.75 2.01 1.21 2.00 2.97 30-40 14.19 9.96 12.66 10.84 9.84 1.28 1.35 1.62 1.87 1.86 40-50 10.92 9.69 12.46 8.51 6.63 1.62 1.41 1.29 1.63 1.80 50-60 15.96 11.90 10.04 8.31 8.96 1.65 0.77 0.64 0.79 1.07 60-70 16.24 11.23 13.84 13.88 8.90 2.04 0.93 1.40 1.23 1.85 >70 20.90 14.31 12.19 11.63 7.98 1.50 1.64 1.59 1.85 1.34 Table 19 Effects of public and private transfers on income distribution Households' Expenditure (Per Capita) Decile Total Net of private Net of Rublic Net of all transfers transfers transfers 1 2061 1979 2042 1961 2 3032 2924 2971 2863 3 3779 3605 3623 3448 4 4530 4325 4417 4213 5 5339 5102 4912 4675 6 6282 6001 5753 5473 7 7519 7108 7162 6751 8 9252 8768 8590 8105 9 12252 11658 11464 10869 10 24214 23556 22904 22246 Total 7826.08 7502.72 7383.81 7060A5 Gini 0.39468 0.40469 0.39847 0.40808 Atkinson (e=2) 0.37795 0.43552 0.39442 0.44754 Per cent Change in 15.3 4.3 18.4 the Aktinson Index Atkinson Indices. A(e) The larger e>0 (the 'inequality aversion parameter'= 0.5, 1, 2) is, the more sensitive A(e) is to income differences at the bottom of the distribution Source: Statistical Office; and World Bank staff estimates based on 1998/99 LSMS data. ANNEX C Page 9 of 10 Table 20: Per capita household expenditure by decile and area -________ NATIONAL DECILE Total expenditure Total expenditure per capita Total expenditure per capita Transfers Transfers Discrepancy Transfers as a share of total Transfers as a share of total per capita net of transfers from abroad net of transfers from abroad as estimated as adiusted per capita hh expenditure per capita bh expenditure [dep-pcl ldepnfopcj as adiusted by MSO from LSMS data by MSO LSMS estimates MSO adjusted as estimated from LSMS data _ a b c d e f _ 1 2060.6 2047.8 2009.7 12.8 50.9 3.98 0.6% 2.5% 2 3032.1 3020.5 2984.7 11.5 474 4.11 _ 0.4% 1.6% 3 3779.2 3708.9 3499.8 70.3 279.4 3.98 1.9% 7 4% 4 4529.7 4452.3 4229.5 77.4 300.2 3.88 1.7% 6.6% 5 5338.8 5231.7 4915.6 107.1 423.2 3.95 2.0% 7.9% 6 6281.7 6138.5 5725.5 143.2 556.2 3.89 2.3% 8.9% 7 7519.3 7404.1 7055.0 115.2 464.3 4.03 1.5% 6.2% 8 9252.4 9040.1 8382.3 212.3 870.1 4.10 2.3% 9.4% 9 12252.2 11881.8 10808.8 370.4 1443.4 3.90 3.0% 11.8% 10 24214.1 23561.5 21659.4 652.6 2554.7 3.91 2.7% 10.6% Total 7826.1 7648.8 7126.8 177.3 699.3 3.94 2.3% 8.9% URBAN DECILE Total expenditure per Total expenditure per Total expenditure per Transfers Transfers Discrepancy Transfers as a share Transfers as a capita capita net of transfers capita net of transfers as estimated from as adiusted by MSO of total per capita hh share of total per [dep_pc] from abroad from abroad LSMS's dataset expenditure capita bh [depnfopcJ as adiusted by MSO LSMS estimates expenditure as estumated from MSO adjusted LSMS data b i l k I m 1 2965.4 2941.2 2869.5 24.2 95S 3.97 0.8% 3.2% 2 4220.3 4163.5 3995.6 56.8 224.7 3.96 1.3% 5.3% 3 5134.5 5000.1 4604.7 134.4 529.8 3.94 2.6% 10.3% 4 6010.2 5877.8 5478.5 132.3 531.7 4.02 2.2% 8.8% 5 6994.8 6933.3 -6745.9 61.5 248.9 4.05 0.9% 3.6% 6 8279.5 8079.2 7498.2 200.3 781.3 3.90 2.4% 9.4% 7 9780.9 9547.4 8787.4 233.5 993.5 4.25 2.4% 10.2% 8 12097.7 11690.1 10492.9 407.6 1604.8 3.94 3.4% 13.3% 9 15910.0 15398.1 13969.3 511.8 1940.7 3.79 3.2% 12.2% 10 30218.8 29431.1 27069.2 787.7 3149.6 4.00 2.6% 10.4% Total 101570 9902.1 9150.5 254.9 1006.5 3.95 2.5% 9.9% Note: MSO Morocco Statistical Office Source: Statistical Office; and World Bank staff estmates based on 1998/99 LSMS data. ANNEX C Page 10 of 10 RURAL DECILE total expenditure per total expenditure per total expenditure per Transfers Transfers Discrepancy Transfers as a share Transfers as a capita capita net of transfers capita net of transfers as estimated from as estimated by MSO of total per capita hh share of total pdepcl from abroad from abroad LSMS's dataset expenditure per capita hh [depnfqpcJ as adiusted by MSO LSMS estrmates expenditure as estimated from MSO adjusted LSMS data n o p r s 1 1689.7 1679.2 1646.3 10.5 43.4 4.13 0.6% 2.6% 2___ _____2463.8 2460.1 2449.1 3.7 147 3.97 0.1% 0.6% 3 2952.5 2934.4 2882.7 18.1 69.8 3.86 0.6% 2.4% 4 3424.4 3381.8 3260.2 42.6 164.2 3.85 1.2% 4.8% 5 3937 4 3840.5 3550.0 96_ 9 387.4 4.00 2.5% 9.8% 6 _45-58.8 -- 4481.0 - 4258.1 77.8 300.7 3.87 1.7% 6.6% 7 5282.1 5195.7 4935.8 86.5 346.3 4.01 1.6% 6.6% 8 6295.6 6128.3 5669.6 167,3 _- 626.0 _ 3.74 2.7% 9.9% 9 7834.2 7711.3 7329.2 122.9 505.0 4.11 1.6% 6.4% 10 12477.3 12241.6 11562.0 235.8 _ 915.3 3.88 I1.9% 7.3% Total 5087.5 5001.4 4749.2 86.1 338.3 3.93 1.7% 6.6% Note: the estimates based on the 1998/99 LSMS data lead to columns e, k, and q. Those estimates were consistently estimated by both Funo and the Moroccans, but the Moroccans preferred to adjust the estimates to match National Accountmg estimates. Columns f, 1, and r report the transfers as adjusted by the Moroccans to account for the under-reportmg affecting LSMS transfers The adjustment is very naive: the values of the transfers as estimated from LSMS are multiplied by 19161.6/4972.8=3.8533, where the numerator is the total amount of transfers as from NA, and the denominator is the total amount of transfers as extrapolated from LSMS. Further details In the MSO document. Source: Statistical Office; and World Bank staff estimates based on 1998/99 LSMS data. ANNEX D Page I of 15 ANNEX D INCIDENCE OF PUBLIC SPENDING IN EDUCATION AND HEALTH 1. Education and Literacy Access. During the 1990s substantial improvements have been achieved in increasing enrollment, particularly among rural girls: net primary enrollment rate has increased from 58.2% in 1991 to 70.2% in 1998 and particularly among rural girls (28.3% in 1991 compared to 46.8% in 1998). Net secondary enrollment rate has reached 31.2% in 1998 compared to 25.9% in 1991 and for girls it has increased to 28% from 21.5% (compared to 30.4% in 1991 and 35.1% in 1998 for boys). In rural areas, the percentage of pupils attending basic schooling in 1998 ranges from about 48% for the lowest per capita expenditure quintile to 77% for the better off in highest expenditure group, compared to 88% and 89% in urban areas. Various factors have contributed to this increase in rural girls enrollment during the last few years, such as: increase in access through additional school construction, improvement in quality of the education, and introduction of various programs that target rural girls (i.e., food aid programs financed byWorld Food Program (WFP), aid to families in few selected BAJ provinces and programs of school supplies financed under the BAJ in 14 provinces). But we don't know to what extent these programs have contributed to the increase in enrollment and once they will be ended the sustainability of the rural girls enrollment may be jeopardized. Table 1: Illiteracy rates by gender 1998/99 1990/91 Age group Urban Rural National Urban Rural National Total 10-14 8.6 41.6 25.3 9.7 50.1 32.8 15-24 16.3 57.3 35.7 15.3 60.8 37.6 Male 10-14 5 4 26.5 16.2 5.1 33 4 21.9 15-24 9.6 38.2 22.7 6.1 39.2 22.0 Female 10-14 11.9 58.2 35.1 13 9 68.4 43.8 15-24 23.2 74.6 48.2 23.8 79.0 51.5 Source Statistical Office, 1990/91 and 1998/99 LSMS data. Because of better access to literacy programs, the decrease in illiteracy rate during 1990-98 mainly took place in urban areas: the illiteracy rate among those aged ten or more has decreased from 55% (40% in urban areas, 68% in rural areas) in 1990/91, to 48% (34% in urban areas, 67% in rural areas) in 1998/99. However, the breakdown of the illiteracy rate by gender shows the persistence of large disparities. For instance, in 1998/99 the illiteracy rate for rural women was 83%, compared to 87.2% in 1990. However this slow reduction in rural women illiteracy rate is mainly due to 'catching-up --- although older women remain illiterate, there are substantial increase in literacy of the younger girls: illiteracy rates for rural girls of 10-14 years of age has declined from 68.4% in 1990/91 to 58.2% in 1998/99, but still about 74.6% of rural girls aged between 15 and 24 years are illiterate (compared to 79% in 1990/91). Among the poor, 49.1% of those living in urban areas are illiterate, a percentage rising to ANNEX D Page 2 of 15 73.5% in rural areas. These figures compare to 24.1% and 60.2%, respectively, for the betler off (see Table 1). FIGURE 1: Impediments to Enrollment by Expenditure Decile for 1998/99 40- 35 30- 25- 01~~~~~~~~~~ - - - - 0 - - - - - - - - - - - - - - - - 5- 1 2 3 4 5 6 7 8 9 10 E:penditure docile E" OPwey T Phr,c aoss Ull*lltdceApvvrdesd | Source: Statistical Office, and 1998/99 LSMS data. The 1998/99 LSMS data distinguishes three broad types of impediments to enrollment: poverty, general attitude towards school, and physical access to schools. According to this information, the lowest deciles non-enrollment is mainly due to both poverty and difficulties to access school facilities (see Figure 1). However, beginning with the third decile the attitude towards the school (by both parents and children) is the single most important obstacle to enrollment. Among the poor, in urban areas lack of sufficient income (being poor) is the most important reason for not attending school (68% in urban areas advocate poverty as the main reason compared to 48% in rural areas) while in rural areas in addition to being poor, if the parents could afford sending their children to school, lack of physical access to school is relevant for their non-enrollment (36% in rural areas compared to 4% in urban areas). Finally parents attitudes toward school influence the gender gap: at the national level, 9% of poor school-age boys are not enrolled for cultural reasons, compared to 19% for girls. ANNEX D Page 3 of 15 Budgetary Cost. Over the last decade, public spending in Morocco in the education sector has increased slightly, from 5.0% of GDP in 1991 to 5.9% in 1998. As a share of total 100% - COMPOSIN OF EXPE)DRE government expenditure it has also 80° r slightly increased (from 19.3% to 0/ 20.4% during the 1991-98 period). 80% In real per capita terms, the public 70% spending on education has 60Y decreased slightly during the first 50l half of the 1990s (on average, -1% per year from 1991 to 1996), but 40% increased thereafter (+4.4% from 30%/_ 1996 to 1998). A functional analysis of public spending in education shows that the burden 10% placed by teaching and non- 0% teaching staff wage bill makes up to 1991 1992 1993 1994 1995 1996 1997 1998 1999 over 80% of the total education E%iowerbaic f*%hi herbasic li%secondac Ohi her eucation budget while only about 9% is allocated to Source: Ministry of Finance investment. In fact during 1991-98, the share of investment in total education budget has declined from 11.5% in 1991 to 9.2% and the share of wages has increased from 77.9% to 81.2%. Over 1991-98, the shares of the Ministry budget allocated to different cycles have remained almost stable: lower basic schooling accounts for about 40%, higher basic for 22%, secondary schooling for 21%, and higher education for 17% (see Figure 2). Over the last decade the cost secondary and higher education per student has increased relative to primary education. Between 1991/92 and 1998/99, real costs per student decreased by about 4% in the lower basic, while they increased in the higher basic (7.8%) and in the secondary education (10.5%): in 1991/92 the cost per student enrolled in secondary education was about three times as much as the cost per student in primary education, to be compared with 3.5 times in 1998/99. However, this increase has not been in response to increase in enrollment at those levels, given that enrollment increased relatively more in basic (+29.9%) than in secondary education (24.2%). As far as higher education is concerned, in 1998/99 the cost per student enrolled in higher education (17,500 DH) was about six times as much the cost per student in basic schooling (3,300 DH) and slightly less than twice the cost per student at the secondary level (10,500 DH). Education Costs and their impact on the Door. Although education is free, the 1998/99 LSMS confirms that families incur non-negligible out of pocket costs (for books, writing materials and food in school) and particularly for the poor the cost of sending their children to school is non-negligible. Overall education is a bigger burden on poor rural household expenditure. In absolute terms, the households' expenditures for education increase with total expenditure, both in urban and in rural areas (see Table 2). On average, the yearly per capita expenditure on education for urban households (263 DH) is more than five times as much as for rural households (50 DH). As a share of total expenditure, at the national level the expenditure for education shows some tendency to be progressive, i.e. the households in the poorest deciles spend a lower share (1.6%) on education than those in the richest deciles (2.7%). However, the pattern of the budget shares for rural households is unambiguously regressive: the poorest spend on education about 1.5% of their total budget, compared to 0.5% for the richest, while in urban areas, the budget shares on education for the poor and the better off are 2.6% and 3.4%, respectively. ANNEX D Page 4 of 15 Table 2 - Household expenditure per capita on education for 1998/99 (current DH, per year). 1 79.2 24.8 32.4 2.6 1.5 1.6 98.8 31.4 53.6 2 2.3 1.3 1.7 3 118.5 36.2 64.1 2.3 1.2 1.7 4 1430 55.3 19.7 2.4 1.6 1.8 5 136.1 52.0 96.1 1.9 1.3 1.8 174.6 51.0 109.9 6 2.1 1.1 1.8 7 202.9 60.2 124.7 2.1 1.1 1.6 8 293.3 62.8 151.1 2.4 1.0 1.6 9 339.2 65.8 217.6 2.1 0.9 1.8 1,039.8 55.4 716.4 10 3.4 0.5 2.7 262 5 49.5 164.3 Aggregate 2.4 0.8 1.5 Note Expenditure groups are deciles ranking households by total expenditure per capita. The table reports (i) the expenditure on education (both public and private), where the amounts paid for supporting members living outside the family ha ve not been included, and (ii) budget shares (in small bold type) Source: Statistical Office and World Bank staff estimates based on 1998S99 LSMS data Distribution of Education Subsidies. Figure 3 shows how government spending (nel: of cost recovery) on primary, secondary and higher education is distributed on per capita basis across expenditure per capita deciles for Morocco in 1998199. Two different patterns characterize education subsidies: (i) the incidence of public spending on the lower basic (Primary 1) is highest for the poorest expenditure group, with a tendency to fall as income rises, (ii) although with considerable disparities, government spending for the upper basic (Primary 2) as well as for both secondary and tertiary education is regressive, i.e. public spending steadily increases as income increases, thereby favoring the better off. In the aggregate - when public spending for all education levels is considered - total government expenditure is found to be strongly regressive: on average, the poorest 10% receive per capita subsidies that are half as much the subsidies accruing to the households around the median (DH 398 and DH 801 per capita per year respectively), while per capita subsidies for the richest 20% of the population are around DH 905 per year. ANNEX D Page 5 of 15 FIGURE 3 - Distribution of Subsidies by Basic, Secondary and Higher Education (1998/99) 400 0- 350 0- 300 0- 250 0- 2500.0 500- 1 2 3 4 5 6 7 a 9 10 Expenditure docile | Pnmaiy 1 *1Pnmary2 I Secondaey 3 Higher Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data The extent to which the education subsidies benefit the households across total expenditure deciles strongly depends on the levels of education. Table 3 provides the breakdown of the incidence of education subsidies by education level: for each decile and education level, the mean per capita subsidy accruing to the decile has been divided by the mean per capita expenditure of the decile. This standardization allows to compare the incidence of education benefits both across decile and across education levels. The main results from Table 3 are as follows: (i) the pattern of subsidies for the lower basic is strongly progressive: the subsidies accruing to the poorest decile amount to 13.9% of their total expenditure, compared to 0.6% accruing to the riches decile; (ii) subsidies for the upper basic are benefiting almost uniformly the first six deciles, showing some tendency to fall for the top 40% of the population; (iii) subsidies for both secondary and higher education are unambiguously regressive (for instance, the bottom 30% receives subsidies for higher education amounting to 0.7% of their total expenditure, compared to 1.6% for the top 30%). In order to investigate the trend of the incidence of benefits, further analysis is needed to compare 1998/99 and 1990/91 LSMS data. ANNEX D Page 6 of 15 Table 3: Distribution of subsidies on education by expenditure decile and education level: Morocco 1998/99. 1 2060.6 0.139 0.035 0.012 0.007 2 3032.1 0.115 0.032 0.014 0 002 3 3779.2 0.090 0.034 0.020 0.012 4 4529.7 0 074 0.032 0.025 0.018 5 5338.8 0.066 0.037 0 028 0.019 6 6281.7 0.055 0 030 0.031 0.026 7 7519.3 0.039 0.025 0.026 0.030 8 9252.4 0.030 0 023 0.023 0.019 9 12252.2 0.023 0.019 0.016 0.019 10 24214.1 0 006 0.007 0.013 0.010 Note. The table reports the ratios between the per capita subsidy accruing to each decile and the mean per capita expenditure of each decile. Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data In order to evaluate the benefits from government expenditure on education accruing to the poor, a simple way is to see whether the poor receive a larger share of the benefits than their share on national per capita expenditure. Thus, if the ratio between the percentage of the benefits received by the poor and their shares of total expenditure exceeds 100, then the benefits accrue more proportionally to the poor than to the better off. Conversely, if the ratio is less than 100, those in poverty receive less than their share in total expenditure. Because in the former case public spending improves the distribution of income while inequality is worsened in the latter, we shall refer to this ratio as the "inequalitv ratio". Table 4 shows that the better off receive proportionally a larger share of the public expenditure in education. However, as noted above with regard to Table 3, there are important differences in the extent of the bias across education levels, with spending in both cycles of primary education being strongly pro-poor, and spending in higher education having the strongest bias towards the rich. Thus, Table 4 allows to zonclude that (i) overall, public spending in education is unambiguously pro-poor, and (ii) if the breakdown by educational level is taken into account, it turns out that the above result is mainly driven by the pro-poor characteristic of primary education, which more-than offset the pro-rich characteristic of the subsidies for both secondary and higher education. Table 4: Distribution of benefits and the poor: Inequality ratios 1998/99 Primary 1 236.8 Primary 2 132.7 Secondary 75.3 Higher 54.5 149.1 TOTAL EDUCATION Note: For an inequality ratio, values greater than 100 denote that public spending benefits are pro-poor, whereas values less than one denote that benefit in favor of the better off. Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data ANNEX D Page 7 of 15 2. Health Care System Morocco's health system is similar to that of many developing countries in which the Government - largely through the Ministry of Health (MOH) - is responsible for basic public health activities, management and regulation of the sector, and is the major provider of services at all levels as well as the social safety net for the poor.' The Ministry of Health delivery system is organized in two networks: basic care and hospital care. The former includes rural dispensaries, rural community health centers, rural local hospitals, and urban health centers. The hospital network is composed of general and specialty hospitals. Among the specialty hospitals, there are two teaching hospitals which provide most of the tertiary care in the county. In principle, all Moroccans are eligible to receive health care in MOH facilities, (health centers, dispensaries, diagnostic centers, and public hospitals), either free of charge, if indigent, or by paying subsidized fees for the better off.2 In terms of formal health insurance, Morocco does not have a compulsory system, but it has a voluntary system for several categories of the population including mutual insurers for civil servants, certain categories of professionals (largely in banking), and public enterprises; mutual and private health insurance for private sector enterprises and individuals; and a compulsory social security system (CNSS - Caisse Nationale de Securiti Sociale) which does not provide medical insurance but does provide some limited health benefits for children through its family allowance system. Formal health insurance coverage is quite limited, covering about 15% of the population, three-fourths of whom are civil servants. Among civil servants, about 80% are voluntarily enrolled in CNOPS (Caisse Nationale des Organismes de Prevoyance Sociale) but since November 1999 the system has become compulsory. According to 1998/99 LSMS, the coverage of CNOPS is urban biased and most of the poor urban wage-earners, who are mainly working in the private sector, are not enrolled: almost 99% of the beneficiaries are non-poor. Extending basic health care to the entire population has been a major objective of the Government since Morocco's independence. However, policies in the past strongly favored hospital construction in the major urban areas, thereby giving low priority to primary health services, particularly in rural areas. The 1998/99 LSMS data indicate that the urban-rural disparity is still far from being reduced: both access and utilization levels are strongly biased toward the urban households. The data acknowledge some progress in improving the delivery of primary health services to rural areas, mainly through the basic dispensaries. Access. The 1998/99 LSMS data indicate that access to health services varies by household living standards. At the national level, the percentage of those who recalled having consulted any medical facility is around 45% in the poorest expenditure quintile, compared to 77% for those in the richest quintile: the same disparity is to be found within both urban and rural areas (see Table 5). Most of the poor use the services of public health facilities while the better-off opt mainly the private sector. Among the sick or injured, the relative majority (52.8%) sought treatment in the private sector, consisting of health care services delivered by private doctors and paramedics.3 However, the data reveal that there is strong evidence of self-selection. The non poor clearly opt out of the public sector: in urban areas, 75% of individuals in the richest expenditure group chose private health care (compared to 23% for the poorest), while in rural areas the percentage is 61% (compared to 28% for the poorest). I See "Morocco Health Financing Brief", World Bank 1999. 2 In principle the poor can obtained a "Carte d'Indigence" through local governments, which will provide coverage for health care services in public facilities. However the system is not efficient and many abuses are reported. 3 Both are often public sector employees who set up private practice after hours. Physicians enjoying dual status (i.e. working two and a half days in public hospitals and the rest of the week in their private practice) have a strong incentive to maintain a long waiting list in the public hospitals and promise to their clients immediate treatment at their clinic. ANNEX D Page 8 of 15 Table 5: Access to health services by region and household consumption expenditure quintile 1,1998/99 Ur,ban _ ........... .'' ''" '''' -.............................. '"'';'''-""'''''.""'""'"''''''':''-"'"""""''' .,aL 7 Quintile I Quintile V Quintile I Quinile V Quintile I Quintile V % who reported being ill & consulted health facilities 2' 59.5 80.0 40,0 73.9 45.1 77.2 Sector: - (o) Public 76.1 20.5 66.8 38 5 70.6 29.3 - (%) Semi-public 0.5 4.7 5.0 0.2 1.6 3.1 - (%) Private 23.4 74.8 28.2 61.3 27.8 67.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 Person consulted: - (%) Doctor 89.1 89.9 68.4 85.6 73.3 89.3 - (%) Pharmacist/Dentists 2.4 6 8 5.4 6.7 2.9 6.4 - (%) Nurse 4.7 0.2 19.1 3.9 16.7 0.8 - (%) Other 3.3 1 8 7.2 3.6 6.3 2.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Distance (kn) 13.7 17.8 34.4 14.7 14.9 Healdt insurance coverage (%) 5.6 43.2 1.8 7.9 2.4 35.0 Notes: 1/ Quiniles refer to total individuals classified by household consumption expenditure per capita 2/ Refers to percentage of people reporting an illness in the preceding month of the survey. Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Morocco' s health delivery system has improved substantially over the past 30 years, but this progress has been uneven across the territory. The travel time and the costs in seeking medical care represent serious access obstacles for households in many of Morocco's rural areas. Aggregate data at the national level mask large urban-rural differentials in terms of availability and use of health services. On average, rural residents are 21 km from a public health facility (31 km from a private one), while urban residents are 5 km from public - and 11 km from private facilities. On average, the rurals' travel time to reach a public health facility (51 minutes) is three times as much as for individual living in urban areas (22 minutes). According to the 1998/99 LSMS data, about 50% of urban respondents were able to reach the health facilities on foot, compared to 14% for the rurals (65% of individuals living in rural areas needed a vehicle). In fact 1998/99 LSMS shows that - within each area - health facilities are located closer to the poor than to the better off. This is mainly because the rich exert more choice over which facilities to attend and can afford not to go to the closest one (see Table 5). Based on the 1998/99 LSMS data, utilization of public/private health facilities and the type of treatment sought by individuals differ within expenditure quintiles (see Figure 5). For instance,, of all those reporting ill in the poorest quintile, about one-third took treatment from primary basic dispensaries (either public or private), about 20% from public hospitals and private doctors, 17% from primary health centers, 3% from private pharmacists, and about 6% from other facilities (e.g., self-treatment, traditional healers, mutualistes, etc.). The data also shows that (i) with the only exception of the poorest quintile, health care provided by private doctors is predominant across all expenditure quintiles (see Figure 4). The reported illnesses treated by private doctors are also an increasing function of per capita expenditure, ranging from 20% for the poorest to 60% for the richest; (ii) visits to private sector exceeded those to public hospitals for all quintiles, and also increased more steeply across household expenditure quintiles; and (iii) recourse to both dispensaries and primary health centers drops systematically from the lI' to the 51h quintile. ANNEX D Page 9 of 15 FIGURE 4: Utilization of Health FacHlities by Expenditures Quintiles Morocco 1998/99 60 0 40 0- 0300 .. l ~~~~~---- --- --- l------ - -____ 1 11 111 IV V _Dispensary 33.4 25.7 18.2 14.6 7.7 *Healthcenter 16.7 15.3 14.3 11.0 3.6 UPubltchos ital 20.9 21.0 231 17.8 171 |DPrvate doctor 20.4 312 35.4 46.0 59.1 |[lPharmacist 2.9 3.1 64 7.2 6.4 IlOther 5.8 3.8 2.6 3.5 6.2 Expenditure quintile Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data LSMS data confirms that both the inefficiencies and low quality of the public health system are clearly perceived by the households. In urban areas, about 60% of the poor sought treatment either in public hospitals (32.4%) or in dispensaries (27.4%), to be compared to 33% for the non poor. In rural areas, dispensaries are the most common choice by the poor (35.8%); however, a relatively high percentage of the poor (20.8%) were treated by private doctors, compared to 15.3% by public hospitals (see Table 6). Table 6 - Utilization of health facilities and the poor (%) Poor Non-Poor Poor Non-Poor Poor Non-Poor Dispensary 27.4 13.0 35.8 17.5 31.6 14.3 Primary health center 15.2 7.8 19.4 12.6 17.3 9.3 Public Hospital 32.4 20.0 15.3 15.7 23.8 18.7 Private doctor 17.5 48.2 20 8 44.6 19.2 47.1 Pharmacist 2.3 6.4 2.6 5.6 2.4 6.2 Other 5.2 4.5 6.3 4.1 5.7 4.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 Source: Statistical Office, and World Bank staff estimates based on 1998199 LSMS data Budgetary costs. Overall, Government health expenditures in Morocco are well below those found in other comparable income countries. Public health expenditures in Morocco were estimated around 0.9-1.2% of GDP between 1990-98, compared with 2.6% in other MENA countries. Throughout 1991 to 1998, total public health expenditure in Morocco grew at a real average annual rate of 2.9%. Since about 86% of the public health spending is for operating expenses, this increase was largely driven by the increase in salaries (+3.7% per year over the same period). If the pattern of growth ANNEX D Page 10 of 15 of the population is taken into account, health expenditures have increased by 1.25% per year: again, this result is mainly driven by the evolution of wages (+2.0% per year on a real per capita basis). A noteworthy characteristic of the functional distribution of health expenditures concerns the evolution of the expenditure for equipment, which has decreased substantially over the last decade (-2.4% per year).4 Moreover a disproportional share of the MOH expenditures are for hospitals (54%) and only 25% of its budget is allocated to preventive care. As a share of total government expenditures, health expenditures have increased slightly, from 3.4% in 1991 to 3.7% in 1998, while as a share of total social expenditures health expenditures have decreased from 9.9% in 1991 to 8.7% in 1998. According to National Health Accounts for 1997/98, Ministry of Health expenditures represent 22% of total health expenditures, other Ministries health expenditures represent 2%, voluntary mutuals and private health insurance account for about 16%, direct out-of-pocket payments from household account for 54%, private enterprises and public organizations represent 4% and the international cooperation and local collectivities account for the remaining 2%. Health costs for the Household. The 1998/99 LSMS data show that the expenditures for health vary with household standard of living, ranging from about 2% of household budget share for the poorest households to 6% for the richest. Households' expenditures for health increase constantly with total expenditure, both in urban and in rural areas. On average, the yearly per capita expenditure on health for urban households (DH 511) is more than two times as much as for rural households (DH 189) (see Table 7). As household budget share, the expenditure for health is strongly progressive: households in the poorest deciles allocate a lower share (2.2%) of there expenditure to health than those in the richest deciles (5.8%). The same pattern is to be found both in urban and rural areas. As far as the poor are concerned, although health care should be free for the indigent, the 1998/99 LSMS show that out-of pocket payments for health services are far from being negligible. At the national level, the household budget share on health for the poor is 2.4%, compared to 4.3% for the better off. In urban areas, the budget shares on health for the poor and the better off are 3.1% and 4.7%, respectively, compared to 2. 1% and 3.7% in rural areas. 4 This pattern is consistent with the presence of quality problems concerning the health facilities, particularly the lack of critical equipment and supplies. Much equipment is over 15 years old: 40% of operating theatres, 22% of reanimating equipment; 39% of lab equipment, and 32% of radiological equipment. For more detalls, see Morocco Health Financing Brief. ANNEX D Page lI of 15 Table 7 - Per capita expenditure on health for Morocco 1998/99 (current DH, per year). 83.4 31.8 46 3 1 2.8 1.9 2.2 1510 59.7 73.1 2 3.6 2.4 2.4 187.8 61.3 112.9 3 3.7 2.1 3.0 241 8 88.0 172.1 4 4.0 2.6 3.8 313.2 115.5 195.6 5 4.5 3.0 3.7 366.9 189.9 228.2 6 4.4 4.2 3.6 523.1 181.3 361.1 7 5.4 3.4 4.8 615.2 214.8 442.4 8 5.1 3.4 4.8 932.9 364.4 627.9 9 5.9 4.7 5.1 1701.1 589.5 1374.5 10 5.7 4.9 5.8 511.5 189.4 363.4 Aggregate 4.5 3.2 3.9 Note: Eypnditure groups are deciles ranking households by total expenditure per capita. The table reports (i) per capita expenditure on health and (ii) budget shares (small- bold-type). Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Distribution of Health Subsidies. In order to measure the extent to which the households benefit from the public spending on health, two main pieces of information are necessary, namely (i) the pattern of use of health facilities across household expenditure groups, and (ii) the estimated unit costs to the govemnment. Unfortunately, these information are not available yet.5 Nevertheless, preliminary estimates of the pattern of utilization of public health facilities provide useful insights (see Table 8). The following observations can be made about each treatment option: X The better-off obtain more often health consultation, no matter what health facility is chosen, particularly in the urban area. At the national level, 45% of the poor have access to medical consultations compared with 77% of the rich. • The better-off often use private consultations no matter their area of residence, while the poor most often resort to public health facilities (dispensary, health center and public hospital). * Public hospitals. At the national level as well as in the rural area, the richest 20% of the population benefits four times more than the 20% poorest individuals from public hospitals. But in the urban area, the poor as well as the rich benefit from public hospitals. 5 The relevant sections of LSMS questionnaire have not been processed by the Moroccan Statistical Office, nor have the MOH estimates of the unit costs by level of services. ANNEX D Page 12 of 15 Health centers. In rural areas, community health centers are used by all expenditure groups , while in urban areas health centers are mostly used by low expenditure groups: the poorest 40%, of urban population benefits two times more from health centers than the 20% richest urban individuals. * Dispensaries. In urban areas, dispensaries are largely targeted to low and middle expenditure groups: the poorest quintile is three times more likely to use dispensaries than the richest quintile. Although in rural areas all expenditure groups use public dispensaries, the richest quintile benefits 2 times more than the poorest quintile from public dispensaries. ANNEX D Page 13 of 15 Table 8: Proportion des malades qui ont rialis6s des consultations medico-sanitaires en 1998/99 National Quintile Urban Rural National 1 59.5 40.0 45.1 2 67.5 40.0 54.3 3 69.7 54.1 64.9 4 729 59.5 71.1 _ _ _ _ 80.0 73.9 77.2 Total 71.7 56.3 66.0 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 9 - R6partition du lieu de consultation m&lico-sanitaire selon les classes des d6penses en 199899 National 1 2 3 4 5 |Total Public sector Dispensary 17.5 22.3 20.1 22.5 17.7 100.0 33 4 25.7 18.2 14.6 7.7 15.8 Health center 13.9 21.0 25.0 26.9 13.2 100.0 16 7 15.3 14.3 11.0 3.6 9.9 Public hospital 9.0 15.0 21.0 22.7 32.3 100.0 20.9 21.0 23.1 17.8 17.1 19.2 Private sector Private office 3.8 9.5 13.8 25.1 47.9 100.0 20.4 31.2 35.4 46.0 59.1 44.8 Mutuals * 5.8 20.5 73.7 100.0 e - 0.5 1.3 3.1 1.5 Fkih/healer 21.3 14.4 21.7 19.3 23.3 100.0 2.6 1.1 1.3 0.8 0.7 1.0 Pharmacy 4.1 7.1 19.2 30.0 39.5 100.0 2.9 3.1 6.4 7.2 6.4 5.8 Home 5.8 23.3 9.8 15.8 45.3 100.0 0.7 1.7 0.6 0.6 1.2 1.0 Other places 19.8 13.8 4.6 18.6 43.1 100.0 2.4 1.0 0.3 0.8 1.2 1.0 Total 8.3 13.7 17.4 24.4 36.2 100.0 100.0C 100.0 100.0 100.0 100.0 100.0 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data ANNEX D Page 14 of 15 Table 10 - Repartition du lieu de consultation midico-sanitaire selon les classes des depenses en 1998/99 Urban 1 2 3 4 5 Total Public sector Dispensary 23.9 18.2 24.9 25.5 7.6 100.0 30.5 17.9 18.5 14.2 3.3 13 9 Health center 19.8 18.7 34.1 17.7 9.8 100.0 15.0 11.0 15.2 5.9 2.6 8.3 Public hospital 16.1 19.5 17.6 25.7 21.1 100.0 30.7 28.7 195 21.4 14.0 20.8 Private sector Private office 4.3 10.0 16.1 24.9 44.8 100.0 18.1 32.9 39.9 46.3 65.9 46.3 Mutuals - 6.1 10.8 20.3 62.8 100.0 - 0.9 1.2 1.7 4.2 2.1 Fkihlhealer 27.9 29.4 9.4 8.1 25.3 100.0 1.6 1.3 0.3 0.2 0.5 0.6 Pharmacy 4.3 12.9 13.9 34.1 34.8 100.0 2.4 5.6 4.6 8.4 6.8 6.2 Home 3.7 23.3 4.8 15.7 52.5 100.0 0.3 1.5 0 3 0.6 1.5 0 8 Other places 16.6 2.8 10.3 32.4 37.9 100.0 1.5 0.2 0 5 1.3 1.2 1.0 Total 10.9 14.1 18.7 24.9 31.4 100.0 100.0 100 0 100.0 100.0 100.0 100.0 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data ANNEX D Page 15 of 15 Table 11- Repartition du lieu de consultation medico-sanitaire selon les classes des dipenses en 1998/99 Rural 1 2_ 3 4 5 Total Public sector Dispensary 11.8 21.1 24.8 20.0 22.5 100.0 26.9 31.9 25.6 16.9 12.7 19.9 Health center 14.7 13.9 21.2 30.2 20.0 0oo.o 22.8 14.2 14.8 17.3 7.6 13.5 Public hospital 9.5 11.9 17.6 21.3 39.7 100.0 17.2_ 14.2 14.8 17.3 7.6 13.5 Private sector Private office 4.1 10.0 18.2 24.6 42.6 100.0 19.4 31.6 39.1 43.3 50.3 41.4 Mutuals - - - - 100.0 100.0 - - - - 0.6 0.2 Fkih/healer 10.1 14.6 16.5 16.2 42.5 100.0 2.2 2 1 1.6 1.3 2.3 1.9 Pharmacy 6.7 I.5 9.6 26.4 45.7 100.0 4.0 4.5 2.6 5.8 6.7 5.2 Home 15.2 8.2 18.3 16.5 41.8 100.0 2.1 0.7 1.1 0.8 1.4 1.2 Other places 41.7 9.6 14.1 8.0 26.7 100.0 4.0 0 8 0.8 0.4 0.9 1.1 Total 8.7 13.2 19.3 23.5 35.3 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data ANNEX E Page 1 of 1( ANNEX E PENSION SYSTEM 1. Introduction The social insurance system in Morocco comprises old age, disability and survivors insurance, health insurance, and fanmly allowances. Schemes are contributory, i.e. these benefits are available only to those workers in the formal sector who make contributions to the schemes, and their families. There are a few exceptions, like limited health care, which is available to all Moroccans free of charge or highly subsidized through the ministry of Health facilities. However the health insurance system is not compulsory. Morocco has a voluntary scheme administered by multiple organizations, like mutual insurers for civil servants, certain categories of professionals, and public enterprises; and mutual and private health insurance for private sector enterprises. Some lirnited health benefits are also provided through the CNSS --Caisse Nationale de Securite Sociale, which is in financed by the old age insurance scheme for private sector employees. The pension system is fragmented and managed by four main schemes: i) the CNSS --Caisse Nationale de Sifcurite Sociale-- for private sector employees; ii) the CMR --Caisse Marocaine de Retraite-- for civil servants; iii) the CIMR --Caisse Interprofessionelle Marocaine de Retraites-- which is voluntary and a supplement to the benefits provided by CNSS; and iv) the RCAR --Regime Collectif d'Allocation de Retraite-- for temporary workers in the public sector. Additionally, six public enterprises offer their own pension plans: Bank Al-Magrib, Office CMrifien des Phosphates (OCP), Office d'Exploitation des Ports (ODEP), Office Nationale des Chemins de Fer (ONCF), Office Nationale d'Electriciti (ONE), and Regie des Tabacs. Finally family allowances are also provided through the CNSS, CMR, and RCAR. Services provided by each of those schemes is sunmarized in Table 1. Table 1: Social Insurance Schemes in Morocco Private sector Civil Service Public enterprises Old age, disability and CNSS, CIMR CMR, RCAR ODEP, ONCF, ONE, OCP, survivors insurance Tabac, Bank Al-Maghrib Health insurance Mutuals/ Priv. Insurance CNOPS/ Mutuals Mutuals Family allowances CNSS CMR ? Source: Annual reports of the Social Security Funds 2. The Pension System - Descrigtion of Main Schemes The three most important dimensions for analyzing the performance of pensions schemes are coverage, eligibility and benefits, and financial status (see Table 2). In respect to the number of contributors and beneficiaries, CMR, CNSS, RCAR, and CIMR dominate over the other plans, as they comprise about 97% of contributors and 94% of beneficiaries.! Together these four schemes collect 87% of total revenues and are responsible for a similar percent of total expenses (note that OCP spends more As CIMR is a complementary plan, and RCAR is for temporary workers in the public sector, there is some overlapping in the number of contributors and beneficiaries to these two schemes and to CNSS and CMR. This is due to the fact that some temporary workers in the public sector later became regular civil servants and some firms participate in both CIMR and CNSS However it has been impossible to obtain information on the number of affiliates to multiple schemes ANNEX E Page 2 of 10 than RCAR -about 5% of total expenses-- in spite of sharing only 1 percent of total contributors). Finally the most important funds are not necessarily the ones with higher reserves (see Table 2). However, in view of the limited coverage of smaller funds the subsequent analysis will concentrate on the biggest four pension schemes. Table 2: Summary Indicators of Different Retirement Schemes, 1998 CMR RCAR CNSS CIMR ODEP ONCF ONE OCP Tabac Contributors 824,860 204,233 997,808 212,619 3,729 11,988 23,749 27,182 2,440 Total 321,678 26,045 195,008 62,973 2,562 9,491 5,609 19,254 1,789 Ratioe 0.38 0.12 0.19 0.29 0.68 0.79 0 23 0.70 0.73 Revenue from 4,618.5 707.67 1,926.3 817.6 58 50 97.5 301.7 558 6 180.4 contributions _ _ . Expenses 3,394.48 257.27 2,252 7 1,147.8 50.40 219.94 242.3 439.9 93.5 Balance 1,224.02 450.4 -326.3 -330.2 8.10 -122 4 59.4 118.6 86.8 Fund reserves 1.98 13,594.4 6,752.2 3,609.0 396.7 n.a. 990.6 5,1183 97.7 N.B: (i) CMR's include mi litary personnel. (ii) The system's dependency ratio is defined as the ratio of total beneficiaries (old aie, disabiliy, and survivors) to total contributors. Source: Annual reports of the Social Security Funds The consolidated analysis reveals a system's dependency ratio of 0.27 (relatively low considering that it includes disabled and survivors), an annual surplus of DH 1,168 million --about half a percent of GDP-- and a total reserve fund representing about 9% of GDP. Closer inspection reveals a number of problems: the high fragmentation into different schemes which may affect labor mobility anti increase administrative costs; the presence of perverse incentives embedded in some of these schernes rules; annual deficits or a very small reserve fund in some of the schemes; and the excessive generosity in light of the available resources and yet inadequate provision of benefits for most of the population. A brief description of the main two schemes is presented below. 2.1 CMR The CMR provides old age, disability, and survivors insurance to civil servants ancd military personnel. It has both a contributory and a non-contributory component. The first one is funded with an employee contribution of 7% of payroll and an employer's contribution of 7%. The non-conitributory scheme comprises work injuries for civil and military personnel, pensions d'anciens risistants financed by the state, and those schemes that were in place before the independence and are now being phased out. In order to qualify for a pension it is required that the individual stop working, and accumnulate 30 years of covered work, or 21 (male) and 15 (female) with a special authorization from a pertinent authority. There is a maximum retirement age of 60 --65 for professors, and 66 for magistrates. The basic annual pension is calculated according to Benefit = base wage x yrs. covered work x 2.5 100 Where the base wage is last year's wage, and the years of covered work are lirmited to 40. That is, after 40 years of covered work the replacement rate is 100%. ANNEX E Page 3 of 10 There is a minimum pension for every affiliate with at least 21 years of covered work. The minimum pension is indexed to the salaries of a particular category within the civil service (traitement de base de l'indice 100), which in 1994 was DH 7,962 per year. Using the average wage in the civil service as a benchmark, this rninimum pension represents about 25 percent of the average salary. Table 3 shows the number of beneficiaries and expenses by type of risk (old age and survivors) for selected years. On average the real value of pensions has gone down, as can be seen by comparing the growth rates of beneficiaries and pension payments. While old age beneficiaries grew 173%, pension benefits grew approximately 129%. The corresponding numbers for survivors pensions reveal a worse scenario, with a grow rate in beneficiaries of 213% and a grow rate in benefits of only 46%. Table 3: Beneficiaries and expenses by type of risk, (in 1998 million DU) 1983 1985 1987 1989 1991 1993 1995 1996 Old age 20,631 24,348 29,035 33,683 38,061 44,903 52,247 56,399 beneficiaries______ ______ Monthly old 39 608 40.963 46.414 56 464 60.438 80.900 85.894 91.008 gePensions __ _ _ ___ _ _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ _ _ __ _ _ _ _ _ _ _ _ _ Survivors 13,626 17,050 21,905 25,155 28,393 32,793 40,615 42,760 Monthly Survivors 17.522 21.825 18.403 18 815 18.659 30.431 25.034 25.621 Pensions _________ N.B.: Only civil forces. Source: CMR annual reports. Regarding the financial status of the CMR, it is currently in surplus. In 1998, the revenues exceeded expenses by approximately DH 1,200 million, in part due to a strong civil servant recruitment policy implemented by the government in the 1980s. This resulted in a relatively young demographic structure in the civil service. At the same time the relative weight of non-contributory regimes is decreasing. However since then there has been a significant decrease in the number of workers recruited by the government, so it is expected that the favorable financial position of this regime will revert in the future. 2.2. CNSS CNSS provides a variety of services to private sector employees: family allowances, family health assistance, short term pensions for maternity, birth and death, long term pensions, and limited additional health care, provided by the operation of 13 public hospitals. Workers in agriculture and forestry were incorporated in 1981. The CNSS is administered by a conseil d'administration, with 24 members. There are three separate reserve funds for family allowances, short term and long term benefits respectively. For family allowances and short term benefits the reserve fund has to be 25% of the average expenses during the previous 3 years (surprisingly enough no rule has been established for the case in which the reserves exceed that amount). For long term benefits, the technical reserve is equal to the excess of revenues over expenses. ANNEX E Page 4 of 10 Table 4: Eligibility and Benefits of CNSS by Type of Risk Conttibution Cap to taxable wage Eligibility Conditions Benefit Rates Old age 6 08 (employer) 5,OOODH per month -- 60 years of age -- 50 percent of average mcithly salary, 3 04 (employee) -- 3,240 days of covered work defined as 1/36 or 1/60 of last 36 or 60 -- 55 years of age for miners with 5 salaries (worker's choice). years of covered work -- Increases I percentage point for each -- Mandatory retirement age at 60, 216 days of covered work above 3,240, unless the employer wants to keep with a 70 percent linit the worker, in which case helshe has -- Disability and old age mtinimum to request authorization from the pensions were fixed in 500DH (in ninistry of labor. 1996) -- In case of retirement, the employer is mandated to get a replacement for .__________ that position. Disability - For 1,080 Underreporting of income: since in most cases the benefit at retirement is based on the last years of earnings (one year in the case of CMR, 3 or 5 in the case of CNSS), there is an incentive to underreport income during the first years of a worker's career. > High replacement rate: the retirement schemes impose a heavy burden on their budgets by promising a very high replacement rate. Civil servants for example can achieve a replacement rate of 100% after 40 years of covered work. Private sector workers cannot receive pensions higher than 70% of pre- retirement income, which is still very high for international standards. > Lax eligibility conditions: civil servant are entitled to unreduced pensions after 30 years of covered work independent of age. Private sector workers are entitled to full pensions after only 15 years of covered work. By not imposing an age condition for retirement workers may end up retiring too early and thus stop contributing to the pension scheme. ANNEX E Page 8 of 10 > Minimum pensions are inadequate: it has been stated that the system is too generous for its available resources. However it does not seem to be providing adequate benefits to the beneficiaries. According to World Bank estimates, about 5% of those receiving pensions in Morocco are poor. The main retirement schemes offer minimum pensions but the amount of these minimum pensions is not sufficient for protecting the elderly from dropping into poverty. For instance in the case of CMR, the minimum pension is about 25% of the average salary for civil servants (or is about 45% of the industrial minimum wage - SMIG). In the case of private sector workers, the minimum pension represents about 20% of the average salary in manufacturing (or about 30% of SMIG). > Low retirement age: both main and complementary retirement schemes in Morocco have a very low retirement age for international standards. CNSS for example allows people to retire at age 60. CMR on the other hand imposes no age requirement for civil servants. This imposes a burden on the plan's accounts as people can retire very young as long as they have achieved the required years of covered work. Thus they claim benefits for a longer period of time. 3.3. Coverage The pension schemes in Morocco taken together encompass about 2,300,000 contributors. This represents around 45% of the urban labor force, a number relatively high for regional standards. However, Morocco has about 15 million people who are between 15 and 59 years of age, and with a labor participation rate estimated at 30%, total labor force (including rural workers) would be abouLt 8 million. This results in a total covered population of about 28%, placing Morocco on the low side of coverage by regional standards. A few points that require special attention: > Urban biased coverage: an additional problem has to do with the type of workers covered by formal pension systems. Even though workers in the agricultural sector are officially covered by CNSS, the number of active contributors from the rural sector is negligible. In 1998 there were about 1 million workers from urban areas contributing to CNSS, while there were only 30,000 rural workers contributing. Moreover, while urban contributors increased 36% from 1990, rural workers contributing to CNSS have remained at the same level.4 > Type of beneficiaries: an issue that may require attention in the civil service scheme has to do with the beneficiaries by type of risk. In some countries the eligibility conditions for survivors benefits are extremely lax, which creates an additional financial burden. In the case of CMR inspection of the data on beneficiaries by types of risk raises two concerns: first the growth in the survivors pensions. Between 1985 and 1995 the number of old age pensions in the civil service increased a little over 100%, while the survivors pensions in the same period increased about 50 percentage points more. The second issue has to do with military and disability pensions. According to CMR annual reports, there are almost as many disabled as old age pensioners among the military, while the number of disabled in the other categories of CMR (civil service, auxiliary forces) is very small. This could be due to historical reasons only, but it is also possible that the rules are specified in a way that creates incentives to claim one type of pension over the other (maybe disability is more generous than old age, or retirement age is lower, and eligibility is lax). 3.4. Financial Status The financial situation of the pension system differs by schemes. Some schemes like RCAR have accumulated significant reserves and still experience annual surpluses. Other schemes like CNSS have been suffering from deficits during the last few years, but still have significant reserves. The civil service on the other hand has benefited from an increase in contribution rates as the base of contributors was aggressively expanded in the 1980s through the inclusion of groups that were not covered before and 4 See CNSS annual reports. ANNEX E Page 9 of 10 through a significant increase in the civil service work force. But that situation has reverted and the financial pressure will be felt in the near future, as the scheme's population ages and retires. There are three main issues that should be taken into account with regards to the long run financial equilibrium of the retirement schemes: > Long term financial viability: recent actuarial evaluations have estimated that some of the reserve funds will be depleted very soon if no changes are implemented. CNSS, CIMR, and some of the smaller funds are already in financial distress as annual expenses are higher than revenues. For example, due to the young population structure and system immaturity, the implicit liabilities of the CNSS to existing contributors is only in order of 18.2% of GDP, but this liability will explode rapidly in the future based on population aging, system maturity and possibly higher wage growth rates. > Cross subsidization: some funds like CNSS are already experiencing annual deficits. However the family allowances fund has significant reserves and has experienced annual surplus for the last 19 years (the old age fund turned red in 1996). This suggests the need for a revision in the type of benefits the social insurance system offers, the need for increasing the transparency in financial flows of the pension system, and restriction in other benefits that the family allowance funds could be used for providing. > Low contribution rates: given the existing benefit formula, pension contributions for CNSS (9.12% of payroll tax) are low by international standards and insufficient for covering the benefits. Based on the recent actuarial projections for CNSS, to ensure the financial sustainability of the pension system, contribution rates should be increased to 10.5%. This means that there is some room for improving the financial condition of the plans by increasing contributions, but doing so would raise the labor cost and create additional labor market distortion and should not be considered in isolation from other social insurance benefits. 3.5. Administrative Issues The main issues identify in the administration of pension funds are: > Too many funds: the international experience suggests that having too many funds may create some problems, as administrative costs are higher than having fewer funds, and as it hinders labor mobility across sectors. Some decision should be adopted concerning the harmonization of rules and the recognition of acquired rights in one scheme by others. > Evasion and mechanisms of control: the low level of coverage in Morocco has to do in part with the difficulty of including workers in the informal sector and in agriculture, but also with lax mechanisms to enforce contributions. The administrative side of the different schemes is an open issue that requires more study. To our knowledge very little has been done to determine how the collection system works in the different schemes and how it can be improved (issues like computerization of records, unifying collection by a central agency, using a unique identification code for social insurance and tax purposes. 4. Reconmmendations There are three main areas of concern with respect to the pension system in Morocco: * Low coverage: Since there is no alternative safety nets in Morocco for old age the coverage of the pension system should increase. The international experience is not very helpful in this area. Recent reform episodes in countries where coverage was low have not resulted in significant increases in the number of contributors and in many cases coverage has decreased. This suggests that the issue of coverage may be related more to the structure of the economy than to the particular features of the pension system. The decision of rernaining in the informal sector is clearly related to the ANNEX E Page 10 of 10 characteristics of the pension system, but there are many other factors affecting this decision. One way to increase coverage for the elderly poor is through a noncontributory pension program. This type of system offers a basic pension to every individual who reaches a certain age. It is financed out of general revenues and can be means tested. The main problem with this type of system is its cost, as they can be very expensive. However in places with a tight farnily structure noncontributoxy pensions have the advantage of providing a safety net not only to the elderly but to their families. Increasing transparency in financial flows of the pension system. Currently family benefit contributions in the private sector (CNSS) yield surpluses which are used to finance pensions through cross subsidization. Therefore the true financial flows of the system are obscured. To insure that financial situations of the pension funds are transparent and are not showing misleading surplus, this cross subsidization need to be reduced or eliminated through strengthening links between contributions and benefits for the pension scheme. * Financial sustainabiity. The system is in no immuediate danger of collapsing. However some of the schemes like CNSS are already experiencing annual deficits. Since there is no unique solution to this problem, the government should explore the feasibility of implementing parametric or structural reforms. Among the first, it is recommnended to review eligibility rules, in particular the years of covered work requirement. However, such a strategy has high political costs and sometimes it is very difficult to be implemented. Among the second type, is the preparation of a comprehensive social security system reform that takes into account the socio-economic characteristics of Morocco and adequately hedges against the risks faced by its population as they grow older. Obviously such a reform would be a lengthy process and it is critical to devote sufficient time for planning it. Such a strategy would involve a) recognizing the fragmented nature of the current pension plans and its inability to provide an equitable and sound retirement income system; and b) creating a second pillar (a widespread individual account system) to channel part of the retirement savings. The CIMR scheme already has a capitalization component, which could be used as a base for a more widespread individual account system. The individual account would act as a supplement to the main pay-as-you- go program. Based on the ongoing actuarial projections for each of the pension schemes, with assistance from the World Bank, the cost of implementing different types of reforns will be estimated. * Administrative efficiency: it is unknown at this moment how many workers do not contribute simply because the collection agencies are not good at enforcing the law. A more thorough study of the different areas in the administration of pension schemes is required. This includes looking at how many agencies perform collection and if it would be more efficient to have one central collection agency, how is compliance verified, if tax authorities use the same identification number as pension authorities, if individuals participating in more than one scheme have a unique identification number, the role of banks and other financial institutions, etc. Finally issues related to responsiveness of the system to beneficiaries should be examined too, for example how long it takes to process benefit claims, how many claims are rejected, how effective is the appeals process (if any), etc.. ANNEX F Page 1 of 10 ANNEX F SOCIAL ASSISTANCE PROGRAMS 1. Consumer Food Subsidies in Morocco 1 Consumer food subsidies were introduced in Morocco in 1941, to stabilize prices of strategic goods with no explicit focus on the poor. The subsidy system for oil and sugar are operated by the Compensation Fund (Caisse de Compensation) and ONICL operates the low- grade flour subsidy (FNBT). Except for FNBT2 which is available at an annual aggregate limit of 10 million quintals, cooking oil and sugar are universally available at subsidized prices in unlimited quantities to anyone who chose to buy them. Given that the main objective of the program is to protect domestic producers, official consumer prices of all three subsidized goods have not changed in the last decade despite annual average inflation of 5%. Cost of food subsidies has reached DH 5.3 billion in 1999 (about 1.6% of GDP compared to 1.3% in 1990). Consumer subsidies is the largest social transfer program in Morocco, representing over 80% of the total social transfers. In earlier stage food subsidies were financed through import duties and licensing controls but since July 1996, import quotas and licensing controls have been eliminated and the system is financed by (i) part of the custom revenues (equivalent tariffs) collected on soft wheat, sugar, oilseeds and their main derivatives; and (ii) direct budget contributions. Distributional Incidence Overall food subsidies in Morocco are not well-targeted to poor consumers and cannot be justified as a mean to redistribute income to the poor. Since Consumer food subsidies are universal, lack of targeting means that only about 25% of the benefits reach the poor. However their welfare effect is stronger for the poor because subsidized products account for a higher proportion of the poor's expenditure and 40% of their caloric intake. In absolute terms, a large portion of subsidy expenditures never reaches the consumer, and of the portion that does, only a small fraction reaches the poor. Incidence of subsidies falls disproportionately on the top quintile of the population, which receives 25% of total subsidy spending compared to only 15% for the bottom quintile (see Table 1).3 Cooking oil in particular favors higher income groups even more, as spending on oil rises substantially with income; this is offset by subsidies on flour, however, which are better targeted to the poor and middle-income groups. Despite high degree of leakages, the subsidies in Morocco constitute an important transfer to the poor. Overall, the poor benefit four times more than the rich in relative terms from food subsidies. Similar to all food related transfers, all the three food subsidies in Morocco are progressive and as share of total per capita expenditures of the household they fall with increase I See "Consumer Food Subsidies in MENA", World Bank, December 1999 and "Morocco: Reforms of Consumer Food Subsidies ", August 1999, Karim El Aynaoui. 2 This high-extraction rate flour is known as farine nationale de b16 tendre (FNBT). 3 This incidence analysis is based on 1990/91 LSMS data. Once the data on detailed food expenditures products will be available for 1998/99 LSMS this incidence analysis will need to be updated. Given the increase in the number of poor during 1990s, it can be expected that a larger share of expenditures is reaching the poor. ANNEX F Page 2 of 10 in income. Per capita spending on flour, for example, represents an average of 1.2% of total spending for households in the poorest quintile, compared to only 0.1% in the richest qulintile. Spending on sugar and cooking oil also declines as income rises. Table 1: Impact of Financial Consumer Food Subsidies, by Quintile, 1995 DH per Person Unless Otherwise Indicated 1 2 3 4 5 Average Absolute Average Subsidies per Capita Flour(FNBT)-TotalPop 25.9 27.3 25.1 20.5 15.4 22.8 Urban 20.0 17 6 15.8 14.8 13 15.2 Rural 29.6 33 34.1 30.1 21 9 13 Sugar - Total Pop. 46 55.9 62.5 65.6 77.9 61.5 Urban 42 46.4 54.1 51 5 65 55.2 Rural 50 5 63.2 71.3 88 2 112.9 68.2 Cooking Oils - Total Pop. 179 23.2 32.8 39 3 53.9 33.4 Urban 17 7 22.3 31 36 4 49.6 36.6 Rural 19.2 25 34 9 41.6 56.1 29.9 Total Food Subsidies 89.9 106.3 120.4 125.3 146.9 117.7 Urban 79.7 86.3 100.9 102.7 127.5 107 Rural 99.3 121.3 140.3 159.9 191 129.1 % of Total Absolute Avg. Subsidies per CaRita (total pop.) Total FNBT 23% 24% 22% 18% 13% 100% Sugar 15% 19% 20% 21% 25% 100% Cooking Oils 11% 13% 20% 24% 32% 100% Total Above Food Subsidies 15% 19% 20% 21% 25% 100% Relative Incidence (total pop.) Avg. per cap. Subsidies as % of Total Avg. per Cap Exp FNBT 1.2% 0.8% 0.5% 0.3% 0 1% 0.3% Sugar 2.1% 1.6% 1.2% 0.9% 0.5% 0.9% Cooking Oils 0 8% 0.7% 0.6% 0 5% 0.3% 0.5% Total Above Food Subsidies 4.0% 3.0% 2.4% 1.7% 0.9% 1.7% Nutritional Importance (total top.) Subsidized Calories as % of Total Calories Acquired FNBT 45% 3.4% 2.7% 1.9% 1.2% 2.4% Sugar 5 3% 4.7% 4.7% 4.5% 4.3% 4.6% Cooking Oils 3.1% 2.8% 3.5% 3.6% 4.2% 3.5% Total Above Food Subsidies 12 9% 10.9% 10.8% 10.0% 9.7% 10 6% Source: Linden and Glewwe, World Bank, 1995, based on 1990/91 LSMS data. Finally, since the subsidized goods are more important to the diets of the poor tha:a the non-poor, the calories from subsidized goods represent a larger portion of total caloric intake by the poor compared to the rich, although the nutritional incidence does not vary significantly across income groups. Link between Consumer Food subsidies and Agriculture sector Policy Food subsidies and associated trade policies in Morocco tend to compensate consumers for the incidence of high border tariffs, as well as to protect crop producers and industrial processors. However in practice a substantial share of subsidies constitute transfers to producers rather than consumers. With respect to flour, for example, on average about 50% of the subsidy goes to producers. For sugar and cooking oil, producer transfers amounted to at least 25% and 21% of total subsidy outlay on each, respectively. ANNEX F Page 3 of 10 At current international prices, nominal protection rates for wheat, sugar and oilseeds, and their major derivatives range from 84 to 157%. In addition, security stock requirements, administered consumer prices, and substantial public transfers to the processing units create a distorted price and incentive structure. Consequently, competition is weak, and inefficient processors and weak performers are maintained. This policy environment provides also significant incentives for fraudulent behavior. Particularly wheat subsidies (FNBT) (i) distort relative price signals and consumers' preferences; (ii) promote mniss-allocation of productive resources; (iii) discourage entries of new competitors; and (iv) reduce incentives to increase productivity. On the agricultural side, persistently high farm-gate prices for soft wheat, sugar crops and oilseeds generate significant economic distortions. Particularly in the case of soft wheat, it encourages substitution effects leading to (i) an undesirable extension of land sowed in soft wheat, contributing to soil erosion; (ii) increasing the variability of yields with no real positive effect on productivity; (iii) discouraging farmers from planting crops which are better suited to the climate. Moreover sugar crops are maintained in areas where this production is economically inadequate. Finally despite strong price support, sunflower yields in the 1990s are remaining at levels observed in the early 1960s, when this crop was first introduced. For oilseeds and sugar, the protection policy has been mainly driven by industrial transformation interests rather than by producer interests. The sugar processing sector consists of a number of uncompetitive refineries that survive behind protective barriers. The towns in which these refineries are located have social concerns, and progress towards privatization or winding down of these refineries is slow. The oilseed refining industry is even more concentrated, and primary refiner is well placed politically to maintain its favored position regardless of whether it procures its inputs from domestic producers or from imports. Reforming Food subsidies for better targeting the poor Moroccan Government is well aware that the food subsidy program coupled with the highly protectionist agricultural policy is costly and inefficient: They cost about 1.6% of GDP; they serve to counteract import tariffs and thus represent effective transfers to producers rather than consumers while distorting agricultural production pattern; and they are poorly targeted and only about 25% of subsidies reach the poor. Therefore the Government has begun assessing options for reform. One option is to elirfinate consumer food subsidies without addressing the agriculture policy and simultaneously reducing tariff protection. This would lead to an increase of all three consumer food prices because in an absence of food subsidies, the consumers would face prices dictated by current producer prices. Given the importance of food subsidies as share of total per capita expenditures for the poor as well as their nutritional impact, an elimination of food subsidies without any complementary scheme therefore would result in an increase of poverty, particularly in rural areas. Given the potential sensitivity of the reform and the need to protect the standard of living of all vulnerable groups, the best reform option could include: (i) parallel reduction of tariff protection and subsidies on food products; and (ii) introduction of targeted assistance to low- income groups negatively affected by reduction of domestic protection and consumer food subsidies. This would allow to reallocate budgetary fund freed up by reduced subsidy expenditures to programs benefiting the poor (e.g., alphabetization, basic services in rural areas, public works programs and assistance programs targeted to the poor). In addition in the medium term strengthening the agricultural policy through focusing on crops where Morocco has a ANNEX F Page 4 of 10 competitive advantage will help to ensure adequate income for rural population, including the poor. To achieve these goals, tariffs need to be reduced and consumer food subsidies gradually eliminated. Therefore the challenge for the Government is to elaborate rational sectoral policies for the oil, sugar and wheat subsectors, notably by removing, or reducing significant'ly the external protection, while mitigating any adverse impact of the reform on the disadvantaged groups. Savings obtain by the reform. For all three products, a simultaneous removal of the subsidy payment and a reduction of the custom protection would have differentiated fiscal effects (see Table 2). However maintaining moderate tariffs where Morocco has a substantial domestic crop production, mainly for soft wheat and sugar crops, would allow the necessary transition time for the reform to gather suoport. Overall eliminating the subsidy program would suppress a budgetary outlay of DH 5.3 1 illion (1.6% of GDP in 1999), or net of trade revenues of around DH 1.5 billion. In addition, cutting the custom tariffs would allow to still generate DH 1.2 billion revenues at the border. Consequently, to address the economic and social costs during the transition period, the State would have a recurrent envelope of about DH 2.7 billion (it is nearly three times the investment budget of the Ministry of Health for fiscal year 2000). Since the Government is not currently speriding sufficiently on targeted programs, the saved budgetary resources could be committed to finance targeted assistance to low-income groups affected by the reform and cost-effective instruments focused on the basic needs of low income groups. Since November 2000, the Government has eliminated the subsidy system for oil by liberalizing oil prices, and substantially reducing the imports tariffs on oilseeds and all by products. As a result the consumer prices on oil have dropped and compensation measures - deficiency payments - for farmers have been introduced. Nevertheless, these reforms needs to be extended to the other two commodities (sugar and v. heat) which are politically more sensitive. Impact of the reform Producers, farmers and processors. The artificially high domestic prices for soft wheat, sugar beet and cane, and oilseeds constitute an income transfer to the farmers. Consequently, the adjustment process will affect everyone to some extent, but mostly 35-40,000 sunflower producers, 60,000 farmers in regions with no comparative advantage for sugar crops. and nearly all cereal producers. Actually, the big cereal producers (4% of the farmers as a whole) would be the most affected. Indeed, if almost every 1.5 million Moroccan farmers cultivate cereals, the bulk of the implicit subsidy (the difference between the international and the domestic price for soft wheat) is hold by this group. In total about 2 million farmers and agriculture workers therefore would be affected by the tariff reduction. To help Ihose adversely affected by these adjustments, compensatory measures need to be implemented. These could include (i) programs to help small land holders to convert to other and better suited alternatives; (ii) expansion of the existing labor intensive public works projects (P') to assist low income household; (iii) basic infrastructure development in affected rural areas to increase access of vulnerable to basic social services (i.e., potable water, electricity, rural roads); and (iv) community development programs in remote rural areas. For oilseed, it is economically feasible to have sunflower producers receiving a direct temporary payment - the cost is estimated at around DH 120 million - to protect their revenue, in front of the full exposition of domestic sunflower price to world market fluctuations. Globally, the efficient industrial processors would have to adjust, while the inefficient will disappear. In the milling activity and the sugar sector (where the public processors have a market share of 30%), increasing the competitive pressure would lead to a desirable restructuring process. Within this context, the four sugar public producers should be immediately privatized when possible, ANNEX F Page 5 of 10 and liquidated for some of them. Here, the State could finance the social costs following a liquidation. On oilseed, a temporary small tariff on meal would allow the unique and today inefficient crusher to adjust. * Consumers. Reduction of tariff protection would lead to domestic price decline which in turn would benefit consumers. At February 1999 international prices, with reduction of external protection, the domestic prices of vegetables oils, flour, would be lower than the present subsidized prices, respectively by about 10 and 14%, and remain stable for sugar. Therefore for all three products, consumers could be substantially compensated for removal of subsidies through a lowering of tariffs which would result in a reduction of domestic prices. Nevertheless the timing for the phasing out subsidies and choice of products to be eliminated at a more rapid pace should be guided by the impact of the removal of the subsidies on the poor and the share of existing subsidies accruing to the poor. Therefore based on the 1998 LSMS the incidence of eliminating subsidies on the poor need to be calculated once the disaggregated food information are available. Based on this incidence analysis introduction of targeted food programs, particularly for the poor children, and there costs should be considered and estimated. Subsidies on cooking oil appear to be suitable for more aggressive elimination because of its lower contribution to total per capita expenditures nutritional intake of the poor. However, sugar and FNBT subsidies should be removed more gradually in light of their relatively larger contribution to the total expenditures and caloric acquisitions of the poor. Table 2: Impact of a tariff reduction and elirination of consumer food subsidies (miiions de DH, unless otherwise noted) External protection Consurmer price Net savings** Additional custom Amount available (Tariff %) (DH/kg) revenues*** Before After Before After OIL Seeds 91-109 0 400 20 420 Oil cakes 98-157 25 Raw oils 87-110 0 Refined oils 10 10 8,5 7,6 FLOUR Soft wheat 101 25 180 680 860 Flours 66 10 2,9 2,5 J SUGAR* Raw 149 53 900 500 1400 Granulated 116 37 4,3 4,3 J Total 1480 1200 2 680 Notes: (*) In this indicative scenario, protection levels are fixed so as to maintain the consumer price at its current level. (**) This is the net savings following a complete elimination of the subsidy and the protection. (+**) Estinmates of the trade revenues collected on the goods still protected Rates appear in the third column of the table Source: World Bank Staff estimates 2. Public Works Pro2ram (Promotion Nationale - PN) Since 1961 the Promotion Nationale (PN), an autonomous directorate under the Ministry of Interior, has been responsible for the implementation of labor-intensive public works. Funds are ANNEX F Page 6 of 10 transferred from the central level to the provinces, where projects are coordinated by a 'd6legation' of the PN. Line ministries represented at that level are providing inputs in the design and supervision of the works to guarantee the quality of infrastructures produced. Its criginal mandate was to generate income for under/unemployed rural populations and to engage in productive investment using the following approach: * Utilize labor intensive construction methods in the provision of works and services (66% of project expenditures to be spent on labor). * Establish a participatory development process whereby communities would be directly involved in the planning and execution of development projects of national and/or local interest. * Decrease the rate of rural migration by improving the social conditions and productivity in less developed rural regions. * Ensure that the provision of basic social and econonic infrastructure in marginalized areas is undertaken utilizing the maximum amount of labor for a minimum amount of capital investments. Today, PN undertakes three types of activities classified as followed: (i) Chintiers d'Equipement which include construction and rehabilitation of rural infrastructure (i.e.. rural roads, rural water supplies, schools, health centers, boarding schools for girls, reforestation, small irrigation canals, etc.); (ii) Chantiers Collectivites (CC) by which admninistrative posts are funded mainly for provincial authorities and local municipalities (i.e., clerical work) as well as salaries of subordinate staff in hospitals, kindergartens etc.; and (iii) Special programs for Saharan regions comprising all types of rural infrastructure to alleviate the negative effects of droughts. Overall PN, which uses force account procedures, has been an important source of temporary employment in rural areas, although more recently it has also shifted some resources towards jobs in urban areas. * During 1990-1999 the PN has created about 104 mnillion person-days of employment (on average about 10.4 million person-days of employment per year or 40,000 person-year) for a total cost of DH 4.5 billion.4 On average the cost of job created per day is about DH 43 (or $4/day), thought there are significant annual and regional variations in number of jobs cieated and funds allocated in this program particularly for each different programs. * The vast majority of those recruited by the PN are unskilled workers, and are paid the agricultural minimum wage, or SMAG (currently DH 41.3/day, about US4$,/day). Particularly during droughts and inactive agricultural period, PN scale up the labor intensive rural activities to generate temporary income for the underemployed and the poor. * Unit cost of PN's projects for social infrastructure (schools, multi-function buildings elc.) is also comparable with simnilar activities undertaken by other agencies (social infrastructure unit cost) while for rural roads PN's project unit cost is about half the cost of other agencies. * Share of administrating the program is less that 6% of the investment cost, which is very low by all standards (i.e., social funds administrative cost is about 10% of the total investment). However PN programs exhibit weaknesses in targeting and cost effectiveness: * Jobs created by PN are not always based on labor intensive mechanisms and the PN's original mandate has been diluted significantly during the last decade: almost half of the resources are spent on supporting local government wage payments to administrative staff and other rinor jobs, rather than directly helping low-income communities through labor intensive activities. 4 During 1997-99, BAJ I project has created about 1 8 million person-days. The cost of person-day is estimated at DH85 ($8.5). This cost is based on the assumption that 90% of the projects are rural roads with a coefficient of labor share of about 55% in average and the remaining 10% of the activities are buildings and rural water supply projects with an average labor share of 35%. ANNEX F Page 7 of 10 Originally, this wage support to local governments, benefiting mainly the low-income unemployed, was expected to be temporary and over time to be transferred to local governments' budget. But, as a result of insufficient local budget, these activities are still financed by the PN. * Costs of the Chantiers Collectivites, account for well over half of PN investment funds. Moreover, almost half of the programs in Saharan regions are also assisting local government. * During 1990-99, less than one-third of PN's budget is genuinely spent on labor-intensive programs and about 40% of the total employment was created in traditional chantiers of PN (excluding the CCs with 50% share of labor in total costs): according to a recent audit report, the labor intensity of jobs for traditional chantiers vary also by type of infrastructure and by region.5 For rural roads the share of labor in total project cost is between 17% and 86%; for water supply projects is between 31% and 47%; and for social infrastructure the share of labor is around 30% and 40%. * Projects are not financially sustainable. Although, once projects are completed, the local communities are involved in their maintenance through the local communities' presidents, the unavailability of resources prevent them from maintaining the projects. In fact there are no practical arrangements put in place to ensure that maintenance will take place and beneficiaries are responsible for it. The dilution in the mandate of the PN, however, should not negate the strength of the PN programs. Overall its main strengths are: (i) high quality basic infrastructure work in low-income communities; (ii) low project cost compared to similar activities undertaken by others; (iii) appropriate and efficient mechanism to create temporary employment for underemployed, and particularly provide income-earning opportunities to the rural poor, although the urban programs are mainly targeted to first job seekers who are not necessarily poor; (iv) ability to respond rapidly to regional and national crisis when funds are available through creating temporary employment; (v) experience in working with local governments in both urban and remote rural areas; (vi) low overhead costs; and (vii) an appropriate adrninistration structure and representativity in each region.6 The organization itself remains a valuable repository of experience in labor intensive works and employment generation. Options for Reforms Despite its weaknesses, PN is currently the most efficient program targeted to the poor in Morocco. Given the potential that public-works employment programs have as an effective poverty reduction mechanism and particularly with the increase in poverty, in the future, the PN could (i) continue to play its role as an emergency agency providing large scale employment in areas hit by droughts and other natural disasters; (ii) provide advise to other government agencies in selecting projects which utilize labor intensive methods; and (iii) initiate contracting projects to SMEs in the construction and services sectors using labor intensive methods. The realization of these goals would require: * Implementation of large scale employment programs: This would require phasing out the Chantiers Collectivites to free funds as well as ensuring that labor intensive methods are applied to implement projects in the most disadvantaged regions of the country. With the existing annual investment budget allocated to PN, amounting to DH 500 million, about 28,000 person years (at 200 person days a year) of employment could be created assumning an 5 See audit report prepared for the BAJ 1, March 2000. 6 The total staff of PN is around 1,200 of which the higher level are around 476 people and about 70 people are nilitary staff. ANNEX F Page 8 of 10 average daily wage of DH 45 and labor share in project at 50%. Given the normal turm over of workers at PN chantiers 7 and assuming each person will work for 20 person days, then the program could reach 280,000 workers. In case that the investment budget of the PN could be doubled (DH 1 billion), the number of employment would also double under the assumnption of 50% labor share. This means that in total about 560,000 workers will be helped. Assuming that household size is about 7 people, this program could reach nearly 75% of the poor (estimated at 5.3 million in 1998). The increase in funding allocated to PN could come either from other Government agencies (Ministry of Agriculture, etc.) or from savings obtained by the consumer food subsidies elimination. In rural areas the activities could focus on rural roads, reforestation, terracing, and small irrigation works. With the increasing urbani2ation, PN activities should also allocate a larger share of their budget to urban works (housing projects, green space programs, etc). * Identification of projects amenable to the PN method (labor intensive and productive) becomes an integral part of the planning process within relevant central and local government agencies: PN could act as an advisory to other Government agencies to ensure that existing public work's projects are executed using labor intensive methods. However this would require effective coordination within the Government, to a certain extent already done at provincial level. • Involving private sector in PN programs: PN could also initiate to contrae out projects to the private sector. At the moment, contractors or suppliers are only used to rent equipment or to provide materials on PN construction sites. Private contractors could be instructed by contract to use labor intensive methods and local workers, similar to the method used by PN. In this process the private sector is involved both as beneficiaries in terms of additional opportunities for small contractors and as an alternative to the public sector in undert iking works and services. This would allow the PN to focus its resources on rural works, specifically the labor intensive activities located in the most disadvantaged regions i ather than scattering its resources throughout the country as it does today. 7 In principle PN procedure mandate that each worker must cede to others in line after working for 10 days, but we are assuniing 20 because the workers should be able to return if they are in need of income support. ANNEX F Page 9 of 10 Table 3: Investment Budget of PN 1990 13.982.556 337.500.000 27.000.000 378.482.556 1991 275 000.000 O 275.000 000 1992 453.000.000 0 453.000.000 1993 400.000.000 130.000.000 530.000.000 1994 500.000.000 0 500.000.000 1995 405.000.000 17.715.400 422.715.400 jer sem. 1996 202.500.000 105.989.658 308.489.658 1996/97 405.000.000 83.963.738 488.963.738 1997198 503.000.000 82.000.000 585.000.000 1998/99 503 000,000 77.000.000 580.000.000 1999/00 503.000.000 330.774.000 833.774.000 Source: PN, 2000. Table 4: Trend in Waee bill of PN 1990 1960 000,00 1991 1 976 000,00 1992 2 173 000,00 1993 2 173 000,00 1994 2 175 460,00 1995 2 052 500,00 1"r semestre 96 957 000.00 1996/97 1 875 000,00 1997/98 1 977 000,00 1998/99 1 859 600,00 1999/00 1 859 600,00 Source: PN, 2000. ANNEX F Page 10 of 10 Table 5: Number of man day created by PN in the 1990s TYPES DE PROGRAMMES 1990 1991 1992 1993 1994 1995 1996 1996/1997 1997/1998 1998/1999 1999/2000 (1990/2000) COLLECTIVITES 4.636.044 4.852.010 4.853.628 5.029.171 5.032.375 5.158.834 2.591.544 5.270.391 5.300.700 1.620.212 2.127.225 46.472.134 EQUIPEMENT 5.263.736 1.647.185 475.681 5.712.025 2.500.918 1.441.892 738.887 1.872.674 2.827.251 5.121.325 3.668.678 31.270.252 SAHARA 2.364.622 2.849.105 3.102.361 3.074.684 3.110.962 3.089.057 1.544.162 3.648.902 4.278.928 3.106.034 3.094.188 33.263.005 BAJ 253.778 760.785 750.295 750.000 2.514.858 TOTAL 12.264.402 9.348.300 8.431.670 13.815.880 10.644.255 9.689.783 4.874.593 11.045.745 13.167.664 10.597.866 9.640.091 113. 620.249 Source: PN, 2000. (i) Previsions STATISTICAL ANNEX Page I of 29 STATISTICAL ANNEX STATISTICAL ANNEX Page 2 of 29 Table 1 - Survey sample size: households by economic region for 1984-85, 1990-91, and 1998-99 Econow.e reWAM urban. . .i: - a i: t rt-ra: South 531 1000 238 240 389 330 Tensift 827 1103 238 240 262 439 Center 2659 1306 232 239 866 394 North-West 2013 1110 237 239 782 405 Center-North 689 898 237 239 262 299 Oriental 536 437 235 237 202 131 Center-South 584 476 233 239 214 156 Total 7839 6366 1650 1673 2977 2154 Source: Statistical Office, 1984-85 Household Survey, and 1990/91 and 1998/99 LSMS data. Table 2 - Survey sample size: households by region for 1998-99 Regions Sud(1) 203 36 239 Souss - Massa - Daraa 186 294 490 Gharb- Chrarda -eniHssen 131 168 299 Chaouia - Ouardigha 132 156 288 Tensift - Al Haoutz 203 310 513 Oriental 202 131 333 Greater Casablanca 555 23 578 Rabat - Sale - Zenmmour - Zaer 400 72 472 DouKala Abda 143 200 343 Tadla Azilal 95 144 239 Mekmes Tafilalet 214 156 370 Fes - Boulemane 190 72 262 Taza Al Hoceima - Taounate 72 227 299 Tanger - Tetouan 251 165 416 Total 2977 2154 5131 Note: (1) Includes Oued Ed-Dahab - Logouira, Laayoune - Boujdour - Sakia El Hamra, and Guelmin - Es-Semara Source:, Statistical Office, 1998/99 LSMS data STATISTICAL ANNEX Page 3 of 29 Table 3 - Private consumption: national accounts compared with survey means (DHperperson, current prices) Private consumption per capita 3830 6384 7812 (national accounts) Mean consumption per capita (Survey 3572 6780 7823 data) Source: Statistical Office, 1998/99 LSMS data, and National Accounts Table 4 - Average household size 1998/99 1 7.4 8.2 8.1 2 6.9 8.0 7.5 3 6.7 7.7 6.9 4 6.2 6.9 6.7 5 6 2 6.6 6.5 6 5.6 7.0 6.0 7 5.7 6.3 5.6 8 4.9 5.8 5.5 9 4.7 5.3 4.7 10 3.8 4.3 4.1 Aggregate 5 6 6.4 5.9 Source Statistical Office, 1998/99 LSMS data. STATISTICAL ANNEX Page 4 of 29 Table 5 - Distribution of Expenditure by decile, 1998/99 Mean Expeaitur:e Cuative( Aerage(o bude-se (%) . --- sU~(Dtlperson/yveswr)- - Ncile - b#t RuraV Nadonaf 1J$~. R~irat ' ~atiana1 bW:-Ra ura1 t:V a Rural : . 2959 1687 2057 2.9 3.3 2.6 44.4 59.2 57.5 2 4216 2461 3029 7.1 8.2 6.5 45.3 60.4 55.9 3 5130 2952 3776 12.2 14.0 11.3 43.8 59.9 53.7 4 6005 3424 4527 18.0 20.7 17.1 44.9 58.5 50.5 5 6989 3934 5337 24.9 28.5 23.9 43.5 59.1 50 4 6 8274 4556 6278 33.1 37.4 32 42.7 55.7 49.1 7 9772 5279 7518 42.7 47 8 41.5 41.9 569 46.7 8 12084 6296 9251 54.6 60.2 53.3 40.6 55.4 45 0 9 15897 7837 12249 703 75.6 68.9 37.5 53.4 42.2 10 30216 12475 24211 100.0 100.0 100.0 30.7 46.3 327 Aggregate 10157.0 5087.5 7826.1 _- - 44.3 59.3 51.2 Source: Statistical Office, 1998199 LSMS data Table 6 - Distribution of population in 1998/99 usebwd p. ca by Urban Rura .- -- decile _ _ _ _ _ _ _ 1 413241 2364030 2777271 2 682319 21119185 2801504 3 994324 1794776 2789100 4 1299628 1507948 2807576 5 1479703 1304294 2783997 6 1627481 1157680 2785151 7 1791731 1033357 2825088 8 1972115 834868 2806983 9 2232115 568341 2800456 10 2552270 236056 2788326 Total 15044927 12931100 27965452 Source: Statistical Office, 1998/99 LSMS data STATISTICAL ANNEX Page 5 of 29 Table 7 - Incidence of poverty by economic region (Headcount index at the upper poverty line) 7 =/ _____ 1* v . -999 - 1999-0 Urban ~Rural Ntou ra Rua Na-iona South 0.21 11 27 7.89 6.03 23.15 15.62 Tensift 5.34 15.01 11.86 13.19 30.70 24.63 Center 1.63 3.03 2.19 5.74 20.84 11.12 North-west 0.47 7.68 3.71 12.22 30.06 18 84 Center-north 4.28 13.65 10.43 24.09 31.04 27.90 Oriental 4.75 19.13 12.08 15.99 18.97 17.18 Center-south 3.00 11.55 7.64 22.79 36 01 28.70 Source: Statistical Office, 1990/91 and 1998/99 LSMS data; Kingdome of Morocco: Poverty Assessment, Report No 11918-MOR, Table 10 Table 8 - Depth of poverty by economic region, 1998/99 (Poverty gap index at the upper poverty line) Region Urar .~sral Na007 . 0t-- 0*n-E--: -- _-- 0-0--tit0on South 0.91 5.31 3.38 Tensift 2.71 7.17 5.62 Center 0.90 3.94 1.98 North-west 2 65 7.46 4.43 Center-north 5.81 8.59 7.33 Oriental 3.34 6.65 4.66 Center-south 4.89 10.18 7.26 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data. Table 9 - Severity of poverty by economic region, 1998/99 (Severity index at the upper poverty line) --- 6as Rd xal Nat1ou0-- South 0.23 2.04 1.24 Tensift 0.86 2.47 1.91 Center 0.25 1.20 0.59 North-west 0.85 2.83 1.59 Center-north 1 98 3.49 2.80 Oriental 0.99 3.08 1.83 Center-south I 1.59 4.12 2.72 Source Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data STATISTICAL ANNEX Page 6 of 29 Table 10 - Distribution of Population and Mean Expenditures by region .op. -eis.'- t% Mean pex capita expenditure MOenp.c.expeiture _ _______,.__________.___'.___'___',____'_______________,__,_____'____ __________".'"_____''._____-.___,:___'___.___: R6gions Sud 2.34% 12,721.2 2,599.2 Souss-Massa-D 10.27% 7,584.9 2,492.3 Gharg-Chrarda 6.17% 6,506.4 2,494.7 Chaouia-Ourd. 5.38% 7,480.3 2,406.9 Tensift Alha. 10.46% 6,713 5 2,450.9 Oriental 7.27% 7,226.2 2,602.4 G.Casablanca 11.07% 11,685.2 3,479.1 Rabat-SaM ZZ 8.21% 10,045 2,858.8 DouKalaAbda 6.71% 6,488.3 2,659.9 Tadla Azilal 5 43% 6,913.7 2,513.5 Mekmes Tafil 6.74% 6,550.8 2,572.0 Fes-Boulmane 5.51% 6,996.2 2,727.5 Taza Alho. Ta 6.57% 6,132.1 2,288.8 Tanger-Tetouan 7.89% 7,067.7 2,570.7 Aggregate 100.00% 7,823.3 2,566.7 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data. Table 11: Distribution of poor and non-poor by region " R6gions Sud 64,957 589,174 654,131 Souss-Massa-D 485,685 2,385,463 2,871,148 Gharg-Chrarda 490,365 1,234,755 1,725,120 Chaouia-Ourd. 146,527 1,357,449 1,503,976 Tensift Alha 790,205 2,134,161 2,924,366 Oriental 349,066 1,682,628 2,031,694 G.Casablanca 154,864 2,941,514 3,096,378 Rabat-Sale ZZ 258,983 2,036,320 2,295,303 DouKala Abda 341,870 1,534,744 1,876,614 Tadla Azilal 318,007 1,199,755 1,517,762 Mekmes Tafil 540,650 1,343,135 1,883,785 Fes-Boulmane 449,966 1,090,095 1,540,061 Taza Alho. Ta 492,296 1,345,125 1,837,421 Tanger-Tetouan 423,840 1,783,401 2,207,241 Aggregate 5,307,281 22,657,719 27,965,000 "Higher poverty line. Source Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data. STATISTICAL ANNEX Page 7 of 29 Table 12 - Incidence, depth and severity of poverty across regions R:egiou I idexz Pbverty gapIndex: S;0 cr. Index Regions Sud 9.9% 0.0199 0.00593 Souss-Massa-D 16 9% 0 03694 0.01387 Gharg-Chrarda 28.4% 0.07001 0.02521 Chaouia-Ourd. 9.7% 0.02265 0.00865 Tensift Alha. 27.0% 0.06445 0.02289 Oriental 17.2% 0.04661 0.01825 G.Casablanca 5.0% 0.00561 0.00113 Rabat-Sal6 ZZ 11.3% 0.02551 0.00824 DouKala Abda 18.2% 0.03228 0.00808 Tadla Azilal 21.0% 0.03998 0.01189 Mekmes Tafil 28.7% 0.07255 0.02722 Fes-Boulmane 29.2% 0.07209 0.02554 Taza Alho. Ta 26.8% 0.07438 0.03014 Tanger-Tetouan 19.2% 0.0438 0.01647 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 13 - Distribution of incidence, depth and severity of poverty across regions Heade0W' *e1 a ~ek ne Regions Sud 1.2% 0.01054 0.00877 Souss-Massa-D 9.2% 0.08586 0.09003 Gharg-Chrarda 9.2% 0.09777 0.09834 Chaouia-Ourd 2 8% 0.02757 0.02943 Tensift Alha. 14.9% 0.15258 0.15137 Oriental 6.6% 0.07666 0.08386 G.Casablanca 2.9% 0.01407 0 00793 Rabat-Sale ZZ 4.9% 0.04741 0.04278 DouKala Abda 6.4% 0.04903 0.03431 Tadla Azilal 6.0% 0.04912 0.04082 Mekmes Tafil 10.2% 0.11063 0.11598 Fes-Boulmane 8.5% 0.08987 0.08894 Taza Alho. Ta 9.3% 0.11064 0.12524 Tanger-Tetouan 8.0% 0.07826 0.0822 Total 100.0% 100.0% 100.0% Source: Statistical Office, and World Bank staff estimates based on 1998199 LSMS data STATISTICAL ANNEX Page 8 of 29 Table 14 - Distribution of poor and poverty incidence (%) by region in 1998/99 Distribution of poor (%) Poverty incidence (%) Regions Urban Rural Total Urban f Rural Total Regions of the South (1) 5.2 13.1 10.4 23.2 15.6 (17.0) (83.0) (100.0) Rabat-Sal6-Zernmour-Zaer, 19.3 11.4 14.1 13.1 29.8 18 6 Gharb-chrarda-Beni Hssen (46.6) (53.4) (100.0) Chaouia-Ouardigha, Talda-Azilal 2.7 11.9 17.7 4.2 22.7 15.4 (10.6) (89.4) (100.0) . Oriental 10.8 4.4 6.6 16.0 19.0 17.2 _______________. _______ __ .(56.0) (44.0) (100.0) _ Grand Casablanca (2) 8.6 2.9 5.4 -- 5.0 (100.0) -- (100.0) Doukala-Abda, Marrakech-Tensift-AI 12.9 25.7 12.4 13.1 29.8 23.6 Haouz (20.7) (79.3) (100.0) Meknes-Tafilalet 13.1 8.7 10.2 22.8 36.0 28.7 (43.9) (79.3) (100.0) Fes-Boulemane, Taza- 20.3 16.4 17 8 24.1 31.0 27.9 Al Hoceima-Taounate (39.0) (61.0) Tanger-Tetouan 7.1 8 4 7.9 10.4 30.5 19.2 _(30.6) (65.9) (100.0) Total 100.0 100.0 100.0 12.0 27.2 19.0 ._________________________________ (34.1) (65.9) (100.0) Total poor (in thousands) 1,814 3,496 5,310 - _ - Notes: (1) The Southern regions include Souss-Massa-Daraa, Oued Ed-Dahab - Lagouira, Laayounc- Benjdour-Sakia El Hamra et Guelrnim- Es- Smara. (2) -- : not representative data Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 15 - Household expenditures by deciles, 1998/99 - National Level (current DU, per capita, per year) - .;.-Vhiwi~T' Food Ilousi C0tw eansport and. Leisure communcation 1 1182.6 508.7 56.7 69.8 46.7 39.9 108.2 2 1692.0 681.7 111.8 118.98 106.5 74.0 159.8 3 2027.5 855.5 156.6 177.2 156.8 86.7 212.2 4 2286.8 1065.5 200.9 261.5 115.4 265.7 2688.4 1190.9 242.3 314.2 298.9 144.5 2968 6 3083 7 1389.8 319 8 373.6 309.7 169.0 417.4 7 . 3509.9 1685 9 380.8 554 3 422.7 225.5 474.7 8 4162.1 2010.3 523.6 676.5 572.9 289.2 715.4 9 5164.3 2669.8 710.6 929.7 854.0 475.0 996.8 10 7922.4 4695.85 1609.1 1882.3 2129.5 1915.4 2775.7 Agr6gat 3371.8 1675.34 431.2 535.8 509.4 353.5 642 3 Source. Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data. STATISTICAL ANNEX Page 9 of 29 Table 16 - Household expenditures by deciles, 1998/99 - Urban Areas (current DH, per capita, per year) Deil flousi; wt}-; ::ClX: oting70; Helt Trilii ~dX lVSUt Lure: U:;- Other90ii c .-- t. -m -.--: - . 3t ni- 1 13138 988 0 86.9 172.2 89.0 92.4 163 8 2 1909.5 1272.3 148.4 274.7 152.9 133.0 230.4 3 2245.4 1506.9 206.0 344.2 284.7 161.9 252.0 4 2697.7 1615.4 299.5 410.9 308.7 194.8 286.7 5 3038.4 1852.0 321.5 531.5 418.5 231.1 405.3 6 3534.5 2139.1 455.6 618.4 485.4 276.0 526.6 7 4092.0 2304.2 507.4 804.0 630.5 413.8 739 0 8 4907.5 2792.2 708.9 957.8 884.3 545.5 898 2 9 5963.1 3482.0 1016.7 1301.0 1157.9 801.3 1436.2 10 ---~9268.2 5825.4 2033.6 2342 35 2750.9 2801.4 3566.3 Aggregate 3896.8 2377.7 578.4 775.7 716.3 565.1 850.4 Source: Statistical Office, and World Bank staff estimates based on 1998199 LSMS data Table 17 - Household expenditures by deciles, 1998/99 - Rural Areas (current DH, per capita, per year) .~1 l4 HClothing, Hllealth =JWSp 1 °eisu,fin 1 999.3 415.2 37.3 46.4 43.8 32.8 78.9 2 1486.3 506.7 91.4 81.8 58 5 39.6 129.5 3 1768.0 570.2 98.1 90.3 120.3 58.3 159.4 4 2001.9 631.2 156.5 120.5 135 7 776 186.6 5 2326.6 686.8 176.3 155.2 168.6 77.6 224.4 6 2537.2 796.3 230.1 245.4 199.4 84.9 300 9 7 -~2005.9 817.4 277.3 243.8 294.4 104.9 356.9 8 3490.4 971.9 337.0 313.9 302.8 126.7 522.1 9 4183.1 1251.7 421 2 494.1 447.0 148.1 552.6 10 5774.9 1861.8 762.1 751.7 896.6 299.91 1473.3 Aggregate 2755.0 850.2 258.3 253.9 266.3 104.9 397.8 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data STATISTICAL ANNEX Page 10 of 29 Table 18 - Distribution of Household Budget, 1998/99 - National Level (percentages) ._us . . _ ... t P- - n- Lf.uV h -)ecie --- Fo-o4 -.OUS4Il. i}g .-. alAfta Transport an, - r Other :: -: __-----l _ - : --- -: communications; 1 24.7% 2.8% 3.4% 2.3% 1.9% 5.3% 2 55.9% 22.5% 3.7% 3.9% 3.5% 2.4% 5 3% 3 53.7% 22.7% 4.2% 4.7% 4.2% 2.3% 5.6% 4 50.5% 23.5% 4.4% 5.8% 4.3% 2.6% 5.9% 5 50.4% 22.3% 4.5% 5.9% 5.6% 2 7% 5.6% 6 49.1% 22.1% 5.1% 5.9% 4.9% 2.7% 6.7% 7 46.7% 22.4% 5.1% 7.4% 5.6% 3.0% 6.3% 8 45.0% 21.7% 5.7% 7.3% 6.2% 3.1% 7.7% 9 42.2% 21 8% 5.8% 7.6% 7.0% 3.9% 8.1% 10 32.8% 19.4% 6.7% 7 8% 8.8% 7.9% 11.5% Aggregate 43.1% 21.4% 5.5% 6.9% 6.5% 4.5% 8.2% Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 19 - Distribution of Household Budget, 1998/99 - Urban Areas (percentages) --- Fod-l: Hosg C.... g ... eilth Transport -nd i Leiwe :-Other . - - -:... ._:- '- - . -. oii - ----- - - -:.. 1 44.4% 33.4% 2.9% 5.8% 3.0% 3.1% 5.5 - 2 45.3% 30.2% 3.5% 6.5% 3.6% 3.2% 5.5% 3 43.8% 29.4% 4.0% 6.7% 5.6% 3.2% 4.9% 4 44.9% 26.9% 5.0% 6.8% 5.1% 3.2% 4.8% 5 43.5% 26.5% 4.6% 7.6% 6.0% 3.3% 5.8% 6 42.7% 25.9% 5.5% 7.5% 5.9% 3.3% 6.4% 7 41.9% 23.6% 5.2% 8 2% 6.5% 4.2% 7.6% 8 40.6% 23.1% 5.9% 7.9% 7.3% 4.5% 7.4% 9 37.5% 21.9% 6.4% 8.2% 7.3% 5.0% 9.0% 10 30.7% 19.3% 6.7% 7.8% 9.1% 9.3% 11.8% Aggregate 38.4% 23.4% 5.7% 7.6%o 7.1% 5.6% 8.4% Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 20 - Distribution of Household Budget, 1998/99 -Rural Areas (percentages) ;De Food. i lot%ng Healt: Transport and Le.is- O __ _ _ _ -_-_: .__:_: .__- - _ cotnm unicalins -_ _ _ _ -- __ __ 59.2% 24.6% 2.2% 2.8% 2.6% 1.9% 4.7% 2 60.4% 20.6% 3.7% 3.3% 2.4% 1.6% 5.3% 3 59.9% 19.3% 3.3% 3.1% 4.1% 2.0% 5.4% 4 58.5% 18.4% 4 6% 3.55 3.9% 2.3% 5.5% 5 59 1% 17.5% 45% 3.9% 4.3% 2.0% 5.7% 6 55.7% 17.5% 5.1% 5.4% 4.4% 1.9% 6.6% 7 569% 15.5% 5.2% 4.6% 5.6% 2 0% 6.8% 8 55.4% 15.4% 5.4% 5.0% 4 8% 2.0% 8.3% 9 53.4% 16.0% 54% 6.3% 5.7% 1.9% 7.1% 10 I 46.3% 14.9% 6.1% 6.0% 7.2% 2.4% 11.8% Aggregate 54.2% 16.7% 5.1% 5.0% 5.2% 2.1% 7.8% Source Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data. STATISTICAL ANNEX Page 11 of 29 Table 21 - Age Com osition of the pqpulation by sender in 1998/99 NAIONAL -UR'' . . . ... '.'........'I Age ltrtst3p M {l FeaJet ; Tota M *;I i:t~ '4Sii qi* : X|;:0I' O 0t : a Ij Mai - enlK -T: E ;:-224;isld 0 - 4 1478076 1400687 2878763 652904 654698 1307602 825172 745989 1571161 S5- 9 1649922 1582526 3232448 757937 732949 1490886 891985 849577 1741562 10 - 14 1717106 1595139 3312245 836559 796255 1632814 880547 798884 1679431 15-19 1573048 1638448 3211496 808209 815803 1624012 764839 822645 1587484 20- 24 1324459 1368169 2692628 761844 726416 1488260 562615 641753 1204368 25-29 1138080 1267361 2405441 710957 790851 - 1501808 427123 476510 903633 30- 34 923295 11 05870 2029165 560133 673866 1233999 363162 432004 795166 35- 39 820470 986344 1806814 493594 607685 1101279 326876 378659 705535 - 40 - 44 764362 827404 1591766 470968 - 40545 o101 513 293394 -286859 580253 45 - 49 564064 579631 1143695 - 327096 339532 666628 236968 240099 477067 50-54 398470 490319 888789 216309 297113 513422 - 182161 193206 375367 55 - 59 365077 389448 754525 206817 215630 422447 158260 173818 332078 60- 64 291466 358175 649641 154900 191420 346320 136566 166755 303321 65 or more 701377 669389 1370766 343480 349661 693141 357897 319728 677625 Total 13709272 14258910 27968182 7301707 7732424 15034131 6407565 6526486 1293405 Table 22- Distributionof oorand non-poor population b 199$/99 0 - 4 717379 2161384 2878763 168394 1139208 1307602 548985 1022176 1571161 5 - 9 832506 2399942 3232448 240999 1249887 1490886 591507 1150055 - 1741562 10- 14 777262 2534983 3312245 262660 1370154 1632814 514602 1164829 1 1679431 15 - 19 658410 2553086 3211496 234889 1389123 1624012 423521 1163963 1587484 20-24 437076 2255552 2692628 163768 1324492 1488260 273308 931060 1204368 25-29 316015 2089426 2405441 132631 1369177 1501808 183384 720249 903633 30-34 308630 1720535 2029165 116927 1117072 1233999 191703 603463 795166 35-39 306855 1499959 1806814 130973 970306 1101279 175882 529653 705535 40-44 242267 1349499 1591766 100162 911351 1011513 142105 438148 580253 45-49 183608 960087 1143695 55414 | 611214 666628 128194 348873 477067 50-54 128819 759970 888789 47104 466318 513422 81715 293652 375367 -,| I t 39 i U10/219 I 647306 75452s 42794 379653 422447 64425 | 267653 332078 60-64 74379 575262 649641 29613 316707 346320 44766 258555 303321 65 or more 192532 1178234 1370766 66752 626389 693141 125780 551845 677625 Total 5282957 22685225 27968182 1793080 13241051 15034131 3489877 9444174 12934051 Source: Statistical Office, and World Bank staff estimates based on I998/99 LSMS data. STATISTICAL ANNEX Page 12 of 29 Table 23 - Distribution of the Population by Education Level of the Household Head by Decile - National Level Decite Ss-xia niveai~ Fondameniatg Secondaire ;Suprieur [ S 1 1820298 471734 15736 489944 2797712 12.36 7.32 - 1.91 10.63 10 2 1768567 449216 25158 10050 543042 2796033 12.01 6.97 1.84 1.22 11.78 10 3 1679485 531428 20678 10772 557195 2799558 11.41 8.25 I.f 1.31 12.09 10.01 4 1684467 608661 17357 1038 482496 2794019 11.44 9.44 1.27 0.13 10.47 9.99 5 1562444 649674 36569 21465 527626 2797778 10 61 10.08 2.68 2.61 11.45 10 6 1563887 675939 47670 16788 491814 2796098 10.62 10.49 3.49 2.04 10.67 10 7 1530314 753790 105176 30109 368463 2787852 10.39 11.7 7.7 3.66 799 9.97 8 1295249 804778 201684 97306 406078 2805095 8.8 12.49 14.77 11.83 8.81 10.03 9 1109090 807734 341904 98568 436418 2793714 Z53 12.53 25.03 11.98 9.47 9.99 10 709162 691712 569510 520916 305841 2797141 4.82 10.73 41.7 63.31 6.64 10 Total 14722963 6444666 1365706 822748 4608917 27965000 100 100 100 100 100 100 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data STATISTICAL ANNEX Page 13 of 29 Table 24 - Distribution of the Population By Education Level of the Household Head by Decile - Urban areas 'Dec Sans niveau TeFondaental Secondair iSueieur . _ T6._. 1 890153 326335 20841 274011 151134i) 13.4 7.58 1.67 - 12.85 10 2 762467 485307 13170 11810 238251 151100:5 11.47 11.27 1 05 1.53 11.17 10 3 884658 398807 25352 11312 187405 150753.1 13.31 926 2.03 1.47 8 79 9.98 4 713750 444937 28655 22348 302377 1512067 10.74 1033 2.29 2.9 14.18 10.01 5 778534 467603 51265 9585 200040 150702'7 11.72 10.86 4.1 1.24 9.38 9.98 6 728579 404956 129094 37001 213993 1513623 10.96 9.4 10.31 4.8 10.04 10.02 7 558623 523181 129167 69030 230337 1510338 8.41 12.15 10.32 8.96 10.8 10 8 545164 481857 201069 71728 210233 151005:1 8.2 11.19 16.06 9.31 9.86 10 9 524729 408367 307370 119952 151571 1511989 7.9 9.48 24.56 15.57 7.11 10.01 10 258498 365824 345667 417716 124227 15119321 3.89 8.49 27.62 54.21 5.83 10.01 Total 6645155 4307174 1251650 770482 2132445 15106906 100 100 100 100 100 100 Source Statistical Office, and World Bank staff estimnates based on 1998/99 LSMS data. STATISTICAL ANNEX Page 14 of 29 Table 25 - Distribution of the Population By Education Level of the Household Head by Decile - Rural areas .-Dedkle Sal iVeaui Fond ienta Secondaire Superieur '''Systeep Total 1 - ~ 845735 229904 ' 5620 207897 1289156 10.47 10.76 10.75 8 39 10 03 2 875239 175768 10116 224540 1285663 10.84 8.22 - 19.35 9.07 10 3 823509 198854 14741 10050 237422 1284576 1019 9.3 12 92 19.23 9.59 9.99 4 831058 185552 - - 270442 1287052 10.29 8.68 - 10.92 10.01 5 757839 221605 10254 - 292849 1282547 9.38 10.37 8.99 - 11.83 9.97 6 849493 145820 4187 - 282430 1281930 10.52 6.82 3 67 - 11.4 9.97 7 792459 218260 - 4593 272162 1287474 9.81 10.21 8.79 10.99 10.01 8 842834 222065 - 225051 1289950 10.43 10.39 - - 9.09 10.03 9 -760938 291387 35030 11972 191293 1290620 9.42 13.63 30.71 22.91 7.72 10.04 10 698704 248277 49844 9915 272386 1279126 8.65 11.62 43.7 18.97 11 9.95 Total 8077808 2137492 114056 52266 2476472 12858094 ..______ 100 100 100 100 100 100 Source: Statistical Office, and World Bank staff estimates based on 1998199 LSMS data STATISTICAL ANNEX Page 15 of 29 Table 26 - Poverty by occupational status of the head of the household ''.' .'t... - . . . T. .i -y-,liou&eh-oldie:i: i'0::i': :i 'i-0 :^; -it.-.Li.-,. .i . 0 L ....................... . .. ......... .... Qce~pa1onal Poo Nonpoo Totl PNon onTti Por Nn poo o hedf Mth wage-earner 1840779 8106385 9947164 770610 6150536 6921146 1070169 1955849 3026018 (%) 34.7 35.8 35.6 42.5 46.3 45.8 30.6 20.9 .23 5 self-employed 2664487 8475790 11140277 571744 2793213 3364957 20927t13 5682577 7775320 (%) 50. ? 37.4 39.8 31.6 21.0 22.3 59 9 60.7 60.5 Employee 14264 716054 730318 14264 562151 576415 0 153903 l'i3903 (%o) 0.3 3.2 2.6 0.8 4.2 3.8 0.0 1.6 1.2 Other 28673 425358 454031 3434 220798 224232 25239 204560 229799 (%°) 0.5 1.9 1.6 0.2 1.7 1.5 0.7 2.2 1.8 Inactive 759078 4934132 5693210 451278 3568878 4020156 307800 1365254 1673054 (%) 14.3 21.8 20.4 24.9 26.8 26.6 8.8 14.6 r3.0 Total 5307281 22657719 27965000 1811330 13295576 15106906 3495951 9362143 12858094 (%) 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 27 - Head of the households by sector of employment and decile fl:'ecie :.kiltr lndsiy ConstrucUi Serie Uuapk l liaie Tota . . . .. , , = -~~~XM . ...................-.. ... _ .-......_ 1 1,636,818 76,136 250,033 421,192 98,851 314,682 2,797,712 (%) 19.5 3.5 14.2 4.8 9.5 5.5 10 0 2 1.302,365 133,381 245,626 541,056 176,580 397,025 2,796,033 (%) 15.5 6.1 13.9 6.1 17.0 7.0 10 0 1,263,956 100,565 249,877 578,356 120,823 485,981 2,799,558 (%°) 150 4.6 14.1 6.5 11.6 8.5 I 10 4 1,022,353 249,141 200,853 698,054 130,138 493,480 2,794,019 (%X°) 12.2 11.4 11.4 7.9 12.5 8.7 I 10 5 914,047 272,014 225,823 857,412 84,254 444,228 2,797,778 (%°) 10 9 12 4 12.8 9.7 8.1 7.8 10 0 6 713,968 251,299 189,817 956,284 105,212 579,518 2,79(:,098 (%) 8.5 11.5 10.7 10.8 10.1 10.2 100 7 570,482 275,865 102,960 963,594 92,460 782,491 2,787,852 (%7b) 6.8 12.6 58 10.9 8.9 13.7 1C 0 8 456,779 247,707 122,641 1,170,518 102,584 704,866 2,805,095 .() 5.4 11.3 6.9 13.2 9.9 12.4 10 0 9 359,355 257,860 114,566 1,167,281 74,999 819,653 2,791,714 (%) 4.3 11.8 6.5 13.2 7.2 14.4 100 10 168,490 327,134 64,753 1,501,567 54,419 680,778 2,791,141 (%k) 2.0 14.9 3.7 17.0 5.2 11.9 1C 0 Total 8,408,613 2,191,102 1,766,949 8,855,314 1,040,320 5,702,702 27,96.,000 (%°) 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Source Statistical Office, and World Bank staff estimates based on 1998199 LSMS data STATISTICAL ANNEX Page 16 of 29 Table 28 - Poverty by sector of employment of the head of the household 0.;~~~~~~~~~~ ~~~~ ~~~~~~~~~~~~~~~~~ :. .. ... M-F.., Agriculture 30.1% 5,072 5 2,337.7 0 2851 0.0682 0.0243 Industry 7.8% 9,389.1 2,771 2 0.1019 0.0246 0.0096 Construction 6.3% 6,092.4 2,591.5 0.3025 0.0767 0.0289 Services 31.7% 9,944.3 2,810.6 0.1218 0.0267 0.0091 Unemployed 3.7% 6,397.6 2,765.9 0.3026 0.0673 0.0229 Inactive 204% 8,794.2 2,802.9 0.1331 0.0291 0 0108 Source. Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 29 - Poverty by principal sector of employment of the head of the household erpendle .xpenditue ., - -s S- - - ndexi Agriculture 30 07 5072.5 2337.7 28.5 6.8 2.4 Industry 7.84 9389.1 2771 2 10.2 2.5 0.9 Construction 6.32 6092 4 2591.5 30.2 7.7 2.89 Commerce 13.14 8449.7 2918.6 16.0 3.3 1.01 Transportation and Communication 3.89 8316.2 2604.1 11.4 2.8 1.07 General Administration 6.22 12640.6 2918.6 3.3 0.5 0.12 Social services 5.96 11340.8 2489.2 12.7 3.6 14.8 Other services 2.45 10307.0 3051.9 14.4 2.5 0.75 Corps Exter. 0.04 54177 - - - Chomeur 3.72 6397.6 2765.9 30.3 6.7 2.29 Femme au foyeur 6.29 9696.5 2858.3 10.0 2.1 0.75 Eleve/Etudiant 0.09 156403 - - - - Vielliard 4.35 6672.0 2548.1 17.15 4.2 1.6 Retraite 5.40 10249.3 3215.3 7.0 0.9 0.2 Rentiers 0.39 12808.5 3085.8 17.8 2.9 0.97 Infirmelmalade 3.65 7214 4 2709.9 22.65 5.8 2.35 Autre inactifs 0.19 5797.6 3702.9 34.2 1.9 0.13 Source Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data STATISTICAL ANNEX Page 17 of 29 Table 30 - Distribution of Elderly Urban RrlNatonal, . tgr- : Male Fem Maea . , .. 0a ne a il: Fe7e Total: ___________ __________ (N~~~~~~~~~~~~~~AtionA~ 60 - 64 154,900 191,420 136,566 166,755 291,466 358,175 649,641 65 or more 343,480 349,661 357,897 319,728 701,377 669,389 1,370,766 Total 498,380 541,081 494,463 486,483 992,843 1,027,564 2,020,4)7 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 31 - Poor and Non-poor Elderly Urban lb*ral ~~~~~~~NaIo-al: .'a ge w group Poor Non poor 1t}00000o wrg '' Poor i-0 T t t - -0 -X-00-- - Non poo -Totat _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ -- i- - : - - ; - :t 0(N atioslt; 60 - 64 29,613 316,707 44,766 258,555 74,379 575,262 649,641 65 or more 66,752 626,389 125,780 551,845 192,532 1,178,234 1,370,766 Total 96,365 943,096 170,546 810,400 266,911 1,753,496 2,020,407 Source. Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 32 - Distribution of Elderly receiving Formal Pension POOR r: uban Rral a-: tiona.i Age group No pension Pension No pension Pension No pension Pension 60 - 64 28,234 1,379 44,766 73,000 1,379 65 or more 59,303 7,449 123,604 2,176 182,907 9,625 Total 87,537 8,828 168,370 2,176 255,907 11,004 NON-POOR Urb5 Rura National Age group No pension Pension No pension Pension No pension Pension 60 - 64 267,030 49,677 252,167 6,388 519,197 56,065 65 or more 505,123 121,266 538,299 13,546 1,043,422 134,812 Total 772,153 170,943 790,466 19,934 1,562,619 190,877 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data STATISTICAL ANNEX Page 18 of 29 Table 33 - Distribution of students attending public school Houhid pr eapit eLxpenditure by :in u,am Fdaetal2: S dr ; T 1 3027 43357 6633 2475 355165 2 370961 60484 11253 1063 443761 3 357383 79764 20577 7358 465082 4 354688 91722 31326 12890 490626 5 363728 123189 41115 16060 544092 6 360258 115266 53725 26206 555455 7 312546 118612 54763 34838 520759 8 291464 132152 59467 28145 511228 9 296769 144335 55395 35617 532116 10 175905 102861 83444 39171 401381 Total enrolled (LSMS) 3186402 1011742 417698 203823 4819665 Total enrolled (MEN and MEST) 3317153 937096 414108 % discrepancy -4.10% 738% 086% - Government expenditure (budgeted, 8474881066 4631869934 4277145000 3596053000 current DHI) Unit cost as from my estimates using 2660.0 4578.0 10240.0 17643 0 1998/99 LSMS Unit cost as by MEN and MEST 2554.87 4942 79 10328 57 - % discrepancy 3.94% -7.96% -0.86% _ Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data; MEN. Table 34 - Distribution of students in poor and non-poor households Poor Non poor Total Fundantental 1 642784 2654516 3297300 Fundamental 2 112666 922877 1035543 Secondary 27466 413425 440891 Higher 9776 203998 213774 Total 792692 4194816 4987508 Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data STATISTICAL ANNEX Page 19 cf 29 Table 35 - Distribution of students by level of education in public schools Fondamental ; 00 F e e ' 2'- -'.'Total Fondaental.0 | Secondaire Highe Decile Students Students Students Students Studen s 1 291830 44243 336073 6633 2475 2 354792 60484 415276 11253 1063 3 345576 79764 425340 20577 7358 4 339258 90703 429961 31326 1289( 5 357778 123189 480967 41115 16060 6 353336 115266 468602 53725 26206 7 296978 116760 413738 53695 35906 8 283772 134004 417776 59467 28145 9 283309 144335 427644 54340 36672 10 160385 104114 264499 83444 39171 Total 3067014 1012862 4079876 415575 205944i Source. Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 36 - Distribution of Education subsidies (in DH) Primary 1 1721141840 7022977812 8744119652 19.68% Primary 2 523463468 5 4221434002 4744897471 11 03% Secondary 282683184 9 4233166800 4515849985 6.26% Higher 170700155 3599108700 3769808855 4.53% Total 2697988649 19076687314 21774675963 12.39% Total Expenditure 116657479680 1287056400384 1403713880064 Share of total expenditure 8.31% 91.69% 8.31% Source: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 37 - Distribution of Education subsidies (including fees paid by the households in DH) 1 ~ 806394931 202325511 68267588 43216334 1120204363 2 980373746 276596438 115817151 18561197 1391348532 3 954907770 364764868 211780816 128479106 1659932560 4 937449650 414789476 322410742 225074161 1899724030 5 988624766 563349622 423160240 280425991 2255560619 6 976350475 527117337 552943789 457586770 2513998371 7 820620065 533949475 552635026 626959878 2534164444 8 784128781 612807173 612041104 491443930 2500420988 9 782849403 660051366 559273439 640335115 2642509323 10 443181479 476118668 858815105 683970517 2462085769 Total 8474881066 4631869934 4277145000 3596053000 20979949000 Sou,ce Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data STATISTICAL ANNEX Page 20 of 29 Table 38 - Distribution of the estimated Education fees paid by the households (in DH) . useper .:.. y I Priary2 Sccouary : igher Totalr 1 4791601 1957662 323700 176310 7249273 2 7271055 3950295 610071 0 11831421 3 7171835 3735307 1732313 208660 12848115 4 6905929 4897542 1806477 267560 13877508 5 6992144 6212114 2811534 249560 16265352 6 8019526 5803636 3078905 1241105 18143172 7 7640408 5766477 3572960 1455530 18435375 8 6270224 7021889 3633370 972630 17898113 9 8164213 7712624 3493914 2160350 21531101 10 6444064 7830477 7368234 2415900 24058675 Total 69670999 54888023 28431478 9147605 162138105 Soarce: Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 39 - Distribution of education subsidies net of the fees paid by the households (in DH) De11 ; P. -- ima.- T -ry-; | - ?rhua"'' . , ' S'c ay , ihr Totaloaai. .:: ° e ' 1 801603330 200367849 67943888 43040024 1112955090 2797712 2 973102691 272646143 115207080 18561197 1379517111 2796033 3 947735935 361029561 210048503 128270446 1647084445 2799558 4 930543721 409891934 320604265 224806601 1885846522 2794019 5 981632622 557137508 420348706 280176431 2239295267 2797778 6 968330949 521313701 549864884 456345665 2495855199 2796098 7 812979657 528182998 549062066 625504348 2515729069 2787852 8 777858557 605785284 608407734 490471300 2482522875 2805095 9 774685190 652338742 555779525 638174765 2620978222 2793714 10 436737415 468288191 851446871 681554617 2438027094 2797141 Total 8405210067 4576981911 4248713522 3586905395 20817810895 27965000 Source Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data STATISTICAL ANNEX Page 21 if 29 Table 40 - Distribution of per capita net Education subsidies (in DH) 20 , , ; g t T9wk Aj l (curriK ;thi14 1 286.5 71.6 243 154 397.8 2060.6 2 348.0 97.5 41.2 6 6 493.4 3032.1 3 338.5 129 0 75.0 45 8 588.3 3779 2 4 333.0 146.7 114.7 80.5 675.0 4529.7 5 350 9 199.1 150.2 100.1 800.4 5338.8 6 346.3 186.4 196 7 163.2 892.6 6281.7 7 291 6 189.5 196.9 224A 902.4 7519.3 8 277 3 216.0 216 9 174 9 885.0 9252.4 9 277.3 233.5 198.9 228.4 938.2 12252.2 10 156.1 167.4 304 4 243.7 871.6 24214.1 Source Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 41 - Education net subsidies as share of mean per capita expenditures i:.Pr y ni I;2 777r H igher 1 0.1390 0 0348 0.0118 0.0075 2 0.1148 0 0322 0.0136 0 0022 3 0 0896 0.0341 0.0199 0 0121 4 0.0735 0.0324 0.0253 0 0178 5 00657 0 0373 0.0281 0.0188 6 0.0551 0 0297 0.0313 0.0260 7 0.0388 0 0252 0.0262 0.0298 8 0 0300 0.0233 0 0234 0.0189 9 0.0226 0.0191 0.0162 0.0186 10 0 0064 0.0069 0.0126 0.0101 Source Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data Table 42 - Households' expenditure on education (in DII) Urban Rural National Urban Rural National 1 20419316 56329828 76749144 607201 863417 1470618 2 44477824 75253984 119731808 3730824 1677804 5408628 3 69910728 78281336 148192064 7472904 536928 8009832 4 106356024 67720000 174076024 10518713 424805 10943518 5 134292000 68533024 202825024 20318576 1923015 22241591 6 169472432 56959168 226431600 26270988 1690831 27961819 7 176427376 52618928 229046304 39541832 3889647 43431479 8 223248784 35221420 258470204 59903776 6829536 66733312 9 299443424 23590372 323033796 130224760 90034 130314794 10 420270464 16569266 436839730 822997824 822997824 Total 1664318372 531077326 2195395698 1121587398 17926017 1139513415 Source. Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data STATISTICAL ANNEX Page 22 of 29 Table 43 - Total households' expenditures on Education -_ . . -- . .- - .. :- :Public.tprivaesectors . Decile Urban Rural National 1 21026517 57193245 78219762 2 48208648 76931788 125140436 3 77383632 78818264 156201896 4 116874737 68144805 185019542 5 154610576 70456039 225066615 6 195743420 58649999 254393419 7 215969208 56508575 272477783 8 283152560 42050956 325203516 9 429668184 23680406 453348590 10 1243268288 16569266 125937554 Total 2785905770 549003343 3334909113 Source. Statistical Office, and World Bank staff estinates based on 1998/99 LSMS data Table 44 - Household per capita expenditure on education (in DH) _ L . : . . Pub J1.. pr ae __ __ __-_ ___-_ . . e....... 1e .- -U. ....b. .. - - . . -- Rura- - .;National 1 50.8 24.2 28.1 2 70.7 36.3 44.6 3 77.8 43.8 55.9 4 91.3 45.3 66.5 5 104.0 53 8 80.4 6 120.3 50.7 91.4 7 1214 54.4 96.7 8 142.6 50.5 115 4 9 192 5 41.8 162.0 10 487.1 70.2 451 8 Total 185.3 42.5 119.3 Per capita expenditures on 565 105 353 education, culture and entertainment Share of education 32.8% 40.4% 33.8% expenditure Source Statistical Office, and World Bank staff estimates based on 1998/99 LSMS data STATISTICAL ANNEX Page 23 of 29 Table 45 - Comparative basic social indicators (1998 or latest year) Greece Yemen .... ... difference clifference Mvf0 Jordan Iran Tunisia Algeria Turkey E-gyt Portugal frmMI rmLM Africa in ~~~1998 in 1990 GNP per capita, Atlas Method (current 3Z40* 1150.0 1650.0 2060.0 1550.0 3160.0 1290.0 10670.0 11740.0 280.0..-- Z~3N ~@-28.7 -38.0 Population Total fertility rate ()4.1 2.7 2.2 3.5 2.4 3.2 1.5 1.3 6.3 20.0 22.0 Population growth rate% 2.8 1.7 1.3 2.1 1.5 1.7 0.2 0.2 2.8 . 54.5 30.0 Urban population (%) 73.1 60.6 64.1 58.8 72.9 44.9 61.2 59.7 243 74,.-687.6 Health Life Expectancy at Birth 71.1 70.7 72 70.6 69.3 66.5 75.2 77.8 55 5 t~. *40.9 -5.0 Infant Mortality Per 1000 Births 4 ~~27.1 26 28.1 34.8 37.9 49.1 8.4 6.1 8244 ~40.3 25.0 Mortality rate, adult, female (per 1,000 119 150 142 123 122 171 7 1 6 1 333 1~ 7.3 female adults) Contraceptive prevalence (bof 53.0 73.0 60.0 57.0 63.0 55.0 66.0 - 20.8 3L 11.5 women 15-49) Education Pnmary School Gross Enrollment Rate Total 70.6 98.4 118 107.5 107.4 101.1 127.6 93.2 70.2 9. 08. -17.1 -38.0 Female . 171.5 95 114.2 101.6 103.7 94.3 124.4 93 39.9 8. 4-22.5 -44.0 Secondary School Enrollment Rate Total 57.4 76.7 64.3 63.3 58.2 78.3 110.7 95.4 34.4 -42.0 -38.0 Female ~$7 58.5 72.6 62.9 61.7 47.8 73.3 115.9 96 14.3 & 4 -54.6 -47.0 Adult Illiteracy Total 11.4 25.4 31.3 34.5 16 46.3 8.6 3.1 55-9 250.4 120.0 Female 17.4 32.6 42.1 45.7 25 58.2 11 4.5 77.3 - 4 7. 270.7 112.0 Basic binfrastructure__ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Potable Water 9 06 23 Electricity . .....99 98 Source: World Development Report (2000), Social Indicators of Developmnent, Statistucal Office, 1998/99 LSMS STATISTICAL ANNEX Page 24 of 29 Table 46 - Central government budget (in million DH current prices) ,, -- : 1: 99Q 199k 12 1993 1994- 199; -19 1997 9 Tot Revenue (excl Privat.) 50976 55550 63659 64888 67565 67311 78838 83796 Tax revenues 46462 50467 57952 57868 59386 61975 71552 76531 Other Revenues 4514 5083 5707 7020 8179 5336 7286 7265 Tot. Expenditures (excl Net lend) 58502 63065 69009 74551 78471 83105 89442 95515 Current 43010 48058 51815 54954 59793 62335 69852 75883 Salaries & Goods 27841 32549 35333 36956 39430 41493 45694 50924 Interest 13200 13308 13527 14722 15817 16783 17070 17274 CFS 1021 1192 1580 1918 3195 2745 5619 6206 Others 948 1009 1375 1358 1351 1314 1469 1479 Capital 15492 15007 17194 19597 18678 207701 19590 19632 Overall balance/deficit -7526 -7515 -5350 -9663 -10906 -15794 -10604 -11719 GDP 212820 242360 242911 249223 279322 281702 319635 319291 346952 GDP deflator growth 5.6 65 4.4 3.6 1 5 8.0 1 2 19 23 GDPdeflator 100.0 1065 111.2 115.2 1169 126.3 127 8 1302 133.2 |CPIgrowth I 7.01 801 57i 52 5.1 6.1 3.0 1.0 2.7 CPIdeflator 100 108.0 114.2 120.1 1262 133.9 1379 139.3 143.1 Source: Ministry of Finance and IMF. STATISTICAL ANNEX Page 25 of 29 Table 47 - Social Sectors Expenditures (in million DH) *00 .X -- -199 l1991 199S2 i; 9 199 199. I99 199 199 1999-- ;1l0 Education 112577 121586 13567.8 142032 149501 15683.6 16373.4 188199 205400 209799 Fondamental 92056 7642.0 82950 8683.0 91250 97060 100470 115740 129585 13106.8 Secondary 2328.5 2832 3 3023 7 3151 9 3305.5 3482 1 3940 0 4230 9 4277 1 Higher 2052.1 2188.1 2440.5 2496.6 2673.3 2672.1 2844.3 3306.0 3350.6 3596.1 Health 1923.5 2160.9 2554.8 2777.5 3048.6 2915.9 3169.4 3622.4 3767.5 4973.4 Total Housing 300 0 300 0 358 0 462 2 559 4 355.4 247 7 277 0 362 1 509 6 Housing 300.0 300 0 358.0 4622 559 4 355 4 247 7 277 0 3621 354 6 Social Housing 155.0 Vocational Training 1/ 300.0 400 0 500 0 500.0 600 0 600 0 800 0 800 0 950.0 950 0 OFPT 1/ 200.0 300.0 400.0 400.0 500.0 500.0 600.0 600.0 660.0 660.0 Active Labor Policies 100 0 100 1000 200.0 200 0 300 0 550 0 700 0 838 0 838 0 Wage Subsidies 2/ 100.0 100 0 100 0 200 0 200.0 300 0 300.0 400 0 450 0 450 0 Micro credits 3/ 150 0 200 0 228.0 228 0 Pepinere 4/ 100.0 100.0 160.0 160.0 Promotion Nationale 3997 290.4 4784 5597 5280 4464 5163 617.8 6084 880.8 Investment 378 5 275 0 453 0 530 0 500 0 422.7 489 0 585 0 580 0 833 8 Overhead 5/ 21.2 15.4 25.4 29.7 28.0 23.7 27.4 32.8 28.4 47.0 Entraide Nationale 200.0 200.0 200.0 200.0 200.0 200.0 199.2 190.0 182.9 167.0 Adult Alphabetisation 10.0 15.0 25.0 34.0 Cons. Food Subsidies 2700 0 3238 0 3034.0 3338 0 4386 0 4430 0 5120 4 5548 5 5626 3 5300 0 Budget 27000 32380 30340 33380 43860 44300 12950 12721 9321 11000 Tariffs 3825.4 4276.4 4694.2 4200.0 Warveterans6/ 20.0 20.0 29.5 36.7 41.1 36.6 36.1 38.8 41.6 41.3 Handicapped 10.0 12.0 12.0 Social Insurance Funds 2400 0 2900 0 3730 0 4489 0 4991 4 5071 9 6837 3 7895.5 8574 9 3865 0 CNSS/Privatesector 2000.0 25000 3000.0 35000 38974 3971 9 45196 4949.8 51749 Alocation Familiale 1107 4 1228.6 1476.2 1667 7 1688 0 Pension 1607 1 1798 1 20649 2254.2 24045 Work Injuries& Maternity 176 1 242 4 241 4 240 9 239 9 Polyclinics subsidies 603 0 304 9 390 2 357.2 340 3 Administrative Costs 403.8 398.0 346.9 429.8 502.2 PublicSector 4000 400.0 7300 989.0 10940 11000 23177 29457 3400.0 38650 CMR 300 0 300 0 550 0 749 0 814.0 750 0 1967 7 2575 7 2800.0 3215 0 CNOPS (Health Insurance) 100.0 100.0 180.0 240.0 280.0 350.0 350.0 370.0 600.0 650.0 Agricultural programs 200 0 200 0 200 0 200 0 200 0 200 0 200 0 200 0 450 0 150 0 Accord Social (CDM) 677.0 640.0 Rural Infrastructure 00 0 0 0 0 00 00 00 0 0 750 0 750 0 900 0 Pager 100.0 100.0 250 0 Rural roads 650.0 650.0 650.0 Total Social Sectors 198009 21967.8 247524 269663 297046 30239.8 34059.9 394849 434058 40241.1 %GDP 93% 91% 102% 108% 10.6% 107% 107% 12.4% 125% #DTVIO! % TOT. GOV. EXP 33.8% 34.8% 35.9% 36.2% 37.9% 36.4% 38.1% 41.3% 43.2% #DIV/0! 1/ Vocational training & OFPT needs to be checked 2/ Wage subsidies include CIOPS, Formation/insertion & Formation/qualifiante 3/ Micro credits include subsidized credits to young entrepreneurs 4/ Pepinere are locations for young unemployed to set-up businesses 5/ The overhead of PN has been estimated at 5 7% of the investment budget 6/ War veterans is the allocation to Haut Commissariat aux Anciens Resistants (need to be checked) Source: Ministry of Finance; sectoral Ministries; World Bank staff estimates. STATISTICAL ANNEX Page 26 of 29 Table 48: Public expenditures in education (in miillion DH) Primary and Seconda!, 199 t 991 1992 199 1995 . 1996 1997 .1998 199 Wages 8120.7 8534.9 9376.8 9868.6 10399.0 11244.7 11858.9 13275 8 14801.9 14928 7 Fondamental 6171.7 6486.5 7000 1 7333.7 7715 7 8401.3 8879.1 9940 0 11113.2 11195.8 CEF 1 4060.4 4267.4 4322.4 4558.0 4820.3 5337.5 5678.8 6357.3 CEF 2 2111.4 2219.1 2677.7 2775 7 2895 4 3063.8 3200 3 3582.7 Secondary 1949.0 2048.4 2376.7 2534.9 2683.3 2843.4 2979.7 3335.8 3688.7 3732.9 Equipment 411 5 471.6 626.4 718 1 7579 7668 7602 840.2 917.5 932.7 Fondamental 267 9 306.2 416.7 459 6 490 4 504.6 494 7 538.8 684 9 693.1 CEF 1 149.5 172.2 242.3 233.3 257.5 275.8 284.0 311.4 CEF 2 118.4 134 0 174.4 226.3 232.9 228.8 210 6 227.4 Secondary 143.6 165.5 209.7 258.5 267.4 262.2 265.6 301.3 232.6 239.6 Investment 673.4 964.0 1124 0 1120 0 1120 0 1000.0 910.0 1398 0 1470 0 1522.5 Fondaniental 827 2 903.0 805 6 675.5 1094.5 1160.4 1217 8 CEF 1 218.2 217.8 211.1 268.9 687.8 CEF 2 609.0 685.2 594 5 406 6 406.6 Secondary 292.8 217.0 194.4 234.5 303.5 309.6 304.6 Total 9205.6 9970.5 11127 3 11706.7 12276.9 13011.5 13529 1 15514.0 17189 4 17383.9 Fondamental 7642.0 8295 0 8683.0 9125.0 9706.0 10047.0 11574.0 12958 5 13106.8 Fondamental 1 4939.0 5074 0 5311.0 5607.0 6049.0 6231 0 7356.0 8379.0 8474 9 Fondamental 2 2703 0 3221.0 3372.0 3518 0 3657 0 3816.0 4218 0 4579 0 4631.9 Secondary ___ 2328.5 2832.3 302!3.7 3151.9 3305.5 3482.1 3940.0 4230.9 4277.1 Higher Education . . _____ __- _ __ ___., ___. Wages 880 6 938 0 1097.7 1112.2 1247 6 1349.6 1503 0 1866.4 190'7.3 2141.1 Equipment 773 7 810.1 842.8 934 3 963.2 922 5 999 2 1029,6 1027.9 1043 0 Investment 397.8 440.0 500.0 450.0 462.5 400.0 342.0 410.0 415.5 412.0 Total Higher Education 2052.1 2188.1 2440.5 2496.6 2673.3 2672.1 2844.3 3306.0 3350.6 3596.1 Source: Ministries of Education and Finance. Table 49: Distribution of Public Expenditures by education levels (%) Prim 0% 63% 61% 61% 61% 62% 61% 61% 63% 62% Fondamental 1 0% 41% 37% 37% 38% 39% 38% 39% 41% 40% Fondamental 2 0% 22% 24% 24% 24% 23% 23% 22% 22% 22% Secondary 0% 19% 21% 21% 21% 21% 21% 21% 21% 20% Higher 18% 18% 18% 18% 18% 17% 17% 18% 16% 17% Total 100% 100% 100% 100% 100% 100% , 100% J 100% 100% 100% Source: Ministries of Education and Finance STATISTICAL ANNEX Page 27 of 29 Table 50- Government health expenditures (in million DH) Current Prices 1990. 199 1992. 1993 1994 1995 1 996 19 97 199& 1999 Wages 1153 5 1256.6 1396 3 1487.7 1590.2 1676 4 1899 6 2102.4 2167 5 3270 0 Equipment 431.8 464 2 606 2 737.5 767 4 739 :5 721.9 800.0 800.0 778.4 Investment 338 2 440 0 552.3 552 3 691 0 500.0 548.0 720 0 800.0 925.0 Total 1923.5 2160.9 2554.8 2777.5 3048.6 2915.9 3169.4 3622.4 3767.5 4973.4 1989 Priceks :. 1990 1 1991 i 1992: 1993 194 1 199 . 1996 :1997 1998 999 Wages 10780 1087.0 11426 1107.6 1177.0 11690 1279.7 1375 1 14037 Equipment 403.6 401 6 496.1 573 9 568.0 515.7 486.3 523.2 518.1 Investment 3161 3806 452.0 4298 511.5 348.7 3692 470.9 5181 Total 1797.7 1869.3 2090.7 2111.4 2256.6 2033.4 2135.2 2369.3 2439.8 Deflator 107.0 115.6 122.2 131.5 135.1 143.4 148.4 152.9 154.4 CGBudgetlcapita 74.35 75.84 83.24 82.53 86.54 77.06 78.85 86.02 87.12 % growth - 2.0% 9.8% -0.9% 4.9% -11.0% 2.3% 9.1% 1.3% Source: Ministry of Health and Finance Table 51 - Healthcare sources of financing (1998/99) (in million DH) Public 583E.5 Health Ministry 3'767.5 Local collectivity 1/ 1 300 CNOPS 21 '186.0 Administration 56 0 Tiers Payant ';08.0 Remboursement des adherents 422.0 Private Mutuelles 3/ 460 0 Private Insurance 4/ 167 0 Households 5/ 4;i50.0 Total Health Expenditures I1 660.5 11 Local Collectivity includes other ministries contributions for their staff, polyctinics of CNSS and enterprise contributions (including private and public) It is equal to almost 30% of the Government health budget based on 1995 data. 2/ Includes civil servants, tiers payant and contributions of the affiliates 3/ Value of 1995 4/ Value of 1997 5/ 1998/99 LSMS data (excluding Tiers payant and Remboursement) Source: Ministry of Health Table 52- Government health expenditure by level of services (in million DH) : : : 199 . : % Share Hospitals 2018 9 55.7% CHU (University) 657 3 Others 1361.6 Preventive caret 1223. 33.80/ii Of which: Curatifs 288.6 8 0% Health programs 166.1 4.6% Total other. costs 398 10$% Admninistration 251 6 Training 43.3 National Laboratories 66 1 Others 18.8 _ Total 3622.4 100.0% Source: Ministry of Health. STATISTICAL ANNEX Page 28 of 29 Table 53 - Caisse Nationale de Securite Sociale (CNSS) (in million DH) 994.. . .. 1997 191S Tot active Affiliates 930195 0 945342 0 982265 0 1052112 0 1073646 0 Invalide 4807.0 Old age 137966 0 Survivors 62802.0 Total 1279221 0 Total Revenues 5091.4 5311.8 6175 6 6014 9 6273.2 Contribution (+penalties) 4302.9 4419 3 5185.2 4935.4 5283 9 Financial 788 5 892.5 990.4 1079.5 989.3 Interest from Banks & TFP 27.2 21.7 21 9 24.0 26 2 Inerest CDG 761.3 870.8 968.4 1055 5 963 1 Total Expenditures 3897 4 3971 9 4519.6 4949.8 5174.9 Total Prestation 2890.6 3269.1 3782 5 4162.8 4332.4 Allocation Farniliale 1107.4 1228 6 14762 1667.7 1688.0 Health & Matemity benefits 176.1 242 4 241.4 240.9 239 9 Aide sanitaire familiale 19 9 24 0 24.4 24.8 28.3 Indemnite Maladie 55.6 94.4 88.9 84.6 79.2 Indemnite Maternity 47.1 64 4 65 2 67 0 700 Matemity leave 8 1 10 2 8.9 8.8 7.0 Death benefits 45.5 49.5 54.0 55.7 55.4 Pension 1607.1 1798 1 2064.9 2254 2 2404.5 Invalidity 35 9 47.5 59.2 73.7 84.5 Old age 1149.7 1418 3 1624.9 1754 5 1828.2 Survivors 261 6 332 3 380.8 425.9 481.5 others (balancing) 160.0 0.0 0.0 0.0 10.3 Subsidies to Polyclinics (6) 603 0 304 9 390.2 357 2 340.3 Admninistration 403.8 398 0 346 9 429 8 502.2 Overall Balance (surplus) 1194.0 1339.8 1655.9 1065.1 1098.3 Source: Ministry of Social Development, Solidarity, Labor and Vocational Training STATISTICAL ANNEX Page 29 of 29 Table 54 - Expenditures on Consumer Food Subsidies (in million DH) 1996 1:. - 997 1998 Cons. Food Subsidies 5120.4 5548.5 5626 3 Budget 1295.0 1272.1 932 1 Tariffs 3825 4 4276.4 4694.2 Total Expenditures 5120.4 5548.5 5626.3 Oil 1684.4 1801.8 1814.3 Sugar 1497.8 1826.3 1846 Flour 1938.2 1920.4 1966 Tariff 3825.4 4276A 4694.2 Oil 1671.9 1411.3 1416.9 Sugar 1007.5 1042.6 941.3 Flour 1146 1822 5 2336 Budget net costs 1295.0 1272.1 932.1 Oil 12.5 390.5 397 4 Sugar 490 3 783.7 904.7 Flour 792 2 97 9 -370.0 Source: Ministry of Finance. MOROCCO t;A0T0L0AtNTIrC :V0 ;00 0 @ 'i(mt}mn s , OCEAN~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.0 Tonuift no 4~~~~Keif4 T.oIo t C E: A N fS. rX iO ..j z Kh..ifl. ~ ~ ~ 4. 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