e%.i 4A.aC#I'SIWU ^" sSocial evelopment- tu, 21249 July 1999 The Social Impact of the Crisis in Indonesia: Results from a Nationwide Kecamatan Survey Sudarno Sumarto Anna Wetterberg and Lant Pritchett The Social Impact of the Crisis in Indonesia: Results from a Nationwide Kecamatan Survey Sudarno Sumarto Anna Wetterberg and Lant Pritchett Support from the Ford Foundation and ASEM Trust Fund is gratefully acknowledged The authors would also like to thank the mnany people who contributed to the design of the questionnaire, including Scott Guggenheim, Sarah Cliffe, Brigitte Duces, Steven Burgess, and Shyaikhu Usman. BPS was responsible for data collection and Peter Gardiner and his team at INSAN HITAWASANA SEJAHTERA carried out the empirical work. The findings and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. The Social Impact of the Crisis in Indonesia: Results from a Nationwide Kecamatan Survey Page Abstract ............................. ii - iii I. Background ..............................1 II. Survey and Methods ............................. 1 III. Provincial and Kabupaten Analysis ................. 4 IV. Specific Crisis Impacts ............................. 13 V. Conclusions and Recommendations ............... 17 Appendix 1: Details of the Survev Appendix 2: Inter-Coder Reliability Appendix 3: Construction of an Index Appendix 4: Survey Indicators Foreword In the middle of 1997, the economies of many East Asian countries experienced a melt down with serious consequences for the development agenda and goals of the region. In responding to the crisis. the World Bank has worked to galvanize action to protect the most vulnerable social groups and preserve the human development gains made in the last two decades. Crisis response has tested the Bank in many ways, including having to bring complex and contentious issues, such as good governance and the rule of law, to the forefront of economic and social policy. Seemingly more simple issues such as the use of monitoring methods to understand the impact of the crisis on different social and regional groups have posed no less a challenge to the Bank's staff, and the other important players at national and regional level. This paper contains the findings of a largely qualitative instrument for measuring the impact of the crisis in Indonesia. In addition to its important finding on the differentiated impact of the crisis, the relatively low cost and quick turnaround characteristics of the methodology will be of interest to development practitioners caught in similar crisis situations that call for rapid and informed response. Kristalina Georgieva Sector Director East Asia Environment and Social Development Unit ii The Social Impact of the Crisis in Indonesia: Results from a Nationwide Kecamatan Survey Abstract This paper is based on a qualitative survey of three expert respondents in every kecamatan (sub-district) in Indonesia, designed to obtain a quick indication of overall impacts of the Indonesian crisis. Questions cover the degree of different types of impacts (migration, access to health and education, food availability), the frequency of different types of coping strategies (selling assets, reducing frequency of meals, etc), and the most severe impacts in each area. Indices were constructed to measure crisis impact along five dimensions. There are three main findings. First, urban areas have been harder hit by the crisis than rural areas. Second, the impact of the crisis is very heterogeneous, with some regions experiencing great difficulties and others doing relatively well. Both rural and urban areas on Java have been hard hit by the crisis. Some of the other islands, particularly large parts of Sumatra, Sulawesi, and Maluku, have experienced minimal negative crisis impact. Other areas show negative impact, but it is unclear whether problems are economic crisis-related or result from drought (East Timor, NTT, NTB) and fires (East Kalimantan). Third. there is little connection between initial poverty levels and the extent to which an area has been hit by the crisis, with some relatively poor areas have not been hard hit while some relatively well off areas have been quite hard hit. This implies that crisis impact targeting and poverty program targeting are two, quite different, exercises. The consistency of the results with other quantitative surveys also shows that this type of quick turnaround. largely qualitative. instrument can give a good overview of degrees of crisis impact in different areas and trends in overall changes. Although results require further validation and cross-checking for use in the design of crisis response programs, this kind of survey can point response efforts in the right direction. Because of its low cost and quick turnaround, a similar survey could also be repeated after six months in an effort to provide on-going monitoring of crisis impacts. . . Dampak Sosial dari Krisis di Indonesia: Hasil dari Survey Nasional Kecamatan Abstrak Makalah ini ditulis berdasarkan hasil-hasil survey kualitatif yang dilakukan terhadap tiga orang ahli di setiap kecamatan di seluruh Indonesia. Survey ini dirancang untuk memperoleh gambaran secara cepat dan menyeluruh mengenai dampak krisis yang terjadi di Indonesia Pertanyaan-pertanyaan yang diajukan mencakup berbagai jenis dampak (migrasi, akses terhadap sarana kesehatan dan pendidikan, ketersediaan pangan), frekuensi penggunaan berbagai jenis kiat penanggulangan (penjualan aset, pengurangan frekuensi makan, dan lain-lain), dan dampak terbesar yang dialami oleh masing-masing daerah. Kemudian disusun beberapa indeks untuk mengukur dampak krisis yang mencakup lima segi (yaitu kesehatan, pendidikan, kesempatan kerja, ketahanan pangan, dan kiat mengatasi krisis). Terdapat tiga temuan utama. Pertama, dampak krisis di daerah perkotaan lebih parah dibandingkan dengan daerah pedesaan. Kedua, dampak krisis ini sangat heterogen, dimana terdapat beberapa daerah yang mengalan-i kesulitan parah sementara daerah-daerah lain relatif baik keadannya. Tetapi di pulau Jawa baik daerah pedesaan maupun perkotaan sama-sama mengalami dampak yang parah. Beberapa daerah di pulau-pulau lain, khususnya sebagian besar Sumatera, Sulawesi, dan Maluku, mengalami dampak krisis yang tidak terlalu besar. Ada juga daerah-daerah yang memperlihatkan keadaan yang memburuk, namun tidak jelas apakah ini merupakan dampak dari krisis ekonomi ataukah akibat dari musim kemarau (Timor Timur, NTT, NTB) dan kebakaran (Kalimantan Timur). Ketiga, terdapat kaitan yang kecil antara tingkat kemiskinan awal dengan derajat besamya dampak krisis, dimana terdapat beberapa daerah yang relatif miskin yang ternyata tidak begitu terkena krisis sementara terdapat beberapa daerah lain yang lebih makmur yang ternyata mengalami dampak krisis yang besar. Implikasi dari hal ini adalah bahwa sasaran dari program penanganan krisis dan sasaran dari program pengentasan kemiskinan merupakan dua hal yang sangat berbeda. Konsistensi dari hasil survey ini dengan hasil penelitian-penelitian lain yang bersifat kuantitatif menunjukkan bahwa suatu instrumen kualitatif dengan jangka waktu pelaksanaan yang singkat seperti yang digunakan dalam penelitian ini mampu memberikan gambaran yang baik mengenai derajat dampak krisis di berbagai daerah dan kecenderungan dari keseluruhan perubahan yang mereka alami. Meskipun hasil penelitian ini memerlukan pengesahan lebih jauh serta pemeriksaan silang untuk dapat digunakan dalam perancangan program-program penanganan krisis, survey jenis ini mampu mengarahkan upaya-upaya penanganan krisis ke arah yang benar. Karena biayanya rendah dan jangka waktunya singkat, survey sejenis dapat diulang setelah enam bulan sebagai suatu usaha untuk melakukan pemantauan berlanjut dari dampak krisis ini. iv I. Background As the economnic and political crisis in Indonesia has worsened over the past year, there has been increasing recognition of the need to identify and track emerging problems, with a view to designing appropriate responses. Efforts to monitor social impacts have concentrated on improving or accelerating existing tools, such as the national expenditure survey (SUSENAS) and the village potential survey (PODES). Although these efforts are crucial for medium-term planning, the time necessary to design instruments, and gather and process data is too long for these instruments to be a useful guide for immediate action. By the time data are processed and analyzed, quickly changing crisis conditions have rendered them obsolete. Other surveys have focused on measuring impact in specific sectors, such as health and education, and have produced good detailed information for a small number of locations and an indication of trends in these sectors. However, they have not been designed to compare crisis impacts across all of Indonesia and identify areas where overall effects have been most severe. To get a snapshot of changes in overall welfare and emerging problems across Indonesia, the Ford Foundation and the World Bank designed a quick turnaround survey to be implemented in every kecamatan (sub-district). The purpose of this tool was to give a first indication of overall crisis impacts, the relative severity of various problems in different parts of Indonesia, and an idea of how to target crisis responses most effectively. -I. Survey and Methods The Kecamatan Rapid Poverty Assessment was a subjective, expert respondent survey of three government officials in each of Indonesia's 4.025 kecamatans. In each sub-district three respondents with kecamatan-wide responsibilities were chosen: the agriculture officer (mantri tani) in rural areas or the development officer (kepala seksi PMD) in urban areas; the kecamatan school supervisor (penilik sekolah); and the health officer (dokter puskesmas). Each respondent was asked a standard set of questions about changes taking place in the kecamatan as a whole, as well as a set of questions about their professional specialty.' The questions focused on the degree of different kinds of impacts (e.g., migration, access to health and education, food availability, etc.), the frequency of different types of coping strategies, and the most severe impacts in each area. All questions were designed to measure proportional change in indicators relative to the same time in 1997, to eliminate seasonal changes. The questions asked of all three respondents were qualitative In addition to the three respondents in each kecamatan, the local representative for the Central Bureau of Statistics (BPS) completed a separate questionnaire, including some quantitative questions regarding changes since the start of the crisis. See Appendix I for more details on survey design and data collection. 1 and asked respondents to rate each indicator's severity on a five-point scale: 1) somewhat improved; 2) about the same; 3) somewhat worse; 4) much worse; and 5) very much 2 worse . The common questions also included a ranking of problems and three questions on existing crisis response programs. The respondent-specific questions were also primarily qualitative, but included a small number of quantitative questions (which duplicated the topics covered qualitatively). There are limitations to every approach and the use of subjective qualitative questions is no exception. With the decision to use this type of instrument, the loss of quantitative precision and relying on a very small number of respondents in each location were the price paid for a rapid and nationally comprehensive survey. For this survey, national coverage was necessary in order to identify crisis-hit areas for program targeting. A quantitative survey using representative sanpling approach was ruled out as demanding kecamatan sample sizes that are simply too large. By asking for qualitative assessments we hoped to get universal coverage with complete response (the use of quantitative questions did dramatically raise the non-response rate in this survey). It was also necessary to limit the number of respondents in each kecamatan to minimize the time between survey distribution and return of questionnaires. Inter-respondent reliabilitv. One concern about using the expert respondent qualitative approach is the inter-respondent reliability. Simply put, do two people, asked the same question about the same kecamatan, tend to give the same answer? Since 21 questions of this survey were general questions that all three respondents answered, we can assess this inter-respondent reliability by comparing responses. Appendix 2 details the outcome of three different measures: the correlation across different respondents: within versus across kecamatan analysis of variance; and the average absolute deviation. All three approaches show an acceptable degree of consistency in response patterns within kecamatan but also show that there is a significant level of disagreement between respondents. The correlations across kecamatans of the responses of any two of the coders from the same kecamatan seldom rise above 0.3 and some cases are closer to 0.2. The average absolute disagreement between two respondents in the same kecamatan (on a scale from I to 5) is between 0.6 and 0.8. While this disagreement is relatively small in an absolute sense, the total variation of the responses is also quite small (standard deviations vary between 0.9 and 1.2). The "within" kecamatan sums of squares (that part of the variation in the data that arises from disagreements among the respondents in the same area) is generally on the order of one-third to one-half of the total 3 variance This degree of uncertainty serves as a warning to confine analysis to comparisons where we can realistically hope that "signal" will overshadow "noise." This means either using aggregates of kecamatan (e.g., provinces or kabupaten and kotamadva) or aggregates of variables. Also, between-kecamatan gross distinctions are fairly reliable, 2 A typical question is "Relative to the same period last year how many families are switching from stable foods to lower quality substitutes?", answered on the scale indicated. 3 Because the respondent-specific questions involve only one respondent per kecamatan we cannot do similar tests to those above. However, one can expect that these qualitative responses are subject to similar imprecision. but fine distinctions (e.g., between "about the same" and "slightly worse") should be made with extreme caution. It should be noted that these limitations imply that, while the broad patterns indicated by this data are useful in targeting of crisis response, used on their own these data alone cannot target program locations at the kecamatan level. Index construction. There are too many questions in the survey to do a detailed analysis of each. Instead, summary indices were constructed from a combination of indicators in each of five dimensions. The construction of each impact index involves several steps: a) choosing variables with sufficient internal reliability and creating a single 4 kecamatan response b) dividing the reliable variables into five categories: 1) use of coping strategies in response to crisis impacts; 2) food security; 3) employment; 4) education; and 5) health; and c) assigning appropriate weights to chosen variables within each category. Using the chosen variables in each category, we applied the principal components technique to summarize the "signal" contained in a set of variables dealing with a common topic. The first principal component of the set of variables for each category, which is that linear combination of all the variables which captures the most common variation in all the variables, was used as the impact index for each category. The variables included in the principal components analysis for each index were5: * Coping: There were 10 questions about coping strategies and reduced involvement in community activities, such as changes in selling animals or consumer durables and changes in the participation and contributions to arisan and ceremony activities. * Food Security: This index combined nine questions on food security, including population reducing the quantitv and quality of food consumption, population unable to afford the staple food, and indications of malnutrition. * Employment: This combined seven individual variables about the fraction of people working more hours, the number of family members working, migration movements of males and females, and local business conditions. 4 For the 21 questions with three respondents, the responses for each kecamatan were combined into a single value. We adopted the following rules to create single variables for each kecamatan: a) if all three respondents had the same response, we used that response/number; b) if two respondents had the same response, we used that response/number; and c) if all three respondents differed, we used the median value of the three responses. 5 For specific questions used and their factor loadings, see Appendix 2, Table 2-4. 3 * Education: The education index combined nine questions from the school official about enrollments and drop-outs at the primary level, and about parental contributions and teacher attendance. * Health: Seven questions from the dokter puskesmas about patient visits, purchasing power for medicines, availability of medicines and contraceptives, and quality of services were used for the health index. The health index was the most disappointing as the common component of the responses was too small to give the index much value added over the individual questions. For this reason we do not include the maps for health below. III. Provincial and Kabupaten Analysis: Regional Heterogeneity and Urban-Rural Differences The indices were used for two stages of analysis: one at the provincial level. distinguishing rural from urban areas. and another at the district level that distinguishes between rural kabupaten (districts) versus kotamadva (municipaliti. . There are three results that come through very clearly from the analysis at both levels: 1) Urban areas have been much harder hit than rural areas by the crisis. 2) There is enormous heterogeneity in the impact of the crisis with some areas suffering enormously while others appear to be absolutely better off. Three prominent patterns emerge: * Java is hard hit, even in rural areas, most likely because of a high degree of integration between urban and rural areas. * Some of the other islands, particularly large parts of Sumatra, Sulawesi. and Maluku, have experienced minimal negative crisis impact. * Other areas show negative impact, but it is unclear whether problems are economic crisis-related or result from drought (East Timor, NTT, NTB) and fires (East Kalimantan). 3) There is little connection between initial poverty and the magnitude of the shock, with many of the areas that were hardest hit were the relatively well-off areas that had booming modern economy sectors pre-crisis. 4 A. Provincial analysis Within each province the values for urban and rural kecamatan are aggregated separately. This produces 51 regions (two for each of 25 provinces6 and one for DKI Jakarta, which has no rural kecamatan). These are then sorted by level of impact (see Table 1). The rankings for each of the five indices are shown in Table 1, for urban and rural areas by province, using standard area abbreviations (e.g., "Kaltim" in boldface type is urban East Kalimantan, while the rural area of the same province is shown in italics). The same areas are also sorted by an overall impact index based on the average of the rankings of three indices (i.e., food security, employment, and coping). According to the overall crisis impact index, the single hardest hit region is urban East Kalimantan while urban Bengkulu is the least hit. Comparing the 40% hardest hit provinces and 40% least hit provinces reveals very clearly that urban areas are, on average, much harder hit than rural areas. Of the 20 hardest hit areas, 14 are urban, while of the 20 least hit areas, 13 are rural. Notably. all areas of Java are included in the 20 hardest hit areas, regardless of urban/rural status. The only other rural areas included in the 20 most affected areas are East Kalimantan and Aceh. The urban areas that fall into the least hard hit 40% are those in provinces where rural areas are also relatively unaffected, such as Jambi, South Sumatra, Bali, North Sulawesi, Central Sulawesi, Maluku, and Bengkulu. These results imply that the crisis impacts are concentrated in urban areas and on Java. Also, some of the eastern provinces have experienced substantial negative impact in both urban and rural areas. Important exceptions are urban areas in provinces that produce export crops or other foreign exchange earning activities (such as tourism). Likely explanations for this regional pattern include the higher integration of rural and urban areas on Java so that a modern sector crisis (e.g., originating in the banking and corporate sector) would spill over more, the recent drought in Eastern Indonesia, and the higher rupiah incomes (due to the currency depreciation) earned from export crops in regions that escaped the recent drought. Since the principal components analysis produces index numbers that are both re- scaled (to have a mean of zero) and are a complicated aggregate of a variety of indicators it is difficult to make statements about the absolute magnitude of changes. While we can say which are the "least hit" areas, it is difficult to say whether this means they are absolutely better off than a year ago or whether their absolute living standards had deteriorated, but just to a lesser degree than other areas. However, looking at the cross tabulation on specific questions does suggest that some areas are absolutely better off than a year ago. 6Irian Jaya is not included in the analysis because of insufficient response from kecamatan in the province. 5 Table 1. Indices by Province and Rural/Urban Status Rank Overall Food Coping Employment Education Health Impact* Security Strategy I Kaltim Timtim DI Aceh NTB Kalbar Dl Yogva 2 NTT r NTT Kaltim Lampung Timtim Bali 3 DI Aceh Timtim Kalsel Sumut NTT Kalbar 4 NTB N7T Jabar Kaltim DI Aceh | Lampung 5 'Kalsel | NTB Lampung Sumsel Kalbar Timtim 6 Jabar 1 Dl Yogva NTT Jateng Sumut Jambi 7V DI Yogya Dl Aceh Jabar Jabar Timtim DI Yogva 8 Lampung Kaltim Kalbar Jateng Dl Aceh Lampung 9 Kalbar DI Yogva NTB Dl Yogya Sumut NTB 10 Jabar Kalbar Kalteng Kalsel Kaltim Bali 11 Timtim Jatim Sumbar I Jabar Sultra Jabar 12 Jateng Jateng DI Aceh INTT NTT Dl Acelt 13 Sumut Kaltim DKI Jakarta NTB Riau Jateng 14 Jateng Jatim Sumut I Dl Aceh DKI Jakarta Sultra 15 Jatim Kalsel Dl Yogya DI Yogya Kalteng Kaltim 16 Kaltim Jabar Kaltim Jatim DI Yogva Sumut 17 Dl Yogva Jateng Jateng DI Aceh Kalsel Jabar 18 DIAceh Jabar Jateng Jatim - Jabar Jatim 19 DKI Jakarta Lampung Timtim Lampung Riau Kalbar 20 Jatim NTB Kalteng Kalteng Jateng NTB 21 Sumbar Sulsel Kalsel Riau Bengkulu Sumut 22 NTT Sumut Jatim I Sumut Bali Kalteng 23 NTB Lampung Riau j DKI Jakarta I Sumbar Maluku 24 Timtim DKI Jakarta 1Sultra Sulsel i Jateng Riau 25 Kalteng DIAce Iz Jambi Sumsel Jambi Sulut 26 Sulsel Sultra NTB ! Sumbar NTB Sumbar 27 i Kalteng Sultra Jatim Timtim Lampung Sulteng 28 ; Lampung Bali - DI Yogva T Kalbar Sumsel Sumsel 29 Kalsel Kalteng Sumbar Bali Sumsel DKI Jakarta 30 Sultra Sumbar Sulsel L Sumbar Jabar Sulsel 31 Riau Maluku Sulteng Kalteng Sumbar I Kalsel 32 Jambi Kalsel N7T F Bali i Jambi [Jatim 33 Sumsel l Bali Sumsel lKalsel Jatim Sulteng 34 Sultra I Kalbar |Lampung Kaltim Sulut I Ben gkulu 35 Kalbar Sulut IKalbar: Jambi Kalsel l Sultra 36 Sumbar Jambi Timtim Kalbar | DI Yogya ! DI Aceh 37 Sumut Riau Sumut Sulsel Sulsel Sulsel 38 Bali Sulsel Sulsel Sulut | Lampung Jateng 39 Bali Kalteng Sultra IBengkulu Sulteng Maluku 40 Sulsel Sumur Sulut Sultra | Kalteng Bengkulu 41 Sulut Sumsel Sumsel NTT Sultra Kaltim 42 Sulteng Sulteng Riau Sultra I Su1teng Sumbar 43 Maluku Bengkulu Sulteng SBengkulu Susel Sulut 44 Surnsel _ Sulteng Jambi Riau Maluku NTT 45 Sulteng Sumbar Bali Timrim Jatim Riau 46 Bengkulu Sumsel Bengkulu Suleng NTB Jambi 47 Rau Maiku JBali iambi Kaltim NTT 48 Jambi Riau| Maluku | Maluku Sulut | Sumsel 49 Sulut Jambi Sulut Sulut Bali Kalsel 50 Maluku Sulut Bengkulu Sulteng Ben. Timtim 51 Bengkulu I Bengkuiu -Maluku Maluku Maluku Kalten * Based on average if three indices (food security, employment and coping mechanism). (Note: Urban areas are shown in bold, rural areas in italics.) 6 Table 2 chooses just one question about the "change in the population selling household goods to meet basic needs" broken down into provinces, and by rural and urban. This table shows the fraction of kecamatans in these areas that reported that, by this indicator, things were worse (any one of the three possible responses for severity). For hard hit areas in Table 1, as measured by the "coping" index, the fraction reporting ,,worse" on this question is very high, such as 93% in urban Aceh, 87.5% in urban West Java, 82.5% in rural west Java. In contrast, of the areas reported as least hard hit as measured by the "coping" index, only a small fraction reported things were "worse". For instance in rural Bengkulu in only 16.7% of kecamatans were asset sales "worse" while in rural Maluku only 26.1% of kecamatans reported things were "worse". In fact, less than half the kecamatans reported asset sales were "worse" in the rural areas of 12 provinces: North Sumatra. West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, East Timor, West Kalimantan, and West, Central, and South Sulawesi. Table 2. Fraction of Kecamatan in Each Area (Province, Urban, Rural) Reporting "People Selling Assets to Meet Basic Needs" as a Coping Mechanism Was "Worse" (of Severity 3, 4 or 5). Total Urban Rural Worse Worse Worse DI Aceh 65.6 93.3 62.0 Sumatra Utara 49.2 68.6 43.8 Sumatra Barat 52.7 75.0 43.9 Riau 37.2 61.1 30.9 Jambi 32.2 53.9 25.5 Sumatra Selatan 28.4 50.0 26.6 Bengkulu 25.8 57.1 16.7 Lampung 43.9 75.0 36.3 Average 41.9 66.8 35.7 Jakarta 88.4 88.4 Java Barat 83.2 87.5 82.5 Java Tengah 73.8 81.6 72.4 Di Yogya 65.4 84.2 59.0 .lava Timor 76.7 86.0 75.4 Average 77.5 85.5 72.3 Bali 56.9 66.6 53.9 NTB 72.1 77.8 71.2 NTT 54.0 73.3 51.4 East Timor 40.3 46.2 38.7 Kal Barat 33.6 100.0 26.7 Kal Tengah 55.2 87.5 50.0 Kal Selatan 70.2 84.6 68.4 KalTimor 71.4 85.7 66.6 Average 56.7 77.7 53.4 Sulawesi Utara 28.0 35.3 26.4 Sulawesi Tengah 29.0 44.4 26.6 Sulawesi Selatan 38.6 58.9 33.1 Sulawesi Southeast 51.6 55.5 51.0 Maluku 30.0 42.9 26.1 Average 35.4 47.4 32.6 7 B. Kabupaten level analysis In this analysis we distinguish between rural and urban areas on the basis of kabupaten and kotamadya which were ranked according to the impact indices and divided into quintiles. We do two types of analysis, a visual analysis of the maps and a statistical comparison of the proportions of various areas falling into the various quintiles of severity. Maps of crisis impact. Quintiles were then mapped to show the spatial distribution of crisis impact. Figure I shows maps for four indicators: la) Coping, lb) Food security, lc) Employment, and Id) Education. These maps tell a very clear story, most striking in the index of "coping" (which is an index driven mainly by asset sales and reduced participation in community activities). On the nationwide map there are five areas that are in the bottom quintile: * West Java and parts of Central Java, * Urban areas (the kotamadva appear as dark spots on all islands, even more clearly on the insert Java-Bali map), * NTT and parts of East Timor, * Scattered areas of West and North Sumatra (Aceh), and * A huge (but sparsely populated) area of Kalimantan. In contrast the unshaded areas, which are the top quintile, shows: * Wide parts of Sumatra, Sulawesi, Maluku, and Bali among the least hard hit, and; * No part of Java in the top fifth, or "least hit" categorv. These same broad patterns show up in the other general crisis indicators. The employment index shows that, in employment terms. there is an even more "island of Java" (and with more rural kabupaten hit hard) and urban crisis, with a few exceptions in Sumatra. Interestingly, an important element of the employment impact index is people "returning to their village." This means that unemployment per se is not measured and that some areas from which people are migrating are equally hard hit as the nearby receiving areas. This is perhaps why more rural parts of Java and Bali appear hard hit on employment, as a spill over from Java. The food security map, which is driven by indicators of eating fewer and lower quality meals, tells again a "Java ant urban story" but with a couple of twists. First, on this index NTT and East Timor show up as much worse hit than by "coping" or "employment" this is an indication of the natural environment dimensions of the problem. Second, parts of West Java and especially the Jabotabek area appear much less 8 Figure la. Kabupaten Level Crisis Impacts on Coping Strategies a W_ s~~~~~~~~~~~~~~~~~~~~nptw. Mrl Wt , mr.ntw ~IS A 9 __ I INDONESI Ab^' r'> MU . 1i,C k JAWA - IMALI ~~~~ ~-~I -i.~ Figure lb. Kabupaten Level Crisis Impacts on Food JAM- RAs- U5 1 i * . A -. ~~W I, -, j^ *f,' ., >9,~~~~~~~ Figure ic. Kabupaten Level Crisis Impacts on Employment _12 ~ ~ ~~~ '~~~~~~~~~.~~ ~ . - .. _t -t i;' --; r. JAM.- MI ,7r w~~~~~~~ Figure Id Kaupte Lee Crisi Impact onEucto A ~ ~ ~ ~ ) INDONESIA p.X Figure Id. Kabupaten Level Crisis Impacts on Education ~~~~~ i,,,,I.,., -~~~~~ ~~~~~~~~~~~~~ 4_ .-, .' ,-~ 10 hard hit on this dimension, which is likely due to the fact that initial incomes were much higher and hence selling assets would take prominence over reducing food intake as a coping mechanism. That is, since the "food security" index captures not just food production but also lowering food intake, the crisis in richer areas (like Jabotabek) will show up more clearly in selling assets. which is a first resort for those that have them, and only later in food intake declines. Statistical analysis. Examining the hardest hit 40% of tingkat II (district level) clearly reinforces the provincial level results. Kotamadvas have generally been more severely hit than rural kabupatens. For both coping and employment indices, 60% of kotamadyas are in the hardest hit 40% of tingkat II, compared to only 38% of rural kabupatens. For the food index, similar proportions of kotamadyas and kabupatens are ranked in the hardest hit 40% (41% of kabupaten versus 45% of kotamadva). Again, as with the provincial results, areas on Java show a different pattern. Both kabupatens and kotamadvas are classified in the two hardest hit quintiles. (See maps where most of areas on Java falls into the categories of hardest hit (black), especially for coping and employment.) Moreover. the coping index for West Java shows that 65% of the tingkat II in the province fall in the worst hit quintile. while an additional 20% are in the second quintile. Five of the six kotamadyas in the province are in those two quintiles, as well as 85% of the rural kabupatens.7 East and Central Java, as well as DI Yogyakarta, are also experiencing similar patterns. C. Has the crisis hit the poorest areas? It is important to remember that the kecamatan survey measures only changes in welfare (and that qualitatively). The results of the survey indicate relative shifts since the crisis began, but are not able to show current absolute welfare or standard of living levels. A question as yet unanswered is whether the crisis hit hardest in areas that were already poor, making them even worse off. or whether the impact has been more severe in relatively well-off areas. A simple test between various indices and pre-crisis levels of the incidence of poverty, based on SUSENAS 1993-1996. across kecamatans reveals very little correlation, statistically insignificant even with 3,900 observations. The same lack of association between pre-crisis poverty and the magnitude of the crisis impact can be seen by comparing the figures which show changes due to the crisis to the pre-crisis poverty levels. Figure 2 shows the proportion below the standard poverty line in each kabupaten, using SUSENAS data averaged from 1993 and 1996. While most of West Java, and especially the area around Jakarta, has very low poverty rates, the crisis has been enormous in those areas. In contrast, Maluku, with very high poverty rates, has perhaps even benefited from the crisis. The fact that severity of crisis impact does not correlate with pre-crisis conditions makes the design of poverty and crisis response instruments extremely complicated because pre-crisis poverty data can not be relied upon for target responses to the crisis. First, some areas which were not poor have been hit relatively hard and may now be A similar pattern is seen for the employment index. 11 Figure 2. Pre-Crisis Poverty Levels (SUSENAS 1993-1996) F.P(rwirr Itn' w 7fl u1r1ii |~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~T11 t1r- Pnrvy t in A- ,: , ,'- a" .,v,<* R . A jl X ,. 4 . - ~~~~' ' . \; y W * > > __4S t __. *. t , ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~- ., . '_ s!_ ; : . .__~~~4_ JAWA - BALI - poorer than other areas. Second, what is even more complicated is that many previously well-off regions (e.g., Jabotabek) have been hit very hard, but almost certainly have not reached the level of absolute poverty of traditionally poor areas. In this case. targeting the crisis impact is not the same as targeting absolute poverty. Needless to say, this creates considerable tensions between the long-term development programs, which have traditionally been aimed at bringing mainly remote and rural areas into the mainstream of growth, and crisis and emergency programs, which will be targeted to urban and rural areas which were, until the crisis, relatively better off and booming. Table 3 illustrates various mixes of crisis effects and pre-crisis poverty levels: relatively well-off and hard hit (e.g., Jabotabek), relatively poor and hard-hit (e.g., NTT), relatively poor and not hard hit (e.g., Maluku), and relatively well-off and not hard hit (e.g., Bali). 12 Table 3. Examples of Differential Impact of Crisis Relatively well-off pre-crisis Relatively poor pre-crisis Hard-Hit Jabotabek, West Java NTT, East Kalimantan Not Hard-Hit Central Sulawesi, Bali Maluku, Jambi IV. Specific Crisis Impacts Beyond the regional heterogeneity of the overall crisis impact there are also regional differences in the various dimensions of the crisis. For some areas the crisis is mainly an economic crisis feeding through labor markets, while in other areas there is a food security crisis driven by natural conditions. Moreover, people who begin from different absolute levels of income will have different responses to the crisis. For instance, middle-class families will respond to a shock by working more, reducing consumption, drawing down savings, and selling assets, but are unlikely to pull children from primary school or suffer malnutrition. In contrast, people near absolute poverty may not have the luxury of these coping strategies so an equally large shock will force them into more drastic measures, such as primary school drop-out and reduced food intake. An examination of how the various indices differ across locations gives some clues to these effects. Result of different indices across locations is presented in Table 4. Table 4. Comparison Between Indices Hardest hit areas 1 Least hard hit # of urban in 20 # of urban in 20 areas hardest hit areas least hit areas Overall impact E. Kalimantan (U) Bengkulu (U) 14 7 Food security E. Timor (U) Bengkulu (U) 12 9 Coping strategies DI Aceh (U) Maluku (U) 15 5 Employment NTB (U) Maluku (U) 13 6 Education W. Kalimantan (U) Maluku (U) 8 14 Health DI Yogya (U) C. Kalimantan (U) 12 11 13 A. Food security As Table 4 shows, the impact of the crisis on food availability has been more severe in urban areas. Further, the hardest hit rural areas are either on Java or in Eastern I!. ;nesia. These findings are consistent with preliminary data reported by the Indonesia Family Life Surveys (IFLS) 2+8. Real per capita expenditures in rural areas have risen in both mean and median. Meanwhile, in urban areas the mean real per capita household expenditures has fallen by around 40% (although median incomes fall by much less, suggesting a larger proportionate compression in expenditures mainly in the richer half of the distribution). Combined with higher food prices, urban dwellers are therefore suffering from sharply reduced purchasing power. In Eastern Indonesia. food security problems are the result of last year's drought. Other data also project severe food shortages in Eastern Indonesia, especially for maize, cassava, sweet potato, and taro9. In addition, individual variables from the survey reveal the following: Increased price of rice - Not surprisingly, half of the respondents indicated that the price of rice has increased by more than 50%. This result is in line with price reports from around the country. The general CPI from May to September 1998 has increased more than 50%, and increases in the rice price have outpaced general CPI. Reduced availabilitv of basic foodstuff - The mean response to the question of changes in availability and accessibility of staples indicates that there has been a dramatic worsening in food security compared to other indicators. In addition to higher food prices, scarcity of staples results from breakdowns in distribution systems and decreased output in drought-struck areas. B. Employment and wages The impact of the crisis on labor markets and access to economic activity has been more severe in urban areas and in Java (Table 1). Four of the seven provinces in the hardest hit rural areas are on Java. The finding that NTB is hardest hit in terms of employment impact is also consistent with preliminary results from IFLS2+, that show that almost 15% of males working in 1997 had lost their jobs in August 1998 (Rand: Measuring Change in Indonesia, 1998). Individual survey variables show the following: Return migration of males - Both urban and rural kecamatans reported a greater than normal in-flow of males, returning because they lost their jobs elsewhere. This result indicates that there is not necessarily a consistent flow of unemployed urban dwellers to These data are very preliminary, as this survey attempts to track the same house' Ids. At this stage the analysis is based on the 80% of the households that have been identified, almost certainly the other 20%, which the survey team is in the process of tracking, are atypical and hence the sample of those who were located is not representative. 9 Garcia-Garcia and Foley, Drought and Food Securiry, Nov. 1997. 14 rural areas or vice versa. In contrast, there has been a smaller increase in the number of women that have returned to the kecamatans compared to the previous year. The data showed less out-migration. Increase in nominal agricultural wages and output prices - More than 85% of the mantri tani reported that wages had increased for hoeing. Responses also indicate that there has been a less dramatic increase in harvesting wages'0. These imply that while assumption of no-change in nominal income is far off the mark'', the data about price changes suggest there have been substantial declines in real wages in many areas. However, on the revenue side, there has been an intermediate increase in non-rice output prices. Mantri tani responses also point to some increase in farm profitability (more than 50% answered that farm profits had increased compared to last year, although the mean response was no change), indicating that increases in output prices have outweighed rising labor costs in some areas. C. Education As discussed above, analysis of survey results indicate that urban areas have been harder hit than rural areas, according to the indices constructed. The exception is the education index, for which 12 of the 20 hardest hit areas are rural. In terms of the least hit 20 areas, only six are rural. Although the individual responses showed that dropout rates have been low, the index points to some problems in rural areas. One possibility is that the increase in agricultural wages raises the opportunity cost of schooling in rural areas, making it more attractive to pull children out of school. Another possibility is that rural families are "nearer the margin' so that an equi-proportionate shock to incomes will cause more rural than urban families to withdraw children from school. For the individual variables, all respondents indicated that taking children out of primary school was not a common response to crisis impacts'2. This result is further reinforced by the responses given by the school supervisors to the sector-specific questions - almost 85% indicated that there had been no change or a reduction in the number of students that dropped out between Kelas 3 and 4 3. On average, school supervisors also indicated no change in the overall level of dropouts during the last school year (compared to the preceding year) or in the numbers of girls and boys that entered Kelas I this school year (compared to last year). However, for the latter, the distribution of answers is skewed towards a decrease in first-year enrollment rates, indicating that parents may be delaying school starts for younger children while letting older children continue. 10 Data from BPS indicate that agricultural wages have increased 30-35% on average for different tasks, with increases ranging from 10% to 50% in different provinces. One recent publication, for instance, placed the numbers in poverty in Indonesia at nearing 100 million in 1998, which essentially assumed that nominal incomes would remain unchanged while prices climbed 80%. This is obviously both analytically unsound and empirically false. 12 The common question did not differentiate between primary or secondary school students. 3 Historically, dropout rates at the primary level are highest between these two grades. There was no difference in responses for girls or boys. Other data sources indicate the problem with drop out is at the junior secondary level and hence our data has little to contribute on this issue. 15 D. Health As shown in Table 4, the impact of the crisis on health is more equally distributed between rural and urban areas. Twelve of the 20 hardest hit areas are urban, and 11 of least hit provinces are also urban. However, the health index is consistent with the other indices in that Java has been severely affected. Except for DKI Jakarta, the four other provinces on Java are in the 20 hardest hit provinces. The responses by the dokter puskesmas show that magnitudes of increases in prices and reduced availability of contraceptives are greater than most of the other impacts included in the survey. The distribution of answers indicates that higher prices may be a bigger problem than availability. E. Ranking of problems Respondents were asked to rank the most to least severe crisis impacts from a list of eight potential problems. From these we identified as "priority" those ranked as the top three of the eight. Response patterns suggest some fairly strong and consistent opinions about which crisis impacts have been most severe. For example, the relatively high ranking of "unemployment" (particularly in urban areas), "finding staple food" and "loss of income" can be compared with the relatively low ranking of "children dropping out of school" or "reduction in health services" (see Table 5). Overall the loss of the real purchasing power of incomes, which is a combination of less employment and rising prices of staples, is the predominant concern. Table 5. Ranking of Priority Problems in Kecamatan % of respondents ranking as priority problem Problem Urban Rural Total Unemployment 6.5 20.1 27 Finding staple food 3.5 17 21 Loss of income 4.5 20 25 Children dropping out of school .3 1.5 2 Reduction in health service .3 1.4 2 16 F. Comparison with other studies There are three other surveys which are producing preliminary results comparing pre- and post-crisis levels of impact: The Indonesia Family Life Surveys (IFLS) 2+. the "100 Villages" surveys, and a recent survey of schools. None of these have nationwide coverage so the comparison of the results is difficult. But the main result of the kecamatan survey are largely consistent with preliminary data from other surveys. Estimates of mean expenditures for rural and urban areas from the IFLS2+ show that mean per capita household expenditures in rural areas have increased in the past year, while those in urban areas have fallen by almost 40%. UNICEF's 100 Villages survey also shows that ownership of durables has increased in rural areas, which reinforces the conclusion that urban areas have been more severely affected across the board. The IFLS2+ survey shows that primary school enrollment has increased for both girls and boys in urban and rural areas. which is consistent with the result that dropout rates have remained the same or dropped slightly. Results from a very recent survey of schools carried out by the World Bank and Ministry of Education and Culture also reveals that overall enrollment at the primary level does not appear to be deviating from their past trend and fell by only 1.5% in 1998/99, about average from the previous four years. The survey also shows that urban Central Java, urban Maluku, and Jakarta experienced significant declines of junior secondary level, while rural South Sulawesi saw an 8% increase. V. Conclusions and Recommendations Of course this survey reconfirms the obvious: Indonesia's population is experiencing enormous social impacts from a severe economic and financial crisis. But what is new is empirical evidence at a national level that suggests the crisis impact is very heterogeneous, both between urban and rural areas and across regions. Some regions have been hard hit, others are relatively less affected. and some are even booming. Ranking of urban and rural areas according to indices of crisis impacts shows that. in general, crisis impacts have been more severe in urban areas. For four of the five indices, the concentration of urban areas in the 20 hardest hit is considerably higher than the concentration of rural locations (12-15 out of 20). Likewise, the number of rural areas in the 20 least affected areas is higher for the food security, coping strategy, and employment indices (11-15 out of 20). The second pattern is that both urban and rural areas on Java have been hard hit by the crisis. All areas of Java rank within the top 20 (of 51) most hard hit regions for the overall impact index (and within or close to the 20 worst affected for the other indices). Other areas that consistently turn up close to the top of the ranking are urban areas in East Kalimantan, West Kalimantan, North Sumatra, DI Aceh, Lampung, NTB, NTT, and East Timor. 17 At the other end of the scale, some provinces seem to have felt relatively little impact of the financial crisis in both urban and rural areas. Notably, North Sumatra, Bengkulu, Central Sulawesi, and Maluku fit into this category. In addition, rural areas in South Sulawesi, Jambi, and Riau also consistently rank among the 20 least affected. Designing specific programs that respond to the crisis is complicated and must balance several objectives, but these data at least suggest that crisis response efforts should therefore target urban centers. in general, and particularly those that have had the relatively largest drops in welfare levels. such as urban areas in East and West Kalimantan, North Sumatra. DI Aceh. Lampung. NTB, NTT, and East Timor. Both rural and urban areas on Java should be included in crisis programs. It is important to note that the magnitude of crisis impacts does not correlate with pre-crisis levels of povertv. This result points to the need to reassess data and assumptions about poverty distributions. While difficult to draw in practice. there is an analytical distinction between targeting for crisis programs and for long-term poverty programs. In designing longer-term poverty interventions there is a deeper, and unresolved. question of whether the crisis has changed fundamental dynamics. and hence calls for a rethinking of long-term poverty programs. or is merely a temporary shock. In terms of the kinds of interventions that should be designed for the crisis, more detailed analysis of the cost-effectiveness in practice of various types of interventions is required. But there appears to be a need for continued efforts to channel rice and other basic foods to needy areas, workfare programs in urban areas, efforts to maintain health services, provision of free or subsidized contraceptives. and continuation of the scholarship program. The consistency of the results with other surveys also show that this tvpe of quick turnaround. largely qualitative. instrument can give a good overview of degrees of crisis impact in different areas and trends in overall changes. Although results require further investigation for design of crisis response programs. this kind of survey can point response efforts in the right direction. Because of its low cost and quick turnaround. a similar survev could also be repeated after six months in an effort to provide on-going monitoring of crisis impacts across Indonesia. 18 APPENDIX 1: DETAILS OF THE SURVEY The survey was distributed through the Central Bureau of Statistics' (BPS) existing network of mantri statistik, who represent BPS at the kecamatan level. The mantri statistik are present in almost every kecamatan and their regular responsibilities include maintaining and updating population, education, and other local data bases. The survey was sent to the BPS office at the kabupaten level, where mantri statistik from each kecamatan picked them up along with instructions to implement the survey and return it to the kabiupaten office within a week. Completed surveys were mailed back to BPS headquarters for data entry and processing. Total turnaround (including administering the survey and processing the data) was approximately one month. Analysis took place over a three week period. In rural areas, the three respondents were: * mnantri tani -- As the kecamatan representative for the Ministry of Agriculture. the mantri tani supervises all agricultural extension workers and, through them. gets regular reports on agricultural production. areas planted. and farm technologies used throughout the kecamatan. - dokter puskesmas -- The dokter putskesmnas is the head of the kecamatan health center and sees patients from different parts of the kecamatan. Some dokter puskesmas also pay regular visits to the smaller supporting health centers in the sub-district. • penilik sekolah -- The penilik sekolalb. or school supervisor, meets regularly with principals and teachers from across the kecamatan. In urban areas. the mantri tani was replaced by the Kepala Seksi PMD, who is in charge of coordinating development activities for the kecamatan. In the instructions accompanying the questionnaire. the respondents were asked to consult with their colleagues (i.e.. agricultural extension worker. nurses and midwives, and principals and teachers) before completing the questionnaire to ensure that answers were representative of the kecamatan as a whole. Translations of the questionnaires used are attached. Five answer choices are given for each of the scaling questions - three are gradations of a worsening in conditions, one registers no change, and one designates an improvement. This particular range of answer choices was based on the need to balance survey objectives of measuring degrees of change with logistical concerns of questionnaire design. Partners at BPS advised that when confronted with several answer choices, respondents would be likely to answer no change. It was therefore decided to limit the scale to five choices. Given this constraint, it was necessary to ensure that there was enough gradation to capture degrees of worsening. In other words, if the crisis had led to a worsening of conditions everywhere, the survey had to register nuances in this worsening, so that comparisons were possible. This consideration led to the decision to include three degrees of worsening and only one choice for improvement, as areas that had benefited from the crisis were of less concern. Instructions were also provided along with the questionnaires, describing the purpose of the survey, defining terms used, and how to register responses. Whenever possible, quantitative guidelines for the degree of change associated with each answer were provided. It was not possible to provide ranges for each of the questions, however, as quantitative data on changes were not always available and, even when they were, they varied greatly across regions. Final Questionnaire: Agricultural Extension Worker I2 3 4 5 I Total number of male residents that retumed to vour A lot i lore Slightly Same Fewer kecamatan during Jan-Aug 98 because they lost their jobs more more elsewhere (compared to Jan-Aug 97) 2 Total number ot fr,naie residents that rcturned to vour A- lot More Slightly Same Fewer kecamatan dunng Jan-Au )98 because thev lost their jobs more |more elsewhere Icompared to Jan-Aug 97) 3 Total number of nale residents that hav c-one to a different A lot %lore Slightly I Same Fewer kecamatan during Jan-Aug 98 to look tor a job (compared more more ito Jan-AuL 97) 4 Total number o female residents that have gone to a A lot More Slightly j Same Fewer different kecamatan during Jan-Aug 98 to look for a job more I more I i___ (compared to Jan-Au,c _97) _ ll 5 Total number or residents replacing staples with lower A lot Mlore Sli-htlv Same Fewer q quality foods (roots, feed. etc.) duonn Jan-Aug 98 inore more i__ I compared to Jan-Aue 97 l I 6 I Total number of residents taking on additional tasks and/or A lot Niore Slightly Same Fewer working longer hours during Jan-Aug 98 compared to Jan- more more I Aug '97 _ _ _ 7 ITotal number of residenits selling assets (jewelry, livestock. A lot More Slightly v Same Fewer TV, etc.) to meet daily needs during Jan-Aug 98 compared more more to Jan-Aug 97 Total number of residents leavin. orisns because they A lot More Slightly I Same Fewer cannot aflord to participate during Jan-Aug -98 compared to more more E___ Jan-Auc''7 19 9 Compared to August 97. amount of unisan payout now A lot less Less Slightly Same More I Number of reli2ious/traditional celebrations during Jan-Aug A lot less Less Slightly Same More '98 compared to Jan-Auc -97 Less II Contributions to reliuious/traditional celebrations durio A lot less Less Slightly I Same More Jan-Au )98 comnared to Jan-Aug (J7 Less I2 Availabilit. o1 stapic lood (rice. corn. ctc.) in stores and A lot Ic.s. L.ss Slightlv Same \lore markets now comnared to Auteust -97 Less 13 Total number of residents that cannot attord to buy stapic A lot More Sli--htlv Same Fewer toods (rice. corn. etc.) during Jan-Aug 98 compared to Jan- more more Aug 97 14 Number of thefts and burglaries during Jan-Aug '98 A lot More I Slightly I Same Fewer i compared to Jan-Aug 97 more more' 15 Below are a series of problems that may be affecting your kecamatan. Please rank them according to how severely thev are affecting your kecamatan (I= most serious: 2=second most serious: 3=third most serious. etc.) l Unemployment Availability of staple foods l Students dropping out of school l Reduced incomes __| Increased crime e_! Crop failure Reduced health services Hunger l___ Other: ..............._....... l O ther: ...................... 16 Below are a series of possible ways residents in your kecamatan may be responding to the crisis. Please indicate the answer that best describes the frequency of each response by residents in your kecamatan. . Substituting lower quality foods for staple foods Very frequent Frequent In-frequent Rare Never Reducing the number of meals per day Very frequent Frequent In-frequent Rare Never Reducing the quantity of food at each meal Very frequent Frequent In-frequent Rare Never Selling livestock Verv frequent Frequent In-frequent Rare Never Selling other assets Very frequent Frequent In-frequent Rare I Never Leaving arisan Very frequent Frequent In-frequent Rare I Never Withdrawing children from school Very frequent Frequent In-frequent Rare Never Sending additional family members to work Very frequent i Frequent In-frequent Rare Never Not holding celebrations Very frequent Frequent In-frequent Rare Never I Reducing contributions to celebrations Very frequent Frequent In-frequent Rare Never 17 In the last year, have there been special programs in your Yes No kecamatan to help people deal with the crisis? I 18 If yes, what was the most widespread program'? ..........ad............................... 19 How effective was this program in helping people affected Very beneficial I Somewhat I Not benelicial by the crisis? I beneficial l l 20 Compared to August '97, the price of medium quality rice A lot I Higher Slightly Same Lower now: higher higher 21 Compared to August '97, the number of stores that are A lot Higher Slightly Same Lower closed down now: hicher higher l 22 Compared to average harvests, the most recent harvest of A lot Lower Slightly Same Higher I the staple crop (specify: ........... ) was: lower lower ' 23 Compared to August '97. the average output price of the A lot Higher Slightly Same I Lower staple crop now: | higher higher l l 24 Compared to the same harvest last year. the profitability of A lot Lower Slightly Same Higher the most recent harvest of the staple crop was: lower lower 25 Compared to August '97, farm wages for hoeing now: A lot Lower Slightly Same f Higher ~~~~~lower Ilower 26 t Compared to August '97, farm wages for weeding now: A lot I Lower Slightly Same Higher I_ _ _ _ _ _ _ _ _ _ _ _ _ __ I I lower lower l l _ I 27 Average output of staple crop (specify: ........ ) during last 1 1998 l harvest compared to same planting season in 1997 1997 _______._l I___I (quintal/ha): 28 Average market price of medium quality nce in August '98 August 1998 ...... l and August '97 (RpAkg): August 1997 29 Average output price of staple crop (specifv i. in August 1998 l |__| August '98 and August '97 (Rp/kg): August 1997 30 Farm wages for hoeing in August '98 and August '97 August 1998 l (Rp/dav): AuLust 1997 1 31 Farm wages for weeding in August'98 and August'97 i August 1998 l |__I (Rp/day): | August 1997 .t.l Final Questionnaire: Director of Education Office If 2 3 [ 4 5 1 Total number of male residents that returned to your A lot More Slightly Same Fewer kecamatan during Jan-Aug '98 because they lost their jobs more more elsewhere (compared to Jan-Aue 97) 2 2 Total number of fenale residents that returned to vour A lot More Sliwhtlv Same F-wer kecamatan during Jan-Aug '98 because they lost tleir jobs more !more elsewhere (compared to Jan-Aug '97) _ 3 Total number of male residents that have gone to a ditferent A lot Mlore Slightly Same Fewer kecamatan dunng Jan-Aug '98 to look for a job (compared more more to Jan-Aue '97) 4 Total number of female residents that have gone to a A lot NMore i Slightly I Same Fewer Ldifferent kecamatan during Jan-Aug '98 to look tor a job more more (compared to Jan-Aun '97) I 5 otal number of'residents replacing staples with lower A lot More Slightlv Same Fewer Lquality foods (roots. Ieed. etc.) durinl Jan-Au '98 more more - . compared to Jan-Au- '97 ( Total number of residents takinu on additional tasks and/or A lot lore Slightly Same Fewer working longer hours during Jan-Aug '98 compared to Jatt- more more Au2'97 7 Total number of residents selling assets (jewelry, livestock. A lot More Slightly Same Fewer TV, etc.) to meet daily needs dunng Jan-Aug '98 comnared more more to Jan-Aua '97 __ '__ S Total number of residents leaving ar-isooZs because they A lot More Slightlv Same Fewer cannot atford to participate during Jan-Aug '98 compared to more more I Jan-Aug'97 9 Compared to August '97. amount of toisein pLxoLit now A lot less l Less Slightly Same Nlore Less :_._ i I ( Number of reliaiousAtraditional celebrations durine Jan-Aug A lot less Less Slightly Same More '98 compared to Jan-AuL '97 Less I I Contributions to religious/traditional celebrations duritiw A lot less Less Slightly Same More Jan-Aug '98 compared to Jain-ALo '97 Less 1 2 Availability ot'staple foods (rice, comr etc.) in stores and A lot less Less Slightly Same Mlore markets now compared to Aueust '97 Less 13 Total number of residents that cannot afford to buy staple A lot More I Slightly Same Fewer foods (rice. com. etc.) during Jan-Aug '98 compared to Jan- more I more Aug 97 ____I _ 14 Number ot thefts and burglaries during Jan-Aug '98 A lot ' lore Slightly Same Fewer compared to Jan-Aut- '97 more more I Below are a senes of problems tlIat may be attecting *Lour kecamatan. Please ranik them according to how scverei thev are at'fectieL your kecamaan i i = most seCrous: 2=>econd msot ierious: 3=third most serious. etc.) Unemployment Availability ot staple toods Students dropping out ot school Reduced incomes Increased cnme Crop failure Reduced health services Hunger Other : .-. Other:...................... iv 16 Below are a series of possible ways residents in your kecamatan may be responding to the crisis. Please indicate the answer that best describes the frequencv of each response bv residents in your kecamatan. Substituting lower quality foods for staple foods Very frequent Frequent In-frequent Rare Never Reducing the number of meals per dav Very frequent Frequent In-frequent Rare Never Reducing the quantity of food at each meal Very frequent i Frequent In-frequent I Rare Never Selling livestock Very trequent Frequent In-frequent i Rare X Never Selling other assets Verv frequent Frequent In-frequent E Rare i Never Leavin- arisan Verv frequent Frequent In-frequent i Rare I Never Withdrawinm children from school j Verv frequent E Frequent In-frequent j Rare | Never Sendine additional familv members to work Verv frequent i Frequent In-frequent | Rare Never Not holding celebrations I Vet- frequent i Frequent In-frequent I Rare | Never ! ____ Reducing contributions to celebrations Vetv frequent Frequent In-frequent I Rare Never | 17 In the last year, have there been special programs in your Yes No kecamatan to help people deal with the crisis'? 18 If ves. what was the most widespread program? ......I............................................ 19 How effective was this program in helping people affected E Very beneficial Somewhat Not beneficial i___ - by the crisis'? beneficial i 20 Compared to August '97. the pnce of medium quality nce A lot Higher Slightly Same i Lower now: higher | higher j 21 Compared to August '97. the number ot stores that are A lot RHiher ISlightly I Same Lower closed down now: higher | higher _ _ I__ 22 Number ot' female students trom pnmarv class 3 that did not I A lot Higher Sli6htiv Same Lower ! continue to class 4 for 1998/99 school year compared to I higher i higher 1997/98 school vear: _ _ 23 Number of male students from pnmary class 3 that did not A lot j Higher Slightly Same Lower i continue to class 4 for 1998/99 school year compared to higher higher S 1997/98 school year: _ 24 Compared to the 1997198 school year, the number of female A lot Lower Slightly Same Higher students entering pnmarv class I in 1998/99 school vear: lower lower l 25 Compared to the 1997/98 school year. the number of male A lot Lower Slightly Same Higher students enterinn primary class I in 1998/99 school year: lower i lower l l 26 Compared to the 1996/97 school year. the number offemale I A lot 1 Higher Slightly Same Lower students that dropped out during 1997/98 school year: higher i higher l l 27 Compared to the 1996/97 school year, the number of male A lot Higher Slightly Same Lower students that dropped out during 1997/98 school year: higher higher- 28 Compared to the 1997/98 school year. the size of parents A lot Lower Slightly Same Higher contributions in 1998/99 school vear: lower lower ' 29 Compared to the 1997/98 school vear, the attendance of A lot Lower Slightly Same Higher teachers in 1998/99 school vear: lower i lower I 30 P The number ol'students commm to school hungry ounng- - A lot Higher SliEhtly Same Lower Jan-Aug 98 compared to Jan-Au2 9? higher I hieher ! 31 Number ot' emale students from pnmary class 3 that did not 1998/99 I continue to class 4 for 1998/99 school year and 1997/98 1997/98 j | school vear: 32 Number ot' male students from pnmarv class 3 that did not 1998/99 i continue to class 4 for 1998/99 school year and 1997/98 1997/98 school year: 33 Number of female students entering primarv class I in 1998/99 1998/99 school year and in 1997/98: 1997/98 34 Number of male students entering primary class I in 1998/99 1998/99 school year and in 1997/98: 1997/98 35 Number offemale students that dropped out during 1997/98 1997/98 school year and during 1996/97: 1996/97 36 Number of male students that dropped out during 1997/98 1997/98 school year and dunng 1996/97: 1996/97 V Final Questionnaire: Public Health Center Doctor = _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 2 3 4 5 1 Total number of male residents that returned to your A lot More Slightly Same Fewer kecamatan during Jan-Aug '98 because they lost their jobs more more elsewhere (compared to Jan-Aug '97) 2 Total number of female residents that returned to your A lot More Slightly Same Fewer kecamatan during Jan-Aug '98 because they Iost their jobs more more elsewhere (compared to Jan-Aug 97) l _ 3 Total number of male residents that have gone to a different A lot More Slightly Same Fewer kecamatan during Jan-Aug '98 to look for a job (compared more more to Jan-Aug '97) 4 Total number of female residents that have gone to a A lot More Slightly Same Fewer different kecamatan during Jan-Aug '98 to look for a job more more (compared to Jan-Aug '97) i _ 5 Total number of residents replacing staples with lower A lot More Slightly Same Fewer quality foods (roots, feed. etc. during Jan-Aug '98 more more compared to Jan-Aug '97 i 6 Total number of residents takintL on additional tasks and/or A lot More Slightly i Same Fewer working longer hours during Jan-Aug '98 compared to Jan- more more Aug '97 _ ! l 7 Total number of residents selling assets (jewelrv, livestock. A lot More Slightly Same Fewer l TV, etc.) to meet daily needs during Jan-Aug '98 compared more more |___ to Jan-Au2 '97 _ , I 8 Total number of residents leaving arisans because they A lot More Slightly Same Fewer cannot afford to participate during Jan-Aug '98 compared to more E more Jan-Aug '97 l 9 Compared to August '97. amount ot'f risan payout now A lot less Less Slightly Same More t | = :~~~~~~~~~~~~~~~~~~ Less I 10 Number of religious/traditional celebrations during Jan-Aug A lot less Less Slightly Same More I '98 compared to Jan-Aug '97 l Less S M I I Contributions to religious/traditional celebrations dunng A lot less Less Slightly Same More Jan-Aug '98 compared to Jan-Aug '97 l j | Less l j 12 Availability of staple foods (rice, corn, etc.) in stores and A lot less Less Slightly I Same More markets now compared to August '97 l Less 1 3 Total number of residents that cannot afford to buy staple A lot More Slightly Same Fewer foods (rice, corn, etc.) dunng Jan-Aug '98 compared to Jan- more more i _ 1.00) Variable Communality i Factor Eigen-value Pct. of Factor Matrix Variance Factor 1 Factor 2 0. 05 0.29255 1 2.99148 32.3 0.53538 0.07696 0.12 0.51952 4 2 1.05991 11.8 0.54712 0.46923 0.13 0.55486 0.67787 0.30879 0.1t6-1 0.61648 0.68407 -0.38539 0.16-2 0.64999 0.68745 -0.42119 0.16-3 0.60955 0.66839 -0.40399 0. 20 0.36236 0.41807 0.43310 0. 26/3 0.14045 0.32832 0.18071 0 Q.30/4T 0.22523 1 0.44286 3 0.17059 11 ! ~~~~~~~~~Reproduced Correlation Matrix Variable Q. 05 0.12 0.13 0.16-1 0.16-2 0.16-3 0.20 0.26/3 0.30/4 0. 05 0.29255 -0.12459 -0.06192 0.01026 -0.13856 -0.12316 -0.14213 -0.08574 ! -0.02492 0.12 0.32903 0.51952 -0.10386 0.02247 0.05534 0.02919 -0.20841 -0.12439 -0.16226 0.13 0.38668 0.51577 0.55486 -0.03892 -0.01048 -0.02761 -0.17305 -0.10970 -0.11704 0.16-1 0.33658 0.19344 0,34471 0.61648 -0.17172 -0.18787 0.01756 -0.01094 -0.03111 0.16-2 0.33563 0.17849 0.33595 0.63259 | 0.64999 -0.12247 0.06146 -0.01300 -0.02106 0.16-3 0.32675 0.17613 0.32833 0.61292 0.62964 0.60995 0.10704 0.01443 -0.06325 0.20 0.25716 0.43196 0.41714 0.11908 0.10499 0.10447 0.36236 -0.09779 -0.11817 0. 26 / 3 0.18969 0.26443 0.27836 0.15495 j 0.14959 0.14644 0.21553 0.14045 -0.10315 0. 3014 0.25023 0.32235 0.35288 0.23721 1 0.23260 J 0.22709 0.25903 0.17710 0.22838 Note: The lower left triangle contains the reproduced correlation matrix; the diagonal, reproduced commonalities and the upper right triangle residuals between the observed correlations and the reproduced correlations. iii Table 3-2 Principal Components Analysis Access to Education Number of Valid Pairs After Pairwise Deletion Variable 0.16-7 0.22/4 0 0.23/4 0.24/4 i Q.25/4 0.26/4 0.27/4 0.28/4 0.29/4 0.16-7 3755 0.22/4 3727 3727 Q. 23/4 3727 3727 3727 0.24/4 3727 3727 r 3727 3727 0.25/4 3727 3727 3727 3727 3727 _______ 0.26/4 3727 3727 3 3727 3727 3727 Q. 2714 3727 3727 3727 3727 3727 3727 3727 0.28/ 4 3727 3727 3727 3727 3727 3727 3727 3727 0. 29/4 3727 3727 3727 3727 3727 3727 3727 3727 Correlation Matrix Variable 0.16-7 Q.22(4 0Q.23/4 Q.24/4 Q.25/4 0.26/4 [Q.2714 0.2814 Q.2914 0.16-7 1.00000 _ 1 _ i 0.22/ 4 0.13877 1.00000 0.23/4 0.14107 0.71552 1.00000 0.24/4 0.06029 0.15283 I 0.16229 [ 1.00000 0.25/4 0.08232 0.12009 0.11389 0.49414 1.00000 0.26/4 0.15369 0.39766 0.41510 0.17473 i 0.16578 1.00000 Q.27/4 0.13347 0.39524 0.38866 0.13644 0.11562 0.60431 1.00000 0. 28/4 0.11356 0.09207 0.06677 0.04350 0.06833 0.13064 0.13239 1.00000 0. 29/4 0.07969 0.22964 0.24572 0.09008 ] 0.10226 | 0.22361 0.25665 0.11448 1.00000 Final Statistics and Factor Matrix (Factors with Eigenvalue > 1.00) Variable Communality Factor Eigen-value | Pct. of Factor Matrix Variance _ Factor 1 Factor 2 Factor 3 0. 16-7 0.36765 1 2.83998 31.6 j 0.29660 0.00508 0.52882 0.22/4 0.68762 2 1.35374 15.0 0.75584 -0.22333 -0.25778 0. 23 / 4 0.70478 3 1.05270 j 11.7 0.76053 -0.22331 1 -0.27660 0.24/4 0.74607 _ _ _ 1 0.38726 0.76230 -0.12248 0.25/4 0.75197 0.35937 0.78883 -0.02404 0. 26/4 0.55614 _____ I 0.73704 -0.11239 0.01673 0.27 / 4 0.54974 - 0.71849 -0.18003 0.03321 0.28/4 0.63281 0.23783 0.01611 0.75894 0. 29 /4 0.24964 0.45517 1 -0.07297 0.19272 iv Reproduced Correlation Matrix Variable 0.16-7 0.22/4 Q.23/4 1 0.24/4 0Q.25/4 0.26/4 Q.2714 Q.28/4 0.29/4 0.16-7 0.36765 0.05203 0.06290 1 0.00632 -0.01557 -0.07320 -0.09629 -0.35840 -0.13886 0.221 4 0.08673 0.68762 0.01951 -0.00122 0.01843 -0.18021 -0.17946 0.10974 -0.08101 0.2314 0.07817 0.69601 0.70478 1 0.00412 0.01009 -0.16590 -0.18878 0.09941 -0.06343 Q. 24 1 4 0.05397 0.15404 0.15817 0.74607 -0.24929 -0.02297 -0.00051 0.03207 -0.00696 0.25/4 0.09788 0.10165 0.10380 i 0.74344 0.75197 -0.01003 0.00023 -0.01160 0.00089 Q 26 / 4 0.22689 0.57787 0.58101 0.19771 0.17581 0.55614 0.05397 -0.05553 -0.12329 0.2714 0.22976 0.57471 0.57745 0.13694 0.11539 0.55034 0.54794 -0.06080 -0.08992 0. 2814 0.47196 0.01947 -0.03264 0.01143 0.07993 0.18618 0.19319 0.63281 -0.13886 0. 29 / 4 0.23655 0.31065 0.30916 0.09704 0.10138 0.34690 0.34657 0.25334 0.24964 Note: The lower left triangle contains the reproduced correlation matrix; the diagonal, reproduced commnonalities and the upper right triangle residuals between the observed correlations and the reproduced correlations. v Table 3-3 Principal Components Analysis Coping Number of Valid Pairs After Pairwise Deletion Variable 0.7 Q. 8 0. 9 0. 10 0Q.11 Q. 16-4 I Q. 16-5 Q. 16-6 Q. 16-9 Q. 16-10 Q. 7 13755 _ _ _ _ _ 0. 8 3755 3755 Q. 9 3755 3755 3755 1 Q. 10 3755 3755 3755 1 3755 1_I_____I 0.11 3755 3755 3755 3755 3755 Q.16-4 3755 3755 3755 3755 3755 I __ _ 0. 16-5 3755 3755 3755 1 3755 3755 3755 3755 0. 16-6 3755 3755 3755 3755 3755 3755 3755 3755 I0.169 3755 3755 3755 3755 3755 1 3755 3755 3755 3755 0.16-10 3755 3755 3755 3755 3755 1 3755 3755 I 3755 3755 3755 Correlation Matrix Variabl 0.7 0.8 0. 9 0. 10 Q.11 Q.16-4 0.16-5 Q.16-60 Q.16-9 0.16-10 I e 0 .7 1.00000 _ ___ 0Q.8 0.45408 1.00000 _ - ___ I 9 10.32616 0.47981 1.00000 0.10 0.35196 0.36644 0.38863 1.00000 I 0.11 0.36549 0.37184 0.39000 0.64114 11.00000 I _ _ _I Q. 16-4 0.30701 0.19953 0.18419 0.21945 0.21805 1.00000 0. 16-5 0.46942 0.31419 0.24882 0.31539 j 0.30499 0.51312 1.00000 I I 0. 16-6 0.28710 1 0.43018 0.37066 0.28471 0.37066 1 0.29529 1 0.44337 11.00000 1 1 0.16-9 0.23473 1 0.26287 j 0.22241 0.40296 1 0.22241 10.23017 0.34948 0.37051 i 1.00000 0. 16- 0.26036 1 0.24845 0.19595 0.35050 1 0.19595 ! 0.24290 0.37582 0.37964 1 0.63905 1 1.00000 Vl Final Statistics and Factor Matrix (Factors with Eigenvalue > 1.00) Variable Communality Factor Eigen-value Pct. ot Factor Matrix Variance Factor 1 Factor 2 Factor 3 Q. 7 0.54188 1 4.08521 40.9 0.63533 | -0.17343 0.32887 0.8 0.60498 2 1.17254 11.7 0.64911 -0.40603 0.13701 0. 9 0.59874 3 1.11272 11.1 0.59190 -0.49738 0.03171 0.10 0.64311 0.68882 -0.23259 -0.33843 0.11 0.63799 0.68631 -0.25864 *0.31636 0.16-4 0.64692 0.51127 0.34144 0.51860 0.16-5 0.71124 0.67787 0.30321 0.41206 0. 16-6 0.45369 0.64019 0.42030 .0.41829 0. 16-9 0.76145 0.63925 0.46437 -0.36904 Q.16-10 0.76047 _ __l Reproduced Correlation Matrix Variable 0.7 Q. 8 0. 9 0.10 0. 11 Q. 16-4 q. 16-5 0.16-6 0.16-9 0.16-10 0. 7 0.54188 -0.07380 -0.14657 -0.01470 | -0.01135 -0.12915 -0.04419 -0.15799 0.03845 0.05613 Q. 8 0.52788 1 0.60498 -011070 -0.12875 -0.13532 -0.06476 -0.05916 0.02477 0.07528 0.07262 0. 9 0.47274 0.59051 0.59874 -0.12404 | -0.13483 0.03494 -0.01467 0.02592 0.06579 0.06025 Q. 10 0.36666 0.49519 0.51267 0.64311 0.00118 | 0.12220 0.05844 -0.09481 -0.08182 -0.10672 0.11 0.37684 0.50716 0.52484 0.63996 0.63799 0.11954 0.04854 -0.08322 -0.11110 -0.06734 0.16-4 0.43616 0.26429 0.14924 0.09725 0.09852 0.64692 -0.15068 -0.14319 -0.02372 -0.0511 0.16-5 0.51360 0.37336 0.26349 0.25696 0.25645 0.66379 0.72124 -0.08504 -0.03957 -0.04625 0.16-6 0.44509 0.40541 0.34474 0.37952 0.37864 0.43848 0.52841 0.45369 -0.02586 -0.02718 0.16-9 | 0.19628 0.18759 0.15662 I 0.48478 j 0.46299 0.25389 0.38905 0.39637 0.76145 -0.11973 0. 16-10 0.20423 0.17583 0.13570 1 0.45722 0.43536 0.29400 0.42206 0.40682 j 0.75878 0.76047 Note: The lower left triangle contains the reproduced correlation matrix; the diagonal. reproduced ommunalities and the upper right triangle residuals between the observed correlations and the reproduced correlations. vii Table 3-4 Principal Components Analysis Access to Economic Activity Number of Valid Pairs After Pairwise Deletion Variable I 0.1 0.2 0.3 Q. 4 0.6 Q.16-8 Q. 21 0.1 3755 I 0. 2 3755 3755 __. 0. 3 3755 3755 3755 0. 4 3755 3755 3755 3755 0. 6 3755 3755 3755 3755 3755 0. 16-8 3755 3755 3755 3755 I 3755 3755 0. 21 3755 3755 3755 3755 3755 3755 3755 Correlation Matrix Variable 0 0.1 1 0.2 0.3 0.4 1 0.6 f 0.164 0.21 ble Q.~1 Q.203 .1 8r ~ 0.1 1.00000 I __ __ 0. 2 0.63876 1.00000 0.3 0.07281 0.03638 1.00000 0. 4 0.07865 0.08504 0.62291 1.00000 0.6 0.01481 0.01155 0.13366 0.07412 1.00000 0. 16-8 0.09169 0.08025 0.08346 0.06069 0.12487 1.00000 0.21 0.17186 0.17202 0.11179 0.10897 0.06969 0.10396 1.00000 Final Statistics and Factor Matrix (Factors with Eigenvalue > 1.00) Variable Communality Factor Eigen-value Pct. of T Factor Matrix Variance 0. 1 0.78705 1 1.92247 1 27.5 Factor 1I Factor2 Factor3 } O.1 0.78705 1 1.92247 1 27.5 0.66171 -0.57610 -0.13151 a0.2 0.79093 2 1.51749 21.7 0.64579 -0.59606 -0.13640 0.3 0.80829 3 1.07800 15.4 0.60237 0.64130 -0.18486 0.4 0.81083 0.60935 0.60413 -0.27304 0.6 0.53038 ] 0.22834 1 0.21296 0.65795 0.16-8 0.53194 0.29697 0.01258 0.66603 0. 21 0.25854 - 0.43941 -0.09251 0.23853 viii Reproduced Correlation Matrix Variable 0.1 0 2 0.3 0.4 0. 6 0.16-8 0.21 0.1 0.78705 -0.14989 | 0.01936 -0.01243 0.07293 -0.00998 -0.14082 0. 2 0.78866 0.79093 0.00441 0.00988 0.08077 -0.01318 -0.13435 0.3 0.05345 0.03196 ! 0.80829 -0.18204 -0.01883 0.01963 -0.04948 0.4 0.09108 0.07066 0.80496 | 0.81083 -0.01403 0.05398 -0.03777 0.6 -0.05812 -0.06922 0.15249 0.08815 0.53038 -0.38383 -0.16789 0.16-8 0.10167 0.09343 0.06383 0.00671 0.50870 0.53194 -0.18423 0. 21 0.31269 0.30637 ' 0.16127 0.14674 0.23757 0.28819 0.25854 Note: The lower left triangle contains the reproduced correlation matrix; ihe diagonal, reproduced commonalities and the upper right triangle residuals between the observed correlations and the reproduced correlations. ix Table 3-5 Principal Components Analysis Access to Health Number of Valid Pairs After Pairwise Deletion Variable I Q.22/3 ! 0.23/3 i0.24/3 0 0.25/3 Q0.2613 0.2713 Q.28/3 0.2913 Q.22/3 37161 _ i 0.23/3 3716 3716 Q.24/3 3716 3716 ' 3716 Q. 25 / 3 3716 j 3716 3716 3716 0.26/3 3716 3716 3716 3716 3716 K 0.27/3 3716 3716 3716 3716 3716 1 3716 0.28/3 3716 3716 3716 3716 3716 3716 3716 0.29/3 3716 3716 - 3716 3716 3716 3716 3716 3716 Correlation Matrix Variable Q0.22/3 Q.23/3 iQ.24/3 Q.25/3 Q0.26/3 0.27/3 Q.28/3 Q.29/3I 0. 22 /3 1.00000j 0.23/3 -0.02826 1.00000 1 0.24 /3 0.08580 0.13023 1.00000 0.25/3 0.01757 0.10973 0.37484 | 1.00000 I 0.26 /3 0.07861 0.14924 0.16605 0.17010 1.00000 1 0.27/3 -0.11565 0.16095 I 0.06064 0.10758 0.17306 1.00000 0.28/3 0.00553 0.10008 0.12443 0.24101 0.12032 0.16306 1.00000 0.29/3 -0.03189 | 0.22553 0.12936 0.15774 0.25268 0.19740 0.32165 1.00000 Final Statistics and Factor Matrix (Factors with Eigenvalue > 1.00) Variable I Communality Factor | Eigen-value Pct. of Variance Factor Matrix __________ IFactor 1 Factor 2 O.22/3 0.40356 1 2.05470 25.7 0.01399 0.63511 0.23/3 0.26431 1 2 1.19803 15.0 0.46067 I -0.22825 Q.24 /3 0.55333 0.52349 0.52847 0. 25/3 0.50264 0.59143 0.39096 0. 26/3 0.28905 0.53558 0.04705 Q. 27 /3 0.43541 0.45374 -'47910 0.28 /3 0.32823 _ 1 1 0.56609 9815 0.29/3 0.47619 _ __ 1 _0.63662 - 29 x L_________ _________ _________ Reproduced Correlation Matrix Variable 0.2213 0.23/3 |j7.24/3 |0Q.25 3 0. 26/3 0.27/3 0. 28/3 0. 29/3 Q. 22/3 0.40356 0.11025 -0.25716 I-0.23900 0.04124 0.18229 0.05360 0.12834 0.23 /3 -0.13582 0.26431 I 0.00970 -0.07349 -0.08675 -0.15742 -0.18082 -0.12851 0.24/3 0.34296 0.12053 0.55333 i -0.14139 -0.13918 0.07631 -0.12533 -0.06317 0. 25/3 0.25658- 0.18322 0.51622 0.50264 -0.16504 0.02653 -0.05933 -0.11467 0.26/3 0.03737 0.23598 0.30523 L 0.33515 0.28905 -0.04741 -0.17872 -0.07575 0. 27 / 3 -0.29794 0.31837 -0.01566 0.08104 0.22047 0.43541 -0.13603 -0.21904 0. 28 /3 -0.04807 0.28090 0.24976 0.30034 0.29904 0.29909 0.32823 -0.06220 0.29/3 -0.16022 0.35405 0.19254 0.27240 0.32843 0.41644 0.38386 0.47619 Note: The lower left tfiangle contains the reproduced correlation matrix; the diagonal. reproduced commonalities and the upper right triangle residuals between the observed correlations and the reproduced correlations. xi Table 3-6. Correlation Matrix of Indices IND FD IND EDU IND COPT IND EMP IND HLT Total IND FD 1.000 0 1 _ IND EDU 0.273 1.000 _ I IND COPT _ 0.621 0.207 ! 1.000 IND EMP 0.421 0.193 0.452 1.000 I IND HLT 0.291 0.099 t 0.241 0.192 1.000 Urban IND FD 1.000 1 IND EDU 0.332 1.000 I IND COPT 0.589 0.239 1.000 i IND EMP 0.423 0.220 0.429 1.000 _ IND HLT 0.145 0.047 0.156 0.085 1.000 Rural IND FD 1 IND EDU 0.263 1 IND COPT 0.625 0.203 1 IND EMP 0.422 0.188 0.459 1 IND HLT 0.299 0.109 0.256 0.216 1 IND FD Availabilirv/Accessibility of Food IND EDU Access to Education IND COPT Financial Coping IND EMP Labor Markets/Access to Employment IND HLT Access to Heath xi. APPENDIX 4: SURVEY INDICATORS Frequency Distributions and Summary Statistics - Combined by Kecamatan (Common questions 1-21 and respondent specific questions by urban/rural status) I. Common Questions Total Std Err Std Status Response Code Total Cases Mean of Dev. Var. 1 2 3 4 5 Mean 0. 1 - ChanRe in males returning to kecamatan due to loss of work Total 3.62 45.19 38.24 11.82 1.12 100.00 3755 2.62! 0.01 0.78 0.61 Urban 4.24 43.64 37.21 13.19 1.73 100.00 637 2.65 0.03 0.83 0.68 Rural 3.50 45.51 38.45 11.55. 0.99 100.00 3118 2.61 0.01 0.77 0.60 0. 2 - Chanme in females returnin2 to kecamatan due to loss of work Total 0.75 8.23 _40.03 42.16 8.84 _100.00 3755 3.50 _0.01 0.80 0.64 Urban 0.78 10.68 36.26 42.39 9.89 100.00 637 3.50 0.03 0.84 0.71 Rural 0.74 7.73, 40.80 42.11 8.63 100.00 3118 3.50 0.01 0.79 0.62 0. 3 - Change in males leaving kecamatan to find work Total 0.37 3.46, 30.63 42.96 22.58 100.00 3755 3.84 0.01 0.82; 0.68 Urban 0.31 4.24 34.38 39.56 21.51 100.00 637. 3.78 0.03 0.84i 0.71 Rural 0.38 3.30 29.86 43.65 22.80 100.00 3118 3.85 0.01 0.82' 0.67 0. 4 - Change in females leaving kecamatan to find work Total 0.16 1.891 20.16 52.81 24.98 100.00 3755 4.01 0.01 0.74! 0.54 Urban 0.31 2.20 21.51 52.75 23.23. 100.00 637 3.96 0.03 0.75 0.56 Rural 0.13 1.831 19.88 52.82. 25.34 100.00 3118 4.01' 0.01 0.73 0.54 0. 5 - Change in population decreasing quality of intake of basic food Total 0.48 2.29 20.08 61.73 15.42 100.00 3755 3.89 0.01 0.69 0.48 Urban 0.47 2.51 19,78 59.65 17.58 100.00 637 3.91 0.03 0.71 0.51 Rural 0.48 2.25 20.14 62.16 14.98 100.00 3118 3.89 0.01 0.69 0.47 0. 6 - Change in population working at more jobs/longer hours Total 0.59 4.21 37.15 45.89 12.17 100.00 3755 3.65 0.01 0.77 0.59 Urban 0.63 3.77 37.21 40.66: 17.74 100.00 637 3.71 0.03 0.82 0.67 Rural 0.58 4.30i 37.14 46.95 11.03 100.00 3118 3.64 0.01 0.76 0.57 0. 7 - Change in population selling household goods to meet basic needs Total 1.33 8.44! 51.48 29.43 9.32, 100.00 3755 3.37 0.01 0.82 0.671 Urban 2.04 10.05. 63.27 17.43, 7.221 100.00 637 3.18_ 0.03 0.781 0.611 Rural 1.19 8.11 49.07 31.88 9.75 100.00 3118; 3.41. 0.01 0.82 0.67 Q. 8 - Change in population leaving arisan due to lack of money Total 0.80 4.66 39.79 47.88 6.871 100.00 3755 3.55i 0.01 0.73i 0.53 Urban 0.78 4.40i 48.19 38.62 8.01 100.00 637. 3.491 0.03 0.741 0.55i Rural 0.80, 4.71 38.07 49.78 6.64! 100.00 3118i 3.571 0.01 0.72Z 0.52 i Status ,Total Std Err Std Response Code Total Cases Mean ot Dev. Var. 1 2 3 4 5 Mean 0. 9 - Change in turnover of arisan Total 1.25 7.46i 37.36 51.85 2.08 _100.00 3755 3.46 0.01 0.72 0.51 Urban 1.10 6.75 43.17 48.04 0.94 100.00 637 3.41 0.03 0.68 0.46 _ural 1.28: 7.601 36.18 52.63 2.31 100.00 3118 3.47 0.01 0.72' 0.52 Q. 10 - Change in level ot community activitieslcelebrations Total 1.231 9.40 51.00 36.80 1.57 100.00 3755 3.28i 0.01 0.70 0.50 Urban 0.31, 10.52: 61.54 27.47 0.16 100.00 637 3.17 0.02 0.611 0.37i Rural 1.41; 9.171 48.85 38.71 1.86i 100.00 3118 3.30 0.01 0.72' 0.52 0. 11 - Change in level of community contributions Total 2.16 11.74i 59.17 25.59 1.33 100.00 3755 3.12 o.o1 0.71 0.50' Urban 2.67 13.03; 66.56 17.43 0.31 100.00 637 3.00 0.03 0.651 0.42 Rural 2.05 11.48i 57.67 27.26 1.54 100.00 3118 3.15 0.01 0.71 0.51 0. 12 - Change in availability of basic foods Total __ 2.61 20.96i 60.64 15.34 0.45 100.00 3755 2.90 0.01 0.69 0.48 Urban 2.98 20.41' 62.79 13.34 0.47 100.00 637 2.88 0.03 0.68 0.46 Rural 2.53 21.07 60.20 15.75 0.45; 100.00 3118 2.91 0.01 0.69 0.48 Q. 13 - Change in ability to purchase basic foods Total 2.08 14.06i 56.70 21.81 5.35 100.00 3755 3.14 0.01 0.80 0.64. Urban 2.20 14.91 60.91 16.64 5.34 100.00 637 3.08 0.03 0.78' 0.61 Rural 2.05, 13.89 55.84 22.87 5.36 100.00 3118 3.16 0.01 0.80 0.64 0. 14 - Change in incidence of theft Total 1.33 8.55 63.25 22.02 4.85 100.00 3755 3.21 0.01 0.71 0.51 Urban 1.57 9.42 70.80 15.86 2.35, 100.00 637 3.08 0.03 0.64 0.40 Rural 1.28. 8.37 61.71 23.28 5.36 100.00 3118 3.23, 0.01 0.73 0.53 Q. 15-a - Issue: Unemployment Total 27.22 27.72 20.03 25.03 _ 100.00_ 3755 2.43 0.02 _1.14 1.29 Urban 38.46 30.30 18.52 12.72 100.00 637 2.05 0.04 1.04 1.08 Rural __ 24.92 27.20 20.33 27.55 100.00 3118 2.51 0.02 1.14 1.30 0. 15-b - Issue: Finding staple food Total 20.77 19.25; 19.17 40.80 100.00 3755 2.80 0.02 1.181 1.39; Urban 20.25 22.61 21.51 35.64i 100.00 637 2.73 0.05 1.151 1.32 Rural 20.88; 18.571 18.70 41.85 100.00 3118 2.82 0.02 1.19 1.41 V. Q. 15-c - Issue: Children dropping out out school Total 1.84 6.231 10.97 80.96 100.00 3755 3.71 0.01 0.661 0.44 Urban 2.04 6.441 13.34 78.18 100.00 637 3.68! 0.03 0.691 0.47 Rural 1.80 6.19[ 10.49 81.53 100.00 3118 3.72 0.01 0.66 0.43 '0. 15-d - Issue: Loss of Income :Total 24.74 40.69; 19.63 14.94 100.00 3755 2.25 0.02 0.99 0.98 Urban 26.53, 39.87! 21.19 12.40 100.00 637 2.19! 0.04 0.97, 0.94s |Rural 24.37 40.861 19.31 15.46 100.00 3118 2.26i 0.02 0.99s 0.991 11 Total Std Err Std Status Response Code Total Cases Mean of Dev. Var. 1 2 3 4 5 Mean 0. 15-e - Issue: Incidence of theft/criminality Total 5.70 9.53 17.04 67.72 100.00 3755 3.47 0.01 0.88i 0.78 Urban 6.281 11.15: 17.58 64.99 100.00 6371 3.41 0.04 0.92i 0.84 Rural 5.58! 9.201 16.93 68.28 100.00 3118 3.481 0.02 0.88 0.77' 0. 15-f - Issue: Harvest tallure+A24 Total 12.04, 12.86 15.79 59.31 100.00 3755 3.22' 0.02 1.07 1.15 Urban 6.591 6.751 10.99 75.67 100.00 637 3.561 0.03 0.88! 0.78r Rural 13,15 14.11 16.77 55.97 100.00 3118 3.16 0.02 1.10 1.20: 0. 15-g - Issue: Reduction In health services Total 1.60 4.13 6.76 87.51 100.00 3755 3.80 0.01 0.58 0.34 Urban 1.88 5.021 7.06 86.03 100.00 637' 3.771 0.02 0.62 0.39 Rural 1.54! 3.94s 6.70 87.81 100.00 3118 3.81! 0.01 0.57 0.33 0. 15-h - Issue: Hunger/starvation Total 1.94 3.49 5.62 88.95 100.00 3755 3.82, 0.01 0.58; 0.34 Urban 2.51 4.55 6.91 _86.03 100.00 637 3.76 0.03 0.65, 0.42 Rural 1.83 3.27 5.36 89.54 100.00 3118 3.83 0.01 0.56 0.32 0. 15-I - Issue: Open-ended response # 1 Total 1.92 0.691 0.59 96.80 100.00 3755 3.92 0.01 0.45 0.20' Urban 1.73, 0.78i 0.47 97.02 100.00 637 3.93; 0.02 0.43i 0.19 Rural 1.961 0.671 0.61 96.76 100.00 3118 3.92 0.01 0.45i 0.20. 0. 15-j - Issue: Open-ended response # 2 Total 0.051 0.21' 0.05 99.68 100.00 3755 3.99 0.00 0.121 0.01. Urban 0.161 0.16 99.69 100.00 637' 4.001 0.00 0.09 0.01 Rural 0.06i 0.221 0.03 99.68 100.00 3118 3.99i 0.00 0.12 0.02 0. 16-1 - Response: Consume less expensive food Total 4.02 31.131 19.63 34.33 10.89 100.00 3755 3.17 0.02 1.11 1.22 Urban 4.87 31.08 22.45 33.75 7.85 100.00 637 3.09 0.04 1.07 1.15 Rural 3.85 31.14 19.05 34.45 11.51 100.00 3118 3.19 0.02 1.11 1.24 Q. 16-2 - Response: Eat less frequently Total 1.54 25.33 23.75 39.36 10.01 100.00 3755 3.31 0.02 1.01 1.01 Urban 1.881 28.26: 28.10 34.69 7.061 100.00 637 3.17 0.04 0.981 0.96 Rural 1.481 24.73 22.87 40.31 10.62 100.00 3118 3.34 0.02 1.01 1.02 Q. 16-3 - Response: Decrease quality of food consumption Total 7.06 54.25 16.17 19.20 3.331 100.00 3755 2.57' 0.02 0.981 0.97 Urban 9.11 53.53i 17.90 17.90' 1.57: 100.00 637' 2.491 0.04 0.941 0.89i Rural 6.64: 54.391 15.81 19.47 3.69 100.00 3118 2.591 0.02 0.99 0.99 0. 16-4 - Response: Sell livestock Total 8.15: 62.16 10.39 15.58 3.73 100.00 3755 2.451 0.02 0.971 0.95 Urban 6.91 51.331 12.56 21.51; 7.69i 100.00 637 2.721 0.04 1.111 1.231 Ru*ral 8.40Y 64.371 9.94-1437 2.921 100.00 3118 2.39. 0.02 0.93 0.87: 111 Total Std Err Std Status Response Code Total Cases Mean of Dev. Var. 1 2 3 4 5 Mean 0. 16-5 - Response: Sell household assets Total 6.92 46.981 17.63 24.71 3.75_ 100.00_ 3755 2.71' 0.02 103 1. 06 Urban 10.36 54.95' 16.33 16.64 1.73 100.00 637 2.44 0.04 0.94 0.89 Rural 6.22 45.35 17.90 26.36 4.17 100.00 3118 2.77 0.02 1.04 1.08 0. 16-6 - Response: Stop attending arisan Total 4.45 36.80 18.96 33.42 6.36 100.00 3755 3.00: 0.02 1.07 1.14 Urban 4.55 44.43 18.37 29.20 3.45 100.00 637 2.83 0.04 1.01 1.03 Rural 4.43 35.25 19.08 34.28 6.96 100.00 3118 3.04 0.02 1.07 1.15 0. 16-7 - Response: Take children out of school Total 0.48 5.03 13.02 53.16 28.31 100.00 3755 4.04' 0.01 0.81 0.66 Urban 0.31 2.83. 12.40 54.00 30.46 100.00 637 4.11 0.03 0.75 0.56 Rural 0.51 5.48 13.15 52.98 27.87 100.00 3118 4.02 0.01 0.82 0.68 Q. 16-8 - Response: Increase number of family members working Total 4.39 58.75 14.91 19.04 2.90 100.00 3755 2.57 0.02 0.94 0.89 Urban 4.24 55.57 18.37 19.47 2.35 100.00 637 2.60 0.04 0.93 0.86 .Rural 4.43. 59.40 14.21 18.95 3.01 100.00 3118 2.57 0.02 0.95 0.89 0. 16-9 - Response: Decease participation in village activities/celebrations Total 3.09 32.30' 22.29 33.26 9.05 100.00 3755 3.13 0.02 1.06 1.13 Urban 3.14 39.72 21.19 29.67 6.28 100.00 637 2.96 0.04 1.03 1.07 Rural 3.08 30.79 22.51 34.00 9.62 100.00 3118 3.16 0.02 1.06 1.13 0. 16-10 - Response: Decrease contributions to village activites/celebrations Total 5.54 44.21 20.19 25.30 4.77 100.00 3755 2.80i 0.02 1.03 1.07 Urban 6.59 52.59 15.86 20.25 4.71 100.00 637. 2.64i 0.04 1.03 1.05 Rural 5.32 42.50 21.07 26.33 4.78 100.00 3118 2.83 0.02 1.03 1.06 Q. 20 - Change in price of medium quality rice Total 49.53 39.28 10.97 0.13 0.08 100.00 3755 1.62, 0.01 0.69 0.47 Urban 48.82 41.13 10.05 100.00 637 1.61 0.03 0.66 0.44 Rural 49.68 38.90 11.16 0.16 0.10 100.00 3118 1.62 0.01 0.69 0.48 0. 21 - Change in number of stores going out of business Total 1.15 4.39 26.90 60.08 7.48. 100.00 3755 3.68. 0.01 0.72 0.52 Urban 1.26 5.49. 37.83 45.84 9.58 100.00 637 3.57! 0.03 0.79 0.62 Rural 1.12' 4.17' 24.66 62.99 7.06' 100.00 3118 3.711 0.01 0.71 0.50 iv II. PMD Total Std Err Std Status Response Code Total Cases Mean of Dev. Var. 1 2 3 4 5 Mean Q. 22 - Change in level of community upkeep activity Total 0.48! 5.39 20.29 67.35 6.50 100.00 631 3.74 0.03 0.68 0.461 Urban 0.48 5.39 20.29 67.35 6.50 100.00 631 3.74 0.03 0.681 0.46 Rural 0 0. 23 - Change in number of Informal sector workers Total 3.80 14.26 43.58 31.22 7.13 100.00 631 3.24! 0.04 0.92 0.84 Urban 3.801 14.26 43.58 31.22 7.13 100.00 631 3.24' 0.04 0.92 0.84 Rtural 0 0. 24 - Change in number of street children Total 2.691 9.98 37.08 40.73 9.51 100.00 631 3.44 0.04 0.89 0.80 Urban 2.69 9.98; 37.08 40.73 9.51 100.00 631 3.44 0.04 0.89 0.80 Rural 0 Ill. Mantri Tani Total Std Err Std Status Response Code Total Cases Mean of Dev. Var. 1 2 3 4 5 Mean 0. 22 - Change in production of main food crop Total 5.53 10.96; 40.63 28.09 14.80 100.00 3094 3.36 0.02 1.04 1.08' Urban 0 Rural 5.53' 10.96 40.63 28.09 14.80 100.00 3094, 3.36, 0.02 1.04 1.08 0. 23 - Change in price of main non-rice crop Total 17.61 31.42 42.60 6.53 1.84 100.00 3094 2.441 0.02 0.92 0.84 Urban 0_ Rural 17.61 31.42 42.60 6.53 1.84 100.00 3094 2.44 0.02 0.92 0.84 0. 24 - Change in level of farm profits Total 3.04 8.40 21.62 17.49 49.45 100.00 3094 4.02 0.02 1.15' 1.32 Urban 0 Rural 3.04 8.401 21.62 17.49 49.45 100.00 3094 4.02i 0.02 1.15. 1.32, Q. 25 - Change in wages for weeding 'Total 0.74 1.71i 2.88 9.47 85.201 100.00 3094 4.771 0.01 0.651 0.43) 'Urban 0; Rural i 0.741 1.711 2.88 9.47 85.20! 100.00 3094 4.771 0.01 0.65i 0.43 Q. 26 - Change in wages for harvesting Total 0.841 1.71 5.43 22.24 69.78: 100.00 3094 4.58, 0.01 0.75i 0.56, Urban 0 Rural 0.84 1.71' 5.43 22.24 69.78 100.00 3094 4.581 0.01 0.751 0.56 v IV. Doktor Puskesmas Total Std Err Std Status Response Code Total Cases Mean of Dev. Var. 1 2 3 4 5 Mean 0. 22 - Change in number of patients visiting puskesmas Total 3.42 13.54 35.25 22.42 25.38 100.00_ 3716 3.53 0.02 1.11 1.24 Urban 4.90' 20.38 39.97 16.11 18.64 100.00 633 3.231 0.04 1.12 1.25' Rural 3.11 12.13; 34.28 23.71 26.76 100.00 3083' 3.59 0.02 1.10 1.21' 0. 23 - Change in number of patients visiting hospital/polyclinic Total 5.38; 9.66i 31.84 44.27 8.85 100.00 3716 3.42i 0.02 0.97 0.94 Urban 4.74, 12.01. 44.55 27.17 11.53 100.00 633 3.29 0.04 0.98 0.96 Rurai 5.51 9.18 29.22 47.78 8.30 - 100.00 3083 3.44, 0.02 0.96 093 0. 24 - Change in price of contraceptives Total 25.03 31.22i 33.13 9.77 0.86 100.00 3716 2.30: 0.02 0.98 0.96 Urban 23.22 35.86 33.49 6.79 0.63 100.00 633 2.26 0.04 0.91 0.83 Rural _ 25.40 30.26 33.05 10.38 0.91 100.00 3083 2.31 0.02 0.99 0.98 Q. 25 - Change in availability of contraceptives Total 14.24 29.49, 29.87 23.20 3.20 100.00 3716 2.72 0.02 1.07 1.14 Urban 14.69 28.59 27.80 24.49 4.42 100.00 633 2.75' 0.04 1.11 1.24 FRural 14.14 29.68 30.30 22.93 2.95 100.00 3083 2.71 0.02 1.06 1.13 Q. 26 - Change in number of malnourished children Total 3.04 9,39! 45.29 30.92 11.36 100.00 3716 3.38i 0.01 0.91 0.83! Urban 3.63 10.90 47.24 29.86 8.37 100.00 633 3.28: 0.04 0.90 0.81' R;ural 2.92 9.08 44.89 31.14 11.97 100.00 3083 3.40 0.02 0.911 0.84 Q. 27 - Change in availability of services at puskesmas Total 0.73 3.04 9.04 69.35 17.84 100.00 3716 4.01 0.01 0.68 0.46 Urban 0.63 2.69 6.16 73.14 17.38 100.00 633 4.04 0.03 0.63 0.401 Rural 0.75 3.11 9.63 68.57 17.94 100.00 3083 4.00 0.01 0.68 0.47 Q. 28 - Change in availability of basic medicines Total 5.97 19.32 35.76 35.17 3.77 100.00 3716 3.11 0.02 0.96 0.92 Urban 5.53 16.11 37.91 36.49 3.95 100.00 633 3.17 0.04 0.94 0.88 Rural 6.07 19.98. 35.32 34.90 3.73 100.00 3083 3.10 0.02 0.96 0.93 Q. 29 - Change in price of basic medecines Total 4.201 10.52, 35.04 47.55 2.69, 100.00 3716 3.34 0.01 0.86. 0.74. Urban 3.16: 9.16i 36.18 48.50 3.00! 100.00 633: 3.391 0.03 0.82 0.67' Rural 4.41 10.80 34.80 47.36 2.63 100.00 3083' 3.33! 0.02 0.87 0.75! vi V. Depikbud Total Std Err Std Status Response Code Total Cases Mean of Dev. Var. 1 2 3 4 5 Mean IQ. 22 - Change in female transition class 3 - 4 in SD Total 1.34 1.69 12.93 55.08 28.95' 100.00 3727 4.09 0.01 0.77 0.60 lUrban 1.11 1.59 14.76 53.49 29.05 100.00 630 4.08 0.03 0.77 0.60 Rural 1.39 1.71 12.56 55.41 28.93 100.00 3097 4.09' 0.01 0.78 0.60 0. 23 - Change in male transition class 3 - 4 in SD Total 1.58 1.96 12.26 54.92 29.27 100.00 3727 4.08 0.01 0.79 0.63, Urban 1.43 1.11 14.29 54.13 29.05 100.00 630 4.08i 0.03 0.77 0.60 Rural 1.61 2.13 11.85 55.09 29.32 100.00 3097 4.08 0.01 0.80 0.64 0. 24 - Change In female entrants to class 1 in SD Total 0.70 2.52 24.23 39.66 32.90 100.00 3727 4.02 0.01 0.86 0.73 Urban 0.79 1.43 23.33 40.79 33.65 100.00 630 4.05 0.03 0.83 0.70 Rural 0.68 2.74 24.41 39.43 32.74 100.00 3097 4.01 0.02 0.86 0.74 0. 25 - Change in male entrants to class 1 in SD Total 0.72 2.66 25.52 41.96 29.14 100.00 3727 3.96 0.01 0.85 0.72 Urban 0.95 1.43 24.29 42.86 30.48 100.00 630 4.00 0.03 0.83 0.69 Rural 0.68 2.91 25.77 41.78 28.87 100.00 3097 3.95 0.02 0.85 0.72 Q. 26 - Change in male dropouts in SD Total 1.45 2.28 20.55 41.67 34.05 100.00 3727 4.05 0.01 0.88 0.77 Urban 1.27 1.59 20.63 44.29 32.22 100.00 630 4.05 0.03 0.84 0.71 Rural 1.49 2.42 20.54 41.14 34.42 100.00 3097 4.05 0.02 0.88 0.78 Q. 27 - Change in female dropouts in SD Total 1.72 2.71 19.86 42.04 33.67 100.00 3727 4.03 0.01 0.89 0.80 Urban 1.11 2.54 18.25 45.08 33.02 100.00 630 4.06 0.03 0.84 0.71 Rural 1.84 2.74 20.18 41.43 33.81 100.00 3097 4.03 0.02 0.90 0.81 0. 28 - Change in receipt of parental school contributions Total 6.14 12.05 36.65 37.43 7.73 100.00 3727 3.29 0.02 0.98 0.97 Urban 6.83 14.44 39.21 30.48 9.05 100.00 630 3.20 0.04 1.02 1.04 Rural __ 6.01 11.56 36.13 38.84 7.46 100.00 3097 3.30 0.02 0.98 0.95 .0. 29 - Change in attendance of teachers Total 0.43 1.40 8.75 68.12 21.30 100.00 3727 4.08 0.01 0.63 0.39 Urban 0.48 0.95 6.19 71.27 21.11 100.00 630 4.12 0.02 0.58 0.34 Rural 0.42 1.49 9.27 67.48 21.34 100.00 3097 4.08 0.01 0.63 0.40 0. 30 - Change in numbers of children in SD coming to school hungry Total 2.36 7.35 32.22 44.19 13.87 100.00 3727 3.60 0.01 0.90 0.81 Urban 1.27 6.51 31.43 44.13 16.67 100.00 630 3.68 0.03 0.87 0.76 Rural 2.58 7.52 32.39 44.20 13.30 100.00 3097 3.58 0.02 0.90 0.82 vii