ࡱ> VXOPQRSTUq` bjbjqPqP ::v LLL VVVY,ZT dddddgegege! # # # 3V ` `$h:Lgegegegege:L8ddO=r=r=rgeLdLd! =rge! =r=r<LLydd pRVAkZL-(e0C6oD yyL=rgegege::q^gegegegegegege dDN$ N LLL  WELFARE, CHILDREN, AND FAMILIES: A THREE-CITY STUDY Wave 1, March-December 1999 USERS GUIDE Principal Investigators Ronald J. Angel, University of Texas, Austin Linda M. Burton, Pennsylvania State University P. Lindsay Chase-Lansdale, Northwestern University Andrew J. Cherlin, Johns Hopkins University Robert A. Moffitt, Johns Hopkins University William Julius Wilson, Harvard University Funding Support Federal Agencies: National Institute of Child Health and Human Development; Office of Disability, Aging, and LongTerm Care Policy, Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services; Administration on Developmental Disabilities, U.S. Department of Health and Human Services; Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services; Office of Research, Evaluation, and Statistics, Office of Policy, Social Security Administration; National Institute of Mental Health. Foundations: The Boston Foundation; The Annie E. Casey Foundation; The Edna McConnell Clark Foundation; The Lloyd A. Fry Foundation; Hogg Foundation for Mental Health; The Robert Wood Johnson Foundation; The Joyce Foundation; Henry J. Kaiser Family Foundation; W. K. Kellogg Foundation; Kronkosky Charitable Foundation; The John D. and Catherine T. MacArthur Foundation; Charles Stewart Mott Foundation; The David and Lucile Packard Foundation; Woods Fund of Chicago. This volume documents the contents of survey data from Welfare, Children and Families: A Three-City Study, Wave 1. The data come from completed interviews conducted between March and December, 1999 with 2,402 children and their caregivers in Boston, Chicago, and San Antonio. The effective N is 2393, because 9 cases interviewed at wave 1 were later determined to be duplicate interviews. These cases are flagged with the variable DUPLICAT. Because the data file has been in the public domain for several years with N=2402, the Three-City Study team decided to preserve that sample size for the current release through ICPSR. Purpose of the study The Welfare, Children and Families Study is a longitudinal study of children and their caregivers in low-income families that were living in low-income neighborhoods in three cities in 1999. The purpose of the study is to investigate the consequences of policy changes resulting from the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (PRWORA). The survey was designed to provide information on the health and cognitive, behavioral, and emotional development of children and on their primary caregivers' labor force behavior, welfare experiences, family lives, use of social service, health, and well-being. A detailed description of the research design can be found in Welfare, Children and Families: A Three City Study: Overview and Design, available at http://www.threecitystudy.jhu.edu or in hardcopy upon request. Nature of the study sample Sample area Survey data were collected in targeted areas in Boston, Chicago and San Antonio. Multi-stage, stratified, area probability sampling was used in each city to obtain a sample of dwelling units from which eligible families and children were selected. The first stage of sampling was selection of primary units, or segments, comprising clusters of 1990 Census blocks. In order to obtain desirable sample sizes for each of the race/ethnicity groups targeted in our sample design (Non-Hispanic white, Non-Hispanic African-American, and Hispanic of any race), we constructed three separate lists in each city. For each list, using 1990 Census data, we ranked the block groups by the percentage of children in that racial/ethnic group who were in families with incomes below the federal poverty line. Then, for each list, we determined a cutoff point for the percent poor and deleted all block groups below that cutoff from further consideration. Among the block groups we selected, 367 had more than 40 percent of households with incomes below the federal poverty line, 213 had 20 to 40 percent of households below the poverty line, and 43 had less than 20 percent of the households with income below the poverty line. Sampling criteria In the selected blocks groups, survey field staff compiled listings of dwelling units (DUs), and the second stage sample was selected from these listings. Sample DUs were visited and a brief screening interview was conducted to determine whether eligible families ( those with incomes below 200 percent of the federal poverty line, female or couple headed, and containing children age birth to 4 or 10 to 14 ( were present. Eligible families were subsampled at varying rates, based on the following family characteristics, to control the distribution of the sample by race/ethnicity, income level, female or couple headship, and receipt of Food Stamps and Medicaid: Three race/ethnicity groups (based on household head) Non-Hispanic African-American Non-Hispanic White Hispanic, any race Two income levels Below the Federal Poverty Level (FPL) Between 100 percent and 200 percent of the FPL Two household types Female-headed Couple-headed Two assistance categories Receiving Food Stamps and Medicaid Not receiving both Food Stamps and Medicaid Because the 24 resulting groups do not occur in equal numbers in the population, we employed conditional selection probabilities in order to obtain suitable sample yields for analysis. For example, in our selected neighborhoods in Chicago, we screened fewer eligible non-Hispanic whites than we did non-Hispanic African-Americans. In order to include the desired number of non-Hispanic whites in our sample, we selected a higher proportion of those screened households for interview compared to the eligible households headed by non-Hispanic African-Americans. If an eligible family was selected for an interview, one eligible child, age birth to 4 or 10 to 14, was chosen to participate, together with the mother or other female primary caregiver. The resulting selection tables are displayed as Tables 1A, 1B, and 1C. If a household contained more than one eligible child, one was selected using equal probability sampling. Table 1A. Household type selection rates, Boston Household Type Determinants and Associated Sampling Rates Income level HH Race/ Ethnicity Food Stamp & Medicaid Rec'd Household Head Race/Ethnicity Stratum Black Hispanic White Sampling Rate Below Poverty Black No Couple 0.350 0.270 0.243  Black No Female Head 0.428 0.240 0.185  Black Yes Couple 0.400 0.540 0.300  Black Yes Female Head 1.000 0.485 0.255         Hispanic No Couple 1.000 0.252 0.252  Hispanic No Female Head 1.000 0.330 0.340  Hispanic Yes Couple 1.000 0.252 0.252  Hispanic Yes Female Head 1.000 1.000 0.980         White No Couple 1.000 0.150 0.150  White No Female Head 1.000 0.298 0.050  White Yes Couple 1.000 1.000 0.260  White Yes Female Head 1.000 1.000 1.000        100%-199% of Poverty Black No Couple 0.350 0.270 0.243  Black No Female Head 0.428 0.270 0.185  Black Yes Couple 0.425 0.540 0.300  Black Yes Female Head 1.000 1.000 1.000         Hispanic No Couple 1.000 0.252 0.252  Hispanic No Female Head 1.000 0.330 0.340  Hispanic Yes Couple 1.000 0.252 0.252  Hispanic Yes Female Head 1.000 1.000 0.980         White No Couple 1.000 0.150 0.150  White No Female Head 1.000 0.910 0.915  White Yes Couple 1.000 1.000 0.260  White Yes Female Head 1.000 0.915 0.915 Table 1B. Household type selection rates, Chicago Household Type Determinants and Associated Sampling Rates Income level HH Race/ Ethnicity Food Stamp & Medicaid Rec'd Household Head Race/Ethnicity Stratum Black Hispanic White Sampling Rate Below Poverty Black No Couple 0.687 0.687 0.687  Black No Female Head 0.576 0.576 0.576  Black Yes Couple 0.687 0.687 0.687  Black Yes Female Head 1.000 1.000 1.000         Hispanic No Couple 0.067 0.067 0.067  Hispanic No Female Head 0.294 0.294 0.294  Hispanic Yes Couple 0.067 0.067 0.067  Hispanic Yes Female Head 1.000 1.000 1.000         White No Couple 0.054 0.054 0.054  White No Female Head 0.314 0.314 0.314  White Yes Couple 0.054 0.054 0.054  White Yes Female Head 1.000 1.000 1.000        100%-199% of Poverty Black No Couple 0.525 0.525 0.525  Black No Female Head 0.576 0.576 0.576  Black Yes Couple 0.525 0.525 0.525  Black Yes Female Head 1.000 1.000 1.000         Hispanic No Couple 0.057 0.057 0.057  Hispanic No Female Head 0.294 0.294 0.294  Hispanic Yes Couple 0.057 0.057 0.057  Hispanic Yes Female Head 1.000 1.000 1.000         White No Couple 0.063 0.063 0.063  White No Female Head 0.314 0.314 0.314  White Yes Couple 0.063 0.063 0.063  White Yes Female Head 1.000 1.000 1.000 Table 1C. Household type selection rates, San Antonio Household Type Determinants and Associated Sampling Rates Income level HH Race/ Ethnicity Food Stamp & Medicaid Rec'd Household Head Race/Ethnicity Stratum Black Hispanic White Sampling Rate Below Poverty Black No Couple 0.525 0.000 na  Black No Female Head 0.450 0.000 na  Black Yes Couple 0.928 0.000 na  Black Yes Female Head 1.000 0.000 na         Hispanic No Couple 0.053 0.053 na  Hispanic No Female Head 0.332 0.325 na  Hispanic Yes Couple 0.147 0.147 na  Hispanic Yes Female Head 1.000 1.000 na         White No Couple 0.000 0.000 na  White No Female Head 0.000 0.000 na  White Yes Couple 0.000 0.000 na  White Yes Female Head 0.000 0.000 na        100%-199% of Poverty Black No Couple 0.413 0.000 na  Black No Female Head 0.800 0.000 na  Black Yes Couple 0.730 0.000 na  Black Yes Female Head 1.000 0.000 na         Hispanic No Couple 0.096 0.096 na  Hispanic No Female Head 0.332 0.325 na  Hispanic Yes Couple 0.290 0.290 na  Hispanic Yes Female Head 1.000 1.000 na         White No Couple 0.000 0.000 na  White No Female Head 0.000 0.000 na  White Yes Couple 0.000 0.000 na  White Yes Female Head 0.000 0.000 na Target population The population of inference is children age birth to 4 and 10 to 14 who have a female primary caregiver, whose caregivers self-identify as non-Hispanic white, non-Hispanic African-American, or Hispanic of any race, and who live in families with incomes below 200 percent of the poverty line, living in low-income neighborhoods in Boston, Chicago, or San Antonio. Cities The design of the Three-City Study allows us to capture some of the ethnic and regional diversity in the United States, while also permitting a focused and detailed analysis of policy changes as they take place at the local level. Policymakers in the states where our cities lie have responded to PRWORA in different ways. Table 2 illustrates the differences in cash assistance policies in each of our three cities in 1999. Table 2. Rules for TANF receipt under PRWORA and in Massachusetts, Illinois, and Texas, 1999  ILLINOIS tc \l1 "ILLINOISMASSACHUSETTS TEXASImplementationtc \l1 "Implementationtc \l1 "tc \l1 "tc \l1 "Date effectiveJuly 1997November 1995November 1996WaiverNoYesYesAdministrative agencyDepartment of Human Services Department of Transitional Assistance Department of Human Services; Texas Workforce CommissionEligibilitytc \l1 "Eligibilitytc \l1 "tc \l1 "tc \l1 "Income limits for family of three for the first month and 13th month$467/$1,110$1,050/$1,050$400/$280Asset limits$3,050 (for a family of 3)$2,500$2,000/$3,000 (if someone is disabled or elderly).Vehicle exemptionExclude the value of the most expensive carFair market value to $5,000Fair market value to $4,650Individual Development AccountsYes (up to $3,000)Not at this timeYes (Up to $10,000)Eligibility of two-parent familiesSame as single-parent familyWork History RuleSame as single-parent familyPaymenttc \l1 "Paymenttc \l1 "tc \l1 "tc \l1 "Maximum benefit level, family of 3 $377 (Can vary by city or county)$565$200Family capYesYesNoChild support pass- through$50$50$50Earnings disregards tc \l1 "Earnings disregardsDisregard 67 percent and TANF eligibility continues until family income reaches FPL.Disregard $120 and 50 percent of remainderDisregard 90% of earnings for the first 4 months of earnings, $120 for the next 8 months, $90 after 12 months.Diversiontc \l2 "DiversionMaximum Paymenttc \l1 "Maximum PaymentNANA$1,000How Often Payment Can Be ReceivedNANAOnce per 12 monthsPeriod of Ineligibility After Receiving Paymenttc \l1 "Period of Ineligibility After Receiving PaymentNANA12 monthsAlternative Resourcestc \l1 "Alternative ResourcesYesNoYesMandatory Job Searchtc \l1 "Mandatory Job SearchNoNoYes, Work orientation requiredWork Requirementstc \l1 "Work Requirementstc \l1 "tc \l1 "tc \l1 "Definition of work activityParticipating at least 25 hours a week in the following: Job search, job readiness, job skill training, employment, self-employment assistance, community service, foster parenting, education, including post-secondary, substance abuse treatment and domestic violence counseling and other activities leading to the family(s self-sufficiency. Participating at least 20 hours a week in the following: Unsubsidized employment, subsidized employment or community service.Participating at least 20 hours a week (30 for 2 parent families) in the following: Job search, job readiness, job skill training, community service, self-employment assistance, employment and subsidized employment.Months before work requirementWhen determined able to engage in work or 24 months, whichever comes first60 days ImmediateExemptions/good cause Families with youngest child under 1 year old (except for families with a teen parent without a high school degree) Non-parent relatives who are taking care of the children Families with youngest child under 6 years old Disability/illness Caring for disabled child, spouse, parent or grandparent Teen parent who is attending high school full time or a GED program or approved training or employment related activity for 30 hours a week Pregnant women in the third trimester Families with youngest child under 4 years old Disability/illness Incarceration/court appearance Domestic violence Lack of transportation, child care or other support services Caring for disabled child Job availabilitySanctionstc \l1 "SanctionsReasons for sanctionsFailure to comply with the following: Work requirements, child support requirements and school attendance requirements.Failure to comply with the following: Work requirements, child support requirements, school attendance requirements for children under 14, and immunization requirements.Failure to comply with the following: Work requirements, child support requirements, school attendance requirements, immunization and medical screening requirements, and parent training requirements  ILLINOIS tc \l1 "ILLINOISMASSACHUSETTS TEXASBenefit reduction1st instance of non-compliance: 50% reduction until compliance. Subsequent Noncompliance after the first instance: 50% reduction in benefit for 3 months (regardless of when compliance begins); if noncompliance continues for 3 months, termination. 1st instance of non-compliance: Removal of adult from benefit until compliance Subsequent instances of noncompliance: termination of benefit1st instance of non-compliance: 1month reduction of $78 if one parent fails to comply or $125 if both parents fail to comply; 2nd instance of noncompliance: 3 months reduction of $78 if one parent fails to comply or $125 if both parents fail to comply; Subsequent instance of non-compliance: 6 months reduction of $78 if one parent fails to comply or $125 if both parents fail to comply. Full family sanctionYesYesNoTime-limitstc \l1 "Time-limitsMonths till termination5 year limit24 out of 60 months, No lifetime limit12, 24, or 36 monthsExemptions Working at least 25 hours a week Teen parent under 18 Full-time college maintaining a 2.5 GPA Disability/illness Caring for disabled person caring for young age child (13 years) because of an absence of available child care Extension for participation in a pay after performance work program.Disability/illness Caring for young age child (2 years) Caring for disabled child, spouse, parent or grandparent Teen parent who is attending high school full time or a GED program or approved training or employment related activity for 30 hours a week Pregnant women in the third trimester Disability/illness Caring for disabled person General hardship/other personal barriers to employment No job available/high local unemploymentFirst families reach time limitJuly 2002December 1998June 1997Support Services tc \l2 "Support Services Transitional Medicaid 12 months12 months18 monthsTransitional childcareNo time-limit for low-income families (but must provide copayments with earned income)12 months12 monthsOutcomestc \l1 "OutcomesState caseload at 1/96663,212242,572714,523State caseload 12/98414,872150,641330,616Percent change37%38%54%  ILLINOIS tc \l1 "ILLINOISMASSACHUSETTS TEXASPercent of State Caseload in the City/County-1998 67%21.3%8.7%Number of welfare families in City/County-1998113,41913,88014,252Percent reduction in number of families since 1994 in City/County28.5%40%36.7%Total reported cases closed, September 1997.Information not available8,53347,398Percent distribution of TANF closed cases by reason of closureInformation not availableEmployment: 56.4% Marriage: 1% 5-year limit: 0% Sanction: 0% State Policy: 9.3% Other: (Can include family voluntarily leaving): 33.3%Employment: 37.1% Marriage: 0.9% 5-year limit: 0% Sanction: 0% State Policy: 39.8% Other: (Can include the family voluntarily leaving): 22.2%Work participation rates (all families/two-parent families)NA31.5/71.119.4/34.3 Sources: Gallagher, L. Jerome, Megan Gallagher, Kevin Perese, Susan Schreiber, and Keith Watson. One Year After Federal Welfare Reform: A Description of State Temporary Assistance for Needy Families (TANF) Decisions as of October 1997. Washington, DC: The Urban Institute, June 1998. Maloy, Kathleen, LaDonna Pavetti, Peter Shin, Julie Darnell, and Lea Scarpulla-Nolan. A Description and Assessment of State Approaches to Diversion Programs and Activities Under Welfare Reform. Washington, DC: The George Washington University Medical Center, Center for Health Policy Research, August, 1998. National Governors( Association Center for Best Practices, Round Two Summary of Selected Elements of State Programs for Temporary Assistance for Needy Families, November, 10, 1998, Washington, DC: National Governors( Association. Commonwealth of Massachusetts, Department of Transitional Assistance, TANF State Plan, September, 1996. Katz, Bruce and Kate Carnevale, The State of Welfare Caseloads in America(s Cities, Washington, DC: The Brookings Institution, Center on Urban and Metropolitan Policy, February 1999. State of Illinois, Department of Human Services, TANF State Plan, July 1997. State of Texas, Department of Human Services, Workforce Commission, TANF State Plan, November 1997. US Department of Health and Human Services, Administration for Children and Families, Office of Planning, Research and Evaluation, Characteristics and Financial Circumstances for TANF Recipients: July-September 1997, August 1998. tc \l1 "Boston Massachusetts is a relatively wealthy state. In 1996-97, the median household income was $41,212, compared with the national median income of $36,656. Reflecting the state's relative wealth, the estimated state poverty rate was 9.7 percent, significantly lower than the national rate of 13.8 percent. Child poverty in the state was estimated at 14.6 percent, compared with a national rate of 20.8 percent. In Suffolk County, where Boston lies, however, the estimated general poverty rate was 17.7 percent and that for children under 18 was 28.3 percent. Massachusetts implemented a welfare policy in the early 1990's that shifted support from cash-based assistance for low-income people to work-oriented programs with significant recipient responsibilities. The state also introduced a number of programs to aid recipients during this transition. Since the revised policies were introduced, welfare caseloads have declined about 54 percent in Suffolk County and in the state as a whole. Suffolk County residents made up about 21 percent of the state caseload in 1998, although they compose only about 11 percent of the state's population. According to the 1990 Census, about 24 percent of Boston's residents were black, 11 percent were Hispanic, and 59 percent were white. (The remainder are Asian or of other races and were not included in our sample.) Among Hispanics, about 42 percent were Puerto Rican. The maximum TANF benefit in the state was between $565 and $579 per month for a family of three in 1999, depending on whether the family was exempt from the state's time limits. This compares with a national state median of about $377. The grant, despite its relative generosity, represents a decrease in real value of about 48 percent since 1970. Chicago Illinois is not quite as wealthy as Massachusetts, but still relatively well-off. The median state household income was $40,873 in 1996-1997, somewhat higher than the national median of $36,656. The state poverty rate was 11.3 percent, and the child poverty rate was 18.5 percent. The general poverty rate for Cook County, which is dominated by the city of Chicago, was above the national average at 14.7 percent, however. The county poverty rate for children was 25.8 percent, significantly higher than that for the U.S. as a whole. Illinois' pre-PRWORA welfare reform plan included changes such as a family cap, increased sanctions, and the mandatory development of a (self-sufficiency plan( for recipients, as well as changes to the earned income disregard. In 1997, however, Illinois declined the offer of a federal waiver to continue that system, and agreed to adapt to the requirements of PRWORA. The central features of the current system are described in table 2. Welfare caseloads decreased about 46 percent from the state's peak in 1995, when about 710,000 people were on the rolls. However, residents of Chicago remain a significant part of the state's caseload. Although caseload size decreased in Cook County during the 1990s, the decline was not as rapid as elsewhere in the state. As a result, the proportion of the caseload living in Cook County increased slightly between 1994, when it was 64 percent, and 1998, when it reached 67 percent. Chicago and Illinois in general have a larger minority population than does Massachusetts. About 46 percent of Chicago residents are non-Hispanic white, 39 percent are black, and 19 percent are Hispanic of any race, according to the 1990 Census. The maximum TANF benefit for a family of three in Illinois varies by county. It is $377 per month in Cook County, comparable to the national median. The grant level remained the same as in 1989, and represents a real decline of 60 percent since 1970. San Antonio Texas is a relatively poor state. The poverty rate ( estimated at 18.5 percent in 1995 ( was significantly above the national average of 13.8 percent. The state's child poverty rate for 1995 was estimated at 26.9 percent. The poverty rate in Bexar County, where San Antonio is, was estimated at 19.4 percent in 1995. The estimated county child poverty rate was almost 30 percent. The state median household income was $34,453 in 1996-1997, compared with the national figure of $36,656. The Texas welfare reform system, approved by the state legislature in 1995, is characterized by a (work first( approach to getting recipients into employment. Consequently, the state's provisions are a mix of stringent and lenient, with exemptions from time limits and work requirements made for individuals with young children or with a disabled family member, and those with personal barriers to work. Compared to Boston and Chicago, San Antonio makes up a relatively small share of the state's population and the state's welfare caseload. About 7 percent of the Texas population resided in Bexar County in 1998, and the county made up about 8.7 percent of the state's welfare cases. While the welfare recipient rate has declined somewhat less slowly in San Antonio than in the state overall, the discrepancy is not as great as that observed in Chicago. The maximum TANF benefit in the state were increased in 1999 to $200 per month, and future benefit levels are indexed to a percentage of the federal poverty level. Even with this increase, however, the state remains in the bottom five states in terms of benefit levels, and its grant level has lost 67 percent of its real value since 1970. Description of in-person interviews Instrument Content and Measurements Adult Portion From adults, we gathered basic demographic information, as well as conventional measures of income, poverty, and family and labor force behavior ( data that are generally useful for studies of the disadvantaged population. Specific questions address household structure, marriage, fertility, cohabitation, education, job history and characteristics, hours of work, earnings and wage rates, and sources of income. We also collect information on current and past welfare program participation, as well as participation in other programs such as Food Stamps, SSI, and so forth. In addition, we focus particular attention on the time respondents spend in welfare-related activities and information on actions related to job search. Child Portion We focus on four main areas of child well-being: behavioral, cognitive, socio-emotional, and physical development. To assess these domains, the instrument combines measures used in large national studies with more detailed, process-oriented information on family functioning and child development using comprehensive measures. The questionnaire is designed to address environments and situations that pertain specifically to children in certain age groups while at the same time attempting to use similar measures across age groups in order to increase the longitudinal and cross-sectional comparability of findings. Throughout the child portion of the instrument, we have chosen measures that have proven validity and reliability in low-income and minority populations. The survey instrument is composed of two computer-assisted personal interviews (CAPI). The first is a 100-minute interview conducted with the primary caregiver of the focal child. The second consists of standardized assessments of the child and a 30-minute interview, conducted if he or she is in the 10-14-year-old age group. Interview questions are organized into modules, each focusing on a different topic related to the lives of the children and caregivers in our study. The modules are listed in Table 3. Items included in the available survey data fall into three categories: original items, recoded items, and constructed items. Original items are those asked of the respondent at the time of interview. Recoded variables are those that include changes to values in an original item. These changes are usually based on information from other items in the data set. One example from the welfare module is variable PWH58A11, a recoded version of survey item PWH58A, in which original values were changed based on additional information from survey items PWH59A and PWH59B. Constructed variables combine information from several variables into a single item. In the Three-City Study data set, welfare receipt status is one such item. Table 3. Modules in the survey instrument Caregiver interview Demographics Education and Training Labor Force, Employment, and Work History Family Background Self-Esteem/Self-Concept Networks Housing Neighborhoods Family Routines Inventory Home Environment Positive Behaviors Scale Child Behavior Checklist  Challenges to Parenting Parenting Style Time Use Father Involvement Child Support Financial Strain Index Welfare Participation and Experiences Income Health and Disability Illegal Activities* Domestic Violence* Brief Symptom Inventory* Focal child interview Physical measurements Ages and Stages (ages 0-2) Woodcock-Johnson (ages 4-14) Letter-Word Identification Applied Problems Schooling (ages 10-14) Child-Mother Relationship Scale* (ages 10-14) Parental Monitoring* (ages 10-14) Father Involvement* (ages 10-14) Father-Child Relationship Scale* (ages 10-14) Delinquency Scale* (ages 10-14) Sex and Pregnancy* (ages 10-14) Brief Symptom Inventory* (ages 10-14) *Starred items were administered by audio computer-assisted self interview (ACASI). The data set contains a separate record for each of the 2,402 surveyed households. For each household, data from interviews with the adult respondent and focal child appear on the same record. Each record contains 3,545 variables. The size of the data set in SAS for Windows Version 8 format is 81 MB. Variable documentation Survey data documentation includes the following information: For all survey questions: Variable names Universe Question wording Response categories Frequencies Skip patterns In addition, all recoded and constructed items include: List of original items used to create new item Notes on how the new item was created The documentation of original items also includes a cross-reference to associated recoded and constructed variables, where appropriate. For example, the constructed variable WELFST11 is a 4-category item indicating when the respondent was most recently receiving welfare, if ever. The variable was constructed from responses to items PWH22A, PWH23A, and PWH24A. In the documentation for each of these original items, a note is included indicating that a constructed variable incorporating information from that item exists on the data set. Format for all original survey items Codebook documentation includes information in the following generic format: >ITEM NAME IN CAPI INSTRUMENT< [U: UNIVERSE] {variable name in data set(Alphanumeric or Numeric)} Cross-reference information for existing recodes, if any Question wording # % Numeric value=value label (>SKIP PATTERN n Percentage TOTAL N 100.00 MISSING n For example, item PWH22A in the data set is documented as: >P_WH22< [U: ALL ADULT RESPONDENTS] {pwh22a(N)} See Welfare recode WELFST11 at the end of this section. Are you or [CHILD/your children] regularly receiving [WELFARE] benefits now? # % 1 YES (> go to WH30 894 37.22 2 NO 1502 62.53 - 1 DON(T KNOW 5 0.21 -2 REFUSED 1 0.04 TOTAL 2402 100 MISSING 0 Formats for recoded and constructed variables Recoded and constructed items are documented using the following generic format: (Note that there are no (item names( because the recoded variables were not in the CAPI instrument.) VARIABLE NAME IN DATA SET Variable label Original items used to create recode/constructed variable Value Value label Frequency Percent TOTAL Missing For example, the constructed item WELFST11 is documented as: WELFST11 Household's welfare receipt status Original items used to create recode: PWH22A, PWH23A, PWH24A Value Value label Frequency Percentage -2 Refused 1 .04 -1 Don't know 5 .21 1 Currently receiving TANF/welfare 894 37.22 2 Received TANF/welfare within last 2 years 469 19.53 3 Received TANF/welfare before last 2 years 430 17.90 4 Never received TANF/welfare 600 24.98 9999 Cannot be determined 3 .12 TOTAL 2402 100.00 Missing 0 We also provide an appendix with extensive documentation on how recoded and constructed items were created. For each new item, the documentation includes a description of the item's purpose, a summary of the logic used to create the item, and the item's frequency distribution or mean. Protections Question wording has been deleted from copyrighted items, including the Brief Symptom Inventory, Child Behavior Checklist, Woodcock-Johnson Letter-Word Identification and Applied Problems, and Ages and Stages. We have included variables that provide sub-scale scores as well as overall scores where appropriate. Contact the instrument designers to obtain the original instrument and question wording. All data in survey items requesting identifying information like names, addresses, and birth dates have been removed. In addition, identifying information, including names and specific locations, have been removed from items where a verbatim response was recorded. The deleted information has been replaced with the letter X. Weights Because this survey is based on a clustered, stratified sample, we recommend that users employ weights in their statistical analyses. The survey contractor, RTI, Inc., developed household weights that take into account clustering, stratification, and non-response; and child-specific weights, which adjust further for the number of children in the household. The principal investigators developed normalized versions of the household and child weights that weight each city equally. (These are also referred to occasionally as equalized weights.) All four weights are included in the public release data set. As of February 2007, the weights have been revised from their original values to correct for inconsistencies that have been identified during the course of data analysis. N=2393 for all weights; weight values are not included for duplicate cases (DUPLICAT=1). The weights included on the file have been trimmed so that weight values are top-coded at the 95th percentile. This step was taken to reduce the impact of outlying values on the weight variables. RTI Household weights: (R1DUT5WT) These weights adjust the individual responses to account for the following factors: Clustering: As noted above, we did not carry out a simple random sample, in which every household in a city would have had an equal probability of being selected. Rather, block groups were ranked according to percent poor for each city-specific racial/ethnic group; then a subset of block groups were randomly selected; and then households were randomly selected from within these block groups. The weights adjust for the probability that a given household was selected. Stratification: A screening interview was conducted to see whether a family fell within one of the cells of the design matrix. Then families in specific cells were sampled at different rates in order to obtain a diverse sample. The weights adjust for the probability that a household in a given cell was selected. Non-response: In addition, the weights were adjusted for the probability that a family responded to the screening interview and the main survey interview. RTI Child weights: (R1CHT5WT) The child weights make one additional adjustment. Because RTI selected one child per household, children in large families were less likely to be chosen than children in small families. The child weight adjusts for the number of age-eligible children in the household. Use of the child weights allows the investigator to generalize to all children in the households selected. Other things being equal, The RTI weighting procedure assigns larger values to households in cities with higher populations because these households had a smaller likelihood of being selected than did households in cities with lower populations. In this data set, respondents from Chicago are weighted higher, all else equal, than respondents in San Antonio, who are in turn weighted higher than respondents in Boston because Chicago is the largest city and Boston is the smallest. So analyses that use the RTI weights will reflect information from Chicago more than information from the other cities, and information from San Antonio more than from Boston. If the investigator wants to report results that are proportional to population size, the RTI weights should be used. Normalized weights: However, the principal investigators of the Three-City Study felt that this population was rather arbitrary and that it might be preferable to report results that weight each city equally and which therefore present the average experiences of households in the three cities. They modified the RTI household and child weights to create normalized (equalized) weights in which the total weights for households in one city equals the total weights in the other cities. The normalized household and child weights are called R1DUE5WT for dwelling units and R1CHE5WT for children. These weights are recommended for data analysis. A note of caution: the normalized weights are applicable only if an analysis includes the entire sample. If a subset is used, that subset could be clustered in some of the cities and not others. And if so, a normalization performed for the whole sample will no longer weight each citys selected households equally. Instead, the RTI weights would have to be normalized anew to preserve the equal-cities property. See Appendix A for a description of this procedure and sample syntax for the SAS system. Should an investigator use the household weights or the child weights? There is no right or wrong answer to this question. The sampling design sampled children -- one per eligible household, and the inclusion of caregivers was ancillary to that choice. So in a statistical sampling sense, this is a sample of children. It could be argued, then, that the child weights are the better choice, and the principal investigators have used the child weights for nearly all of their analyses. However, if one is concerned solely about the adult caregivers, one might opt for the household weights; otherwise, the experience of caregivers with many children will be weighted more heavily than the experience of those with fewer children (because the child weights are larger for children with larger numbers of age-eligible siblings in their household). Identifying variables ZRID: Unique identifier, wave 1 survey data HHID: Unique household identifier to match across waves NEWID: Cross-wave caregiver-specific unique identifier. A household may have multiple associated caregivers over time. For example, a child may reside with her biological mother at wave 1, with her grandmother at wave 2, and again wither her biological mother at wave 3. In order to facilitate tracking caregivers across waves, NEWID was created. Each caregiver at each wave is assigned a value on NEWID. NEWID takes the value HHID+01 for the wave 1 caregiver. Where a new caregiver is introduced, his/her values of NEWID is HHID+02. (After data cleaning, no cases were found where a child resided with one new caregiver at wave 2 and a different new caregiver at wave 3. Had that been the case, NEWID would equal HHID+03 for the second new caregiver.) Recommendations to users Delete 9 duplicate cases for analysis, unless you are replicating an analysis published prior to February 2007. The 9 duplicate cases may be identified in either of two ways: Using the variable DUPLICAT, remove all cases where DUPLICAT=1 Using any of the weight variables, remove all cases where a weight value is missing. In a cross-wave analysis of caregivers, merge caregiver files by the variable NEWID. A household may have multiple associated caregivers over time. For example, a child may reside with her biological mother at wave 1, with her grandmother at wave 2, and again wither her biological mother at wave 3. In order to facilitate tracking caregivers across waves, NEWID was created. Each caregiver at each wave is assigned a value on NEWID. NEWID takes the value HHID+01 for the wave 1 caregiver. Where a new caregiver is introduced, his/her values of NEWID is HHID+02. (After data cleaning, no cases were found where a child resided with one new caregiver at wave 2 and a different new caregiver at wave 3. Had that been the case, NEWID would equal HHID+03 for the second new caregiver.) Use normalized (equalized) weights for analysis. Re-normalize as necessary (following guidelines above). Data users familiar with earlier versions of the data file may find that a handful of values on the variable PHHEX_2 (focal childs gender) have changed. In the course of cross-wave analysis, researchers on the Three-City Study team found inconsistent reports for the focal childs gender. These cases were investigated, and the current values of PHHEX_2 are correct and consistent across waves. Appendix A Renormalizing sampling weights The standard weights that are used in analyses based on Three-City Study data are R1CHE5WT and, to a lesser extent, R1DUE5WT. R1CHE5WT is the normalized focal child weight, as described in the introduction to this codebook. R1DUE5WT is the normalized dwelling unit weight. These weights are normalized versions of the original sampling weights provided by RTI, Inc., the survey contractor. The normalized weights were created in order to give equal weight to each of the three cities in the sample. The normalized weight is the ratio between a respondents original weight and the adjusted mean weight for that persons city. The adjusted mean weight for each city is obtained by dividing the sum of the weights in that city by the average number of people in each city. The average number of people in each city is equal to the number of people in the sample divided by the number of cities in the sample. When the total sample minus duplicate cases is used, the N in each city is 1/3 of that sample size, or 797.6667. The mean of the normalized weights is 1. Normalizing the sampling weights allows each city to make an equal contribution to analysis. The original sampling weights reflect the fact that the cities in our sample are not of equal in population. The weights for our families in a large city like Chicago are much larger than those in a small city like Boston. Using the original weights, the effect of this difference in city size and weights would be that people from Chicago would have a much larger say in the results than would people from Boston and San Antonio. In order to equally represent each city in analysis (1/3 Boston, 1/3 Chicago, 1/3 San Antonio), a variable representing the ratio described above is provided. The normalized weight works by neutralizing the variation in the probability of selection among cities and focusing only on differences in selection between individuals. R1CHE5WT and R1DUE5WT are correct only if the entire sample is being analyzed. The normalized weights need modification if data users restrict their analysis to a subgroup that is not equally divided across the three cities. For example, if an analysis is restricted to current welfare recipients only (more in Chicago, fewer in San Antonio) or Hispanics only (more in San Antonio, fewer in Chicago), the average of the weights in each city will change. Taking the example of current welfare recipients only, the mean of R1CHE5WT is going to be substantially lower than 1 in San Antonio for that subgroup because there are fewer welfare recipients in San Antonio. Conversely, the mean of R1CHE5WT will be slightly greater than 1 in Chicago because welfare recipients are slightly more numerous in that city. Therefore, an analysis based on these skewed weights would give more weight to Chicago than to San Antonio, meaning each of the 3 cities would not be equally represented. In order to correct for this problem, it is necessary to renormalize the weights. In order to do so, the data user will go through the same steps that were used to create the first set of normalized weights, but first restricting their sample to the subgroup of interest. This is appropriate because the data user is really interested only in the weight of each observation relative to all the other observations in the subgroup. This requires that the normalized weight for the subgroup has a mean of 1. Renormalizing will also allow the Ns in each city to sum to the unweighted total for the subgroup of interest. The following steps show the logic the data user should use in order to renormalize the weights appropriately. The specific SAS syntax appears at the end of this appendix. This example is for normalized focal child weights only. The normalized weights are based on the original sampling weight called R1CHT5WT. For that reason, this variable must be included on the working data set. Create a working data set that contains only the observations of interest. Look at the mean for R1CHE5WT overall and for each city in your new subgroup to see to what extent the weighting is affected by the restriction. Determine whether you need to renormalize. (There is no hard and fast rule to guide this decision.) Renormalize the weights for your subgroup by obtaining the new average sampling weight within each city, and dividing that average into each respondent's individual sampling weight. The method for creating the average sampling weight will be different in each statistical software package. In SAS, the technique is to: Create a variable with a constant value that is equal to the number of observations in the subgroup. The number of observations for any variable in the renormalized, weighted data should be equal to this number. Create another variable with a constant value equal to the number of cities observed in the subgroup. This information is required so that the cities are divided into equal sample sizes when the data are weighted. Obtain the adjusted mean weight for each city. As described above, this is equal to the sum of all the original sampling weights within a city, divided by the average number of respondents in that city. The average number of respondents is equal to 1/3 of the sample size in cases where all 3 cities are included in the subgroup, or ( the sample size where only 2 cities are in the subgroup. Create a new item with a total of 3 values, each value equal to the sum of the weights in a city divided by the average number of respondents in a city (sum of weights/average # of respondents). Replace existing values of normalized weights with new values by dividing individuals' original sampling weights by the mean weight for each city. Now the weights are renormalized for the subgroup of interest. To double-check, look at the mean for R1CHE5WT to make sure that it is now 1. Data users can add this syntax to their programs each time it is necessary to renormalize, or simply create a macro containing this syntax that can be invoked as required. An example of the effects of renormalizing The program used to create this output looks first at the means of R1CHE5WT overall and in each city in the full sample, and then shows the weighted frequencies for city size and for a dichotomous variable, IBELP11 (Whether R(s income is below the poverty level). Notice that the Ns sum to 233. R1CHE5WT: Normalized focal child weight, overall mean VariableObsMeanStd. Dev.MinMaxr1che5wt239311.444219.0147788.878365 R1CHE5WT: Normalized focal child weight, mean by city CityObsMeanStd. Dev.MinMaxBoston923.8642109.8560644.06411664.648709Chicago7581.0523311.398555.0147787.213307San Antonio7121.1203131.985677.07378488.878365 CITY11: Weighted city sizes are equal with full sample CityFreqPercentCum FreqCum PercentBoston797.733.33797.733.33Chicago797.733.331595.366.67San Antonio797.733.332393100.00 IBELP11: Whether Rs household income below poverty line, imputed Value labelFreqPercentCum FreqCum PercentAbove poverty636.226.6636.226.6Below poverty175673.42392100.00 In the next step, the sample is restricted only to cases where the respondent is currently receiving welfare (WELFST11=1). The N for this subgroup is 889. The same descriptives appear below. Notice that the number of observations sums to 889 in the table showing mean values of R1CHE5WT in each city, but the mean for each city has changed, and the overall mean is no longer 1. EQSZCHWT: Normalized focal child weight, overall mean VariableObsMeanStd. Dev.MinMaxr1che5wt889.83517481.159243.0147788.878365 EQSZCHWT: Normalized focal child weight, mean by city CityObsMeanStd. Dev.MinMaxBoston315.68797.6823773.06446974.648709Chicago3611.0834331.401692.0147787.213307San Antonio213.63211471.193456.07419798.878365 The weighted frequency for CITY11 and IBELP11 in this subsample is only 742.5 (see below). It should be 889. This discrepancy is a result of the skewed weights. CITY11: N by city, weighted, pre-renormalizing CityFreqPercentCum FreqCum PercentBoston216.729.19216.729.19Chicago391.152.68607.881.87San Antonio134.618.13742.5100.00 IBELP11: Whether Rs household income below poverty line, imputed Value labelFreqPercentCum FreqCum PercentAbove poverty87.7511.8387.7511.83Below poverty653.988.17741.7100.00 After doing the renormalizing procedure described above, the mean for EQSZCHWT averages to1again, and the N for the subsample sums to 889. EQSZCHWT: Normalized focal child weight, overall mean VariableObsMeanStd. Dev.MinMaxr1che5wt88911.569492.011196619.5406 EQSZCHWT: Normalized focal child weight, mean by city CityObsMeanStd. Dev.MinMaxBoston315.9407408.9330931.08815696.356717Chicago361.8208681.061998.01119665.465194San Antonio2131.3912362.626707.163303919.5406 CITY11: N by city, after renormalizing CityFreqPercentCum FreqCum PercentBoston296.3.333296.3.333Chicago296.3.333592.7.667San Antonio296.3.3338891.00 IBELP11: Whether Rs household income below poverty line, imputed Value labelFreqPercentCum FreqCum PercentAbove poverty106.612.0106.612.0Below poverty781.488.0887.9100.0 So the renormalization altered the distribution of IBELP11, but by less than two-tenths of one percentage point (88.0 vs. 12.0, compared to 88.17 vs. 11.83). In many cases the net effect will be this modest, but in subsamples that are highly skewed to one or two cities, the difference can be greater. Census geocodes The following variables appear on the wave 1 restricted-use data files: Variable nameVariable descriptionGDTSFIPS2000 Census FIPS Code, StateGDTCFIPS2000 Census FIPS Code, CountyGDTTR2000 Census Tract IDGDTBLKGR2000 Census Block Group ID SAS Syntax for the renormalization procedure Step 1: When creating the subgrouped data set, be sure to keep the following variables: city11, r1dut5wt, and r1dut5wt. libname libref pathname; data working data set (keep= city11, r1dut5wt, r1dut5wt, varlist); set libref.filename; if ; Step 2: Sort subgrouped data by the unique identifier, ZRID. Count up the number of cases in the subgroup by using the retain statement below. This step will create two new variables, n and count. proc sort data=working data set; by zrid; data macro1; set working data set; by zrid; n=1; retain count; count=sum(count, n); Step 3: Create a variable with a constant value equal to the number of cases in the subgroup. The renormalized, weighted data should add up to this number. In this case, the new variable is called allcount, and it is output to a temporary data set called counted. proc sort; by n; proc univariate noprint data=macro1; by n; var count; output out=counted max=allcount; Step 4: Merge the new variable back on to the working data set. Create another variable with a constant value equal to the number of cities observed in the subgroup. This information is needed to make sure that the cities are divided into equal sample sizes when weighted. This step will create three new variables, c and citycnt, which are added to the temporary data set macro3, and allcity, which is output to a temporary data set called countcty. data macro2; merge macro1 counted; by n; c=1; proc sort data=macro2; by city11; data macro3; set macro2; by city11; retain citycnt; if first.city11 then citycnt=sum(citycnt, c); proc sort; by c; run; proc univariate noprint data=macro3; by c; var citycnt; output out=countcty max=allcity; Step 5: Merge allcity back onto working data set. data macro4; merge macro3 countcty; by c; proc sort; by city11; Step 6: Obtain the mean weight for each city. This is equal to the sum of all the weights within a city, divided by the average number of respondents in that city. The average number of respondents is equal to 1/3 of the sample size (in cases where all 3 cities are in the subgroup) or ( of the sample size (where only 2 cities are in the subgroup); data macro5; set macro4; by city11; Step 6a: Sum the weights within each city. This step creates two new variables, sumchwgt and sumduwgt. retain sumchwgt sumduwgt; if first.city11 then sumchwgt=.; sumchwgt=sum(sumchwgt, r1dcht5wt); if first.city11 then sumduwgt=.; sumduwgt=sum(sumduwgt, r1dut5wt); Step 6b: Create two new items each with up to 3 values, each value equal to the sum of the weights in each city. This step outputs two new variables, allchwgt and allduwgt to a temporary data set, weights. proc univariate noprint data=macro5; by city11; var sumchwgt sumduwgt; output out=weights max=allchwgt allduwgt; run; data new working data set (drop=n c sumduwgt sumchwgt citycnt); merge macro5 weights; by city11; Step 6c: Create a new item that is equal to the average number of respondents in each city (subsample size/# of cities). This step creates one new variable, countnum. countnum=allcount/allcity; Step 6d: Create two new items each with 3 values, each value equal to the sum of the weights in each city divided by the average number of respondents in each city (sum of weights/average # of respondents). Two new variables are created at this step, eqchwgt and eqduwgt. eqchwgt=allchwgt/countnum; eqduwgt=allduwgt/countnum; Step 6e: Replace existing values of normalized weights with new values (Original sampling weight divided by the mean weight for each city). The two new weights take the name of the previous normalized weights, eqszchwt and eqszduwt. r1che5wt = r1cht5wt /eqchwgt; r1due5wt = r1dut5wt /eqduwgt; run; Step 7: delete temporary data sets used in macro. proc delete data=macro1; proc delete data=macro2; proc delete data=macro3; proc delete data=macro4; proc delete data=macro5; proc delete data=counted; proc delete data=countcty; run;  One interviewer may have falsified some data, especially in the sections with repetitive items (IDACC=1518738 19). In investigating the resulting composite scores from this interviewer, repeatedly, his cases had scores significantly different from those of the total sample. For these 11 cases, the composite scores and original variables were coded to missing. The survey is one component of a multidisciplinary project that also includes an embedded developmental study of about 600 children age 2 to 4 in 1999 and an ethnographic study of about 250 families residing in the same neighborhoods as the survey families.     1 This is the income eligibility limit for a recipient family of three with no unearned income or child care expenses. The 13th month is shown reflecting the changes in the limits because of changes in earned income disregards. 2 Limit is based on rules that apply to families subject to the time limits. Income eligibility limits are lower for families exempt from the time limits. 3 New legislation in 1999 authorized the creation of a pilot program to establish IDAs for low-income workers. 4 This is for a non-exempt family with a rent allowance. 5 The maximum grant is linked at 17% of the FPL and adjusted upwards annually pending appropriations. Additionally, TANF families will receive a once-a year $60 per child per grant in August to help with back to school expenses. 6 For families not subject to the time limit, the disregard remains $120 and one-third but without time limit. 7 The 25 hour-a-week qualification is through September 1999; it will increase to 30 hours in October 1999. 8 Parents whose youngest child is between two and six may become nonexempt if work activities become available. Parents are exempt while the youngest child is under three months old for children not in the assistance unit. 9 The exemption is based on the youngest child at the time of the initial application, regardless of a new child is added to the assistance unit. New legislation in 1999 lowers this exception by 2002 to families with children under one to comply with Federal regulations. 10 The 12-month time limit applies to recipients with 18 or more months of recent work experience and a high school diploma, a GED, or a certificate from a post-secondary or vocational school. The 24-month time limit applies to recipients with 6 to 17 months of recent work experience or education through 11th grade but less than a high school degree. The 36-month time limit applies to recipients with less than 6 months work and education less than 11th grade. For the first two groups, the time limit begins once the recipient is notified of an opening in the JOBS program. The third group has a one-year waiting period before the time limit begins. 11 The 25 hour-a-week qualification is through September 1999; it will increase to 30 hours in October 1999. 12 Since 1997 was a transition year for the new welfare program (TANF), states that started their programs after April 1997 were not required to report work rates.  =>B[\hiq ONOQeiv}PQTx y ߺ֢߮ߺںߺߛߛߔhNEh9^Jh9OJQJ^J hRjh9 jBh9hRj0J>*B*phjh90JH*U h95\jha0JU hRjhRjh XhRj h9>*h9 h#h[ h[ h%"j h#h%"j h96]1 >[\ijklmnopq P0h$a$gd%"jgd%"j $ P0ha$N[ E q PQfg# 5vF V&f!6$1$^ P0hh`h P0hST 9:O]kl h1$ & F gdNE & FgdRj & FgdRj & FgdRjgdRj P0h P0hh1$`hx y tddd$ h$1$Ifa$Ykd$$Ifx$t04 xaK$ h:$1$Ifa$ d$If h1$ hh1$`h ^!_!d!|!!!!!d&e&&&&&&K'L'Q'i'n'|'}'~'Q,R,,,,,,<-=-B-Z-_-m-n-o-1 2y335555򸯸򸯸򪦪hNEh9^JhNEh9CJ^JaJ hNEh9h9 h9>*hNEh9^Jh9OJQJ^J h?y5CJOJQJ\^JaJ h95CJOJQJ\^JaJh?yCJOJQJ^JaJh9CJOJQJ^JaJ1 !!!!!!2!3!4!5!6!7!F!G!^! d$If$ h:$1$Ifa$$ h$1$Ifa$^!_!`!a!b!c!d!XIIII@ d$If h:$1$Ifkd$$Ifx4rG- %$`t```04 xaKd!j!k!t!u!{! d$If$ h:$1$Ifa${!|!}!~!2## h:$1$Ifkd$$Ifx4֞G- %$ t   04 xaK~!!!!!!.kd&$$Ifx4rG- %$ t   04 xaK$ h:$1$Ifa$ d$If h:$1$If!!!!!!!!!!!!!!!$ h:$1$Ifa$ h:$1$If d$If!!!3* d$Ifkde$$Ifx֞G- %$t04 xaK!!!!!!!!!!!!!!$ h:$1$Ifa$ d$If h:$1$If !!!3* d$Ifkds$$Ifx֞G- %$t04 xaK!!!""""""""""#"$ h:$1$Ifa$ d$If h:$1$If #"$"%"3* d$Ifkd$$Ifx֞G- %$t04 xaK%"&"'"-"."2"3"?"@"F"G"M"N"T"$ h:$1$Ifa$ d$If h:$1$If T"U"V"3* d$Ifkd$$Ifx֞G- %$t04 xaKV"W"X"Y"Z"["\"]"^"_"`"a"b"c"$ h:$1$Ifa$ d$If h:$1$If c"d"e"3* d$Ifkd$$Ifx֞G- %$t04 xaKe"f"g"p"q"t"u"|"}""""""$ h:$1$Ifa$ d$If h:$1$If """3* d$Ifkd $$Ifx֞G- %$t04 xaK""""""""""""""$ h:$1$Ifa$ d$If h:$1$If """3* d$Ifkd $$Ifx֞G- %$t04 xaK""""""""""""""$ h:$1$Ifa$ d$If h:$1$If """3* d$Ifkd $$Ifx֞G- %$t04 xaK"""######## #!#'#$ h:$1$Ifa$ d$If h:$1$If '#(#)#3* d$Ifkd $$Ifx֞G- %$t04 xaK)#*#+#,#-#.#/#0#1#2#3#4#5#6#$ h:$1$Ifa$ d$If h:$1$If 6#7#8#3* d$Ifkd $$Ifx֞G- %$t04 xaK8#9#:#@#A#D#E#L#M#S#T#Z#[#a#$ h:$1$Ifa$ d$If h:$1$If a#b#c#3* d$Ifkd$$Ifx֞G- %$t04 xaKc#d#e#k#l#o#p#|#}######$ h:$1$Ifa$ d$If h:$1$If ###3* d$Ifkd$$Ifx֞G- %$t04 xaK##############$ h:$1$Ifa$ d$If h:$1$If ###3* d$Ifkd $$Ifx֞G- %$t04 xaK##############$ h:$1$Ifa$ d$If h:$1$If ###3* d$Ifkd$$Ifx֞G- %$t04 xaK##############$ h:$1$Ifa$ d$If h:$1$If ###3* d$Ifkd)$$Ifx֞G- %$t04 xaK#$$$$$ $'$($.$/$5$6$<$$ h:$1$Ifa$ d$If h:$1$If <$=$>$3* d$Ifkd7$$Ifx֞G- %$t04 xaK>$?$@$F$G$J$K$W$X$^$_$e$f$l$$ h:$1$Ifa$ d$If h:$1$If l$m$n$3* d$IfkdE$$Ifx֞G- %$t04 xaKn$o$p$v$w${$|$$$$$$$$$ h:$1$Ifa$ d$If h:$1$If $$$3* d$IfkdS$$Ifx֞G- %$t04 xaK$$$$$$$$$$$$$$$ h:$1$Ifa$ d$If h:$1$If $$$3* d$Ifkda$$Ifx֞G- %$t04 xaK$$$$$$$$$$$$$$$ h:$1$Ifa$ d$If h:$1$If $$$3* d$Ifkdo$$Ifx֞G- %$t04 xaK$$$$$$$$$$$$%%$ h:$1$Ifa$ d$If h:$1$If %%%3* d$Ifkd}$$Ifx֞G- %$t04 xaK% % %%%%%$%%%+%,%2%3%9%$ h:$1$Ifa$ d$If h:$1$If 9%:%;%3* d$Ifkd$$Ifx֞G- %$t04 xaK;%<%=%F%G%K%L%S%T%Z%[%a%b%h%$ h:$1$Ifa$ d$If h:$1$If h%i%j%3* d$Ifkd$$Ifx֞G- %$t04 xaKj%k%l%u%v%z%{%%%%%%%%$ h:$1$Ifa$ d$If h:$1$If %%%3* d$Ifkd$$Ifx֞G- %$t04 xaK%%%%%%%%%%%%%%$ h:$1$Ifa$ d$If h:$1$If %%%3* d$Ifkd$$Ifx֞G- %$t04 xaK%%%%%%%%%%%%%%$ h:$1$Ifa$ d$If h:$1$If %%%3* d$Ifkd$$Ifx֞G- %$t04 xaK%%%%%%%%%%%%&&$ h:$1$Ifa$ d$If h:$1$If &&&3* d$Ifkd$$Ifx֞G- %$t04 xaK& & &&&&&&&$&%&+&,&2&$ h:$1$Ifa$ d$If h:$1$If 2&3&4&3* d$Ifkd $$Ifx֞G- %$t04 xaK4&5&6&<&=&A&B&N&O&U&V&\&]&c&$ h:$1$Ifa$ d$If h:$1$If c&d&e&&&3*** h1$kd!$$Ifx֞G- %$t04 xaK&&&&&&&&&&&&&&&zzzzzzzz$ h$1$Ifa$Ykd"$$Ifx$t04 xaK$ h:$1$Ifa$ d$If&&&&''''' '!'"'#'$'3'4'K' d$If$ h:$1$Ifa$$ h$1$Ifa$K'L'M'N'O'P'Q'XIIII@ d$If h:$1$Ifkd#$$Ifx4rG- %$`t```04 xaKQ'W'X'a'b'h' d$If$ h:$1$Ifa$h'i'j'k'2## h:$1$Ifkd$$$Ifx4֞G- %$ t   04 xaKk'l'm'n'|'}'.kd!&$$Ifx4rG- %$ t   04 xaK$ h:$1$Ifa$ d$If h:$1$If}'~''''''''''''''$ h:$1$Ifa$ h:$1$If d$If'''3* d$Ifkd`'$$Ifx֞G- %$t04 xaK''''''''''''''$ h:$1$Ifa$ d$If h:$1$If '''3* d$Ifkdn($$Ifx֞G- %$t04 xaK'''''''''(( ( (($ h:$1$Ifa$ d$If h:$1$If (((3* d$Ifkd|)$$Ifx֞G- %$t04 xaK(((((( (,(-(3(4(:(;(A($ h:$1$Ifa$ d$If h:$1$If A(B(C(3* d$Ifkd*$$Ifx֞G- %$t04 xaKC(D(E(F(G(H(I(J(K(L(M(N(O(P($ h:$1$Ifa$ d$If h:$1$If P(Q(R(3* d$Ifkd+$$Ifx֞G- %$t04 xaKR(S(T(](^(a(b(i(j(p(q(w(x(~($ h:$1$Ifa$ d$If h:$1$If ~(((3* d$Ifkd,$$Ifx֞G- %$t04 xaK(((((((((((((($ h:$1$Ifa$ d$If h:$1$If (((3* d$Ifkd-$$Ifx֞G- %$t04 xaK(((((((((((((($ h:$1$Ifa$ d$If h:$1$If (((3* d$Ifkd.$$Ifx֞G- %$t04 xaK(((((((())) )))$ h:$1$Ifa$ d$If h:$1$If )))3* d$Ifkd/$$Ifx֞G- %$t04 xaK)))))))))) )!)")#)$ h:$1$Ifa$ d$If h:$1$If #)$)%)3* d$Ifkd0$$Ifx֞G- %$t04 xaK%)&)')-).)1)2)9):)@)A)G)H)N)$ h:$1$Ifa$ d$If h:$1$If N)O)P)3* d$Ifkd1$$Ifx֞G- %$t04 xaKP)Q)R)X)Y)\)])i)j)p)q)w)x)~)$ h:$1$Ifa$ d$If h:$1$If ~)))3* d$Ifkd2$$Ifx֞G- %$t04 xaK))))))))))))))$ h:$1$Ifa$ d$If h:$1$If )))3* d$Ifkd4$$Ifx֞G- %$t04 xaK))))))))))))))$ h:$1$Ifa$ d$If h:$1$If )))3* d$Ifkd5$$Ifx֞G- %$t04 xaK))))))))))))))$ h:$1$Ifa$ d$If h:$1$If )))3* d$Ifkd$6$$Ifx֞G- %$t04 xaK)*** * * *****"*#*)*$ h:$1$Ifa$ d$If h:$1$If )***+*3* d$Ifkd27$$Ifx֞G- %$t04 xaK+*,*-*3*4*7*8*D*E*K*L*R*S*Y*$ h:$1$Ifa$ d$If h:$1$If Y*Z*[*3* d$Ifkd@8$$Ifx֞G- %$t04 xaK[*\*]*c*d*h*i*p*q*w*x*~***$ h:$1$Ifa$ d$If h:$1$If ***3* d$IfkdN9$$Ifx֞G- %$t04 xaK**************$ h:$1$Ifa$ d$If h:$1$If ***3* d$Ifkd\:$$Ifx֞G- %$t04 xaK**************$ h:$1$Ifa$ d$If h:$1$If ***3* d$Ifkdj;$$Ifx֞G- %$t04 xaK**************$ h:$1$Ifa$ d$If h:$1$If ***3* d$Ifkdx<$$Ifx֞G- %$t04 xaK***+++++++++ +&+$ h:$1$Ifa$ d$If h:$1$If &+'+(+3* d$Ifkd=$$Ifx֞G- %$t04 xaK(+)+*+3+4+8+9+@+A+G+H+N+O+U+$ h:$1$Ifa$ d$If h:$1$If U+V+W+3* d$Ifkd>$$Ifx֞G- %$t04 xaKW+X+Y+b+c+g+h+t+u+{+|++++$ h:$1$Ifa$ d$If h:$1$If +++3* d$Ifkd?$$Ifx֞G- %$t04 xaK++++++++++++++$ h:$1$Ifa$ d$If h:$1$If +++3* d$Ifkd@$$Ifx֞G- %$t04 xaK++++++++++++++$ h:$1$Ifa$ d$If h:$1$If +++3* d$IfkdA$$Ifx֞G- %$t04 xaK++++++++++++++$ h:$1$Ifa$ d$If h:$1$If +++3* d$IfkdB$$Ifx֞G- %$t04 xaK+++++,, , ,,,,,,$ h:$1$Ifa$ d$If h:$1$If , ,!,3* d$IfkdC$$Ifx֞G- %$t04 xaK!,",#,),*,.,/,;,<,B,C,I,J,P,$ h:$1$Ifa$ d$If h:$1$If P,Q,R,,,3*** h1$kdD$$Ifx֞G- %$t04 xaK,,,,,,,,,,,,,,,zzzzzzzz$ h$1$Ifa$YkdE$$Ifx$t04 xaK$ h:$1$Ifa$ d$If,,,,,,,,------$-%-<- d$If$ h:$1$Ifa$$ h$1$Ifa$<-=->-?-@-A-B-XIIII@ d$If h:$1$IfkdF$$Ifx4rG- %$`t```04 xaKB-H-I-R-S-Y- d$If$ h:$1$Ifa$Y-Z-[-\-2## h:$1$IfkdG$$Ifx4֞G- %$ t   04 xaK\-]-^-_-m-n-.kdI$$Ifx4rG- %$ t   04 xaK$ h:$1$Ifa$ d$If h:$1$Ifn-o-}-~------------$ h:$1$Ifa$ h:$1$If d$If---3* d$Ifkd[J$$Ifx֞G- %$t04 xaK--------------$ h:$1$Ifa$ d$If h:$1$If ---3* d$IfkdiK$$Ifx֞G- %$t04 xaK--------------$ h:$1$Ifa$ d$If h:$1$If ---3* d$IfkdwL$$Ifx֞G- %$t04 xaK---........".#.&.$ h:$1$Ifa$ d$If h:$1$If &.'.(.3* d$IfkdM$$Ifx֞G- %$t04 xaK(.).*.+.,.-.../.0.1.2.3.4.5.$ h:$1$Ifa$ d$If h:$1$If 5.6.7.3* d$IfkdN$$Ifx֞G- %$t04 xaK7.8.9.B.C.F.G.N.O.U.V.\.].`.$ h:$1$Ifa$ d$If h:$1$If `.a.b.3* d$IfkdO$$Ifx֞G- %$t04 xaKb.c.d.m.n.q.r.~.......$ h:$1$Ifa$ d$If h:$1$If ...3* d$IfkdP$$Ifx֞G- %$t04 xaK..............$ h:$1$Ifa$ d$If h:$1$If ...3* d$IfkdQ$$Ifx֞G- %$t04 xaK..............$ h:$1$Ifa$ d$If h:$1$If ...3* d$IfkdR$$Ifx֞G- %$t04 xaK..............$ h:$1$Ifa$ d$If h:$1$If ...3* d$IfkdS$$Ifx֞G- %$t04 xaK../// / ///// /!/$/$ h:$1$Ifa$ d$If h:$1$If $/%/&/3* d$IfkdT$$Ifx֞G- %$t04 xaK&/'/(/.///2/3/?/@/F/G/M/N/Q/$ h:$1$Ifa$ d$If h:$1$If Q/R/S/3* d$IfkdU$$Ifx֞G- %$t04 xaKS/T/U/[/\/`/a/h/i/o/p/v/w/z/$ h:$1$Ifa$ d$If h:$1$If z/{/|/3* d$IfkdW$$Ifx֞G- %$t04 xaK|/}/~////////////$ h:$1$Ifa$ d$If h:$1$If ///3* d$IfkdX$$Ifx֞G- %$t04 xaK//////////////$ h:$1$Ifa$ d$If h:$1$If ///3* d$IfkdY$$Ifx֞G- %$t04 xaK//////////////$ h:$1$Ifa$ d$If h:$1$If ///3* d$Ifkd-Z$$Ifx֞G- %$t04 xaK/////00000000 0$ h:$1$Ifa$ d$If h:$1$If 0!0"03* d$Ifkd;[$$Ifx֞G- %$t04 xaK"0#0$0*0+0/0007080>0?0E0F0I0$ h:$1$Ifa$ d$If h:$1$If I0J0K03* d$IfkdI\$$Ifx֞G- %$t04 xaKK0L0M0S0T0X0Y0e0f0l0m0s0t0w0$ h:$1$Ifa$ d$If h:$1$If w0x0y03* d$IfkdW]$$Ifx֞G- %$t04 xaKy0z0{0|0}0~000000000$ h:$1$Ifa$ d$If h:$1$If 0003* d$Ifkde^$$Ifx֞G- %$t04 xaK00000000000000$ h:$1$Ifa$ d$If h:$1$If 0003* d$Ifkds_$$Ifx֞G- %$t04 xaK00000000000000$ h:$1$Ifa$ d$If h:$1$If 0003* d$Ifkd`$$Ifx֞G- %$t04 xaK00000000011 1 1 1$ h:$1$Ifa$ d$If h:$1$If 1113* d$Ifkda$$Ifx֞G- %$t04 xaK111111 1,1-13141:1;1>1$ h:$1$Ifa$ d$If h:$1$If >1?1@13* d$Ifkdb$$Ifx֞G- %$t04 xaK@1A1B1C1D1E1F1G1H1I1J1K1L1M1$ h:$1$Ifa$ d$If h:$1$If M1N1O13* d$Ifkdc$$Ifx֞G- %$t04 xaKO1P1Q1W1X1[1\1c1d1j1k1q1r1u1$ h:$1$Ifa$ d$If h:$1$If u1v1w13* d$Ifkdd$$Ifx֞G- %$t04 xaKw1x1y111111111111$ h:$1$Ifa$ d$If h:$1$If 1113* d$Ifkde$$Ifx֞G- %$t04 xaK11111111111111$ h:$1$Ifa$ d$If h:$1$If 1113* d$Ifkdf$$Ifx֞G- %$t04 xaK11111111111111$ h:$1$Ifa$ d$If h:$1$If 111 23** h1$kdg$$Ifx֞G- %$t04 xaK 2x3y33)5*55555555555 h $1$If h $1$If d$If h1$ hh1$`h5555555555555555555555555666667777777777Ƶԥ}sdsddsddsd}}jhNEh9U^JhNEh9^JhNEh95\^J"jhNEh95U\^JhNEh9^JhNEh95CJ\^JaJ!hNEh95CJ\^JaJhNEh9CJ^JaJ$jhNEh9CJU^JaJhNEh9CJ^JaJhNEh95\^J'555555 h $1$Ifmkdh$$Ifh4\D#81z z z 4 hah556 6.6<6 h $1$Ifkkdi$$Ifh\D#81z z z 4 hah<6=6D6G6K6O6 h $1$Ifkkdzj$$Ifh\D#81z z z 4 hahO6P6f6666 h $1$Ifkkd=k$$Ifh\D#81z z z 4 hah666777 h $1$Ifkkdl$$Ifh\D#81z z z 4 hah77n7z777f & p@ P ! $1$If h $1$Ifkkdl$$Ifh\D#81z z z 4 hah777'7(7d7f7l7m7z77777 8889@9A9P9Q9R9S9[9\9]9^9f9g9i9j9Ƹƣƚƚƣƚzpz^Q^^Q^hNEh95\^J"jhNEh95U\^JhNEh9^JjhNEh9U^J jhNEh90JH*U^JhNEh9^J(jhNEh90JCJH*U^JaJhNEh9CJH*^JaJhNEh9CJ^JaJhNEh9CJ^JaJ$jhNEh9CJU^JaJhNEh95\^J777777 h $1$Ifkkdm$$Ifh\D#81z z z 4 hah77 858Q8m8uuu h $1$If h $1$IfkkdIn$$Ifh\D#81z z z 4 hahm8n88888 h $1$Ifkkd o$$Ifh\D#81z z z 4 hah8889979u h $1$If h $1$Ifkkdo$$Ifh\D#81z z z 4 hah7989@9R9]9h9 h $1$Ifkkdp$$Ifh\D#81z z z 4 hahh9i99999 h $1$IfkkdUq$$Ifh\D#81z z z 4 hahj9r9s99999:::6:7:::2;3;4;<;E;F;H;J;Z;[;r;s;;;;%<&<7<M<N<k<l<x<<<<<<<<ѼѼѰѼѠޠѰѰѰѰцwjhNEh9U^JhNEh9^J!hNEh95CJ\^JaJhNEh95CJ\^JaJhNEh95\^J(jhNEh90JCJH*U^JaJhNEh9CJ^JaJ$jhNEh9CJU^JaJhNEh9CJ^JaJ+999999 h $1$Ifkkdr$$Ifh\D#81z z z 4 hah9999:: h $1$Ifkkdr$$Ifh\D#81z z z 4 hah:::8:::(;wwww h $1$If  h$1$Ifkkds$$Ifh\D#81z z z 4 hah(;);3;G;H;I;uuu h $1$If h $1$Ifkkdat$$Ifh\D#81z z z 4 hahI;J;Z;v;y;;uuu h $1$If h $1$Ifkkd$u$$Ifh\D#81z z z 4 hah;;;;;; h $1$Ifkkdu$$Ifh\D#81z z z 4 hah;;;)<,<6<uuu h $1$If h $1$Ifkkdv$$Ifh\D#81z z z 4 hah6<7<M<p<s<w<uuu h $1$If h $1$Ifkkdmw$$Ifh\D#81z z z 4 hahw<x<<<<<uuu h $1$If h $1$Ifkkd0x$$Ifh\D#81z z z 4 hah<<<< == h $1$Ifmkdx$$Ifh;\D#81z z z 4 hah<<<<== = = ====== =U=V=|>}>,A-A.A/A_A`AdBBBBBB>CGCHCȻȻڨxh__xUxU_hi2CJ^JaJhNEh9^J j=hNEh9CJ^JaJ(jhNEh90JCJH*U^JaJhNEh9CJ^JaJhNEh9CJ^JaJ$jhNEh9CJU^JaJhNEh95\^J"jhNEh95U\^JhNEh95\^JjhNEh9U^JhNEh9^J!==<=>?? h $1$Ifkkdy$$Ifh\D#81z z z 4 hah?? @T@]@g@ h $1$Ifkkd}z$$Ifh\D#81z z z 4 hahg@h@~@@@,A-Aw]]> ,p@ P !$$1$If & F VZ$1$If^Zgdi2 h $1$If h$1$Ifkkd@{$$Ifh\D#81z z z 4 hah-A.A/A0AaAtAA9B_B`BBv[ & F hx$1$If^x`gdi2  $1$If & F 2<$1$If^<gdi2! ,p@ P !$ $1$If  ) p@ P !$$1$Ifgdi2 ,p@ P !$$1$If BBBBC,C=C>C[kkd|$$Ifh\D#81z z z 4 hah & F hx $1$If^x`gdi2 & F hx$1$If^x`gdi2>CHC\C]C^C_CuCQkkd|$$Ifh\D#81z z z 4 hah! ,p@ P !$ $1$If! ,p@ P !$ $1$IfHCICQCZC[CuCCD_E`EbEdElEmEnEoEwEEEEEqcRqBhChi25CJ\^JaJ!hChi25CJ\^JaJhChi2CJ^JaJ$jhChi2CJU^JaJhChi25\^JhChi2^JhChi2CJ^JaJhi2h9CJOJQJ^JaJhNEh9^JhNEh9CJ^JaJ!hNEh95CJ\^JaJhNEh9CJ^JaJ$jhNEh9CJU^JaJuCCD^E_E`EbEcEQOOkkd}$$Ifh\D#81z z z 4 hah! ,p@ P !$ $1$If! ,p@ P !$ $1$IfcEdEmEnEEEEE^pkdL~$$Ifh\D#81z z z 4 hah h $1$Ifgd$ h $1$Ifgd$ d$Ifgd$EEF.GHHHHHHmpkd~$$Ifh\D#81z z z 4 hah! ,p@ P !$ $1$If EEEEEFFFFFFG.G/G1GLGGGGG+HPHHHHHHHH[I\IIImJnJJJLɽvviihChCCJ^JaJ(jhChi20JCJH*U^JaJ!hChi25CJ\^JaJhChi2CJ^JaJ$jhChi2CJU^JaJhChi25\^JhChi2^JhChi2CJ^JaJ!hChi26CJH*]^JaJhChi26CJ]^JaJ%HHHHHHmLLL! ,p@ P !$ $1$If! ,p@ P !$ $1$Ifpkd$$Ifh\D#81z z z 4 hahHHI IGI]Immmm! ,p@ P !$ $1$Ifpkdk$$Ifh\D#81z z z 4 hah]I^IiIjIIIoN33 & F $1$If^`gdC! ,p@ P !$ $1$If ,p@ P !$$1$Ifpkd.$$Ifh\D#81z z z 4 hahIIIILJJJJKKKyyyyyN & F m#8p @ xHP X !(#$%`'(0*+-h./812$1$If^`gdC & F  $1$If^`gdC & F $1$If^`gdC KKKKLoooN & F m#8p @ xHP X !(#$%`'(0*+-h./812$1$If^`gdCB n$h8p @ xHP X !(#$%`'(0*+-h./812 $1$IfLELFLfL>! ,p@ P !$ $1$Ifpkd$$Ifh\D#81z z z 4 hahP & F m#8p @ xHP X !(#$%`'(0*+-h./812 $1$If^`gdCfLpLLLLLLLLmL! ,p@ P !$ $1$Ifpkd$$Ifh\D#81z z z 4 hah! ,p@ P !$ $1$IfLLLLLLtM|M}M~MMMM NNNNNNN#N+N,N@NNNNNNN OO*Q+QAQDQϾݱϾݱϾ~~i\h95OJQJ\^J(jhChi20JCJH*U^JaJhChi26CJ]^JaJh9CJOJQJ^JaJhChi25\^JhChi2^JhChi2CJ^JaJ!hChi25CJ\^JaJhChi2CJ^JaJ$jhChi2CJU^JaJhChi25CJ\^JaJ#LLLLLLmmmm! ,p@ P !$ $1$Ifpkd$$Ifh\D#81z z z 4 hahLLM_MiMsMmmmm! ,p@ P !$ $1$IfpkdH$$Ifh\D#81z z z 4 hahsMtM}MMMMmLLL! ,p@ P !$ $1$If! ,p@ P !$ $1$Ifpkd $$Ifh\D#81z z z 4 hahMMMMMMmmmm! ,p@ P !$ $1$Ifpkd΅$$Ifh\D#81z z z 4 hahMMMMMMmmmm! ,p@ P !$ $1$Ifpkd$$Ifh\D#81z z z 4 hahMMNNN Nmmmm! ,p@ P !$ $1$IfpkdT$$Ifh\D#81z z z 4 hah N NNNNNNk_M_ h $1$Ifgd$ d$Ifgd$! ,p@ P !$ $1$Ifpkd$$Ifh\D#81z z z 4 hahN:N;NANBNuNyNNNpOOOO! ,p@ P !$ $1$Ifpkdڈ$$Ifh\D#81z z z 4 hah d$Ifgd$ h $1$Ifgd$NNNNNNmLmm! ,p@ P !$ $1$If! ,p@ P !$ $1$Ifpkds$$Ifh\D#81z z z 4 hahNN OOOOmLmm! ,p@ P !$ $1$If! ,p@ P !$ $1$Ifpkd6$$Ifh\D#81z z z 4 hahOOO O!O"Ommmm! ,p@ P !$ $1$Ifpkd$$Ifh\D#81z z z 4 hah"O#OPOjOpOwOmmmm! ,p@ P !$ $1$Ifpkd$$Ifh\D#81z z z 4 hahwOxOOOOOPP!PmmNNNNN ,p@ P !$$1$If! ,p@ P !$ $1$Ifpkd$$Ifh\D#81z z z 4 hah!PXPjPyPPPPPPNpkdB$$Ifh\D#81z z z 4 hah ,p@ P !$$1$If! ,p@ P !$ $1$IfPPPPPP(Q,Q6Q@Qkrkd$$Ifh\D#81z z z 4 hah! ,p@ P !$ $1$If @QAQBQCQDQNQbRSTssssPPP" ,p@ P !$01$^`0 ,p@ P !$1$pkd̎$$Ifh\D#81z z z 4 hahDQNQQ0RR#SSSS8TpTqT U2U3U;UV)W6W9W:W;WCWDWJW4^;^````ee1f2fUfVfJhKhUhVhl״竧~wpp~w jBh9 j@h9 jAh9 h9>*h95CJ\aJ jh95CJU\aJh9hCh9^J$ j=hCh96CJ]^JaJ j=hCh9CJ^JaJhCh96CJ]^JaJhCh9CJ^JaJhCh95\^J)TTUURV8W9W:WKWyYnO ,p@ P !$1$` ,p@ P !$1$ ,p@ P !$ ,p@ P !$" ,p@ P !$01$^`0! ,p@ P !$0^`0 yY[\3^4^<^X`bcdeeeg}iEklllllll ,p@ P !$1$ ,p@ P !$1$`lllllmmooeutuuu#w8wwxxxyz&}=}/)*\]Ƀ45?@ !_h̅х҅݅ޅAL+2ŒČ͌Ό]hC j@h9 jAh9 j=h9 h9< jBh9 h96] h9>*h9 h95\Llooorrtwwwwxxx+xUxgxxx" ,p@ P !$01$^`0 ,p@ P !$1$ ,p@ P !$1$`xxxxxxxxxy'y0yCyQyhyyyyyyyyyyzzz ,p@ P !$1$z7zTzozzzzzz {+{8{X{x{{{{{{" ,p@ P !$`1$^`` ,p@ P !$1$` ,p@ P !$1${%}&}=}>}|}}}}}}}}}}}'~ & F ) p@ P !$1$gdC ,p@ P !$1$` ,p@ P !$1$'~V~|~}~€À?tuvy d$If" ,p@ P !$1$^` ,p@ P !$1$` ,p@ P !$1$ & F ) p@ P !$1$gdC vÁȁiJi< h h1$`h ,p@ P !$1$` ,p@ P !$1$Ykd$$Ifx04 xax! ,p@ P !$:$1$Ifȁ TUyx2kd'$$Ifx4 xa$$ p:$1$If d$If $$ p1$$$ pp1$^p` 1$ h h1$`h "ATk~ʃ˃ 1$ $ 8 1$$$ 8 `1$^``$$ 8 1$ $$ p1$  !^_`iʅ˅H߆ *8 81$^8` 8 1$ *8 1$ *8  1$^ ` !@AMN*+34efߏ `1$^ `1$ 01$ 01$` 8 1$ *8 1$def{~Ǒ86H&'./|[c{š+-39XY_fp8_`Qaksǿhe8hmB*phhXTiB*phhXTi hXTi5h9B*phh9 h95\h%"j h9>*h9hChdhdH*hdF()56ĚŚœ,-YN & F `1$gdXTi `gd9 `1$ `1$^_`aIJ}rs `1$ `gdXTi `1$gdXTi>?IJKL­حYbĵ/&Թݹ)/GN4= #PRSɿ hbnhbnCJOJQJ^JaJhdhdOJQJ^Jh9OJQJ^Jh9CJOJQJ^JaJ j=h9 h95\ h9>* j2h9 h96]hdh9?sμϼ12 01$^`0  & F 1$gdd 1$^`gdd 01$^`0 001$^`0 & F !$1$gddQRpppWppp # p@ P !$1$gdd # p@ P !$01$^`0( & F : p@ P  8 xHX (#1$gdd( & F : p@ P  8 xHX (#1$gdd! ) p@ P !$01$^`0 RS\`eosw$1$Ifgd$l! # p@ P !$01$^`0gddwx8$$$$1$Ifgd$lkd$$IflֈdP % f t0644 la$kdH$$IflֈdP % f t0644 la$1$Ifgd$l$1$Ifgd$l # p@ P !$01$^`0 !"*,[\ 23WXYŸŪh9CJOJQJ^JaJhbnhbnOJQJ^J hbnhbnCJOJQJ^JaJhbnCJOJQJ^JaJh9CJOJQJ^JaJhdh9OJQJ^JC8$$$$1$Ifgd$lkd$$IflֈdP % [ t0644 la"+,$kd$$IflֈdP % [ t0644 la$1$Ifgd$l,48AJR[$1$Ifgd$l[\hlu8$$$$1$Ifgd$lkdX$$IflֈdP % [ t0644 lau~$kd$$IflֈdP % [ t0644 la$1$Ifgd$l$1$Ifgd$l! ) p@ P !$01$^`0$ ) p@ P !$01$^`0gdbn K77777$1$Ifgd$lkd$$IflrdP n 8 t0644 la%,2K77777$1$Ifgd$lkdR$$IflrdP n 8 t0644 la23?EKPWK77777$1$Ifgd$lkd$$IflrdP n 8 t0644 laWXYK**! ) p@ P !$01$^`0kd$$IflrdP n 8 t0644 la7kdD$$IflrdP n 8 t0644 la$1$Ifgd$l7kd$$IflrdP n 8 t0644 la$1$Ifgd$l .6"#YZ[_|ӍӍӍӍ|ӍӍӍӍӍӍӍӍ h9h9CJOJQJ^JaJ hbnh9CJOJQJ^JaJh9CJOJQJ^JaJh9CJOJQJ^JaJ h9^Jhbnh9^Jh9OJQJ^Jh9CJOJQJ^JaJ hbnhbnCJOJQJ^JaJhbnCJOJQJ^JaJ0 7kd$$IflrdP n 8 t0644 la$1$Ifgd$l$1$Ifgd$l & p@ P !$1$gdbn! ) p@ P !$01$^`0 8$$$$1$Ifgd$lkd6$$IflֈdP % [w t0644 la!"$kd$$IflֈdP % [w t0644 la$1$Ifgd$l"#$Z[`disw{$1$Ifgd$l! ) p@ P !$01$^`0$ ) p@ P !$01$^`0gd9 {|8$$$$1$Ifgd$lkd$$IflֈdP % [ t0644 la$kdL$$IflֈdP % [ t0644 la$1$Ifgd$l$1$Ifgd$l8$$$$1$Ifgd$lkd$$IflֈdP % [ t0644 la$kd$$IflֈdP % [ t0644 la$1$Ifgd$lX[y| &(./45:;@AFHhiϼ᠖h9OJQJ^Jh9h9OJQJ^Jh9CJOJQJ^JaJh9CJOJQJ^JaJh9h9CJ^JaJ h9^Jh9h9^Jh9h9^J hbnh9CJOJQJ^JaJh9CJOJQJ^JaJ5 '$1$Ifgd$l & p@ P !$1$gd9! ) p@ P !$01$^`0 '(/5;AGK77777$1$Ifgd$lkdJ$$IflrdP n 8 t0644 laGHPV\bhK77777$1$Ifgd$lkd$$IflrdP n 8 t0644 lahiu{K77777$1$Ifgd$lkd$$IflrdP n 8 t0644 laK***! ) p@ P !$01$^`0kd<$$IflrdP n 8 t0644 la$1$Ifgd$l! ) p@ P !$01$^`0"(K77777$1$Ifgd$lkd$$IflrdP n 8 t0644 la!"')PQRCFGHIQRZ[bdPQRxzоh9CJOJQJ^JaJ h$h9CJOJQJ^JaJh$CJOJQJ^JaJ hbnh$CJOJQJ^JaJh9CJOJQJ^JaJh$ h$h9h9OJQJ^Jh9CJOJQJ^JaJ hbnh9CJOJQJ^JaJ-()7=CIPK77777$1$Ifgd$lkd$$IflrdP n 8 t0644 laPQRK*%*gd$! ) p@ P !$01$^`0kd.$$IflrdP n 8 t0644 la"'159$1$Ifgd$l! ) p@ P !$01$^`09:CGI8$$$$1$Ifgd$lkdԡ$$IflֈdP % J t0644 laIR[cd$kd$$IflֈdP % J t0644 la$1$Ifgd$lde$1$Ifgd$l! ) p@ P !$01$^`0 8$$$$1$Ifgd$lkd4$$IflֈdP % [ t0644 la$kdޣ$$IflֈdP % [ t0644 la$1$Ifgd$l $1$Ifgd$l)-68$$$$1$Ifgd$lkd$$IflֈdP % [ t0644 la6?HPQ$kd2$$IflֈdP % [ t0644 la$1$Ifgd$lQRyz$1$IfgdXl! ) p@ P !$01$^`0$ ) p@ P !$01$^`0gd$z~CEPQUV]^fgrtŷ hbnhCJOJQJ^JaJhCJOJQJ^JaJh9CJOJQJ^JaJh9CJOJQJ^JaJhCJOJQJ^JaJh/RCJOJQJ^JaJ hbnh/RCJOJQJ^JaJh/RCJOJQJ^JaJ2K77777$1$IfgdXlkdܥ$$IflrdP n 8 t0644 laK77777$1$IfgdXlkd$$IflrdP n 8 t0644 laK77777$1$IfgdXlkd($$IflrdP n 8 t0644 laDK***! ) p@ P !$01$^`0kdΧ$$IflrdP n 8 t0644 laDEQV^gs$1$IfgdXl! ) p@ P !$01$^`0stK77777$1$IfgdXlkdt$$IflrdP n 8 t0644 laK77777$1$IfgdXlkd$$IflrdP n 8 t0644 laK*%*gd! ) p@ P !$01$^`0kd$$IflrdP n 8 t0644 la5;>OQY[.<Jnz{ļĴĪ hshs j2hsh9hs hsh9hOJQJ^Jhh>*hh5hhB*phhh>*B*phh9OJQJ^Jh hh9hCJOJQJ^JaJ7IJXmnwczkdf$$Ifl0,"(p t0644 la$Ifgd lgd `gd qq$Ifgd lzkdŪ$$Ifl0,"(p t0644 laqq$Ifgd lzkd$$$Ifl0,"(p t0644 laqq$Ifgd lzkd$$Ifl0,"(p t0644 la,-c^^^^^^^^gds! ) p@ P !$01$^`0zkd$$Ifl0,"(p t0644 la 45&'4STYZhi~gds 23CDrs%BCYZgdsNOij?DETUpqgds7Pi! ) p@ P !$01$^`0gds:E 0HIJMNOQRTUWX[\@ABLMNklmFGH()*;=oqƾƾƾƾƴhCh X0J^JhCh XCJ^JaJhCh X^JhCh XH*^Jjh Uh h Xjh X0JH*Uha hahahahaH*jhaha0JH*Uh98HIMNPQSTVWYZ[AMlG); Z1$^Z  ,Z* j: zJ"$Z1$^Zgda dd[$\$gda;;{! ) p@ P !$01$^`0  ,Z* j: zJ"$Z1$^Z ,Z* j: zJ"$Z1$^Z ,Z* j: zJ"$Z1$^Z;=h9h hCh X^JhCh X0J^Jh X0J^J/0P/ =!"#$% 2 00P/ =!"#$% 2 00P/ =!F"#$% 50P:pNE/ =!J"#8$8% 50P:pNE/ =!J"#8$8% 50P:pNE/ =!J"#8$8% /0P/ =!F"#$% 200P= /!F"#$% 5 000P= /!"#$% 5 000P= /!"#$% 200P= /!F"#$% /0P/ =!F"#$% 2 00P/ =!F"#$% 2 00P/ =!F"#$% H 00P/ =!F"#$% P0   2 00P/ =!F"#$% /0P/ =!F"#$% H 00P/ =!F"#$% P0 2 00P/ =!F"#$% 2 00P/ =!"#$% 2 00P/ =!F"#$% 2 00P/ =!F"#$% 2 00P/ =!F"#$% /0P/ =!F"#$% 2 00P/ =!F"#$% 2 00P/ =!F"#$% /0P/ =!F"#$% 2 00P/ =!F"#$% 8 00P:p9/ =!F"#$% $$IfK!vh5$#v$:V x05t/ 44 xaK=$$IfK!vh5t5555#vt#v#v#v#v:V x40++++5t5555/ / / / / 44 xaKM$$IfK!vh5t555555#vt#v#v#v#v#v:V x40++++5t55555/ / / / 44 xaK=$$IfK!vh5t5555#vt#v#v#v#v:V x40++++5t5555/ / / / / 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK$$IfK!vh5$#v$:V x05t/ 44 xaK=$$IfK!vh5t5555#vt#v#v#v#v:V x40++++5t5555/ / / / / 44 xaKM$$IfK!vh5t555555#vt#v#v#v#v#v:V x40++++5t55555/ / / / 44 xaK=$$IfK!vh5t5555#vt#v#v#v#v:V x40++++5t5555/ / / / / 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK$$IfK!vh5$#v$:V x05t/ 44 xaK=$$IfK!vh5t5555#vt#v#v#v#v:V x40++++5t5555/ / / / / 44 xaKM$$IfK!vh5t555555#vt#v#v#v#v#v:V x40++++5t55555/ / / / 44 xaK=$$IfK!vh5t5555#vt#v#v#v#v:V x40++++5t5555/ / / / / 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK $$IfK!vh5t555555#vt#v#v#v#v#v:V x05t55555/ 44 xaK$$Ifh!vh55z 5z 5z #v#vz :V h455z / / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h;55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z / / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z / / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifh!vh55z 5z 5z #v#vz :V h55z /  / / / / 44 hah$$Ifx!vh5#v:V x05/ 44 xaxc$$If!vh5#v:V x5/ 44 xa$$If!vh555555#v#v#v#v#v#v:Vl t6555555a$$If!vh555555#v#v#v#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh555555#v#v#v#v#v:Vl t6555555a$$If!vh555555#v#v#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh555555#v#v#v#v:Vl t6555555a$$If!vh555555#v#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh555555#v#v#v:Vl t6555555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a$$If!vh55555#v#v#v#v#v:Vl t655555a]$$If!vh5(5p#v(#vp:Vl t65(5p]$$If!vh5(5p#v(#vp:Vl t65(5p]$$If!vh5(5p#v(#vp:Vl t65(5p]$$If!vh5(5p#v(#vp:Vl t65(5p]$$If!vh5(5p#v(#vp:Vl t65(5pL@L Normal1$7$8$H$CJ_HaJmH sH tH @@@  Heading 1@&5CJ\aJ8@8  Heading 2@&5\DA@D Default Paragraph FontRi@R  Table Normal4 l4a (k@(No List <&@< Footnote Reference6O6 Hypertext >*B*ph:O: Level 10^`04O!4 footnote refH*<O2< footnote texCJaJB@BB Header !$CJaJ`OR` _level18 # p@ P 0^`0tObt Body Text InA ,h8 xHX (#h^h`DZ@rD  Plain TextCJOJ QJ ^J aJ>@> Rj Footnote TextCJaJv@v bn Table Grid7:V01$7$8$H$>>@> %"jTitle$1$7$8$H$a$5\N quxl//0112U5_9:[AA*I+s yeR$)*-`=FBI:O]jpqqrsuy_}”  1 e  Ю  > o  گ  E v  ް ( \  ױ  ? s  ۲  @ t  ٳ >[\ijklmnopqEqPQfg ST 9:O]klxy234567FG^_`abcdjktu{|}~#$%&'-.23?@FGMNTUVWXYZ[\]^_`abcdefgpqtu|} !'()*+,-./0123456789:@ADELMSTZ[abcdeklop|} '(./56<=>?@FGJKWX^_eflmnopvw{|  $%+,239:;<=FGKLSTZ[abhijkluvz{  $%+,23456<=ABNOUV\]cde !"#$34KLMNOPQWXabhijklmn|}~          , - 3 4 : ; A B C D E F G H I J K L M N O P Q R S T ] ^ a b i j p q w x ~  !!! !!!!!!!!!!!!!! !!!"!#!$!%!&!'!-!.!1!2!9!:!@!A!G!H!N!O!P!Q!R!X!Y!\!]!i!j!p!q!w!x!~!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!""" " " """""""#")"*"+","-"3"4"7"8"D"E"K"L"R"S"Y"Z"["\"]"c"d"h"i"p"q"w"x"~""""""""""""""""""""""""""""""""""""""""""""""""""""######### #&#'#(#)#*#3#4#8#9#@#A#G#H#N#O#U#V#W#X#Y#b#c#g#h#t#u#{#|#######################################################$$ $ $$$$$$ $!$"$#$)$*$.$/$;$<$B$C$I$J$P$Q$R$$$$$$$$$$$$$$$$$$$$$$$$%%%%%%$%%%<%=%>%?%@%A%B%H%I%R%S%Y%Z%[%\%]%^%_%m%n%o%}%~%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%&&&&&&&&"&#&&&'&(&)&*&+&,&-&.&/&0&1&2&3&4&5&6&7&8&9&B&C&F&G&N&O&U&V&\&]&`&a&b&c&d&m&n&q&r&~&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&''' ' ''''' '!'$'%'&'''('.'/'2'3'?'@'F'G'M'N'Q'R'S'T'U'['\'`'a'h'i'o'p'v'w'z'{'|'}'~''''''''''''''''''''''''''''''''''''''''''''''''(((((((( (!("(#($(*(+(/(0(7(8(>(?(E(F(I(J(K(L(M(S(T(X(Y(e(f(l(m(s(t(w(x(y(z({(|(}(~((((((((((((((((((((((((((((((((((((((((((((((((()) ) ) )))))))) ),)-)3)4):);)>)?)@)A)B)C)D)E)F)G)H)I)J)K)L)M)N)O)P)Q)W)X)[)\)c)d)j)k)q)r)u)v)w)x)y)))))))))))))))))))))))))))))))))))))))))))) *x+y++)-*-----------------. ...<.=.D.G.K.O.P.f......////n/z///////// 050Q0m0n0000000117181@1R1]1h1i111111111111122228222(3)333G3H3I3J3Z3v3y333333333)4,46474M4p4s4w4x44444444 555<56777 8T8]8g8h8~888,9-9.9/909a9t999:_:`:::::;,;=;>;H;\;];^;_;u;;<^=_=`=b=c=d=m=n======>.?@@@@@@@@@@@@A AGA]A^AiAjAAAAAALBBBBCCCCCCDEDFDfDpDDDDDDDDDDDDDDE_EiEsEtE}EEEEEEEEEEEEEEEFFF F FFFFFF:F;FAFBFuFyFFFFFFFFF GGGGGG G!G"G#GPGjGpGwGxGGGGGHH!HXHjHyHHHHHHHHHHH(I,I6I@IAIBICIDINIbJKLLMMRN8O9O:OKOyQST3V4Vu|u}uuuuuuuuu'vVv|v}vxxxxyy?ytyuyvyyyyyyyy zzzTzUzyzzzzzzz{{"{A{T{k{~{{{{{{|||||||| } }}} }!}^}_}`}i}}}}}}~~H~~~~ !@AMN*+34ef߇()56ĒŒ”,-YN_`aIJ}rsδϴ12ĹŹQRS\`eoswxڻۻ"+,48AJR[\hlu~ɼμӼۼ %,23?EKPWXYȽɽ׽ݽ ˿̿տٿ޿!"#$Z[`disw{| '(/5;AGHPV\bhiu{"()7=CIPQR"'159:CGIR[cde )-6?HPQRyzDEQV^gstIJXmnw,-45&'4STYZhi~ 23CDrs%BCYZNOij?DETUpq7PiHIMNPQSTVWYZ[AMlG);;00000000000000000000000000000000000000000000000000 0 0 000 0 000 0 000 0 00000000 0 0000000 0000000 00000 000000 00 0 0 0 0 0 00 00 00 0 0 0 0 0 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00000 0 0000000 0000000 00000 000000 00 0 0 0 0 0 00 00 00 0 0 0 0 0 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00000 0 0000000 0000000 00000 000000 00 0 0 0 0 0 00 00 00 0 0 0 0 0 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00000000000 00 00 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 00000 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 000 00 00 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0  0  0 0 0  0  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 00 00 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 000000 000000 0 0 0 0 0 0 0 0 0 0 0 0000000000000009O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O 09O 09O 09O 09O 09O 09O09O09O 09O 09O09O09O09O09O09O09O09O09O09O09O09O09O 09O 09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O 09O 09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O09O 09O 09O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O 08O08O 08O08O 08O08O 08O08O 08O08O 08O08O 08O08O 08O08O@08O 08O08O08O08O08O08O08O08O08O08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O08O08O08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O08O08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O08O08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O08O08O08O08O08O08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O 08O0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 000000 0 0 0 0 0 0 0 0 0 0 0 0 0 0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00000000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O08O008O0000000000000000000000000000000000000000008O000000000000000000000000000000000000000@0@0@000u@0I00@0I00@0I00@0I00I00t@0@0@0@0@0@0@0@0@0@0@0@0@000 ==AFBFw+,!"{|hi()PQ9:cdPQmnHK00 00} K00 00} K00K00K00K001 K0010 K0 0 <20 K00K0 0 2 K0 0 20 K00K00 K00@0 K00T30 K00T30 K0030 K0030 K00<@0 K0 0 @0 K00K0 0 K0 0 @0 K00K00 K00@0 K0'0(:0K0'0( \0 K0+0,8:0 K0+0,8:0 K0/00:@0 K0 0 @0 0b0c0b00d0e0b00f0g0b00h0i0b00j0k0b0 00K00  00  57j9<HCELDQlzw}>ELW^bj{~ ^!d!{!~!!!!!!#"%"T"V"c"e"""""""'#)#6#8#a#c#########<$>$l$n$$$$$$$%%9%;%h%j%%%%%%%&&2&4&c&&&K'Q'h'k'}'''''((A(C(P(R(~(((((())#)%)N)P)~)))))))))*+*Y*[*********&+(+U+W+++++++++,!,P,,,<-B-Y-\-n-------&.(.5.7.`.b.........$/&/Q/S/z/|/////// 0"0I0K0w0y0000000 11>1@1M1O1u1w111111 255<6O66777m8879h999:(;I;;;6<w<<=?g@-AB>CuCcEEHH]IIKLfLLLsMMMM NNNNO"OwO!PP@QTyYlxz{'~vȁsRw,[u2W"{'Gh(P9Id6QDs;xz{|~      !"#$%&'()*+,-./0123456789:;<=?@ABCDFGHIJKMNOPQRSTUVXYZ[\]_`acdefghiklmnopqrstuvwxyz|}y8@0(  B S  ?#*%.clZ]x%%%%%%#&%&]&_&&&&&&&!'#'N'P'w'y'''''((F(H(t(v((((( ) );)=)r)t)))))))@EJE{IIbJgJsJzJ{JJJJLMȯ\_jlǾտؿ`cFO!GQ BL $MQEM8?w~:AY`el2:7?DLV^_g,45=iqrz{JRU]^nnuzNNPPQQSTVWYZ[04yenZ***-2-33NIIIIbJJKK M Moorr,s0s@yHy*z4zzzzz{ {{}}   '+47Z`io39DFsw%*CGOUjl?BEIU^"7;PTimNNPPQQSTVWYZ[3333333333333333333333333333333333333333333333333333333333333333333333333333333333333 iNO68-3XXY_D35;>OQY[:?DE 011GGL[G;NNPPQQSTVWYZ[@T      !"#$%&'()*+,-./012G4/o:!B&(9L"^Y(K;i@:}YAT`&Efd$H.bK mvm%Fw:yt{"*$$$$$$$$...()()()))$$$$$$$$...()()()))$$$$$$$$...()()()))...()()()))$$$$$$$$...()()()))...()()()))$$$$$$$$...()()()))...()()()))...()()()))$$$$$$$$...()()()))$$$$$$$$...()()()))...()()()))$$$$$$$$...()()()))...()()()))$$$$$$$$...()()()))$$$$$$$$...()()()))$$$$$$$$...()()()))$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$................h^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJ QJ o(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJ QJ o(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJ QJ o(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJ QJ o(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hH^`o(.) ^`hH. pLp^p`LhH. @ @ ^@ `hH. ^`hH. L^`LhH. ^`hH. ^`hH. PLP^P`LhH.h^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJ QJ o(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJQJo(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJ QJ o(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJ QJ o(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJ QJ o(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJ QJ o(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJ QJ o(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohpp^p`OJ QJ o(hHh@ @ ^@ `OJQJo(hHh^`OJ QJ ^J o(hHoh^`OJ QJ o(hHh^`OJQJo(hHh^`OJ QJ ^J o(hHohPP^P`OJ QJ o(hHutPvtvtwt|wtG4`&E:!BytK;i@bK$HmvmYALFwY(ut @h h^h`OJQJo($\vt @V V^V`OJQJo($vt @2 2^2`OJQJo($$wt @ t^`tOJQJo($wt @ 0^`0OJQJo($@ Bullet List ParaNumbers1 Bullet List ParaNumbers1 Bullet List ParaNumbers11 ParaNumbers1 Bullet List ParaNumbers11 ParaNumbers1 Bullet List ParaNumbers11 ParaNumbers12 ParaNumbers1 Bullet List ParaNumbers1 Bullet List ParaNumbers14 ParaNumbers1 Bullet List ParaNumbers12 ParaNumbers1 Bullet List ParaNumbers1 Bullet List ParaNumbers1 Bullet List ParaNumbers1 Bullet List Bullet List Bullet List Bullet List Bullet List Bullet List Bullet List Bullet List Bullet List1 Bullet List Bullet List Bullet List AutoList21 AutoList2@                           Q_                                                                                W2y"! X9 Z6&sa[ !!$e81JjhUXTi%"jbnzTi2NEC 9d/RRjms9?yX#2F^_`abcjt{|}~#$&-2?FMTUWY[]_acdfpt| '(*,.024679@DLSZabdko|'.5<=?FJW^elmov{ $+29:<FKSZahikuz $+235<ANU\cd3KLMNOPWahijklm|}      , 3 : A B D F H J L N P Q S ] a i p w ~  ! !!!!!!!!!!#!$!&!-!1!9!@!G!N!O!Q!X!\!i!p!w!~!!!!!!!!!!!!!!!!!!!!!!!!!!"" """"")"*","3"7"D"K"R"Y"Z"\"c"h"p"w"~""""""""""""""""""""""""""""#####&#'#)#3#8#@#G#N#U#V#X#b#g#t#{##############################$ $$$$ $"$)$.$;$B$I$P$Q$$$$$$%$%<%=%>%?%@%A%H%R%Y%Z%[%\%]%^%m%n%}%%%%%%%%%%%%%%%%%%%%%%%%%&&&&"&&&'&)&+&-&/&1&3&5&6&8&B&F&N&U&\&`&a&c&m&q&~&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&' ''' '$'%'''.'2'?'F'M'Q'R'T'['`'h'o'v'z'{'}''''''''''''''''''''''''''(((( (!(#(*(/(7(>(E(I(J(L(S(X(e(l(s(w(x(z(|(~(((((((((((((((((((((((((() ) ))))),)3):)>)?)A)C)E)G)I)K)M)N)P)W)[)c)j)q)u)v)x))))))))))))))))))))))))-----------. ...<.=.D.G.K.O.P.f......////n/z///////// 050Q0m0n0000000117181@1R1]1h1i11111111111112228222(3)333G3H3I3J3Z3v3y333333333)4,46474M4p4s4w4x44444444 555<56777 8T8]8g8h8809`:=;>;H;\;];^;_;u;;<^=_=b=c=m=====>.?@@@@@@@@@@@@A AGA]A^AjABCEDFDfDpDDDDDDDDDDDDDDE_EiEsEtE}EEEEEEEEEEEEEEEFFF F FFFF:FAFBFuFyFFFFFFFFF GGGGGG G!G"G#GPGjGpGwGxGGGXHHHHHHHH(I,I6I@IAIuyyyzzzS\`eoswxۻ"+,48AJR[\hlu~ɼμӼۼ %,23?EKPWXȽɽ׽ݽ ̿տٿ޿!"[`disw{| '(/5;AGHPV\bhiu{"()7=CIPQ"'159:CGIR[cd )-6?HPQzEQV^gstJXmnw@CutePDF WriterCPW2:CutePDF WriterCutePDF WriterCutePDF WriterS odXXLetterPRIV0''''\KhCu^mSMTJCutePDF WriterResolution600dpiPageSizeLetterPageRegionLeadingEdgeInputSlotOnlyOneCutePDF WriterS odXXLetterPRIV0''''\KhCu^mSMTJCutePDF WriterResolution600dpiPageSizeLetterPageRegionLeadingEdgeInputSlotOnlyOneT @Unknown Gz Times New Roman5Symbol3& z ArialkWP TypographicSymbolsCourier NewGGoudy Old StyleQBaskerville Old FaceE& Century Gothic7&  VerdanaC5  SAS Monospace?5 z Courier New;Wingdings" hhF[ ] {n] {n! xxdjj 2Q ?922Welfare, Children and Families: A Three-City StudyWFSTUDY Paula Fomby @                           ! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? Oh+'0 ,8 X d p|4Welfare, Children and Families: A Three-City StudyWFSTUDYNormal Paula Fomby16Microsoft Office Word@;@9 @~ə] {՜.+,0  hp  wfstudynj 3Welfare, Children and Families: A Three-City Study Title  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./013456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>@ABCDEFHIJKLMNWRoot Entry FDvYData A1Table2WordDocumentSummaryInformation(?DocumentSummaryInformation8GCompObjq  FMicrosoft Office Word Document MSWordDocWord.Document.89q