Data and Methods. To better understand couple dynamics, the DHS men’s questionnaire asks husbands about their reproductive preferences and attitudes toward family planning. For husbands in a polygynous marriage, the questions are asked for each of their wives/partners. This analysis uses DHS matched couples’ data from 14 sub-Saharan countries: Benin, Burkina Faso, Ghana, and Mali from Western Africa; Chad from central Africa; and Ethiopia, Kenya, Malawi, Mozambique, Namibia, Rwanda, Uganda, Zambia, and Zimbabwe from eastern and southern Africa. All surveys in this analysis were conducted between 1999 and 2004. The data for women are based on women age 15-49, while the data for men are based on men age 15-59 (with the exception of Kenya, Malawi, Uganda, and Zimbabwe, where the interviewed men are age 15- 54; and Benin, where the interviewed men are age 15-64). The men’s questionnaire is similar in structure to the women’s questionnaire but shorter. To the extent possible, the questions and response categories in the two questionnaires are worded identically to be comparable across countries. The section on fertility preferences includes a question on fertility intentions and ideal number of children. For fertility intentions, women and men were asked, “Would you like to have (a/another) child or would you prefer not to have any (more) children?” For ideal number of children (ideal family size), women and men were asked one of two questions, depending on whether or not they had children. Those who did not have children were asked, “If you could choose exactly the number of children to have in your lifetime, how many would that be?” Respondents who had at least one living child were asked, “If you could go back to the time you did not have children and could choose exactly the number of children to have in your lifetime, how many would that be?” In this study, a woman is defined as infecund if she had no births and no pregnancies in the past five years but has had a birth or pregnancy at some time, and has been married for the past five years but did not use contraception during that period.
Data and Methods. 4.1 Data This study used 2010 data from the China Family Panel Studies (CFPS) (2010) to examine the relevant factors that influence Chinese household decision of gift-exchange. The Institute of Social Science Survey (ISSS) of Peking University launched this annual longitudinal survey in 2010 to collect individual-, family-, and community-level data in contemporary China. There have been multiple waves of surveys conducted, among which 2010, 2011, and 2012 are released. This paper used the 2010 survey results, which cover 25 out of 34 provincial-level administrative divisions that include 23 provinces, four Municipalities, five Autonomous Regions, and two Special Administrative Regions (for additional details see Appendix Table 1). Except for Shanghai and Gansu provinces, of which the share of community numbers is relatively higher than their population share in the entire Chinese population, the weights in other provinces are fairly reasonable compared to the Chinese population distribution. Applying the sample weights in the dataset can solve this issue. As for the age-structure, the median and mean of individual age are both around 45 years old; the average percentage of people over 60 years old is about 18%. This nationally representative survey involves 57,155 individuals that come from 14,960 households across China. 33.52% of the households come from urban residential communities (Xx Xxx Xxx), while 66.48% of them come from villages (Cun Xxx Xxx). The minority ethnicity consists of approximately 10% of the sample. As for occupations, 28.45% of the adults are employed. To analyze the potential impact of occupation on gift-exchange decisions, individuals that were marked as household representations were also categorized into ten industries (for additional details see Appendix Table2). There are several reasons for which only the 2010 dataset was used. First of all, compared to the datasets in other years, it contains a wealth of information about household level financial decision-making, particularly the part that is relevant to the present study on gift-exchange. Also, it provides enough demographic and other relevant information to be controlled in regression analyses. Moreover, the present study does not focus on the over-time changes of behaviors; therefore using one of the panel datasets will not only suffice the purpose of the study, but also avoid the issues that exist potentially in panel datasets.
Data and Methods. Below, we provide a summary of the survey methodology and measured variables. A more elaborate description of the survey questionnaire, methodology and variables is given in a working paper (Xxxxxxx, Xxxxxx, Xxxxxxxxx, xxx Xxxxxxx, & xxx xxx Xxxxxxx, 2015). The survey sample consisted of 2,193 PhD graduates who obtained a PhD from Utrecht University (a broad research university), Delft University of Technology (engineering and technology), Wageningen University (an agricultural university), or Erasmus University Rotterdam (focused on medicine and social sciences, especially economics and management) between April 2008 and March 2009 or from Leiden University (a broad research university) between January 2008 and May 2012. An invitation to the survey (which was open from 23 October 2013 until 21 January 2014) was sent through email or LinkedIn, in which the prospective respondents were informed on the purpose and content of the survey in the invitation, and strict confidentiality guaranteed, only aggregate results (impossible to trace back to individuals) to be published. Furthermore, a test of the survey showed the survey took 20 minutes to complete on average, which was also written in invitation letter, so the respondents would know which response burden to expect. In the online survey itself, the instructions made explicit it was possible to quit the survey. Up to three reminders were sent if respondents had not completed the survey. In total, 1,133 started the survey (52%), and 960 progressed to the final question (44%). Survey data were anonymized before analysis and the key to the respondents’ names and unique survey analysis ID stored in a secured folder. Non-response analysis showed that the respondents were representative of the survey set regarding gender, age, year of PhD, and city of PhD (Waaijer et al., 2015). However, Dutch nationals seemed to be overrepresented in the survey compared to the country of birth of the entire sample. In this study, we used variables on type of job, perception of career prospects, research performance and personal characteristics. Three sectors of employment were distinguished: academic R&D (dubbed academia in the paper for brevity), non-academic R&D (dubbed non-academic research) and non-R&D (dubbed outside research). The classification of respondents into these categories was based on two variables: involvement in R&D and type of employer. We follow the Organisation for Economic Co-operation and Development’s (O...
Data and Methods. DESIGN OVERVIEW In order to test these hypotheses, I conducted a vignette experiment (see Appendix 1). The vignettes were distributed to a nationally representative sample of United States adults via Time-Sharing Experiments for the Social Sciences (TESS) and GfK (formerly Knowledge Networks). 1013 respondents were asked to review and rate a profile of one job candidate. The job candidates in the vignettes are all parents, coupled, of the same race (all implied white), and equally qualified for the position, but vary in terms of gender (male/female), sexual orientation (heterosexual/homosexual), and caregiving position (not indicated/primary/secondary). Male and female respondents were randomly assigned to one of the twelve conditions of the 2x2x3 experimental design. While I make no prediction about respondent gender on job candidate ratings, this design allows for investigating if and how male and female evaluators respond differently to these manipulations. This design allows me to tackle an issue that still plagues the U.S. workplace but from a novel perspective. While Xxxxxxx et al. (2007) focus on comparing parents to non- parents and looking at the intersection with race of worker, I focus solely on white, partnered parents to test how worker evaluations are impacted by job candidate gender, sexual orientation, and caregiving position and their interactions. Further, while there is a growing literature on gay and lesbian couples in the workplace, studies typically employ census data. This approach is very helpful for analyzing trends and shifts in earnings over time and between groups, but does not offer the benefits of an experimental design, which allows for control of potentially confounding variables. Determining whether penalties and premiums arise from differences in choices or expectations around gender performance is possible in an experimental design. In such a design, all parents can be presented as having made the same choices, save for the experimentally manipulated ones. Further, while it is increasingly relevant to today’s workplace, there is scant research at this point in time on how and whether gay and lesbian parents face workplace penalties and premiums associated with parenthood. To isolate these particular main effects and interactions of gender, sexual orientation, and caregiving, I hold other variables constant. The variables that were held constant for each vignette include race (which is manipulated as white by using U.S. bi...
Data and Methods. Data Approximately 247 million people live in Indonesia across 6,000 inhabited islands divided into 34 provinces, which are further split into 500 districts, 7,000 sub-districts, and over 80,000 villages[2]. This analysis used data from the 2012 Indonesia Family Life Survey, East (IFLS, E), a large-scale multi-topic household and community survey that was conducted in seven provinces in Eastern Indonesia [23, 24]. The 2012 survey was designed and implemented by the National Team for Acceleration of Poverty Alleviation (Xxx Nasional Percepatan Penanggulangan Kemiskinan or TNP2K), Poverty Reduction Support Facility, and Australian Aid by SurveyMETER with a structure based on a large ongoing longitudinal IFLS that has been collecting data since 1994 [24]. Sampling was completed in four stages to be representative of the Indonesian population living in eastern provinces. First, two provinces were selected with equal probability from Kalimantan and Sulawesi regions while the remaining five were selected without sampling. Xxxxxxxxxx Xxxxx, Xxxxxxxx Xxxxxxxx, Xxxx Tenggara Timur, Maluku, Maluku Utara, Papua Barat, and Puapa were the seven final provinces included in the survey. Second, 14 villages were drawn from each province without replacement and with equal probability. Third, administrative unit levels were identified; these areas consisted of approximately 100-150 households and then were further divided into smallest local area (SLS) units. One SLS group from each village was randomly selected. The fourth and final step involved listing all households within the selected SLS. From this list, a simple random sample without replacement was taken for 30 rural households or 20 urban households. A more detailed description of IFLS East survey methods are described elsewhere [23]. For each level, both household and community level cross-sectional surveys were conducted to collect information associated with health, education, and socioeconomic status. In this analysis, we focused exclusively on household level data. Outcome variables Three binary vaccination statuses were evaluated in this study: fully vaccinated, vaccinated with three doses of DTP-HepB vaccine (DTP-HepB3), and vaccinated with first dose of measles vaccine (MCV1). Each status was based on the child’s vaccination card, or if the card was not available, by caretaker recall. Fully vaccinated status was defined as the child receiving all recommended doses of vaccine. In Indonesia this includes: one ...
Data and Methods. Data Our data are drawn from the Child Support Noncustodial Parent Employment Demonstration (CSPED) evaluation, a randomized controlled trial conducted in certain counties across eight states in the U.S. To participate, noncustodial parents (NCPs) were required to have established paternity, at least one open child support order, either current or likely future difficulty in making child support payments, and be facing employment difficulty even though they were legally and medically able to participate in gainful employment.1 All participants were at least 18 years of age and not incarcerated at the time of enrollment in the study. Baseline surveys were administered to all participants at the date of their enrollment, between October 2013 and September 2016 (Cancian, et al., 2019). Baseline surveys gathered background information about socioeconomic characteristics, relationships with children, employment status, receipt of public assistance, family of origin characteristics, and motivations for enrolling in the CSPED program. About one year after participants completed the baseline survey, a follow-up survey was conducted. CSPED also collected administrative data from each partner state which we use exclusively for child support payment-related information. 1A small percentage of participants (about 2%) did not owe current child support but were enrolled because they were anticipated to begin to owe. For simplicity, we describe the sample as all owing support. Sample We restrict our analytic sample to NCPs who responded to both a baseline and follow-up survey with CSPED. Our analysis sample consists of noncustodial parents from six of the eight participating states. NCPs (N = 853) from Texas and Iowa were excluded because of data limitations.2 Additionally, we exclude N = 466 individuals who did not identify as fathers, N = 101 NCPs without nonresident children, and N = 449 NCPs with missing data across any of our selected outcomes. We also restrict our child-level outcome data to NCPs’ biological minor children who did not reside with the NCP at both the baseline and follow-up surveys (or who were not yet born at baseline and did not reside with the NCP at follow-up) as measured by the NCP reporting spending fewer than 16 nights in the same place as the child in the past thirty days. This leaves a final analytic sample of N = 2,409. After completing an analysis drawing on data from all six states, we conducted a Wisconsin-only analysis using the same ap...
Data and Methods. Data and sample Our sample consists of divorced parents in Cohorts 28 through 33 of the Wisconsin Court Record Database (CRD).1 We include parents filing for divorce between October 2007 and December 2013 in 21 counties in Wisconsin (including Milwaukee, the largest urban county in the state); the data are weighted to be representative of all divorcing parents in those counties. Data were collected from court records and include demographic information about parents and children; and information about parents’ income, child placement arrangements, and child support obligations at the time of the divorce’s final judgment. Data are linked to administrative records from several sources: the state child support system, which provides information about child support and maintenance payments and receipts; state public assistance records, which provide information about public assistance benefits; records of unemployment compensation payments; and wage records from the Unemployment Insurance (UI) program, which provide information on quarterly earnings for jobs in Wisconsin. The sample is limited to parents with at least one child born when the case initially came to court (the petition date) and with all children under age 13 at the final judgment. It excludes cases that are missing a social security number for one or both parents (required for matching with earnings data). Our base sample includes 2,009 couples. We classify the cases according to placement arrangements, differentiating among sole-mother placement, shared placement (at least 25 percent time with each parent, in accordance with the definition in state statutes), and sole-or other placement types. Within the shared placement group, our initial analyses further classify cases as either mother primary, equal shared, or father primary, based on a counting of 1 We did not include earlier cohorts in this analysis because complete information on unemployment compensation benefits before and after divorce is not available for earlier cohorts, and we wanted to have uniform data sources for the whole sample. overnights to be spent with each parent. For most of our analyses, we further limit the analysis to the subset in the sole-mother, mother-primary, and equal-shared placement groups (n=1,832), due to the very limited numbers in father-primary and sole-father categories, and to long-term trends that suggest that the shift over time has been largely from sole-mother to either mother- primary or equal shar...
Data and Methods. For the county level analysis, the unit of analysis is each county, and the independent variable is a binary variable that codes for whether the majority of members of each county’s elections board is white. The dependent variable is the rejection rate (in percent) of minorities in each county compared to their percentage of the voting age 10 The only county I encountered that does not have an explicitly partisan balanced board is XxXxxxxx county, which awards 4 seats to the party that received the most votes in the past general election, and 3 to the party that received the second most votes. population and registered electorate in each county. For this model, unlike the individual level model, I used data for 2014 and 2015 only. The reason for using only 2014 and 2015 is twofold. First, they are the only two complete years in the rejected data set (it contains July-December 2013 and January-July 2016). Second, 2014 accounts for 66% of all the rejections, most likely because it was an election year. Being a non-election year, 2015 serves as a good comparison to see whether registration or rejection practices change during an election year. Further, using a cross-sectional analysis simplifies this model, as the Elections Boards were constantly changing throughout the 3-year span. I collected the names of each county’s elections board members for 2013-2016, and later only used those from 2014-15. Those members from 2014 were compared only with rejections, Voting Age populations and registered voter counts from 2014—the same, respectively, for 2015. To collect the names of elections board members, I first sent an e-mail to the Secretary of State’s office asking if they had a current list of the Elections Boards for the counties. I followed this request up in-person with an elections assistant on the phone, but was told that they “would look for it,” and never heard back. I then e-mailed every county elections director and chief registrar using the contacts provided on the Secretary of State’s office website. In that e-mail I expressed that I was a student and resident of Georgia and requested the names of the board members from 2013-2016, as well as information on the appointment or elections process through which people end up on the board. From that initial e-mail I heard back from about 70 counties. It is worth noting that the information I was requesting is simply the names of current or recently serving public officials. I was not asking for any informa...
Data and Methods. Given that infrastructure projects have to adapt to local conditions, there is a significant variation in the cost per acre-foot (af) of water for different projects, even within similar categories. To account for this variation, we obtained information for as many projects as we could for each alternative, then we determined unit costs for each project, and finally we calculated the ranges and distribution of costs for each category. To facilitate comparisons, all results are in unit cost terms (dollars per acre-foot per year in 2018 dollars).
Data and Methods. I conducted a cross-sectional quantitative survey with a qualitative supplement through: 120 surveys on Gichagi residents' awareness, attitudes and utilization of community health services; and a focus group discussion with 10 Gichagi community health workers assessing their views of the program.
