This study is an impact evaluation of the KALAHI-CIDSS (KC) program. The impact evaluation's key research questions can be divided into the following four themes: 1. Socio-Economic Effects: Does KC increase household consumption? Does KC increase labor force participation? 2. Governance Effects: Does KC increase government leader responsiveness to community needs? Does KC reduce corruption and increase transparency? 3. Community Empowerment Effects: Does KC increase participation in local governance? Does KC increase collective action and contribution to local public goods? 4. Social Capital Effects: Does KC build groups and networks? In what ways are these networks applied? Does KC enhance trust? In order to isolate KC's effects, a randomized control trial evaluation design was chosen. The impact evaluation sample consists of 198 municipalities (with 33 to 69 percent poverty incidence), spread over 26 provinces and 12 regions. The 198 municipalities were paired based on similar characteristics (99 pairs) and then randomly assigned into treatment and control groups through public lotteries. The sample size is large enough to be able to detect MCC's projected eight percent change in household income as well as other smaller effects. As part of the impact evaluation, baseline quantitative data were collected in the study area from April to July 2012. The quantitative data came from 5,940 household surveys in 198 barangays (one from each municipality) and 198 barangay surveys implemented in these same barangays

Abstract

The study includes data and materials (do files, survey instruments) necessary for the replication of the paper: "Unilateral Facilitation Does Not Raise International Migration from the Philippines" by Emily A. Beam, David McKenzie, Dean Yang. According to the study, signifcant income gains from migrating from poorer to richer countries have motivated unilateral (source-country) policies facilitating labor emigration. However, their effectiveness is unknown. The investigators conducted a large-scale randomized experiment in the Philippines testing the impact of unilaterally facilitating international labor migration. Their most intensive treatment doubled the rate of job offers but had no identifable effect on international labor migration. Even the highest overseas job-search rate they induced (22%) falls far short of the share initially expressing interest in migrating (34%). They conclude that unilateral migration facilitation will at most induce a trickle, not a food, of additional emigration.

Geographic coverage

42 barangays from six municipalities in Sorsogon Province

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

Early in 2010, we randomly selected 42 barangays from six municipalities in Sorsogon Province in which to conduct the baseline survey. We collected a household roster from each barangay that included a list of households, and we used these to set barangay-specific target sample sizes proportional to population. We targeted approximately 5% of the total population from each barangay, or roughly 26%of households. We sorted households randomly and selected the first listed households to be our target. When a household could not be located or had no eligible members, we replaced it with the next household on the list.

From each household, interviewers screened the first member they met who had never worked abroad and was age 20-45. Subsequent to the baseline survey, we learned from recruitment agencies that most individuals over age 40 would not be eligible for overseas work, so we restricted our baseline sample to the 4,153 individuals age 20-40 we interviewed. Houses selected were typically far enough apart from each other that concerns about information spillovers are second order; to the extent that there were spillovers, our treatment estimates are lower bounds on the differential impact of more information. The passport assistance was only offered to the respondents themselves, and so it is not subject to such spillovers.

Mode of data collection

Face-to-face [f2f]

Research instrument

Attached

Response rate

We obtained measures of whether the respondent migrated abroad for work from full, proxy, or log surveys for 4,089 respondents, or 98.5% of our sample. Of those, 73% were surveys with the respondents themselves, 20% were proxy surveys, and 7% were log surveys. Excluding the log surveys, we have a 91% response rate for their full set of job search and migration outcome variables.

Abstract

The 2017 Philippines National Demographic and Health Survey (NDHS 2017) is a nationwide survey with a nationally representative sample of approximately 30,832 housing units. The primary objective of the survey is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS 2017 collected information on marriage, fertility levels, fertility preferences, awareness and use of family planning methods, breastfeeding, maternal and child health, child mortality, awareness and behavior regarding HIV/AIDS, women’s empowerment, domestic violence, and other health-related issues such as smoking.

The information collected through the NDHS 2017 is intended to assist policymakers and program managers in the Department of Health (DOH) and other organizations in designing and evaluating programs and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents) and all women age 15-49 years resident in the sample household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the NDHS 2017 is based on a two-stage stratified sample design using the Master Sample Frame (MSF), designed and compiled by the PSA. The MSF is constructed based on the results of the 2010 Census of Population and Housing and updated based on the 2015 Census of Population. The first stage involved a systematic selection of 1,250 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 20 or 26 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the pre-selected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on domestic violence.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used for the NDHS 2017: the Household Questionnaire and the Woman’s Questionnaire. Both questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, universities, and international agencies.

Cleaning operations

The processing of the NDHS 2017 data began almost as soon as fieldwork started. As data collection was completed in each PSU, all electronic data files were transferred via an Internet file streaming system (IFSS) to the PSA central office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the PSU. Secondary editing involved resolving inconsistencies and the coding of openended questions; the former was carried out in the central office by a senior data processor, while the latter was taken on by regional coordinators and central office staff during a 5-day workshop following the completion of the fieldwork. Data editing was carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage, because it maximized the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for more effective monitoring. The secondary editing of the data was completed by November 2017. The final cleaning of the data set was carried out by data processing specialists from The DHS Program by the end of December 2017.

Response rate

A total of 31,791 households were selected for the sample, of which 27,855 were occupied. Of the occupied households, 27,496 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 25,690 women age 15-49 were identified for individual interviews; interviews were completed with 25,074 women, yielding a response rate of 98%.

The household response rate is slightly lower in urban areas than in rural areas (98% and 99%, respectively); however, there is no difference by urban-rural residence in response rates among women (98% for each).

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the Philippines National Demographic and Health Survey (NDHS) 2017 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the NDHS 2017 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the NDHS 2017 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix C of the survey final report.

This dataset contains clustered precinct-level data about the 2016 Philippine vice-presidential elections scraped from the official electoral commission (COMELEC) website, pilipinaselectionresults2016.com. The script used for scraping data is at https://github.com/ianalis/scraper2016 but the data for clustered precinct 67060001 was manually added because it did not follow the assumption of the script (it only transmitted results for VP, party lists and vice governor). The transmission timestamp is based on raw data, as of 12 May 2016 15:45, made available by NAMFREL (http://www.elections.org.ph/2016/results/raw-data.php). This dataset is recent as of 26 May 2016 18:00 UTC.The fields contained in this dataset are:* region* province* municipality* barangay* clustered_precinct* cayetano* escudero* honasan* marcos* robredo* trillanes* registered_voters* ballots_cast* precincts* polling_center* timestampThis dataset contains data from 90642 clustered precincts. However, the source website notes that there were 90655 election returns (ERs) received out of the 93754 ERs expected. The citizens' arms NAMFREL and PPCRV both expect data to come from 94276 clustered precincts. The discrepancies in the numbers of received and expected ERs are still unresolved.