The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM(r) classification, census block group, and latitude-longitude. PRIZM(r) classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM(r) classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because of its higher spatial accuracy than other databases describing US Census geographies, including those provided by the US Census. This database includes data only for environmental improvement: In regard to the following environmental and quality of life issues, I'd like you to tell me if you have experienced improvement, declined or remained the same in the past few years." a) cleanliness of streets and sidewalks, b) availability of parks and open spaces, c) quality of parks and open spaces, d) safety and security, e) air quality, and f) water quality." The response is the percentage of respondents in that Prizm class who had an index value in the "seeing improvement" range for the index. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

In this dataset we present two maps that estimate the location and population served by domestic wells in the contiguous United States. The first methodology, called the “Block Group Method” or BGM, builds upon the original block-group data from the 1990 census (the last time the U.S. Census queried the population regarding their source of water) by incorporating higher resolution census block data. The second methodology, called the “Road-Enhanced Method” or REM, refines the locations by using a buffer expansion and shrinkage technique along roadways to define areas where domestic wells exist. The fundamental assumption with this method is that houses (and therefore domestic wells) are located near a named road. The results are presented as two nationally consistent domestic-well population datasets. While both methods can be considered valid, the REM map is more precise in locating domestic wells; the REM map had a smaller amount of spatial bias (nearly equal vs biased in type 1 error), total error (10.9% vs 23.7%,), and distance error (2.0 km vs 2.7 km), when comparing the REM and BGM maps to a California calibration map. However, the BGM map is more inclusive of all potential locations for domestic wells. The primary difference in the BGM and the REM is the mapping of low density areas. The REM has a 57% reduction in areas mapped as low density (populations greater than 0 but less than 1 person per km), concentrating populations into denser regions. Therefore, if one is trying to capture all of the potential areas of domestic-well usage, then the BGM map may be more applicable. If location is more imperative, then the REM map is better at identifying areas of the landscape with the highest probability of finding a domestic well. Depending on the purpose of a study, a combination of both maps can be used. For space concerns, the datasets have been divided into two separate geodatabases. The BGM map geodatabase and the REM map database.

Replication files for David Slichter, "The Employment Effects of the Minimum Wage: A Selection Ratio Approach to Measuring Treatment Effects,” Journal of Applied Econometrics, forthcoming

Firstly, I’ve provided a .do file called sr.do which contains general code for implementing the selection ratio approach, with detailed instructions written as comments in the code.

For the minimum wage application, the main data file is mw_final.dta. A .csv version is also provided. Observations are a county in a time period. I have added self-explanatory variable labels for most variables. A few variables warrant a clearer explanation:

adj1-adj14: List of FIPS codes of all counties which are adjacent to the county in question. Each variables holds one adjacent county, and counties with fewer than 14 neighbors will have missing values for some of these variables.

change, logchange: Minimum wage this quarter - minimum wage last quarter, measured either in dollars or in logs.

time, t1-t108: The variable "time" converts years and quarters into a univariate time period, with time=1 in 1990Q1 and time=108 in 2016Q4. t1-t108 are indicators for each of these time periods.

lnemp_1418, lnearnbeg_1418, lnsep_1418, lnhira_1418, lnchurn_1418: Logs of employment, earnings, separations, hires, and churn, respectively, for 14-18 year olds.

gt1-gt6: Dummies for inclusion in each of the six comparisons used for the main (i.e., not spillover-robust) analysis. All treated counties which neighbor a control country take value 1 for each of these variables; all other treated counties take value 0. Among control counties, gt1=1 if the county neighbors a treated county and 0 otherwise, gt2=1 if the county has gt1=0 but neighbors a gt1=1 county, gt3=1 if county has gt1=gt2=0 but neighbors a gt2=1 county, etc.

h2-h6: Dummies for inclusion in each of the first spillover-robust (i.e., excluding border counties only) comparisons. Among control counties, h2-h6 are equal to gt2-gt6. Among treated counties, h2-h6 are equal to 1 if the treated county has gt1=0 but borders a gt1=1 county, and 0 otherwise.

k3-k6: Dummies for inclusion in each of the second spillover-robust (i.e., excluding two layers) comparisons. Among control counties, these variables are equal to gt3-gt6. Among treated counties, all observations take value 1 except those with gt1=1 or h2=1.

The data sources are as follows. The minimum wage law series is taken from David Neumark's website (https://www.economics.uci.edu/~dneumark/datasets.html). The economic variables are taken from the QWI, which I accessed via the Ithaca Virtual RDC. County adjacency files were downloaded from the Census Bureau (https://www.census.gov/geo/reference/county-adjacency.html).

The file main.do then runs the analyses. The resulting output file containing results is results.dta.

For the incumbency application, the main data file is incumb_final.dta. A .csv version is also provided. This file is drawn from Caughey and Sekhon's (2011) data; see their description of most variables here: https://doi.org/10.7910/DVN/8EYYA2

The key added variables are _IDistancea1-_IDistancea50, which are dummies for inclusion in the 50 comparisons used in the paper. Treated observations (i.e., Democratic wins) with margin of victory below 5 points have each of these variables equal to 1. Control observations have these variables equal to 1 if they fall within the margin of victory range, e.g., _IDistancea9=1 for control observations with Republican margin of victory between 8 and 9 points. Note that these variables are redefined by the code for the analyses of treatment effects away from the discontinuity. Lastly, there is a variable called RepWin which is the treatment variable when treatment is defined as a Republican winning.

The file sr_incumb.do then performs the analysis.

Please contact me with any questions at slichter@binghamton.edu.

Many residents of New York City speak more than one language; a number of them speak and understand non-English languages more fluently than English. This dataset, derived from the Census Bureau's American Community Survey (ACS), includes information on over 1.7 million limited English proficient (LEP) residents and a subset of that population called limited English proficient citizens of voting age (CVALEP) at the Community District level. There are 59 community districts throughout NYC, with each district being represented by a Community Board.

As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.

Abstract

The Labour Force Survey (LFS) is a study of the employment circumstances of the UK population. It is the largest household study in the UK and provides the official measures of employment and unemployment.The first Labour Force Survey (LFS) in the United Kingdom was conducted in 1973, under the terms of a Regulation derived from the Treaty of Rome. The provision of information for the Statistical Office of the European Communities (SOEC) continued to be one of the reasons for carrying out the survey on an annual basis. SOEC co-ordinated information from labour force surveys in the member states in order to assist the EC in such matters as the allocation of the Social Fund. The survey was carried out biennially from 1973 to 1983 and was increasingly used by UK government departments to obtain information which would assist in the framing of social and economic policy. By 1983 it was being used by the Employment Department (now the Department for Work and Pensions) to obtain information which was not available from other sources or was only available for Census years. From 1984 the survey was carried out annually, and since that time the LFS has consisted of two elements:

• a quarterly survey conducted in Great Britain throughout the year, in which each sampled address was called on five times at quarterly intervals, and which yielded about 15,000 responding households in every quarter;
• a 'boost' survey in the spring quarter (March-May), which produced interviews at over 44,000 households in Great Britain and over 4,000 households in Northern Ireland.

Users should note that only the data from the spring quarter and the 'boost' survey were included in the annual datasets for public release, and that only data from 1975-1991 are available from the UK Data Archive. The depositor recommends only considered use of data for 1975 and 1977 (SNs 1757 and 1758), as the concepts behind the definitions of economic activity changed and are not comparable with later years. Also the survey methodology was being developed at the time and so the estimates may not be reliable enough to use.

During 1991 the survey was developed, so that from spring 1992 the data were made available quarterly, with a quarterly sample size approximately equivalent to that of the previous annual data. The Quarterly Labour Force Survey series therefore superseded the annual LFS series, and is held at the Data Archive under GN 33246.

The study is being conducted by the Office for National Statistics (ONS), the government's largest producer of statistics. They compile independent information about the UK's society and economy which provides evidence for policy and decision making, and for directing resources to where they are needed most. The ten-yearly census, measures of inflation, the National Accounts, and population and migration statistics are some of our highest-profile outputs.

Geographic coverage

The whole country.

Analysis unit

• Individuals
• Families/households

Universe

• Households
• All persons normally resident in private households in the United Kingdom

Kind of data

Sample survey data [ssd]

Sampling procedure

Stratified multi-stage sample; for further details see annual reports. Until 1983 two sampling frames were used; in England, Northern Ireland and Wales, the Valuation Roll provided the basis for a sample which, in England and Wales, included all 69 metropolitan districts, and a two-stage selection from among the remaining non-metropolitan districts. In Northern Ireland wards were the primary sampling units. In Scotland, the Address File (i.e. post codes) was used as the basis for a stratified sample.From 1983 the Postoffice Address File has been used instead of the Valuation Roll in England and Wales. In 1984 sample rotation was introduced along with a panel element, the quarterly survey, which uses a two-stage clustered sample design.

The sample comprises about 90,000 addresses drawn at random from the rating lists in 190 different areas of England and Wales With such a large sample, it Will happen by chance that a small number of addresses which were selected at random for the 1979 survey Will come up again In addition 2,000 addresses in 8 of the areas selected in 1979 have been deliberately re-selected again this time (me Interviewers who get these addresses In their work w,ll receive a special letter to take with them.)

The sample is drawn from the "small users" sub-file of the Postcode Address File (PAF), which is a list of all addresses (delivery points) to which mail is delivered, prepared by the Post OffIce and held on computer. "Small users" are delivery points that receive less than 25 afiicles of mail a day and include all but a small proportion of private households. The PAF is updated regularly by the Post Office but, as mentioned in Chapter 1, there was an interruption in the supply of updates in the period leading up to the 1988 msurvey. As a result one third of the sample was drawn from the PAF as at March 1986 and two thirds from the sample as at September 1986. Although the PAF includes newly built properties ahead of their actual occupation, the 1988 sample does seem to have been light in the most recently built properties.

Sampling deviation

One of the limitations of the LFS is that the sample design provides no guarantee of adequate coverage of any industry, as the survey is not industrially stratified. The LFS coverage also omits communal establishments, except NHS housing, students in halls of residence and at boarding schools. Members of the armed forces are only included if they live in private accommodation. Also, workers under 16 are not covered. As in previous years, the sample for the boost survey was drawn in a single stage in the most densely populated areas, in two stages elsewhere. The areas where the sample was drawn in a single stage were:

(I) local authority districts in the metropolitan counties and Greater London; (II) districts which, based on the 1981 Census.

Mode of data collection

Face-to-face [f2f]

Research instrument

All questions in the specification are laid out using the same format. Some questions (for instance USUWRKM) have a main group routed to them, but subsets of this group are asked variations of the question. In such cases the main routing is at the foot of the question as usual, and the subsets are listed separately above it, with the individual aspect of the routing indented slightly from the left of the page.

Cleaning operations

Information Technology Centres provides one-year training and practical work experience course in the use of computers and word processors and other aspects of information technology (eg teletex, editing, computer maintenance).

Response rate

The response rate achieved averaged between 83 percent. The method of calculating response rates is the following: The response rate indicates how many interviews were achieved as a proportion of those eligible for the survey. The formula used is as follows: RR = (FR + PR)/(FR + PR + OR + CR + RHQ + NC + RRI*) where RR = response rate, FR = full response, PR = partial response, OR = outright refusal, CR = circumstantial refusal, RHQ = refusal to HQ, NC = non contact, RRI = refusal to re-interview, *applies to waves two to five only.

Sampling error estimates

As with any sample survey, the results of the Labour Force Survey are subject to sampling errors. In addition, the results of any sample survey are affected by non-sampling errors, i.e. the whole variety of errors other then those due to sampling.

Data appraisal

Day of birth and date of birth variables have been removed from the annual LFS datasets, in the same way that they have been removed from the quarterly LFS datasets from 1992 onwards, as this information is now considered to be disclosive. The variable AGEDFE (age at proceeding 31 August) has been added to all annual datasets.

A dataset listing Maine cities by population for 2024.

A dataset listing Iowa cities by population for 2024.