This commuter mode share data shows the estimated percentages of commuters in Champaign County who traveled to work using each of the following modes: drove alone in an automobile; carpooled; took public transportation; walked; biked; went by motorcycle, taxi, or other means; and worked at home. Commuter mode share data can illustrate the use of and demand for transit services and active transportation facilities, as well as for automobile-focused transportation projects.

Driving alone in an automobile is by far the most prevalent means of getting to work in Champaign County, accounting for over 69 percent of all work trips in 2023. This is the same rate as 2019, and the first increase since 2017, both years being before the COVID-19 pandemic began.

The percentage of workers who commuted by all other means to a workplace outside the home also decreased from 2019 to 2021, most of these modes reaching a record low since this data first started being tracked in 2005. The percentage of people carpooling to work in 2023 was lower than every year except 2016 since this data first started being tracked in 2005. The percentage of people walking to work increased from 2022 to 2023, but this increase is not statistically significant.

Meanwhile, the percentage of people in Champaign County who worked at home more than quadrupled from 2019 to 2021, reaching a record high over 18 percent. It is a safe assumption that this can be attributed to the increase of employers allowing employees to work at home when the COVID-19 pandemic began in 2020.

The work from home figure decreased to 11.2 percent in 2023, but which is the first statistically significant decrease since the pandemic began. However, this figure is still about 2.5 times higher than 2019, even with the COVID-19 emergency ending in 2023.

Commuter mode share data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Means of Transportation to Work.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (18 September 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (14 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

The data provided is the electron phase space density in s^3/m^6 as a function of pitch angle from 33.31 keV to 972.3 keV inside and outside of the event. This data is used to create an energy distribution, which is used to estimate the growth rate.

SV test to demonstrate all aspects of the Target Locate phase of Mode II target acquisition.

The Challenger Glider Mission is a re-creation of the first global scientific ocean survey conducted by the HMS Challenger from 1872-1876. The goals of the mission are to establish a collaborative international network of autonomous underwater glider ports, to assess global ocean model predictive skill while contributing real-time profile data for assimilation in ocean forecast models by operational centers worldwide, and to crowd source student-based ocean research and discovery. Glider is providing temperature, conductivity, salinity, density and current profile observations. The current profiles are logged via external pd0 files and are not available in this delayed mode dataset. RU29 is flying a transect across the Anegada Passage and box pattern throughout the surrounding island regions to monitor heat transport between the Caribbean Sea and the Tropical North Atlantic. This region includes the US Virgin Islands, British Virgin Islands, and Anguilla, areas commonly impacted by tropical cyclones. This delayed mode dataset was created from the high-resolution d/ebd pairs downloaded after the glider was recovered.

Matlab scripts, source C-code, mex compiled C-code, and figure data points for the paper entitled “Semi-Analytical Analytical Modelling of Linear Mode Coupling in Few -Mode Fibers”.

Folders: 0_differential_equations_solver Matlab scripts based on the Symbolic Math Toolbox for the derivation of a semi-analytical solution to the differential equations describing linear mode coupling in few-mode fibres. Scripts available for 3, 4, 5 and 6 modes.

1_C_code_for_high_precision_solution_of_polynomials C-code for the numerical evaluation of the 6-modes semi-analytical solutions obtained in 0_differential_equations_solver. Two versions: “highPrecRootFind_6M_doubleIO” uses always the same seed for the root finding section; “highPrecRootFind_6M_doubleIO_rand” uses a randomized seed for the root finding section.

2_crosstalk_vs_radial_displacement Script for plotting typical fibre coupling coefficients and plotting of the crosstalk introduced by a single fibre displacement as a function of the radial displacement and averaged in the azimuth coordinate.

3_solutions_precision Script for the evaluation of the precision of the semi-analytical solutions proposed against Runge-Kutta-Fehlberg Method (RKF45) numerical solutions.

98_poly_solvers_mex_files_compiled_for_R2014b_64bit Compiled mex C-code at 1_C_code_for_high_precision_solution_of_polynomials. Compiled for Mex Matlab R2014b 64bit.

99_fibre_parameters Typical fibre parameters used in this dataset.

100_figures_data_poins Excel files containing the data points in the figures presented in the paper.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This represents the Service data reported to the NTD by transit agencies to the NTD. In versions of the data tables from before 2014, you can find data on service in the file called "Transit Operating Statistics: Service Supplied and Consumed." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

Full details of every data item available on both the Maternity Indicators dataset and National Community Child Health Database are available through the NWIS Data Dictionary: http://www.datadictionary.wales.nhs.uk/#!WordDocuments/datasetstructure20.htm From 1st April 2019 health service provision for residents of Bridgend local authority moved from Abertawe Bro Morgannwg to Cwm Taf. For more information see the joint statement from Cwm Taf and Abertawe Bro Morgannwg University Health Boards (see weblinks). The health board names have changed with Cwm Taf University Health Board becoming Cwm Taf Morgannwg University Health Board and Abertawe Bro Morgannwg University Health Board becoming Swansea Bay University Health Board. Data for Abertawe Bro Morgannwg and Cwm Taf are available for previous years in this table by selecting the tick boxes in the Area drop-down box.

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the downloads tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

With some exceptions, all samples are probability based and nationally representative of the resident adult population. The coverage area is the entire country including rural areas, and the sampling frame represents the entire civilian, non-institutionalized, aged 15 and older population. For more details on the overall sampling and data collection methodology, see the World poll methodology attached as a resource in the downloads tab. Specific sampling details for each country are also attached as technical documents in the downloads tab. Exclusions: NA Design effect: 1.43

Mode of data collection

Face-to-Face [f2f]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 3.7. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

Data appraisal

The variable WORRIED was not considered in the computation of the published FAO food insecurity indicator based on FIES due to the results of the validation process.

Contents 1 Data set description 1.1 Data set overview 1.2 Parameters 1.3 Processing 1.4 Data 1.5 Ancillary data 1.6 Software 1.7 Media/Format 2 Confidence level note 2.1 Confidence level overview 2.2 Review 2.3 Data quality 1. Data set description 1.1. Data set overview The output data of CENA sensor is basically neutral particle counts.CENA sensor operates in 3 instrument modes (coincidence Mode, counter M ode and Engineering Mode) and the content of the data coming from the CENA sensor is dependent on the instrument mode and the format of the data depends on the telemetry mode. The telemetry modes are Mass Accumulation Mode, TOF Accumulation Mode and Count Accumulation Mode.In Mass accumulation mode, TOF accumulation mode and Count accumulation mode, data coming from the sensor is being sorted by lookup tables and is being summed up into two types of accumulation matrixes (the accumulation matrix and the accumulation scaling matrix) during a time period. The accumulation matrix size changes depending on the binning parameters (energy, channel, phase and mass bins).For details on the CENA sensor of the SARA experiment and the data products, see the EAICD in the DOCUMENT directory. 1.2. Parameters The measured parameter is basically raw neutral particle counts. 1.3. Processing No processing beyond unpacking has been applied to the telemetry data. 1.4. Data Each data product contains all data from one orbit. The data product contain housekeeping data as well as science data as scaling matrix(total counts) and accumulation matrix except for the counter mode operation of CENA where there will be no scaling matrix.The instrument mode and telemetry mode is reflected in the file name (refer to the EAICD in the DOCUMENT directory).CENA data is archived using the storage format of PDS ARRAY of COLLECTION objects.Each CENA PDS data product f ile contains an ARRAY of records of CENA measurements in one orbit. Ea ch record is [truncated!, Please see actual data for full text]

Received wisdom in survey practice suggests that using web mode in the first wave of a panelstudy is not as effective as using interviewers. Based on data from a two-wave mode experiment for the Swiss Household Panel (SHP), this study examines how the use of online data collection in the first wave affects participation in the second wave, and if so, who is affected. The experiment compared the traditional SHP design of telephone interviewing to a mixed-mode design combining a household questionnaire by telephone with individual questionnaires by web and to a web-only design for the household and individual questionnaires. We looked at both participation of the household reference person (HRP) and of all household members in multi-person households. We find no support for a higher dropout at wave 2 of HRPs who followed the mixed-mode protocol or who participated online. Neither do we find much evidence that the association between mode and dropout varies by socio-demographic characteristics. The only exception was that of higher dropout rates among HRPs of larger households in the telephone group, compared to the web-only group. Moreover, the mixed-mode and web-only designs were more successful than the telephone design in enrolling and keeping all eligible household members in multi-person households in the study. In conclusion, the results suggest that using web mode (whether alone or combined with telephone) when starting a new panel shows no clear disadvantage with respect to second wave participation compared with telephone interviews.

This dataset details data on hours worked by public transportation employees and the head counts of employees for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years at the mode and type of service level.

NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Transit Agency Employees database files.

In years 2015-2021, you can find this data in the "Employees" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data.

If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

Abstract

The FAO has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). The FAO conducted round 3 of the DIEM-Monitoring household survey between 8 August and 7 September 2022 to monitor changes in agricultural livelihoods and food security in Chad. Data was collected in face-to-face surveys in the provinces of Kanem, Lac, Moyen-Chari and Wadi Fira. A total of 14 departments were targeted and 3704 households interviewed. The data collection for round 3 took place during the rainy (lean) season, whereas the previous survey took place in December 2021, after the harvest. For more information, please go to https://data-in-emergencies.fao.org/pages/monitoring

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

Data was collected in face-to-face surveys in the provinces of Kanem, Lac, Moyen-Chari and Wadi Fira. A total of 14 departments were targeted and 3704 households interviewed. The data collection for round 3 took place during the rainy (lean) season, whereas the previous survey took place in December 2021, after the harvest.

Mode of data collection

Face-to-face [f2f]

Research instrument

A link to the questionnaire has been provided in the documentations tab.

Cleaning operations

The datasets have been edited and processed for analysis by the Needs Assessment team at the Office of Emergencies and Resilience, FAO, with some dashboards and visualizations produced. For more information, see https://data-in-emergencies.fao.org/pages/countries.

This dataset details stations for each agency and mode for stations reported to the National Transit Database in report years 2022 and 2023. These data include the type of facility and the decade in which it was built.

In many cases, stations are reported by each mode and type of service that uses them. For example, a single station used by bus - directly operated, bus - purchased transportation, and commuter bus - directly operated would be reported three times. For more detail, please see the NTD Policy Manual.

Rural reporters do not report passenger stations and are not included in this file. Modes Demand Response, Demand Response - Taxi, Vanpool, and Publico also do not report stations and are also excluded.

NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Facility Inventory database files.

In years 2015-2021, you can find this data in the "Stations" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data.

If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

The Southern Annular Mode (SAM) is an index that describes climate variation around the South Pole and Antarctica, as far north as New Zealand. It indicates short-term climate variations that can influence New Zealand’s climate. Such climate variations can impact on our environment, industries, and recreational activities. The variation is caused by the movement of a low-pressure belt that generates westerly winds. During a negative phase, the low pressure belt moves north, towards the equator. In New Zealand, this can cause increased westerly winds, unsettled weather, and storm activity over most of the country. Over the southern oceans, there are relatively less westerly winds and less storm activity. During a positive phase, the low pressure belt moves south towards Antarctica. In New Zealand, this can cause relatively light winds and more settled weather. Over the southern oceans, there is increased westerly winds and storm activity. This dataset relates to the "Southern annular mode" measure on the Environmental Indicators, Te taiao Aotearoa website.

Abstract

The census date was midnight, the 23rd of July 2018.

The Census is the official count of population, household and dwellings in Wallis & Futuna and it gives a general overview of the country at one specific point in time: 23rd of July 2018. Since 1969 until 2003, Census has been taken once in every 7 or 6 years and every 5 years from 2003.

The census can be the source of information for allocation of public funding, more particularly in areas such as health, education and social policy. The main users of the information provided by the Census are the government, education facilities (such as schools and tertiary organizations), local authorities, businesses, community organizations and the public in general.

The objectives of Census changed over time shifting from earlier years where they were essentially household registrations and counts, to now where a national population census stands supreme as the most valuable single source of statistical data for Wallis & Futuna. This Census allowed to determine the legal population of Wallis and Futuna in all geographical aspects: Wallis island, Futuna island, the 3 "circonsriptions" (Alo, Sigave, Uvea) and 5 districts (Alo, Sigave, Hahake, Hihifo, Mua).

Census data is now widely used to evaluate: - The availability of basic household needs in key sectors, to identify disadvantaged areas and help set priorities for action plans; - Benefits of development programmes in particular areas, such as literacy, employment and family planning;

In addition, census data is useful to asses manpower resources, identify areas of social concern and for the improvement in the social and economic status of women by giving more information about this part of the population and formulating housing policies and programmes and investment of development funds.

Geographic coverage

National coverage.

Analysis unit

Households and Individuals.

Universe

The Census is covering all people alive on the reference date (23rd of July 2018), that are usually living in Wallis and Futuna - whichever nationality they are, for at least 12 months. The Census covered all household and communitiy members. Communities are considered to be: boarding schools, gendarmerie, retirement homes, religious communities, but also people living in mobile dwelling (e.g. boats) and homeless people.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable as it is a full coverage.

Mode of data collection

Face-to-face [f2f]

Research instrument

There are two types of questionnaire for this Census:

Individual sheet (Feuille de Logement or "FL"): describing the dwelling characteristics and enlisting all the individuals living in it; Individual form (Bulletin Individuel or "BI"): information on each individual that are usually living in the household.

The questionnaires were distributed in French and are available in the "External Resources" section.

Cleaning operations

Data editing was done by SPC in collaboration with Wallis and Futuna NSO.

Sampling error estimates

Not applicable.

Abstract

This research is a survey of unregistered businesses conducted in Peru from June 10 to July 20, 2010. Data from 480 enterprises were analyzed.

Questionnaire topics include general information about a business, infrastructure and services, sales and supplies, crime, sources and access to finance, business-government relationship, assets, bribery, workforce composition, obstacles to get registration, reasons for not registering, and benefits that an establishment could get from registration. The mode of data collection is face-to-face interviews.

The Informal Surveys aim to accomplish the following objectives: 1) To provide information about the state of the private sector for informal businesses in client countries; 2) To generate information about the reasons of said informality; 3) To collect useful data for the research agenda on informality; 4) To provide information on the level of activity in the informal sector of selected urban centers in each country.

Geographic coverage

National

Analysis unit

The primary sampling unit of the Informal Surveys is an unregistered establishment. For Peru, informal firms were defined as those not registered with the Superintendencia Nacional de Administración Tributaria (SUNAT).

Universe

The whole population, or the universe, covered in the survey is the non-agricultural informal economy.

At the beginning of each survey, a screening procedure is conducted in order to identify eligible interviewees. At this point, a full description of all the activities of the business owner or manager is taken; based on its principal activity, a business is then classified in the manufacturing or services stratum using a list of activities developed from previous iterations of the survey. Certain activities are excluded such as strictly illegal activities (e.g., prostitution or drug trafficking) as well as individual activities that are forms of selling labor like domestic servants or windshield washers.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Informal Surveys are conducted in selected urban centers, which are intended to coincide with the locations for the implementation of the main Enterprise Surveys. The overall number of interviews is pre-determined.

In Peru, the urban centers identified were Lima and Arequipa. The target sample for both urban centers was 240 interviews.

Sampling in the Informal Surveys is conducted within clearly delineated sampling zones, which are geographically determined divisions within each urban center. Sampling zones are defined at the beginning of fieldwork, and are delineated according to the concentration and geographical dispersion of informal business activity.

The number of sampling areas, and the geographical area they contain, is determined with the goal that each sector will yield four effective interviews.

In Peru, each sampling area was designed to contain a physical area, on average, of no less than the equivalent of eight city blocks. These sampling areas may or may not correspond to the administrative districts of the urban center.

In both Lima and Arequipa, for a total of 240 interviews in each city, 60 sampling areas were identified (240/4 = 60 sampling areas), respectively.

In order to provide information on diverse aspects of the informal economy, the sample is designed to have equal proportions of services and manufacturing (50:50). These sectors are defined by responses provided by each informal business to a question on the business's main activity included in the screener portion of the questionnaire.

As a general rule, services must constitute an ongoing business enterprise and so exclude the sale of manual labor Manufacturing activity in the informal sector includes business activity requiring inputs and/or intermediate goods. Thus, for example, the processing of coffee, sugar, oil, dried fruit, or other processed foods is considered manufacturing, while the simple selling of these goods falls under services. If an informal business conducts a mixture of these activities, the business is considered under the manufacturing stratum.

Each sampling zone was designed with the goal of obtaining two interviews in services and two interviews in manufacturing. In order to ensure a degree of geographical dispersion within each sampling zone, two starting points were identified.

Each starting point was designed to correspond to five city blocks, which were numbered sequentially. The first starting point was identified as Starting Point A and the second as Starting Point B.

Proceeding from each starting point, interviewers were instructed to begin on block 1, defining the starting block and corner. Each interviewer was instructed to attempt to achieve two interviews from each starting point, ideally one interview in manufacturing and one in services.

Interviewers were instructed to proceed clockwise around block 1 from Starting Point A; if the target interviews were not achieved, interviewers proceeded to block 2, Starting Point A, and so forth until completing a circuit of block 5. After achieving two interviews from Starting Point A, interviewers were instructed to cease work in the blocks assigned to that given Starting Point and repeat the sameprocedure from Starting Point B, beginning with block 1.

Using local knowledge, within each block all houses and shops were checked for unregistered businesses, following the pre-fixed route described above, until the allotted quota of interviews for each starting point was reached. Often interviewers used referrals by neighbors and locals in order to identify informal businesses. When a referral was obtained, the pre-determined route was followed until reaching the address of the referral. It should be noted that when referrals were obtained, interviewers were instructed to maintain the sampling procedure noted above; i.e., in the case that an interviewer encountered an informal business in the process of following a referral, an attempt was made to interview the former business first.

Each sampling zone, including its two starting points, were marked using Google maps, with the GPS coordinates of the starting points being systematically recorded.

Additionally, when obtaining a complete interview, the exact address of the informal business (or where the interview took place) was registered by the interviewer. Once in the office, this address was searched in Google maps, and its GPS coordinates were registered in a fieldwork report.

If no address was immediately available, using local knowledge, the GPS coordinates were determined using imaging via Google maps. In order to preserve confidentiality, the exact coordinates of businesses are not published.

Due to issues of non-response, in the process of fieldwork, the implementing contractor was unable to obtain the targeted four interviews in each of the originally delineated sampling areas.

As a result, replacement sampling areas were delineated, ex post. In sum, there were 70 sampling areas (60 original, 10 replacement) in Arequipa and 72 zones in Lima (60 original, 12 replacement).

Complete information regarding the sampling methodology as well as maps of starting points can be found in "Description of Peru Informal Survey Implementation" and "Mapping of starting points for sampling in Peru Informal Survey 2010" in "Technical Documents" folder.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instrument is available: - Informal Questionnaire.

The survey topics include general information about a business, infrastructure and services, sales and supplies, crime, sources and access to finance, business-government relationship, assets, bribery, workforce composition, obstacles to get registration, reasons for not registering, and benefits that an establishment could get from registration.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

The overall survey response rate among contacted, eligible businesses for the Peru Informal Survey 2010 was estimated at 25%.

Here you can find symbols and pictograms for all transport modes to use in your apps, products and other projects. Symbols and icons are available in various formats, while all can be found as vector files that can be opened directly in software such as Adobe Illustrator.

Replication Data for "Determined by Mode? Representation and Measurement Effects in a Dual Mode Statewide Survey"