analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

This replication package describes the dataset sources used in the paper and the STATA do files used in the empirical analysis together with the log files resulting from running them. As explained in more detail in the readme.pdf file, data from the Encuesta de Estrategias Empresariales (ESEE) is proprietary data and we are not allowed to pass the data to third parties. In case of being interested in replicating the results in this paper, one can send an e-mail to desee@fundacionsepi.es, specifying the title of the paper and the authors, and they will give access to the data for the only purpose of replicating the paper. Data on annual stock and installations of robots by industry and country has been obtained from the International Federation of Robotics (IFR). The IFR data are also proprietary. The data can be obtained from https://ifr.org/worldrobotics/ Two additional data files are distributed in this package: Consumer Price Index data for Spain covering the period 2006-2016 in STATA format and data for intermediate goods imports of Spain from its trading partners between 2006 and 2016 downloaded from the World Trade Integrated Solution (WTIS) and distributed in Microsoft Excel format.

This study delves into the intricate political dynamics that influence legislators’ policy stances concerning the import of US meat into Taiwan over the last decade. It specifically centers on the instances of US beef importation in 2012 and US pork importation in 2021. Within this folder, you will find two datasets along with a Stata .do file, all of which are instrumental for the analysis of quantitative data as presented in the paper. Additionally, the folder encompasses a spreadsheet that facilitates the creation of Figure 5.

Replication package for "Escaping Import Competition in China" by Ana Cecilia Fieler and Ann E. Harrison

The package contains all Stata and Fortran programs used for the results in the paper above. Data on tariffs and sector concordance are included, but data on Chinese establishments are proprietary.

analyze the survey of consumer finances (scf) with r the survey of consumer finances (scf) tracks the wealth of american families. every three years, more than five thousand households answer a battery of questions about income, net worth, credit card debt, pensions, mortgages, even the lease on their cars. plenty of surveys collect annual income, only the survey of consumer finances captures such detailed asset data. responses are at the primary economic unit-level (peu) - the economically dominant, financially interdependent family members within a sampled household. norc at the university of chicago administers the data collection, but the board of governors of the federal reserve pay the bills and therefore call the shots. if you were so brazen as to open up the microdata and run a simple weighted median, you'd get the wrong answer. the five to six thousand respondents actually gobble up twenty-five to thirty thousand records in the final pub lic use files. why oh why? well, those tables contain not one, not two, but five records for each peu. wherever missing, these data are multiply-imputed, meaning answers to the same question for the same household might vary across implicates. each analysis must account for all that, lest your confidence intervals be too tight. to calculate the correct statistics, you'll need to break the single file into five, necessarily complicating your life. this can be accomplished with the meanit sas macro buried in the 2004 scf codebook (search for meanit - you'll need the sas iml add-on). or you might blow the dust off this website referred to in the 2010 codebook as the home of an alternative multiple imputation technique, but all i found were broken links. perhaps it's time for plan c, and by c, i mean free. read the imputation section of the latest codebook (search for imputation), then give these scripts a whirl. they've got that new r smell. the lion's share of the respondents in the survey of consumer finances get drawn from a pretty standard sample of american dwellings - no nursing homes, no active-duty military. then there's this secondary sample of richer households to even out the statistical noise at the higher end of the i ncome and assets spectrum. you can read more if you like, but at the end of the day the weights just generalize to civilian, non-institutional american households. one last thing before you start your engine: read everything you always wanted to know about the scf. my favorite part of that title is the word always. this new github repository contains t hree scripts: 1989-2010 download all microdata.R initiate a function to download and import any survey of consumer finances zipped stata file (.dta) loop through each year specified by the user (starting at the 1989 re-vamp) to download the main, extract, and replicate weight files, then import each into r break the main file into five implicates (each containing one record per peu) and merge the appropriate extract data onto each implicate save the five implicates and replicate weights to an r data file (.rda) for rapid future loading 2010 analysis examples.R prepare two survey of consumer finances-flavored multiply-imputed survey analysis functions load the r data files (.rda) necessary to create a multiply-imputed, replicate-weighted survey design demonstrate how to access the properties of a multiply-imput ed survey design object cook up some descriptive statistics and export examples, calculated with scf-centric variance quirks run a quick t-test and regression, but only because you asked nicely replicate FRB SAS output.R reproduce each and every statistic pr ovided by the friendly folks at the federal reserve create a multiply-imputed, replicate-weighted survey design object re-reproduce (and yes, i said/meant what i meant/said) each of those statistics, now using the multiply-imputed survey design object to highlight the statistically-theoretically-irrelevant differences click here to view these three scripts for more detail about the survey of consumer finances (scf), visit: the federal reserve board of governors' survey of consumer finances homepage the latest scf chartbook, to browse what's possible. (spoiler alert: everything.) the survey of consumer finances wikipedia entry the official frequently asked questions notes: nationally-representative statistics on the financial health, wealth, and assets of american hous eholds might not be monopolized by the survey of consumer finances, but there isn't much competition aside from the assets topical module of the survey of income and program participation (sipp). on one hand, the scf interview questions contain more detail than sipp. on the other hand, scf's smaller sample precludes analyses of acute subpopulations. and for any three-handed martians in the audience, ther e's also a few biases between these two data sources that you ought to consider. the survey methodologists at the federal reserve take their job...

Fieldwork: October 2003. Data collecting agency: Gallup. Board: Gabriel Bădescu, Ionica Berevoescu, Mircea Comşa, Cristian Pîrvulescu, Camil Postelnicu, Cosima Rughiniş, Manuela Stănculescu. Program officers: Ovidiu Voicu. Database and press release retrieved by Ovidiu Voicu & Bogdan Voicu. ************************************* Datasets are provided in SPSS and Stata (13+) formats. For R, we recommend using package readstata13 in order to import the database. Labels are provided for the moment only in Romanian language. No weighting system is provided, for the mere reason that sample structure reflects correctly population structure, and there is no need for weighting (all weights should be equal to 1).

Fieldwork: October 2010. Data collecting agency: Gallup. Board: Mircea Comşa, Cristian Pîrvulescu, Dumitru Sandu, Manuela Stănculescu. Program officers: Ovidiu Voicu. Database and press release retrieved by Ovidiu Voicu & Bogdan Voicu. ************************************* Datasets are provided in SPSS and Stata (13+) formats. For R, we recommend using package readstata13 in order to import the database. Labels are provided for the moment only in Romanian language. No weighting system is provided, for the mere reason that sample structure reflects correctly population structure, and there is no need for weighting (all weights should be equal to 1).

Dataset from a special wave of the Public Opinion Barometer, by Open Society Foundation, devoted to "Family and Couple Life". Please check the dataverse for details on the series of surveys. A full report, including extended analyses, is also enclosed. Fieldwork: May 2007. Data collecting agency: Gallup. Team: Gabriel Bădescu, Mircea Kivu, Raluca Popescu, Cosima Rughiniş, Dumitru Sandu, Ovidiu Voicu. Database and press releases retrieved by Ovidiu Voicu & Bogdan Voicu. ************************************* Datasets are provided in SPSS and Stata (13+) formats. For R, we recommend using package readstata13 in order to import the database. Labels are provided for the moment only in Romanian language. No weighting system is provided, for the mere reason that sample structure reflects correctly population structure, and there is no need for weighting (all weights should be equal to 1).

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

This dataset includes a complete record of the 36,066 public comments submitted to the Commodity Futures Trading Commission (CFTC) in response to notices of proposed rule-making (NPRMs) implementing the Dodd-Frank Act over a 42-month period (January 14, 2010 to July 16, 2014). The data was exported from the agency’s internal database by the CFTC and provided to the authors by email correspondence following a cold call to the CFTC public relations department. The source internal database is maintained by the CFTC as part of its internal compliance with the Administrative Procedures Act (APA) and includes all rule-making notices that appear in the Federal Register. Owing to the salience and publicity of the Dodd-Frank Act, the CFTC made a special tag in its database for all comments submitted in response to rules proposed under the authority of the Dodd-Frank Act. This database thus includes all comments which the CFTC considers relevant to the Dodd-Frank reform. In short, the CFTC gave t..., This dataset was exported by the CFTC from their internal database of public comments in response to NPRMs. The uploaded file is the exact raw data generated by the CTFC and provided to the authors. An updated version of the data file including the author's classifications based on the organization value will be uploaded when the related work is accepted for publication., , # Dodd Frank Financial Reform at the CFTC - Public Comments, January 14th, 2010 to July 16th, 2014

Description of the data and file structure

NOTE: The Comment Text ( and variables) are longer than the maximum character count of Microsoft Excel cells (32,767 characters). All analysis should take this into account and import the .txt file directly into your analysis program (R, Stata, etc.) rather than attempt to edit or modify the data in Excel before using computational analysis.

There are two files provided:

1. DoddFrankCommentsAll(uncompressed).txt - the full raw data file from the CFTC internal database of all 36,066 comments on NPRMs
2. (2014-07-30) CFTC Original Codebook.xlsx - the codebook provided by the CTFC with the raw data. Originally provided as email text, formatted in Excel by authors.

Codebook:Â

| Variable | Explanation ...

Fieldwork: June 2002. Data collecting agency: IMAS. Board: Gabriel Bădescu, Ionica Berevoiescu, Mircea Comşa, Cristian Pârvulescu, Manuela Stănculescu. Program officer: Ovidiu Voicu. Database and press release retrieved by Ovidiu Voicu & Bogdan Voicu. ************************************* Datasets are provided in SPSS and Stata (14+) formats. For R, we recommend using package readstata13 in order to import the database. Labels are provided for the moment only in Romanian language. No weighting system is provided, for the mere reason that sample structure reflects correctly population structure, and there is no need for weighting (all weights should be equal to 1).

In 1998-1999, the Public Opinion Barometer was renamed as “Human resources barometer”. Surveys collected in October 1999, by CURS SA. Board: Sebastian Lăzăroiu, Manuela Stănculescu, Ionica Berevoescu, Cristian Pârvulescu, Horea Băcanu, Irina Culic, Mircea Comşa, Gabriel Bădescu, Ticu Constantin. Programme Director: Ionel David. Press release were retrieved by Ovidiu Voicu. They are enclosed as .ppt files. Database was partly curated by Bogdan Voicu, in 2001. ************************************* Datasets are provided in SPSS and Stata (13+) formats. For R, we recommend using package readstata13 in order to import the database. Labels are provided for the moment only in Romanian language. No weighting system is provided, for the simple reason that in all cases there is no need for weighting (all weights should be equal to 1).

In 1998-1999, the Public Opinion Barometer was renamed as “Human resources barometer”. This wave was collected by MetroMedia transilvania (MMT) in May 1998. Programme Director: Ionel David. Database was partly curated by Bogdan Voicu, in 2001. ************************************* Datasets are provided in SPSS and Stata (13+) could be used to read the data. For R, we recommend using the readstata13 package in order to import databases. Labels are provided for the moment only in Romanian language. No weighting system is provided, for the mere reason that in all cases there is no need for weighting (all weights should be equal to 1).

Fieldwork: May 2005. Data collecting agency: Gallup. Board: Gabriel Bădescu, Mircea Comşa, Cristian Pîrvulescu, Dumitru Sandu, Manuela Stănculescu si Cosima Rughiniş. Program officers: Ovidiu Voicu. Database and press release retrieved by Ovidiu Voicu & Bogdan Voicu. Commented data analysis is provided in the .pdf file by the board of this BOP wave. ************************************* Datasets are provided in SPSS and Stata (13+) formats. For R, we recommend using package readstata13 in order to import the database. Labels are provided for the moment only in Romanian language. No weighting system is provided, for the mere reason that sample structure reflects correctly population structure, and there is no need for weighting (all weights should be equal to 1).