While fixed effects (FE) models are often employed to address potential omitted variables, we argue that these models’ real utility is in isolating a particular dimension of variance from panel data for analysis. In addition, we show through novel mathematical decomposition and simulation that only one-way FE models cleanly capture either the over-time or cross-sectional dimensions in panel data, while the two-way FE model unhelpfully combines within-unit and cross-sectional variation in a way that produces un-interpretable answers. In fact, as we show in this paper, if we begin with the interpretation that many researchers wrongly assign to the two-way FE model—that it represents a single estimate of X on Y while accounting for unit-level heterogeneity and time shocks—the two-way FE specification is statistically unidentified, a fact that statistical software packages like R and Stata obscure through internal matrix processing.

This article challenges Fixed Effects (FE) modelling as the 'default™' for time-series-cross-sectional and panel data. Understanding differences between within- and between-effects is crucial when choosing modelling strategies. The downside of Random Effects (RE) modelling (correlated lower-level covariates and higher-level residuals)“ is omitted-variable bias, solvable with Mundlak's (1978a) formulation. Consequently, RE can provide everything FE promises and more, and this is confirmed by Monte-Carlo simulations, which additionally show problems with Pluemper and Troeger'™s FE Vector Decomposition method when data are unbalanced. As well as incorporating time-invariant variables, RE models are readily extendable, with random coefficients, cross-level interactions, and complex variance functions. We argue not simply for technical solutions to endogeneity, but for the substantive importance of context and heterogeneity, modelled using RE. The implications extend beyond political science, to all multilevel datasets. However, omitted variables could still bias estimated higher-level variable effects; as with any model, care is required in interpretation.

Abstract

Panel data possess several advantages over conventional cross-sectional and time-series data, including their power to isolate the effects of specific actions, treatments, and general policies often at the core of large-scale econometric development studies. While the concept of panel data alone provides the capacity for modelling the complexities of human behaviour, the notion of universal panel data - in which time- and situation-driven variances leading to variations in tools, and thus results, are mitigated - can further enhance exploitation of the richness of panel information. The NPS Universal Panel Questionnaire (UPQ) consists of both survey instruments and datasets, meticulously aligned and engineered with the aim of facilitating the use of and improving access to the wealth of panel data offered by the NPS. The NPS-UPQ provides a consistent and straightforward means of conducting not only user-driven analyses using convenient, standardized tools, but also for monitoring MKUKUTA, FYDP II, and other national level development indicators reported by the NPS.

The design of the NPS-UPQ combines the four completed rounds of the NPS - NPS 2008/09 (R1), NPS 2010/11 (R2), NPS 2012/13 (R3), and NPS 2014/15 (R4) - into pooled, module-specific survey instruments and datasets. The panel survey instruments offer the ease of comparability over time, with modifications and variances easily identifiable as well as those aspects of the questionnaire which have remained identical and offer consistent information. By providing all module-specific data over time within compact, pooled datasets, panel datasets eliminate the need for user-generated merges between rounds and present data in a clear, logical format, increasing both the usability and comprehension of complex data.

Geographic coverage

Regional coverage

Analysis unit

Households

Universe

The universe includes all households and individuals in Tanzania with the exception of those residing in military barracks or other institutions.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING PROCEDURE While the same sample of respondents was maintained over the first three rounds of the NPS, longitudinal surveys tend to suffer from bias introduced by households leaving the survey over time, i.e. attrition. Although the NPS maintains a highly successful recapture rate (roughly 96% retention at the household level), minimizing the escalation of this selection bias, a refresh of longitudinal cohorts was done for the NPS 2014/15 to ensure proper representativeness of estimates while maintaining a sufficient primary sample to maintain cohesion within panel analysis. A newly completed Population and Housing Census (PHC) in 2012, providing updated population figures along with changes in administrative boundaries, emboldened the opportunity to realign the NPS sample and abate collective bias potentially introduced through attrition.

To maintain the panel concept of the NPS, the sample design for NPS 2014/2015 consisted of a combination of the original NPS sample and a new NPS sample. A nationally representative sub-sample was selected to continue as part of the “Extended Panel” while an entirely new sample, “Refresh Panel”, was selected to represent national and sub-national domains. Similar to the sample in NPS 2008/2009, the sample design for the “Refresh Panel” allows analysis at four primary domains of inference, namely: Dar es Salaam, other urban areas on mainland Tanzania, rural mainland Tanzania, and Zanzibar. This new cohort in NPS 2014/2015 will be maintained and tracked in all future rounds between national censuses.

Mode of data collection

Face-to-face [f2f]

Research instrument

The format of the NPS-UPQ survey instrument is similar to previously disseminated NPS survey instruments. Each module has a questionnaire and clearly identifies if the module collects information at the individual or household level. Within each module-specific questionnaire of the NPS-UPQ survey instrument, there are five distinct sections, arranged vertically: (1) the UPQ - “U” on the survey instrument, (2) R4, (3), R3, (4) R2, and (5) R1 – the latter 4 sections presenting each questionnaire in its original form at time of its respective dissemination.

The uppermost section of each module’s questionnaire (“U”) represents the model universal panel questionnaire, with questions generated from the comprehensive listing of questions across all four rounds of the NPS and codes generated from the comprehensive collection of codes. The following sections are arranged vertically by round, considering R4 as most recent. While not all rounds will have data reported for each question in the UPQ and not each question will have reports for each of the UPQ codes listed, the NPS-UPQ survey instrument represents the visual, all-inclusive set of information collected by the NPS over time.

The four round-specific sections (R4, R3, R2, R1) are aligned with their UPQ-equivalent question, visually presenting their contribution to compatibility with the UPQ. Each round-specific section includes the original round-specific variable names, response codes and skip patterns (corresponding to their respective round-specific NPS data sets, and despite their variance from other rounds or from the comprehensive UPQ code listing)4.

• Household identification;
• Survey staff details;
• Household member roster;
• Education,
• Health,
• Labour;
• Food outside the household;
• Subject welfare;
• Food security;
• Housing, water and sanitation;
• Consumption of food over the past one week;
• Non-food expenditures (past one week & one month);
• Non-food expenditures (past twelve months);
• Household assets;
• Family/household non-farm enterprises;
• Assistance and groups;
• Credit;
• Finance;
• Recent shocks to household welfare;
• Deaths in the household;
• Household recontact information;
• Filter questions;
• Anthropometry.

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute for Statistics (RSIS), the Federal Office of Statistics (FOS) and the Agency for Statistics of Bosnia and Herzegovina (BHAS), carried out a Living Standards Measurement Survey (LSMS).

The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows: 1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs. 2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household. 3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and is also providing funding for a further two years of data collection for a panel survey, to be known as the Household Survey Panel Series (HSPS). Birks Sinclair & Associates Ltd. are responsible for the management of the HSPS with technical advice and support being provided by the Institute for Social and Economic Research (ISER), University of Essex, UK.

The aim of the panel survey is to provide longitudinal data through re-interviewing approximately half the LSMS respondents for two years following the LSMS, in the autumn of 2002 and again in 2003. The LSMS constitutes wave 1 of the panel survey so there will be three years of panel data available for analysis under current funding plans. For the purposes of this document we are using the following convention to describe the different rounds of the panel survey: Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel Wave 2 Second interview of 50% of LSMS respondents in Autumn/Winter 2002 Wave 3 Third interview with sub-sample respondents in Autumn/Winter 2003

The panel data will allow the analysis of key transitions and events over this period such as labour market or geographical mobility and observe the consequent outcomes for the well-being of individuals and households in the survey.

The panel data will provide information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty is experienced by different types of households and individuals over the three year period. And most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within FBiH and RS at a time of social reform and rapid change.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation, Republic

Analysis unit

Households, Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The panel survey sample is made up of over 3,000 households drawn from the Living Standards Measurement Survey (LSMS) conducted by the World Bank in co-operation with the SIs in 2002. Approximately half the households interviewed on the LSMS were selected and carried forward into the panel survey. These households were re-interviewed in 2003 and will be interviewed for a third time in September 2004.

Sampling Frame

The 5,400 households interviewed on LSMS formed the sampling frame for the panel survey. The aim was to achieve interviews with approximately half of these (2,700) at wave 2 (1,500 in FBiH and 1,200 in RS). A response rate of 90% was anticipated (as the sample is based on households that have already co-operated with LSMS) and therefore the selected sample consisted of 3,000 households. Unlike the LSMS, the HSPS does not have a replacement element to the sample, only the original 3,000 issued addresses. This approach was new to the Supervisors and Interviewers and special training was given on how to keep non-response to a minimum.

The LSMS Sample

The LSMS sample design process experienced some difficulties which resulted in a sample with a disproportionately high number of households being selected in urban areas. Work by Peter Lynn from ISER identified the source of this problem by establishing the selection probabilities at each stage of the LSMS sampling process. Essentially, the procedures used for selecting households within municipalities would have been appropriate had municipalities been selected with equal probabilities. But in fact municipalities had been selected with probability proportional to size, and using different overall sampling fractions in each of three strata. The details are documented in a memo by Peter Lynn dated 25-3-2002. Consequently, household selection probabilities varied considerably across municipalities.

Compensating for the LSMS sample imbalance

Having established the selection probability of every LSMS household, it became possible to derive design-based weights that should provide unbiased estimates for LSMS. However, the considerable variability in these weights means that the variance of estimates (and hence standard errors and confidence intervals) is greatly increased. For the HSPS, there was an opportunity to reduce the variability in weights by constructing the subsample in a way that minimised the variability in overall selection probabilities. The overall selection probability for each household would be the product of two probabilities - the probability of being selected for LSMS, and the probability of being selected for HSPS, conditional upon having been selected for LSMS, i.e. P(HSPS) = P(LSMS) * P(HSPS)/(LSMS)

Ideally, then, we would have set the values of P(HSPS)/(LSMS) to be inversely proportional to P(LSMS). This would have resulted in each HSPS household having the same overall selection probability, P(HSPS), so that there would no longer be an increase in the variance of estimates due to variability in selection probabilities. However, this was not possible due to the very considerable variation in P(LSMS) and the limited flexibility provided by a large overall sampling fraction for HSPS (3,000 out of 5,400).

The best that could be done was to minimise the variability in sampling fractions by retaining all the LSMS households in the (mainly rural or mixed urban/rural) municipalities where LSMS household selection probabilities had been lowest and sub-sampling only in the municipalities where LSMS selection probabilities had been much higher. In 16 of the 25 LSMS municipalities, all households were retained for HSPS. In the other 9 municipalities, households were sub-sampled, with sampling fractions ranging from 83% in Travnik to just 25% in Banja Luka and Tuzla.

To select the required number of households within each municipality, every group of enumeration districts (GND) was retained from LSMS. The sub-sampling took place within the GNDs. Households were sub-sampled using systematic random sampling, with a random start and fixed interval. For example, in Novo Sarajevo, where the sampling fraction was 1 in 2, 6 households were selected out of the 12 LSMS households in each GND by selecting alternate households. In Prijedor, where the fraction was 1 in 3, 4 out of 12 were selected by taking every third LSMS household. And so on.

The total selected sample for the HSPS consists of 3,007 households (1681 in the FBIH and 1326 in the RS).

The overall design weight for the HSPS sample will be the product of the LSMS weight for the household and this extra design weight (which will of course tend to increase the size of the smallest LSMS weights).

Panel design

Eligibility for inclusion

The household and household membership definitions are the same standard definitions as used on the LSMS (see Supervisor Instructions, Annex A). While the sample membership status and eligibility for interview are as follows: i) All members of households interviewed at wave 1 (LSMS) have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules and the definition of 'out-of-scope'

The panel design means that sample members who move from their previous wave address at either wave 2 or 3 must be traced and followed to their new address for interview. The LSMS sample was clustered and over the two waves of the panel some de-clustering will occur as people move. In some cases the whole household will move together but in others an individual member may move away from their previous wave household and form a new split-off household of their own.

Following rules

All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This means that a four person household at Wave 1 could generate three additional households at wave 2 if three members, either OSMs or

Abstract

Panel data possess several advantages over conventional cross-sectional and time-series data, including their power to isolate the effects of specific actions, treatments, and general policies often at the core of large-scale econometric development studies. While the concept of panel data alone provides the capacity for modeling the complexities of human behavior, the notion of universal panel data – in which time- and situation-driven variances leading to variations in tools, and thus results, are mitigated – can further enhance exploitation of the richness of panel information.

The Basic Information Document (BID) provides a brief overview of the Nigerian General Household Survey (GHS) but focuses primarily on the theoretical development and application of panel data, as well as key elements of the universal panel survey instrument and datasets generated by the four rounds of the GHS. As the BID does not describe in detail the background, development, or use of the GHS itself, the wave-specific GHS BIDs should supplement the information provided here.

The Nigeria Universal Panel Data (NUPD) consists of both survey instruments and datasets from the two survey visits of the GHS - Post-Planting (PP) and Post-Harvest (PH) - meticulously aligned and engineered with the aim of facilitating the use of and improving access to the wealth of panel data offered by the GHS. The NUPD provides a consistent and straightforward means of conducting user-driven analyses using convenient, standardized tools.

The design of the NUPD combines the four completed Waves of the GHS Household Post-Planting and Post-Harvest Surveys – Wave 1 (2010/11), Wave 2 (2012/13), Wave 3 (2015/16), and Wave 4 (2018/19) – into pooled, module-specific survey instruments and datasets. The panel survey instruments offer the ease of comparability over time, with modifications and variances easily identifiable as well as those aspects of the questionnaire which have remained identical and offer consistent information. By providing all module-specific data over time within compact, pooled datasets, panel datasets eliminate the need for user-generated merges between rounds and present data in a clear, logical format, increasing both the usability and comprehension of complex data.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

Please see the GHS BIDs for each round for detailed descriptions of the sample design used in each round and their respective implementation efforts as this is a compilation of datasets from all previous waves.

Mode of data collection

Face-to-face [f2f]

Research instrument

The larger GHS-Panel project consists of three questionnaires (Household Questionnaire, Agriculture Questionnaire, Community Questionnaire) for each of the two visits (Post-Planting and Post-Harvest). The GHS-NUPD only consists of the Household Questionnaire.

GHS-Panel Household Questionnaire: The Household Questionnaire provides information on demographics; education; health (including anthropometric measurement for children); labor; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; and other sources of household income.

The Household Questionnaire is slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

Cleaning operations

Please see the GHS BIDs for each round for detailed descriptions of data editing and additional data processing efforts as this is a compilation of datasets from all previous waves.

Whether democratic and nondemocratic regimes perform differently in social provision policy is an important issue to social scientists and policy makers. Since political regimes are rarely changing, their long-term or dynamic effects on the outcome are of concern to researchers when they evaluate how political regimes affect social policy. However, estimating the dynamic effects of rarely changing variables in the analysis of time-series cross-sectional (TSCS) data by conventional estimators may be problematic when the unit effects are included in the model specification. This article proposes a model to account for and estimate the correlation between the unit effects and explanatory variables. Applying the proposed model to 18 Latin American countries, this article finds evidence that democracy has a positive effect on social spending both in the short and long term.

Sunspots - Monthly Activity since 1749

We include Stata syntax (dummy_dataset_create.do) that creates a panel dataset for negative binomial time series regression analyses, as described in our paper "Examining methodology to identify patterns of consulting in primary care for different groups of patients before a diagnosis of cancer: an exemplar applied to oesophagogastric cancer". We also include a sample dataset for clarity (dummy_dataset.dta), and a sample of that data in a spreadsheet (Appendix 2).

The variables contained therein are defined as follows:

case: binary variable for case or control status (takes a value of 0 for controls and 1 for cases).

patid: a unique patient identifier.

time_period: A count variable denoting the time period. In this example, 0 denotes 10 months before diagnosis with cancer, and 9 denotes the month of diagnosis with cancer,

ncons: number of consultations per month.

period0 to period9: 10 unique inflection point variables (one for each month before diagnosis). These are used to test which aggregation period includes the inflection point.

burden: binary variable denoting membership of one of two multimorbidity burden groups.

We also include two Stata do-files for analysing the consultation rate, stratified by burden group, using the Maximum likelihood method (1_menbregpaper.do and 2_menbregpaper_bs.do).

Note: In this example, for demonstration purposes we create a dataset for 10 months leading up to diagnosis. In the paper, we analyse 24 months before diagnosis. Here, we study consultation rates over time, but the method could be used to study any countable event, such as number of prescriptions.

The mechanism for the association between democratic development and the wealth gap has always been the focus of political and economic research, yet with no consistent conclusion. The reasons for that often are, 1) challenges to generalize the results obtained from analyzing a single country’s time series studies or multinational cross-section data analysis, and 2) deviations in research results caused by missing values or variable selection in panel data analysis. When it comes to the latter one, there are two factors contribute to it. One is that the accuracy of estimation is interfered with the presence of missing values in variables, another is that subjective discretion that must be exercised to select suitable proxies amongst many candidates, which are likely to cause variable selection bias. In order to solve these problems, this study is the pioneeringly research to utilize the machine learning method to interpolate missing values efficiently through the random forest model in this topic, and effectively analyzed cross-country data from 151 countries covering the period 1993–2017. Since this paper measures the importance of different variables to the dependent variable, more appropriate and important variables could be selected to construct a complete regression model. Results from different models come to a consensus that the promotion of democracy can significantly narrow the gap between the rich and the poor, with marginally decreasing effect with respect to wealth. In addition, the study finds out that this mechanism exists only in non-colonial nations or presidential states. Finally, this paper discusses the potential theoretical and policy implications of results.

Spurious regression analysis in panel data when the time series are cross-section dependent is analyzed in the article. The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components. We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran’s common correlated effects approach. This result is used to design individual and panel cointegration test statistics that accommodate the presence of structural breaks that can induce parameter instabilities in the deterministic component, the cointegration vector and the common factor loadings.

Missing data is a growing concern in social science research. This paper introduces novel machine-learning methods to explore imputation efficiency and its effect on missing data. The authors used Internet and public service data as the test examples. The empirical results show that the method not only verified the robustness of the positive impact of Internet penetration on the public service, but also further ensured that the machine-learning imputation method was better than random and multiple imputation, greatly improving the model’s explanatory power. The panel data after machine-learning imputation with better continuity in the time trend is feasibly analyzed, which can also be analyzed using the dynamic panel model. The long-term effects of the Internet on public services were found to be significantly stronger than the short-term effects. Finally, some mechanisms in the empirical analysis are discussed.

Consumer panel data on long-enough time series for brand purchases across different retail product categories. To examine the short-term and the long-term sales-to-advertising effectiveness of offline and online media for a broad set of brands in the CPG industry and derive empirical generalizations. In addition, we will assess the existence of cross-media effects among the offline and online media, thereby accounting for carryover effects. Based on the results of our analyses we will make recommendations on how CPG brands should optimally allocate their advertising budget and in which media they should either increase or decrease their spending.

Methodology to estimate malaria incidence rates from a commonly occurring form of interval-censored longitudinal parasitological data—specifically, 2-wave panel data—was first proposed 40 years ago based on the theory of continuous-time homogeneous Markov Chains. Assumptions of the methodology were suitable for settings with high malaria transmission in the absence of control measures, but are violated in areas experiencing fast decline or that have achieved very low transmission. No further developments that can accommodate such violations have been put forth since then. We extend previous work and propose a new methodology to estimate malaria incidence rates from 2-wave panel data, utilizing the class of 2-component mixtures of continuous-time Markov chains, representing two sub-populations with distinct behavior/attitude towards malaria prevention and treatment. Model identification, or even partial identification, requires context-specific a priori constraints on parameters. The method can be applied to scenarios of any transmission intensity. We provide an application utilizing data from Dar es Salaam, an area that experienced steady decline in malaria over almost five years after a larviciding intervention. We conducted sensitivity analysis to account for possible sampling variation in input data and model assumptions/parameters, and we considered differences in estimates due to submicroscopic infections. Results showed that, assuming defensible a priori constraints on model parameters, most of the uncertainty in the estimated incidence rates was due to sampling variation, not to partial identifiability of the mixture model for the case at hand. Differences between microscopy- and PCR-based rates depend on the transmission intensity. Leveraging on a method to estimate incidence rates from 2-wave panel data under any transmission intensity, and from the increasing availability of such data, there is an opportunity to foster further methodological developments, particularly focused on partial identifiability and the diversity of a priori parameter constraints associated with different human-ecosystem interfaces. As a consequence there can be more nuanced planning and evaluation of malaria control programs than heretofore.

One of the most well-known facts about unit root testing in time series is that the Dickey–Fuller (DF) test based on ordinary least squares (OLS) demeaned data suffers from low power, and that the use of generalized least squares (GLS) demeaning can lead to substantial power gains. Of course, this development has not gone unnoticed in the panel unit root literature. However, while the potential of using GLS demeaning is widely recognized, oddly enough, there are still no theoretical results available to facilitate a formal analysis of such demeaning in the panel data context. The present article can be seen as a reaction to this. The purpose is to evaluate the effect of GLS demeaning when used in conjuncture with the pooled OLS t-test for a unit root, resulting in a panel analog of the time series DF–GLS test. A key finding is that the success of GLS depend critically on the order in which the dependent variable is demeaned and first-differenced. If the variable is demeaned prior to taking first-differences, power is maximized by using GLS demeaning, whereas if the differencing is done first, then OLS demeaning is preferred. Furthermore, even if the former demeaning approach is used, such that GLS is preferred, the asymptotic distribution of the resulting test is independent of the tuning parameters that characterize the local alternative under which the demeaning performed. Hence, the demeaning can just as well be performed under the unit root null hypothesis. In this sense, GLS demeaning under the local alternative is redundant.

The mechanism of the relationship between democratization and inequality has been one of the main focuses of political and economic research, albeit with no consensus. The presence of missing values, the voluminous social, economic, environmental measures across countries and the discretion needed to select proxies/measures, leading to difficulties in conducting multinational panel data analysis. This study utilizes one of the machine-learning techniques, the random forecast model, to interpolate missing values and select candidates of control variables based on importance ranking, which greatly reduces the variable selection bias and increases the continuity of data in time series. We then constructed unbalance panel data analysis on data from 151 countries (1993-2017). Results from different models come to a consensus that the promotion of democracy can significantly reduce income inequality, but this effect has declined slightly, both in terms of wealth and in the long run. However, such mechanism only exists in non-colonial nations and presidential states.

The British Household Panel Survey (BHPS) ran for 19 waves, from 1991-2009, and was conducted by the ESRC UK Longitudinal Studies Centre (ULSC), together with the Institute for Social and Economic Research (ISER) at the University of Essex. The ULSC, established in 1999, is a continuation of the research resource component of the ESRC Research Centre on Micro-Social Change (MISOC), established in 1989. In addition to running panel studies, ISER undertakes a programme of research based on panel data, using Understanding Society (see below), the BHPS and other national panels to monitor and measure social change.

The main objective of the BHPS was to further understanding of social and economic change at the individual and household level in Britain, and to identify, model and forecast such changes and their causes and consequences in relation to a range of socio-economic variables. It was designed as an annual survey of each adult member (aged 16 years and over) of a nationally representative sample of more than 5,000 households, making a total of approximately 10,000 individual interviews. The same individuals were re-interviewed in successive waves and, if they left their original households, all adult members of their new households were also interviewed. Children were interviewed once they reach the age of 16; there was also a special survey of household members aged 11-15 included in the BHPS from Wave 4 onwards (the British Youth Panel, or BYP). From Wave 9, two additional samples were added to the BHPS in Scotland and Wales, and at Wave 11 an additional sample from Northern Ireland (which formed the Northern Ireland Household Panel Study or NIHPS), was added to increase the sample to cover the whole of the United Kingdom. For Waves 7-11, the BHPS also provided data for the European Community Household Panel (ECHP). For details of sampling, methodology and changes to the survey over time, see Volume A of the documentation (Introduction, Technical Report and Appendices). From Wave 19, the BHPS was subsumed into a new longitudinal study called Understanding Society, or the United Kingdom Household Longitudinal Study (UKHLS), conducted by ISER. The BHPS Wave 19 is part of Understanding Society Wave 2 (January 2010-March 2011) (see under SN 6614). Further information is available on the Understanding Society series webpage.

BHPS Geographic data and other related studies:

• BHPS Medium-level Geographical Identifiers and Low-level Geographical Identifiers are available to registered users, subject to Special Licence access conditions;
• British National Grid postcode grid references for each BHPS household surveyed are also available, subject to Secure Access conditions;
• Several datasets from ISER-based BHPS research, and teaching/sampler data are also available;
• For details of all related data, see the BHPS series webpage.

The British Household Panel Survey, Waves 1-18, 1991-2008: 1991 Ward, Census Code Range (WARDC91) dataset contains the 1991 Electoral Ward (Census code range) geographic variable for each wave of the BHPS to date, and a household identification serial number for file matching to the main BHPS data. These data have more restrictive access conditions than those available under the standard End User Licence (see 'Access' section below). Those users who wish to make an application for these data should contact the HelpDesk for further details.

For the second edition (January 2014) revised geographic data files for each wave have been deposited. The documentation has also been updated.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

The dataset included with this article contains three files describing and defining the sample and variables for VAT impact, and Excel file 1 consists of all raw and filtered data for the variables for the panel data sample. Excel file 2 depicts time-series and cross-sectional data for nonfinancial firms listed on the Saudi market for the second and third quarters of 2019 and the third and fourth quarters of 2020. Excel file 3 presents the raw material of variables used in measuring the company's profitability of the panel data sample

This study is part of a time-series collection of national surveys fielded continuously since 1952. The American National Election Studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. The data for this collection are derived from an interviewing program across three studies: the 1956 Presidential Pre- and Post-Election (AMERICAN NATIONAL ELECTION STUDY, 1956 [ICPSR 7214]), 1958 Congressional (AMERICAN NATIONAL ELECTION STUDY, 1958 [ICPSR 7215]), and 1960 Presidential Pre- and Post-Election Studies (AMERICAN NATIONAL ELECTION STUDY, 1960 [ICPSR 7216]).

Data for Figure 10.11 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure. 10.11 shows attribution of historic precipitation change in the Sahelian West African monsoon during June to September. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Doblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has 5 subpanels. Data for all subpanels is provided. --------------------------------------------------- List of data provided --------------------------------------------------- The data is annual June-September (JJAS) precipitation means for: - Observed anomalies over 1920-2018 respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N) - Model anomalies over 1920-2018 respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N) - Observed precipitation difference 1980-1990 mean - 1950-1960 mean - Model differences between 1.5x and 0.2x aerosol scalings over 1955-1984 - Trends in relative precipitation anomalies (baseline 1955-1984) over decline (1955-1984) and recovery (1985-2014) period over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N) --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel (a): Observed (CRU TS) timeseries anomalies over 1920-2018 in respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N): - Data file: Fig_10_11_panel-a_timeseries_obs.csv Panel (b): Observed (CRU TS) precipitation difference 1980-1990 mean - 1950-1960 mean: - Data file: Fig_10_11_panel-b_mapplot_pr_change_CRU_single_mean.nc Panel (c): Model differences between 1.5x and 0.2x aerosol scalings over 1955-1984: - Data file: Fig_10_11_panel-c_mapplot_pr_diff_SMURPHS_single_mean.nc Panel (d): Model timeseries anomalies over 1920-2018 respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N) for CMIP6 hist all-forcings (red), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), CMIP6 hist-aer (grey) and CMIP6 hist-GHG (pale blue): - Data file: Fig_10_11_panel-d_timeseries_cmip6.csv Panel (e): Observed and modelled OLS linear trends in relative precipitation anomalies (baseline 1955-1984) over decline (1955-1984) and recovery (1985-2014) period over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N): observed data (GPCC, CRU TS: black crosses), 34 CMIP5 models (dark blue circles), individual members of CMIP6 hist all-forcings (red circles), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading): - Data file: Fig_10_11_panel-e_trends.csv; Acronyms: CMIP - Coupled Model Intercomparison Project, CRU TS- Climatic Research Unit Time Series, SMURPHS - Securing Multidisciplinary UndeRstanding and Prediction of Hiatus and Surge events, DAMIP - Detection and Attribution Model Intercomparison Project, GHG - Greenhouse Gases, GPCC - GLOBAL PRECIPITATION CLIMATOLOGY CENTRE, SMILEs -single model initial-condition large ensembles, CSIRO - Commonwealth Scientific and Industrial Research Organisation, MIROC - Model for Interdisciplinary Research on Climate, MPI - Max-Planck-Institut für Meteorologie, d4PDF - Database for Policy Decision-Making for Future Climate Change, OLS - ordinary least squares regression. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- The code for ESMValTool is provided. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report component containing the figure (Chapter 10) - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11 - Link to the code for the figure, archived on Zenodo.