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.

This Basic Information Document (BID) provides a brief overview of the Tanzania National Panel Survey (NPS), 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 NPS. As this Basic Information Document (BID) for the UPD does not describe in detail the background, development, or use of the NPS itself, the round-specific NPS BIDs should supplement the information provided here.

The NPS Uniform Panel Dataset (UPD) 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-UPD 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-UPD 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

Designed for analysis of key indicators at four primary domains of inference, namely: Dar es Salaam, other urban, rural, Zanzibar.

Analysis unit

• Households
• Individuals

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

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-UPD 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-UPD survey instrument, there are five distinct sections, arranged vertically: (1) the UPD - “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 UPD and not each question will have reports for each of the UPD codes listed, the NPS-UPD 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 UPD-equivalent question, visually presenting their contribution to compatibility with the UPD. 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 UPD code listing)4.

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.

This paper studies the empirical relevance of precautionary and other motives for household portfolio behaviour using recent panel data from the Netherlands. Dutch households' portfolios exhibit low degrees of risk taking and diversification. It is possible that this is the outcome of a rational, precautionary response to unavoidable exposure to background risk (stemming from the labour market or health conditions, etc.). We consider as alternative explanations liquidity needs and habits. The endogenous variable is the fraction of clearly safe in total financial assets at the household level. Parametric and semi-parametric censored regression models for pooled cross-sections and random and fixed effects models for panel data show that both heteroscedasticity and unobserved heterogeneity are of major importance in the data. With subjective indicators of income uncertainty we find a limited role for precautionary motives.

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

This paper extends the Common Correlated Effects Pooled (CCEP) estimator to homogeneous dynamic panels. In this setting CCEP suffers from a large bias when the time span (T) of the dataset is fixed. We develop a bias-corrected CCEP estimator that is consistent as the number of cross-sectional units (N) tends to infinity, for T fixed or growing large, provided that the specification is augmented with a sufficient number of cross-sectional averages, and lags thereof. Monte Carlo experiments show that the correction offers strong improvements in terms of bias and variance. We apply our approach to estimate the dynamic impact of temperature shocks on aggregate output growth.

Complete data, syntax, and log files for Market Orientation and National Homicide Rates. Abstract: We studied the influence of market orientation on national homicide rates. Multiple theoretical traditions equate the development and dominance of markets with higher crime rates. Some traditional sociological theoretical claims, however, suggest market expansion should reduce violence. Elias argued economic interconnectedness demands stable societies, increased sensitivity to others, and self-control. Durkheim maintained that greater division of labor and integration result in fewer offenses against the person, especially with concomitant development of a religion of humanity. Further, empirical evidence from multiple fields shows that market integration positively covaries with fairness and prosociality, marketoriented societies are more averse to unethical behavior, and globalization reduces national homicide rates. We tested these competing hypotheses using panel data for 88 nations, 2000-2019. We obtained national homicide rates from the World Health Organization’s Mortality Database and employed the Fraser Institute’s Economic Freedom of the World Index to operationalize market orientation. We used pooled cross-sectional models with fixed effects, controlling for common structural covariates of homicide rates. Results revealed a negative and significant association between market orientation and homicide rates, a substantively meaningful effect size, that the effect appears to be concentrated in nations with lower market orientation, and the findings remained consistent across several stability checks.

The Common Correlated Effects (CCE) approach enjoys considerable popularity for estimating factor-augmented panel data models. A key benefit is that by orthogonalizing the data on the available cross-section averages, the unobserved components are eliminated from the data, regardless of their order(s) of integration. This obviates the need for such knowledge, and makes CCE particularly attractive for macroeconomic applications, where the set of unobservables might contain both stationary and non-stationary variables. Despite of these benefits, it is often neglected that the pooled CCE (CCEP) estimator suffers from an asymptotic bias in TN−1→τ>0 panels, which too is common in macroeconomic research. This bias is highly disruptive for inference but cannot generally be remedied with analytical corrections. As such, we establish in this paper the validity of the cross-section bootstrap under general unknown factors for TN−1→τ

Abstract This paper measures the tax effort of a group of fifty-nine developed and developing countries over the period 1996-2015 by comparing a country’s actual tax/GDP ratio with the ratio predicted derived from an international tax function which relates tax revenue to various measures of a country’s taxable capacity such as the level of per capita income; the share of trade in GDP; the productive structure, and the level of financial deepening. The tax function is estimated using cross section data; pooled time series/cross section data, and panel data using a fixed effects estimator. The results are compared and show a range of tax effort from South Africa with the highest effort and Switzerland with the lowest effort. Implications for policy are drawn. The paper is critical of studies that include institutional variables (and other variables not related to the tax base of countries) to measure tax effort when they are really explanations of why the tax ratio differs between countries not of tax effort itself.

Affordable health care is often a result of increased government spending on the health sector. Out-of-pocket expenses remain the primary health care funding source in many South Asian nations. Lack of adequate public funding for health services, difficulty in obtaining health insurance, and high out-of-pocket costs can result in indebtedness, reductions in actual consumption, and decreased access to health care services. The study examines life expectancy and health care spending in South Asian countries. The life expectancy of South Asian countries is studied as a health outcome in relation to health care spending, gross national income per capita, and expected years of schooling. This study is based on secondary data from World Bank statistics that covers eight South Asian countries from 2000 to 2021, for a total of 176 pooled time series and cross-sectional observations. The data were analysed using econometric models, including the cross sectional dependency test, panel unit root test, panel co-integration test, vector error correction model, pair-wise Granger causality test, and Wald test statistics. The vector error correction model results indicate that health care spending, anticipated years of schooling, and gross national income per capita have a long-run association with health outcomes. Health care spending, per capita gross national income, and expected years of education have all greatly enhanced life expectancy in South Asian countries. An effective health strategy is necessary to increase people’s healthy life expectancy and well-being. To accomplish this, government may need to promote school enrolment to help people become more health literate and aware of their health outcomes. As a result, persons with more years of schooling have better health, higher levels of well-being, healthier habits, and ultimately, a longer life expectancy. This study will provide an important insight to policymakers in improving health outcomes through targeted and sustained health care spending in South Asia.

This paper considers estimation of factor-augmented panel data regression models. One of the most popular approaches towards this end is the common correlated effects (CCE) estimator of Pesaran (Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 2006, 74, 967-1012, 2006). For the pooled version of this estimator to be consistent, either the number of observables must be larger than the number of unobserved common factors, or the factor loadings must be distributed independently of each other. This is a problem in the typical application involving only a small number of regressors and/or correlated loadings. The current paper proposes a simple extension to the CCE procedure by which both requirements can be relaxed. The CCE approach is based on taking the cross-section average of the observables as an estimator of the common factors. The idea put forth in the current paper is to consider not only the average but also other cross-section combinations. Asymptotic properties of the resulting combination-augmented CCE (C3E) estimator are provided and tested in small samples using both simulated and real data.

Affordable health care is often a result of increased government spending on the health sector. Out-of-pocket expenses remain the primary health care funding source in many South Asian nations. Lack of adequate public funding for health services, difficulty in obtaining health insurance, and high out-of-pocket costs can result in indebtedness, reductions in actual consumption, and decreased access to health care services. The study examines life expectancy and health care spending in South Asian countries. The life expectancy of South Asian countries is studied as a health outcome in relation to health care spending, gross national income per capita, and expected years of schooling. This study is based on secondary data from World Bank statistics that covers eight South Asian countries from 2000 to 2021, for a total of 176 pooled time series and cross-sectional observations. The data were analysed using econometric models, including the cross sectional dependency test, panel unit root test, panel co-integration test, vector error correction model, pair-wise Granger causality test, and Wald test statistics. The vector error correction model results indicate that health care spending, anticipated years of schooling, and gross national income per capita have a long-run association with health outcomes. Health care spending, per capita gross national income, and expected years of education have all greatly enhanced life expectancy in South Asian countries. An effective health strategy is necessary to increase people’s healthy life expectancy and well-being. To accomplish this, government may need to promote school enrolment to help people become more health literate and aware of their health outcomes. As a result, persons with more years of schooling have better health, higher levels of well-being, healthier habits, and ultimately, a longer life expectancy. This study will provide an important insight to policymakers in improving health outcomes through targeted and sustained health care spending in South Asia.

These data include law-level information for 2,703 legislative actions by the 50 American states from 2005-2016. There are also aggregated composite measures that pool across years to make a single cross-section, that create a panel structure, and that pool across years to make scores by policy area.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

Affordable health care is often a result of increased government spending on the health sector. Out-of-pocket expenses remain the primary health care funding source in many South Asian nations. Lack of adequate public funding for health services, difficulty in obtaining health insurance, and high out-of-pocket costs can result in indebtedness, reductions in actual consumption, and decreased access to health care services. The study examines life expectancy and health care spending in South Asian countries. The life expectancy of South Asian countries is studied as a health outcome in relation to health care spending, gross national income per capita, and expected years of schooling. This study is based on secondary data from World Bank statistics that covers eight South Asian countries from 2000 to 2021, for a total of 176 pooled time series and cross-sectional observations. The data were analysed using econometric models, including the cross sectional dependency test, panel unit root test, panel co-integration test, vector error correction model, pair-wise Granger causality test, and Wald test statistics. The vector error correction model results indicate that health care spending, anticipated years of schooling, and gross national income per capita have a long-run association with health outcomes. Health care spending, per capita gross national income, and expected years of education have all greatly enhanced life expectancy in South Asian countries. An effective health strategy is necessary to increase people’s healthy life expectancy and well-being. To accomplish this, government may need to promote school enrolment to help people become more health literate and aware of their health outcomes. As a result, persons with more years of schooling have better health, higher levels of well-being, healthier habits, and ultimately, a longer life expectancy. This study will provide an important insight to policymakers in improving health outcomes through targeted and sustained health care spending in South Asia.

Affordable health care is often a result of increased government spending on the health sector. Out-of-pocket expenses remain the primary health care funding source in many South Asian nations. Lack of adequate public funding for health services, difficulty in obtaining health insurance, and high out-of-pocket costs can result in indebtedness, reductions in actual consumption, and decreased access to health care services. The study examines life expectancy and health care spending in South Asian countries. The life expectancy of South Asian countries is studied as a health outcome in relation to health care spending, gross national income per capita, and expected years of schooling. This study is based on secondary data from World Bank statistics that covers eight South Asian countries from 2000 to 2021, for a total of 176 pooled time series and cross-sectional observations. The data were analysed using econometric models, including the cross sectional dependency test, panel unit root test, panel co-integration test, vector error correction model, pair-wise Granger causality test, and Wald test statistics. The vector error correction model results indicate that health care spending, anticipated years of schooling, and gross national income per capita have a long-run association with health outcomes. Health care spending, per capita gross national income, and expected years of education have all greatly enhanced life expectancy in South Asian countries. An effective health strategy is necessary to increase people’s healthy life expectancy and well-being. To accomplish this, government may need to promote school enrolment to help people become more health literate and aware of their health outcomes. As a result, persons with more years of schooling have better health, higher levels of well-being, healthier habits, and ultimately, a longer life expectancy. This study will provide an important insight to policymakers in improving health outcomes through targeted and sustained health care spending in South Asia.

Abstract

The Integrated Household Survey is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO) roughly every 3-5 years to monitor and evaluate the changing conditions of Malawian households. The IHS data have, among other insights, provided benchmark poverty and vulnerability indicators to foster evidence-based policy formulation and monitor the progress of meeting the Millennium Development Goals (MDGs), the goals listed as part of the Malawi Growth and Development Strategy (MGDS) and now the Sustainable Development Goals (SDGs).

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop
• Market

Universe

Members of the following households are not eligible for inclusion in the survey: • All people who live outside the selected EAs, whether in urban or rural areas. • All residents of dwellings other than private dwellings, such as prisons, hospitals and army barracks. • Members of the Malawian armed forces who reside within a military base. (If such individuals reside in private dwellings off the base, however, they should be included among the households eligible for random selection for the survey.) • Non-Malawian diplomats, diplomatic staff, and members of their households. (However, note that non-Malawian residents who are not diplomats or diplomatic staff and are resident in private dwellings are eligible for inclusion in the survey. The survey is not restricted to Malawian citizens alone.) • Non-Malawian tourists and others on vacation in Malawi.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IHS5 sampling frame is based on the listing information and cartography from the 2018 Malawi Population and Housing Census (PHC); includes the three major regions of Malawi, namely North, Center and South; and is stratified into rural and urban strata. The urban strata include the four major urban areas: Lilongwe City, Blantyre City, Mzuzu City, and the Municipality of Zomba. All other areas are considered as rural areas, and each of the 27 districts were considered as a separate sub-stratum as part of the main rural stratum. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS5 strata are composed of 32 districts in Malawi.

A stratified two-stage sample design was used for the IHS5.

Note: Detailed sample design information is presented in the "Fifth Integrated Household Survey 2019-2020, Basic Information Document" document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

HOUSEHOLD QUESTIONNAIRE The Household Questionnaire is a multi-topic survey instrument and is near-identical to the content and organization of the IHS3 and IHS4 questionnaires. It encompasses economic activities, demographics, welfare and other sectoral information of households. It covers a wide range of topics, dealing with the dynamics of poverty (consumption, cash and non-cash income, savings, assets, food security, health and education, vulnerability and social protection). Although the IHS5 household questionnaire covers a wide variety of topics in detail it intentionally excludes in-depth information on topics covered in other surveys that are part of the NSO’s statistical plan (such as maternal and child health issues covered at length in the Malawi Demographic and Health Survey).

AGRICULTURE QUESTIONNAIRE All IHS5 households that are identified as being involved in agricultural or livestock activities were administered the agriculture questionnaire, which is primarily modelled after the IHS3 counterpart. The modules are expanding on the agricultural content of the IHS4, IHS3, IHS2, AISS, and other regional agricultural surveys, while remaining consistent with the NACAL topical coverage and methodology. The development of the agriculture questionnaire was done with input from the aforementioned stakeholders who provided input on the household questionnaire as well as outside researchers involved in research and policy discussions pertaining to the Malawian agriculture. The agriculture questionnaire allows, among other things, for extensive agricultural productivity analysis through the diligent estimation of land areas, both owned and cultivated, labor and non-labor input use and expenditures, and production figures for main crops, and livestock. Although one of the major foci of the agriculture data collection effort was to produce smallholder production estimates for major crops, it is also possible to disaggregate the data by gender and main geographical regions. The IHS5 cross-sectional households supply information on the last completed rainy season (2017/2018 or 2018/2019) and the last completed dry season (2018 or 2019) depending on the timing of their interview.

FISHERIES QUESTIONNAIRE The design of the IHS5 fishery questionnaire is identical to the questionnaire designed for IHS3. The IHS3 fisheries questionnaire was informed by the design and piloting of a fishery questionnaire by the World Fish Center (WFC), which was supported by the LSMS-ISA project for the purpose of assembling a fishery questionnaire that could be integrated into multi-topic household-surveys. The WFC piloted the draft instrument in November 2009 in the Lower Shire region, and the NSO team considered the revised draft in designing the IHS5 fishery questionnaire.

COMMUNITY QUESTIONNAIRE The content of the IHS5 Community Questionnaire follows the content of the IHS3 & IHS4 Community Questionnaires. A “community” is defined as the village or urban location surrounding the enumeration area selected for inclusion in the sample and which most residents recognize as being their community. The IHS5 community questionnaire was administered to each community associated with the cross-sectional EAs interviewed. Identical to the IHS3 and IHS4 approach, to a group of several knowledgeable residents such as the village headman, the headmaster of the local school, the agricultural field assistant, religious leaders, local merchants, health workers and long-term knowledgeable residents. The instrument gathers information on a range of community characteristics, including religious and ethnic background, physical infrastructure, access to public services, economic activities, communal resource management, organization and governance, investment projects, and local retail price information for essential goods and services.

MARKET QUESTIONNAIRE The Market Survey consisted of one questionnaire which is composed of four modules. Module A: Market Identification, Module B: Seasonal Main Crops, Module C: Permanents Crops, and Module D: Food Consumption.

Cleaning operations

DATA ENTRY PLATFORM To ensure data quality and timely availability of data, the IHS5 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHS5, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. In Survey Solutions, Headquarters can then see the location of the dwellings plotted on a map of Malawi to better enable supervision from afar – checking both the number of interviews performed and the fact that the sample households lie within EA boundaries. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (NSO management) assigned work to supervisors based on their regions of coverage. Supervisors then made assignments to the enumerators linked to their Supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHS5 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to STATA for other consistency checks, data cleaning, and analysis.

DATA MANAGEMENT The IHS5 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHS5 Interviews were collected in “sample” mode (assignments generated from headquarters) as opposed to “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data

Affordable health care is often a result of increased government spending on the health sector. Out-of-pocket expenses remain the primary health care funding source in many South Asian nations. Lack of adequate public funding for health services, difficulty in obtaining health insurance, and high out-of-pocket costs can result in indebtedness, reductions in actual consumption, and decreased access to health care services. The study examines life expectancy and health care spending in South Asian countries. The life expectancy of South Asian countries is studied as a health outcome in relation to health care spending, gross national income per capita, and expected years of schooling. This study is based on secondary data from World Bank statistics that covers eight South Asian countries from 2000 to 2021, for a total of 176 pooled time series and cross-sectional observations. The data were analysed using econometric models, including the cross sectional dependency test, panel unit root test, panel co-integration test, vector error correction model, pair-wise Granger causality test, and Wald test statistics. The vector error correction model results indicate that health care spending, anticipated years of schooling, and gross national income per capita have a long-run association with health outcomes. Health care spending, per capita gross national income, and expected years of education have all greatly enhanced life expectancy in South Asian countries. An effective health strategy is necessary to increase people’s healthy life expectancy and well-being. To accomplish this, government may need to promote school enrolment to help people become more health literate and aware of their health outcomes. As a result, persons with more years of schooling have better health, higher levels of well-being, healthier habits, and ultimately, a longer life expectancy. This study will provide an important insight to policymakers in improving health outcomes through targeted and sustained health care spending in South Asia.

Affordable health care is often a result of increased government spending on the health sector. Out-of-pocket expenses remain the primary health care funding source in many South Asian nations. Lack of adequate public funding for health services, difficulty in obtaining health insurance, and high out-of-pocket costs can result in indebtedness, reductions in actual consumption, and decreased access to health care services. The study examines life expectancy and health care spending in South Asian countries. The life expectancy of South Asian countries is studied as a health outcome in relation to health care spending, gross national income per capita, and expected years of schooling. This study is based on secondary data from World Bank statistics that covers eight South Asian countries from 2000 to 2021, for a total of 176 pooled time series and cross-sectional observations. The data were analysed using econometric models, including the cross sectional dependency test, panel unit root test, panel co-integration test, vector error correction model, pair-wise Granger causality test, and Wald test statistics. The vector error correction model results indicate that health care spending, anticipated years of schooling, and gross national income per capita have a long-run association with health outcomes. Health care spending, per capita gross national income, and expected years of education have all greatly enhanced life expectancy in South Asian countries. An effective health strategy is necessary to increase people’s healthy life expectancy and well-being. To accomplish this, government may need to promote school enrolment to help people become more health literate and aware of their health outcomes. As a result, persons with more years of schooling have better health, higher levels of well-being, healthier habits, and ultimately, a longer life expectancy. This study will provide an important insight to policymakers in improving health outcomes through targeted and sustained health care spending in South Asia.

Affordable health care is often a result of increased government spending on the health sector. Out-of-pocket expenses remain the primary health care funding source in many South Asian nations. Lack of adequate public funding for health services, difficulty in obtaining health insurance, and high out-of-pocket costs can result in indebtedness, reductions in actual consumption, and decreased access to health care services. The study examines life expectancy and health care spending in South Asian countries. The life expectancy of South Asian countries is studied as a health outcome in relation to health care spending, gross national income per capita, and expected years of schooling. This study is based on secondary data from World Bank statistics that covers eight South Asian countries from 2000 to 2021, for a total of 176 pooled time series and cross-sectional observations. The data were analysed using econometric models, including the cross sectional dependency test, panel unit root test, panel co-integration test, vector error correction model, pair-wise Granger causality test, and Wald test statistics. The vector error correction model results indicate that health care spending, anticipated years of schooling, and gross national income per capita have a long-run association with health outcomes. Health care spending, per capita gross national income, and expected years of education have all greatly enhanced life expectancy in South Asian countries. An effective health strategy is necessary to increase people’s healthy life expectancy and well-being. To accomplish this, government may need to promote school enrolment to help people become more health literate and aware of their health outcomes. As a result, persons with more years of schooling have better health, higher levels of well-being, healthier habits, and ultimately, a longer life expectancy. This study will provide an important insight to policymakers in improving health outcomes through targeted and sustained health care spending in South Asia.