The pooled cross-sectional dataset contains 2643 observations including 51% manufacturing and 41% service firms.

• The dataset includes data from wave 1 (2005-2007), wave 2 (2008-2010), and wave 3 (2016-2018) of the Nigerian business innovation surveys.
• The year variable identifies the different survey waves. Wave 1 was completed in 2008, wave 2 in 2011, and wave 3 in 2019.
• The service variable sorts the observations broadly into manufacturing and services.
• The id variable identifies each unique firm. REPEATEDNESS WAS IGNORED BECAUSE REPEATED CASES ARE ONLY ABOUT 2.5%.
• As much as possible, variables have been matched across the three waves.
• Due to coding changes and some inconsistencies in the survey instrument, a few variables could not be matched.
• Any variable that could not be matched is retained in its original form.
• Some of the variables have notes attached to them. The notes are consistent with what is in the accompanying codebook (Excel document).
• Item numbering on the questionnaire for the three waves is not consistent. Thus, rather than use question numbers for variable names as is commonly done, intuitive variable names and labels (defined in detail in the accompanying codebook) are used.
• Definitions of main concepts can be found in the accompanying codebook.
• It is strongly recommended that users thoroughly familiarize themselves with the accompanying codebook as well as the questionnaires for each of the waves before applying the dataset. This is crucial especially because of the skip patterns. While everything was done to ensure that the skip patterns were all correctly established, there can be no guarantee of perfection.
• It is also strongly recommended that users be familiar with the nature of business innovation surveys as this will help in understanding how to treat the data for analysis. The Oslo Manual (third and fourth editions), which are freely available online, are very useful resources.

To have a feel of the sectoral distribution of the sample, type in Stata: tab service year

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.

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.

This study investigates the intricate dynamics of international multilateral climate finance disbursements from 2003 to 2022 via an extensive dataset from the Climate Funds Update (CFU). By employing panel data econometric models, including pooled ordinary least squares (OLS), fixed effects (FE), and random effects (RE) models, the study elucidates the impact of grants and approved funds on disbursement levels across different income groups. The analysis reveals that while grants do not significantly influence disbursements, the approval of funds plays a critical role in enhancing disbursement efficiency. The random effects model, validated through the Hausman test, emerges as the optimal model for this context. The findings underscore the importance of streamlined approval processes in ensuring effective climate finance disbursements and highlight the need for further investigation into the non-significance of grants. The forecasting results indicate a positive trend in disbursements from 2023 to 2027, with potential fluctuations driven by external factors. This study provides valuable insights for policymakers and stakeholders to optimize climate finance mechanisms and improve fund utilization for sustainable development.

This study investigates the intricate dynamics of international multilateral climate finance disbursements from 2003 to 2022 via an extensive dataset from the Climate Funds Update (CFU). By employing panel data econometric models, including pooled ordinary least squares (OLS), fixed effects (FE), and random effects (RE) models, the study elucidates the impact of grants and approved funds on disbursement levels across different income groups. The analysis reveals that while grants do not significantly influence disbursements, the approval of funds plays a critical role in enhancing disbursement efficiency. The random effects model, validated through the Hausman test, emerges as the optimal model for this context. The findings underscore the importance of streamlined approval processes in ensuring effective climate finance disbursements and highlight the need for further investigation into the non-significance of grants. The forecasting results indicate a positive trend in disbursements from 2023 to 2027, with potential fluctuations driven by external factors. This study provides valuable insights for policymakers and stakeholders to optimize climate finance mechanisms and improve fund utilization for sustainable development.

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.

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.

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.