This zipped file contains the code and data to replicate the results in Burlig, Preonas, and Woerman (2020). See README.rtf for more information.

This is the appended data set of the two waves of ACCESS survey from 2014-15 and 2018 for panel data analysis.

Codes for simulations, controlling time fixed effects by time-varying intercepts

The authors investigate how the Global South’s gross domestic product (GDP) is impacted by trade with China. While the current literature on the growth impacts of trade (by leading partner countries) often neglects the properties of macro panel data, such as cross-sectional dependence, heterogeneity and structural breaks, their models take these features into account. Their empirical results based on 22 major developing countries from 2000Q1 to 2016Q4 identify positive contributions of imports from China to GDP in the studied sample, although these effects are smaller compared to imports from other emerging and developing economies (excluding China) (EME) and advanced economies (AdE). The authors also show that, in contrast with considerable impacts of exports to EME and AdE, exports to China have limited effects on the growth of its partners. However, the global financial crisis marks a turning point of China’s role as a major driver of growth in the South. Namely, while the positive growth effects of trade with China after the global crisis are on the rise, the opposite is true for EME and AdE. Examining the effects by individual countries, the authors present that the distance between China and its partners, economic and institutional development levels of its partners are almost irrelevant to the contributions of imports from China to its partners’ growth. Based on these findings they provide some important policy recommendations for the economies of the Global South.

I have submitted a paper to a journal. The paper is based on a dataset. When the paper is published, the dataset will be uploaded here.

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.

DOI Data collected based on secondary sourcesThe use of panels where the number of time periods and cross section units varies across applications creates a number of challenges for statisticians and econometricians, as well as for economic theory where network interactions are of interest. One very common form of interaction is spatial. Closeness or geographical contiguity is observable and there is a well developed field of spatial econometrics that deals with these issues. When the interaction is unobservable it may be that there is a common factor at work-global warming, for example, or a world financial crisis with pervasive effects globally. But there can also be more local forms of interaction which in addition to spatial patterns could take place in more abstract spaces such as social or economic networks.These abstract interactions can be both strong and weak. Strong interactions do not die away as the number of agents increases or as we move away from a 'neighbourhood'. Weak interactions do.This project will address these issues by developing econometric techniques for taking account of these interactions in a wide range of applications in economics.

This paper re-examines health-growth relationship using an unbalanced panel of 17 advanced economies for the period 1870–2013 and employs panel generalised method of moments estimator that takes care of endogeneity issues, which arise due to reverse causality. We utilise macroeconomic data corresponding to inflation, government expenditure, trade and schooling in sample countries that takes care of omitted variable bias in growth regression. With alternate model specifications, we show that population health proxied by life expectancy exert a positive and significant effect on both real income per capita as well as growth. Our results are in conformity with the existing empirical evidence on the relationship between health and economic growth, they, however, are more robust due to the presence of long-term data, appropriate econometric procedure and alternate model specifications. We also show a strong role of endogeneity in driving standard results in growth empirics. In addition to life expectancy, other constituent of human capital, education proxied by schooling is also positively associated with real per capita income. Policy implication that follows from this paper is that per capita income can be boosted through focussed policy attention on population health. The results, however, posit differing policy implications for advanced and developing economies.

Abstract The need for changes in the current energy matrix is a reality due to the possibility of a shortage of fossil fuels and the environmental damage caused by emissions related to fossil fuel use. The correct prescription of public policies for energy markets depends on the knowledge of demand elasticities. Hence, the aim of this work was to estimate the main determinants of light fuel demands in Brazil. Dynamic and non-dynamic estimators were used, and the results indicated that both demands respond more to changes in gasoline prices than changes in ethanol prices. Therefore, public policies that aim to change consumption patterns should focus on gasoline prices.

Raw data used in analysis of determinants of dividend policy - a case of banking sector in Serbia.

This panel dataset presents information on the impact of democracy and political stability on economic growth in 15 MENA countries for the period 1983-2022. The data are collected from five different sources; the World Bank Development Indicators (WDI), the World Bank Governance Indicators (WGI), the Penn World Table (PWT), Polity5 from the Integrated Network for Societal Conflict Research (INSCR), and the Varieties of Democracy (V-Dem). The dataset includes ten variables related to economic growth, democracy, and political stability. Data analysis was performed using statistical methods such as R in order to ensure data reliability through imputing missing data; hence, enabling future researchers to explore the impact of political factors on growth in various contexts. The data are presented in two sheets, before and after the imputation for missing values. The potential reuse of this dataset lies in the ability to examine the impact of different political factors on economic growth in the region.

In this paper, we study a varying–coefficient panel data model with both nonstationarity and partially observed factor structure. Two approaches are proposed in this paper. The first approach proposed in the main text considers a sieve based method to estimate the unknown coefficients as well as the factors and loading functions simultaneously, while the second approach proposed in the online supplementary document involving the principal component analysis provides an alternative estimation method. We establish asymptotic properties for them, compare the asymptotic efficiency of the two estimation methods and examine the theoretical findings through extensive Monte Carlo simulations. In an empirical study, we use our newly proposed model and a method to study the returns to scale of large U.S. commercial banks, where some overlooked modelling issues in the literature of production econometrics are addressed.

We revisit the panel data analysis of Acemoglu et al. (forthcoming) on the relationship between democracy and economic growth using state-of-the-art econometric methods. We argue that panel data settings are high-dimensional, resulting in estimators to be biased to a degree that invalidates statistical inference. We remove these biases by using simple analytical and sample-splitting methods, and thereby restore valid statistical inference. We find that debiased fixed effects and Arellano-Bond estimators produce higher estimates of the long-run effect of democracy on growth, providing even stronger support for the key hypothesis of Acemoglu et al.

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.

The data and programs replicate tables and figures from "Reworking Wild Bootstrap Based Inference for Clustered Errors", by Webb. Please see the ReadMe file for additional details. Note: There are two master files, one reruns the entire set of Monte Carlos. The other reproduces tables from previously stored Monte Carlo p-values.

Data for the 28 countries of the European Union as well as Norway on following variables influencing the countries' energy consumption: - self-constructed weighted average price index - HDD - longitude - latitude - age - average floor area - GDP per capita - home ownership - share of apartment - share of new buildings - share of district heating

The data provided in this repository is the dataset of the article “Disaggregated Approach of Local Government Expenditures and Poverty Reduction: Empirical Evidence from Panel Data Analysis”. The raw data are gathered from The Ministry of Finance in Indonesia and the Central Bureau of Statistic Indonesia. In addition, this article uses a panel data set of 24 districts/cities in South Sulawesi for the period of 2015 to 2020.

The panel data are used in an econometric analysis estimating Cobb-Douglas production functions that are subsequently used to calculate the shadow price (current marginal value) or irrigation water in 11 major groundwater depleting countries.

Tables from econometric analysis with Swiss firm panel data. A detailed description can be found in the study on the EPO's homepage (title of the study: "Knowledge spillovers from product and process inventions and their impact on firm performance"): https://www.epo.org/learning-events/materials/academic-research-programme/research-project-grants.html Funding by the "European Office Academic Research Programme" is gratefully ackknowledged.

This record contains the underlying research data for the publication "Disentangling greenhouse warming and aerosol cooling to reveal Earth's climate sensitivity" and the full-text is available from: https://ink.library.smu.edu.sg/soe_research/1845Earth's climate sensitivity has long been subject to heated debate and has spurred renewed interest after the latest IPCC assessment report suggested a downward adjustment of its most likely range(1). Recent observational studies have produced estimates of transient climate sensitivity, that is, the global mean surface temperature increase at the time of CO2 doubling, as low as 1.3 K (refs 2,3), well below the best estimate produced by global climate models (1.8 K). Here, we present an observation-based study of the time period 1964 to 2010, which does not rely on climate models. The method incorporates observations of greenhouse gas concentrations, temperature and radiation from approximately 1,300 surface sites into an energy balance framework. Statistical methods commonly applied to economic time series are then used to decompose observed temperature trends into components attributable to changes in greenhouse gas concentrations and surface radiation. We find that surface radiation trends, which have been largely explained by changes in atmospheric aerosol loading, caused a cooling that masked approximately one-third of the continental warming due to increasing greenhouse gas concentrations over the past half-century. In consequence, the method yields a higher transient climate sensitivity (2.0 +/- 0.8 K) than other observational studies.