Measuring the usage of preferential tariffs in the world

This study examined the impact of human resources development on economic growth in Nigeria from 1980 to 2019. To achieve this objective, data were collected on the real gross domestic product, government expenditure on education, government expenditure on health and human development index from Central Bank of Nigeria Statistical bulletin, World Bank -World Development Indicator and UNDP. The study adopted the Augmented Dickey-Fuller unit root test, Johansen Co-integration test and Error Correction Mechanism (ECM) methods of econometric to analyse the collected data. Evidence from the findings revealed that all the variables were individually integrated of Order One and have a long-run relationship. The parsimonious ECM result revealed that an increase in government expenditure on education, government expenditure on health, as well as human development index, do not significantly increase economic growth in Nigeria during the period of study. The study concluded that human resources development via public spending in the education sector, health sector, as well as an increase in human development index remains crucial in the process of achieving sustainable economic growth in Nigeria. Based on these findings, the study recommended among others that crucial effort should be made by the government in channelling more funds to the health sector in order to improve health standards and reduce the mortality rate of the citizens since a healthy population and workforce is a major ingredient for rapid and sustainable productivity and growth. Enough funds should be allocated to education for proper utilization of potential productive and social benefits that will help to boost the real sector of the economy.

PurposeThe authors extend the World Bank's Logistics Performance Index (LPI) for 30 additional countries and 13 additional years. The authors develop an inexpensive method for extending survey data when frequent, universal surveys are unavailable. The authors identify groups of country characteristics that influence LPI scores.Design/methodology/approachUsing data from the World Development Indicators—the broadest global dataset of country socioeconomic features—the authors test machine learning algorithms for their ability to predict the LPI. The authors examine importance scores to identify factors that influence LPI scores.FindingsThe best performing algorithm produces predictions on unseen data that account for nearly 90% of observed variation, and are accurate to within 6%. It performs twice as well as an OLS model with per capita income as the only predictor. Explanatory factors are business environment, economic structure, finance, environment, human development, and institutional quality.Practical implicationsMachine learning offers a simple, inexpensive way of extending the coverage of survey data. This dataset provides a richer picture of logistics performance around the world. The factors the authors identify as predicting higher LPI scores can help policymakers and practitioners target interventions.Originality/valueThis paper is one of the first applications of machine learning to extend coverage of an index based on an international survey. The authors use the new data to provide the most wide-ranging analysis of logistics performance across countries and over time. The output is an important resource for policymakers tracking performance, and researchers particularly in smaller and lower income countries. The authors also examine a wider range of explanatory factors for LPI scores than previous work.

The present study empirically investigates the relevance of the various specifications of relative Income hypothesis (RIH) in the case of Pakistan. World Development Indicators’ data is analyzed over the period of 1986 to 2016. This study tests the existence of income, consumption, and habit ratchet effects at the National level. Based on stationarity tests the techniques of Box Jenkin, Two Stage Least Square, Generalized Methods of Moments, and Limited Information Maximum Likelihood Method are utilized to estimate the four versions of relative income hypothesis. Findings of this research validate the significant prevalence of three types of ratchet effects in case of Pakistan. In Duesenberry-Eckstein-Formm (DEF) model, habit ratchet effect is found to be stronger than income ratchet effect. Whereas in modified Davis model Consumption ratchet effect exceeds the habit ratchet effect. It is observed that long run marginal propensities to consume are closer to one which demonstrates smooth consumption behavior over long period in Pakistan. The estimated short run marginal propensities to consume are observed to be less than long run marginal propensities to consume that accords with the existing consumption theory. These findings imply that all ratchet effects put pressure upon consumers to maintain the highest standard of living enjoyed in the past. In this process households do not retain balance between savings and consumption. Based on these findings, it is suggested to consider the impact of societal factor in formulating policies to shape the consumption patterns.

This study analyzes data coverage in five World Bank databases (World Development Indicators, Doing Business, Gender Statistics, Statistical Capacity Indicators, and Millennium Development Goals) across seven countries: Australia, Burundi, Haiti, Spain, Mexico, Turkmenistan, and Ukraine. The study employs statistical variation metrics to evaluate data completeness and identify information gaps. The analysis reveals significant data coverage heterogeneity, particularly in databases with over 200 indicators spanning more than 20 years. The findings indicate that the presence of indicators does not guarantee data availability, with some metrics having minimal or single-year entries. The World Development Indicators and Gender Statistics databases show coverage of up to 32–35 units out of 63 possible units. The study concludes that although the World Bank platform provides extensive resources, data completeness remains a significant challenge. The authors attribute these gaps to factors including economic development levels, industry structures, and historical circumstances. They recommend using standard indicators for research while consulting national statistical offices for specialized data needs.

This study examined modelling the trend of heart diseases in Nigeria using regression model a case study of incidence of Tuberculosis (TB). The accumulated data used for this study was a secondary statistical data extracted from survey information performed in Nigeria on incidence of Tuberculosis (TB) from the World Development Indicators- World Bank from 2000 to 2021. It was found out that based on p-value and Pearson (rho), there was a strong and positive relationship. The logistic regression model was best fit and was used to make forecast of the trend of the incidence of tuberculosis in Nigeria. It clearly showed that incidence of tuberculosis cases will be on the decreased within the next 12 months starting from January, 2022 to December, 2022.

The political climate of any country has become one of the pillars of decision-making for investment and any other financial business. Zimbabwe is one of the countries that have experienced political instability for the longest time. Many studies have looked at the impact of political risk on the stock market and other macroeconomics variables without considering the impact of political risk on the demand for foreign exchange in a country. Using secondary data from the World Bank - World Development Indicator (WDI), International Country Risk Guide (ICRG), and Reserve Bank of Zimbabwe (RBZ), this study examined the effects of political risk on the foreign currency demand in Zimbabwe. The regression results showed a negative and significant association between political risk and foreign exchange reserve both in the short run and long-run period, implying an increase in foreign currency demand as political risk increases. The study also applied an alternate indicator for foreign exchange demand to test whether the main findings are robust to different dependent variable measures. The results from the robustness test are consistent with the main findings of the study. The study makes recommendations for policymaking by the Zimbabwean government and other regulators.

We present a comparison of coverage and values for five inequality data sets that have worldwide or major international coverage and independent measurements that are intended to present consistent coefficients that can be compared directly across countries and time. The comparison data sets are those published by the Luxembourg Income Studies (LIS), the OECD, the European Union’s Statistics on Incomes and Living Conditions (EU-SILC), and the World Bank’s World Development Indicators (WDI). The baseline comparison is with our own Estimated Household Income Inequality (EHII) data set of the University of Texas Inequality Project. The comparison shows the historical depth and range of EHII and its broad compatibility with LIS, OECD, and EU-SILC, as well as problems with using the WDI for any cross-country comparative purpose. The comparison excludes the large World Incomes Inequality Database (WIID) of UNU-WIDER and the Standardized World Income Inequality Database (SWIID) of Frederick Solt; the former is a bibliographic collection and the latter is based on imputations drawn, in part, from EHII and the other sources used here.

PPP-based national accounts have become an important part of the database for macroeconomists, development economists, and economic historians. Frequently used global data come from the Penn World Table (PWT) and the World Bank's World Development Indicators; a substantial fraction of the world is also covered in the PPP accounts produced by the OECD and the European Union. This paper provides an overview of how these data are constructed, and discusses both the theory and the practical problems of implementing it. All of these data are underpinned by the International Comparison Program (ICP), which collects data on prices worldwide. The most recent round of the ICP was for 2005 with final results published in early 2008; version 7.0 of the Penn World Table will soon incorporate these results. The 2005 ICP, like earlier rounds, involved substantial revisions to previous data, most notably revising downwards the size of the Chinese (40 percent smaller) and Indian (36 percent) economies. We discuss the reasons for the revisions, and assess their plausibility. We focus on four important areas: how to handle international differences in quality, the treatment of urban and rural areas of large countries such as China, India, and Brazil, how to estimate prices for government services, health, and education, and the effects of the regional structure of the ICP. All of these affect the interpretation of previous data, as well as the current revisions. We discuss previous revisions of the PWT, and their effects on various kinds of econometric analysis. The paper concludes with health warnings that should be kept in mind when using these data, which are not always suitable for the purposes to which they are put. Some international comparisons are close to impossible, even in theory, and in others, the practical difficulties make comparison exceedingly hazardous.

World Development constitutes of the development and growth of each country. There are various factors that come into play for the development of the country. Through the process of ranking, one can compare the strengths and weaknesses of one's nation. Thus it is necessary to identify correct ranking mechanism that will help us to perform this comparatative analysis. Modified Fuzzy Distance Base Approach (MFDBA) is one such approach that helps to rank the countries and study various factors affecting the same. There are many tools that can perform this ranking on the basis of single factor, but none that studies multiple factors. Hence, through this paper Multi-Criteria Decision Making is used for determining the rank of 27 countries. It also helps in further predicting the future trend and study the progress of various nations.

Agriculture still employs large shares of the workforce in many BRICS and BRICS-aligned economies, yet output per worker remains uneven across countries. This paper examines the drivers of farm labor productivity in ten BRICS+ members, namely Brazil, Russia, India, China, South Africa, Egypt, Ethiopia, Indonesia, Iran, and the United Arab Emirates, using a balanced panel for 2001-2021. Drawing on data from the World Development Indicators, FAOSTAT, and UNDP, we estimate country fixed-effects models of agricultural value added per worker on lagged human development (HDI or schooling), arable land per worker, fertilizer use per hectare, national income (GNI per capita), and a time trend. The results show that higher HDI and greater land availability per agricultural worker are robustly associated with substantial productivity gains, whereas fertilizer intensity has no stable independent effect, and the influence of income largely operates through human development. A strong positive time trend suggests additional technological and institutional progress. Policy priorities should therefore combine investments in human development, land and tenure reforms, and innovation systems to foster inclusive productivity growth in BRICS+ agrifood systems.

The dynamic interplay between macroeconomic variables and Foreign Direct Investment (FDI) in Nigeria has become increasingly significant, particularly amid recent economic challenges and policy shifts. This study investigates the influence of key macroeconomic determinants—specifically inflation rate, interest rate, and exchange rate—on FDI inflows from 2002 to 2023. Utilizing a quantitative research design, data was collected from credible sources, including the Central Bank of Nigeria and World Development Indicators. The Autoregressive Distributed Lag (ARDL) model was employed to analyze the data, addressing both short-term and long-term relationships among the variables. The findings reveal that inflation (CPI) positively influences FDI inflows, while interest rates (MPR) exert a negative impact. Although the exchange rate (REER) also positively affects FDI, its statistical insignificance suggests limited influence. The study fills a significant gap in the existing literature by providing updated empirical evidence on the combined effects of these macroeconomic variables on FDI in Nigeria. It is recommended that policymakers implement strategies that stabilize these macroeconomic factors to foster a more conducive environment for foreign investment.

This study examined the impact of tax revenue on human capital development in Nigeria from 2013 to 2022.Data on five significant taxes in Nigeria were observed as explanatory variables: Petroleum Profit Tax (PPT), Educational Tax (EDT), Value Added Tax (VAT), Company Income Tax (CIT), and Customs and Excise Duties (CED). At the same time, Nigerian Human Development Index (HDI) were utilized to measure Human Capital Development. The study used secondary data obtained from the Central Bank Nigeria annual statistical bulletin, World Development Indicator and Federal Inland Revenue Service publications. The Augmented Dickey-Fuller Unit root test was used to determine the stationarity and order of integration of the research variables. Thereafter, these variables were integrated into order one. Ordinary Least Square (OLS) Log –Log Model was adopted to estimate parameters for analysis. Results showed that CIT and VAT positively and significantly affect Human Capital Development. PPT and EDT showed positive but insignificant effect, while CED has a negative insignificant effect on Human Capital Development in Nigeria. It was recommended, among other things, that Nigerian government, through the fiscal policy makers should utilize significant proportion of tax revenue in financing human capital development infrastructure.

This study seeks to establish the impact of tax revenue and infrastructural development (through investment) on economic growth in Nigeria. It is expected that tax revenue should serve as an incentive for infrastructural development to strive and yield economic growth. The data used in the study was obtained majorly from World Development Indicator (WDI) Database 2022. Tax revenue was proxied as the actual total tax revenue collected from VAT, and CIT, and PPT. This formed the independent variables as well as Gross Capital formation (GCF) to represent infrastructural development. While the dependent variable is RGDP. The ARDL model was employed after variables were stationary at both levels and at first difference. The study found a significant long-run relationship among the variables. Specifically, PPT was found to be a strong contributor to economic growth in Nigeria. VAT was only positively significant at 15% accounting for economic growth. GCF and CIT were not significant in the study. The research employs a quantitative method of data analysis. These findings connote that GCF, VAT and CIT are yet to fully be additive components in the Nigerian economy. Government economic policy and financing henceforth should reflect good economic policy direction that will open up these components for economic growth in the country.

The research investigates the effect of renewable energy consumption on economic growth as well as analyzing the dynamic relationship between both variables from 1990 to 2022. Annual secondary data covering the period between 1990 and 2022 were used in the study. Data on real gross domestic product, renewable energy consumption (% of total final energy consumption), gross capital formation (% of GDP), labor force, total and trade (% of GDP) were sourced from World Development Indicator. The study applies Johansen co-integration test and Vector Error Correction Model (VECM) to examine the effect of renewable energy consumption on economic growth and other explanatory variables as well as using pairwise granger causality test to examine the causal relationship between both variables. The empirical evidence revealed long-term relationship between renewable energy consumption and economic growth. Evidence shows that there is no causality either unidirectional or bi-directional between both variables. In short run, renewable energy consumption revealed a significant positive impact on economic growth. This shows that renewable energy may stimulate economic growth, particularly as it has a greater short-term impact on economic growth than capital formation. The key policy implication drawn from the results indicate the need for investment in renewable energy technologies and infrastructure, which can help to increase the availability and affordability of renewable energy, improve workforce development in Nigeria renewable energy sector.

This paper analyzes the impact of government expenditure and efficiency on economic growth of Sub Saharan African low income countries. The paper uses a panel data of 25 Sub-Saharan African low income countries spanning from 2002 – 2015 which are obtained from World Development Indicators (WDI) database. The paper executes panel unit root tests by using Im- Pesaran-Shin and Fisher ADF tests. The paper also uses Pedroni test to accomplish panel cointegration tests. Finally Generalized Methods of Moments (GMM) is applied to answer the two research questions. The results demonstrate that increasing government expenditure accelerates economic growth of low income countries in Sub Saharan Africa. However, when government expenditure is interacted with government efficiency we find no evidence for government efficiency to boost the impacts of government expenditure on economic growth. Fiscal policy makers in Sub Saharan African low income region should consider the rationale for using their spending to accelerate economic growth. Keywords: Government expenditure, efficiency, economic growth, Sub Saharan Africa