The short-run effects of health aid in low-income countries: evidence from panel data analysis

The system GMM estimator for dynamic panel data models combines moment conditions for the model in first differences with moment conditions for the model in levels. It has been shown to improve on the GMM estimator in the first differenced model in terms of bias and root mean squared error. However, we show in this paper that in the covariance stationary panel data AR(1) model the expected values of the concentration parameters in the differenced and levels equations for the cross‐section at time t are the same when the variances of the individual heterogeneity and idiosyncratic errors are the same. This indicates a weak instrument problem also for the equation in levels. We show that the 2SLS biases relative to that of the OLS biases are then similar for the equations in differences and levels, as are the size distortions of the Wald tests. These results are shown to extend to the panel data GMM estimators.

This study examines the role of official development assistance (ODA) in affecting adolescent fertility rates in low- and middle-income countries. Approximately 95% of adolescent births occur in middle- and low-income countries, and the average adolescent fertility rate in low-income countries is five times higher than in high-income countries. Aid donors typically pursue multiple objectives when providing development aid, many of which can go beyond changes in per capita GDP and include human capital factors such as adolescent fertility and infant mortality. Using data over the period from 1995 to 2015, our results suggest that total ODA has a beneficial impact by lowering the adolescent fertility rate either directly or through the channel of income growth. Our results also show that this beneficial effect on the adolescent fertility rate is stronger in low-income countries than in middle-income countries. Findings based on sectoral aid suggest that health aid has a stronger effect than aid in other sectors.

Objectives: To analyze the effect or impact of Digital Services Trade on economic growth (GDP) of a panel of Low, Middle and High Income Countries.
 Study Design: Panel Quantitative Study.
 Methodology: Dynamic Difference GMM (Diff-GMM) and System GMM (Sys-GMM), Panel pooled OLS (POLS) and Fixed Effects (FE) models were employed in the analyses.
 Results: The System GMM estimator seems to predict that, ceteris paribus, a 1 unit increase in digital services exports significantly impacts GDP growth in Low and High Income countries panels in the short run by 5.7% and 52.4% respectively. The panel POLS models estimate that digital services exports cause a significant long run increase in GDP in High income countries by 39.67% relative to 6.68% in the panel of Middle Income countries and negative growth in Low income countries of 7.74%. The FE models predict that for every 1 unit increase in the number of people using the internet, GDP significantly increases by 42.7%, 27.8% and 0.03% in the Middle, High, and Low Income countries panels respectively.
 Conclusion: The findings of this study indicate that generally, digital services trade seems to have a significant positive effect on GDP of all country panels. However, Low and Middle Income countries are lagging behind. Therefore, this study recommends that, to promote digital trade driven economic growth, the panel of Low and Middle Income countries’ policy makers should increase investments in both institutional and physical digital infrastructure that enable more people, Small and Medium enterprises(SMEs) and rural populations have access to stable, high speed and affordable digital services.

Whether health aid has a positive, negative, or no effect on the health status of recipient countries is controversial. The current paper examines the long-run effect of health aid on health status in low-income countries. The long-run health function was estimated using infant mortality as a proxy for health status and panel data constructed from 34 low-income countries from 2000 to 2017. For the estimation, fixed effect, random effect, and Hausman-Taylor estimators were employed. The estimation results indicate that health aid has a beneficial and statistically significant long-run effect on the health status of low-income countries. Doubling health aid saves the lives of 44 infants per 1000 live births in the long run. This study shows that health aid could be one of the best interim tools with which the health status of low-income groups could get improved and helps meet the target of universal health coverage. Despite the favorable effect of health aid observed in this study, recipient countries need to find ways of promoting surrogate domestic health financing systems, as external assistance cannot be an everlasting means of improving population health.

The system GMM estimator for dynamic panel data models combines moment conditions for the model in first differences with moment conditions for the model in levels. It has been shown to improve on the GMM estimator in the first differenced model in terms of bias and root mean squared error. However, we show in this paper that in the covariance stationary panel data AR(1) model the expected values of the concentration parameters in the differenced and levels equations for the crosssection at time t are the same when the variances of the individual heterogeneity and idiosyncratic errors are the same. This indicates a weak instrument problem also for the equation in levels. We show that the 2SLS biases relative to that of the OLS biases are then similar for the equations in differences and levels, as are the size distortions of the Wald tests. These results are shown in a Monte Carlo study to extend to the panel data system GMM estimator.

We propose a new framework for testing the “mean stationarity” assumption in dynamic panel data models, required for the consistency of the system GMM estimator. In our set up the assumption is obtained as a parametric restriction in an extended set of moment conditions, allowing the use of a LM test to check its validity. Our framework provides a ranking in terms of power of the analyzed test statistics, in which our approach exhibits better power than the difference-in-Sargan/Hansen test that compares system GMM and difference GMM, that is, on its turn, more powerful than the Sargan/Hansen test based on the system GMM moment conditions.

How should resources be reallocated between physicians and nurses in Africa and Latin America?

PROTOCOL: Effects of interventions for infant and young child feeding (IYCF) promotion on optimal IYCF practices, nutrition, growth and health in low- and middle-income countries: a systematic review.

Does inequality drive the Dutch disease? Theory and evidence

Does Inequality Drive the Dutch Disease?: Theory and Evidence

<abstract> This paper seeks to analyse the relationships between listed high-tech firms' financing choices and intellectual capital (IC). It also analyses the impact of ownership concentration on IC investments in high-tech firms. The data set was gathered from the Datastream database for a sample of listed high-tech firms in 14 Western European countries for the period between 2004 and 2015. The data set has an unbalanced panel structure, with the number of years of observations on each firm varying between 3 and 12. We use dynamic panel data models, the GMM system (1998) estimator. Results suggest that internal finance and equity issues are positively while debt is negatively related to IC in high-tech firms. High-tech firms seem to rely on equity issues and internal finance, avoiding debt to fund IC assets. Ownership concentration is negatively related to IC investments in high-tech firms. To the authors' knowledge, this is the first study exploring the relationships between financing choices and IC in high-tech firms. The findings also contribute to the literature by analysing the impact of ownership concentration on IC investments of high-tech firms. </abstract>

This chapter investigates the influence of mobile phone penetration on economic growth in developing economies. It is widely agreed that telecommunications infrastructure has a positive effect on GDP growth. The empirical evidence concerns mainly fixed line telephony and is mostly conducted with samples from developed countries. Mobile telephony, on the other hand, may be particularly important in those low-income countries, where landlines are not accessible to all. As there also is some evidence that mobile technology may encourage innovative entrepreneurial activity, it is likely that mobile telephony has a great positive impact on welfare especially in developing economies. To examine the role of mobile telephone penetration in economic growth, we use difference and system GMM estimators with a sample of low-income and lower middle-income countries and find mobile telephony to boost economic growth. This result suggests that extensive mobile cellular network coverage facilitates economic development in developing countries.

This chapter investigates the influence of mobile phone penetration on economic growth in developing economies. It is widely agreed that telecommunications infrastructure has a positive effect on GDP growth. The empirical evidence concerns mainly fixed line telephony and is mostly conducted with samples from developed countries. Mobile telephony, on the other hand, may be particularly important in those low-income countries, where landlines are not accessible to all. As there also is some evidence that mobile technology may encourage innovative entrepreneurial activity, it is likely that mobile telephony has a great positive impact on welfare especially in developing economies. To examine the role of mobile telephone penetration in economic growth, we use difference and system GMM estimators with a sample of low-income and lower middle-income countries and find mobile telephony to boost economic growth. This result suggests that extensive mobile cellular network coverage facilitates economic development in developing countries.

This chapter investigates the influence of mobile phone penetration on economic growth in developing economies. It is widely agreed that telecommunications infrastructure has a positive effect on GDP growth. The empirical evidence concerns mainly fixed line telephony and is mostly conducted with samples from developed countries. Mobile telephony, on the other hand, may be particularly important in those low-income countries, where landlines are not accessible to all. As there also is some evidence that mobile technology may encourage innovative entrepreneurial activity, it is likely that mobile telephony has a great positive impact on welfare especially in developing economies. To examine the role of mobile telephone penetration in economic growth, we use difference and system GMM estimators with a sample of low-income and lower middle-income countries and find mobile telephony to boost economic growth. This result suggests that extensive mobile cellular network coverage facilitates economic development in developing countries.

Access to effective medicines (e.g. antibiotics) is a fundamental human right. However, in contrast to high-income countries (HICs), many low- and middle-income countries (LMICs) lack appropriate and effective antibiotics. This is a paradox, and an inequitable scenario, as LMICs can have significantly higher burdens of infectious diseases than HICs and especially require appropriate antibiotics. Inequitable access to appropriate antibiotics results in patients being treated with substandard antibiotics, treatment failure, the emergence of antimicrobial resistance (AMR) and, inevitably, morbidity and mortality. Factors that hinder access to appropriate antibiotics in LMICs include: poor political will, weak health systems, complex bureaucratic protocols, poor implementation of National Action Plans on AMR, inadequate expertise in regulatory science, unfavourable macroeconomic policies and a poor investment climate. Clearly, multisectoral, collaborative approaches are required to effectively mitigate inequitable access to appropriate antibiotics in LMICs. Also, efforts (such as the African Medicines Regulatory Harmonization Initiative and the African Medicines Agency) to streamline bureaucratic processes and improve the registration and entry of appropriate antibiotics into LMICs are required. This review discusses factors responsible for inequitable access to appropriate antibiotics in LMICs, and makes recommendations to mitigate the problem. With rising rates of AMR, a dwindling antibiotic pipeline, and the dangers of a post-antibiotic era, it is clear that the time to act is now, as inequitable access to appropriate antibiotics in LMICs reduces the quality of healthcare, and threatens the achievement of Universal Health Coverage and, ultimately, the Sustainable Development Goals.