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Register a new userEconomic and environmental impacts of ballast water management on Small Island Developing States and Least Developed Countries
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The Ballast Water Management Convention can decrease the introduction risk of harmful aquatic organisms and pathogens, yet the Convention increases shipping costs and causes subsequent economic impacts. This paper examines whether the Convention generates disproportionate invasion risk reduction results and economic impacts on Small Island Developing States (SIDS) and Least Developed Countries (LDCs). Risk reduction is estimated with an invasion risk assessment model based on a higher-order network, and the effects of the regulation on national economies and trade are estimated with an integrated shipping cost and computable general equilibrium modeling framework. Then we use the Lorenz curve to examine if the regulation generates risk or economic inequality among regions. Risk reduction ratios of all regions (except Singapore) are above 99%, which proves the effectiveness of the Convention. The Gini coefficient of 0.66 shows the inequality in risk changes relative to income levels among regions, but risk reductions across all nations vary without particularly high risks for SIDS and LDCs than for large economies. Similarly, we reveal inequality in economic impacts relative to income levels (the Gini coefficient is 0.58), but there is no evidence that SIDS and LDCs are disproportionately impacted compared to more developed regions. Most changes in GDP, real exports, and real imports of studied regions are minor (smaller than 0.1%). However, there are more noteworthy changes for select sectors and trade partners including Togo, Bangladesh, and Dominican Republic, whose exports may decrease for textiles and metal and chemicals. We conclude the Convention decreases biological invasion risk and does not generate disproportionate negative impacts on SIDS and LDCs.
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During the global spread of COVID-19, Japan has been among the top countries to maintain a relatively low number of infections, despite implementing limited institutional interventions. Using a Tokyo Metropolitan dataset, this study investigated how these limited intervention policies have affected public health and economic conditions in the COVID-19 context. A causal loop analysis suggested that there were risks to prematurely terminating such interventions. On the basis of this result and subsequent quantitative modelling, we found that the short-term effectiveness of a short-term pre-emptive stay-at-home request caused a resurgence in the number of positive cases, whereas an additional request provided a limited negative add-on effect for economic measures (e.g. the number of electronic word-of-mouth (eWOM) communications and restaurant visits). These findings suggest the superiority of a mild and continuous intervention as a long-term countermeasure under epidemic pressures when compared to strong intermittent interventions.
Vaccination has been perceived as a key to reaching herd immunity in the current COVID-19 pandemic. This paper examines effectiveness of different vaccination strategies. We investigate the effects of two key elements in mass vaccination, which are allocations and timing of first and second doses and types of vaccines, on the spread of COVID-19. Amid limited supply of approved vaccines and constrained medical resources, the choice of a vaccination strategy is fundamentally an economic problem. We employ standard time-series and panel data models commonly used in economic research with real world data to estimate the effects of progress in vaccination and types of vaccines on health outcomes. Potential confounders such as government responses and peoples behavioral changes are also taken into account. Our findings suggest that the share of people vaccinated with at least one dose is significantly negatively associated with new infections and deaths. Conditioning on first dose progress, full vaccination offers no further reductions in new cases and deaths. For vaccines from China, however, we find weaker effects of vaccination progress on health outcomes. Our results support the extending interval between first and second dose policy adopted by Canada and the UK among others for mRNA-based vaccines. As vaccination progressed, peoples mobility increased and it offset the direct effects of vaccination. Therefore, public health measures are still important to contain the transmission by refraining people from being more mobile after vaccinated.
When facing threats from automation, a worker residing in a large Chinese city might not be as lucky as a worker in a large U.S. city, depending on the type of large city in which one resides. Empirical studies found that large U.S. cities exhibit resilience to automation impacts because of the increased occupational and skill specialization. However, in this study, we observe polarized responses in large Chinese cities to automation impacts. The polarization might be attributed to the elaborate master planning of the central government, through which cities are assigned with different industrial goals to achieve globally optimal economic success and, thus, a fast-growing economy. By dividing Chinese cities into two groups based on their administrative levels and premium resources allocated by the central government, we find that Chinese cities follow two distinct industrial development trajectories, one trajectory owning government support leads to a diversified industrial structure and, thus, a diversified job market, and the other leads to specialty cities and, thus, a specialized job market. By revisiting the automation impacts on a polarized job market, we observe a Simpsons paradox through which a larger city of a diversified job market results in greater resilience, whereas larger cities of specialized job markets are more susceptible. These findings inform policy makers to deploy appropriate policies to mitigate the polarized automation impacts.
Entrepreneurship is often touted for its ability to generate economic growth. Through the creative-destructive process, entrepreneurs are often able to innovate and outperform incumbent organizations, all of which is supposed to lead to higher employment and economic growth. Although some empirical evidence supports this logic, it has also been the subject of recent criticisms. Specifically, entrepreneurship does not lead to growth in developing countries; it only does in more developed countries with higher income levels. Using Global Entrepreneurship Monitor data for a panel of 83 countries from 2002 to 2014, we examine the contribution of entrepreneurship towards economic growth. Our evidence validates earlier studies findings but also exposes previously undiscovered findings. That is, we find that entrepreneurship encourages economic growth but not in developing countries. In addition, our evidence finds that the institutional environment of the country, as measured by GEM Entrepreneurial Framework Conditions, only contributes to economic growth in more developed countries but not in developing countries. These findings have important policy implications. Namely, our evidence contradicts policy proposals that suggest entrepreneurship and the adoption of pro-market institutions that support it to encourage economic growth in developing countries. Our evidence suggests these policy proposals will be unlikely to generate the economic growth desired.
In this work, we explore the relationship between monetary poverty and production combining relatedness theory, graph theory, and regression analysis. We develop two measures at product level that capture short-run and long-run patterns of poverty, respectively. We use the network of related products (or product space) and both metrics to estimate the influence of the productive structure of a country in its current and future levels of poverty. We found that poverty is highly associated with poorly connected nodes in the PS, especially products based on natural resources. We perform a series of regressions with several controls (including human capital, institutions, income, and population) to show the robustness of our measures as predictors of poverty. Finally, by means of some illustrative examples, we show how our measures distinguishes between nuanced cases of countries with similar poverty and production and identify possibilities of improving their current poverty levels.
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