No Arabic abstract
The Cooperation Council for the Arab States of the Gulf (GCC) is generally regarded as a success story for economic integration in Arab countries. The idea of regional integration gained ground by signing the GCC Charter. It envisioned a closer economic relationship between member states.Although economic integration among GCC member states is an ambitious step in the right direction, there are gaps and challenges ahead. The best way to address the gaps and challenges that exist in formulating integration processes in the GCC is to start with a clear set of rules and put the necessary mechanisms in place. Integration attempts must also exhibit a high level of commitment in order to deflect dynamics of disintegration that have all too often frustrated meaningful integration in Arab countries. If the GCC can address these issues, it could become an economic powerhouse within Arab countries and even Asia.
Every nation prioritizes the inclusive economic growth and development of all regions. However, we observe that economic activities are clustered in space, which results in a disparity in per-capita income among different regions. A complexity-based method was proposed by Hidalgo and Hausmann [PNAS 106, 10570-10575 (2009)] to explain the large gaps in per-capita income across countries. Although there have been extensive studies on countries economic complexity using international export data, studies on economic complexity at the regional level are relatively less studied. Here, we study the industrial sector complexity of prefectures in Japan based on the basic information of more than one million firms. We aggregate the data as a bipartite network of prefectures and industrial sectors. We decompose the bipartite network as a prefecture-prefecture network and sector-sector network, which reveals the relationships among them. Similarities among the prefectures and among the sectors are measured using a metric. From these similarity matrices, we cluster the prefectures and sectors using the minimal spanning tree technique.The computed economic complexity index from the structure of the bipartite network shows a high correlation with macroeconomic indicators, such as per-capita gross prefectural product and prefectural income per person. We argue that this index reflects the present economic performance and hidden potential of the prefectures for future growth.
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.
Inferring the uncertainties in economic conditions are of significant importance for both decision makers as well as market players. In this paper, we propose a novel method based on Hidden Markov Model (HMM) to construct the Economic Condition Uncertainty (ECU) index that can be used to infer the economic condition uncertainties. The ECU index is a dimensionless index ranges between zero and one, this makes it to be comparable among sectors, regions and periods. We use the daily electricity consumption data of nearly 20 thousand firms in Shanghai from 2018 to 2020 to construct the ECU indexes. Results show that all ECU indexes, no matter at sectoral level or regional level, successfully captured the negative impacts of COVID-19 on Shanghais economic conditions. Besides, the ECU indexes also presented the heterogeneities in different districts as well as in different sectors. This reflects the facts that changes in uncertainties of economic conditions are mainly related to regional economic structures and targeted regulation policies faced by sectors. The ECU index can also be easily extended to measure uncertainties of economic conditions in different fields which has great potentials in the future.
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.
This paper studies reputation in the online market for illegal drugs in which no legal institutions exist to alleviate uncertainty. Trade takes place on platforms that offer rating systems for sellers, thereby providing an observable measure of reputation. The analysis exploits the fact that one of the two dominant platforms unexpectedly disappeared. Re-entering sellers reset their rating. The results show that on average prices decreased by up to 9% and that a 1% increase in rating causes a price increase of 1%. Ratings and prices recover after about three months. We calculate that identified good types earn 1,650 USD more per week.