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Register a new userIdentifying poverty traps based on the network structure of economic output
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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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The existing theorization of development economics and transition economics is probably inadequate and perhaps even flawed to accurately explain and analyze a dual economic system such as that in China. China is a country in the transition of dual structure and system. The reform of its economic system has brought off a long period of transformation. The allocation of factors is subjected to the dualistic regulation of planning or administration and market due to the dualistic system, and thus the signal distortion will be a commonly seen existence. From the perspective of balanced and safe growth, the institutional distortions of population birth, population flow, land transaction and housing supply, with the changing of export, may cause great influences on the production demand, which includes the iterative contraction of consumption, the increase of export competitive cost, the widening of urban-rural income gap, the transferring of residents income and the crowding out of consumption. In view of the worldwide shift from a conservative model with more income than expenditure to the debt-based model with more expenditure than income and the need for loose monetary policy, we must explore a basic model that includes variables of debt and land assets that affecting money supply and price changes, especially in China, where the current debt ratio is high and is likely to rise continuously. Based on such a logical framework of dualistic system economics and its analysis method, a preliminary calculation system is formed through the establishment of models.
The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional evaluation approaches rely heavily on repeated in-person field surveys to measure changes in economic well-being and thus program effects. However, this is known to be costly, time-consuming, and often logistically challenging. Here we provide the first evidence that we can conduct such program evaluations based solely on high-resolution satellite imagery and deep learning methods. Our application estimates changes in household welfare in the context of a recent anti-poverty program in rural Kenya. The approach we use is based on a large literature documenting a reliable relationship between housing quality and household wealth. We infer changes in household wealth based on satellite-derived changes in housing quality and obtain consistent results with the traditional field-survey based approach. Our approach can be used to obtain inexpensive and timely insights on program effectiveness in international development programs.
The compact city, as a sustainable concept, is intended to augment the efficiency of urban function. However, previous studies have concentrated more on morphology than on structure. The present study focuses on urban structural elements, i.e., urban hotspots consisting of high-density and high-intensity socioeconomic zones, and explores the economic performance associated with their spatial structure. We use nighttime luminosity (NTL) data and the Loubar method to identify and extract the hotspot and ultimately draw two conclusions. First, with population increasing, the hotspot number scales sublinearly with an exponent of approximately 0.50~0.55, regardless of the location in China, the EU or the US, while the intersect values are totally different, which is mainly due to different economic developmental level. Secondly, we demonstrate that the compactness of hotspots imposes an inverted U-shaped influence on economic growth, which implies that an optimal compactness coefficient does exist. These findings are helpful for urban planning.
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.
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.
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