Measuring economic downside risk and severity - Growth at Risk

Output collapses, and crises are a fact of life. Severe economic downturns occur periodically, and have grave consequences on the poor. The authors propose a new measurement for economic downside risk, and severity: Growth at risk. Similar to the concept of Value at Risk in finance, Growth at Risk summarizes the expected maximum economic downturn over a target horizon at a given confidence level. After providing a taxonomy of growth risks, the authors construct a panel data, set on Growth at Risk for 84 countries, over the period 1980-98. On average, different regional groups experience very distinct Growth at Risk patterns over time. 1) Non-OECD countries experience a higher downturn risk, while OECD countries'downturn risks for both big, and small recessions are the lowest among all groups. 2) East Asia countries, which had been growing faster, had a high Growth at Risk for big downturns, at around six percent, and it rose dramatically at the end of the 1990s. 3) Latin America, and Sub-Saharan Africa also maintained high Growth at Risk for both big, and small recessions through 1980-98. But for Latin America, Growth at Risk for big recessions declined in the 1990s. The authors then investigate the relationship between downside risks, and long-term average growth in a cross-country analysis. They find that higher perceived levels of downside growth risk, seem to be negatively associated with long-term growth. When a country's perceived level of downside growth risk is relatively high, both domestic, and foreign investors might be deterred from making long-term investments in the country, and instead invest elsewhere. The results suggest that prudent, and consistent pursuit of socioeconomic, and political stability, contributes to long-term growth, and that risk management in a broader sense, should be a vital part of the pro-growth, and poverty reduction strategy.Public Health Promotion,Economic Theory&Research,Economic Conditions and Volatility,Health Monitoring&Evaluation,Labor Policies,Achieving Shared Growth,Economic Growth,Economic Theory&Research,Governance Indicators,Health Monitoring&Evaluation

Sources of China's economic growth, 1952-99 : incorporating human capital accumulation

China's performance in economic growth, and poverty reduction has been remarkable. There is an ongoing debate about whether this growth is mainly driven by productivity, or factor accumulation. But few past studies have incorporated information on China's human capital stock, and thus contained an omission bias. The authors construct a measure of China's human capital stock from 1952 to 1999, and, using a simple growth accounting exercise, incorporate it in their analysis of the sources of growth, during the pre-reform (1952-77), and the reform period (1978-99). They find that the accumulation of human capital in China (as measured by the average years of schooling for the population aged 15 to 64) was quite rapid, and contributed significantly to growth, and welfare. After incorporating human capital, they also find that the growth of total factor productivity, still plays a positive, and significant role during the reform period. In contrast, productivity growth was negative in the pre-reform period. The results are robust to changes in labor shares in GDP. The recent declining rate of human capital accumulation is a cause for concern, if China is to sustain its improvements in growth, and welfare in the coming decade. Funding for basic education is unevenly distributed, and insufficient in some poor regions.Labor Policies,Environmental Economics&Policies,Capital Markets and Capital Flows,Economic Theory&Research,Public Health Promotion,Achieving Shared Growth,Health Monitoring&Evaluation,Economic Theory&Research,Economic Growth,Environmental Economics&Policies

Strong convergence in the infinite horizon of numerical methods for stochastic differential equations

The strong convergence of numerical methods for stochastic differential equations (SDEs) for $t\in[0,\infty)$ is proved. The result is applicable to any one-step numerical methods with Markov property that have the finite time strong convergence and the uniformly bounded moment. In addition, the convergence of the numerical stationary distribution to the underlying one can be derived from this result. To demonstrate the application of this result, the strong convergence in the infinite horizon of the backward Euler-Maruyama method in the $L^p$ sense for some small $p\in (0,1)$ is proved for SDEs with super-linear coefficients, which is also a a standalone new result. Numerical simulations are provided to illustrate the theoretical results.Comment: 16 pages, 2 figure

Bond-Order Time Series Analysis for Detecting Reaction Events in Ab Initio Molecular Dynamics Simulations.

Ab initio molecular dynamics is able to predict novel reaction mechanisms by directly observing the individual reaction events that occur in simulation trajectories. In this article, we describe an approach for detecting reaction events from simulation trajectories using a physically motivated model based on time series analysis of ab initio bond orders. We found that applying a threshold to the bond order was insufficient for accurate detection, whereas peak finding on the first time derivative resulted in significantly improved accuracy. The model is trained on a reference set of reaction events representing the ideal result given unlimited computing resources. Our study includes two model systems: a heptanylium carbocation that undergoes hydride shifts and an unsaturated iron carbonyl cluster that features CO ligand migration and bridging behavior. The results indicate a high level of promise for this analysis approach to be used in mechanistic analysis of reactive AIMD simulations more generally