Dispersion of Human Capital and Economic Growth

Based on a theoretical consideration of human capital production technology, this study empirically investigates the growth implication of dispersion of population distribution in terms of educational attainment levels. Based on a pooled 5-year interval time-series data set of 94 developed and developing countries for 1960 to 1995, the study finds that dispersion index as well as average index of human capital positively influences productivity growth. Given limited social resources for human capital investment, the finding implies that education policy that creates more dispersion in the human capital will promote growHuman capital, Dispersion, Educational attainment, Economic Growth

Exchange Rate Based Stabilization with Sudden Restrictions on Capital Flows

This study examines the dynamics associated with an economy implementing an Exchange Rate Based Stabilization (ERBS) programs when they are subject to sudden restrictions in international capital flows. In the context of a simple theoretical model, we describe the pressures on a country's central bank, implementing such a program, to sell its foreign exchange reserves when the country experiences an unanticipated shock in the form of an external borrowing constraint. The theory and the empirical investigation in the paper support the view that current account deficits coupled with sudden restrictions on capital flows can account for several of the stylized facts associated with the ERBS plans. The analysis is particularly successful in explaining the reserve and real interest rate dynamics observed prior to the collapse of these plans, a feature which has largely been ignored by the ERBS literature. The paper also captures the more well documented boom-bust cycles associated with these programsExchange rate, inflation, capital flows, borrowing constraints

Has Financial Liberalization Improved Economic Efficiency in the Republic of Korea? Evidence from Firm-Level and Industry-Level Data

This study analyzes the effects of financial liberalization on the lending behavior of banks and non-bank financial institutions (NBFIs) before and after the 1997 Asian financial crisis, using panel regressions on Republic of Korea firm-level and industry-level data of the period 1991 - 2007. It also develops a financial liberalization index to incorporate the multifaceted nature of financial reform. Findings show that financial liberalization has led banks and NBFIs to allocate more of their loans to small and medium-sized firms with good performance histories, thereby helping these entities to improve their total factor productivity growth. This paper does not find similar effects of financial liberalization on efficiency at large firms or at the industry level. Heavier reliance on direct financing after the crisis has not improved the productivity of large firms

Information Technology Investment and National Productivity

Using the country-level information technology (IT) expenditures and productivity data for the period from 1992 to 2000, we estimate production function augmented with IT capital stock in the first-difference form. As discussed in prior studies, we confirm that IT expenditures have significant positive effects on national productivity growth. The effects of IT expenditure on productivity growth hold for a short-term (1-year) as well as for a longer-term (4-year and 8-year). Using two theory-based measures of IT maturity, we find that the IT maturity is an important factor that explains the relationship between IT expenditures and national productivity. In addition, we find that the effect of IT expenditures is even higher when the countries are at the mature stage of IT expenditures. Furthermore, we present evidence that IT externalities improve the effect of IT expenditures on productivity growth

Improving Scene Text Recognition for Character-Level Long-Tailed Distribution

Despite the recent remarkable improvements in scene text recognition (STR), the majority of the studies focused mainly on the English language, which only includes few number of characters. However, STR models show a large performance degradation on languages with a numerous number of characters (e.g., Chinese and Korean), especially on characters that rarely appear due to the long-tailed distribution of characters in such languages. To address such an issue, we conducted an empirical analysis using synthetic datasets with different character-level distributions (e.g., balanced and long-tailed distributions). While increasing a substantial number of tail classes without considering the context helps the model to correctly recognize characters individually, training with such a synthetic dataset interferes the model with learning the contextual information (i.e., relation among characters), which is also important for predicting the whole word. Based on this motivation, we propose a novel Context-Aware and Free Experts Network (CAFE-Net) using two experts: 1) context-aware expert learns the contextual representation trained with a long-tailed dataset composed of common words used in everyday life and 2) context-free expert focuses on correctly predicting individual characters by utilizing a dataset with a balanced number of characters. By training two experts to focus on learning contextual and visual representations, respectively, we propose a novel confidence ensemble method to compensate the limitation of each expert. Through the experiments, we demonstrate that CAFE-Net improves the STR performance on languages containing numerous number of characters. Moreover, we show that CAFE-Net is easily applicable to various STR models.Comment: 17 page

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

Time-series forecasting models often encounter abrupt changes in a given period of time which generally occur due to unexpected or unknown events. Despite their scarce occurrences in the training set, abrupt changes incur loss that significantly contributes to the total loss. Therefore, they act as noisy training samples and prevent the model from learning generalizable patterns, namely the normal states. Based on our findings, we propose a reweighting framework that down-weights the losses incurred by abrupt changes and up-weights those by normal states. For the reweighting framework, we first define a measurement termed Local Discrepancy (LD) which measures the degree of abruptness of a change in a given period of time. Since a training set is mostly composed of normal states, we then consider how frequently the temporal changes appear in the training set based on LD. Our reweighting framework is applicable to existing time-series forecasting models regardless of the architectures. Through extensive experiments on 12 time-series forecasting models over eight datasets with various in-output sequence lengths, we demonstrate that applying our reweighting framework reduces MSE by 10.1% on average and by up to 18.6% in the state-of-the-art model.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 202