Asymmetric effects of housing wealth on college enrolment

We investigate the impact of housing wealth, credit availability and financial distress on college enrolment decisions. We find that housing wealth is negatively related to enrolment in public schools and positively related to enrolment in private schools. This evidence suggests that, on average, students substituted away from private schools towards public institutions during the recent financial crisis.     

Dynamic response of bank-intermediated trade finance to a global liquidity shock

This paper investigates the bank-level responses of a bank-intermediated instrument of trade finance to a negative global liquidity shock in Korea. Using a factor-augmented vector autoregression approach, the results show that there exists significant heterogeneity in bank-level trade-finance responses to a global liquidity shock. In addition, we find that the source of the heterogeneity is the bank-level foreign currency liquidity condition; banks with a better foreign currency liquidity condition may dampen the negative impact of a global liquidity shock on trade-finance.     

Market impact: a systematic study of the high frequency options market

This paper deals with a fundamental subject that has seldom been addressed in recent years, that of market impact in the options market. Our analysis is based on a proprietary database of metaorders—large orders that are split into smaller pieces before being sent to the market—on one of the main Asian markets. In line with our previous work on the equity market [Said, E., Bel Hadj Ayed, A., Husson, A. and Abergel, F., Market impact: A systematic study of limit orders. Mark. Microstruct. Liq., 2018, 3(3&4), 1850008.], we propose an algorithmic approach to identify metaorders, based on some implied volatility parameters, the at the money forward volatility and at the money forward skew. In both cases, we obtain results similar to the now well-understood equity market: Square-Root Law, Fair Pricing Condition and Market Impact Dynamics.     

Personal Income Tax Reform in China in 2018 and Its Impact on Income Distribution

In the context of personal income tax (PIT) reform in China in 2018, this paper examines some of the major issues of concern regarding the reform and income distribution. Using the China Personal Income Tax Micro‐simulation model, the paper compares the differences between the 2011 and 2018 PIT systems, and finds that residents relying on different income sources may face a large degree of real tax rate change. Once the tax system is altered to PIT 2018, the coverage of PIT for wage earners will decrease from 46.9 to 23.4 percent, the income redistributive effect will drop from 1.95 to 1.22 percent and the PIT's role in fiscal revenue will also be negatively affected. Nevertheless, if individual income continues to grow, the share of PIT in fiscal revenue is expected to return to the 2018 level in 2022, but its income redistribution function is difficult to recover in the short term. The paper finds that the effect of PIT on income distribution depends on the tax structure. Gradual transition to an “entirely comprehensive” tax system when conditions are appropriate will achieve better income redistribution results at a lower average tax rate.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Policy induced increases in private healthcare financing provide short-term relief of total healthcare expenditure growth: Evidence from OECD countries

Healthcare reforms have long been advocated as a cure to the increasing healthcare expenditures in advanced economies. Nevertheless, it has not been established whether a market solution via private financing, rather than public financing, curb aggregate healthcare expenditures. To our knowledge, this paper is the first that quantifies the impact of reforms that significantly increases (decreases) the private (public) share of healthcare financing on total healthcare expenditures relative to income in 20 OECD countries. Our reform measure is based on structural break testing of the private share of total expenditures, and verification using evidence of policy reforms. To quantify the effect of these reforms we apply Propensity Score Matching and Inverse Probability Weighted regression analysis. Over a 5-year evaluation period the reforms lead to an accumulated cost saving 0.45 percentage points of GDP. The yearly effects of the reforms are largest in the first years in the post-reform period and decreases in size as a function of time since the reform. Our findings suggest that the investigated healthcare reforms have a relatively short-lived effect on aggregate health spending relative to GDP. The findings are robust to various sensitivity tests.     

我国物流业转型升级研究综述

中国经济已经到了只有转型升级才能持续健康发展的关键阶段,产业转型升级在国家政策文件和研究文献中出现的词频较高。文章以我国物流业为研究对象,从现有涉及到物流业转型升级方面的文献挖掘有效信息,梳理我国物流业转型升级研究的历史脉络,从供应链与价值链、产业集群、现代科学技术应用和产业联动四个视角对现有的物流业转型升级文献进行了总结和归纳,并从物流业转型升级的动力研究、阶段性划分研究和演进规律研究三个方面指出了现有文献的研究不足和以后可能的研究方向。其中,供应链与价值链视角侧重于物流企业的纵向角度,产业集群视角侧重于物流企业的横向角度。文章对于物流企业战略设计、政府政策的制定和理论研究具有一定的参考作用。     

Will China's WTO accession worsen farm household incomes?

Many fear China's accession to the World Trade Organization (WTO) will impoverish its farmers via greater import competition in its agricultural markets. We explore that possibility bearing in mind that, even if producer prices of some (land-intensive) farm products fall, prices of other (labor-intensive) farm and nonfarm products could rise. New estimates, from the global, economy-wide numerical simulation model known as Global Trade Analysis Project (GTAP), of the likely changes in agricultural and other product prices as a result of WTO accession, are drawn on to examine empirically the real income implications of China's WTO accession. The results suggest farm–nonfarm income inequality may well rise within China but rural–urban income inequality need not. The article concludes with some policy suggestions for alleviating any pockets of farm household poverty that may emerge as a result of WTO accession.     

Testing Rival Theories of Budgetary Decision–making in the US States

This article tests three common budgetary decision–making theories in the US states. Pooled time series cross–section analysis is used from 1960 to 1996 to test the garbage can theory and incrementalism, and from 1989 to 1996 to test various theories of rational budgeting. The results demonstrate that there was some support for all three theories in terms of their impact on reducing state budget outputs. Rational budgeting reduces expenditures in aggregate, incrementalism has relatively low explanatory power below the aggregates, and garbage can budgeting is more prevalent in functional areas than for government as a whole. These findings imply that the future research agenda on budgetary decision–making theories should focus more on a system–wide approach, which takes into account many of the characteristics of all three rival models of decision–making, rather than exclusively focusing on each one singly.