Capital Flows to Latin America (2003–17): A Critical Survey from Prebisch’s Business Cycle Theory

ABSTRACT

The economic literature on capital flows to developing countries has shared two important commonalities since the 1990s. Published works (whether they focus on the external situation or stress the domestic determinants of capital flows) tend to assume a beneficial effect of capital inflows, which leads to an improvement of peripheral institutions, whose deficiencies are ostensibly the main cause of economic turmoil and/or failure in attracting capital flows, in continuity with New Institutional Economics. In doing so, mainstream economists deliberately overlook the asymmetric characteristics of the international monetary system and the persisting hegemony of dollar. Raul Prebisch’s pioneering work on business cycles in Latin America provide an alternative view, one capable of amending the existing mainstream literature. On the one hand, Prebisch stressed the destabilizing role of capital inflows on Latin American economies, particularly short-term speculative capital. On the other hand, Prebisch designed a set of counter cyclical monetary policies in order to contrast capital volatility, particularly during downturns. An analysis of stylized facts shows that, when correctly updated, Prebisch’s theory has remarkable explanatory potential when applied to Latin America’s current economic and financial situation.     

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.     

Increasing linkages among European regions. The role of sectoral composition

This paper analyses changes in economic regional interlinkages in Europe over time and investigates the factors that could explain the dynamics of these changes. Our four main findings are the following: (i) we detect a significant surge in regional synchronisation after the Great Recession; (ii) we identify the regions most interrelated with the rest of Europe, namely, Ile de France, Inner London and Lombardia; (iii) we find that sectoral composition explains regional synchronisation in Europe, mainly after the Great Recession and (iv) we document that sectoral composition has important implications for aggregate economic fluctuations, in particular, that similarities in services-related sectors across regions explain a nonlinear relationship between sectoral composition and regional business cycle synchronisation. We also propose a new method to measure time-varying synchronisation in small samples that combines regime-switching models and dynamic model averaging.     

Operating cash flow asymmetric timeliness in Australia

Operating cash flow (CFO) asymmetric timeliness occurs when CFO reflects bad news more quickly than good news. We examine the presence and determinants of CFO asymmetric timeliness in Australia, where substantial differences in reporting requirements of cash flow components, in characteristics of listed companies and in the degree of conservative financial reporting produce contrasting findings to those in the United States. We find supportive evidence for the novel ‘sticky cost behaviour’ explanation and also the product-pricing strategy, but not the life cycle hypothesis. These findings are useful for investors and analysts concerned with forecasting the future values of companies.     

我国中小企业三维差异化特征比较研究

作为推动国民经济增长和促进社会稳定的基础力量,中小企业在市场中扮演着最活跃的经济主体。与大企业相比,中小企业有其鲜明的成长特征,从动态演进视角选取体量规模、生命周期、要素需求三个维度,系统探讨了中小企业的差异化特征。结论显示:体量规模特征反映在个体与群体以及经济板块之间;生命周期特征体现在死亡陷阱和空间格局演变上;要素需求特征会根据阶段创新内容的不同做出适时调整。     

地方财政政策逆周期调节：财政分权还是债务增发*

财政政策工具实际操作偏离最初设计将会降低政策工具的调控效果，导致宏观经济波动加剧。针对地方政府财政政策的逆周期性调控目标，本文关注财政分权深化和政府债务增发的逆周期性效应。研究发现：地方政府财政政策总体呈现顺周期特征。财政分权程度的提高放大了地方财政政策的顺周期性。地方政府债务增发使地方财政政策呈现逆周期特征。地方政府财政政策及其收入面、支出面均呈现顺周期性，由“顺”转“逆”的债务平衡点分别为0137，0113，0209。当前绝大部分地区的债务规模都在债务平衡点之下。驱动地方政府债务逆周期调控的内在因素是官员晋升激励。晋升激励的程度越高，地方债务逆周期调控的力度就越大。但外部市场融资环境的波动削弱了地方政府债务逆周期调控的力度。本文的政策主张是，不宜过度财政分权，应该扩大地方债发行规模，促使地方财政政策逆周期调节。     

The electoral migration cycle

This paper proposes a new test of Tiebout sorting that relies on the exogenous time structure of recurrent local elections. The test is based on the idea that competitive elections represent periodic perturbations to the Tiebout equilibrium of local public good provision and allocation of households to communities, so that their schedule should affect the timing of households' sorting decisions. On the other hand, internal migration flows that have nothing to do with the demand for public goods over which localities vote recurrently ought to be orthogonal to the timing of elections in a reduced-form migration equation. I exploit the staggered schedule of mayoral elections in Italy to analyze migration, elections, and public budget data across several thousands of municipalities, and find evidence of a systematic influence of the electoral calendar on the timing of sorting decisions.     

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization*

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.     

Welfare Cost of Fluctuations When Labor Market Search Interacts with Financial Frictions

We study the welfare costs of business cycles in a search and matching model with financial frictions. The model replicates the volatility on labor and financial markets. Business cycle costs are sizable. Indeed, the interactions between labor market and financial frictions magnify the impact of shocks via (i) a credit multiplier effect and (ii) an endogenous wage rigidity inherent to financial frictions. In addition, in a nonlinear framework, large welfare costs of fluctuations are explained by the high average unemployment and the low job finding rates with respect to their deterministic steady‐state values.     

Ethnic discrimination in hiring,labour market tightness and the business cycle - evidence from field experiments

Several studies using observational data suggest that ethnic discrimination increases in downturns of the economy. We investigate whether ethnic discrimination depends on labour market tightness using data from correspondence studies. We utilize three correspondence studies of the Swedish labour market and two different measures of labour market tightness. These two measures produce qualitatively similar results, and, opposite to the observational studies, suggest that ethnic discrimination in hiring decreases in downturns of the economy.