Domestic institutions and export performance: Evidence for Cambodia

The paper analyzes the relevance of domestic institutions for export performance of Cambodia. Regulatory quality, control of corruption, rule of law, government effectiveness, and political stability are introduced in an augmented gravity model with a panel data set over 1996–2015. The research is the first application to Cambodia, until 2015 a least developed country which is generally believed to have poorly developed institutions. Due to high multicollinearity among the variables, the institutional variables are introduced in the model one by one. Estimation is by the Hausman–Taylor method, which reduces or removes the correlation between the composite error terms and the included variables. All institutional variables show a highly significant positive relationship with Cambodia's exports, with rule of law having the largest impact. It is concluded that the government should give high priority to the further improvements of the legal environment and to strong enforcement of property rights and contracts.     

大数据与监视型资本主义

本文首先介绍了监视型资本主义的概念,描述信息时代的资本是如何通过收集个人数据,无视私隐,筛选用户,在不受法律监控的情况下进行帝国式扩展的。进而,本文提出数据的附身性和数码资本错位这两个特征,用以分析技术如何在数据收集和应用上激活数据的私人财产属性,以及资本如何利用价值错位和技术优势去诱使用户参与这一资本增值的进程。数据的附身性揭示了个体如何被卷入大数据时代的生产关系,数码资本错位揭示了资本具体实现监视型资本主义的技术。这两个概念将完善监视型资本主义这一理论体系,进一步展现这个时代隐蔽的不平等状况。     

General or central government? Empirical evidence on political cycles in budget composition using new data for OECD countries

Previous studies used general government data to examine whether national governments’ electoral motives and ideology influenced budget composition in OECD countries. General government data includes, however, the state and local level. Using new data for general and central government over the period 1995–2016, I reexamine political cycles in budget composition. The results suggest that, both at the general and central government level, leftwing governments spent more on education and less on public services than rightwing governments. Defense expenditure was somewhat lower under leftwing than rightwing governments and in election years; especially in federal states. Effects of government ideology on the individual expenditure categories are larger at the central than general government level. Scholars need to re-examine results on ideology-induced effects that have been derived from general government data where central government data should have been used.     

Do vertical spillovers differ by investors’ productivity? Theory and evidence from Vietnam

Developing countries are eager to host foreign direct investment to receive positive technology spillovers to their local firms. However, what types of foreign firms are desirable for the host country to achieve spillovers best? We address this question using firm‐level panel data from Vietnam to investigate whether foreign Asian investors in downstream sectors with different productivity affect the productivity of local Vietnamese firms in upstream sectors differently. Using endogenous structural breaks, we divide Asian investors into low‐, middle‐, and high‐productivity groups. The results suggest that the presence of the middle group has the strongest positive spillover effect. The differential spillover effects can be explained by a simple model with vertical linkages and productivity‐enhancing investment by local suppliers. The theoretical mechanism is also empirically confirmed.     

The Cinderella moment: Exploring consumers' motivations to engage with renting as collaborative luxury consumption mode

Past literature argued that the purchase of luxury goods is driven by people's motivation to conform or fit into our economic and social system. In this study, the authors focus on a new aspect of consumption, that is, renting instead of purchasing luxury goods, backed by the emerging opportunities of sharing economy platforms. Drawing upon the analysis of spontaneous consumers' online communications (in the form of tweets), this study aims to investigate the motivations to engage with luxury garment renting within a collaborative consumption context. To this end, a series of automatic content analyses, via two studies, were conducted using the tweets posted with respect to the Run the Runway collaborative consumption platform. Results demonstrate consumers' increased willingness to show their social status through renting rather than owning luxurious apparel based on five main motivators (need to wear new clothes for a special event, inspirations created by the products/brands, possibility to explore a new way of consuming luxury goods, need to make more sustainable choices, and to increase the life cycle of each luxury product). The implications of these findings are discussed, while they pave the way for future research in collaborative consumption of luxury retailing.     

Using paradata to collect better survey data: Evidence from a household survey in Tanzania

Data are a key component in the design, implementation, and evaluation of economic and social policies. Monitoring data quality is an essential part of any serious, large‐scale data collection process. The purpose of this article is to show how paradata should be used before, during, and after data collection to monitor and improve data quality. To do this we use timestamps, global positioning system (GPS) coordinates, and other paradata collected from an 800‐household survey conducted in Tanzania in 2016. We demonstrate how key paradata can be used during each phase of a research project to identify and prevent issues in the data and the methods used to collect it. Our results corroborate the importance of collecting and analyzing paradata to monitor fieldwork and ensuring data quality for micro data collection in developing countries. Based on these findings we also make recommendations as to how researchers can make better use of paradata in the future to manage and improve data quality. We argue for an expansion in the understanding and use of varied paradata among researchers, and a greater focus on its use for improving data quality.     

A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo,Egypt

Abstract

The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequently lead to biased conclusions and poorly designed countermeasures. This is particularly true for imbalanced data where the number of samples in one class far outnumber the other. To improve the classification performance of the injury severity, the paper presents a robust noise filtering technique to deal with the mislabels in the imbalanced crash dataset using the advanced machine learning algorithms. We examine the state-of-the-art filtering algorithms, including Iterative Noise Filtering based on the Fusion of Classifiers (INFFC), Iterative Partitioning Filter (IPF), and Saturation Filter (SatF). In the case study of Cairo (Egypt), the empirical results show that: (1) the mislabels in crash data significantly influence the injury severity predictions, and (2) the proposed M-IPF filter outperforms its counterparts in terms of the effectiveness and efficiency in eliminating the mislabels in crash data. The test results demonstrate the efficacy of the M-IPF in handling the data noise and mitigating the impacts thereof.     

Employment Dynamics and Linkages in the Rural Economy: Insights from Senegal

Evidence on rural wage employment is thin and lacks nuance for different employment sources, insights on dynamic effects, and an understanding of the channels of effects. We assess conceptually and empirically the direct and indirect welfare effects of entry and continuation in different types of wage employment in rural Senegal. Using panel data, fixed effects and first-difference estimation, we show substantial positive welfare and linkage effects. We find that participation in wage employment increases per capita income by 143%, and reduces poverty, poverty gap and food insecurity by, respectively, 63%, 89% and 48%. While the direct effect on income is larger for non-agricultural and contractual wage employment, the indirect income effects through self-employment are more pronounced for agricultural and casual wage employment. Our results imply that job creation is important for rural development, that wage employment in agriculture can lead to considerable growth multiplier effects, and that synergies exist between large-scale and small-scale agriculture.     

Metafrontier efficiency analysis for hospital pharmacy services using dynamic network DEA framework

Medication management is a complex but critical process within the healthcare delivery system. The pharmacy department in healthcare facilities is responsible for acquisition, distribution and dispensing of medication to patients that involves a series of network process. This process can be categorized into two main subprocesses: i) pharmacy store and ii) patient services (i.e., ambulatory and inpatient pharmacy). The objective of this study was to investigate the relative efficiency level of the two subprocesses of pharmacy services in Malaysia's public hospitals. We used a nonconvex metafrontier technology to estimate technical efficiency of hospital pharmacies between specialist and nonspecialist hospital categories under the dynamic network data envelopment analysis (DNDEA) framework. For overall efficiency, both specialist and nonspecialist categories had similar mean efficiency scores. However, when measuring the efficiency level according to pharmacy store and patient services separately, the nonspecialist category had a higher mean efficiency score in pharmacy store, whereas for patient service efficiency, both categories had similar results. The patient service efficiency level was lower consistently over time, suggesting that the overall inefficiency of hospital pharmacy is greatly influenced by the inefficiency of the patient service. This suggests that systematic improvements in patient service efficiency should be a higher priority than pharmacy store efficiency to further increase the overall efficiency level. We believe that our findings will provide valuable information to policymakers towards achieving an efficient public healthcare service.     

Comparison of dimension reduction methods for DEA under big data via Monte Carlo simulation

Data with large dimensions will bring various problems to the application of data envelopment analysis (DEA). In this study, we focus on a “big data” problem related to the considerably large dimensions of the input-output data. The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation, including principal component analysis (PCA-DEA), which is based on the idea of aggregating input and output, efficiency contribution measurement (ECM), average efficiency measure (AEC), and regression-based detection (RB), which is based on the idea of variable selection. We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test. In addition, we discuss the effect of initial variable selection in RB for the first time. Based on the results, we offer guidelines that are more reliable on how to choose an appropriate method.