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Book part
Publication date: 10 November 2014

Maria Bampasidou, Carlos A. Flores, Alfonso Flores-Lagunes and Daniel J. Parisian

Job Corps is the United State’s largest and most comprehensive training program for disadvantaged youth aged 16–24 years old. A randomized social experiment concluded that, on…

Abstract

Job Corps is the United State’s largest and most comprehensive training program for disadvantaged youth aged 16–24 years old. A randomized social experiment concluded that, on average, individuals benefited from the program in the form of higher weekly earnings and employment prospects. At the same time, “young adults” (ages 20–24) realized much higher impacts relative to “adolescents” (ages 16–19). Employing recent nonparametric bounds for causal mediation, we investigate whether these two groups’ disparate effects correspond to them benefiting differentially from distinct aspects of Job Corps, with a particular focus on the attainment of a degree (GED, high school, or vocational). We find that, for young adults, the part of the total effect of Job Corps on earnings (employment) that is due to attaining a degree within the program is at most 41% (32%) of the total effect, whereas for adolescents that part can account for up to 87% (100%) of the total effect. We also find evidence that the magnitude of the part of the effect of Job Corps on the outcomes that works through components of Job Corps other than degree attainment (e.g., social skills, job placement, residential services) is likely higher for young adults than for adolescents. That those other components likely play a more important role for young adults has policy implications for more effectively servicing participants. More generally, our results illustrate how researchers can learn about particular mechanisms of an intervention.

Details

Factors Affecting Worker Well-being: The Impact of Change in the Labor Market
Type: Book
ISBN: 978-1-78441-150-3

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Book part
Publication date: 23 November 2020

Masao Yamaguchi

Recent empirical studies have improved methodologies for identifying the causal effects of policies especially on a minimum wage hike. This study identifies causal effects of…

Abstract

Recent empirical studies have improved methodologies for identifying the causal effects of policies especially on a minimum wage hike. This study identifies causal effects of minimum wage hikes across 47 prefectures in Japan from 2008 to 2010 on employment, average hourly wage, work hours, full-time equivalent employment (FTE), total wage costs, average tenure, separation and new hiring in establishments using a micro dataset of business establishments in restaurant, accommodation, and food takeout and delivery industry. Various regression specifications including controls for time-varying regional heterogeneity are implemented by using the bite of the minimum wage in each establishment. First, this study finds that the effects of a revision of minimum wage on employment and FTE in the establishment are statistically insignificant, but the effects on hourly wages and total wage costs are statistically significant. Subsequently, it considers how the establishments react to the increase in total wage costs caused by the revised minimum wage, and finds that separation from the establishment may decrease, and average tenure of workers may increase.

Details

Change at Home, in the Labor Market, and On the Job
Type: Book
ISBN: 978-1-83909-933-5

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Article
Publication date: 11 September 2017

Francesco Caracciolo and Marilena Furno

Several approaches have been proposed to evaluate treatment effect, relying on matching methods propensity score, quantile regression, influence function, bootstrap and various…

Abstract

Purpose

Several approaches have been proposed to evaluate treatment effect, relying on matching methods propensity score, quantile regression, influence function, bootstrap and various combinations of the above. This paper considers two of these approaches to define the quantile double robust (DR) estimator: the inverse propensity score weights, to compare potential output of treated and untreated groups; the Machado and Mata quantile decomposition approach to compute the unconditional quantiles within each group – treated and control. Two Monte Carlo studies and an empirical application for the Italian job labor market conclude the analysis. The paper aims to discuss these issue.

Design/methodology/approach

The DR estimator is extended to analyze the tails of the distribution comparing treated and untreated groups, thus defining the quantile based DR estimator. It allows us to measure the treatment effect along the entire outcome distribution. Such a detailed analysis uncovers the presence of heterogeneous impacts of the treatment along the outcome distribution. The computation of the treatment effect at the quantiles, points out variations in the impact of treatment along the outcome distributions. Indeed it is often the case that the impact in the tails sizably differs from the average treatment effect.

Findings

Two Monte Carlo studies show that away from average, the quantile DR estimator can be profitably implemented. In the real data example, the nationwide results are compared with the analysis at a regional level. While at the median and at the upper quartile the nationwide impact is similar to the regional impacts, at the first quartile – the lower incomes – the nationwide effect is close to the North-Center impact but undervalues the impact in the South.

Originality/value

The computation of the treatment effect at various quantiles allows to point out discrepancies between treatment and control along the entire outcome distributions. The discrepancy in the tails may differ from the divergence between the average values. Treatment can be more effective at the lower/higher quantiles. The simulations show the performance at the quartiles of quantile DR estimator. In a wage equation comparing long and short term contracts, this estimator shows the presence of an heterogeneous impact of short term contracts. Their impact changes depending on the income level, the outcome quantiles, and on the geographical region.

Details

Journal of Economic Studies, vol. 44 no. 4
Type: Research Article
ISSN: 0144-3585

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Book part
Publication date: 18 January 2022

Cheng Hsiao, Yan Shen and Qiankun Zhou

Panel data provide the possibilities of estimating individual treatment effects for multiple individuals. Two issues are considered: (1) differences in the estimated individual…

Abstract

Panel data provide the possibilities of estimating individual treatment effects for multiple individuals. Two issues are considered: (1) differences in the estimated individual treatment effects are due to heterogeneity or a chance mechanism? (2) what is the best way to estimate the average treatment effects? Testing and aggregation methods are suggested. Monte Carlo simulations are also conducted to shed light on these two issues. An empirical analysis on the involvement of underground organization in China’s Peer-to-Peer (P2P) activities through the “anti-gang” campaign is also provided.

Details

Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology
Type: Book
ISBN: 978-1-80262-065-8

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Book part
Publication date: 7 October 2019

Xiqian Liu and Victor Borden

Without controlling for selection bias and the potential endogeneity of the treatment by using proper methods, the estimation of treatment effect could lead to biased or incorrect…

Abstract

Without controlling for selection bias and the potential endogeneity of the treatment by using proper methods, the estimation of treatment effect could lead to biased or incorrect conclusions. However, these issues are not addressed adequately and properly in higher education research. This study reviews the essence of self-selection bias, treatment assignment endogeneity, and treatment effect estimation. We introduce three treatment effect estimators – propensity score matching analysis, doubly robust estimation (augmented inverse probability weighted approach), and endogenous treatment estimator (control-function approach) – and examine literature that applies these methods to research in higher education. We then use the three methods in a case study that estimates the effects of transfer student pre-enrollment debt on persistence and first year grades. The final discussion provides guidelines and recommendations for causal inference research studies that use such quasi-experimental methods.

Article
Publication date: 13 April 2020

Evy Rombaut and Marie-Anne Guerry

The main goal of employee retention is to prevent competent employees from leaving the company. When analysing the main reasons why employees leave and determining their turnover…

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Abstract

Purpose

The main goal of employee retention is to prevent competent employees from leaving the company. When analysing the main reasons why employees leave and determining their turnover probability, the question arises: Which retention strategies have an actual effect on turnover and for which profile of employees do these strategies work?

Design/methodology/approach

To determine the effectiveness of different retention strategies, an overview is given of retention strategies that can be found in the literature. Next, the paper presents a procedure to build an uplift model for testing the effectiveness of the different strategies on HR data. The uplift model is based on random forest estimation and applies personal treatment learning estimation.

Findings

Through a data-driven approach, the actual effect of retention strategies on employee turnover is investigated. The retention strategies compensation and recognition are found to have a positive average treatment effect on the entire population, while training and flexibility do not. However, with personalised treatment learning, the treatment effect on the individual level can be estimated. This results in an ability to profile employees with the highest estimated treatment effect.

Practical implications

The results yield useful information for human resources practitioners. The personalised treatment analysis results in detailed retention information for these practitioners, which allows them to target the right employees with the right strategies.

Originality/value

Even though the uplift modelling approach is becoming increasingly popular within marketing, this approach has not been taken within human resources analytics. This research opens the door for further research and for practical implementation.

Details

International Journal of Manpower, vol. 41 no. 8
Type: Research Article
ISSN: 0143-7720

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Article
Publication date: 7 March 2016

Ricardo Monge-González, Juan Antonio Rodríguez-Alvarez and Juan Carlos Leiva

The purpose of this paper is to estimate the impact of one productive development program (PROPYME) in a developing nation like Costa Rica. This program seeks to increase the…

Abstract

Purpose

The purpose of this paper is to estimate the impact of one productive development program (PROPYME) in a developing nation like Costa Rica. This program seeks to increase the capacity of small and medium-sized firms (SMEs) to innovate.

Design/methodology/approach

Impacts have been estimated assuming that beneficiary firms are trying to maximize their profits and that PROPYME aims to increase these firms productivity. The impacts were measured in terms of three result variables real average wages employment demand and the probability of exporting. A combination of fixed effects and propensity score matching techniques was used in estimations to correct for any selection bias. The authors worked with panel data companies treated and untreated for the period 2001-2011.

Findings

PROPYME’s beneficiaries performed better than other firms in terms of labor demand and their probability of exporting. In addition, the dose and the duration of the effects of the treatment (timing effects) are important.

Originality/value

The authors study the impact in ways that go beyond the average treatment effects on the treated (ATT) usually estimated in the existing literature. Specifically, the research focusses on the identification of the timing or dynamic effects (i.e. how long should we wait to see results?) and treatment intensity (dosage effects).

Propósito

Se estima el impacto de un programa de desarrollo productivo (Propyme) en un país en vías de desarrollo como Costa Rica. El Propyme busca incrementar la capacidad innovadora de las pequeñas y medidas empresas (pymes) costarricenses.

Diseño/metodológico

el impacto se ha estimado y evaluado asumiendo que las pymes beneficiaras buscan maximizar sus beneficios y que Propyme se enfoca en incrementar la productividad de esas empresas. El impacto se valoró en función de tres variables: salarios reales medios, empleo demandado y la probabilidad de exportar. Se utilizó una combinación de técnicas de efectos fijos y emparejamiento en las estimaciones con el fin de prevenir sesgos de selección. Se trabajó con un panel de datos, incluyendo empresas tratadas (beneficiarias de Propyme) así como no tratadas para el periodo 2001-2011.

Hallazgos

los beneficiarios de Propyme tuvieron mejor desempeño que las restantes empresas en términos de empleo demandado y su posibilidad de exportar. Adicionalmente los efectos dinámicos (dosis y duración) de los tratamientos son importantes.

Originalidad y valor

este artículo evalúa el impacto de una forma que va más allá de lo usual en la literatura por medio de los efectos promedios de los tratamientos sobre los beneficiarios. Esto por cuanto se enfoca en efectos dinámicos como la duración así como la intensidad.

Article
Publication date: 11 March 2020

KonShik Kim

The purpose of this study is to determine the extent to which R&D subsidy can affect the innovation process of manufacturing venture firms by examining the output additionality…

Abstract

Purpose

The purpose of this study is to determine the extent to which R&D subsidy can affect the innovation process of manufacturing venture firms by examining the output additionality measured as both proximal indicators of innovation and distal indicators of growth. Further, the differences in output additionality between the clusters in the subcontracting regime were examined to investigate whether the effect of R&D subsidy can vary depending on subcontracting practices and structure among large enterprises and venture firms.

Design/methodology/approach

This study uses survey data of the Korea Venture Business Association conducted in 2012, 2013, 2014, 2015, and 2016 respectively, which selects a random sample from venture firms by stratified random sampling method based on the industry sector, size and location for each survey year. This study analyzed the data using an endogenous treatment effects model to estimate the average treatment effect of R&D subsidy, yielding more accurate estimates than a traditional treatment effects model by controlling the unobserved endogenous components.

Findings

This research found that R&D subsidy may not facilitate the process of transformation of innovation into financial growth even though R&D subsidy can facilitate the innovation process and contribute to producing new and improved products. This research also reveals that the relationship between R&D subsidy and innovation performance for firms heavily dependent on subcontracting is generally much weaker than those for independent subcontractors. Further, the present study exhibits that public R&D subsidy for independently subcontracting venture firms is more effective for the growth in both employment and sales than those for subcontracting with large enterprises or other subcontractors.

Research limitations/implications

R&D subsidy for venture firms does not relieve the burden of liability of newness and smallness of venture firms, especially the disadvantage in market penetration and competition. In addition, venture firms subcontracting with large enterprises or other prime subcontractors tend to achieve incremental innovation with the help of the technology and competence of large companies and run stable businesses through a predetermined market.

Practical implications

R&D subsidy for venture firms does not relieve the burden of liability of newness and smallness of venture firms, especially the disadvantage in market penetration and competition. Further policy measures should be implemented so as to identify and eliminate barriers to market acceptance for new products of venture firms.

Originality/value

This research verifies that the effect of R&D subsidy may harmful to the sales growth of venture firms and the output additionality differs with the degree of dependency on subcontracting practices and structure.

Details

European Journal of Innovation Management, vol. 24 no. 2
Type: Research Article
ISSN: 1460-1060

Keywords

Book part
Publication date: 13 October 2015

Catherine C. Eckel, Haley Harwell and José Gabriel Castillo G.

This paper replicates four highly cited, classic lab experimental studies in the provision of public goods. The studies consider the impact of marginal per capita return and group…

Abstract

This paper replicates four highly cited, classic lab experimental studies in the provision of public goods. The studies consider the impact of marginal per capita return and group size; framing (as donating to or taking from the public good); the role of confusion in the public goods game; and the effectiveness of peer punishment. Considerable attention has focused recently on the problem of publication bias, selective reporting, and the importance of research transparency in social sciences. Replication is at the core of any scientific process and replication studies offer an opportunity to reevaluate, confirm or falsify previous findings. This paper illustrates the value of replication in experimental economics. The experiments were conducted as class projects for a PhD course in experimental economics, and follow exact instructions from the original studies and current standard protocols for lab experiments in economics. Most results show the same pattern as the original studies, but in all cases with smaller treatment effects and lower statistical significance, sometimes falling below accepted levels of significance. In addition, we document a “Texas effect,” with subjects consistently exhibiting higher levels of contributions and lower free-riding than in the original studies. This research offers new evidence on the attenuation effect in replications, well documented in other disciplines and from which experimental economics is not immune. It also opens the discussion over the influence of unobserved heterogeneity in institutional environments and subject pools that can affect lab results.

Details

Replication in Experimental Economics
Type: Book
ISBN: 978-1-78560-350-1

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Book part
Publication date: 13 May 2017

Luke Keele, Scott Lorch, Molly Passarella, Dylan Small and Rocío Titiunik

We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a…

Abstract

We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a treated and a control area. This type of geographically discontinuous treatment assignment can be analyzed in a standard regression discontinuity (RD) framework if the exact geographic location of each unit in the dataset is known. Such data, however, is often unavailable due to privacy considerations or measurement limitations. In the absence of geo-referenced individual-level data, two scenarios can arise depending on what kind of geographic information is available. If researchers have information about each observation’s location within aggregate but small geographic units, a modified RD framework can be applied, where the running variable is treated as discrete instead of continuous. If researchers lack this type of information and instead only have access to the location of units within coarse aggregate geographic units that are too large to be considered in an RD framework, the available coarse geographic information can be used to create a band or buffer around the border, only including in the analysis observations that fall within this band. We characterize each scenario, and also discuss several methodological challenges that are common to all research designs based on geographically discontinuous treatment assignments. We illustrate these issues with an original geographic application that studies the effect of introducing copayments for the use of the Children’s Health Insurance Program in the United States, focusing on the border between Illinois and Wisconsin.

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

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