Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs

Ashenfelter, Orley; Card, David

doi:10.2307/1924810

Cited by 943 publications

(531 citation statements)

References 4 publications

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“…Essentially, DID framework requires two groups (treated and control), at least two time periods (before and after) and an outcome variable of interest (Card & Krueger, 1994;Ashenfelter & Card, 1985;Hastings, 2004). In the context of this study, the treatment is AP (implemented only by government schools).…”

Section: Empirical Methodologymentioning

confidence: 99%

Automatic Promotion and Student Dropout: Evidence from Uganda, Using Propensity Score in Difference in Differences Model

Okurut¹

2018

JEL

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The impact of automatic promotion practice on students dropping out of Uganda's primary education was assessed using propensity score in difference in differences analysis technique. The analysis strategy was instrumental in addressing the selection bias problem, as well as biases arising from common trends over time, and permanent latent differences between the treated and control groups. Probit regression results indicate a negative effect on the probability of students dropping out, but only at P3. There seems to be no policy effect at P6. Decomposing the effect incidence along school location shows the policy as having had an effect only on P3 students studying in urban schools; otherwise, there is no effect among students at P3 rural, P6 rural or P6 Urban. In terms of the gender component, automatic promotion appears to have had an effect on P3 male and female students and no effect on either sex at P6.

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Section: Empirical Methodologymentioning

confidence: 99%

Automatic Promotion and Student Dropout: Evidence from Uganda, Using Propensity Score in Difference in Differences Model

Okurut¹

2018

JEL

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“…We employ a difference-in-difference strategy that is integrated with the Poisson, generalized Poisson, and ZIP regression models. The DID method innovated by Ashenfelter and Card (1985) is used as an evaluation method to make causal inferences [26]. This method is widely applied to evaluate the impact of public policies.…”

Section: Methodsmentioning

confidence: 99%

The Impacts of the Mass Rapid Transit System on Household Car Ownership in Taipei

Huang¹,

Chao²

2014

J. sustain. dev. energy water environ. syst.

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This paper investigates the impacts of Taipei Mass Rapid Transit (MRT) system on household car ownership and analyses how socioeconomic characteristics affect household car ownership. We employ a difference-in-difference (DID) strategy integrated with generalized Poisson regression models to examine the effects of MRT. The results are as follows: first, the establishment of Taipei MRT significantly reduced the level of household car ownership. Expanding the network of MRT system can be a feasible policy to control car ownership. Second, the levels of household car ownership are related to household's socioeconomic characteristics. Third, households with high dependence on public transport own fewer cars after Taipei MRT began operation. Hence, the traffic authority should adopt more effective methods to encourage public transit use in order to decrease household car ownership.

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“…The use of D-i-D method became very widespread in the field of evaluating the impact of a policy or program, especially after research work by Ashenfelter and Card (1985). Other early scholars greatly associated with this estimation method include Card and Krueger (1994), Meyer (1995), andHastings (2004).…”

Section: Analysis Technique/ Modelmentioning

confidence: 99%

Examining the Effect of Automatic Promotion on Students’ Learning Achievements in Uganda’s Primary Education

Okurut¹

2015

WJE

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This study employed a difference-in-differences analysis technique to estimate the average treatment effect of automatic promotion on students' cognitive learning outcomes in Uganda's primary education. Regression results indicate a positive policy effect on learning achievements in literacy and numeracy at primary three (P3) and primary six (P6). Specifically, the implementation of automatic promotion policy has translated in to an increase in learning outcomes in reading and mathematics at P3 and P6, all statistically significant at conventional levels. Decomposing the effect along gender and school location (rural or urban) dimensions reveals positive and statistically significant effect on literacy and numeracy in both grades. The effect on students' scores in rural areas is higher than that on students' in urban schools. In terms of gender, the effect is relatively similar for female students and their male counterparts. These results are contrary to the popular belief among many Ugandans, but consistent with earlier scholarly works that have attributed automatic promotion with positive impact on learning outcomes.

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Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs

Cited by 943 publications

References 4 publications

Automatic Promotion and Student Dropout: Evidence from Uganda, Using Propensity Score in Difference in Differences Model

Automatic Promotion and Student Dropout: Evidence from Uganda, Using Propensity Score in Difference in Differences Model

The Impacts of the Mass Rapid Transit System on Household Car Ownership in Taipei

Examining the Effect of Automatic Promotion on Students’ Learning Achievements in Uganda’s Primary Education

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