788 resultados para Difference-in-differences
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Using American panel data from the National Education Longitudinal Study of 1988, this article investigates the effect of working during grade 12 on attainment.We employ, for the first time in the related literature, a semiparametric propensity score matching approach combined with difference-in-differences. We address selection on both observables and unobservables associated with part-time work decisions, without the need for instrumental variable. Once such factors are controlled for, little to no effects on reading and math scores are found. Overall, our results therefore suggest a negligible academic cost from part-time working by the end of high school.
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Background Despite the importance of HIV testing for controlling the HIV epidemic, testing rates remain low. Efforts to scale-up testing coverage and frequency in hard-to-reach and at-risk populations commonly focus on home-based HIV testing. This study evaluates the effect of a gift (a food voucher for families, worth US$ 5) on consent rates for home-based HIV testing.
Methods We use data on 18,478 men and women who participated in the 2009 and 2010 population-based HIV surveillance carried out by the Wellcome Trust Africa Centre for Health and Population Studies in rural KwaZulu-Natal, South Africa. Our quasi-experimental difference-in-differences approach controls for unobserved confounding in estimating the causal effect of the intervention on HIV testing consent rates.
Results Allocation of the gift to a family in 2010 increased the probability of family members consenting to test in 2010 by 25 percentage points (95% CI 21-30; p<0.001). The intervention effect persisted, slightly attenuated, in the year following the intervention (2011), further increasing intervention value for money.
Conclusions In HIV hyperendemic settings a gift can be highly effective at increasing consent rates for home-based HIV testing. Given the importance of HIV testing for treatment uptake and individual health, as well as for HIV treatment-and-prevention strategies and for monitoring the population impact of the HIV response, gifts should be considered as a supportive intervention for HIV testing initiatives where consent rates have been low.
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Background: National physical activity data suggest that there is a considerable difference in physical activity levels of US and Australian adults. Although different surveys (Active Australia and BRFSS) are used, the questions are similar. Different protocols, however, are used to estimate “activity” from the data collected. The primary aim of this study was to assess whether the 2 approaches to the management of PA data could explain some of the difference in prevalence estimates derived from the two national surveys. Methods: Secondary data analysis of the most recent AA survey (N = 2987). Results: 15% of the sample was defined as “active” using Australian criteria but as “inactive” using the BRFSS protocol, even though weekly energy expenditure was commensurate with meeting current guidelines. Younger respondents (age < 45 y) were more likely to be “misclassified” using the BRFSS criteria. Conclusions: The prevalence of activity in Australia and the US appears to be more similar than we had previously thought.
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The present research represents a coherent approach to understanding the root causes of ethnic group differences in ability test performance. Two studies were conducted, each of which was designed to address a key knowledge gap in the ethnic bias literature. In Study 1, both the LR Method of Differential Item Functioning (DIF) detection and Mixture Latent Variable Modelling were used to investigate the degree to which Differential Test Functioning (DTF) could explain ethnic group test performance differences in a large, previously unpublished dataset. Though mean test score differences were observed between a number of ethnic groups, neither technique was able to identify ethnic DTF. This calls into question the practical application of DTF to understanding these group differences. Study 2 investigated whether a number of non-cognitive factors might explain ethnic group test performance differences on a variety of ability tests. Two factors – test familiarity and trait optimism – were able to explain a large proportion of ethnic group test score differences. Furthermore, test familiarity was found to mediate the relationship between socio-economic factors – particularly participant educational level and familial social status – and test performance, suggesting that test familiarity develops over time through the mechanism of exposure to ability testing in other contexts. These findings represent a substantial contribution to the field’s understanding of two key issues surrounding ethnic test performance differences. The author calls for a new line of research into these performance facilitating and debilitating factors, before recommendations are offered for practitioners to ensure fairer deployment of ability testing in high-stakes selection processes.
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It is often reported that females lose less body weight than males do in response to exercise. These differences are suggested to be a result of females exhibiting a stronger defense of body fat and a greater compensatory appetite response to exercise than males do. Purpose This study aimed to compare the effect of a 12-wk supervised exercise program on body weight, body composition, appetite, and energy intake in males and females. Methods A total of 107 overweight and obese adults (males = 35, premenopausal females = 72, BMI = 31.4 ± 4.2 kg·m−2, age = 40.9 ± 9.2 yr) completed a supervised 12-wk exercise program expending approximately 10.5 MJ·wk−1 at 70% HRmax. Body composition, energy intake, appetite ratings, RMR, and cardiovascular fitness were measured at weeks 0 and 12. Results The 12-wk exercise program led to significant reductions in body mass (males [M] = −3.03 ± 3.4 kg and females [F] = −2.28 ± 3.1 kg), fat mass (M = −3.14 ± 3.7 kg and F = −3.01 ± 3.0 kg), and percent body fat (M = −2.45% ± 3.3% and F = −2.45% ± 2.2%; all P < 0.0001), but there were no sex-based differences (P > 0.05). There were no significant changes in daily energy intake in males or females after the exercise intervention compared with baseline (M = 199.2 ± 2418.1 kJ and F = −131.6 ± 1912.0 kJ, P > 0.05). Fasting hunger levels significantly increased after the intervention compared with baseline values (M = 11.0 ± 21.1 min and F = 14.0 ± 22.9 mm, P < 0.0001), but there were no differences between males and females (P > 0.05). The exercise also improved satiety responses to an individualized fixed-energy breakfast (P < 0.0001). This was comparable in males and females. Conclusions Males and premenopausal females did not differ in their response to a 12-wk exercise intervention and achieved similar reductions in body fat. When exercise interventions are supervised and energy expenditure is controlled, there are no sex-based differences in the measured compensatory response to exercise.
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Research and practice regarding LO students usually has focussed upon defining and supplementing deficiencies rather than seeking unique talents and capability patterns for learning and expression. This study examined nine dimensions that may constitute artistic or creative talent and compared LDs with "regular-class" students, pair-wise and as groups, for levels and distributions of the dimensions. For 14 LO and 9 "regular-class" elementary-school subjects, both genders, data were taken by direct observation, from a standardized test and assessments by two practicing artists. Assessments by artists were in concord. LOs improved more in "Composition". No other significant class, age or gender-related differences were found.
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Childhood is characterised by diversity and difference across and within societies. Street children have a unique relationship to the urban environment evident through their use of the city. The everyday geographies that street children produce are diversified through the spaces they frequent and the activities they engage in. Drawing on a range of children-centred qualitative methods, this article focuses on street children's use of urban space in Kampala, Uganda. The article demonstrates the importance of considering variables such as gender and age in the analysis of street children's socio-spatial experiences, which, to date, have rarely been considered in other accounts of street children's lives. In addition the article highlights the need for also including street children's individuality and agency into understanding their use of space. The article concludes by arguing for policies to be sensitive to the diversity that characterises street children's lives and calls for a more nuanced approach where policies are designed to accommodate street children's age and gender differences, and their individual needs, interests and abilities.
Resumo:
Childhood is characterised by diversity and difference across and within societies. Street children have a unique relationship to the urban environment evident through their use of the city. The everyday geographies that street children produce are diversified through the spaces they frequent and the activities they engage in. Drawing on a range of children-centred qualitative methods, this article focuses on street children's use of urban space in Kampala, Uganda. The article demonstrates the importance of considering variables such as gender and age in the analysis of street children's socio-spatial experiences, which, to date, have rarely been considered in other accounts of street children's lives. In addition the article highlights the need for also including street children's individuality and agency into understanding their use of space. The article concludes by arguing for policies to be sensitive to the diversity that characterises street children's lives and calls for a more nuanced approach where policies are designed to accommodate street children's age and gender differences, and their individual needs, interests and abilities.
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Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we can model the heteroskedasticity of a linear combination of the errors. We show that this assumption can be satisfied without imposing strong assumptions on the errors in common DID applications. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative inference method that relies on strict stationarity and ergodicity of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment periods. We extend our inference methods to linear factor models when there are few treated groups. We also derive conditions under which a permutation test for the synthetic control estimator proposed by Abadie et al. (2010) is robust to heteroskedasticity and propose a modification on the test statistic that provided a better heteroskedasticity correction in our simulations.
Resumo:
Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative application of our method that relies on assumptions about stationarity and convergence of the moments of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment groups. We extend our inference method to linear factor models when there are few treated groups. We also propose a permutation test for the synthetic control estimator that provided a better heteroskedasticity correction in our simulations than the test suggested by Abadie et al. (2010).
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Sex differences in seasonal timing include differences in hatch- or birth-date distribution and differences in the timing of migration or maturation such as protandrous arrival timing (PAT), which is early male arrival at breeding sites. I describe a novel form of protandrous arrival timing, as a sex difference in birth-date distribution in a live-bearing fish (Dwarf Perch, Micrometrus minimus). In this species, birth coincides with arrival at breeding sites because newborn males are sexually active. A series of samples of pregnant females and young of year was collected in Tomales Bay, CA. I analyzed the daily age record in otoliths to estimate the conception date of broods and the age that young-of-year individuals were born. Males were born at a younger age than females, as indicated by the daily age record and also by the predominance of females in broods from which some young had already been born, which was a common occurrence in pregnant females with older embryos. Sex ratio of broods varied with conception date such that early-season broods were predominantly male, possibly as a result of temperature-dependent sex determination. The combined effects of the sex difference in age at birth and seasonal shift in sex ratio were to shift the mean birth date of males relative to females by five days. The most likely ultimate explanation for PAT in the Dwarf Perch is that it arises from exploitation (scramble) competition for mating opportunities among recently-born young-of-year males.
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This study examined gender differences in emotional and behavioral responses to an experience of being invisible to others. Invisibility was defined as being ignored, slighted and overlooked by others. Participants recalled their own experience and answered questions about it and their responses on an anonymous web-based survey. Although such experiences could be very unpleasant, people may respond to such negative experiences very differently. It was hypothesized that in a patriarchal society like the United States in which men hold more power than women, that men would show emotion that was more aggressive such as anger, and respond more violently to incidents they were not respected. Women, on the other hand, were expected to be more subservient in their behavior and responses, show submissive emotions such as sadness, and respond less violently when they were not respected.