Learning in the Public Sector: Evaluation of the SSA Title IV-E Intervention Program of Virginia Department of Social Services

Organizational learning has attracted both scholars and practitioners for decades, but the public sector has rarely been the focus for empirical investigation. We conducted a summative evaluation of the Social Security Act Title IV-E program to take stock of organizational learning in local level public sector service organizations in Virginia. We chose this case for scholarly investigation because it affords an opportunity to uniquely contribute to discovering learning constraints in public sector organizations. We used mixed methods to collect quantitative data on the monthly penetration rate (ratio of federal to state funding) from 2001 to 2006 and qualitative data generated from in-depth interviews of seven informants and a focus group. The quantitative analysis could not detect evidence of the learning that should have been institutionalized in the Local Department of Social Services. The qualitative analysis revealed that complex organizational structure, distorted economic incentive system, and rapid staff turnover are some factors inhibiting learning and retention of knowledge in this public sector organization. Generalizing from these results, these findings can contribute to both theory and practice in the public sector by enriching the understanding of public sector organizations’ learning constraints

Adolescents with disabilities and caregivers experience of COVID-19 in rural Nepal

Introduction: Intersecting vulnerabilities of disability, low socio-economic status, marginalization, and age indicate that adolescents with disabilities in low-and middle-income countries were uniquely affected by the COVID-19 pandemic. Yet, there has been limited research about their experience. We conducted participatory research with adolescents with disabilities in rural, hilly Nepal to explore their experience of the pandemic and inform understanding about how they can be supported in future pandemics and humanitarian emergencies. Methods: We used qualitative methods, purposively sampling adolescents with different severe impairments from two rural, hilly areas of Nepal. We collected data through semi-structured interviews with five girls and seven boys between the age of 11 and 17 years old. Interviews used inclusive, participatory, and arts-based methods to engage adolescents, support discussions and enable them to choose what they would like to discuss. We also conducted semi-structured interviews with 11 caregivers. Results: We found that adolescents with disabilities and their families experienced social exclusion and social isolation because of COVID-19 mitigation measures, and some experienced social stigma due to misconceptions about transmission of COVID-19 and perceived increased vulnerability of adolescents with disabilities to COVID-19. Adolescents who remained connected with their peers throughout lockdown had a more positive experience of the pandemic than those who were isolated from friends. They became disconnected because they moved away from those they could communicate with, or they had moved to live with relatives who lived in a remote, rural area. We found that caregivers were particularly fearful and anxious about accessing health care if the adolescent they cared for became ill. Caregivers also worried about protecting adolescents from COVID-19 if they themselves got ill, and about the likelihood that the adolescent would be neglected if the caregiver died. Conclusion: Contextually specific research with adolescents with disabilities to explore their experience of the pandemic is necessary to capture how intersecting vulnerabilities can adversely affect particular groups, such as those with disabilities. The participation of adolescents with disabilities and their caregivers in the development of stigma mitigation initiatives and strategies to meet their needs in future emergencies is necessary to enable an informed and inclusive response

Model Validation and DSGE Modeling

The primary objective of this paper is to revisit DSGE models with a view to bringing out their key weaknesses, including statistical misspecification, non-identification of deep parameters, substantive inadequacy, weak forecasting performance, and potentially misleading policy analysis. It is argued that most of these weaknesses stem from failing to distinguish between statistical and substantive adequacy and secure the former before assessing the latter. The paper untangles the statistical from the substantive premises of inference to delineate the above-mentioned issues and propose solutions. The discussion revolves around a typical DSGE model using US quarterly data. It is shown that this model is statistically misspecified, and when respecified to arrive at a statistically adequate model gives rise to the Student’s t VAR model. This statistical model is shown to (i) provide a sound basis for testing the DSGE overidentifying restrictions as well as probing the identifiability of the deep parameters, (ii) suggest ways to meliorate its substantive inadequacy, and (iii) give rise to reliable forecasts and policy simulations

Wolf Lethal Control and Livestock Depredations: Counter-Evidence from Respecified Models

<div><p>We replicated the study conducted by Wielgus and Peebles (2014) on the effect of wolf mortality on livestock depredations in Montana, Wyoming and Idaho states in the US. Their best models were found to be misspecified due to the omission of the time index and incorrect functional form. When we respecified the models, this replication failed to confirm the magnitude, direction and often the very existence of the original results. Wielgus and Peebles (2014) reported that the increase in the number of wolves culled the previous year would increase the expected number of livestock killed this year by 4 to 6%. But our results showed that the culling of one wolf the previous year would decrease the expected number of cattle killed this year by 1.9%, and the expected number of sheep killed by 3.4%. However, for every wolf killed there is a corresponding 2.2% increase in the expected number of sheep killed in the same year. The increase in sheep depredation appears to be a short term phenomenon.</p></div

Number of cattle depredated regressed on the number of wolves killed, number of wolf breeding pairs, number of cattle and time index.

<p>Number of cattle depredated regressed on the number of wolves killed, number of wolf breeding pairs, number of cattle and time index.</p

Number of sheep depredated regressed on the number of wolves killed, number of wolf breeding pairs, number of sheep and time index.

<p>Number of sheep depredated regressed on the number of wolves killed, number of wolf breeding pairs, number of sheep and time index.</p

Number of cases and prevalence of specific CKD per 100,000 population in 2019.

Number of cases and prevalence of specific CKD per 100,000 population in 2019.</p