972 resultados para household investment decisions
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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Despite clear evidence of correlations between financial and medical statuses and decisions, most models treat financial and health-related choices separately. This article bridges this gap by proposing a tractable dynamic framework for the joint determination of optimal consumption, portfolio holdings, health investment, and health insurance. We solve for the optimal rules in closed form and capitalize on this tractability to gain a better understanding of the conditions under which separation between financial and health-related decisions is sensible, and of the pathways through which wealth and health determine allocations, welfare and other variables of interest such as expected longevity or the value of health. Furthermore we show that the model is consistent with the observed patterns of individual allocations and provide realistic estimates of the parameters that confirm the relevance of all the main characteristics of the model.
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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Report on applying agreed-upon procedures to the Villisca Municipal Power Plant’s accounting procedures, cash and investment balances and compliance with Code of Iowa requirements for the period February 1, 2007 through December 31, 2010
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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There have been a multitude of programs providing assistance to the state of Iowa in the past 18 months. Springtime 2008 disasters resulted in tornado damage and widespread flood damage to large fractions of the state. In consequence, there was a very large flow of federal and state resources dedicated to assisting community and statewide recovery efforts. The nation was in recession as well and continued to be in recession through much of 2009. A sizeable amount of assistance found its way to Iowa under the American Recovery and Reinvestment Act of 2009 in the forms of infrastructure stimulus spending, income supports and other safety net spending for households, and stabilization assistance for essential public services like education. On top of that, the state of Iowa authorized the I Jobs program as an additional infrastructure development program, and as a jobs stimulus program. The total amount of spending for all types of programs, disaster or economic recovery related, is perhaps as high as $7.5 billion over the next few years.
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A-1 - Monthly Public Assistance Statistical Report Family Investment Program
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The objective of this paper is to identify the political conditions that are most likely to be conducive to the development of social investment policies. It starts from the view put forward by theorists of welfare retrenchment that in the current context of permanent austerity, policy is likely to be dominated by retrenchment and implemented in a way that allows governments to minimise the risk of electoral punishment (blame avoidance). It is argued that this view is inconsistent with developments observed in several European countries, were some welfare state expansion has taken place mostly in the fields of childcare and active labour market policy. An alternative model is put forward, that emphasises the notion of "affordable credit claiming". It is argued that even under strong budgetary pressures, governments maintain a preference for policies that allow them to claim credit for their actions. Since the traditional redistributive policies tend to be off the menu for cost reasons, governments have tended to favour investments in childcare and active labour market policy as credit claiming tools. Policies developed in this way while they have a social investment flavour, tend to be rather limited in the extent to which they genuinely improve prospects of disadvantaged people by investing in their human capital. A more ambitious strategy of social investment sees unlikely to develop on the basis of affordable credit claiming. The paper starts by presenting the theoretical argument, which is then illustrated with examples taken from European countries both in the pre-crisis and in the post-crisis years.
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AIM: Phylogenetic diversity patterns are increasingly being used to better understand the role of ecological and evolutionary processes in community assembly. Here, we quantify how these patterns are influenced by scale choices in terms of spatial and environmental extent and organismic scales. LOCATION: European Alps. METHODS: We applied 42 sampling strategies differing in their combination of focal scales. For each resulting sub-dataset, we estimated the phylogenetic diversity of the species pools, phylogenetic α-diversities of local communities, and statistics commonly used together with null models in order to infer non-random diversity patterns (i.e. phylogenetic clustering versus over-dispersion). Finally, we studied the effects of scale choices on these measures using regression analyses. RESULTS: Scale choices were decisive for revealing signals in diversity patterns. Notably, changes in focal scales sometimes reversed a pattern of over-dispersion into clustering. Organismic scale had a stronger effect than spatial and environmental extent. However, we did not find general rules for the direction of change from over-dispersion to clustering with changing scales. Importantly, these scale issues had only a weak influence when focusing on regional diversity patterns that change along abiotic gradients. MAIN CONCLUSIONS: Our results call for caution when combining phylogenetic data with distributional data to study how and why communities differ from random expectations of phylogenetic relatedness. These analyses seem to be robust when the focus is on relating community diversity patterns to variation in habitat conditions, such as abiotic gradients. However, if the focus is on identifying relevant assembly rules for local communities, the uncertainty arising from a certain scale choice can be immense. In the latter case, it becomes necessary to test whether emerging patterns are robust to alternative scale choices.
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The Iowa Department of Management requested the Iowa Department of Corrections to accept the Pew Center on the States’ invitation to be trained in assessing the return on investment to taxpayers from criminal justice programs utilized by the State of Iowa. Using the Results First model, a nationally recognized, peer-reviewed tool developed by the Washington State Institute for Public Policy (WSIPP), the Department of Corrections has calculated the rate of return on investment for Iowa adult offender programs for each program area included in the model.
