Unemployment, labor costs, and recessions: implications for the inflation outlook

Economists have been arguing about the connection between unemployment and infl ation for decades. Critics claim that the connection is unreliable and leads policymakers astray, while others argue that the relationship is useful for forecasting. We examine the more direct connections between elevated unemployment levels and the rate of increase in wage and labor costs, more generally. We fi nd that wage and labor cost growth has declined markedly following recent recessions. It has again declined sharply in the most recent recession. We also fi nd that compensation typically remains subdued during the initial phases of recent recoveries. This is again the case in the current recovery, making labor costs a significant restraining force on inflation going forward.Recessions ; Inflation (Finance) ; Inflation targeting

The growing difference in college attainment between women and men

Workers with more education typically earn more than those with less education, and the difference has been growing in recent decades. Not surprisingly, the percentage of the population going after and getting a college degree has been rising as well. Since the late 1970s, though, the increase in college attainment has stalled for men and gathered steam for women. Among college-age individuals, more women now graduate than men. Changes in labor market incentives appear to explain the increased investment in education made by women. But men’s investments in education have been much less responsive to the same incentivesEducation ; Women - Education

Center for Population Dynamics Quarterly Brief July 2016: Population Loss and Development Trends in Cleveland

When a place loses population, it’s assumed that’s because people leave. But it’s not that simple. What’s lost in translation is today’s households have fewer people living in them than they did years prior. For instance, 40% of mothers aged 40 to 44 had four or more children in 1976. Today, it’s 14%. Overall, the average American household contracted from 3.14 people in 1970 to 2.54 today. This can explain the apparent paradox of population loss in “shrinking” communities while the number of occupied households grows. Such was the case in Cuyahoga County. There were 1.72 million residents in the county in 1970. By 2010 the population fell by 440,713, to just over 1.28 million. How much of that loss was due to change in family composition? If the average household size remained at 1970 levels (3.10 people per house), the population of Cuyahoga County would be 1,692,323 in 2010 given its current household totals—412,201 more than the actual number. Taken together, 93.5% of Cuyahoga County’s population loss since 1970 can be explained by change in household composition, not necessarily entire households leaving. Does this mean outmigration is not a factor locally? No. This is particularly so for the City of Cleveland. Cleveland’s population approached its peak in 1950 with 914,808 residents. In 2010 the population was 396,830—a decline of 517,978 . While the average household size dropped considerably from 1950 (3.44) to 2010 (2.37), the change in household composition accounted for only 34.6% of the city’s population losses since 1950. The remainder was likely due to the decline in the actual number of households, which dropped by nearly 100,000. That is, people left, and not many people arrived, and this was manifested in the erosion of occupied residencies and net outmigration

Center for Population Dynamics Quarterly Brief July 2016: Population Loss and Development Trends in Cleveland

When a place loses population, it’s assumed that’s because people leave. But it’s not that simple. What’s lost in translation is today’s households have fewer people living in them than they did years prior. For instance, 40% of mothers aged 40 to 44 had four or more children in 1976. Today, it’s 14%. Overall, the average American household contracted from 3.14 people in 1970 to 2.54 today. This can explain the apparent paradox of population loss in “shrinking” communities while the number of occupied households grows. Such was the case in Cuyahoga County. There were 1.72 million residents in the county in 1970. By 2010 the population fell by 440,713, to just over 1.28 million. How much of that loss was due to change in family composition? If the average household size remained at 1970 levels (3.10 people per house), the population of Cuyahoga County would be 1,692,323 in 2010 given its current household totals—412,201 more than the actual number. Taken together, 93.5% of Cuyahoga County’s population loss since 1970 can be explained by change in household composition, not necessarily entire households leaving. Does this mean outmigration is not a factor locally? No. This is particularly so for the City of Cleveland. Cleveland’s population approached its peak in 1950 with 914,808 residents. In 2010 the population was 396,830—a decline of 517,978 . While the average household size dropped considerably from 1950 (3.44) to 2010 (2.37), the change in household composition accounted for only 34.6% of the city’s population losses since 1950. The remainder was likely due to the decline in the actual number of households, which dropped by nearly 100,000. That is, people left, and not many people arrived, and this was manifested in the erosion of occupied residencies and net outmigration

Preparing for Growth: An Emerging Neighborhood Market Analysis Commissioned by Mayor Frank G. Jackson for the City of Cleveland

In anticipation of the development of a Neighborhood Transformation Initiative, in 2016, Mayor Frank G. Jackson commissioned Cleveland State University’s Center for Population Dynamics to analyze the City of Cleveland housing market. The resulting study, “Preparing for Growth,” provides a foundation for the Mayor’s Neighborhood Transformation Initiative acknowledging neighborhoods where private investment is strong but, and most significantly, identifying emerging neighborhood markets where focused planning and the leverage of public dollars will attract private investment to the benefit of existing residents and businesses in these neighborhoods

Glutathionylation at Cys-111 Induces Dissociation of Wild Type and FALS Mutant SOD1 Dimers

Mutation of the ubiquitous cytosolic enzyme Cu/Zn superoxide dismutase (SOD1) is hypothesized to cause familial amyotrophic lateral sclerosis (FALS) through structural destabilization leading to misfolding and aggregation. Considering the late onset of symptoms as well as the phenotypic variability among patients with identical SOD1 mutations, it is clear that nongenetic factor(s) impact ALS etiology and disease progression. Here we examine the effect of Cys-111 glutathionylation, a physiologically prevalent post-translational oxidative modification, on the stabilities of wild type SOD1 and two phenotypically diverse FALS mutants, A4V and I112T. Glutathionylation results in profound destabilization of SOD1WT dimers, increasing the equilibrium dissociation constant Kd to ~10−20 μM, comparable to that of the aggressive A4V mutant. SOD1A4V is further destabilized by glutathionylation, experiencing an ~30-fold increase in Kd. Dissociation kinetics of glutathionylated SOD1WT and SOD1A4V are unchanged, as measured by surface plasmon resonance, indicating that glutathionylation destabilizes these variants by decreasing association rate. In contrast, SOD1I112T has a modestly increased dissociation rate but no change in Kd when glutathionylated. Using computational structural modeling, we show that the distinct effects of glutathionylation on different SOD1 variants correspond to changes in composition of the dimer interface. Our experimental and computational results show that Cys-111 glutathionylation induces structural rearrangements that modulate stability of both wild type and FALS mutant SOD1. The distinct sensitivities of SOD1 variants to glutathionylation, a modification that acts in part as a coping mechanism for oxidative stress, suggest a novel mode by which redox regulation and aggregation propensity interact in ALS

Preparing for Growth: An Emerging Neighborhood Market Analysis Commissioned by Mayor Frank G. Jackson for the City of Cleveland

In anticipation of the development of a Neighborhood Transformation Initiative, in 2016, Mayor Frank G. Jackson commissioned Cleveland State University’s Center for Population Dynamics to analyze the City of Cleveland housing market. The resulting study, “Preparing for Growth,” provides a foundation for the Mayor’s Neighborhood Transformation Initiative acknowledging neighborhoods where private investment is strong but, and most significantly, identifying emerging neighborhood markets where focused planning and the leverage of public dollars will attract private investment to the benefit of existing residents and businesses in these neighborhoods

Insomnia disorder: State of the science and challenges for the future

Insomnia disorder comprises symptoms during night and day that strongly affect quality of life and wellbeing. Prolonged sleep latency, difficulties to maintain sleep and early morning wakening characterize sleep complaints, whereas fatigue, reduced attention, impaired cognitive functioning, irritability, anxiety and low mood are key daytime impairments. Insomnia disorder is well acknowledged in all relevant diagnostic systems: Diagnostic and Statistical Manual of the American Psychiatric Association, 5th revision, International Classification of Sleep Disorders, 3rd version, and International Classification of Diseases, 11th revision. Insomnia disorder as a chronic condition is frequent (up to 10% of the adult population, with a preponderance of females), and signifies an important and independent risk factor for physical and, especially, mental health. Insomnia disorder diagnosis primarily rests on self-report. Objective measures like actigraphy or polysomnography are not (yet) part of the routine diagnostic canon, but play an important role in research. Disease concepts of insomnia range from cognitive-behavioural models to (epi-) genetics and psychoneurobiological approaches. The latter is derived from knowledge about basic sleep–wake regulation and encompass theories like rapid eye movement sleep instability/restless rapid eye movement sleep. Cognitive-behavioural models of insomnia led to the conceptualization of cognitive-behavioural therapy for insomnia, which is now considered as first-line treatment for insomnia worldwide. Future research strategies will include the combination of experimental paradigms with neuroimaging and may benefit from more attention to dysfunctional overnight alleviation of distress in insomnia. With respect to therapy, cognitive-behavioural therapy for insomnia merits widespread implementation, and digital cognitive-behavioural therapy may assist delivery along treatment guidelines. However, given the still considerable proportion of patients responding insufficiently to cognitive-behavioural therapy for insomnia, fundamental studies are highly necessary to better understand the brain and behavioural mechanisms underlying insomnia. Mediators and moderators of treatment response/non-response and the associated development of tailored and novel interventions also require investigation. Recent studies suggest that treatment of insomnia may prove to add significantly as a preventive strategy to combat the global burden of mental disorders