Ohio Public Media Cooperative meeting August 5, 2013.

Ohio Public Media Cooperative meeting at Bexley Public Radio set for 4:00 p.m. Monday August 5, 2013.

The meeting location will be announced on the prior Friday morning to individuals who RSVP by the prior Thursday.

Community residents are welcome. 

Admission is $10.00 per person.

Cash, check, money order and ID.

Please RSVP to wcrxlp@yahoo.com or voice mail to (614) 235-2929 no later than the Thursday prior to the meeting.

Ohio Public Media Cooperative is a public  committee of

Bexley Public Radio Foundation

2700 E. Main St., Suite 208

Columbus, OH 43209

The Green Radio Choice. Bexley Public Radio.

Green. The public radio green choice is still WCRX-LP, 102.1 FM. Celebrate the lower energy requirements for LPFM.

Big power blasters like WOSU and WCBE consume lots of energy. These big stations have gluttonous appetites for anthracite.

WOSU alone engorges itself on almost a shovel full of coal every eighteen minutes of broadcast.

Think WOSU. Think coal. Think pollution.

Reflect on WCRX-LP. Visualize low energy use. Know that Bexley Public Radio means responsible stewardship of energy resources.

To know green, think Bexley Public Radio.

HELP BEXLEY PUBLIC RADIO UPGRADE ITS ANTENNA. SEND YOUR MONEY PROMPTLY. BE GENEROUS.

Bexley Public Radio Foundation broadcasting as
WCRX-LP, 102.1 FM, Local Power Radio
2700 E. Main St., Suite 208
Columbus, OH 43209
Voice (614) 235 2929
Fax (614) 235 3008
Email wcrxlp@yahoo.com
Blog http://agentofcurrency.blogspot.com

Bexley Public Radio Foundation is exempt from federal taxes under IRC Section 501(c)(3). Donations are deductible from federal income taxes for individuals who itemize. Checks may identify the payee as Bexley Public Radio Foundation or WCRX-LP, 102.1 FM.

Design is copyright 2013. All rights reserved. Bexley Public Radio Foundation. Text is copyright 2009 through 2013. All rights reserved. Bexley Public Radio Editorial Collective.

POSTED BY WCRX-LP EDITORIAL COLLECTIVE AT 8:54 AM 0 COMMENTS

Recent College Graduates. Good Jobs Available?

 

June 27, 2013

New York Federal Reserve Bank

"Are Recent College Graduates Finding Good Jobs?"

Jason R. Abel and Richard Deitz

Stories abound about recent college graduates who are struggling to find good jobs in today’s economy, especially with student debt levels rising so quickly. But just how bad are the job prospects for recent college graduates when one moves beyond anecdotes and looks at the data? How widespread is unemployment? And how common is it for college graduates to work in a job that doesn’t require a bachelor’s degree—that is, how widespread is underemployment? We examined these questions at today’s economic press briefing at the Federal Reserve Bank of New York.

        In our presentation, we show that both unemployment and underemployment for young graduates are in fact higher now compared to, say, a decade ago. At the same time, however, we show that it is not unusual for newly minted college graduates to take some time to transition into the labor market and find jobs that utilize their education. In other words, young graduates typically have relatively high unemployment and underemployment rates as they start their careers, but those rates drop pretty rapidly by the time they hit their late twenties.

        Perhaps not surprisingly, college major plays a key role in how well recent graduates have fared in the labor market. We show that there are large differences in unemployment rates, underemployment rates, and average wages across majors. In particular, we show that those with degrees in majors that provide technical training, such as “Engineering” and “Math & Computers,” or in those that are geared toward growing parts of the economy, such as “Education” and “Health,” have tended to do pretty well when compared to the rest of the pack. At the other end of the spectrum, those with a “Liberal Arts” or “Leisure & Hospitality” major tend to have lower wages, higher unemployment, and higher underemployment.

        Regardless of major, though, we show that those with a college degree still tend to do better than those without. In fact, even recent college graduates who take a job that typically does not require a college degree tend to earn more than those with only an Associate’s degree or high school diploma—and this pattern is true for people with degrees in the lowest-paying majors. So, while times may have gotten tougher for recent college graduates since the Great Recession, obtaining a college degree still appears to provide significant economic benefits.

        For more information on the job prospects of recent college graduates in the nation and in the region, see the New York Fed’s regional economic press briefing web page.
Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

---

Jaison R. Abel is a senior economist in the Federal Reserve Bank of New York's Research and Statistics Group.

Richard Deitz is an assistant vice president in the Research and Statistics Group.

Posted by Blog Author at 12:00:00 PM in Education

September 12, 2013. John B. Friedman comments on Les Secrets de Li'Histoire naturelle.

Penelope writing to Ulysses.
Image by Robinet Testard (b. 1470, d. 1531)
Image source "Robinet Testard" wikipedia.org
Image is in public domain because it was created in 16th century.


The Thursday September 12, 2013 lecture in the Aldus Society Program series is a presentation by John B. Friedman based on his Commentary on Les Secrets de l'Histoire naturelle. 

Les Secrets is a geographical compendium of the marvels and wonders of the world, dated 1485 and prepared by Robinet Testard.

Testard was an illuminator whose botanical drawings, social illustrations and geographical renderings are memorable documentation of cultural life and the environment during the late 16th century.

His illustrations also included mythical creatures like mermaids and unicorns.

Testard's work is colorful and dazzles the eye with the richness of color.

Testard was a prolific artist.  For those who are not familiar with Testard's work, Friedman's commentary will provide an introduction to the work of  this important artist.

The Aldus Society meets the second Thursday of each month at the Thurber Center, 91 Jefferson Avenue.

Programs at the Aldus Society are free and anyone is welcome. Doors open at 7:00 the program begins at 7:30 p.m.

Antique market for furniture made by African-American craftsmen.

Help wanted.  Producer and research assistant.  Volunteer positions at Bexley Public Radio Foundation, WCRX-LP, 102.1 FM.   

Produce radio feature on African-American furniture makers.  Feature may also include other decorative and functional domestic objects produced by African-American craftsmen.   

Email resume to wcrxlp@yahoo.com.  Producer reports to vice president, production and programming at Bexley Public Radio, WCRX-LP, 102.1 FM.

WCRX-LP Community Programming Advisory Committee meeting set for August 5, 2013.

WCRX-LP Community Programming Advisory Committee meeting for Bexley Public Radio set for 4:30 p.m. Monday August 5, 2013.

The meeting location will be announced on the prior Friday morning to individuals who RSVP by the prior Thursday.

Community residents are welcome. 

Admission is $5.00 per person.

Cash, check, money order and ID.

Please RSVP to wcrxlp@yahoo.com or voice mail to (614) 235-2929 no later than the Thursday prior to the meeting.

Community Programming Advisory Committee is a public  committee of

Bexley Public Radio Foundation

2700 E. Main St., Suite 208

Columbus, OH 43209

Bexley Comedy Wrtiers Guild meeting set for August 5, 2013

Bexley Comedy Writers Guild meeting at Bexley Public Radio set for 4:00 p.m. Monday August 5, 2013.

The meeting location will be announced on the prior Friday morning to individuals who RSVP by the prior Thursday.

Community residents are welcome. 

Admission is $35.00 per person.

Cash, check, money order and ID.

Please RSVP to wcrxlp@yahoo.com or voice mail to (614) 235-2929 no later than the Thursday prior to the meeting.

Bexley Comedy Writers Guild is a public  committee of

Bexley Public Radio Foundation

2700 E. Main St., Suite 208

Columbus, OH 43209

Introduction Financial Crises (1620-1920). A series of installments.

Federal Reserve Bank of New York  Liberty Street Economics  June 24, 2013 "Crisis Chronicles:  300 Years of Financial Crises (1620-1920)."   James Narron and David Skeie As momentous as financial crises have been in the past century, we sometimes forget that major financial crises have occurred for centuries—and often. This new series chronicles mostly forgotten financial crises over the 300 years—from 1620 to 1920—just prior to the Great Depression. Today, we journey back to the 1620s and take a fresh look at an economic crisis caused by the rapid debasement of coin in the states that made up the Holy Roman Empire. The Kipper und Wipperzeit (1619–23) The Kipper und Wipperzeit is the common name for the economic crisis caused by the rapid debasement of subsidiary, or small-denomination, coin by Holy Roman Empire states in their efforts to finance the Thirty Years’ War (1618–48). In a 1991 article, Charles Kindleberger—author of the earlier work   Manias, Panics and Crashes and originally a Fed economist—offered a fascinating account of the causes and consequences of the 1619–23 crisis.  Kipper refers to coin clipping and Wipperzeit refers to a see-saw (an allusion to the counterbalance scales used to weigh species coin). Despite the clever name, two forms of debasement actually fueled the crisis. One involved reducing the value of silver coins by clipping shavings from them; the other involved melting the coins, mixing them with inferior metals, re-minting them, and returning them to circulation. As the crisis evolved, an early example of Gresham’s Law took hold as bad money drove out  good. As Vilar notes in A History of Gold and Money, once  “agriculture laid down the plow” at the peak of the crisis and farmers turned to coin clipping as a livelihood, devaluation, hyperinflation, early forms of currency wars, and crude capital controls were either firmly in place or not far behind. From Self-Sufficiency to Money and Markets The period preceding and including the early 1600s was marked by a fundamental shift from feudalism to capitalism, from medieval to modern times, and from an economy driven by self-sufficiency to one driven by markets and money. It is within this social and economic context that various states in the Holy Roman Empire attempted to finance the Thirty Years’ War by creating new mints and debasing subsidiary coins, leaving large-denomination gold and silver coins substantially unaffected. In a simple example, subsidiary coin might initially be minted using only silver, then gradually undergo a shift in metallic content as a growing percentage of copper was added during re-mintings, until the monetary system was effectively on a copper rather than a gold  or silver standard. This shift in metallic content created a divergence  between a coin’s nominal value and its intrinsic metal value, which led to the rapid debasement of coin. As Kindleberger notes, “Bad money was taken by debasing states to their neighbors and exchanged for good [money]. The neighbor typically defended itself by debasing its own coin.” Owing to trade and the easy circumvention of laws that forbade the removal of coin from a city, states found that early forms of capital controls were ineffective and that a large portion of circulating coin originated elsewhere. Given this porosity, individual states determined that reforming their own minted coinage by returning to a silver standard did not necessarily allow them to reform the currency circulating within the state. So states sought greater revenue through seigniorage—the difference between the cost of production (including the price of the metal contained in the coin) and the nominal value of the coin—by minting more money and by taking debased coin abroad, exchanging it, and bringing home good coin and re-minting it. The rapid debasement up to 1622 created a European boom, which turned to mania by early 1622 when average citizens turned to coin clipping as a livelihood, then hyperinflation in 1622 and 1623. Many became rich by exploiting the unknowing—typically peasants. This ultimately led to a widespread breakdown in trade as peasants, fearing that they would be paid in debased coin, refused to bring products to market, creating the spillover to the broader economy. Cry Up, Cry Down, or Call In One response to the crisis was for states to “cry up” good coins by raising the denomination or “cry down” bad coins by lowering their denomination. Another response was to “call in” coin and re-mint it. A third response was to enforce minting standards. But central authority was so weak that no one state could solve the crisis without the help and support of neighboring states. States were finally able to solve the crisis through mint treaties and by setting exchange rates, with hyperinflation subdued by a return to the Imperial Augsburg Ordinance of 1559. Because the public became so wary of clipped and debased coin, it took months to convince the masses that coin was good once it was restored. History Repeating Itself Like markets and money, crises evolve easily but lessons learned often last only a lifetime and are easily forgotten. Over the coming year, we’ll share with you how elements of this crisis were repeated during the Great Re-Coinage of 1696, the Mississippi Bubble of 1720, the Dutch Commodities Crash of 1763, and the Continental Currency Crisis of 1779, with each crisis adding a unique twist. In the meantime, as we reflect upon the states’ struggles to manage their domestic economies of the 1620s at a time of evolving money, markets, and trade, and amid pressure to finance the Thirty Years’ War, we pose the following question: Is it possible to draw any parallels between the events of the 1620s and the current objectives of the Group of Seven to meet “respective domestic objectives using domestic instruments”? Tell us what you think. Disclaimer The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.   James Narron is a senior vice president in the Federal Reserve Bank of New York's Executive Office.   David Skeie is a senior economist in the Bank's Research and Statistics Group.

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Jazz Concert by Tom Battenberg and Friends Tuesday evening June 18.

Tuesday June 18 at 7:30 p.m. at Trinity Lutheran Seminary in Bexley. 

Jazz Concert by Tom Battenberg and Friends. Tom Battenberg on trumpet, Michael Cox on sax, Mark Flugge on piano, Terry Douds on bass and Jim Rupp on drums.

This is the 29^th annual summer music series of three concerts at Trinity Lutheran Seminary. 

There is free parking at the Trinity parking lot on College Avenue in Bexley.

This performance are at Trinity Lutheran Seminary, 2199 E. Main St, Columbus, OH  43209

There is a free will offering at each performance.

Hospitals paying penalties under new federal rules.

Will New York City Hospitals That Treat Many Low-Income Patients Face The Heaviest Penalties Under New Federal Reimbursement Policies?

Prepared by Christina Fiorentini, New York City Independent Budget Office.

Two new federal policies tying Medicare reimbursements to quality of care took effect in October 2012. Hospitals are now penalized for excess readmissions. An additional penalty or bonus can be awarded, based on adherence to clinical standards and ratings on patient surveys.

Hospitals that serve the city’s poor−public hospitals and private safety-net hospitals−generally face heavier penalties than other hospitals. Penalties for 8 out of 12 public hospitals and 4 out of 6 safety-net hospitals exceed the citywide average of 0.97 percent of reimbursements.

Medicare accounts for $777 million of expected inpatient revenues in 2013 at Health & Hospitals Corporation facilities, about 30 percent of all anticipated inpatient revenues, ranging from $21 million at North Central Bronx Hospital to $112 million at Bellevue.




SOURCES: United Hospital Fund; U.S. Centers for Medicare & Medicaid Services

NOTES: Some hospitals have more than one campus, but penalties/bonuses are systemwide. Private safety-net hospitals, as defined by the United Hospital Fund (UHF), are those where Medicaid and uninsured patients comprised more than 50 percent of admissions, other than births, in 2008.

Prepared by Christina Fiorentini

New York City Independent Budget Office

PDF version here.

New York City By The Numbers IBO Homepage

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Urban Decline in Rust-Belt Cities.

May 20, 2013

Economic Commentary

'Urban Decline in Rust-Belt Cities'

Author:  Daniel Hartley.  

The author is a research economist at the Federal Reserve Bank of Cleveland.  The views expressed are his owns and not necessarily those of the Federal Reserve Bank of Cleveland or the Board of Governors of the Federal Reserve System or its staff..

Summary:  Many Rust-Belt cities have seen almost half their populations move from inside the city borders to the surrounding suburbs and elsewhere since the 1970s. As populations shifted, neighborhoods changed—in their average income, educational profile, and housing prices. But the shift did not happen in every neighborhood at the same rate. Recent research has uncovered some of the patterns characterizing the process. 

Complete research report:

Most major Rust-Belt cities have seen their populations shrink since their heydays, and with that decline, the average income of the remaining residents has fallen as well. Cities such as Buffalo, Cleveland, Detroit, and Pittsburgh have each lost more than 40 percent of their populations over the last four decades. However, the losses have not been uniform across neighborhoods. Some neighborhoods have declined more rapidly than others.

The uneven population decline across neighborhoods implies that the distributions of income, house prices, and human capital have also shifted within cities and larger metropolitan areas over time. While the challenges posed by the decline in overall population are well recognized, the movement of the population across neighborhoods within these Rust-Belt cities creates additional challenges. Policymakers and city planners need to understand how such neighborhood dynamics evolve and, ultimately, how the underlying dynamics interact with the provision of public services and infrastructure.

Recent research on population and income dynamics in four Rust-Belt cities shows that neighborhoods with the lowest housing prices are the ones that experience the steepest declines in population, but that income falls more sharply in neighborhoods with middle-tier house prices. These patterns are the reverse of a gentrification process. Both processes involve the borders of poor and rich neighborhoods. But where gentrification typically leads to an outward expansion of high-income neighborhoods into low-income neighborhoods, reverse gentrification involves an inward contraction of high-income neighborhoods, as the border areas become low-income.

This Commentary describes the reverse gentrification process and its consequences in four cities—Buffalo, Cleveland, Detroit, and Pittsburgh—from 1970 to 2006. While reverse gentrification occurred to some degree in all four cities, there are distinct differences across them. In addition, to show how neighborhood dynamics in the central city influence the surrounding suburbs, the Cleveland-Akron metropolitan area is explored more closely, focusing on changes in the inner-ring and outer-ring suburbs.

Patterns of Urban Decline in Rust-Belt Cities.

Cleveland, Detroit, Buffalo, and Pittsburgh are alike in many ways. All lie within close proximity to one another, and all were centers of manufacturing activity and still relatively large in 1970. Remarkably, from 1970 to 2006 all four of these cities lost about 45 percent of their population (see table 1). Median household incomes fell in all of them (in real terms), though less in Buffalo and Pittsburgh. Incomes in Cleveland and Detroit fell by about 30 percent, Buffalo by 20 percent, and Pittsburgh by 10 percent.¹ Median home prices have not changed much in any of these cities. In fact, in Cleveland the median price of a home changed almost exactly 0 percent from 1970 to 2006.

Table 1. Comparison of Population, Income, House Prices, and Education in Cleveland, Detroit, Buffalo, and Pittsburgh in 1970 and 2006

	Buffalo			Pittsburgh
	1970	2006	Change (%)	1970	2006	Change (%)
Population	462,783	257,758	−44	520,167	297,061	−43
Median household income (2009 dollars)	38,395	29,637	−23	37,477	33,818	−10
Median home value (2009 dollars)	71,477	64,702	−9	69,570	78,749	13
Fraction with college or higher degree	6.7	20.4	13.7	9.0	31.3	22.3

	Cleveland			Detroit
	1970	2006	Change(%)	1970	2006	Change(%)
Population	751,046	406,427	−46	1,511,336	834,116	−45
Median household income (2009 dollars)	41,674	28,238	−32	46,438	30,184	−35
Median home value (2009 dollars)	92,826	92,477	0	86,108	93,966	9
Fraction with college or higher degree	4.4	12.0	7.6	6.2	11.3	5.1

Sources: U.S. Census Bureau, 1970 Census and 2006 American Community Survey.

One area in which the cities have differed more is educational attainment—a measure of what economists call “human capital.” Cleveland and Detroit had the lowest proportion of residents over the age of 25 with a college degree or higher in 1970 (4 percent and 6 percent, respectively), and both experienced relatively small gains in this share by 2006, leaving both at about 12 percent. In contrast, Buffalo and Pittsburgh were slightly more highly educated in 1970 (7 percent and 9 percent, respectively) but are now much more highly educated (20 percent and 31 percent, respectively).

To examine how the population has shifted across neighborhoods over time, and how these changes have affected other neighborhood characteristics like income, housing prices, and educational attainment, I looked at groups of neighborhoods based on their home prices in 1970. I used home prices because they provide a summary measure of the amenities and characteristics of the neighborhoods. To create the groups, I ranked the census tracts in each city by their median home price and then divided each city’s set of tracts into 10 similarly sized groups. (I label these deciles 1 through 10, ordered from the lowest to the highest median home price in 1970).

In terms of population changes within the city, Cleveland displays a pattern similar to that of Detroit, Pittsburgh, and Buffalo (figure 1). While population dropped in tracts all across the city, it dropped the most in the initially low-price tracts and the least in the highest-price tracts.



With respect to income growth, Cleveland and Detroit are again similar; both saw the steepest drops in income in the middle deciles (figure 2). The highest deciles do not drop in income by that much, but the next-highest deciles experience a big drop in income as lower-income residents move in. These trends can be described as a shrinking of the high-income neighborhoods inward toward their core.




In Buffalo and Pittsburgh the overall pattern is the same, but growth rates in most neighborhoods are higher than in Cleveland and Detroit. It is worth noting that in Pittsburgh and Buffalo, incomes surge ahead in the highest-home-price neighborhoods between 1970 and 2006—by almost 50 percent in Pittsburgh and 20 percent in Buffalo—a phenomenon that is not present in Cleveland or Detroit. This reflects the fact that these neighborhoods are situated near centers of higher education, which have attracted highly skilled residents. By contrast, some of the neighborhoods closest to Cleveland’s major higher education institutions are outside the city limits.

The top decile neighborhoods of Pittsburgh and Buffalo have also experienced sizable gains in educational attainment (figure 3). By contrast, gains in educational attainment in Cleveland and Detroit are much more modest and somewhat flat across the neighborhood price deciles. Even the lowest-decile neighborhoods in Pittsburgh saw a dramatic increase in educational attainment. This change is consistent with the high-income growth that occurred in these areas and suggests that there is some degree of gentrification occurring in Pittsburgh.



Home-price growth in Cleveland and Detroit is very small in all deciles except for the two lowest-price deciles (figure 4). Some of the price increases in the lowest-price deciles may be attributable to what statisticians call “mean reversion.” Mean reversion—the return to a long-run trend—might occur due to measurement error or transient local shocks. Either way, the more interesting part of the figure is seen in the difference in growth rates between Cleveland and Detroit and Pittsburgh and Buffalo. While Cleveland and Detroit show less than 20 percent growth in all of the upper five deciles, Pittsburgh and Buffalo show much stronger growth rates in the top deciles (especially in the top decile). The relatively stronger home-price growth rates in Pittsburgh and Buffalo’s top deciles may be driven by increases in educational attainment in neighborhoods close to these cities’ major higher education institutions.



Overall, Cleveland experienced similar, although less severe, patterns to those found in Detroit. Population declines were steepest in the lowest-price tracts, while incomes fell most sharply in tracts in the middle deciles. At the same time, none of the neighborhood deciles in either city saw large increases in educational attainment.

These patterns are consistent with the patterns of urban decline (or reverse gentrification) described in recent research by Veronica Guerrieri, Daniel Hartley, and Erik Hurst. These researchers show that this type of urban decline occurs when low (citywide) housing demand leads to population loss in the lowest-price neighborhoods, and falling prices allow lower-income households to move into formerly middle income neighborhoods. As this happens, housing prices in those middle neighborhoods fall (or rise less than in the other neighborhoods).

In contrast, despite similar declines in population across the distribution of neighborhoods in Pittsburgh and Buffalo, these cities experienced income growth in the top three housing-price deciles. Underlying that achievement are large gains in educational attainment in these neighborhoods. As a result, these neighborhoods also experienced increases in home prices. These price increases may be due to the increasing desirability of these neighborhoods as the neighborhood residents become more highly educated (possibly influencing school quality and other local public goods).

A Closer Look at the Cleveland Metropolitan Area.

For the remaining part of my analysis, I broaden the geographical scope to look at the entire metropolitan statistical area (MSA), the region around a city (or cities) that, practically speaking, is seen as part of the same economic and social community. I limit my focus to Cleveland, so that I can present maps of the area which reveal more of the spatial details regarding how neighborhoods have changed in this larger region from 1970 to the present. While the City of Cleveland has been losing population since 1950, the combined population of the seven counties of the MSA—Cuyahoga, Geauga, Lake, Medina, Portage, and Summit—has been relatively stable. It dropped from about 3 million residents in 1970 to 2.75 million in 1990 and went back up to 2.84 million in 2000. (I omit Ashtabula County, which is part of the present-day Cleveland-Akron-Elyria combined statistical area, because tract-level data are not available for Ashtabula in 1970.)

Figures 5–8 show how different variables are distributed across the census tracts of the Cleveland-Akron-Elyria MSA. Each variable is broken into 20 bins ranging from lowest to highest, dividing the set of census tracts into 20 ranking groups based on the value of the variable.

In 1970 home prices in the City of Cleveland were uniformly lowest in the neighborhoods closest to the center of the city and then a bit higher in the neighborhoods that were farthest away from downtown (figure 5). Prices generally rose as one crossed the border into the inner-ring suburbs. In 1970 there were three groupings of high-home-price census tracts: one in the western suburbs, one in the southern suburbs, and one in the eastern suburbs.



By the time data was collected for the 2005–2009 American Community Surveys, the high-housing-price areas had pushed outward a bit farther from downtown Cleveland (figure 6). At the same time, the home-price rankings of census tracts in a number of the inner-ring suburbs had fallen dramatically. These changes are most striking in a group of southeastern suburbs: Highland Hills, North Randall, Warrensville Heights, Bedford Heights, Bedford, and Maple Heights. Less striking, but similar patterns can be seen to the southwest in Parma, Parma Heights, Brooklyn, Brook Park, Middleburg Heights, North Olmsted, Berea, Fairview Park, and to the northeast in Euclid, South Euclid, East Cleveland, the northern half of Cleveland Heights, Richmond Heights, Willowick, and Wickliffe.



The same sets of suburbs that experienced a drop in home-price ranking over this period also tended to experience large drops in their household-income ranking (figure 7). In general, it appears that the boundaries of the high-income areas that were present in 1970 have pulled outward, away from downtown Cleveland.



A few notable exceptions to this pattern can be found in the southern part of Cleveland Heights and in parts of Shaker Heights, possibly bolstered by their proximity to cultural attractions in University Circle and the Case Western Reserve University campus. A few other pockets where housing prices and income rankings have not fallen by as much as other places with similar proximity to downtown Cleveland include Bratenahl and the northern part of Lakewood (which have the amenity of being situated on the Lake Erie coastline), and Independence and Valley View (situated along the Cuyahoga Valley National Park).

The Importance of Amenities.

Why do housing prices rise in neighborhoods where the average income of the residents rises, and fall where income declines? One possibility is that the housing stock in these areas changes. When higher-income residents move in, for example, they might make improvements to the housing stock, or when lower-income residents move in, they may be more likely to defer home maintenance when finances are tight.

However, the change in housing prices might have more to do with other kinds of neighborhood characteristics that change when the income of the residents goes up or down. In the same way that natural amenities like ocean views or pleasant weather conditions make an area more desirable, living near high-income neighbors offers amenities that affect the demand for housing in the neighborhood as well—good schools, lower crime, greater entertainment options, and so on.

One way to explore how much those amenities are influencing housing prices is to construct a measure that can tease out changes in home prices which are not due to changes in the housing stock (and thus reflect changes in land values). Figure 8 shows such a measure. It controls for changes in the structural characteristics of the homes in each census tract, such as the fraction that are single-family detached houses, the number of bedrooms, and the average age of homes. Comparing the map of changes in income to the map of changes in this land-value measure reveals similarities in many places. In fact, at the tract-level, the change in household income and the change in the land-value measure have a correlation of 65 percent.



This correlation suggests that it is likely that changes in land values are driven by changes in the features of neighborhoods that are associated with the income of the residents, such as school quality, crime rates, restaurants, and entertainment options. It is also noteworthy that the maps of changes in income and land values also show the retreat of the high-income areas deeper into the suburbs, but they also indicate a little bit of gentrification in the Tremont, Ohio City, and Edgewater neighborhoods, close to downtown Cleveland.
On the whole, the Cleveland MSA shows spatial patterns of urban decline that are similar to other cities that have been affected by large drops in labor demand, such as the retreating boundaries of the high income areas. However, there do seem to be signs of gentrification in a few neighborhoods, and resiliency to urban decline in another set of neighborhoods. It also appears that high-income households have moved further out into the countryside during this period.

Some of this movement may be due to households sorting into jurisdictions that provide the mix of public goods and tax levels that they prefer. Since the City of Cleveland has a relatively small geographic footprint and there are many small cities and towns nearby, different bundles of taxes and public good provision are readily available.

Footnote

1. All dollar amounts and growth rates are in real terms, expressed in 2009 dollars.[Back]

References

“Endogenous Gentrification and Housing-Price Dynamics,” Veronica Guerrieri, Daniel Hartley, and Erik Hurst, 2013. Journal of Public Economics.
“Within-City Variation in Urban Decline: The Case of Detroit,” Veronica Guerrieri, Daniel Hartley, and Erik Hurst, 2012. American Economic Review, 102:3.

Timo Weiland.. 

This name sounds vaguely familiar.

TIMO Weiland.

Timo WEILAND.

TIMO WEILAND.

Search Google Images for Timo Weiland.  Review his handiwork.
 
What are the antecedents of this young man.

And should listeners of WCRX-LP pay attention?

What to do? Underwater mortgage debt. Eminent domain as a public financial strategy.

New York Federal Reserve Bank Current Issues in Economics and Finance

"Paying Paul and Robbing No One: An Eminent Domain Solution for Underwater Mortgage Debt"

Volume 19, Number 5    2013

Author: Robert Hockett

In the view of many analysts, the best way to assist “underwater” homeowners—those who owe more on their mortgages than their houses are worth—is to reduce the principal on their home loans.

Yet in the case of privately securitized mortgages, such write-downs are almost impossible to carry out, since loan modifications on the scale necessitated by the housing market crash would require collective action by a multitude of geographically dispersed security holders.

The solution, this study suggests, is for state and municipal governments to use their eminent domain powers to buy up and restructure underwater mortgages, thereby sidestepping the need to coordinate action across large numbers of security holders.

College graduates earn more but possible increased earnings aren't enough to motivate some people to attend college.

Federal Reserve Bank of Richmond
Economic Brief -  June 2013

Implications of Risks and Rewards in College Decisions

Despite a large and growing earnings premium for college graduates, growth in college enrollment and especially college attainment in the United States has been quite slow. The labor market's apparent lack of responsiveness to the earnings premium may be driven in part by the risks that marginally prepared students face when they go to college. Failing or dropping out could leave them with low wealth, high debt, and low earnings. Recent research indicates that neither further increases in the earnings premium nor reductions in college costs are likely to produce large increases in the college completion rate. And if technological change continues to increase the demand for skilled labor, both the earnings premium and income inequality will continue to grow.



The full report is available at:

http://www.richmondfed.org/publications/research/economic_brief/2013/eb_13-06.cfm?WT.mc_id=110006

Drilling Down into Core Inflation: Goods Versus Services.

June 5, 2013  Liberty Street Economics Federal Reserve Bank of New York M. Henry Linder, Richard Peach, and Robert Rich Among the measures of core inflation used to monitor the inflation outlook, the series excluding food and energy prices is probably the best known and most closely followed by policymakers and the public. While the conventional “ex food and energy” measure is a composite of the price changes of a large number of different products and services, almost all models developed to explain and forecast its behavior do not distinguish between the goods and services categories. Is the distinction important? Here, we highlight the different behavior and determinants of goods inflation and services inflation and suggest, based on preliminary analysis, that we can improve the forecast accuracy of this conventional core inflation measure by combining separate inflation forecasts of the two categories. Every Picture Tells a Story (Don’t It?) As specified in the Federal Reserve Reform Act of 1977, the Federal Reserve’s mandate is “to promote effectively the goals of maximum employment, stable prices, and moderate long-term interest rates.” Given long and variable lags between changes in monetary policy and the subsequent impact on the economy, meeting these goals is greatly facilitated by being able to accurately forecast the behavior of inflation over a one-to-two-year horizon. This, of course, is easier said than done, as headline inflation measures, such as Consumer Price Index (CPI), tend to be quite volatile, due in large part to sharp swings in energy and food prices.         Because of the volatility in headline inflation, policymakers have relied on core inflation measures designed to differentiate between transitory and persistent price changes to help guide their decision making. Among the measures of core inflation, the “ex food and energy” series has been the most widely adopted for this purpose (see this paper by Timothy Cogley for a discussion of other core inflation measures). This measure, shown in the chart below, is a much less volatile series that is indicative of lower-frequency changes of the general price level and has also proved to be a more accurate predictor of headline inflation than past headline inflation.         However, models developed to explain and forecast core inflation—such as Phillips curve models—do not have a particularly good track record, to the point that there is disagreement regarding the fundamental determinants of inflation.         One possible explanation for this state of affairs is that core inflation is a composite of the price changes of a large number of different products and services that behave quite differently over time. The next chart presents core inflation at one level of disaggregation—commodities less food and energy commodities (or core goods) and services less energy services (or core services). Note that the absolute level of inflation of these two categories is quite different as are their weights in the core CPI; the weight of core goods was 34 percent in 1985, but was just 26.1 percent in 2012. Additionally, over the past decade, the two inflation rates have generally moved inversely to each other.         It seems likely that the divergence in the behavior of goods inflation and services inflation may also carry over to their determinants. To explore this idea, we examine the relationship over the period since 1985 between each inflation series in the previous chart and two variables considered to be important in predicting inflation: long-run inflation expectations and the level of domestic resource utilization. Resource utilization provides a gauge of the balance between aggregate demand and supply in an economy. One of the most widely used measures of domestic resource utilization is the unemployment gap—the difference between the unemployment rate and the estimate of the time-varying Non-Accelerating Inflation Rate of Unemployment (NAIRU) from the Congressional Budget Office (CBO).         The next two charts present scatter plots of the four-quarter-ahead inflation rates (period t to t+4) of core services and core goods, respectively, less a measure of ten-year expected CPI inflation (period t) from the U.S. Survey of Professional Forecasters, versus the CBO unemployment gap (period t). For core services, there is a nonlinear, negative relationship between the inflation rate and the unemployment gap. For core goods, however, no such relationship is present. What Explains Movements in Services Inflation and Goods Inflation? Based on the three charts above, the behavior and determinants of core services inflation and core goods inflation differ significantly. Can we say more about this? To do so, we have developed and estimated separate models for core CPI services inflation and core CPI goods inflation using quarterly data from 1986:Q1 to 2012:Q4. We can provide the following summary of the results.         The core services inflation model draws upon the modeling approach outlined in this Federal Reserve Bank of Boston paper by Jeffrey Fuhrer, Giovanni Olivei, and Geoffrey Tootell. We find a strong relationship (both economically and statistically speaking) between core services inflation and long-term inflation expectations. There is also an important nonlinear relationship between core services inflation and the unemployment gap, indicating that the impact of changes in labor market slack on core services inflation depends on the level of slack itself.         For the core goods inflation model, the results suggest a very different set of factors influencing the behavior of the series. We find persistence in the series, that is, core goods inflation depends on its own past value. Relative import price inflation—growth in (non-petroleum) import prices less core goods inflation—also matters, suggesting goods prices act as the linkage between supply shocks and core inflation. There is also evidence of a relationship between core goods inflation and expected inflation, but that the relevant inflation expectations are associated with a short-term (one-year) horizon. Last, we find no meaningful effect of the unemployment gap on core goods inflation, consistent with commentators who contend that it is global (and not domestic) economic slack that impacts core goods inflation. The Whole versus the Sum of the Parts Taken together, the evidence supports the view that the behavior and determinants of core services and core goods inflation are quite different. Why might this matter? Based on some preliminary work, there appear to be gains in the forecast accuracy of aggregate core inflation from using separate models for core services inflation and core goods.         The estimated models can be used to generate forecasts of core services inflation and core goods inflation, which can then be combined using the relative weights of each category in the core CPI. To provide a basis of comparison, we also produce forecasts from an estimated Phillips curve model of aggregate core CPI inflation that uses long-term inflation expectations, the unemployment gap, and relative import price inflation as explanatory variables.         We estimate the models from 1985:Q1 to 2004:Q4, and then forecast out-of-sample for the post-2004 Q4 period. To construct forecasts of the “composite model,” we use weights of 28 percent for core goods and 72 percent for core services—the relative weights in the core CPI in 2004. As shown above, the forecasts from the composite model capturing the differences in the determinants of the inflation process of core goods and core services are over 65 percent more accurate than the forecasts from the Phillips curve model ignoring those differences. While both models generally track the slowing in core inflation during the recent recession, the forecasts from the composite model have done a better job picking up the subsequent rebound in core inflation. Although they are not shown, we obtain similar results based on the post‑2007 Q4 period.         While we recognize our analysis is preliminary, the results suggest that a further exploration of core services inflation and core goods inflation and their role in the core inflation process is warranted. Disclaimer The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors. M. Henry Linder is a research associate in the Research and Statistics Group of the Federal Reserve Bank of New York. Richard Peach is a senior vice president in the Research and Statistics Group of the Federal Reserve Bank of New York. Robert Rich is an assistant vice president in the Research and Statistics Group of the Federal Reserve Bank of New York. Posted by Blog Author at 07:00:00 AM in Macroecon, Monetary Policy

African-American furniture-makers. Producer and research assistant needed for radio feature.

Help wanted.  Producer and research assistant.  Volunteer positions at Bexley Public Radio Foundation, WCRX-LP, 102.1 FM.   

Produce radio feature on African-American furniture makers.  Feature may also include other decorative and functional domestic objects produced by African-American craftsmen.   

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The public radio green choice is still Bexley Public Radio.

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