Demographics

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The French will have to build a wall …

Published by Anonymous (not verified) on Sun, 09/08/2020 - 5:30pm in

If Brexit in fact is actually happening – and so far it seems it is – then unsurprisingly, its spirit is gradually dying of its own contradictions. Immigration is just one area when taking back control means actually relying on the co-operation of the er, French. True we already rely on French co-operation, but that... Read more

Introduction to Heterogeneity Series III: Credit Market Outcomes

Published by Anonymous (not verified) on Tue, 07/07/2020 - 10:00pm in

Rajashri Chakrabarti

 Credit Market Outcomes

Average economic outcomes serve as important indicators of the overall state of the economy. However, they mask a lot of underlying variability in how people experience the economy across geography, or by race, income, age, or other attributes. Following our series on heterogeneity broadly in October 2019 and in labor market outcomes in March 2020, we now turn our focus to further documenting heterogeneity in the credit market. While we have written about credit market heterogeneity before, this series integrates insights on disparities in outcomes in various parts of the credit market. The analysis includes a look at differing homeownership rates across populations, varying exposure to foreclosures and evictions, and uneven student loan burdens and repayment behaviors. It also covers heterogeneous effects of policies by comparing financial health outcomes for those with access to public tuition subsidies and Medicare versus those not eligible. The findings underscore that a measure of the average, particularly relating to policy impact, is far from complete. Rather, a sharper picture of the diverse effects is essential to understanding the efficacy of policy.

Here is a brief look at each post in the series:

Inequality in U.S. Homeownership Rates by Race and Ethnicity1. Inequality in U.S. Homeownership Rates by Race and Ethnicity

Andrew Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw investigate racial gaps in homeownership rates and, importantly, explore the reasons behind these differences. They find:

  • The Black-white and Black-Hispanic homeownership gaps widened after the Great Recession, markedly more so after 2015.
  • The foreclosure crisis disproportionately affected areas with majority Black or Hispanic populations.
  • Explanations for the homeownership gap may include differential effects of tightening underwriting standards across areas with a majority Black or Hispanic population versus those with a majority white population, differences in labor market outcomes across these areas during and following the Great Recession, and larger incidence of student debt in these areas.

Who Has Been Evicted and Why?2. Who Has Been Evicted and Why?

Andrew Haughwout, Haoyang Liu, and Xiaohan Zhang explore the reasons behind evictions, who is more likely to be evicted, and the possibility of owning a home and gaining access to credit following evictions. Their findings reveal:

  • Large shares of low-income households have been evicted.
  • Income or job loss and change in building ownership are important reasons behind evictions.
  • Renters with a past eviction history are less likely to have access to credit cards and auto loans.

Measuring Racial Disparities in Higher Education and Student Debt Outcomes3. Measuring Racial Disparities in Higher Education and Student Debt Outcomes

Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw investigate whether (and how) differences in college attendance rates and types of college attended may lead to student debt borrowing and default. The key takeaways include:

  • There are noticeable disparities in college attendance and graduation rates between majority white, majority Black, and majority Hispanic neighborhoods, with graduation rates the lowest in majority Black neighborhoods.
  • Students from majority Black neighborhoods are more likely to hold student debt and in larger amounts.
  • Borrowers from majority Black neighborhoods are more likely to default, and this pattern is more prominent for borrowers from two-year colleges than those from four-year colleges.

Do College Tuition Subsidies Boost Spending and Reduce Debt? Impacts by Income and Race4. Do College Tuition Subsidies Boost Spending and Reduce Debt? Impacts by Income and Race

Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw investigate the effect of tuition subsidies, specifically merit-based aid, on other debt and consumption outcomes. The main findings include:

  • Cohorts eligible for these tuition subsidies have higher credit card balances and higher delinquencies in their early-to-mid-twenties. These patterns are more evident for borrowers from low-income and predominantly Black neighborhoods.
  • Eligible cohorts are more likely to own cars (as captured by auto debt originations) in their early-to-mid-twenties. This pattern is more prominent for borrowers from low-income and predominantly Black neighborhoods.
  • The patterns indicate substitution away from student debt (as net tuition declines) to other forms of consumer debt for eligible cohorts in college-going ages, a pattern more prominent for borrowers from low-income and Black neighborhoods.

Medicare and Financial Health across the United States5. Medicare and Financial Health across the United States

Paul Goldsmith-Pinkham, Maxim Pinkovskiy, and Jacob Wallace investigate the effect of access to health insurance programs, as captured by Medicare eligibility, on financial health of individuals. They find:

  • Medicare eligibility markedly improves financial health, as captured by declines in debt in collections.
  • Access to Medicare drastically reduces geographic disparities in financial health.
  • The improvements in financial health are most evident in areas with a high share of Black, low-income, and disabled residents and in areas with for-profit hospitals.

As these posts will demonstrate in greater detail tomorrow, the average outcome doesn’t provide a full picture of credit market outcomes. There is considerable heterogeneity in different segments of the credit market both in terms of outcomes, as well as the in the effects of specific policies. Outcomes vary by a range of factors, such as differences in race, income, age, and geography. We will continue to study and write about the importance of heterogeneity in the credit market and other segments of the economy.

Chakrabarti_rajashriRajashri Chakrabarti is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

How to cite this post:

Rajashri Chakrabarti, “Introduction to Heterogeneity Series III: Credit Market Outcomes,” Federal Reserve Bank of New York Liberty Street Economics, July 7, 2020, https://libertystreeteconomics.newyorkfed.org/2020/06/introduction-to-he....

Related Reading:

Series One

Introduction to Heterogeneity: Understanding Causes and Implications of Various Inequalities

Series Two
Introduction to Heterogeneity: Labor Market Outcomes




Disclaimer

The views expressed in this post are those of the author 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 author.

Demographic Trends in the Philosophy Major Might Be Mostly Due to Pre-College Factors (guest post)

Published by Anonymous (not verified) on Thu, 02/07/2020 - 1:47am in

This guest post* looks at two questions related to demographic trends among philosophy majors. First, are women disproportionately less interested in the philosophy major at the beginning of their first year of study? And second, is the recent apparent increase in interest in philosophy reflected in first-year intention to major? 

Authored by Eric Schwitzgebel (UC Riverside), Morgan Thompson (Pittsburgh), and Eric Winsberg (South Florida), the post first appeared at The Splintered Mind.


Félix Armand Heullant, “Edle junge Dame im Salon”

Demographic Trends in the Philosophy Major Might Be Mostly Due to Pre-College Factors
by Eric Schwitzgebel, Morgan Thompson, and Eric Winsberg

As we mentioned last month, we recently obtained data from the Higher Education Research Institute (HERI) on intention to major in philosophy among first-year students in the U.S.

Today we will explore two questions.

First, it’s well known that undergraduate philosophy majors in the U.S. are disproportionately men. For example, recent data from the National Center for Education Statistics show 36% of graduating Philosophy majors in the U.S. to be women, compared to 57% of graduating majors overall. Our first question is this: Are women also disproportionately less interested in the Philosophy major at the beginning of their first year of study?

The answer to this question is crucial to understanding the causes of the low proportion of women among graduating philosophy majors. If women begin their studies with less interest in philosophy than undergraduates as a whole, then the causes of disproportion trace back to something prior to college enrollment. In contrast, if women begin their studies with approximately proportionate interest in the Philosophy major, then their underrepresentation among Bachelor’s recipients in Philosophy suggests that something in students’ college experience is driving the disproportion.

Second, as Eric Schwitzgebel noticed last fall, the Philosophy major seems to be back on the rise in popularity while other humanities majors continue to fall. We wanted to see if the recent apparent increase in interest in Philosophy was also reflected in first-year intention to major. This is relevant to evaluating both the causes of and the likely persistence of the trend that Eric S. noticed last year.

First-Year Intention to Major by Sex, 2000-2016

Every fall, HERI gathers information from first-year undergraduates at a sample of U.S. colleges and universities, with about 200,000-400,000 respondents per year. One question asks respondents’ sex, with response categories “male” and “female”. About half of one percent of respondents decline to state. Another question asks for intended major, with “Philosophy” as one among dozens of choices.

This graph shows the percentage answering “female” among first-year students, both overall and in Philosophy, excluding students who declined to state.

As you can see from the figure, first-year student respondents were about 56%-59% female across all majors throughout the period (54%-55% if nonresponse bias is taken into account; see below). From 2000-2012, 32% to 36% of first-year student respondents intending to major in philosophy were female. This compares with about 30-34% of women among graduating majors in Philosophy in the same period. Thus, female students appear to be disproportionately less interested in the Philosophy major from the beginning of their undergraduate studies. These results match with some earlier analyses of the HERI database by Christopher Dobbs and Philippe Lemoine.

There may be some further loss of interest among women—about 2% in absolute percentage terms (32-36% vs 30-34%)—between first year intention to major and completion of the major, but due to differences in methodology between HERI and NCES it’s difficult to be confident about effects of this size, and we note that “female” and “woman”, though approximately comparable, are not identical categories.[1]

The second striking feature of this graph is the recent increase in percentage of respondents intending to major in Philosophy who reported being female: 40%-43% in 2013-2016. This suggests that the increased percentage of women among Philosophy BA recipients that appeared in the NCES data from 2018, which we noticed last fall, may not be a blip but might be the beginning of a trend that showed up in first-year students in 2013. In fact, the timing is perfect. With a national average of five years to Bachelor’s degree, a change in first-year students in the 2013-2014 academic year should be reflected in a change in graduating majors in the 2017-2018 academic year.

The change could be explained either by an increase in female students’ interest in the Philosophy major or a decrease in male students’ interest or both. This is a slightly complicated question which will first require us to address changes over time in the Philosophy major in general.

One big methodological caveat here is that the HERI data have some nonresponse and sampling problems: Not all colleges are included, with lower prestige public colleges especially undersampled, and not all students respond, and this skews the HERI demographic data.[1] Furthermore, the number of participating colleges declined substantially over the period in question. Some preliminary analyses we’ve tried suggest that nonresponse and over/undersampling might be an especially big issue with student race (which we hope to analyze in a future post), but only a minor issue with sex.

HERI provides researchers with a calculated variable “Student Weight”, which represents their best attempt to overweight the responses of students from underrepresented portions of the sample and underweight the responses of students from overrepresented portions of the sample, with the hope that the weighted responses are representative of first-year students in the U.S. as a whole. (The NCES data, in contrast, are reported by administrators and are approximately complete.)

The results above are based on raw responses. We attempted to correct for sampling and nonresponse bias by multiplying all responses by HERI’s Student Weight variable, but statistical noise became a problem. For example, using this method, estimates of the percentage of philosophy majors who were female jumped implausibly from 27% to 37% from 2013 to 2014. Since the Student Weight variable weights some students’ responses several times more than others, it should be expected to amplify noise, and given the small numbers of female philosophy major respondents (207 in 2013), it’s unsurprising that noise might be a limiting factor.

Overall, all trends reported in this post are confirmed when data are weighted by HERI’s Student Weight variable. However, the percentage of philosophy majors overall might actually be somewhat lower than reported (due to disproportionate representation of elite schools, where Philosophy is more commonly chosen as a major) and the percentage of female students might be slightly lower (due to slightly higher response rates among female students at the included schools).

While History and English Continue to Fall, Philosophy Has Partly Recovered

In 2017, Eric S. noted sharp declines in completed Philosophy, History, and Language majors in the NCES database, followed the next year by a slight recovery or stabilization in Philosophy, while the other big humanities majors continued to decline.

We were curious to see if this would also reflected in the HERI data on first year intention to major. As with the data on sex, examination of the HERI patterns could give us insight into mechanisms (are these changes due to something happening before college or in college?) and also perhaps some basis for projection into the future.

This chart shows rise and decline in intention to major, normed to the year 2000.

As you can see, the percentage of students majoring in History and English is about 2/3 of what it was in 2000. Philosophy showed an equally sharp decline in the early 2010s but seems to have partly recovered and is now at 86% of 2000 levels, while History and English continue to fall. As with gender, the timing shows a nice offset between HERI and NCES: The decline in first-year intention to major started in about 2010, while in the decline in completed Bachelor’s degrees started in about 2014 or 2015.

As with sex, the timing offset and similar pattern in the HERI and NCES data suggest that the primary factors behind these demographic trends are pre-college.

The decline and partial recovery of interest in the philosophy major interacts with sex, as shown in this figure:

As you can see, the percentage of female first-year students’ intending to major in Philosophy has recovered fully to 2000 levels, but not so for male first-year students.

We conclude that those of us who are interested in exploring the causes of demographic trends in the philosophy major should look more carefully than is usually done at factors that might be influencing students’ perceptions and intentions even before they enroll in college.

notes

[1] Most non-women in the undergraduate population are men, but a small percentage will be non-binary. We are unaware of any good data source on the rates at which non-binary students choose to major in philosophy. This dataset from HERI does not ask for gender, so it is possible that many respondents are answering with gender rather than sex. HERI’s Freshman Survey did not revise the question to be about one’s gender identity until 2018 and did not add a question about whether the student is trans or cis until 2019. Unfortunately, we were unable to access those data due to temporary embargoes on more recent years’ data.

[2] Unlike the NCES data, which is reported to the U.S. government by adminstrators at each institution, HERI collects data by selling U.S. universities and colleges the results of their survey for that particular institution. Wealthier institutions appear to be more likely to pay for this data collection and thus more likely to be represented in the HERI Freshman survey dataset than lower prestige colleges. The “Student Weight” variable discussed below is partly intended to help correct for demographic differences between wealthier, higher-prestige institutions and lower prestige public colleges.

The post Demographic Trends in the Philosophy Major Might Be Mostly Due to Pre-College Factors (guest post) appeared first on Daily Nous.

Distribution of COVID-19 Incidence by Geography, Race, and Income

Published by Anonymous (not verified) on Mon, 15/06/2020 - 9:00pm in

Rajashri Chakrabarti and William Nober

Distribution of COVID-19 Incidence by Geography, Race, and Income

In this post, we study whether (and how) the spread of COVID-19 across the United States has varied by geography, race, income, and population density. Have urban areas been more affected by COVID-19 than rural areas? Has population density mattered in the spread? Has the coronavirus's impact varied by race and income? Our analysis uncovers stark demographic and geographic differences in the effects of the pandemic thus far.

We use county-level data as of June 11, compiled by the New York Times and the New York City Department of Health (NYC Health) on numbers of cases and deaths for our analysis. The New York Times compiles a daily series of confirmed cases and deaths by county for almost every county in the United States. Its data set aggregates New York City, which consists of five counties, into a single entity. To get a breakdown of deaths and cases by New York City’s boroughs, we use data from NYC Health. Since race and income data for affected individuals are not available in all states, we match our county-level COVID-19 data with county-level data on race, income, urban status, and population density from the 2014-18 five-year American Community Survey to understand the dispersion of COVID-19 by these factors.

To understand the spread of COVID-19 by race, we investigate whether majority-minority (MM) counties were affected differently than other counties. For our analysis, we define majority-minority counties as those in which at least half the population is Hispanic and/or non-Hispanic black. As of June 11th, MM counties had COVID-19 cases at a rate of 7.2/1,000 people, while individuals in other counties contracted it at a rate of 5.9/1,000 people. We find that MM counties had a death rate of 35/100,000 people while other counties had a death rate of 33/100,000 people.

The map below shows the geographic distribution (in population-weighted terciles) of cases per thousand, differentiating between the spread in MM counties and other counties. The visibly higher rate in MM counties is driven mostly by the Bronx, Brooklyn, and several counties in Northern New Jersey (although these areas are too small to discern in the map), but some cities, such as New Orleans and Philadelphia, and less urban places, such as Southwestern Georgia and the Mississippi Delta, have very high rates of infection. In analysis not reported here, we find that the geographic distribution of deaths across counties is qualitatively similar to the geographic distributions of cases.

Distribution of COVID-19 Incidence by Geography, Race, and Income

However, such simple univariate descriptive analysis leaves open the question of whether the higher incidence of the virus in MM areas is best explained by their racial distribution or by other factors such as income, population density, and urban status. We define urban counties as counties that lie within metropolitan statistical areas. To parse out these effects, we next present results from some multivariate regressions. All regressions below control for time-invariant characteristics of the states and exploit within-state variation to understand patterns. We define low-income counties as those that fall in the bottom quartile of the population weighted distribution of median household income.

As seen in the first column of the table below, MM counties have 3.8 more cases per 1,000 than other counties after controlling for low-income and urban status, see the first column of the table below. Similarly after controlling for the other statuses, urban counties have 1.8 more cases per 1,000 than rural counties and low-income counties have 0.6 more cases per 1,000 than counties that are not low income. Thus, each of the MM, urban, and low-income counties has a significantly higher rate of cases than their corresponding counterparts. Notably, this effect is the strongest in MM counties. We find the patterns are similar for death rates.

We extend the model by including county population density as an additional covariate in columns 2 and 4. Counties in urban areas tend to have higher population density (the correlation coefficient is 0.59), so a relevant question is whether the higher incidence of COVID-19 in urban areas is attributable to their urban status or to their higher population density. Specifically, including population density in the model enables us to investigate whether it drives the more extensive spread in MM, urban, and low-income areas.

Controlling for population density yields a very different picture for urban versus rural areas—we find that urban areas have lower case rates (column 2) and lower death rates (column 4) than rural counties. This implies that the higher incidence in case and death rates in urban areas above was driven by higher population density in these areas. The lower susceptibility of urban areas may be because of better medical facilities and easier access to essential goods and services in these areas relative to rural areas. On the other hand, even after controlling for population density, we continue to find markedly larger incidence of COVID-19 in both low-income and MM communities, as captured by case rates (column 2) and death rates (column 4).

In column 3, we build on the model in column 2 and add an interaction term between low-income and MM dummies. The purpose is to investigate whether counties that are both low income and majority-minority have different patterns. We find that low-income counties that are also MM have a markedly larger number of cases than low-income counties that are not MM. Specifically, low-income MM counties have 3.79 more cases per 1,000 people than low-income counties that are not MM. Low-income MM counties have 4.7 more cases per 1,000 people than non-low income counties that are not MM. Similarly, MM counties that are low income have a higher rate of cases than MM counties that are not low-income.

Distribution of COVID-19 Incidence by Geography, Race, and Income

Finally, the chart below reveals the role of population density in the timing and severity of local outbreaks. We group all counties with similar population density and plot the per-capita case spread by population density (vertical axis) and time (horizontal axis). The size of the bubbles represents the severity of the outbreak as captured by number of cases per capita.

We find that population density played a major role in the spread of the virus. The scatter reveals that denser counties were the first to see cases and the rate of cases has been markedly larger in denser counties. The bubbles in the upper rows start growing well before the bubbles in lower rows do and they are markedly larger in size than the bubbles in the lower rows. In a chart, not included here, we find that the picture looks qualitatively very similar if we replace cases by deaths in the scatter chart below. The higher susceptibility of dense areas is because it is relatively difficult to socially distance in places with higher population density, which increases the risk of COVID-19 infection.

Distribution of COVID-19 Incidence by Geography, Race, and Income

In this post, we have studied heterogeneity in incidence of COVID-19 (cases and death rates) by urban, minority, and low-income status. We find that urban areas, majority-minority communities, and low-income communities have been impacted markedly more than other communities. Delving deeper, we find that the higher incidence of COVID-19 cases and deaths in urban areas is due to their higher population density. Controlling for population density, we find that urban areas are likely to have lower case and death rates. This may be due to better medical care facilities (hospitals, doctors, medical equipment) and better/easier availability of essential commodities and services. The larger vulnerabilities in low-income and majority minority communities continue to remain prominent even after controlling for the effect of population density. In ongoing work, we are studying the underlying reasons behind the differences in vulnerabilities in majority minority and low-income areas. Is this due to differences in pre-existing comorbidities? Is it because of differences in access to the health care system? Is it because of higher exposure in certain jobs (for example, essential versus non-essential) for which social distancing is more difficult? These are questions we are continuing to study; stay tuned for forthcoming postings in this area.

Rajashri Chakrabarti
Rajashri Chakrabarti is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

William Nober
William Nober is a senior research analyst in the Bank’s Research and Statistics Group.

How to cite this post:

Rajashri Chakrabarti and William Nober, “Distribution of COVID-19 Incidence by Geography, Race, and Income,” Federal Reserve Bank of New York Liberty Street Economics, June 15, 2020, https://libertystreeteconomics.newyorkfed.org/2020/06/distribution-of-co....




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.

Curry on George Floyd and the “Fake Outrage” of Academic Philosophy

Published by Anonymous (not verified) on Wed, 03/06/2020 - 11:16pm in

“The fake outrage of academic philosophy amazes me.”

Those are the words of Tommy J. Curry, professor of philosophy at the University of Edinburgh, in a recent public Facebook post.


Frank Bowling, “Middle Passage”

Professor Curry specializes in critical race studies, social and political philosophy, and black male studies, and is the author of The Man-Not: Race, Class, Genre, and the Dilemmas of Black Manhood. Readers may recall that Professor Curry was the target of racist harassment and death threats while he was a professor at Texas A & M (see also here and here).

Here’s the full post:

The fake outrage of academic philosophy amazes me. Let’s ask ourselves something for the last 6 years we have had verifiable evidence that what happened to George Floyd happens to almost 300 Black men every year… how many panels have been held at APAs or organizational conferences addressing the murder of Black men and boys in the United States.

Now ask yourselves: How many panels have been held about MeToo and excluded Black men on those panels and in those sessions despite Black men reporting the highest rates of sexual assault in the United States.[*]

At a certain point we have to realize—its just built that way. You don’t care about most Black people, esp. Black men and boys.

Professor Curry’s remarks prompt consideration of what the institutions of academic philosophy should be doing in regard to issues facing black men and boys. Discussion of this is welcome, as are pointers to existing and planned work and events on these matters.

(*In response to questions from others about this claim, Curry points to The National Intimate Partner and Sexual Violence Survey and his article, “Expendables for Whom: Terry Crews and the Erasure of Black Male Victims of Sexual Assault and Rape“.)

The post Curry on George Floyd and the “Fake Outrage” of Academic Philosophy appeared first on Daily Nous.

The Sexual Orientations of First-Year Philosophy Undergrads

Published by Anonymous (not verified) on Fri, 15/05/2020 - 11:20pm in

What’s the distribution of sexual orientations among first-year undergraduates who are majoring in philosophy? Eric Schwitzgebel (Riverside), Morgan Thompson (Pittsburgh), and Eric Winsberg (South Florida) looked at data from Higher Education Research Institute’s “Freshman Survey” to find out that and other information.


[photograph by Muholi Zanele]

They conclude that “Students intending to major in philosophy are more likely to identify as non-heterosexual than are students in other majors.” Here are the numbers:

Overall, across all majors, 92% identified as straight, 4% as bisexual, 2% as other, and the remaining groups 1% each. Philosophy majors were more likely to report non-heterosexual sexual orientation: 88% straight, 6% bisexual, 3% other, 2% gay, 1% queer, and < 1% lesbian. (p < .001, comparing the proportion straight).

Non-response rates of 10% for philosophy majors and 8% for students over all was “an issue”, they say, but “the proportions, absolute numbers, and effect sizes are large enough to permit some confidence in the conclusion”.

They add, “Unsurprisingly, philosophy isn’t the queerest of all disciplines.” That designation would go to Women’s and Gender Studies, with 42% of the students identifying as non-heterosexual.

They also found that “43% (485/1132) of intended philosophy majors were women (1%, or 7 total, declined to state), compared to 58% of first-year students overall.”

They attempted to determine the proportion of philosophy students who are transgender, but, they say, “given the small number of self-reported transgender students and these resulting interpretative difficulties, we are hesitant to draw conclusions about the proportion of students who are transgender or about whether philosophy students were more likely than other students to be transgender.”

You can read more about their study here.

Related: “The Personality of Philosophy Majors“, “The Political Views of Philosophy Majors“, “Philosophy Majors Are Less Likely To Marry Each Other“, “The Philosophy Major Sees Increase in Numbers and Diversity“, “Proportion of Philosophy Majors Who Are Women Varies Widely Across Schools“.

The post The Sexual Orientations of First-Year Philosophy Undergrads appeared first on Daily Nous.

Fatstock for slaughter

Published by Anonymous (not verified) on Wed, 29/04/2020 - 5:00pm in

I have already drawn attention to the diabetes and metabolism problems that appear to leave some people more than usually susceptible to COVID-19. Even more persuasive evidence has arrived in the ‘European Scientist’: Public Health England have said now is the best time to quit smoking, citing research from China concluding that smokers were 14... Read more

Affordable housing, homelessness and the upcoming federal budget

Published by Anonymous (not verified) on Fri, 20/03/2020 - 10:14am in

I’ve written a ‘top 10’ overview of things to know about affordable housing and homelessness, as they relate to Canada’s upcoming federal budget. The overview is based on the affordable housing and homelessness chapter in the just-released Alternative Federal Budget.

A link to the ‘top 10’ overview is here.

Affordable housing, homelessness and the upcoming federal budget

Published by Anonymous (not verified) on Fri, 20/03/2020 - 10:14am in

I’ve written a ‘top 10’ overview of things to know about affordable housing and homelessness, as they relate to Canada’s upcoming federal budget. The overview is based on the affordable housing and homelessness chapter in the just-released Alternative Federal Budget.

A link to the ‘top 10’ overview is here.