US Census 2010

The US population counts have ben released. The red states have gained more population than the blue states. Michigan reported a slight loss.

Also, the increase in population in the states that grew the most was a result of increase in immigration and higher birth rates amongst the non-white Hispanics.

For details, visit: http://2010.census.gov/2010census/data/index.php

 

 

From Audrey Singer, Senior Fellow, Metropolitan Policy Program

December 21, 2010 —

Nevada, Arizona, Utah and Idaho had the fastest growth this decade while slow growth was seen in Rhode Island, Louisiana, Ohio and New York. Michigan was the only state to see a decline.
Most people poring over the 2010 state counts are doing so with an eye to future elections, but it makes sense to proceed cautiously before drawing any hard conclusions about what that data mean in the political context.
Attention is focused on 12 seats in the House of Representatives that will be leaving slow-growth states in the Northeast and Midwest and moving to the South and Southwest as populations there continued to grow quickly.
Census 2010: America grew at slowest rate since the Depression
Texas is the big winner, with four new House seats. Since the states losing seats are usually regarded as blue, tending to elect Democrats, and those gaining seats are normally considered red, tending to elect Republicans, it would be easy to assume Tuesday's census release is good news for that party and alarming news for Democrats.
It's not that simple.
If we regard simple population change as an indicator of political power, the buildup in red states is indeed significant. However, early next year, the Census Bureau will release counts of the population by race and ethnicity. That number will show shifts of the population that official estimates have pointed to all along: The diversifying of the population is more extensive in areas of fast population growth.
Many of the states that have gained in their head count have gained non-white minorities, especially Hispanics. Estimates already show that four states that gained seats -- Texas, Florida, Arizona and Georgia -- are highly ranked in the Top 10 states for growth in the Hispanic population during this decade.
Moreover, more than half the population growth in those states alone came from increases in the Latino population. These additions were a result of net immigration and births in those states. While the large increase in Hispanics in these high-growth states includes some immigrant newcomers ineligible to vote, eligible Latinos tend to vote Democratic in most of the states that gained seats. That may change by 2012, but much will happen between now and then.
Putting this into further perspective, the U.S. population grew by 9.7% between 2000 and 2010, slower than any decade since the Great Depression in the 1930s. With the Great Recession taking hold at the end of the 2000s, slowing immigration and birth rates, it is possible that slower growth will continue, at least for the short term. Thus, the sizeable increase in minority populations comes at a time when the overall population is growing more slowly.
Thus the sweeping demographic changes going on in our nation are much more significant than just shifts in where people live. We are becoming more diverse, and, as many analysts have noted, we will be a white-minority nation in about 30 years. So simply looking at geography may not yield correct results when we're talking about how new populations may behave and particularly how they may vote.
The excitement that demographers feel about this once-a-decade population snapshot is likely overwhelmed by the enthusiasm of political analysts, who are busy discovering what state-level population shifts mean for state reapportionment and redistricting within states.
But as we can see by looking more carefully at the data, there is more to population shifts than a simple rearranging of the map based on total counts.
We're in for a much more interesting and challenging time than many people may believe.

Where is Toronto’s missing middle class? It has suburbanized out of Toronto

The recent study by the University of Toronto’s (U of T) urban researchers has raised alarms about the increase in income polarization in the City of Toronto where the very rich and the very poor have expanded and the middle class has shrunk over the years. The study raises concerns about the implications of the rising income inequalities in the City of Toronto.

The report is indeed an important contribution to the ongoing dialogue on how to create socially just societies and thus serves as a scorecard on how the City performs on income inequality. The report also mentioned that the  growing number of the very poor in the City of Toronto are immigrants and visible minorities.

While I agree with the general findings of the report, and consider it a must read for those interested in cities, I have though some concerns about the methodology and some implied, yet unstated, conclusions. First the methodology. The authors have focussed on the City of Toronto and have only tangentially mentioned the possibility that the middle class may have migrated to the outer suburbs. I call this phenomenon of suburbanizing middle classes ‘the brown flight’, since immigrants from South and South-East Asia constitute a large number of the new middle class, which has settled in Toronto’s outer suburbs.

Second is the implied assumption that growing income inequality by default is at odds with social cohesion and long-term economic and social viability of the region. This assumes that the social safety nests, which are predominantly supported by the taxes imposed on high and very-high income earners, are incapable of providing short- and long-term services for the very poor in the City. Furthermore, the breakdown of neighbourhoods by income also gives the impression that there may be a socially-optimum breakdown of neighbourhoods by income categories.

I will address below only the first concern in this blog.

I have repeated the analysis done by the authors of the U of T study with two differences. Instead of using individual income, I have based my analysis on household incomes. This does not fundamentally change the distribution  of income profiles mentioned in the U of T study, however it is theoretically more consistent because income manifests itself at the household level more than it does at the individual level. For instance, a poor household is unlikely to have both poor and not poor members of the household. The pooling of income within a household  can help the household deal with poverty better than a single-person, and therefore a single-income, household. My own research on household dynamics in the Peel region suggests that single-person households are more vulnerable to poverty than the rest because of lack of monetary support from other household members. Thus using household income as a metric to study income polarization is better than using individual income.

The City of Toronto is part of the larger urban system or conurbation known as the Greater Toronto Area. Statistics Canada defines Toronto’s conurbation as the Toronto Census Metropolitan Area (population 5.1 million, Census 2006) and includes the City of Toronto (population 2.5 million, Census 2006) and the neighbouring suburban municipalities. Some even include Hamilton in the same conurbation.

In an earlier blog on the topic I had suggested that Toronto’s middle class has migrated out of the City of Toronto and has settled in the outer suburbs of Toronto, which are part of the Toronto conurbation. The reason for outward migration of the middle class are rooted in demographics and affordable housing. The middle class comprises of growing families with children who need more shelter space at affordable prices, which is abundantly available in the outer suburbs. Furthermore, financially stable and multigenerational immigrant households abandon Toronto at an increasing rate than the rest since they crave affordable shelter space more than the rest. This creates the spatial mismatch between the shelter needs of the middle class and the small-sized, yet prohibitively expensive, housing stock available in the City of Toronto.

See the map to the right (click on it to see the larger version), which shows the percentage of households with 4 or more persons. The brown and red-coloured census tracts in the suburbs act as proof for the claim that the growing households have left for the suburbs. Similarly, the blue-coloured census tracts in Toronto along the waterfront and in areas of higher income suggest small-sized households comprising either young professionals or empty-nesters, who outbid households with children for shelter space in the choice neighbourhoods.

In this blog I will present evidence to my claim that income polarization in the City of Toronto is a result of the arbitrary boundaries used to define Toronto. These boundaries conform to the political and administrative boundaries of the City, but not to the housing and employment markets observed in the City. The evidence for this argument lies in the commuting patterns observed in the City. My own calculations from the 2006 place of work data from Statistics Canada suggest that almost 600,000 work trips cross City of Toronto’s boundary every day. These are the individuals who either live in Toronto (186,555 trips) and work in the suburbs, or the suburbanites (407,535) who work in Toronto and cross the arbitrary boundaries, which delineate the City, twice every day.

See the map to the right (click on it to see the larger version),  where I have depicted municipal boundaries in grey colour. Also shown are the pre-amalgamation boundaries of Toronto’s former inner suburbs. The green colour neighbourhoods in the map depict the middle class, which is primarily concentrated in Toronto’s outer suburbs. The blue colour low-income cohorts are increasingly concentrated in the amalgamated City of Toronto making a U-shaped poverty concentration around the high-income neighbourhoods concentrated along the Yonge Street.

The correlation between the location of rail tracks in the City of Toronto and the spread of poverty in proximity to rail tracks has not been discussed in detail in the past. The U-shaped poverty visible in the above map is along the rail/subway tracks. Furthermore, I also noticed a significantly large number of homicides committed in the vicinity of rail racks, which are also neighbourhoods depicting poverty (see the map to the right).

The egalitarian suburbs

Toronto’s outer suburbs are far more egalitarian than City of Toronto itself. When I reviewed the income distribution, I was able to replicate the findings from the U of T’s report. Consider the following histogram of household income that illustrates more than 60% residents in the City of Toronto (2.5 million) earn at least 20% less than the average household income ($91,579 for 2005) observed for Toronto CMA (5.1 million people).

The graph also shows that almost 8% of the population lives in Census Tracts (approximate population 4,000) where the average household income is more than 40% of the average household income in Toronto CMA. The numbers presented here are slightly different from what is reported in the U of T study because the U of T study computed the percentage of the census tracts (i.e., land area) and not the percent of population, which I have presented in the above-mentioned graph. The following table is based on the data presented in Map 3 on Page 5 of the U of T study.

The middle class found in suburbia

The most interesting finding emerges when one plots the income distribution for the outer suburbs of the  Toronto CMA, i.e.,  after excluding the City of Toronto from the data set. The resulting histogram is presented below, which is computed for the 2.6 million suburbanites who live in the “905 municipalities”.

The missing middle class is right in the middle of the graph where 60% of the suburbanites live in neighbourhoods where the average household income is within the +/- 20% of the average household income for Toronto CMA. The symmetrical distribution presented in the above graph suggests that the extreme income disparities seen in the City of Toronto do not exist in the outer suburbs of Toronto.

The other main concern in methodology adopted by the U of  T study is the presence of high extreme values in the income variable. The literature on income inequality discusses the issues related with extreme values. The histogram below confirms the existence of the problem where a small number of census tracts report very high incomes in the range of $250,000 and $800,000. A naive method of correcting this problem is to use a trimmed data set that excludes extreme values. However, more statistically robust methods are readily available and have already been programmed in some econometrics software. I will address this issue later in another blog.

There exists in fact a greater heterogeneity in the income distributions within the region. I have plotted income distributions for the nine main municipalities, which are home to 4.6 million of the 5.1 million residents of Toronto CMA. The following graph has been truncated at a maximum household income of $250,000. The graphs below show that the peaks observed for the suburban municipalities are concentrated around the average household income observed in the region ($91,579), whereas the results are skewed for Toronto with a large number of low income concentration as well as a small number of very large income households.

Another way of looking at this issue is to examine the share of population for each income category distributed amongst the select 9 municipalities, including Toronto. The following table illustrates the concept. Notice that 95% of those whose incomes are at least 40% less than the regional average, and 76% of those whose incomes are at least 20% less than the regional average  reside in the City of Toronto. Similarly, 53% of those whose incomes are more than 40% above the regional average also reside in the City of Toronto. This confirms the findings of income equality in the U of T study.

However, more interesting results are reported for the middle income categories. 68% of those whose income falls within +/- 20% of the regional average income, i.e., the middle class, lives in Toronto’s external suburbs. Furthermore, 77% of the slightly well-off households whose incomes are between 20% to 40% higher than the regional average also reside in the outer suburbs.

Also note that the very low income category is non-existent in  Markham, Milton, Oakville, Richmond Hill, and Vaughan.

Lastly, consider the Lorenz curves for the City of Toronto (bottom left) and the 905 municipalities (bottom right).  The Lorenz curve for the City of Toronto reveals a unique distribution, confirming the trends mentioned above where a small number of households earn very large incomes. On the other hand, the Lorenz curve for the outer suburbs suggest mild income inequality.

The good, the bad, and the ugly in Toronto

What happened to the middle class in Toronto? University of Toronto's urban/social geographer, David Hulchanski, and colleagues argue that the middle class has disappeared from Toronto in a recently released update on the growing income disparity in the City of Toronto. They argue that the income disparities have reached new heights where wealth and poverty in extremes define the City.

The report also raises alarm about the urban poor, which in Toronto's case appear to be visible minorities and immigrants who do not posses adequate education or job skills required by the Toronto's specialized job market.

I have a hypothesis, which may help locate Toronto’s missing middle-class. I believe demographics and affordable housing are behind the trends  that the authors have highlighted in their report. The missing middle-class from Toronto, which comprise to a large extent of  immigrants from South Asia and the Asia-Pacific, has perhaps migrated to the outer suburbs in the Durham, Peel, and York regions of the Greater Toronto Area. Furthermore, non-immigrant households with children have also abandoned Toronto in search of affordable housing in the suburbia.

The question to ponder is why financially “successful” immigrants (well-educated professionals who have migrated in the past 20 years to Canada from South Asia and Asia-Pacific) not settle in droves in Toronto after they have gained some economic traction in Canada. And why those immigrants who struggle in the job market stay behind in Toronto?

It appears that the financially “successful” immigrants leave Toronto for demographic reasons. Multi-generational households with children and grandparents need larger homes, which are available in plenty in the outer suburbs and at affordable prices. A 4-bedroom house in a reasonable neighbourhood in Toronto costs in excess of $550,000. The same-size housing unit in a newer neighbourhood in the suburbs costs at least $100,000 less.

Larger household size of the new middle class immigrant households, which cannot be readily fit into the small-sized housing units in the transit accessible, and prohibitively expensive, neighbourhoods in Toronto, may partially explain why the “brown flight” has taken place.

Source: Statistics Canada, 2006 Census

The missing middle class has abandoned Toronto for the outer suburbs where affordable housing and new infrastructure is in plenty. The housing markets in Mississauga and Brampton, one of the fastest growing places in Canada with a combined population of almost a million people, are to a large extent driven by the immigrants from South Asia. Chinese immigrants drive the housing markets in the York Region, which is situated to the north of the City of Toronto.

The following graphic from the report reveals almost non-existence of extreme poverty in the outer suburbs in the greater Toronto Area, which is now home to the missing middle class from Toronto.

The City of Toronto's housing stock is in fact more conducive for smaller-sized households. Unlike the recent immigrants from South Asia, who have larger-sized families, former immigrants and their descendants from Western and Eastern Europe have small-sized families, which could be accommodated with ease in the available housing stock in the City of Toronto. 

So what about the immigrants who stay behind? The answer lies in the availability of social services, e.g., shelters for homeless, subsidized housing and other similar facilities, which are more prevalent in Toronto than they are in the outer suburbs. It would make sense for households that rely on state's assistance to stay in Toronto to avail readily available social services.

Thus, Toronto's inner suburbs are now acting as the staging grounds or intermediate neighbourhoods for the new immigrants who save enough while living their to buy a home in the outer suburbs and eventually relocating their to start a new suburban life as the missing middle class of Toronto.

While the University of Toronto's study does not explain if the income polarization is causing further harm by limiting or preventing access to health-care, education, or the like for the low income households, it is still a very important contribution to the on-going debate on how to create  a socially just and egalitarian society.

Shrinking middle class makes Toronto a city of socioeconomic extremes - The Globe and Mail

The return of the non-creative class: wealthy suburbs

The recent release of census data in the United States shows that suburban communities in Virginia lead the United States in wealth generation.  At the same time, the highest incidence of poverty was concentrated in American Indian reservations in South Dakota.

The above findings regarding suburban communities should be of interest to urban planners who often believe the suburbs to be undesirable landscapes inhabited by obese and lifeless individuals, whose lives are soon to be buried under the unsustainable mortgage debt.  Not only the latest data show an increase in wealth in the suburban communities, but also a concentration of highly educated individuals.  According to the data, seven counties out of a total of 17 where more than 50% of the adults possessed a a bachelors degree were located in suburban communities.

The data from the American Community Survey also revealed that immigrant populations are fast spreading in the United States into communities where they had not set foot in the past.  The immigrants have started to move into suburban and rural communities whereas in the past, immigrants concentrated primarily in urban communities.

--

From the New York Times,

By SABRINA TAVERNISE and ROBERT GEBELOFF

WASHINGTON — The three places in the country with the highest median household income are all in Virginia, according to census datareleased on Tuesday, while those with the highest rates of poverty are in four American Indian reservations, all in South Dakota.

The Virginia counties of Fairfax and Loudoun and the city of Falls Church had the highest median income, according to the data, which spans 2005 to 2009. Falls Church was the highest at $113,313, up by 17 percent from 2000. The lowest median income was in Owsley County, Ky., at $18,869.

Of the five counties with poverty rates higher than 39 percent, four contain or are in American Indian reservations in South Dakota. The fifth, Willacy County, Tex., is on the Gulf Coast.

The data is from the Census Bureau’s American Community Survey, which samples 1 in 10 Americans on a variety of social, economic and demographic topics. It is the single largest release of data in the bureau’s history, with 11 billion individual estimates covering 670,000 geographic locations. It gives details on the characteristics of American society based on surveys, and is separate from the 2010 Census, which will provide a precise count of all Americans.

In another finding, the immigrant population has spread out to areas that previously had none. The number of immigrants increased by 40 percent in the nearly 2,500 counties where they had comprised less than 5 percent of the population in 2000. Immigrants have traditionally settled in cities, but in the last decade, they have followed jobs to rural and suburban areas, particularly ones with housing booms.

Among the sharpest increases were in Frederick County, Md., where the foreign-born population nearly tripled to almost 10 percent of the population. Other fast risers were Stafford County, Va., where the foreign-born population also tripled, and Newton County, Ga., where it jumped fourfold.

Meanwhile, the nation’s traditional immigrant centers — the 16 counties where immigrants made up more than 30 percent of the population, according to the data — saw their foreign-born population grow by only a quarter million.

The suburbs of Washington also contained the highest concentration of people with higher education. Seven of the 17 counties where more than half of people 25 and older had a bachelor’s degree were in the city’s suburbs. Three counties were in Colorado (Boulder, Douglas and Pitkin) and two were in California (Marin and San Francisco) and Maryland (Howard and Montgomery).

There were 62 counties where less than 10 percent of the adult population had a bachelor’s degree. Fourteen of these counties were in Georgia, nine in Tennessee, eight in Kentucky and five each in Florida and West Virginia.

Sabrina Tavernise reported from Washington, and Robert Gebeloff from New York.

A sneak peak at the new census questionnaire

While the controversy over the 2011 census in Canada continues, Statistics Canada, the agency responsible for holding Census in Canada, has released the draft questionnaire for review.

There are a few changes in the questionnaire that may make transport planners happy.

Here is a brief list of the changes that I could spot:

1. Question 33 about unpaid work or “care economy” has been removed. It appears that the National Statistics Council had already decided upon this question, but made it look like a new suggestion in their press release two days ago. This needs more probing. The exact timing of NSC’s recommendation to exclude question about unpaid work should be established.
2. Questions about childcare expenses have been included.
3. Also included are questions about child support paid to former spouses.
1. This development is interesting. The question about unpaid work, which predominantly dealt with women’s non-monetised contribution to economy and society, has been removed and a question about child support, that affects men predominantly, has been included.
4. Transport planners should be delighted to note that in addition to the questions about the mode of travel to work, and work location, the questionnaire in 2011 will collect info on the following:
1. Average travel time for home to work trip
2. Departure time for home to work trip
3. Car pooling, i.e., number of people travelling together to work in the same vehicle
4. The mode of travel question has also been enhanced by asking details about public transit, i.e., differentiating between buses, subways, street cars, and regional rail.
1. The question is phrased poorly as it will most likely report lower mode split for buses for multi-modal trips.
5. And yes, the survey will be voluntary, not mandatory!

Lastly, I think it is not too early to start thinking about putting up a legal challenge to the government’s decision to make the survey voluntary. It is indeed  not going to be easy because the courts would not like to interfere/override the authority of the minister (executive) as enshrined in the constitution. However, there are ways to make this happen and universities with faculties of law and businesses with legal departments may want to start looking for those whose welfare will be adversely impacted by this decision and help such aggrieved parties litigate.

Related articles by Zemanta

• New census deletes questions about unpaid work (ctv.ca)
• Voluntary census deletes questions about unpaid work (theglobeandmail.com)
• Coalition urges Conservatives to reverse census stand (canada.com)
• Canada Logs Opposition to Simpler Census (online.wsj.com)

STATISTICS ACT of Canada

According to the act it is not merely the refusal to respond to Census, but wilfully falsifying a response to Census or any other info sought under the act may lead to a prison term.

The Statistics Act states:

False or unlawful information

31. Every person who, without lawful excuse,

(a) refuses or neglects to answer, or wilfully answers falsely, any question requisite for obtaining any information sought in respect of the objects of this Act or pertinent thereto that has been asked of him by any person employed or deemed to be employed under this Act, or

(b) refuses or neglects to furnish any information or to fill in to the best of his knowledge and belief any schedule or form that the person has been required to fill in, and to return the same when and as required of him pursuant to this Act, or knowingly gives false or misleading information or practises any other deception thereunder is, for every refusal or neglect, or false answer or deception, guilty of an offence and liable on summary conviction to a fine not exceeding five hundred dollars or to imprisonment for a term not exceeding three months or to both. 1970-71-72, c. 15, s. 29.

 

For the complete text of Statistics Act of Canada, please visit:

http://www.statcan.gc.ca/about-apercu/act-loi-eng.htm

 

From Coats to Sheikh

Last week Dr. Munir Sheikh resigned as Statistics Canada’s chief statistician to register his protest at the government’s attempt to misrepresent the agency about its views on converting the mandatory census into a voluntary one.

Dr. Sheikh told the parliamentary industry committee that  the “fact that in the media and in the public that there was this perception that Statistics Canada was supporting a decision that no statistician would, it really casts doubt on the integrity of that agency, and I as head of that agency cannot survive in that job."

It is only appropriate to remember Robert H. Coats who was the first chief statistician of Canada who laid the foundation of Statistics Canada after being appointed as the Controller of Census in 1915.

Wikipedia recalls Robert Coats as follows:

Robert Hamilton Coats (July 25, 1874 – February 7, 1960) was Canada's first Dominion Statistician.

He was born in Clinton, Huron County, Ontario in 1874, the son of Charles Coats, who came to Canada from Scotland. In 1896, Coats received a B.A. from the University College in Toronto. He worked as a journalist for the Toronto World and then the Toronto Globe until 1902 when, at the request of Prime Minister Mackenzie King, he became editor of the Labour Gazette; King himself had been the first editor of this publication which included statistical information related to labour. Coats was named Chief Statistician for the Department of Labour in 1905. In 1915, he was appointed Dominion Statistician and Controller of the Census. Coats helped establish the Dominion Bureau of Statistics, now Statistics Canada.

He also served on statistical committees with the League of Nations. After he retired in 1942, Coats served as statistical advisor to the government of Ontario and the United Nations. He served four years as visiting professor of statistics in the Department of Political Economy at the University of Toronto.

Coats, one of the first residents of Rockliffe Park, died in Ottawa at the age of 85. He had been married twice, first to Marie Hollbeister, and then, after his first wife's death, to Maida Skelly.

The R.H. Coats Building in Ottawa was named after him.

References

• Robert Hamilton Coats fonds, Archives Canada
• Herbert Marshall, The Canadian Journal of Economics and Political Science / Revue canadienne d'Economique et de Science politique, Vol. 26, No. 3. (Aug., 1960), pp. 482-483.
• John A. Stevenson, The American Statistician, Vol. 14, No. 2. (Apr., 1960), pp. 28-29.

Related articles by Zemanta

• Munir Sheikh shows us what integrity and leadership look like (theglobeandmail.com)
• Ex-StatsCan head defends mandatory census (cbc.ca)
• Statistics Canada, Dr. Munir Sheikh, and the long form (ekonometrics.blogspot.com)

Self-selection bias and census

What happens when one tries to analyse women’s labour force participation using data collected from a voluntary survey. There will obviously be a self-selection bias, that is, we will observe labour force participation choice made by only those women who chose to fill out the census form. As for those women who opted not to fill the census form, we would have no knowledge about their decision to participate in labour force or otherwise.

Now armed with a data set that suffers from self selection bias, we would have to face the challenge of analysing wages for only those women who chose to fill out the form and then chose to participate in the labour force. There are now two self-selection biases in play: one dealing with filling out the census, and the other dealing with participating in the labour force.

 This poses interesting challenges in econometrics, as pointed out by Mathew Kahn in the Christian Science Monitor. He writes:

“The only winner here is Jim Heckman [recipient of the the 2000 Nobel Memorial Prize in Economic Sciences (with Daniel McFadden)] as his cite count will go up as smart researchers in sociology, demography and economics will have to come up with a selection correction to model who bothers to fill out the form and then a researcher studying women's labor force participation would need to estimate a second selection correction. I'd like to see somebody work out the "three step" standard error formula in this case!”

What Mathew means is that a researcher has to account for not just one, but two self-selection biases to be able to estimate unbiased coefficients in an econometric model that explains wage differentials between men and women. Obviously, this fine point will be lost on those who never made it beyond Statistics 101. For them, Mathew Kahn offers a simpler example:

“Suppose that highly motivated busy people don't bother [filling out the Census], then the "average" person in Canada will look "lazy" because the sample who fills out the survey will omit the high achievers.”

Put simply, the voluntary census long form will make Canada lazier than it really is!

Lastly, a paper published on census response rates suggested that racially and socio-economically diverse communities reported lower response rates at the county level. This implies that racial minorities are more involved in census when there numbers increase within the community.

Furthermore, the US Census Bureau in 2003 compared the response rates for the mandatory American Community Survey by sending a portion of the respondents the same survey but indicating that their response was voluntary. The change from mandatory to voluntary survey resulted in a huge 20.7% decline in response rate. At the same time the Bureau observed that the highest decline in response rate occurred in communities with high response rates recorded for the Census conducted in 2000.

These issues have serious implications for converting the mandatory long form into a voluntary survey.

Community Composition and Collective Action: Analyzing Initial Mail Response to the 2000 Census

Jacob L. Vigdor (Duke University)

The Review of Economics and Statistics, February 2004, 86(1): 303–312

Abstract

This paper analyzes how community heterogeneity influences resident decisions to undertake actions generating public benefits. The decision in question is completing and returning the 2000 Census questionnaire, an action which secures a significant amount of federal grants for the community. The model developed to explain this action allows members of societal groups to differentially value public benefits that accrue to other group members. Racial, generational, and socioeconomic class heterogeneity all predict significantly lower response rates at the county level. The potential for endogenous sorting into heterogeneous counties implies that the magnitude of true behavioral effects exceeds these estimates. Copyright (c) 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Related articles by Zemanta

• The Statistics Council's compromise on Census hurts Women interests! (ekonometrics.blogspot.com)
• European census alternatives have privacy concerns of their own (theglobeandmail.com)
• Government-appointed advisory group says Canadians need full census info (thestar.com)
• Come to your census, expert tells Tories (canada.com)
• Come to your census, expert tells Tories (canada.com)

Statistics Canada needs guts

Industry minister Tony Clement has asked Canadians to trust Statistics Canada for his decision to scrap the mandatory long census form and replace it with a voluntary questionnaire. The Canadians would trust Statistics Canada if they knew where it stood on this matter.

Dr. Munir A. Sheikh, Canada’s chief statistician who was handpicked by the Harper government in 2008 to lead the agency, has refused to speak on the matter.  He has declined an interview request from The Canadian Press. His silence on the matter has made Minister Clement the de facto chief spokesperson for the agency.

Canadians expect strong leadership from their chief statistician. The former chief statistician of Canada, Dr. Ivan Fellegi, has told The Canadian Press that he would have quit his job in protest had the government tried to pull this stunt during his tenure.

Statistics Canada has already lost the trust of most Canadians by following an executive order instead of first consulting with those who rely on its data. Dr. Sheikh must speak directly to Canadians and explain why he believes the unanimous opposition by Canadian academics, businesses, charities, and medical professionals to scrap the mandatory long census form is misplaced.