Household Dynamics and Commuting Patterns in

Canada

Michael Haan

Canada Research Chair in Population and Social Policy

University of New Brunswick

mhaan@unb.ca

Recent Economics Journals Journal of Population Economics (1987-) Journal of Urban Economics (1978-) Journal of Socio-Economics (1981-) Journal of Economic Inequality (2003-) Journal of Education Economics (1969-) Journal of Health Economics (2000-) Journal of Housing Economics (1991-) Journal of Economics of Religion (2006-) Journal of Labor Economics (1982-)

A (Brief) History of Migration Theory

Ravenstein’s Laws of Migration (1889) Zipf’s Gravity Model (1946) Stouffer’s “intervening obstacles” model

(1940; Galle and Taeuber, 1960) Zelinsky’s mobility transition (1971) Model Migration Schedule (Rogers and

Willekens 1986) Borjas, Castles and Miller, Massey, Jasso,

etc.

Complex Network Research on Human Mobility

The Levy Walk (Brockman, Hufnagel, and Giesel 2006), originally used to study hunting patterns of sharks, birds, etc.

Random walk (Yasuda, 1975), first used to study the foraging patterns of horses, cows.

Brownian Movement (Klafter, Schlesinger, and Zumofen, 1996) – first used to understand the floating patterns of pollen on water by Robert Brown (1827).

Gravity Model (Zipf, 1949; Gonsalez, Hidalgo, and Barabási, 2008), based on Newton’s laws of gravity.

Radiation Model, the Universal Model of Human Mobility and Migration (Simini et. al, 2012)

Two assumptions (Zipf, 1949):

1. Humans do not enjoy moving,

2. They take the nearest opportunity with an unknown that improves their circumstances.

Every region of population size n has a job ‘benefit distribution’ p(z) derived from a combination of income, working hours, conditions, etc..

The Radiation Model

Where are the people?

Radiation model: the individual chooses the closest job to his/her home, whose benefits z are higher than the best offer available in his/her home region (Simini et al., 2012).

This explains 93% of all local human labour movements (Simini et. al, 2012). The model contains no individual information. The model does not explicitly define ‘closer’ or

‘easier’.

Mobility and Work in Canada

onceptual and empirical divide Mobility is linked to age (Dion and Coloumbe

2008; Finnie 1998, 1999; Turcotte and Vezina 2010)

Mobility is shaped by gender (Green and

Meyer 1997; Hiller and McCaig 2007; Turcotte 2005)

The extent and type of mobility is dependent upon industrial sector (Green

and Meyer 1997b; Cubukgil and Miller 1982; Moos and Skaburkis 2010)

Mobility is regionally/provincially contingent (Green and Meyer 1997a; Hiller 2009; Turcotte 2005; Statistics Canada 2008)

Defining ‘easier’ and ‘closer’

Is it ‘easier’ to commute 6500 kilometres to work a ‘21 on, 7 off’ shift than it is to move?

Is it ‘closer’ to commute 200+ kms so that your family is close to the amenities they want/need? Is it easier for a man or a woman to commute? Someone with/without children?

Are individuals the appropriate unit of analysis?

Excess Commuting

Defined as those that travel 200+ kms to their usual place of work.

Identifies a population that conducts a move that is neither easy nor close (as defined by Simini et. al., 2012).

Heterogeneous population. Commuters from Toronto to Ottawa,

Victoria to Vancouver, Calgary to Edmonton, St. John’s to Fort McMurray, etc.

Is Mobility the Future of Migration?

Northern Alberta alone has over 55,000 work camp spots. This does not include campgrounds, hotels,

permanent dwellings, or exploratory missions. Projected worker shortage of 200,000 by 2020. Migration is likely to increase. Most work the “21 on, 7 off” shift. We know nothing about one of Canada’s

largest migratory trends.

Data

2006 Census of Canada Master File Household file consisting of

married/common-law husbands and wives. Both are age 18-64 and working full-time.

Use individual, household and spousal characteristics to predict excess commuting.

Variables

Marital status Number of Children (linear and quadratic). Age (linear and quadratic) Education Household Income Homeownership status Housing value Province of residence Industry of employment

Dependent Variables: whether husband/partner (equation #1) or wife/partner (equation #2) engages in excess commuting.

Analytical Technique Seemingly unrelated bivariate probit

model Jointly model individual propensity to

‘excess commute’, using household, individual and spousal characteristics as predictors.

bivariate normal disturbance term. Produced a substantially better fitting

model than two independent equations.

Descriptive Results 65% of ‘excess commuters’ are male. Young people more likely to commute

than older people. Childless couples more likely to

commute than those with children. Homeowners more likely to commute.

Multivariate ResultsVariable Men Women

Number of Children No effect -5%

Quebec (vs. NL) -77% -62%

Homeowner No Effect -16%

Income quartile 5% No effect

Mining/Oil/Gas (comp. to Agric.)

100% 94%

# Couples 4989807

Spousal EffectsVariable Men Women

Age of spouse No effect -3%

University Degree -3% (+4%) -3%(+7%)

Mining/Oil/Gas (comp. to Agric.)

-27%(100%) 22.3% (94.4%)

Finance/Insurance -15.6%(1.6%) -13.4% (57%)

# Couples 4989807

Predicted and Conditional Probabilities

Man and Woman Excess Commute 0.1%

Man Commutes, Woman doesn't 1.0%

Woman Commutes, Man Doesn't 0.5%

Neither Commutes 98.4%

Man Commutes, Cond. On Woman 20.1%

Woman Commutes, Cond. On Man 11.0%

Conclusions

Individuals do not make the mobility/migration decision alone.

‘Easier’ and ‘Closer’ are subjective, and need to be defined/operationalized.

Migration/commuting research is currently in a data desert.

Limitations Census data heavily under-report excess

commuting. Census moment (May 16, 2006) is between

tourist and oil-production seasons. These are conservative (and likely biased)

estimates. Commuting information is capped at 200

kilometres. Labour force survey will soon begin to ask

place of residence and place of work.