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The demography of agricultural supply and demand Natalie Jackson © Professor of Demography...
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The demography of agricultural supply and demand Natalie Jackson © Professor of Demography Director, Population Studies Centre >> National Institute of Demographic and Economic Analysis 2010 1
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Transcript of The demography of agricultural supply and demand Natalie Jackson © Professor of Demography...
The demography of agricultural supply and demand
Natalie Jackson ©
Professor of DemographyDirector, Population Studies Centre >>National Institute of Demographic and Economic Analysis
20101
Key points• Older average age of farmers well
known– but seemingly invisible in debate over who
may/ can / should buy NZ farms, farm prices etc
• Industry’s market focus is on size and growth rates of national populations– Yet markets are driven less by size than
composition (market ‘segment’ - age, sex, culture, income, labour force/marital status etc)
• Population ageing will significantly affect both
2
Population Ageing
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-84
85+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
NZ 1976
percentage at each age
Male Female
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-84
85+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
NZ 2010
percentage at each age
Male Female
Internal momentum of decline
Internal momentum of increase
3
What has the ageing of farmers got to do with
foreign ownership?Who may (can/should) buy NZ
farms?
Stats NZ Customised Database
Grain, Sheep and Beef Farmers
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65+
4,0003,0002,0001,000 0 1,0002,000
Self-Employed and Employers
Self-Employed, Without Em-ployees
Employer
Number at each age
age
Male Female
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65+
4,000 3,000 2,000 1,000 0 1,000 2,000
Total
Self-Em-ployed, Without Employees
Employer
Paid Em-ployee
Unpaid Family Worker
Not Else-where
Number at each age
age
Male
Female
N=38,634; av 48 years 5
Stats NZ Customised Database
Dairy Farmers (2006)
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65+
2,500 1,500 500 500 1,500 2,500
Self-Employed and Employers
Self-Em-ployed, Without Em-ployees
Employer
Number at each age
Age
MaleFemale
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65+
2,500 1,500 500 500 1,500 2,500
Total
Self-Em-ployed, Without Em-ployees
Employer
Paid Em-ployee
Unpaid Family Worker
Not Else-where
Number at each age
Age
Male Female
N=33,507; av 41 years 6
Stats NZ Customised Database
Other Livestock Farmers (2006)
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65+
1,000
750500
250 0250
500750
1,000
Self-Employed and Employers
Self-Employed, Without Em-ployees
Employer
Number at each age
Age
Male Female
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65+
1,000 750 500 250 0 250 500 750 1,000
Total
Self-Em-ployed, Without Employees
Employer
Paid Em-ployee
Unpaid Family Worker
Not Else-where
Number at each age
Male
Female
N=9,303; av 47.5 years 7
Who you ‘gonna call?
8Stats NZ Estimated Resident Population 2009, Waikato and NZ (unshaded)
Males Females
Demographic Dividend
Where are you farming?
Matamata-Piako (16.5% 65+)
0-4
10-14
20-24
30-34
40-44
50-54
60-64
70-74
80-84
6 4 2 0 2 4 6
Males Females
Waipa (15.0% 65+)
0-4
10-14
20-24
30-34
40-44
50-54
60-64
70-74
80-84
6 4 2 0 2 4 6
MalesMales Females
Percentage at each age 2009 (2006 unshaded) 9
NZ: TA’s with negative entry/exit ratios
2006 2007 2008 200926
27
28
29
30
Percen
tage
10
39%
36%
Enter: a demographically tight labour market
Australia 2007-200911
Succession planning is critically needed
Who ‘may’ buy NZ farms? Who ‘can’ ?
Who [maybe] ‘should’ ?
12
What has population ageing got to do with farming
markets?Who consumes what?
13
Who eats ice-cream? Who eats beef?
Let’s imagine..
• That kids eat most of the ice-creams *
• That adults eat most of the beef ** 14
China’s age structure 2000-2010
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
2000
percentage at each age
Male Fe-male
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
2010
percentage at each age
Male Female
Source: US Census Bureau International Database http://www.census.gov/ipc/www/idb/ 15
China’s age structure 2020-2030
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
2020
percentage at each age
Male Fe-male
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
2030
percentage at each age
Male Female
Source: US Census Bureau International Database http://www.census.gov/ipc/www/idb/ 16
Projected Population of China2000
2003
2006
2009
2012
2015
2018
2021
2024
2027
2030
2033
2036
2039
2042
2045
2048
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Billion
17Source: US Census Bureau International Database http://www.census.gov/ipc/www/idb/
Assumption: 2 ltr per capita per year
Ice-cream consumption in Chinaprojected by crude and age-weighted rates
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2042
2044
2046
2048
2050
0.0
0.5
1.0
1.5
2.0
2.5
3.0
crude rate
age-weighted
litr
es (
billion
)
Assumption: 2 kg per capita per year
Beef consumption in Chinaprojected by crude and age-weighted rates
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2042
2044
2046
2048
2050
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
crude rate
age-weighted
kg
(b
illion
)
India (2000 and 2010)
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
2000
percentage at each age
Male Fe-male
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
2010
percentage at each age
Male Female
Source: US Census Bureau International Database http://www.census.gov/ipc/www/idb/
India (2020 and 2030)
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
2020
percentage at each age
Male Fe-male
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
2030
percentage at each age
Male Female
India will overtake China
2000
2004
2008
2012
2016
2020
2024
2028
2032
2036
2040
2044
2048
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
India
Billion
s
22Source: US Census Bureau International Database http://www.census.gov/ipc/www/idb/
Assumption: 2 ltr per capita per year
Ice-cream consumption in Indiaprojected by crude and age-weighted rates
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.52000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
litre
s (b
illio
n)
crude rate
age-weighted
23
Assumption: 2 kg per capita per year
Beef consumption in Indiaprojected by crude and age-weighted rates
0.00.51.01.52.02.53.03.54.04.52000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
kg (
bill
ion)
crude rate
age-weighted
24
Japan….
2010
6.0 4.0 2.0 0.0 2.0 4.0 6.0
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
percentage at each age
Male Female
2020
6.0 4.0 2.0 0.0 2.0 4.0 6.0
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
percentage at each age
Male Female
25Source: US Census Bureau International Database http://www.census.gov/ipc/www/idb/
Russia…*
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
2010
percentage at each age
Male Fe-male
0-4 5-9
10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+
6.0 4.0 2.0 0.0 2.0 4.0 6.0
2020
percentage at each age
Male Female
26Source: *as defined by US Census Bureau International Database http://www.census.gov/ipc/www/idb/
Projected Population of Russia*2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2042
2044
2046
2048
2050
0
20
40
60
80
100
120
140
160
Million
27Source: *as defined by US Census Bureau International Database http://www.census.gov/ipc/www/idb/
Summary
• Pay attention to age structure. Population ageing changes the consumption patterns of the population; slows, then stops growth– In the short to medium term, significant growth at
middle to older ages assured– Need to follow the target / tailor the product
• Population ageing means competition for labour and industry participation of the young– Labour supply will be short – and cost more– The lack of sufficient numbers of young farmers in
the pipeline needs to be considered when deliberating who may/can/should buy NZ farms..
28
• Population Studies Centre /
• National Institute of Demographic and Economic Analysis (NIDEA)
Thankyou
29
Why do populations age and stop growing?
• Increasing life expectancy causes more babies to live ~ eventually they turn into more elderly
• Declining fertility rates cause the base of the population pyramid to compress >> the top to expand
• Eventually the elderly outnumber the young, growth slows and stops because deaths first match, then exceed, births
30
Assumptions (Ice-Cream) (Beef)Ice-Cream (%) Beef (%)
0-4 5 1
5-9 10 2
10-14 20 4
15-19 20 8
20-24 15 10
25-29 10 10
30-34 5 10
35-39 5 10
40-44 5 10
45-49 2 10
50-54 1 10
55-59 1 10
60-64 1 1
65-69 1
70+ 1
75-79 1
80+ 1
TOTAL 100 100
