Assignment 1 Finland

EKHM30: Population and Living StandardsAssignment 1

Ahmad IdrusKemal Burçak Kaplan

Mario-Alessander BauwensRicardo Meilman L. Cohn

Finland: 1750 - 1913

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The content

• Introduction• Data• Long Term Analysis• Conclusions for Long Term Analysis • Short Term Analysis• Conclusions for Short Term Analysis• Limitations

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Introduction

• Population grew from 421,500 in 1750 to 3,035,800 in 1913 (and 5,375,276 in 2010)

• Average annual rate of increase of 1.2%

• Relation living standards and demographic variables in the long and short term

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Data

• Data span: 1750 - 1913• Source: Statistics Finland and Reykjavik (1987)– Live births, deaths, population (31.12),

marriages(women), infant mortality, real wage index

• CBR, CDR, CMR• Growth rate, moving average, log

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1750175617621768177417801786179217981804181018161822182818341840184618521858186418701876188218881894190012

12.5

13

13.5

14

14.5

15

-

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

Log Population Real wage

Figure 1: Population and 25 year centered moving average real wage index

1st. Period 3rd. Period

Long Term AnalysisPopulation and Real Wage

War

War of Finland

Cholera

Great Famine

Real

Wag

e in

dex

2nd. Period

Log

Popu

latio

n

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Long Term AnalysisNuptiality and Fertility

Figure 2: 11 year centered moving average for both CMR and CBR

1755 1765 1775 1785 1795 1805 1815 1825 1835 1845 1855 1865 1875 1885 1895 1905 1915 1925 1935 1945 1955 1965 1975 1985 1995 2005 3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

-

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

CMR (MA-11) CBR (MA-11)

CMR

(per

thou

sand

)

CBR

(per

thou

sand

)

7Figure 3: 11 year centered moving average both for CDR and IMR

1756 1766 1776 1786 1796 1806 1816 1826 1836 1846 1856 1866 1876 1886 1896 1906 1916 1926 1936 1946 1956 1966 1976 1986 1996 -

5.0

10.0

15.0

20.0

25.0

30.0

35.0

-

50.0

100.0

150.0

200.0

250.0

300.0

CDR (MA-11) IMR (MA-11)

Long Term AnalysisMortality and Infant Mortality

CDR

(per

thou

sand

)

IMR

(per

thou

sand

)

8Figure 4: 11 year centered moving average both for CDR and real wage index

17551761176717731779178517911797180318091815182118271833183918451851185718631869187518811887189318991905 15.0

17.0

19.0

21.0

23.0

25.0

27.0

29.0

31.0

33.0

35.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

cdr Real Wage

Long Term AnalysisMortality and Real Wage

Real

Wag

e in

dex

CDR

(per

thou

sand

)

WarWar of Finland

Cholera

Great Famine

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Figure 5: 5 year centered moving average CBR and 11 year centered moving average real wage index

175217581764177017761782178817941800180618121818182418301836184218481854186018661872187818841890189619021908 30.0

32.0

34.0

36.0

38.0

40.0

42.0

44.0

46.0

48.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

CBR Real Wage

Long Term AnalysisFertility and Real Wage

Real

Wag

e in

dex

CBR

(per

thou

sand

)

10Figure 6: 11 year centered moving average both for CBR and CDR

1755176417731782179118001809181818271836184518541863187218811890189919081917192619351944195319621971198019891998 5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

CBR (MA-11) CDR (MA-11)

Long Term AnalysisFertility and Mortality

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Figure 7: 25 year centered moving average CMR and 11 year centered moving average real wage index

1762 1768 1774 1780 1786 1792 1798 1804 1810 1816 1822 1828 1834 1840 1846 1852 1858 1864 1870 1876 1882 1888 1894 1900 1906 6.0

6.5

7.0

7.5

8.0

8.5

9.0

25.0

35.0

45.0

55.0

65.0

75.0

85.0

95.0

CMR (MA-25) Real Wage (MA-11)

Real

Wag

e in

dex

CMR

(per

thou

sand

)

Long Term AnalysisNuptiality and Real Wage

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• The period from 1750 to 1913 can be divided in 3 sub-periods with specific dynamics.

• Fertility and mortality and their connection to economic factors present a complex picture.

• The period can be decomposed in two demographic transition.

• Only the first period hints to Malthusian mechanisms, but …

• Historical events played an important role.• In the first period, although there is negative correlation

between mortality and real wages in the first period, we can not establish a clear causal relationship.

Long Term AnalysisConclusions

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Short-term analysis Introduction

• Econometrical analysis of main demographic variables– Autoregressive model with 4 lags– Similar to the analyses of Lee (1981)

• In the first part the whole period(1754-1911) is analyzed.

• In the next part, we divide the observations into 3 sub-periods– 1754-1810: War years– 1811-1868: Cholera years and great famine– 1867-1911: Modern economic growth era

Short-term analysis 1754-1911

•Lee (1981) uses prices as the dependent variable and his significant variables are L0 and L1, both<0.

•As prices and wages are negatively related, we expect positive coefficients; L1 is significant and >0.

•Higher real wages encourage marriage.


•Controlled for wages

•Signs of significant coefficients are same with England

•Yet coefficients are much lower: aggregate 5 year effect is 0.05 (<0.30 england, 0.25<theory


•Higher wages(lower prices) also encourage fertility. Our only significant coefficient is positive.

•Our regression tells that the positive effect comes with 1 year lag: 9-months childbearing effect.

•Lee (1981) found negative and significant coefficients up to 3 lag years.


•During and after mortality crisis, reduction in baptism, empirics tell.

•Several reasons are possible.

•But when controlled for wage levels, only we see contemporaneous effect is negative and significant.

•Lee (1981)’s significant coefficients are L0<0,L1<0, L2>0, L3<0


•Lee (1981) found weak relationship between the two. Similar to my results negative relationship in early lags and positive relationship in the 4th lag.

•4th year positive relationship is interpreted as cumulative effect of survivors of early years.

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Short-term analysis Sub periods

• Relationship between wages on marriages are not clear as coefficient between periods are not unanimous– Coefficient alternate between

positive and negative

• But general pattern can be seen between periods. There is stronger relationship in period of 1868-1911 compared to earlier.

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• Wages positively affect fertility after one year lag.– Partly explained by positive relationship

between wages and marriage shown earlier

• Shape of coefficent remains similar but magnitude slightly stronger for the later period

• Lag 0 is not significant as it takes as least 9 months for the fertility data to come up in statistics


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• Similar pattern across periods. Sharp decline in fertility for current but rebound for the next period, and relatively flat onwards.

• Period of 1869-1911 did not rebound but stay negative:– Could be due to general decline in

fertility?– Turpeinen (1979): lower fertility

observed in Finland post 1850 due to increased literacy, medical progress etc.


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• Clear contrast between sub-periods

• Pattern remains similar across periods but effects weakened on later periods– Strong relationship in the earlier

periods due constant occurrence of crisis such as war and disease. People may strongly postpone marriage in crisis periods compared to non-crisis periods.

– Fewer crises on the modern period thus causing weakened relationship.


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• Mortality affected by wage on 0 and 1-year lag.

• Stronger effect of wages on mortality in the later period

• Contrast the result obtained by Lee for England– Mortality should have less effect in the later

periods– Lee used wheat price instead of wages.– Wheat price becomes less relevant as

compared to income from industrial sources as time progresses

• Turpeinen (1979): relationship between crop failures and mortality is not clear.– Finland’s mortality does not always increase

following crop failure & vice versa


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Short-term analysis Conclusions

• There is no single factor explanation.

• Whole period analysis: findings for Finland have parallel aspects to Lee(1981)’s findings for England.

• Sub period analysis: clear differences across periods, impact either gets stronger or weaker.

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Limitations• Endogeneity problems, reverse causations• Yearly data dampens the effect compared to monthly

data.• Our long term analysis, based on Wrigley & Schofield

(1980), is limited to descriptive statistics and does not provide any econometrical evidence or suggests causal relationships.

• Crude rates used are aggregates and therefore limited. A detailed breakdown by age would help to give a more precise explanation since it can reveal the sources of change.

Thank you for your attention.

Assignment 1 Finland

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