R: Marriages, Births and Deaths in Grossarl

Grossarl {strucchange} | R Documentation

## Marriages, Births and Deaths in Grossarl

### Description

Data about the number of marriages, illegitimate and legitimate births, and deaths in the Austrian Alpine village Grossarl during the 18th and 19th century.

### Usage

    data("Grossarl")

### Format

`Grossarl` is a data frame containing 6 annual time series (1700 - 1899), 3 factors coding policy interventions and 1 vector with the year (plain numeric).

marriages

  time series. Number of marriages,

illegitimate

  time series. Number of illegitimate births,

legitimate

  time series. Number of legitimate births,

legitimate

  time series. Number of deaths,

fraction

  time series. Fraction of illegitimate births,

lag.marriages

  time series. Number of marriages in the previous year,

politics

  ordered factor coding 4 different political regimes,

morals

  ordered factor coding 5 different moral regulations,

nuptiality

  ordered factor coding 5 different marriage restrictions,

year

  numeric. Year of observation.

### Details

The data frame contains historical demographic data from Grossarl, a village in the Alpine region of Salzburg, Austria, during the 18th and 19th century. During this period, the total population of Grossarl did not vary much on the whole, with the very exception of the period of the protestant emigrations in 1731/32.

Especially during the archbishopric, moral interventions aimed at lowering the proportion of illegitimate baptisms. For details see the references.

### Source

Parish registers provide the basic demographic series of baptisms and burials (which is almost equivalent to births and deaths in the study area) and marriages. For more information see Veichtlbauer et al. (2006).

### References

Veichtlbauer O., Zeileis A., Leisch F. (2006), The Impact Of Policy Interventions on a Pre-Industrial Population System in the Austrian Alps, forthcoming.

Zeileis A., Veichtlbauer O. (2002), Policy Interventions Affecting Illegitimacy in Preindustrial Austria: A Structural Change Analysis, In R. Dutter (ed.), Festschrift 50 Jahre Österreichische Statistische Gesellschaft, 133-146, Österreichische Statistische Gesellschaft.

### Examples

    data("Grossarl")

    ## time series of births, deaths, marriages
    ###########################################

    with(Grossarl, plot(cbind(deaths, illegitimate + legitimate, marriages),
      plot.type = "single", col = grey(c(0.7, 0, 0)), lty = c(1, 1, 3),
      lwd = 1.5, ylab = "annual Grossarl series"))
    legend("topright", c("deaths", "births", "marriages"), col = grey(c(0.7, 0, 0)),
      lty = c(1, 1, 3), bty = "n")

    ## illegitimate births
    ######################
    ## lm + MOSUM
    plot(Grossarl$fraction)
    fm.min <- lm(fraction ~ politics, data = Grossarl)
    fm.ext <- lm(fraction ~ politics + morals + nuptiality + marriages,
      data = Grossarl)
    lines(ts(fitted(fm.min), start = 1700), col = 2)
    lines(ts(fitted(fm.ext), start = 1700), col = 4)
    mos.min <- efp(fraction ~ politics, data = Grossarl, type = "OLS-MOSUM")
    mos.ext <- efp(fraction ~ politics + morals + nuptiality + marriages,
      data = Grossarl, type = "OLS-MOSUM")
    plot(mos.min)
    lines(mos.ext, lty = 2)

    ## dating
    bp <- breakpoints(fraction ~ 1, data = Grossarl, h = 0.1)
    summary(bp)
    ## RSS, BIC, AIC
    plot(bp)
    plot(0:8, AIC(bp), type = "b")

    ## probably use 5 or 6 breakpoints and compare with
    ## coding of the factors as used by us
    ##
    ## politics                   1803      1816 1850
    ## morals      1736 1753 1771 1803
    ## nuptiality                 1803 1810 1816      1883
    ##
    ## m = 5            1753 1785           1821 1856 1878
    ## m = 6       1734 1754 1785           1821 1856 1878
    ##              6    2    5              1    4    3

    ## fitted models
    coef(bp, breaks = 6)
    plot(Grossarl$fraction)
    lines(fitted(bp, breaks = 6), col = 2)
    lines(ts(fitted(fm.ext), start = 1700), col = 4)

    ## marriages
    ############
    ## lm + MOSUM
    plot(Grossarl$marriages)
    fm.min <- lm(marriages ~ politics, data = Grossarl)
    fm.ext <- lm(marriages ~ politics + morals + nuptiality, data = Grossarl)
    lines(ts(fitted(fm.min), start = 1700), col = 2)
    lines(ts(fitted(fm.ext), start = 1700), col = 4)
    mos.min <- efp(marriages ~ politics, data = Grossarl, type = "OLS-MOSUM")
    mos.ext <- efp(marriages ~ politics + morals + nuptiality, data = Grossarl,
      type = "OLS-MOSUM")
    plot(mos.min)
    lines(mos.ext, lty = 2)

    ## dating
    bp <- breakpoints(marriages ~ 1, data = Grossarl, h = 0.1)
    summary(bp)
    ## RSS, BIC, AIC
    plot(bp)
    plot(0:8, AIC(bp), type = "b")

    ## probably use 3 or 4 breakpoints and compare with
    ## coding of the factors as used by us
    ##
    ## politics                   1803      1816 1850
    ## morals      1736 1753 1771 1803
    ## nuptiality                 1803 1810 1816      1883
    ##
    ## m = 3       1738                     1813      1875
    ## m = 4       1738      1794           1814      1875
    ##              2         4              1         3

    ## fitted models
    coef(bp, breaks = 4)
    plot(Grossarl$marriages)
    lines(fitted(bp, breaks = 4), col = 2)
    lines(ts(fitted(fm.ext), start = 1700), col = 4)

[Package strucchange version 1.5-2 Index]