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READING A COMPLEX TABLE

Reading across table 22.11, we see that among urban families with at least an eighth-grade education and three or fewer children, 43 out of 59, or 73%, are above the poverty line. Among urban families with at least an eighth-grade education and more than three children, only 20% (20/100) are above the poverty line. Among rural families with at least an

1

Rural

Urban

>3 children

<3 children

Row

totals

>3 children

<3 children

Row

totals

>8th

grade

<8th

grade

>8th

grade

< 8th grade

>8th

grade

< 8th grade

>8th

grade

< 8th grade

Not poor

34

20

16

14

84

20

10

43

18

91

Poor

36

77

14

39

166

80

22

16

41

159

Column totals

70

97

30

53

250

100

32

59

59

250

Rural

Urban

>8th

grade

<8th

grade

Row

totals

>8th

grade

<8th

grade

Row

totals

>3 children

70

97

167

100

32

132

<3 children

30

53

83

59

59

118

Column totals

100

150

250

159

91

250

X2 = .77 ns (not significant)

X2 = 17.86, p <.001, OR = 3.13

than three children, according to these data. This throws new light on the subject, and begs to be explained. We know that higher education without small families does not produce an increase in economic status for these poor migrants. We know, too, that most people, whether urban or rural, keep having large families, although large families are less prevalent among urbanites than among rural residents (132 out of 250 vs. 167 out of 250).

To understand this case still further, consider table 22.13, which cross-tabulates family size by wealth, controlling for both education and residence. This table shows that neither

Table 22.13 Family Size by Wealth, Controlling for Education and Residence

Rural

Urban

>8th grade

<8th grade

Row

totals

>8th grade

<8th grade

Row

totals

Poor

Not

poor

Poor

Not

poor

Poor

Not

poor

Poor

Not

poor

>3 children

34

36

20

77

167

20

80

10

22

132

<3 children

16

14

14

39

83

43

16

18

41

118

Column totals

50

50

34

116

250

63

96

28

63

250

wealth nor education influences family size among rural informants. For urban residents, however, the story is quite different. As expected, those urban informants who have both increased their education and increased their wealth have small families.

Go through table 22.13 carefully and make the appropriate comparisons across the rows and between the two halves. Compare also the results of this table with those of table 22.11, in which wealth status was the dependent variable.

From these tables, we can now hazard a guess about how these variables interact. We can draw a conceptual model of the whole process we’ve been looking at. It’s in figure 22.1.

Most people in our sample are poor. Sixty-six percent of rural informants (166/250) and 64% of urban informants (159/250) are below the poverty line by our measurements. Among rural informants, education provides an edge in the struggle against poverty, irrespective of family size, but for urban migrants, education only provides an edge in the context of lowered family size.

Among those who remain in the villages, then, education may lead either to accumulation of wealth through better job opportunities, or it may have no effect. The chances are better, though, that it leads to wealth. Once this occurs, it leads to control of fertility. Among urban informants, education leads either to control of natality or not. If not, then education has practically no effect on the economic status of poor migrants. If it leads to lowered natality, then it may lead, over time, to a favorable change in economic status.

We can check this model by going back to our data on wealth status by number of

FIGURE 22.1.

Model of how wealth, education, and family size interact in urban and rural environments for informants in tables 21.11 and 21.13.

years in the city to see if those migrants who are economically successful over time have both increased their education and lowered their natality. Plausible assumptions about time ordering of variables are crucial in building causal models. Knowing, for example, that wealthy villagers never move to the city rules out some alternative explanations for the data presented here.

You get the picture. The elaboration method can produce subtle results, but it is quite straightforward to use and depends only on your imagination, on simple arithmetic (percentages), and on basic bivariate statistics.

 
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