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Russel-Research-Method-in-Anthropology

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658 Chapter 21<br />

FOR RURAL<br />

INFORMANTS<br />

EDUCATION<br />

FOR URBAN<br />

INFORMANTS<br />

EDUCATION<br />

WEALTH ACCUMULATION<br />

LOWER FAMILY SIZE<br />

LOWER FAMILY SIZE<br />

WEALTH ACCUMULATION<br />

Figure 21.1. Model of how wealth, education, and family size <strong>in</strong>teract <strong>in</strong> urban and<br />

rural environments for <strong>in</strong>formants <strong>in</strong> tables 21.11 and 21.13.<br />

Most people <strong>in</strong> our sample are poor. Sixty-six percent of rural <strong>in</strong>formants<br />

(166/250) and 64% of urban <strong>in</strong>formants (159/250) are below the poverty l<strong>in</strong>e<br />

by our measurements. Among rural <strong>in</strong>formants, education provides an edge <strong>in</strong><br />

the struggle aga<strong>in</strong>st poverty, irrespective of family size, but for urban<br />

migrants, education only provides an edge <strong>in</strong> the context of lowered family<br />

size.<br />

Among those who rema<strong>in</strong> <strong>in</strong> the villages, then, education may lead either<br />

to accumulation of wealth through better job opportunities, or it may have no<br />

effect. The chances are better, though, that it leads to wealth. Once this occurs,<br />

it leads to control of fertility. Among urban <strong>in</strong>formants, education leads either<br />

to control of natality or not. If not, then education has practically no effect on<br />

the economic status of poor migrants. If it does lead to lowered natality, then<br />

it may lead, over time, to a favorable change <strong>in</strong> economic status.<br />

We can check this model by go<strong>in</strong>g back to our data on wealth status by<br />

number of years <strong>in</strong> the city to see if those migrants who are economically<br />

successful over time have both <strong>in</strong>creased their education and lowered their<br />

natality. Plausible assumptions about time order<strong>in</strong>g of variables are crucial <strong>in</strong><br />

build<strong>in</strong>g causal models. Know<strong>in</strong>g, for example, that wealthy villagers never<br />

move to the city rules out some alternative explanations for the data presented<br />

here.<br />

You get the picture. The elaboration method can produce subtle results, but<br />

it is quite straightforward to use and depends only on your imag<strong>in</strong>ation, on<br />

simple arithmetic (percentages), and on basic bivariate statistics.<br />

Partial Correlation<br />

Of course, I haven’t proven anyth<strong>in</strong>g by all this lay<strong>in</strong>g out of tables. Don’t<br />

misunderstand me. Elaboration tables are a great start—they test whether your

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