Archive for the ‘Sociology’ Category

1.) All data are from the American Time Use Survey.

2.) All data for Table 1, Chart 1, Chart 2 reflect the amount of hours per week that the average high school student puts in to various activities.  Chart 1 and Chart 2 are visualizations of Table 1.

3.) Data for Table 2 and Chart 3 reflect the average number of hours that mothers spend with their children on educational activities and all other activities.

4.) These averages control for differences across groups in the number and age of children, education of the mother and marital status.

5.) . This U.S. government survey measures the time use of thousands of individuals from 2003 to 2009 based on time diaries, which are considered the most accurate way to measure time use. It includes data on individuals ages 15 and older.

A few findings:

  • Not all Asian mom’s are hardcore tiger mom’s.
  • White students spend time doing a diverse array of activities.
  • Not many differences in terms of mother’s spending time with their kids with regards to educational activities. Though 0.6 hours in the large scheme of things may mean a lot.
  • More or less, it’s fairly concrete, you can infer your own conclusions.

Table 1

Chart 1Chart 2

Table 2

Chart 3


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There was a discussion about the average age of marriage for males and for females on a forum that I frequent and decided to see how they differed in terms of the age gap between the two genders.

The long-term trend is towards convergence but stabilizing at the male being 1.5 years older.

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I noticed I had a plethora of data regarding fertility and female illiteracy. These two variables are highly correlated with one another. In fact female literacy correlates more strongly than general literacy. The issue here is that men’s literacy starts to go up at an earlier date than female literacy.

As you poke around the data, the interesting thing you notice that the numbers go all over the map for female literacy, the Muslims/Hindus don’t consistently top one another. But outside of Madhya Pradesh, Muslims have consistently higher TFR’s than the Hindus do.

Sources: International Institute for Population Sciences, National Family Health Survey 1998-1999, ORC, Macro, WorldBank Dataset

The table appears small, here is a link to the table.

These next two tables show rank differences by state.

I subtracted (Muslim – Hindu) for both variables.

The higher the number, the greater the disparity is in the Hindu’s favor for the first chart, it is measuring illiteracy. A lower number indicates a Muslim edge.

This is literally measuring how many more kids Muslims have than Hindus. Ranging from as high as 3.2 to zero.

Islam tends to have a pro-natalist effect, even in that states where Muslim women have lower illteracy rates, they have higher fertility rates than Hindu women.

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The data is from the GSS on the distribution of children by sex. As the numbers imply, the median female is more reproductively successful than the median male and this trend was likely the norm throughout human history.

Some of it has to do with biology, i.e. one man can impregnate multiple women (at the fastest pace probably every two hours), while once a woman is pregnant she has to wait 9 months. There is a near infinite supply of sperm compared to egg. Other times it has to do with conditions relating to the rise of plural marriage in certain groups. In 2003 there was a groundbreaking paper in population genetics that showed the spread of Genghis Khan’s Y-Chromosome and that approximately ~0.5% of all males were his direct descendant.

If the male population wasn’t being diluted somehow (usually through battle and other conflict) than they were excommunicated (and becoming “Lost Boys”) as we see with the FLDS groups based out of certain counties in Utah and Arizona. Otherwise the social equilibrium would be unsustainable.

The chart below shows the CDF, which is cumulatively summing up each preceding value. The females rise at a slower rate.

The weighted average, counting 8+ as just 8, which is a little imprecise but low enough as to not skew the average too dramatically. We get an expected value of 1.827 for males and 2.087 for females, a difference of 0.26.

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