Discussion of One Child Policy on RTHK Radio 3

Earlier this week, I participated in a panel discussion on the future of the One Child Policy on the show Backchat on RTHK Radio 3.  RTHK is public radio here in HK, and Radio 3 is there English language service.

The panelists were Stuart Basten at Oxford, Kerry Brown at the University of Sydney, Shaun Rein at China Marketing Research, and of course yours truly.  I thought the discussion was very high quality, and covered a lot of ground.

The show is available online, broken into two thirty minute halves: http://programme.rthk.hk/channel/radio/programme.php?name=radio3/backchat&d=2013-12-03&p=514&e=242059&m=episode The first half starts about 30 minutes into the first (8:30-9:15) link, and finishes with the second link (9:15-9:30).

This was my first time in a radio studio. I was struck by how quickly the hosts could shift from their regular voice during conversation in breaks, to their ‘radio voice’ once the light came on.  And of course it is always amazing to me that anyone can make it through so many spoken sentences without an awkward pause, an “Uhhhh”, “Well…” or some utterance.

The future of marriage in China

Reading Leta Hong Fincher’s CNN piece on changes in women’s attitudes about marriage in China reminded me of a prediction that I have been making for the past two or three years to anyone who will listen:

Within a decade, marriage patterns in mainland China will resemble those everywhere else in East Asia, with high proportions of women marrying late or not at all. Similarly, high proportions of men, especially poorly educated ones with poor economic prospects, will be unable to marry. This is already happening in Beijing, Shanghai, and other prosperous cities. Based on what happened in Taiwan, South Korea, and Japan after 1990 or so, I am guessing the changes, when they occur, will be sudden and dramatic. These changes will be much larger and more important than any of the ones associated with imbalanced sex ratios at birth, and would occur even if the sex ratio at birth were normal.  More speculatively, I expect that mainland China will continue to resemble other East Asian societies in terms of having very low rates of non-marital childbearing. As proportions married collapse, the fertility rate will fall even further.

When I look at what is happening in mainland China right now, and what has happened elsewhere in East Asia, this all seems obvious.  All of the factors that seemed to be associated with rapid marriage change elsewhere in East Asia seem to be present in mainland China right now: dramatic and rapid economic and social change, rising levels of female education, changing patterns of inter-generational relations,  and changing expectations about career and marriage on the part of both young men and women.

One piece of indirect evidence suggests that there is pent-up demand or at least curiosity about the possibilities associated with delaying marriage, at least for women: at least according to Joy Chen’s website, the Chinese version of her straightforwardly titled book Do Not Marry Before Age 30 seems to be selling well.   I haven’t read the book and probably never will since I am not part of the target audience, but it is refreshing to see someone writing a book that is the exact opposite of the usual nonsense offering women advice on how to bag a man, on how to avoid spinsterhood, and so forth.

Nevertheless, many observers, Chinese and foreign, seem wedded in some vague way to a notion that ‘tradition’ will somehow prevent the same changes taking place in China that took place elsewhere in East Asia.  ‘Tradition’ and ‘cultural values’ did not serve as a bulwark against marriage change elsewhere in East Asia in the last two decades, so I don’t understand why they would prevent change in China now.  Indeed they have not done much to prevent changes in marriage patterns among young adults in China’s largest and most developed cities, notably Beijing and Shanghai, where the average age at marriage is already high, and the proportions of people marrying are falling.  ‘Tradition’ and ‘culture’ may help us understand why specific phenomenon persist to the present, but they have a terrible track record of predictors of future behavior.  Sometimes this assumption of continuity is explicit, but in many cases it is implicit, for example, in the assumptions about marriage preferences that demographers simulating the effects of sex ratio imbalances build into their projection models.

The best example of how useless tradition is as a predictor of future trends is probably the recent rise in divorce rates in China.  Rates of divorce in China used to be very low.  Most people, including myself, assumed that they would remain low, because of ‘culture’ or ‘tradition’ that encouraged unhappy couples to remain married.  Yet when China changed divorce laws around a decade ago to make it easier to divorce, rates skyrocketed.  Low divorce rates apparently had more to do with institutional and legal barriers than with any ‘culture’ or ‘tradition’ that discouraged divorce.  Rapid increases in divorce rates elsewhere in East Asia over the last two decades were similarly unexpected.

Somewhat perplexing for me is the continuing concern on the part of pundits and academics about a topic that for me is a not much more than a side issue: the potential effects on marriage of imbalanced sex ratios at birth.  This is not to dismiss concern about imbalanced sex ratios at birth.  There are many important reasons to be concerned about imbalanced sex ratios at birth, not the least of which is what they reflect about gender attitudes.  However, I think the effects of imbalanced on sex ratios on marriage patterns will turn out to be fairly small because the affected cohorts will be coming of age at a time when much more dramatic shifts in marriage patterns are occurring.  No matter what the sex ratio of births is or was, the numbers of men and women not marrying is probably going to increase dramatically.  While some of the men who do not marry might be unmarried because of the imbalanced sex ratio, many more will be unmarried because none of the single women are willing to marry them, or they themselves choose not to marry.

As to the implications of what I think will be a very rapid shift in marriage patterns in mainland China, I can only speculate.  It certainly won’t be a disaster.  Other places in East Asia seem to have experienced these rapid shifts in the last decade or two without collapsing.  I would guess that twenty-somethings in China will spend more and more of their time working, spending time with friends, and pursuing individual interests, and less and less time meeting and assessing potential spouses.  And I suspect that as elsewhere in East Asia, members of senior generations will finally realize the world has changed, and stop pressuring their adult children, nephews, and nieces to find a spouse and have children.  As I noted earlier, in light of the very low levels of nonmarital childbearing in China, the most important effect of delayed or foregone marriage there may be further reductions in the birth rate.

I would certainly like to see commentators, journalists, pundits, academics, and policymakers acknowledge the possibility that marriage may change rapidly.  At the very minimum, demographers should allow for a wider range of possibilities for marriage preferences when they run projections to examine possible impacts of imbalanced sex ratios.  If we’re lucky, the degrading and artificial term ‘sheng nv’ will be banished from the language, and will no longer be used either by domestic commentators, or foreign journalists who uncritically accept the term as an organic one and reuse it, even though it was actually coined and put into widespread use as part of a systematic effort to belittle unmarried women.  Best of all would be accommodation on the part of the government, commentators and senior generations to the changing reality, and abandonment of efforts to pressure young people, especially women, into marrying by a certain age.

Academics and policymakers need to engage in a thoughtful and open-minded assessment of why marriage is changing that goes beyond repeating tired and sometimes offensive platitudes, especially ones about young women having expectations that are too high, or young people in general being too selfish, irresponsible, and consumption-oriented.  The former is especially unappealing because implicitly, it argues that women should be the ones who make sacrifices in order to marry, not men.

Serious consideration needs to be given to the fact that marriage may be unappealing to women because labor markets and household gender roles combine to make the prospect of being a working mother especially unappealing.  In many China, as in many societies, women are responsible for many domestic duties including child care and elder care, even if they are also working.  The financial burden associated with buying a home and paying for a child’s education, meanwhile, make staying at home unrealistic as an option.  Given a choice between remaining a single and working, or being married and working and doing most of the domestic work, remaining single seems an eminently sensible option.

 

SJTU 2013 Social Demography Final Project

Social Demography
SJTU Summer Short Semester
2013

Due 7/25 at the beginning of class

You are to write an original research paper that uses the IPUMS website to carry out a comparative study of time trends and age patterns of the demographic and socioeconomic characteristics by education, income, ethnicity, race, region, sex, or some other variables.  The emphasis is on comparison.  If you are interested in a particular ethnicity, for example, you still need to compare it to other ethnicities or the population as a whole to establish what is distinct about it.

Please read the following directions carefully.  Since you have nearly two months to complete the project, there is no excuse for not complying with the instructions.

Your research paper should be 2000 words of text (roughly 4 single-spaced pages or 8 double-spaced pages) and 6 tables based on computations at the IPUMS site.   The paper should be organized as the text, followed by the references, followed by the tables, with each table on a separate page.  All tables should be publication quality according to the specifications below, not simply copied and pasted from the website.  Do not insert tables into the main text.  Please number all pages, and make sure that your name is on the first page.

The text should consist of four sections: Introduction, Background, Results, and Conclusion.  Below I suggest guidelines for the lengths of each of these sections.  These guidelines are not rigid, and depending on your topic and your findings the actual word count may differ.  You may end up with more or fewer words in each section than

The Introduction should explain the overall focus of the paper and specify the questions that you are interested in.   250 words should be adequate.

The Background section that provides whatever information from other published sources you think may be necessary to help a reader understand the object of your study.  For example, if your tables focus on comparison of different ethnic groups, you might provide a brief history of each group’s history in the United States that focuses on features relevant to the analysis.  If you are comparing several major cities, you might want to mention key features of each relevant to your analyses.  500 words should be sufficient.

A Results section that discusses the tables one by one, and interprets their contents in light of hypotheses or theories in the introduction.  The tables should be numbered consecutively, and referred to in the text as Table 1, Table 2 etc.

The Conclusion reviews the most interesting results in the paper and suggests further work.  250 words should be sufficient.

Tables

Each of the tables should examine relationships among a distinct set of variables.  In other words, the tables should not be repetitions of the same basic tabulation but with different filters.  At least two tables should make use of demographic or other variables unique to the American Community Survey (ACS) data, which are annual starting in 2001.  At least two tables should make use of variables from the Decennial Census data.

You may also use the Current Population Survey (CPS) data at the IPUMS site.  It tends to have much richer detail on labor force and employment characteristics.  It may also be harder to use.

For some of your tables, you may also use General Social Survey (GSS) data, which is available at a different website (http://sda.berkeley.edu/cgi-bin/hsda?harcsda+gss10).  It can be analyzed via a web interface like the one that you are already familiar with at IPUMS.  The GSS includes questions on topics like religion, political views, and so forth that are not covered in the Census.  Keep in mind that if you want to use the GSS, the tables you create should have something to do with demographic behavior, broadly defined.

Each table should also have a self-explanatory title, and the row and column headings should be sufficient to allow a reader to interpret the table without referring to your text.  Each table should include a totals column and/or totals row as appropriate.  Please format the tables so that there are no vertical lines, and only four horizontal lines: one between the title and the column headings, one between the column headings and the table contents, one between the table contents and the totals row, and one at the bottom.  Basically the table should be formatted like the ones you see in the papers in the assigned reading.  You will notice that in publications, tables almost never have vertical lines, and generally have a limited number of horizontal lines.

Either the title of the table or a note at the bottom of the table should specify any restrictions that were applied in selecting observations to be included in the calculation.  Typically this means specifying the ages that were included in the calculation, the the years.

The tables should not be copied and pasted directly from the site, but rather should be prepared to look like they were publication quality, following the guidelines above.

The tables may be frequencies or cross-tabulations like the ones you are already used to.  You are also encouraged to take advantage of some of the other tools available at the site.  You are most likely to find the comparison of means tool (https://sda.usa.ipums.org/helpfiles/helpan.htm#means) the most useful.   This allows you to calculate the mean of one variable for different combinations of other variables.  For example, you could calculate mean income (INCTOT) for different combinations of RACE and YEAR.  If you are more adventurous, you may try using the correlation or regression tools, but these can take a long time.

Filter variables to restrict the observations included in the analysis

In constructing your tables, make sure to select or filter observations correctly to make sure the ones you include are relevant.  You can restrict the valid range of a variable used in the analysis to achieve the same effect as a filter: https://sda.usa.ipums.org/helpfiles/helpan.htm#range

Depending on the analysis that you are doing, you may want to use a filter to restrict to people of particular ages, or people with particular characteristics.  For example, when looking at completed education, EDUC, you will almost always want to restrict to people aged 25 or over, so you will only be looking at people who have completed their education.  Similarly, most of the income and occupation variables are only relevant for people of working ages, 18-55.  For details on using the selection filter at IPUMS, please see https://sda.usa.ipums.org/helpfiles/helpan.htm#filter

Recode continuous variables like income, age etc. into a manageable number of categories

When constructing tables that are tabulations, you will also want to use recode for any variable that is continuous (a quantity), not discrete (a category).  Examples include age, year of birth, and almost any of the income variables.  If you are working with age, instead of having a separate row or column for each single year of age (1,2,3, etc.) you will want to have a limited number of age groups: 1-9, 10-19 and so on.  Similarly, If you want to use total income (INCTOT), income from wages (INCWAGE), or other variables that record an amount in dollars, not a category, you will definitely need to recode the original values into into categories.

If you attempt to carry out a tabulation in which one of the income variables is a row, column, or control variable, and don’t record, the tabulation will almost certainly fail, with an error message indicating that there are too many rows or columns.  The definition of your income categories will depend on the year that you are looking at.  Because of inflation, typical incomes change dramatically over time.  See  https://sda.usa.ipums.org/helpfiles/helpan.htm#recode on how to carry out a recode.

Exclude observations with missing or not available (N/A) values

You will also need to exclude missing or not available (N/A) values, especially if you are computing a mean.  In the IPUMS data, when information is missing for a variable in a particular observation, that is typically represented with a numeric value that will be included in any mean that you compute, unless you exclude it.  This is especially important for income variables.  In total income (INCTOT), missing data is represented by 9999999: https://usa.ipums.org/usa-action/variables/INCTOT/#codes_section.  For wage income (INCWAGE), missing is represented as 999999: https://usa.ipums.org/usa-action/variables/INCWAGE/#codes_section.  For the socioeconomic index (SEI), N/A is represented as 0: https://usa.ipums.org/usa-action/variables/SEI/#codes_section And so on.  If you fail to exclude the numeric codes for missing values from the calculation of a mean, you may get peculiarly high values (if N/A was being represented as 999999) or particularly low values (if N/A was being represented as 0).  If you are using other variables, you will need to check the documentation for them to see how missing or N/A was coded, and then exclude those values.

Demographic and Socioeconomic Characteristics to Treat as Outcomes/Dependent Variables

Basic demographic and socioeconomic variables available in most of the decennial Censuses that you might want to consider as outcomes (dependent variables) include but are not limited to:

  • Current marital status (MARST)
  • Number of children born (CHBORN)
  • Age at first marriage (AGEMARR)
  • Total individual income (INCTOT)
  • Poverty status (POVERTY)
  • Educational attainment (EDUC)
  • Socioeconomic index (SEI) – this is a commonly used measure of the standing of an individual’s occupation.
  • Of course if you have found another variable that you are interested in, you are welcome to use that.  Some of you have mentioned school enrollment, home ownership, type of school, health insurance, and so forth.

The ACS also includes a rich set of demographic variables that could be used as outcomes.  The ACS are the data that show up annually since 2000 for 2001, 2002, 2003 etc.  The most interesting relevant to the class are some variables for very recent years that indicate whether certain events have occurred in the last year, and could be the basis of the calculation of rates, as opposed to percentages:

These lists are only meant as suggestions, and if you have other interests that can be addressed with other variables you have found, you may pursue them.

Demographic  and Socioeconomic Characteristics to Treat as Explanatory/Independent Variables

Generally your explanatory variables should precede your outcome variables in time.  That doesn’t always  mean they have a causal effect on the outcome, but a causal interpretation is at least more plausible.  So, for example, you might examine number of children born (CHBORN) for women aged 45 according to their level of education (EDUC), but you probably won’t think about studying the education of women aged 45 according to their number of children.  The variables are of course the same in both cases, but the interpretation of which is an outcome and which is explanatory differs.

  • Race (RACE) – Note that since 2000, Race includes codes identifying people who have said they were two or more races.   There are also codes since 2000 for single races, for example, RACASIAN
  • Hispanic (HISPAN) – Note that Hispanic status is separate from race.
  • A variety of other nativity and ancestry variables are available at http://usa.ipums.org/usa-action/variables/group/race_eth.  The availability of these variables tends to change over time, so there isn’t really one nativity or ancestry variable that is available on a continuous basis since 1850.  I will post a separate guide to using some of the key variables.
  • Geographic identifiers in http://usa.ipums.org/usa-action/variables/CITY#codes_section.  Note that the IPUMS doesn’t offer any more detail than City, so with IPUMS you can’t compare different neighborhoods in the same city.
  • Of course you can use EDUC, INCTOT and other variables as explanatory variables, just make sure that your dependent variable comes after them in time.

Examples of tables you could construct

  • Use the comparison of means to look at mean number of children born for people of difference races in different years.  In this case, you would select number of children as your dependent variable, and RACE and YEAR as row and column variables.  You would probably want to filter to restrict to (for example) women who were old enough to have completed their childbearing, say 50 years old.  You might want to restrict to decennial census years.
  • Use the comparison of means to look at mean income for people of different ages with different levels of education.  In this case you would select income as your dependent variable, and age and education as your rows and columns.  You would probably want to set a filter to restrict to ages when people might actually have incomes, for example, 25-55.  You would want to recode age so that instead of having fifty rows, one for each age, you have three rows, one for each ten year age group.

Reminders

  • My posts with IPUMS tips and tracks are accessible via http://camerondcampbell.me/category/ipums/ Make sure to review to see if there is anything that helps you.
  • If you are trying to use an income variable such as INCTOT as a row or column variable, you will need to record it into a limited number of categories in order for a table to work.  If you simply specify INCTOT or another income variable as a row or column variable, the table won’t run, because there are too many distinct values, requiring thousands of columns or rows.  You will need to use the recode to regroup incomes into a manageable number of categories, and of course exclude 9999999 and 9999998.
  • Most if not all of the income variables, including INCTOT, FINCTOT, and HINCTOT, code missing values or not available as 9999999,  9999998, 999999, 999998, or some variant thereof.  INCTOT codes missing values as 9999999: https://usa.ipums.org/usa-action/variables/INCTOT/#codes_section.  If you are carrying out a comparison of means, you need to exclude those observations because the average shouldn’t include these values.  You could do this by putting inctot(*-9999997) in the filter.
  • Similarly, If you are categorizing income, make sure that the highest category of income doesn’t include 9999998 and 9999999.  For example, inctot(r:0-9999;10000-19999;20000-29999;30000-39999;40000-49999;50000-9999997)
  • Many of the fertility variables use 0 to indicate missing or no response, 1 to indicate no births or no children.  For example, the ACS variable FERTYR is 0 for Not Available, 1 for no births in the last year, and 2 for one or more births in the last year: https://usa.ipums.org/usa-action/variables/FERTYR#codes_tab .  Similarly, CHBORN is 0 for not available, 1 for no children, 2 for one child, and so forth: In those cases, 2 often means 1 child, 3 means 2 children and so forth: https://usa.ipums.org/usa-action/variables/CHBORN#codes_tab   Be attentive to this when you interpret .  If you are computing mean number of children, or mean numbers of births, you will often want to subtract one from the numbers you present.
  • If you are computing averages of any variables via Comparison of Means, make sure to inspect the detailed documentation for those variables to find out how missing values are coded, and use a selection filter to exclude them.
  • Again, use selection filters to make sure that the observations you include are relevant to the question you are interested in.  For example, if you want to use school to look at whether or not someone is currently enrolled in school, you would want to restrict to people who have a chance of being currently enrolled by applying a selection filter based on age.  Restricting to age(14-18), for example, would let you look at people who were eligible to be eligible to be in high school.  If you are looking at completed education, normally you would want to restrict to ages 25 and above.
  • Remember that not every variable is available in every year.  For the variables you are interested in, check to see which years they are available in.  Some very interesting variables are only available in one or two years.  The variables related to ethnicity, nativity, and origin are especially prone to change.
  • Remember that 2001-2009 are based on the ACS.  If you just want to present data from the decennial Census, you would restrict to years 1850-2000, and if you just wanted ACS data, you would restrict to 2001-2009.
  • Keep in mind that the ACS has some nice variables that allow for direct computation of certain demographic rates, like whether or not someone has married in the last year, whether or not someone has had a birth in the last year, and so forth.

 

Final Project (Sociology 116 W13)

Sociology 116
Winter 2013
Final Project

Due Friday 3/15 at 11:59pm via TurnItIn.

You are to write an original research paper that uses the IPUMS to carry out a comparative study of time trends and age patterns of the demographic and socioeconomic characteristics by education, income, ethnicity, race, region, sex, or some other variables.  The emphasis is on comparison.  If you are interested in a particular ethnicity, for example, you still need to compare it to other ethnicities or the population as a whole to establish what is distinct about it.

Please read the following directions carefully.  Since you have nearly two months to complete the project, there is no excuse for not complying with the instructions.

Your research paper should be 2500 words of text (roughly 5 single-spaced pages or 10 double-spaced pages) and 7 tables based on computations at the IPUMS site.   The paper should be organized as the text, followed by the references, followed by the tables, with each table on a separate page.  All tables should be publication quality according to the specifications below, not simply copied and pasted from the website.  Do not insert tables into the main text.  Please number all pages, and make sure that your name is on the first page.

The text should consist of four sections: Introduction, Background, Results, and Conclusion.  Below I suggest guidelines for the lengths of each of these sections.  These guidelines are not rigid, and depending on your topic and your findings the actual word count may differ.

The Introduction should explain the overall focus of the paper and specify the questions that you are interested in.   250 words should be sufficient.

The Background section that provides whatever information from other published sources you think may be necessary to help a reader understand the object of your study.  For example, if your tables focus on comparison of different ethnic groups, you might provide a brief history of each group’s history in the United States that focuses on features relevant to the analysis.  If you are comparing several major cities, you might want to mention key features of each relevant to your analyses.  500 words should be sufficient.

A Results section that discusses the tables one by one, and interprets their contents in light of hypotheses or theories in the introduction.  The tables should be numbered consecutively, and referred to in the text as Table 1, Table 2 etc.

The Conclusion reviews the most interesting results in the paper and suggests further work.  250 words should be sufficient.

Tables

Each of the tables should examine relationships among a distinct set of variables.  In other words, the tables should not be repetitions of the same basic tabulation but with different filters.  At least two tables should make use of the American Community Survey (ACS) data, which are annual starting in 2001.  At least two tables should make use of Decennial Census data.

The tables should not be repetitions of ones you have already constructed for a class assignment.

You may also use the Current Population Survey (CPS) data at the IPUMS site.  It tends to have much richer detail on labor force and employment characteristics.

For some of your tables, you may also use General Social Survey (GSS) data, which is available at a different website (http://sda.berkeley.edu/cgi-bin/hsda?harcsda+gss10).  It can be analyzed via a web interface like the one that you are already familiar with at IPUMS.  The GSS includes questions on topics like religion, political views, and so forth that are not covered in the Census.  Keep in mind that if you want to use the GSS, the tables you create should have something to do with demographic behavior, broadly defined.

Each table should also have a self-explanatory title, and the row and column headings should be sufficient to allow a reader to interpret the table without referring to your text.  Each table should include a totals column and/or totals row as appropriate.  Please format the tables so that there are no vertical lines, and only four horizontal lines: one between the title and the column headings, one between the column headings and the table contents, one between the table contents and the totals row, and one at the bottom.  Basically the table should be formatted like the ones you see in the papers in the assigned reading.  You will notice that in publications, tables almost never have vertical lines, and generally have a limited number of horizontal lines.

Either the title of the table or a note at the bottom of the table should specify any restrictions that were applied in selecting observations to be included in the calculation.  Typically this means specifying the ages that were included in the calculation, the the years.

The tables should not be copied and pasted directly from the site, but rather should be prepared to look like they were publication quality, following the guidelines above.

The tables may be frequencies or cross-tabulations like the ones you are already used to.  You are also encouraged to take advantage of some of the other tools available at the site.  You are most likely to find the comparison of means tool (https://sda.usa.ipums.org/helpfiles/helpan.htm#means) the most useful.   This allows you to calculate the mean of one variable for different combinations of other variables.  For example, you could calculate mean income (INCTOT) for different combinations of RACE and YEAR.  If you are more adventurous, you may try using the correlation or regression tools, but these can take a long time.

In constructing your tables, make sure to select or filter observations correctly to make sure the ones you include are relevant.  You can restrict the valid range of a variable used in the analysis to achieve the same effect as a filter: https://sda.usa.ipums.org/helpfiles/helpan.htm#range

Depending on the analysis that you are doing, you may want to use a filter to restrict to people of particular ages, or people with particular characteristics.  For example, when looking at completed education, EDUC, you will almost always want to restrict to people aged 25 or over, so you will only be looking at people who have completed their education.  Similarly, most of the income and occupation variables are only relevant for people of working ages, 18-55.  For details on using the selection filter at IPUMS, please see https://sda.usa.ipums.org/helpfiles/helpan.htm#filter

When constructing tables that are tabulations, you will also want to use recode for any variable that is continuous (a quantity), not discrete (a category).  Examples include age, year of birth, and almost any of the income variables.  If you are working with age, instead of having a separate row or column for each single year of age (1,2,3, etc.) you will want to have a limited number of age groups: 1-9, 10-19 and so on.  Similarly, If you want to use total income (INCTOT), income from wages (INCWAGE), or other variables that record an amount in dollars, not a category, you will definitely need to recode the original values into into categories.  If you attempt to carry out a tabulation in which one of the income variables is a row, column, or control variable, and don’t record, the tabulation will almost certainly fail, with an error message indicating that there are too many rows or columns.  The definition of your income categories will depend on the year that you are looking at.  Because of inflation, typical incomes change dramatically over time.  See  https://sda.usa.ipums.org/helpfiles/helpan.htm#recode on how to carry out a recode.

You will also need to exclude missing or not available (N/A) values, especially if you are computing a mean.  In the IPUMS data, when information is missing for a variable in a particular observation, that is typically represented with a numeric value that will be included in any mean that you compute, unless you exclude it.  This is especially important for income variables.  In total income (INCTOT), missing data is represented by 9999999: https://usa.ipums.org/usa-action/variables/INCTOT/#codes_section.  For wage income (INCWAGE), missing is represented as 999999: https://usa.ipums.org/usa-action/variables/INCWAGE/#codes_section.  For the socioeconomic index (SEI), N/A is represented as 0: https://usa.ipums.org/usa-action/variables/SEI/#codes_section And so on.  If you fail to exclude the numeric codes for missing values from the calculation of a mean, you may get peculiarly high values (if N/A was being represented as 999999) or particularly low values (if N/A was being represented as 0).  If you are using other variables, you will need to check the documentation for them to see how missing or N/A was coded, and then exclude those values.

Demographic and Socioeconomic Characteristics to Treat as Outcomes/Dependent Variables

Basic demographic and socioeconomic variables available in most of the decennial Censuses that you might want to consider as outcomes (dependent variables) include but are not limited to:

  • Current marital status (MARST)
  • Number of children born (CHBORN)
  • Age at first marriage (AGEMARR)
  • Total individual income (INCTOT)
  • Poverty status (POVERTY)
  • Educational attainment (EDUC)
  • Socioeconomic index (SEI) – this is a commonly used measure of the standing of an individual’s occupation.
  • Of course if you have found another variable that you are interested in, you are welcome to use that.  Some of you have mentioned school enrollment, home ownership, type of school, health insurance, and so forth.

The ACS also includes a rich set of demographic variables that could be used as outcomes.  The ACS are the data that show up annually since 2000 for 2001, 2002, 2003 etc.  The most interesting relevant to the class are some variables for very recent years that indicate whether certain events have occurred in the last year, and could be the basis of the calculation of rates, as opposed to percentages:

These lists are only meant as suggestions, and if you have other interests that can be addressed with other variables you have found, you may pursue them.

Demographic  and Socioeconomic Characteristics to Treat as Explanatory/Independent Variables

Generally your explanatory variables should precede your outcome variables in time.  That doesn’t always  mean they have a causal effect on the outcome, but a causal interpretation is at least more plausible.  So, for example, you might examine number of children born (CHBORN) for women aged 45 according to their level of education (EDUC), but you probably won’t think about studying the education of women aged 45 according to their number of children.  The variables are of course the same in both cases, but the interpretation of which is an outcome and which is explanatory differs.

  • Race (RACE) – Note that since 2000, Race includes codes identifying people who have said they were two or more races.   There are also codes since 2000 for single races, for example, RACASIAN
  • Hispanic (HISPAN) – Note that Hispanic status is separate from race.
  • A variety of other nativity and ancestry variables are available at http://usa.ipums.org/usa-action/variables/group/race_eth.  The availability of these variables tends to change over time, so there isn’t really one nativity or ancestry variable that is available on a continuous basis since 1850.  I will post a separate guide to using some of the key variables.
  • Geographic identifiers in http://usa.ipums.org/usa-action/variables/CITY#codes_section.  Note that the IPUMS doesn’t offer any more detail than City, so with IPUMS you can’t compare different neighborhoods in the same city.
  • Of course you can use EDUC, INCTOT and other variables as explanatory variables, just make sure that your dependent variable comes after them in time.

Examples of tables you could construct

  • Use the comparison of means to look at mean number of children born for people of difference races in different years.  In this case, you would select number of children as your dependent variable, and RACE and YEAR as row and column variables.  You would probably want to filter to restrict to (for example) women who were old enough to have completed their childbearing, say 50 years old.  You might want to restrict to decennial census years.
  • Use the comparison of means to look at mean income for people of different ages with different levels of education.  In this case you would select income as your dependent variable, and age and education as your rows and columns.  You would probably want to set a filter to restrict to ages when people might actually have incomes, for example, 25-55.  You would want to recode age so that instead of having fifty rows, one for each age, you have three rows, one for each ten year age group.

Reminders

  • My posts with IPUMS tips and tracks are accessible via http://camerondcampbell.me/category/ipums/ Make sure to review to see if there is anything that helps you.
  • If you are trying to use an income variable such as INCTOT as a row or column variable, you will need to record it into a limited number of categories in order for a table to work.  If you simply specify INCTOT or another income variable as a row or column variable, the table won’t run, because there are too many distinct values, requiring thousands of columns or rows.  You will need to use the recode to regroup incomes into a manageable number of categories, and of course exclude 9999999 and 9999998.
  • Most if not all of the income variables, including INCTOT, FINCTOT, and HINCTOT, code missing values or not available as 9999999,  9999998, 999999, 999998, or some variant thereof.  INCTOT codes missing values as 9999999: https://usa.ipums.org/usa-action/variables/INCTOT/#codes_section.  If you are carrying out a comparison of means, you need to exclude those observations because the average shouldn’t include these values.  You could do this by putting inctot(*-9999997) in the filter.
  • Similarly, If you are categorizing income, make sure that the highest category of income doesn’t include 9999998 and 9999999.  For example, inctot(r:0-9999;10000-19999;20000-29999;30000-39999;40000-49999;50000-9999997)
  • Many of the fertility variables use 0 to indicate missing or no response, 1 to indicate no births or no children.  For example, the ACS variable FERTYR is 0 for Not Available, 1 for no births in the last year, and 2 for one or more births in the last year: https://usa.ipums.org/usa-action/variables/FERTYR#codes_tab .  Similarly, CHBORN is 0 for not available, 1 for no children, 2 for one child, and so forth: In those cases, 2 often means 1 child, 3 means 2 children and so forth: https://usa.ipums.org/usa-action/variables/CHBORN#codes_tab   Be attentive to this when you interpret .  If you are computing mean number of children, or mean numbers of births, you will often want to subtract one from the numbers you present.
  • If you are computing averages of any variables via Comparison of Means, make sure to inspect the detailed documentation for those variables to find out how missing values are coded, and use a selection filter to exclude them.
  • Again, use selection filters to make sure that the observations you include are relevant to the question you are interested in.  For example, if you want to use school to look at whether or not someone is currently enrolled in school, you would want to restrict to people who have a chance of being currently enrolled by applying a selection filter based on age.  Restricting to age(14-18), for example, would let you look at people who were eligible to be eligible to be in high school.  If you are looking at completed education, normally you would want to restrict to ages 25 and above.
  • Remember that not every variable is available in every year.  For the variables you are interested in, check to see which years they are available in.  Some very interesting variables are only available in one or two years.  The variables related to ethnicity, nativity, and origin are especially prone to change.
  • Remember that 2001-2009 are based on the ACS.  If you just want to present data from the decennial Census, you would restrict to years 1850-2000, and if you just wanted ACS data, you would restrict to 2001-2009.
  • Keep in mind that the ACS has some nice variables that allow for direct computation of certain demographic rates, like whether or not someone has married in the last year, whether or not someone has had a birth in the last year, and so forth.

 

Sociology 226A Introduction to Social Demography (UCLA Spring 2012) Announcement

I will teach Sociology 226A again in Spring 2013.  This is the first half of our department’s two-quarter graduate seminar on social demography.  The goal of the seminar is to introduce students to major issues and debates in demography, broadly defined.  I am still working on the syllabus.

The course will be similar in format and organization to the last offering in fall 2010: http://www.sscnet.ucla.edu/soc/faculty/campbell/2010_226A_F/226A_2010_fall_syllabus.htmI do expect to update the readings.  Broadly speaking, the emphasis will be on interactions between 1) population and the economy, 2) population composition with an emphasis on techniques for measurement and projection, 3) demographic transition, and 4) mortality.  There will be an initial discussion of fertility, which will be dealt with in more detail in 226B.

Sociology 116 Social Demography (W13) Syllabus

Sociology 116

Social Demography
Winter 2013

Class web page at UCLA Social Science Computing
Check enrollments at the registrar’s entry
Evaluations from my Fall 2011 offering of Sociology 116

12/14/2012 Revision

 

INTRODUCTION

This is an overview of population and social demography intended to familiarize students with key concepts, major debates, and recent research.  Topics will be a balanced mixture of academic interest, contemporary relevance, and policy concern.  Along the way, the class will introduce methods and data sources used in the study of population and social demography.  Readings will include academic publications that are examples of classic or recent work in key issues of population or social demography.  Students should come away with the class with an awareness of the range of issues considered in population studies and social demography, a basic understanding of relevant data and methods, and an ability to read articles related to population in an informed and critical fashion.

Some of the topics to be covered include:

  • The history of world population
  • Relationships between population growth, economic development, and the environment
  • Contemporary trends in the family, including marriage, cohabitation, and divorce
  • Race, ethnic, socioeconomic and other differences in demographic behavior and family organization in the United States
  • International differences in demographic trends and patterns, with an emphasis on comparison between East Asia and the West
  • Interactions between differentials in birth, death, and marriage patterns and population composition

INSTRUCTOR

Cameron Campbell, camcam@ucla.edu. See class website for location, office hours, phone number.  If you email, please review this guide to ‘etiquette guide’ for emailing professors:  http://www.wikihow.com/Email-a-Professor.  It is most important that in any email you send me, you provide your full name as it appears on the roster, and that you use the account that is on file at URSA.  I usually ignore class-related emails that do not clearly identify the sender, or which come from an email account other than the one on file with the university.  I may also ignore emails requesting information that is already provided in the syllabus.

REQUIRED TEXTS

These should be available at Ackerman, and also on reserve at Powell.

Livi-Bacci, Massimo.  2007.  A Concise History of World Population.  Fifth Edition.  Blackwell Publishing.  (Fourth edition is also OK)

Malthus, Thomas Robert. 2008. An Essay on the Principle of Population, edited by Geoffrey Gilbert. Oxford University Press. ISBN-13: 978-0199540457

Note that Malthus’ Essay is available in many versions. The one I referenced above is the one I submitted in the requisition to the bookstore, and Powell reserves

Additional readings listed below will be available as PDF files on the web, either on a password-protected class website, or else at an external site like JSTOR (www.jstor.org).  Access to external sites will normally require that you have a computer with a UCLA IP address.  This means that you must either use a computer on campus, or if you are off campus, connect to the net through the UCLA VPN or Proxy Server.  You can download the UCLA VPN client for free here: http://www.bol.ucla.edu/services/vpn/.  If you are unsure of how to use the UCLA VPN or Proxy Server, I strongly recommend that you download all the readings when you are on campus.

There will not be a reader available for purchase.  Students are responsible for obtaining access to the readings listed below.

i>clickers

We will be using i>clickers this quarter to test comprehension, poll the class about opinions, and generally make the class more fun and interactive. You will not be graded on whether you answer questions correctly, but rather the frequency of your participation, as described below.

You can purchase a new or used i>clicker 2 at Ackerman Union for $46 (new) or
considerably less for a used one.  You will then need to register the remote.  I have added an i>clicker ‘block’ at the Course website where you should be able to register your device.     To receive credit for participation, you will have to register your clicker and bring it to class.

You may not use a clicker registered to another student in class.  Neither are you allowed to give your clicker to another student to bring to class and use on your behalf.  Any such
cases will be treated as academic dishonesty and referred to the Dean of Students.

I assume there may be some technical problems in the first week, thus assignment of credit based on i>clickr participation will begin in the second week of class.

GRADING

  • Research paper, due at the end of 10th week: 15%
  • Completion of course evaluation at end of quarter: 1%.
    • I will receive a list of names for students who completed the evaluation by the deadline. Obviously, I will not receive anything but the list of names, and will not be given access to any individual responses.
  • Lecture attendance (demonstrated via participation in i-Clickr surveys): 5%
    • Each class, I will carry out one or more surveys about topics related to class via i-Clickr.
    • Participation in these surveys via i-Clickr will be recorded.  Because the i-Clickr will be used to promote interaction in class, not for evaluation, the content of responses will be anonymous.  In other words, I will know that you responded to a question, and what the distribution of responses for the class is, but I will not know how any individual student responded.
    • Each class will be weighted equally, and your score for a class will be based on the proportion of surveys that day which you participate in.
    • Each student is allowed two ‘free’ class absences over the course of the quarter.
  • Section attendance and participation: 4%
    • The TAs will take attendance at every section meeting.  This will account for half of the 4%
    • TAs will assign the other half of the 4% based on participation in class discussion.
  • Assignments: 45%
    • There will be one assignment due roughly every other week, for a total of 4-6 over the course of the quarter.  Early in the quarter, assignments will be more frequent because they are intended to introduce you to important web resources.
    • Prompts for the assignments will be posted on the class website.
    • Some of the exercises will require you to visit websites to gather demographic data, carry out some basic demographic calculations, and write up results.
    • Submit assignments at turnitin.com
      • Access turnitin.com via the link in the course entry at MyUCLA, not by going directly to www.turnitin.com
      • When you enter your name at turnitin.com, please make sure it matches the one on the class roster.
    • Late policy on essays and exercises
      • Within 1 days (24 hours) of the original posted time: 2.5 points will be deducted
      • Within 2 days (48 hours) of the original posted time: 5 points will be deducted
      • Within 1 week of the original posted time: 10 points will be deducted.
      • Within 2 weeks of the original posted time: 20 points will be deducted.
      • After 2 weeks, but before the last day of finals week: 25 points will be deducted.
      • This policy is designed so that if you are a few minutes or hours late on the occasional essay, or very late with one essay, it should have little impact on your grade, but if you are habitually late, or seriously late on more than one essays, it will affect your grade.
  • Midterm exam: 10%
    • There will be a multiple-choice midterm exam assessing knowledge of key facts and concepts during the first half of the quarter.
    • The final exam will be open book and open note.  Tablets, e-readers, or laptops may be used but must be in ‘airplane mode’ i.e. internet connectivity turned off.
  • Final exam: 20%
    • There will be a multiple-choice final exam assessing knowledge of key facts and concepts covered during the quarter.
    • The final exam will be open book and open note.  Tablets, e-readers, or laptops may be used but must be in ‘airplane mode’ i.e. internet connectivity turned off.
  • Scale: 96.7 A+, 93.3 A, 90.0 A-, 86.7 B+ and so forth.
  • All scores will be available at MyUCLA.

Research paper

  • The research paper will describe and interpret patterns and trends in demographic and socioeconomic characteristics of an ethnic group, state or other geographic region (city etc.), or other well-defined subpopulation, using data from IPUMS USA (http://usa.ipums.org/usa/).
  • Characteristics of interest may include age and sex distribution, marital status, childbearing, educational attainment, For the paper, students will carry out tabulations at the IPUMS website, produce tables or graphs, and write accompanying text that refers to relevant literature to interpret observed trends.
  • The paper should include about 8-10 double-spaced pages of text, that is 2000-2500 words.  Tables, graphs, and references will follow at the end.
  • Please begin familiarizing yourself with the IPUMS website as soon as possible.  In addition to visiting the main IPUMS USA page (http://usa.ipums.org/usa/), please make sure to visit the main page for the Online Data Analysis system (ODA) that you will be using to do the calculations for your research paper, and some of the assignments: http://usa.ipums.org/usa/sda/.  There is also a short set of instructions for using the ODA at: http://usa.ipums.org/usa/resources/sda/sdainstructions.pdf
  • If you are especially interested in economic characteristics of your population of interest, you may also want to consider using Current Population Survey (CPS) data: http://cps.ipums.org/cps/.  The Online Data Analysis system for the CPS is available at: http://cps.ipums.org/cps/sda

Essays

  • Some assignments will require you to write an essay.
  • Essays are expected to be 500-700 words each.
  • Essays outside the word range may be penalized. The penalty is not automatic, and depends on the essay content.  If the essay is too long because it is unfocused, rambling, or includes irrelevant material, that may be penalized.  But an essay that is on target and well-written may not be penalized.  Essays under the minimum are more likely to be penalized because in my experience they usually leave our key material that I am looking for.
  • Topics for essays will be announced via email and posted on the announcements section of the class web page. They will normally cover the material in the week in which they were posted.. They will be due the following week, usually on Thursday or Friday
  • Essays and exercises will be graded out of 100 points.
  • The lowest grade will not be dropped.
  • Spelling errors, incorrect or inconsistent word usage, incoherent writing, run-on sentences and other typographical and grammatical errors will all be penalized. You are strongly encouraged to make use of the spell-checker that is no doubt already part of the software you are using for word processing. You should also make use of a grammar checker such as Grammatik. Microsoft Word, and many other packages, now include one.
  • Your essays should demonstrate that you have read all of the assigned material and paid attention in lecture. Failure to demonstrate a careful reading of the assigned material will be penalized.
  • Essays that are substantially off-topic will not receive credit, no matter how long or well-written.
  • Please do not use unusual fonts, line spacing, or other special effects.
  • Essays will be submitted via TurnItIn.
  • Mysterious software and hardware problems that cause your work to vanish after you have completed it but before you have had a chance to submit it, or after you think you submitted it, are not acceptable as excuses for turning in late work.  I strongly suggest that you keep a copy of whatever you submit on your computer, and also confirm after uploading that it has been received by TurnItIn.
  • The written work you submit each week must be your own. Unattributed use of the work of others is plagiarism, and is not acceptable. If you do feel the need to include text from another source, set it off in quotes and include a proper citation. If you have any questions about how to attribute sources, how to use quotations, etc., PLEASE ASK ME OR THE TA! Do not put yourself in jeopardy by submitting an essay that includes material that appears to be plagiarized. I will refer to the Dean any essays that appear to contain material that is not original. Keep in mind that I have complete files of every essay submitted in this class since I began teaching it and electronically compare essays with those submitted in previous years via TurnItIn.
  • In general, I prefer you to paraphrase, not quote. By successfully paraphrasing, you demonstrate your understanding of the material. By providing quotations, you just demonstrate that you can type. If your essay has too many quotations, it will be penalized.
  • If you make a claim or assertion that is not clearly based on material from lecture or the reading, and the validity of it is not self-evident, you must provide evidence to back it up, in the form of a citation or a brief argument. If you can’t do that, you at least must clarify that what you are saying represents a personal opinion by prefacing the claim with “I believe that…” or something equivalent
  • I will grade each essay on a 100 point scale.

POLICIES

  • Announcements will be made via the class web page, and all assignments posted there. You are responsible for checking the web page frequently.
  • If you have an inquiry the answer to which you think would be of general interest to the class, please post it to the discussion board. Thus questions about grading policies, due dates, assignments, lecture material, and so forth should all go to the discussion board. If you contact me with a question that I believe should be posted to the discussion board, I will tell you to post it
  • Otherwise, the best way to reach me is via email.
  • I will try to leave some time at the end of each lecture for questions and discussion. Because the class is large and time is limited, if you have additional questions about the readings or the content of the lectures, please post them to the discussion board. I will do my best to respond promptly. Your classmates are also encouraged to respond.
  • You are always welcome to come to my office. I am guaranteed to be there during office hours.  I am in my office most days 9-5 except when I am teaching or in a meeting.

Starting from the 2nd week of class, I will require participation in class polls via  i>clicker.

SCHEDULE

Important dates

2/12     Tuesday         Midterm, covering material through the end of Week 5.
3/15     Friday             Research Paper Due at Midnight via TurnItIn

Location and time of final TBD.  Please check MyUCLA rather than asking me.

There will be some adjustment to the readings before or during the quarter. 

Changes during the quarter will be announced via email or a post at the class website.  Please check the syllabus before printing ou/downloading the readings for each week.

Week 1 – What is social demography?

Introduction

Sources for the study of social demography

Population growth over the long term

Population studies and the social sciences

Reading

Livi-Bacci, Chapter 1

Preston, Samuel H.  1993.  “The Contours of Demography: Estimates and Projections.”  Demography.  30(4):593-606.  J

McFalls, Joseph.  2007.  “”Population: A lively introduction.  Fifth Edition.”  Population Bulletin.  62(1).  Link

Optional, not required

Keyfitz, Nathan. 1975. “How do we know the facts of demography?” Population and Development Review 1(Dec):267-288. J.

Week 2 – Population, the economy, and the environment

Reading

Livi-Bacci, Chapters 2 and 3

Malthus, An Essay on the Principle of Population, Chapters I-VII, XVI-XIX.  If you have the Geoffrey Gilbert edition, please read his introduction.

Boserup, Ester. 1976. “Environment, population, and technology in primitive societies.” Population and Development Review. 2(March): 21-36. J.

Optional, not required

De Souza, Roger-Mark, John S. Williams, Frederick A.B. Meyerson.  2003’  “Critical Links: Population, Health, and the Environment.”  Population Bulletin.  58(3):1-48.
http://www.prb.org/Source/58.3CriticalLinksPHE_Eng.pdf

Week 3 – Demographic measures and methods

Fertility: Crude birth rates, total fertility rates

Measuring mortality: crude rates, age-specific rates, cause-specific rates and standardized rates, the life table

Reading

Haupt, Arthur.  2004.  Population Handbook.  Fifth Edition.  Washington: Population Reference Bureau.  http://www.prb.org/pdf/PopHandbook_Eng.pdf  Chapters 3, 5

Week 4 – Population Composition

Population aging, causes and consequences of changes in population composition.

Reading

Haupt, Arthur.  2004.  Population Handbook.  Fifth Edition.  Washington: Population Reference Bureau.  http://www.prb.org/pdf/PopHandbook_Eng.pdf  Chapter 2, 9, 11..

Hout, Michael and Joshua Goldstein. 1994. “How 4.5 million Irish immigrants became 40 million Irish Americans: Demographic and Subjective Aspects of Ethnic Composition of White Americans.” American Sociological Review 59:64-82.  J.

Hout, Michael, Andrew Greeley, Melissa J. Wilde. 2001. “The Demographic Imperative in Religious Change in the United States.” American Journal of Sociology. 107(2):468-500. http://www.journals.uchicago.edu/AJS/journal/contents/v107n2.html

Lee, Ronald, and Shripad Tuljapurkar. 1997. “Death and taxes: Longer life, consumption, and social security.” Demography 34: 67-81. J

Week 5 –The Demographic Transition

Reading

Haupt, Arthur.  2004.  Population Handbook.  Fifth Edition.  Washington: Population Reference Bureau.  http://www.prb.org/pdf/PopHandbook_Eng.pdf  Chapter 12.

Livi-Bacci, Chapter 4

Cutler, David and Grant Miller.  2005.  “The Role of Public Health Improvements in Health Advances: The Twentieth-Century United States.”  Demography.  42(1):1-22.  http://muse.jhu.edu/journals/demography/v042/42.1cutler.pdf

Lee, Ronald.  2003.  “The Demographic Transition: Three Centuries of Fundamental Change.”  Journal of Economic Perspectives.  17(4):167-190.  J

Special topic: Fertility in historical and contemporary China, readings TBA

Optional, not required

Caldwell, John C. 1986. “Routes to low mortality in poor countries.” Population and Development Review 12(2):171-220. J.

Cleland, John and Christopher Wilson. 1987. “Demand theories of the fertility transition: An iconoclastic view.” Population Studies.   41:5-30. J.

Population Reference Bureau.  2004.  “Transitions in World Population.”  Population Bulletin.  59(1).  http://www.prb.org/Source/ACFFF4.pdf

Week 6 – Health and Mortality in Developed Countries

Health and mortality differentials by race, gender, socioeconomic status, and other characteristics

MIDTERM (2/12)

Reading

Elo, Irma T. and Samuel H. Preston. 1996. “Education differentials in mortality: United States, 1979-1985.” Social Science and Medicine.  42(1):47-57.

Hayward, Mark D.  2004.  “The Long Arm of Childhood: The Influence of Early-Life Social Conditions on Men’s Mortality.”  Demography.  41(1):87-107.  http://muse.jhu.edu/journals/demography/v041/41.1hayward.pdf

Optional

Palloni, Alberto and Elizabeth Arias.  2004.  “Paradox Lost: Explaining the Hispanic adult mortality advantage.”  Demography.  41(3):385-415.  http://muse.jhu.edu/journals/demography/v041/41.3palloni.pdf

Rogers, Richard G., Robert A. Hummer, Charles B. Nam and Kimberly Peters.  1996.  “Demographic, Socioeconomic, and Behavioral Factors Affecting Ethnic Mortality by Cause.”  Social Forces.  74(4):1419-1438. http://links.jstor.org/sici?sici=0037-7732%28199606%2974%3A4%3C1419%3ADSABFA%3E2.0.CO%3B2-L

Week 7 –Marriage and Cohabitation

Haupt, Arthur.  2004.  Population Handbook.  Fifth Edition.  Washington: Population Reference Bureau.  http://www.prb.org/pdf/PopHandbook_Eng.pdf  Chapters 7, 10.

Qian, Zhenchao and Daniel T. Lichter.  “Social Boundaries and Marital Assimilation:  Interpreting Trends in Racial and Ethnic Intermarriage.” American Sociological Review 72: 68-94.  http://www.ingentaconnect.com/content/asoca/asr/2007/00000072/00000001/art00004

Waite, Linda J. 1995.  “Does Marriage Matter?”  Demography.   32(4):483-507.
http://links.jstor.org/sici?sici=0070-3370%28199511%2932%3A4%3C483%3ADMM%3E2.0.CO%3B2-J

Axinn, William G. and Arland Thornton.  1992.  “The Relationship between Cohabitation and Divorce: Selectivity or Causal Influence?”  Demography.   29(3): 357-374.  http://links.jstor.org/sici?sici=0070-3370%28199208%2929%3A3%3C357%3ATRBCAD%3E2.0.CO%3B2-6

Optional

Schwartz, C. R., and R. D. Mare. 2005. “Trends in Educational Assortative Marriage from 1940 to 2003.”  Demography 42: 621-46.  http://muse.jhu.edu/journals/demography/v042/42.4schwartz.pdf

Oppenheimer, Valerie Kincade, Matthijs Kalmijn, and Nelson Lim. 1997. “Men’s Career Development and Marriage Timing During a Period of Rising Inequality.” Demography.  34(3):311-330.  http://links.jstor.org/sici?sici=0070-3370%28199708%2934%3A3%3C311%3AMCDAMT%3E2.0.CO%3B2-7

Qian, Zhenchao, Sampson Lee Blair, and Stacey Ruf.  2001.   “Asian American Interracial and Interethnic Marriages: Differences by Education and Nativity.” International Migration Review 35: 557-586.  http://links.jstor.org/sici?sici=0197-9183%28200122%2935%3A2%3C557%3AAAIAIM%3E2.0.CO%3B2-9

Week 8 – Reproduction

Billari, Francesco and Hans-Peter Kohler.  2004. “Patterns of Low and Lowest-Low Fertility in Europe.” Population Studies 58(2), 161-176.  http://taylorandfrancis.metapress.com/link.asp?id=67t0ppuum5f714kk

Morgan, S. Philip. 1996. “Characteristic features of modern American fertility.” Pp. 19-63 in John B. Casterline, Ronald D. Lee, and Karen A. Foote (eds.), Fertility in the United States: New Patterns, New Theories.  New York: The Population Council.  http://links.jstor.org/sici?sici=0098-7921%281996%2922%3C19%3ACFOMAF%3E2.0.CO%3B2-I

Morgan, S. Philip.  2003.  “Is Low Fertility a Twenty-First Century Demographic Crisis?”  Demography.  40(4):589-604.
http://links.jstor.org/sici?sici=0070-3370%28200311%2940%3A4%3C589%3AILFATD%3E2.0.CO%3B2-M

Week 9 – Divorce and Union Dissolution

Cherlin, Andrew. 1999. “Going to extremes: Family structure, children’s well-being, and social science.” Demography. 36: 421-428.
http://links.jstor.org/sici?sici=0070-3370%28199911%2936%3A4%3C421%3AGTEFSC%3E2.0.CO%3B2-Z

Goldstein, Joshua R. 1999. “The Leveling of Divorce in the United States.” Demography.  36: 409-14.
http://links.jstor.org/sici?sici=0070-3370%28199908%2936%3A3%3C409%3ATLODIT%3E2.0.CO%3B2-H

Martin, Teresa Castro and Larry Bumpass. 1989. “Recent Trends in Marital Disruption.” Demography 26:37-51.
http://links.jstor.org/sici?sici=0070-3370%28198902%2926%3A1%3C37%3ARTIMD%3E2.0.CO%3B2-Q

Smock, Pamela J., Wendy D. Manning, and Sanjiv Gupta. 1999.  “The Effect of Marriage and Divorce on Women’s Economic Well-Being.” American Sociological Review. 64(6):794-812.
http://links.jstor.org/sici?sici=0003-1224%28199912%2964%3A6%3C794%3ATEOMAD%3E2.0.CO%3B2-S

Optional, not required

Preston, Samuel H. and John McDonald. 1979. “The Incidence of Divorce Within Cohorts of American Marriages Contracted Since the Civil War.” Demography 16(1):1-25.
http://links.jstor.org/sici?sici=0070-3370%28197902%2916%3A1%3C1%3ATIODWC%3E2.0.CO%3B2-T

Week 10 – Migration

Durand, Jorge, William Kandel, Emilio A. Parrado, Douglas S. Massey. 1996. “International migration and development in Mexican communities.”  Demography.  33:249-264.
http://links.jstor.org/sici?sici=0070-3370%28199605%2933%3A2%3C249%3AIMADIM%3E2.0.CO%3B2-Z

Bruch, Elizabeth and Robert Mare.  2006.  “Neighborhood Choice and Neighborhood Change.”  American Journal of Sociology.
http://www.journals.uchicago.edu/AJS/journal/issues/v112n3/090192/090192.web.pdf

WEB LINKS

Historical Demography: Data, Methods, Sources (UCLA Sociology 285B Spring 2012) Syllabus

Sociology 285B
Historical Demography: Data, Methods, and Debates
M 1-4PM

Cameron Campbell
Haines 202
x51031
This seminar will examine 1) new data and methods that have recently expanded the scope of historical demography, 2) classic debates and controversies in population history, including relevant broader debates in economic and social history,3) recent work using new data and methods to investigate topics of longstanding interest in historical demography such as mortality, fertility, and marriage, as well as newer topics such as migration and stratification, and 4) recent work that has used historical demographic data to address issues of contemporary relevance, especially in health and mortality.  Weekly class discussions will not only cover the substance of the readings and relevant debates, but methodological issues related to the creation, management and analysis of datasets.  Students who complete the class will have a detailed understanding of major debates and controversies in historical demography as well as knowledge of the major sources of data and the methods for organizing and analyzing them, and should be adequately prepared to initiate a study on a topic of their choice with a historical dataset.

Background
Historical demography began as an effort to reconstruct demographic regimes in populations in the past.  Central concerns were reconstruction of historical trends in basic indices of mortality, fertility, and nuptiality, and examination of the relationship of those trends to economic and social conditions.  Practitioners made creative use of individual-level data in parish registers, genealogies, and other sources to produce estimates of aggregate demographic indices.  Such work illuminated demographic processes before and during the demographic transition, and provided a foundation for ambitious efforts to examine relationships between population, the economy, and social organization in the past.
New data and new methods have transformed historical demography.  They have not only broadened its scope by making possible the investigation of a much wider variety of historical topics, but they have also increased the relevance of historical demography for our understanding of contemporary demographic phenomena.  One key development has been the construction of large databases of longitudinal and in some cases multigenerational individual level data from historical household registers, genealogies, and other archival sources.  Application of event-history analysis and other regression-based techniques to these data has led to a new focus on describing, comparing, and understanding patterns of differentials within populations.  Historical demographic studies now routinely examine differences in marriage, reproduction, and death according to community and family context, socioeconomic status, and other individual and family characteristics.  Increasingly, historical demography takes advantage of these novel data and methods to focus on new topics such as migration and stratification, and examine topics of contemporary importance such as the influence of conditions in early life on socioeconomic and health outcomes at later age, and multigenerational processes.

Prerequisites

Students should have completed Sociology 210ABC, or have completed 210AB and be enrolled in 210C.  Students in other departments should have completed courses equivalent to 210ABC, for example, an econometrics or statistics sequence that covers linear regression as well as methods for limited dependent variables.  Students should have basic familiarity with data management and analysis in STATA or another statistical package.  Prior coursework in social demography or demographic methods is useful but not required.

Grading

Weekly response papers – 50%
In preparation for the discussion at each week’s class meeting, students will write a one page (500-600 words) essay responding to and reflecting on the assigned readings for that week.  Responses may either critique individual readings, or synthesize key points from all readings to identify questions and topics on which to focus class discussion.  Students will post their essays to the discussion forum at the class website in advance of each class meeting.

Class participation – 10 %
Students are expected to attend all class meetings and contribute to discussion.  Ten percent of the grade will be assigned based on attendance and participation in discussion.

Research paper and presentation – 40%
Students will write a research paper on a topic of their choice based on analysis of historical population data.  The data should be individual level and longitudinal, and may consist of household registers, genealogies, linked Censuses, or other linked archival sources.  Cross-sectional data such as historical Censuses may be acceptable but the analysis should include a time dimension.  Students are encouraged to make use of publicly available datasets that are readily downloadable such as the China Multigenerational Panel Dataset – Liaoning (CMGPD-LN), the North Atlantic Population Project, the Historical Sample of the Netherlands, the Union Army Study, or the IPUMS Linked Representative Samples.  Exploratory and descriptive analysis of novel historical datasets Students may also make use of contemporary datasets that are longitudinal or intergenerational such as PSID, L.A. FANS and so forth, but will need to check with me to confirm the suitability of their topic.  They are also responsible for obtaining access to the data.  There are other potential sources of data with varying degrees of accessibility and I will be happy to discuss possibilities with students.
Students who do not have the appropriate background or experience to carry out an analysis of data may choose instead to write a comprehensive literature review on a specific topic related to historical demography, broadly defined.  They will need to discuss their proposed topic with me and have it approved.

Schedule (Tentative)
Readings are subject to modification.  The readings emphasize recent scholarship in which advanced methods are applied to novel individual-level, longitudinal demographic databases.  Starting in Week 3, there will be in-depth discussion of a specific dataset every week.  Time will also be available every week for students to discuss questions and problems they have about the dataset they are analyzing.  Discussion of issues related to data management are especially welcome.
Week
Topic
1
Origins of historical demography
Population and economy in the past
Recent developments and new directions
Bengtsson, Tommy, Cameron Campbell, James Lee, et al.  2004.  Life Under Pressure: Mortality and Living Standards in Europe and Asia, 1700-1900.  Cambridge: MIT Press.  Chapters 1 2, and 3.
Tsuya, Noriko, Wang Feng, George Alter, James Z. Lee et al.  2010.  Prudence and Pressure: Reproduction and Human Agency in Europe and Asia, 1700-1900Cambridge: MIT Press.  Chapters 1-2, 5.
康文林 (Cameron Campbell).  2012.  “人口歷史(Population History)”.  Chapter 8 in 人口學 (Demography).  北京(Beijing):人民大學出版社 (People’s University Press).  I will distribute the English language original.
2
Old and New Sources
Data entry, cleaning and linkage
Dataset management
Analysis
Bengtsson, Tommy, Cameron Campbell, James Lee, et al.  2004.  Life Under Pressure: Mortality and Living Standards in Europe and Asia, 1700-1900.  Cambridge: MIT Press.  Appendix A.
Campbell, Cameron and James Lee.  2002 (publ. 2006).  “State views and local views of population: Linking and comparing genealogies and household registers in Liaoning, 1749-1909.”  History and Computing.  14(1+2):9-29.  [LINK]
Ruggles, Steven.  2002 (publ. 2006).  “Linking historical Censuses: A new approach.”  History and Computing.  14(1+2):213-224.  [LINK]
Tsuya, Noriko, Wang Feng, George Alter, James Z. Lee et al. Prudence and Pressure: Reproduction and Human Agency in Europe and Asia, 1700-1900Cambridge: MIT Press.  Chapter 3.
Please visit the websites of the Union Army Study, the Historical Sample of the Netherlands, by following the links above. 
Please also visit the websites of the following historical population databases, which while not yet publicly available, are nevertheless of potential importance because data are available through application, or may be released:
PRDH (genealogical database constructed from Quebec parish registers)
Enquête TRA (A French project to link the demographic and administrative records of individuals whose surnames begin with the letters TRA)
Program for Historical Demography (Taiwanese household registers from the Japanese colonial era)
For reference
Gutmann, Myron and Etienne van de Walle.  1978.   “New Sources for Social and Demographic History: The Belgian Population Registers.”  Social Science History. 2(2): 121-143. http://www.jstor.org/stable/10.2307/1171005
Kurosu, Satomi.  2002.  “Studies on Historical Demography and Family in Early Modern Japan.”  Early Modern Japan: An Interdisciplinary Journal.  10(1):3-21.  https://kb.osu.edu/dspace/handle/1811/603
Lee, James, Cameron Campbell and Wang Feng. 1993. “An introduction to the demography of the Qing imperial lineage, 1644-1911.” Schofield, Roger and David Reher eds. Old and New Methods in Historical Demography.  Oxford: Oxford University Press, 361-382.
Park, Hyunjoon and Sangkuk Lee.  2008.  “A survey of data sources for studies of family and population in Korean history.”  The History of the Family.  13(8):258-267.  http://www.tandfonline.com/doi/abs/10.1016/j.hisfam.2008.05.005

3
Classic issues

Fertility behavior before the demographic transition

Bengtsson, Tommy and Martin Dribe.  2006.  “Deliberate control in a natural fertility population: Southern Sweden, 1766-1864.”  Population Studies.  43(4): 727-746.   http://www.jstor.org/stable/4137215
Campbell, Cameron and James Lee.  2010.  “Fertility control in historical China revisited: New methods for an old debate.”  History of the Family 15:370-385. doi:10.1016/j.hisfam.2010.09.003.
Tsuya, Noriko, Wang Feng, George Alter, James Z. Lee et al. Prudence and Pressure: Reproduction and Human Agency in Europe and Asia, 1700-1900Cambridge: MIT Press.  Chapter 6.
Van Bavel, Jan. 2004. “Deliberate birth spacing before the fertility transition in Europe: evidence from nineteenth-century Belgium.” Population Studies. 58(1): 95-107.  http://www.tandfonline.com/doi/abs/10.1080/0032472032000167706
Van Bavel, Jan and Jan Kok.  2010.  “A mixed effects model of birth spacing for pre-transition populations: Evidence of deliberate fertility control from nineteenth century Netherlands.”  History of the Family.  15(2):125-138.  http://www.tandfonline.com/doi/abs/10.1016/j.hisfam.2009.12.004
Dataset: CMGPD-LN and CMGPD-SC.  Please review the CMGPD-LN User Guide in preparation, especially pages 1-25.
4
Demographic transition
Brown, John C. and Timothy W. Guinnane.  2002.  “Fertility transition in a rural, Catholic population: Bavaria, 1880-1910.”  Population Studies.  56(1):35-49.  http://www.jstor.org/stable/3092940
Cutler, David and Grant Miller.  2005.  “The role of public health improvements in health advances: The twentieth-century United States.”  Demography.  42(1):1-22.  http://muse.jhu.edu/journals/demography/v042/42.1cutler.html
Hacker, J. David.  2003.  “Rethinking the “early” decline of marital fertility in the United States.”  Demography.  40(4):605-620.  http://www.springerlink.com/content/e334t244gl5173j3/
Dataset: Qing Imperial Lineage Genealogy

For reference


Hacker, J. David.  1999.  “Child naming, religion and the decline of marital fertility in nineteenth-century America.”  History of the Family.  4(3): 339-365. http://www.tandfonline.com/doi/abs/10.1016/S1081-602X(99)00019-6

5
Household organization, family formation, marriage, and adoption
Bengtsson, Tommy, Cameron Campbell, James Lee, et al.  2004.  Life Under Pressure: Mortality and Living Standards in Europe and Asia, 1700-1900.  Cambridge: MIT Press.  Chapters 4, 5.
Chen Shuang, Cameron Campbell, James Lee.  2008.  “Institutional, Household, and Individual Influences on Male and Female Marriage and Remarriage in Northeast China, 1749-1912.”  CCPR Working Paper 061-08.   http://papers.ccpr.ucla.edu/papers/PWP-CCPR-2008-061/PWP-CCPR-2008-061.pdf
Kurosu, Satomi 2011.  “Divorce in Early Modern Rural Japan: Household and Individual Life Course in Northeastern Villages, 1716-1870.”. Journal of Family History.  36:118-141.  http://jfh.sagepub.com/content/36/2/118.short
Tsuya, Noriko, Wang Feng, George Alter, James Z. Lee et al. Prudence and Pressure: Reproduction and Human Agency in Europe and Asia, 1700-1900Cambridge: MIT Press.  Chapter 4.
Dataset: Korean household registers

Historical data for contemporary topics
6
Migration
Abramitzky, Ran, Leah Platt Boustan, Katherine Erikkson.  2010.  “Europe’s tired, poor, huddled masses: Self-selection and economic outcomes in the age of mass migration.”  NBER Working Paper No. 15684.  http://www.nber.org/papers/w15684
Arrizabalaga, Marie-Pierre.  2005.  “Basque Women and Urban Migration in the 19th Century.” The History of the Family.  10:99-117.  http://www.tandfonline.com/doi/abs/10.1016/j.hisfam.2004.01.015

Campbell, Cameron and James Lee.  2001.  “Free and unfree labor in Qing China: Emigration and escape among the bannermen of northeast China, 1789-1909.”  The History of the Family: An International Quarterly.  6(4):455-476.  [LINK]

Kesztenbaum L.  2008.  “Cooperation and coordination among siblings: Brothers’ migration in France, 1870-1940” The History of the Family. 13(1):85-104. http://www.tandfonline.com/doi/abs/10.1016/j.hisfam.2008.01.006
Dataset: TBA
For reference
Boustan, Leah Platt, Price V. Fishback, and Shawn Kantor.  2010.  “The effect of internal migration on local labor markets: American cities during the Great Depression.”  Journal of Labor Economics.  28(4):719-746.  http://www.jstor.org/stable/10.1086/653488
7
Stratification and social mobility
Campbell, Cameron and James Lee.  2008.  “Kin Networks, Marriage, and Social Mobility in Late Imperial China.”  Social Science History 32(2):175-214. http://ssh.dukejournals.org/content/32/2/175.short
Long, Jason and Joseph Ferrie.  2005.  “A Tale of Two Labor Markets: Intergenerational Occupational Mobility in Britain and the U.S. Since 1850.”  NBER Working Paper 11253.  http://www.nber.org/papers/w11253.pdf. Forthcoming in American Economic Review as “Intergenerational Occupational Mobility in Britain and the U.S. Since 1850.”
Xie Yu and Alexandra Achen Killewald.  2010.  “Historical trends in social mobility: Data, methods and farming.”  PSC Research Report No. 10-716.  http://www.psc.isr.umich.edu/pubs/pdf/rr10-716.pdf  Forthcoming in American Economic Review.
Arrondel, Luc and Cyril Grange.  2006.  “Transmission and inequality of wealth: An empirical study of wealth mobility from 1800 to 1938 in France.”  Journal of Economic Inequality.  4(2):209-232.  http://www.springerlink.com/content/d2880h0463895p29/
For reference
Van Leeuwen, Marco H.D.  2009.  “Social inequality and mobility in history: introduction.”  Continuity and Change.  24(3):399-419.
Van Leeuwen, Marco H.D., Ineke Maas, and Andrew Miles.  2004.  “Creating a historical international standard classification of occupations: An exercise in multinational interdisciplinary cooperation.”  Historical Methods.  37(4):186-197.
Van Leeuwen, Marco H.D. and Ineke Maas.  2010. “Historical studies of social mobility and stratification.” Annual Review of Sociology. 36:429-451.
Dataset: TBA
8
Long-term effects of conditions in early and mid-life
Bengtsson, Tommy, and Göran Broström.  2009.  “Do conditions in early life affect old-age mortality directly and indirectly?  Evidence from 19th century rural Sweden.”  Social Science and Medicine.  68(9):1583-1590.
Costa, Dora. L.  2000.  “Understanding the twentieth-century decline in chronic conditions among older men.”  Demography.  37(1):53-72.
Ferrie, Joseph.and Karen Rolf.  2011.  “Socioeconomic status in childhood and health after age 70: A new longitudinal analysis for the U.S., 18952005.”   Explorations in Economic History.  48(4):445-460.
Smith, Kenneth R., Geraldine P. Mineau, Gilda Garibotti, Richard Kerber.  2009.  “Effects of childhood and middle-adulthood family conditions on later-life mortality: Evidence from the Utah Population Database, 1850-2002.” Social Science Medicine.  68(9)1649-59.
Smith, Kenneth R., Geraldine P. Mineau, Lee Bean. 2002.  “Fertility and post-reproductive longevity.” Social Biology. 49(3-4):185-205.
Dataset: TBA

9
Kinship networks and multigenerational perspectives
Campbell, Cameron and James Z. Lee.  2011.  “Kinship and the Long-Term Persistence of Inequality in Liaoning, China, 1749-2005.”  Chinese Sociological Review. 44(1):71-104.  [LINK]
Garibotti, Gilda, Ken R. Smith, Richard A. Kerber, Kenneth M. Boucher.  2006.  “Longevity and Correlated Frailty in Multigenerational Families.”  The Journals of Gerontology.  61A(12):1253-61.
Dataset: TBA
10
Presentations, and final discussion

Social Mobility and Demographic Behaviour: A Long-Term Perspective

The complete collection of papers from the December 2008 IUSSP Scientific Panel on Historical Demography seminar “Social Mobility and Demographic Behaviour: A Long-term Perspective” that Martin Dribe, Jan Van Bavel, and I organized at the UCLA California Center for Population Research (CCPR) is now available as a special collection at Demographic Research: http://www.demographic-research.org/special/10/  The meeting received generous support not only from the IUSSP, but also from a number of UCLA units, including CCPR the International Institute, the Dean of Social Sciences, and the Department of Sociology.  Participating scholars came from Asia, Europe, and the Americas, and represented a variety of disciplines, including sociology, economics, and history.

The papers had already appeared individually as they completed the review and production process, and with the addition of our introduction, the collection is now complete.

These papers all address the interaction of demographic behavior with social mobility.  Most of them apply advanced quantitative techniques to longitudinal historical population databases such as the Historical Sample of the Netherlands, but some used contemporary data.  Specific of the many questions that the papers addressed included changes over time in the influence of family size of origin on socioeconomic attainment, and interactions between social mobility and  assortative mating.  A detailed introduction to the papers is available here: http://www.demographic-research.org/Volumes/Vol26/8/default.htm

Historical Demography: Data, Methods, and Applications (Spring 2012 Sociology 285B)

Sociology 285B
Historical Demography: Data, Methods, and Applications
Spring 2012
Haines A78, M 1-4PM

Cameron Campbell
Haines 202
x51031
Preliminary and subject to substantial revision
This seminar will examine 1) new data and methods that have recently expanded the scope of historical demography, 2) classic debates and controversies in population history, including relevant broader debates in economic and social history,3) recent work using new data and methods to investigate topics of longstanding interest in historical demography such as mortality, fertility, and marriage, as well as newer topics such as migration and stratification, and 4) recent work that has used historical demographic data to address issues of contemporary relevance, especially in health and mortality.  In discussion of new data, publicly released datasets such as the China Multigenerational Panel Dataset – Liaoning (CMGDP-LN), the Historical Sample of the Netherlands (HSN), the Union Army Study, and IPUMS Linked Samples will receive particular attention. Weekly class discussions will not only cover the substance of the readings and relevant debates, but methodological issues related to the creation, management and analysis of datasets. 
The course is intended not only for students interested in social or economic history, but also for students interested in contemporary social demography and population who would like to learn more about relevant historical demographic research and data.  Students who complete the class will have a detailed understanding of major debates and controversies in historical demography as well as knowledge of the major sources of historical demographic data and the methods for organizing and analyzing them, and should be adequately prepared to initiate a study on a topic of their choice with a historical dataset.

Background
Historical demography began as an effort to reconstruct demographic regimes in populations in the past.  Central concerns were reconstruction of historical trends in basic indices of mortality, fertility, and nuptiality, and examination of the relationship of those trends to economic and social conditions.  Practitioners made creative use of individual-level data in parish registers, genealogies, and other sources to produce estimates of aggregate demographic indices.  Such work illuminated demographic processes before and during the demographic transition, and provided a foundation for ambitious efforts to examine relationships between population, the economy, and social organization in the past.
New data and new methods have transformed historical demography.  They have not only broadened its scope by making possible the investigation of a much wider variety of historical topics, but they have also increased the relevance of historical demography for our understanding of contemporary demographic phenomena.  One key development has been the construction of large databases of longitudinal and in some cases multigenerational individual level data from historical household registers, genealogies, and other archival sources.  Application of event-history analysis and other regression-based techniques to these data has led to a new focus on describing, comparing, and understanding patterns of differentials within populations.  Historical demographic studies now routinely examine differences in marriage, reproduction, and death according to community and family context, socioeconomic status, and other individual and family characteristics.  Increasingly, historical demography takes advantage of these novel data and methods to focus on new topics such as migration and stratification, and examine topics of contemporary importance such as the influence of conditions in early life on socioeconomic and health outcomes at later age, and multigenerational processes.
Prerequisites
Because the class requires a research paper involving quantitative analysis of a historical demographic dataset, students should already be familiar with using statistical packages (preferably STATA) to estimate linear regressions as well as regressions for limited dependent variables.  Students should have basic familiarity with data management and analysis in STATA or another statistical package, or be willing to invest the time during the course to acquire these skills.  Prior coursework in social demography or demographic methods is useful but not required.  Students who are interested in the class but who do not have prior coursework or experience in quantitative analysis may consult with me on alternative topics for the research paper.

Grading

Weekly response papers – 50%
In preparation for the discussion at each week’s class meeting, students will write a one page (500-600 words) essay responding to and reflecting on the assigned readings for that week.  Responses may either critique individual readings, or synthesize key points from all readings to identify questions and topics on which to focus class discussion.  Students will post their essays to the discussion forum at the class website in advance of each class meeting.

Class participation – 10 %
Students are expected to attend all class meetings and contribute to discussion.  Ten percent of the grade will be assigned based on attendance and participation in discussion.

Research paper and presentation – 40%
Students will write a research paper on a topic of their choice based on analysis of historical population data.  The data should be individual level and longitudinal, and may consist of household registers, genealogies, linked Censuses, or other linked archival sources.  Cross-sectional data such as historical Censuses may be acceptable but the analysis should include a time dimension.  Students are encourage to make use of publicly available datasets that are readily downloadable such as the China Multigenerational Panel Dataset – Liaoning (CMGPD-LN), the Historical Sample of the Netherlands, the Union Army Study, or the IPUMS Linked Representative Samples.  There are other potential sources of data with varying degrees of accessibility and I will be happy to discuss possibilities with students.  Students who do not have prior coursework or other experience in quantitative analysis may consult with me on the possibility of writing a paper on an alternative topic.

Schedule (Tentative)
Week
Topic
1
Origins of historical demography
Recent developments
New sources

2
Data entry, cleaning and linkage
Dataset management
Analysis

Classic issues

3
Population and economy in the past
1) Malthusian and Boserupian processes
2) Demographic regimes and economic growth before the Industrial Revolution
3) Differential demographic responses to economic pressure

4
Reproductive behavior before and during the demographic transition

5
Household organization, family formation, marriage, and adoption

Historical data and contemporary topics

6
Migration

7
Social mobility

8
Long-term effects of conditions in early life

9
Kinship networks and multigenerational perspectives

10
Presentations, and final discussion