IPUMS Int.l Online Data Analysis
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1 Minnesota Population Center Training and Development IPUMS Int.l Online Data Analysis Exercise 1 OBJECTIVE: Gain an understanding of how the IPUMS dataset is structured and how it can be leveraged to explore your research interests. This exercise will use the IPUMS dataset to explore cell phone ownership, fertility, and migration patterns. 10/24/2012
2 Page1 IPUMS-I Training and Development Research Questions What are the patterns of cell phone ownership in South Africa? What is the trend in fertility in China? What is the relationship between income and migration in India? Objectives Select datasets and variables of interest the data using sample code Validate data analysis work using answer key IPUMS-I Variables CELL: Cellular phone availability URBAN: Urban/rural status CHBORN: Children ever born to the individual CHBORNF/CHBORNM: Children ever born to the individual, female/male MGCAUSE: Cause of migration INCWAGE: Wage and salary income per week SDA Code to Review Field Row Column Control Selection Filter Purpose Represents the primary variable of interest Divides the analysis of the variable of interest into categories Creates a separate chart for each category of the control Allows you to select cases; ex: year(2000-*) -> all years 2000-onward Review Answer Key (page 6) Common Mistakes to Avoid 1 Choosing numerical instead of categorical variables for the Frequencies/Cross Tabulation Program. For these, use the Compar ison of Means Program instead 2 Forgetting to specify the years of interest
3 Page2 Step 1 Select a Sample Getting Started Go to and on the left-hand side of the page, select " Data Online" You will have the option to analyze single year or multi-year samples of one country, or datasets of all countries within a region for all available years Choose the multi-year sample for South Africa The default analysis is frequencies and cross-tabulation Step 2 Research Variables of Interest Search on the main IPUMS-I site for variable descriptions, codes, universe, etc, or browse under Household and Person variable categories Row and Column are the variables of interest that you will perform the cross-tabulation on Filters select only specified cases A Control creates multiple tables for row and column variables, separated by a third categorical variable. For example, if you include the variable SEX as a control, you will get two frequency tables The Weight default is person weight (wtper), which extrapolates the sample to represent the entire population
4 Page3 Part I - South Africa Cell Phone Ownership A) What are the codes and universe for CELL? Is this a person variable or a household variable? B) What are the respective shares of households for which a cell phone was available? i. Rural households across all samples? ii. Urban households? Row: cell Column: urban Weight: wthh Selection Filters: cell(1-2), urban(1-2) C) Compare the share of rural or urban households where a cell phone was available in 1996 to Comment on the change over time. Row: cell Column: urban Weight: wthh Control: year Selection Filters: cell(1-2), urban(1-2)
5 Page4 Part II Fertility in China Choose the multi-year sample for China. In the upper-left corner, hover over Analysis and then select Comparison of Means. A) What is the universe for CHBORN? Why would a frequency table by CHBORN and SEX not be informative? B) What is the code in CHBORN for NIU (Not in Universe)? Should they be excluded from a comparison of means? C) Compare the means of CHBORN between 1982 and D) What was the ratio of male children ever born to female Dependent: chborn Row: year Selection filter: sex(2), chborn(*-30) children ever born in 1990? Either return to the main SDA page and choose the China 1990 sample, or create a year (1990) filter. Dependent: chbornf, chbornm Row: year Filter: chbornf(*-30), chbornm(*-30), sex(2)
6 Page5 Part III Migration and Income in India Choose the sample for India Use the Frequencies and Crosstabulations first. A) What are the reasons for migration in India in 1983? Most common reason? Least common reason? Row: mgcause Selection filter: mgcause(10-69) B) Now change to Comparison of Means. What was the average income for migrants who moved due to: i. Natural disaster? ii. Social and political problems? iii. Job relocation? Why might this be? Dependent: incwage Row: mgcause Selection filter: mgcause(10-69), incwage(* ) Complete! Validate Your Answers
7 Page6 ANSWERS: Part I - South Africa Cell Phone Ownership A) What are the codes and universe for CELL? Is this a person variable or a household variable? Codes: 0 NIU (not in universe); 1 Yes; 2 No; 9 Unknown; Universe: South Africa 1996: Private households; South Africa 2001: Non-homeless households; South Africa 2007: Private household; CELL is a household variable B) What are the respective shares of households for which a cell phone was available? i. Rural households across all samples? Yes: 33%, No: 67% ii. Urban households? Yes: 56.7% No: 43.3% Row: cell Column: urban Weight: wthh Selection Filters: cell(1-2), urban(1-2) C) Compare the share of rural or urban households where a cell phone was available in 1996 to Comment on the change over time. 1996: Yes: 3.9% No: 96.1%; 2007: Yes: 71.6% No: 28.4% The availability of cell phones to rural households in South Africa is converging on the availability of cell phones in urban households. Row: cell Column: urban Weight: wthh Control: year Selection Filters: cell(1-2), urban(1-2)
8 Page7 ANSWERS: Part II Fertility in China Choose the multi-year sample for China. In the upper-left corner, hover over Analysis and then select Comparison of Means. A) What is the universe for CHBORN? Why would a frequency table by CHBORN and SEX not be informative? Females ages 15 to 64. There are no men in the universe of this question. B) What is the code in CHBORN for NIU (Not in Universe)? Should they be excluded from a comparison of means? The code for NIU is 99, and 98 is Unknown. If these aren't excluded, they will be included in the means of children ever born and bias the estimates upwards. C) Compare the means of CHBORN between 1982 and : Average of 2.61 children ever born; 1990: Average of 2.1 Dependent: chborn Row: year Selection filter: sex(2), chborn(*-30) D) What was the ratio of male children ever born to female children ever born in 1990? The mean number of male children ever born was 1.09, and mean number of female children born was 1.01, for a ratio of 1.09/1.01= boys to each girl. Either return to the main SDA page and choose the China 1990 sample, or create a year (1990) filter. Dependent: chbornf, chbornm Row: year Filter: chbornf(*-30), chbornm(*-30), sex(2)
9 Page8 ANSWERS: Part III Migration and Income in India Choose the sample for India Use the Frequencies and Crosstabulations first. A) What are the reasons for migration in India in 1983? Most common reason? Marriage or union Least common reason? Natural disaster Row: mgcause Selection filter: mgcause(10-69) B) Now change to Comparison of Means. What was the average income for migrants who moved due to: i. Natural disaster? rupees ii. Social and political problems? iii. Job relocation? Why might this be? The income for migrants who moved due to natural disasters are likely to earn much less than average in the past week because they may have lost everything or did not have enough capital to rebuild and stay in the same place. Families that are slightly wealthier than average might be able to afford to move due to security. Job relocators are paid the most because the pay must be higher to exceed the costs of moving. Dependent: incwage Row: mgcause Selection filter: mgcause(10-69), incwage(* )
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