Analysis of Census and Survey Data for Social Science Research University of Cape Coast Stata Workshop, July 11-20, 2011

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1 Analysis of Census and Survey Data for Social Science Research University of Cape Coast Stata Workshop, July 11-20, 2011 Graphs and weights Summary: This module teaches you how to use Stata to produce graphs, including histograms, pie charts and bar diagrams. We will also learn why most datasets include sample weights, and when and how to use them. Open Stata and load the 2000 Ghana census file Graphs Stata can produce a wide variety of graphs specific to our research needs. At the beginning of this course we mentioned that Stata has a comprehensive menu system through which commands can be selected. We have, however, only concentrated on using Stata at the command line level. This is because in most cases using the command line is far quicker and easier than using the menu system. However, graphing syntax can be rather long and somewhat complicated, especially if you are unfamiliar with it. It is therefore easier to learn how to generate graphs using the drop-down menu. After generating a graph through the menu system, Stata will automatically display the necessary code in the results window. Looking at this code is an excellent way of becoming familiar with the correct syntax and once you have gained some confidence you can switch from using the graphing menu to entering syntax at the command line level! We will explain how to use the drop-down menu but also subsequently present the correct code so that the graphs are easily reproducible. If you click on the Graphics tab in the Stata menu, you can see that Stata can generate a large number of different graphs. We will learn how to create and interpret three of the most widely used kinds of graphs -- histograms, pie graphs, and bar charts. We will learn about another type of graph, scatter plots, at a later point. Histogram Probably the easiest graph to create is a histogram. Histograms are a graphical tool that tell us the fraction (or percent) of observations, for any given variable, that fall within different ranges. Histograms are used for continuous variables. If we have a continuous variable, such as income or age, a histogram will tell us what percentage or fraction of the sample fall into different income bins or groups. We will present the basics of graphing histograms in Stata by working through the example we present below. To draw a histogram, click on the Graphics drop-down menu and then on the histogram option. A window will open up on your screen. Take time to look at this window. You should notice a number of tabs running across the top with different titles. These tabs allow us to manipulate our graph in various ways and we will go through the most important ones as we progress through this module. For now let s focus on the tab that is open, the main tab. This tab is where we enter the most important information, that is the variable we are interested in examining. There are a few other specifications we also have to choose on this tab but we are lucky that Stata does most of the hard work for us. The main tab To graph the histogram we simply need to do the following on the main tab:

2 Use the drop-down list of variables to choose a variable or just type in variable name in the box for variable. Let s use the variable age. Make sure that Data are continuous is selected (since age is a continuous variable) Under Y-axis select Percent (this is the most intuitive and easily interpretable measure to have on the Y-axis.) Click OK Stata then produces the histogram, which shows up in a new window. Also notice that the code for this histogram appears in the results window and in the command window. This makes it easy to re-use the command to make modifications. The first line of code shows us what we would have entered at the command line level to generate this graph. It s not too complicated, but as soon as we start specifying options, the command-line code will become more complicated. Nonetheless, try to get a feel for it as it is important to be able to use both the drop down menus and the command line for graphing. What does the histogram tell us about the age distribution in Ghana? This graph is a good start, but Stata allows for many more specifications. For example, instead of using "percent", we could have specified density, frequency, or fraction. Each would produce a similar looking histogram but each would have a different y-axis. For now, let's continue with percent and focus on improving the graph in other ways. The titles tab Graph titles are an important part of presentation that we should not overlook. Titles make graphs not only more understandable but also give them a more professional appearance. Furthermore, they allow us to include important additional information such as the source of the data used. Let s reopen the histogram menu and add titles to our graph. As you may notice, Stata remembers all the information you put in the last time. Click on the title tab, and enter the following information in the appropriate lines: Title: Age distribution in Ghana (2000) Subtitle: (Unweighted) Note: Source: IPUMS 1% subsample of Ghanaian Census 2000 The x and y axis tabs We may, in our endeavour to make the graph more informative, also want to format the axes. Stata allows for a wide range of alterations. We can change the scale, title, thickness, or colour of major and minor ticks/labels. We will concentrate only on the basic axis formatting options and leave further experimentation up to you. Before we begin, it will be useful to clarify what we mean when we refer to major and minor ticks/labels. Very simply, ticks are the small lines that come off the axes. They normally indicate the scale of the graph by dividing up the space between major ticks into sections of equal lengths. Major ticks have numerical labels while minor ticks do not. For example, on the x-axis of the graph above, we only have major ticks; each representing a 20 year increase in age. Stata will generally use its own default formula to calculate the interval between ticks but we can often improve upon this to make our graph more easily readable and interpretable. Now let s begin by turning our attention to the x-axis first: Click on the x axis tab and then add a new x axis title ( Age ). Then click on the major ticks/label properties. When we click on Major tick/label properties, a new window will open. In this window, we can view the different rules with 2

3 which we can specify the spacing of the labels on the x axis. You can experiment with the different options, but we prefer to change the selection from default to Range/Delta. This option requires us to specify a minimum, a maximum and delta, where delta is the interval between major ticks on the x-axis (i.e. the increments). From our previous graph, we know that age does not exceed 100, so we set this as the maximum. We set the minimum to 0, and we try a delta of 5. Once we have done this we click Accept. You could do a similar thing with minor ticks. Now click on the y axis tab and on Minor tick/label properties. Click on Range/Delta and enter 0 as minimum, 10 as maximum and a delta of 1. Then click Accept. Click ok. Notice that the code for creating this histogram with all the specified options is now much longer and more complicated than the code for our simple histogram. Can you see what each term in this code is doing? The if tab We might be interested in the age distribution not only for Ghanaians as a whole, but for men and women separately. To restrict the sample to females, click on the if tab and enter the restriction sex==2. The by tab Often, it is also nice to be directly able to compare whether two distributions look similar. Do men and women have similar age patterns or are there important differences? We can answer this question by clicking on the by tab. Click on the box Draw sub graphs for unique values of variables and then enter the variable of interest the space below this (in our case sex). Then go back to the if tab and get rid of the restriction sex==2. Also get rid of the major tick property changes that we made under the x axis tab to make the graph less cluttered. Now click ok. Your graph should now show two histograms, one for men and one for women. Try making a histogram for the number of children ever born for women aged Use the width of bin option, setting it to 1. Beware missing value codes! Pie charts Similar to histograms, pie graphs are graphical tools which inform the reader of the distribution of any particular variable. In contrast to histograms, however, Pie graphs are used for categorical variables only. We will start our explanation of pie charts with an easy example: suppose we want to know whether there are an equal number of men and women in our dataset. One way we could find out whether this is the case is by producing a pie chart for gender. (Note: If we want to know whether there are an equal number of men and women in Ghana, we should really either use the full census or use weights. We will learn more about weights later today). We create the pie chart as follows: Click on the graphics drop down menu tab and select the pie chart option (like we did for the histogram section). Once again a window will appear, but this time the window will be for graphing pie charts. Note that this window has very similar tabs to the histogram window, so you will be familiar with how to use these tabs already. The window will open on the main tab. Make sure the Graph by categories option is selected. Enter the name of the variable of interest under category variable. Let s use the variable sex. Then click ok. 3

4 A new window with the pie chart will pop up. The relevant syntax for this graph is: graph pie, over(sex) What does this graph tell us? The if/in tab Now suppose we want to dig a bit deeper and investigate the gender distribution of newborns and infants in our dataset for Ghana. To do this we need to restrict on the age variable to be either 0 or 1. In the pie chart window, click on the if/in tab. Type in our restriction: age==0 age==1 Could we have written the age restriction in a different way? Click ok. What does this new graph tell us? Notice that the code for this new pie chart is now graph pie if age==0 age==1, over(sex) Now, let s look at a categorical variable with more than two categories. Pie charts can be extremely helpful for exploring patterns when there are a number of categories. Suppose we re interested in the distribution of cooking fuel across households. One difference from variables like age or gender is that this is a household variable. When you make tables or graphs with household-level information you need to take that into account. If you don t control for the number of people in the household you will count large households more often than smaller ones. This creates bias and your results will not reflect the true distribution. So we only want to use one observation per household for these variables. How do we do this? The egen command is very helpful here. If you type help egen Stata gives you a list of potential functions that you can use with egen. Scroll down until you get to the function tag. This is exactly what we want: This will tag one observation for each distinct group defined by the variables we choose. In our case we want Stata to choose one person per household. Since the cooking fuel question is a household question and the answer is therefore identical for all household members, it doesn t matter which household member we choose. Type: egen hh_tag=tag(serial) since serial is a household identifier code that will be the same for all members of a household, but unique across households. Now let s see whether the command worked properly. Type sort serial order serial hh_tag You should see that only one person per household has a value of hh_tag equal to 1 if you browse the data, whereas all other household members are assigned a 0 for this variable. We can now proceed with our pie graph of cooking fuels. Open the pie graph window. Remember to reset all the information that we entered previously. Enter fuelck as the new variable of interest. In the if/in tab now specify the restriction hh_tag==1 4

5 Then click on the by tab. We may not only be interested in the distribution of different cooking fuels, but also in how this distribution differs between rural and urban areas. Therefore, check the box Draw subgraphs for unique values of variables, enter the variable urban in the appropriate line and click ok. You should now see two pie charts next to each other, showing the distribution of cooking fuels in rural and urban areas. What is the most popular cooking fuel in rural and urban areas, respectively? Tip: Since pie charts only represent fractions of the total population in rural and urban areas, they do not tell you differences in the use of cooking fuels in absolute numbers. If you wanted to know whether more households use kerosene in urban than in rural areas, for example, you should use tabulate to check this! Let s make this graph look a bit nicer. First, you may want to reduce the number of categories: We are not interested in values of the variable that are labelled as NIU or unknown/missing. Set these values to missing (remember that when you are working on your research projects, it may be helpful to create a new variable to keep the original values in case you may want to go back to them at some point in the future). To do this, type numlabel, add tab fuelck Do you remember what the numlabel command does? Then type replace fuelck=. if fuelck==0 fuelck==99 Next, go back to the pie chart window and enter a title in the titles tab. Then click on the slices tab. You will see that the window is divided into two sections: Slice Properties and Labels. In each of these sections you can either customize all the slices together or each individual slice separately. We choose for simplicity sake to customize all slices or labels at once. Let s start with the slice properties: - make sure that Customize all slice properties is selected; - Click on Slice Properties (all). A new window will appear. Here we simply: - select the Explode slice option; - Click on accept. We could also add some labels to our graph. To do that: - make sure the Customize all slice labels option is selected; - click on the Label Properties button. Once again, a new window will appear: - change the label type to percent ; - change the colour option to white; - click Accept. Click ok and look at the new graph and notice the effects of our formatting. The explode option has made our slices look as if they have exploded out of the pie chart! This can be particularly effective if we just make one slice that we want to emphasize explode out. As you can see, the labels didn t work so well in our case since we have many categories with 5

6 very small percentages. So you would either have to do without labels, change their size and orientation in the slice menu options to make sure that they are not on top of each other, or collapse some of the small categories into one by using the replace command to have fewer categories. We will do some reformatting in our next example. Bar graphs Bar graphs can be a useful alternative option to pie charts when you have a variable with many categories. Bar charts can be used not only for categorical variables but also for continuous variables. The reason for this is that the bar chart is quite diverse in its applicability as it allows us to incorporate summary statistics in our graphs. Suppose we want to visualize the mean number of persons in a household in every region by using a bar chart. You will find a bar graph option on the graphics drop down menu. Clicking on it will call up the bar graph window. As usual, we start by focusing on the Main tab. As you see, the Main tab looks rather different in comparison to the histogram and pie chart windows. Under Type of data the bar graph gives us the option of each bar reflecting the actual data or a specific summary statistic. In most situations you will want to keep the summary statistics option selected: - make sure the graph by creating summary statistics option is selected. Next we enter our variable of interest in the first row of the Statistics to Plot section: - enter the persons variable in the first row under the variable heading. You may have noticed that we can also choose the summary statistic which the bar chart will use. In our first example we will stick to using the mean (but there are also a range of other options that you can play around with). Remember that persons is a household-level variable. Therefore, go to the if/in tab and enter the restriction hh_tag==1 as before. Now click on the categories tab. This is where we specify our grouping variable, in our case region. - click on the Categories tab; - select Group 1 ; - enter the regngh variable as the grouping variable. - Click ok Clearly this graph is in need of some formatting. Below we change the angle of the group 1 labels so that they do not overlap. - In the categories tab click on Properties (for group one); - In the sub-menu change the angle of labels to 45 degrees; - Click accept. Suppose we also want to see differences between rural and urban areas in terms of the average number of people per household in each region. - Click on the categories tab - Tick group 2 and enter urban as the grouping variable - Click ok It is often useful to add numerical labels to the bars (it is also useful to take note of the numerous bar formatting options available in this tab). To do this: - Click on the bars tab - Under bar labels select Label with bar height 6

7 To make the graph less cluttered, let s get rid of the group 2 grouping variable under the categories tab. Then click ok. Sample weights Most survey and census data include sample weights. In household surveys these often reflect issues in sample design, such as an oversample of certain subgroups. A census is supposed to include everyone in the population, so the weights come about for a different reason. They are typically based on ex-post enumeration surveys that indicate that certain groups are underrepresented in the census. The weights adjust the census to be more representative of what is thought to be the true population. In our case, we are using a random subsample of the actual census, so we can use weights to make the analysis for our sample representative of the whole population. Calculating how many people lived in Ghana in 2000 on Day 1 by using the display command is one example where you have already seen weights being used. Each observation in the data can be thought of as representing some larger group of people in the total population. How large the group is depends on various factors such as where that person lives and the person s age, ethnicity and gender. All of this information is summarized in a person's weight. To make your results representative of the population, you tell Stata to use the weight provided in the data set. Stata uses this weight to weigh some observations more heavily than others. Weights are typically defined at both the individual and household level. Whether you use household or individual weights depends on whether your analysis is at the household or individual level. If you are looking at the percentage of households with electricity, for example, you would use household weights. If you are looking at the distribution of education for individuals you would use individual weights. IPUMS uses the same weight variables in all of the data sets. Find the weight variables in the data set: lookfor weight There are four weight variables: wtper, wtper_new, wthh, wthh_new. The wtper and wthh variables are the person weight and household weight created by IPUMS. The wtper_new and wthh_new are new household and person weights that we created that account for the fact that we drew a subsample from the IPUMS sample. This 2000 file is a 10% sample of the IPUMS sample, so we multiplied the IPUMS weights by 10. Look at the summary statistics for these weights: sum wt* We will want you to use the wtper_new and wthh_new weights, with the choice depending on whether your analysis is at the individual or household level. The syntax for producing weighted results is the same in most Stata commands: you specify the weight variable inside square brackets at the end of the command but before the comma. Below are some examples: sum age [w=wtper_new], detail tab age [w=wtper_new] Since the above tab of age includes everyone, the weighted total at the bottom should be approximately the total population of Ghana in tab age [w=wtper_new], sum(hrswrk1) sum persons if relate==1 [w=wthh_new] Let s look at how much difference the weights make in various calculations: sum age sum age [w=wtper_new], detail Is there a difference between the weighted and unweighted distributions for age? Why? 7

8 Now let s see how things look for another country. Open the file ipums_tanzania2002.5%.dta, which is a 5% subsample of the original IPUMS file for Tanzania (which was itself a 10% subsample of the complete census). Repeat the summary statistics for age from above. Is there a difference between weighted and unweighted distributions for age? Why? You should get in the habit of using weights for all of your results, though (as in this case) in most cases the results should be similar with and without weights. Let s go back to our Ghana census dataset for the next section. Population pyramids Population pyramids are a very effective tool that demographers use to look at age structure. We can use the Ghana 2000 census data to look at the age structure of the Ghanaian population. It will be simplest to work with five year age groups. Here is a convenient way to create a five year age group variable: gen age5=5*int(age/5) label var age5 Five year age group tab age age5 Stata is very good at making age pyramids using horizontal twoway bar graphs. There is even a nice example in the Stata help tutorial, which you can find if you type: search pyramid This will take you to the instructions for the twoway_bar command. The following steps walk through a modified version of the same approach: Use egen to generate the sum of the sample weights for males and females in each five year age group and for the total population. Then create a variable that gives the proportion of the population in each five year age group/gender cell. egen pop5=sum(wtper_new), by(age5 sex) egen totpop=sum(wtper_new) gen pop5m=100*pop5/totpop if sex==1 gen pop5f=-100*pop5/totpop if sex==2 bysort age5 sex: gen count=_n Note that the pop5f variable is given negative values, a trick that makes the bar graph look like a pyramid. The last command just creates a simple way to limit the graph to one observation per age/gender cell, making the graph faster to generate and print. We now have what we need for a population pyramid. Here is a simple version: twoway bar pop5m pop5f age5 if count==1 & age5<100, horizontal Here is a more complicated version that makes the graph look much better (this is in the do file agepyramid.do): twoway bar pop5m pop5f age5 if count==1 & age5<100, horizontal xtitle("female Male" "Percent of Population") xline(0) barwidth(4 4) xlabel(-7.5 "7.5" -5 "5" -2.5 "2.5" ) legend(off) ylabel(0 10 to 95) title("age Pyramid, Ghana 2000") To insert this into a paper it is nice to export it to a file in Windows MetaFile (.wmf) or TIFF (.tif) format. graph export agepyr_gh2000.wmf, replace graph export agepyr_gh2000.tif, replace The do file agepyramid.do shows how to generate age pyramids for people with and without electricity separately. The same approach could be used to make age pyramids for different years, for native born versus immigrants, etc. 8