Archive

Monthly Archives: July 2013

If you had hundreds of millions of lines of contribution data, what would you want to know? Well here at ActBlue, we have an insane amount of data, and we’re always looking to learn more about our donors and how they use our site.

So we recently recently posed the question:

Who donates more…men or women?

The answer turns out to be women, but only if you approach things from the right perspective.

Before I go on, I’d like to say that I by no means want to perpetuate the gender binary; everyone at ActBlue respects and values people all across the gender spectrum.

We all know some of the basic election gender data – more women went for Obama, more men for Romney. But, political contributions involve personal investment, so I wanted to see how it breaks down on our site, which is obviously exclusive to Democrats. There was just one hiccup in my data-nerd fantasy: we don’t collect any information on our donors’ gender identification.

The easiest way to get around this problem is to use approximate name-gender matching. While many databases available for this purpose are either costly, unreliable, or both, I did eventually find a source which I felt comfortable using (an academic paper available for free in which the authors explained their methodology). So after digging into our database and crunching the numbers, I came out with some answers. I’ll give an overview of my results first and then explain my methodology and some statistical issues I want to highlight in a bit more detail further down.

I found that for individual contributions, women give about 15.0% smaller dollar amounts than men do. I also found, however, that women are 12.4% more likely to make a recurring contribution than men are. (Assume all of these values are statistically significant, but if you’re interested read more on that below.)

So the obvious question was: what happens once you factor in future installments of a recurring contribution, and not just the initial dollar amount? I crunched the numbers again, but it turned out not to change anything — women still donated about 16.6% smaller dollar donations than men. This was a big surprise, so I started racking my brain for possible explanations.

You’ve probably already figured it out, but I made quite an oversight in my initial assumptions. It’s well documented that the gender wage gap still persists; 77 cents is a popular estimate for how much a woman earns for doing the same amount of work a man is payed one dollar to do. This is incredibly unjust, but it is also directly relevant to my project — women are unfairly earning less income than men, so it makes sense that they’d have less disposable income from which they are willing and able to make political contributions, all else equal.

So I did what every progressive has always dreamed of. I punched a few computer keys and voilà– the gender wage gap disappeared! After this adjustment for equality, women turned out to make about 12.9% higher dollar contributions than men, and when factoring in the entirety of recurring donations, they donated 11.4% more than men. Quite the change from my initial findings, indeed. (This kind of broad and general adjustment is bound to be approximate, but in my opinion it was actually a fairly conservative change. But, see below for some discussion of that.)

Given ActBlue’s focus on grassroots donors, I wondered what would happen if I trimmed my dataset to include only donations that were $100 or less. Well, I did that and was left with about 95% of my original sample, which really does demonstrate the extent to which ActBlue is all about small-dollar donations. After trimming the dataset (and continuing to use adjusted donation amounts), I found that women were donating higher dollar amounts than men to an even greater extent than before, at 21.1%!

As many of you know, ActBlue Express Accounts allow donors to securely store their payment information with us and donate with just one click. I found that women and men in my sample donated using an ActBlue Express Account at a remarkably similar rate– within 1 percentage point. This just goes to show how egalitarian ActBlue Express Accounts are!

Now there are several important takeaways here. It looks like on ActBlue, for example, women tend to donate higher dollar amounts than men (after adjusting for the gender wage gap), and also tend to give recurring contributions more often than men. But for me, the biggest lesson was to be vigilant about understanding what outside factors might be affecting the internal nature of your data.

Before I move on to some nitty-gritty technical comments, I want to say that I really did mean the question that opened this blog post. So, readers, what would you want to know if you had that much data? I really enjoyed sharing these results with you, so please shoot me a note at martin [at] actblue [dot] com to let me know what you’d like our team to dig into for the next post!

My discussion below is a bit more technical and intended for other practitioners or very curious general readers.

As I mentioned above, name-to-gender matching is difficult for several reasons. In “A Name-Centric Approach to Gender Inference in Online Social Networks”, C. Tang et al. combed Facebook pages of users in New York City and, after using some interesting techniques, came up with a list of about 23k names, each of which was associated with the number of times a user with that name identified as male and female. I definitely recommend reading through their study– you might not think it’s perfect, but it could provide some inspiration for the aspiring data miners among you. In any case, I then did some further pruning of their list for suitability reasons, the effects of which were minimal. I combined their name-gender list with a n=500k random sample of contributions made on ActBlue since 2010, matching only names that appear on both lists for obvious reasons.

At that point, I had a dataset that included, on a contribution-basis, the donor’s name, estimated gender (the authors of the study pegged their matching accuracy at about 95%), and some other information about the contribution. Of the 500k sample, the matching spat out about 50.4% females.

When I say “other information”, I’m specifically referring to factors that I know from past analyses directly affect contribution amount (for instance, whether the donor is an ActBlue Express User or not). I took this extra information since I knew I’d need to control for these factors when evaluating the effect of gender on donation amount. This is a good reminder of why it’s super important to know your data really well by staying current with trends and performing frequent tests– otherwise you might end up omitting important explanatory variables, choosing a misspecified model, or making other common mistakes.

With my dataset ready, I tried a few different types of models, but landed on one in which the dependent variable (contribution amount) was in logarithmic form, so it looked like:

ln(contribution_amount) = β0 + β1female + some other stuff + u

This model was best for a few different, yet boring (even for practitioners) reasons, so I’ll spare you the discussion :)

As I noted in my general discussion, all of the results I found were “statistically significant”, but there was an issue I wanted to address. In my case, yes, beta coefficients were significant at p<.0001, as was the overall significance of the regression and joint significance of groups of regressors I thought it important to test. But with n=500k, I think saying certain things were “statistically significant” can be a bit insincere or misleading if not explained properly, unless you’re talking to someone fairly comfortable with statistics. What I mean is pretty obvious if you just think about how a t statistic is actually computed, why it’s done that way, and what that means.

At huge sample sizes, very small differences can be “significant” at very high confidence levels, and lead to misinterpreting your results. Moreover, just because something is statistically significant doesn’t mean that it is practically significant. There are a few different ways to deal with this, none of which are perfect, though. In my case, I saw that 95% CIs of the regressor coefficients were really tight, and would certainly consider 10%-14% differences practically significant (don’t get me wrong—of course there are times when small differences like 0.3% can be practically significant, but this isn’t one of them). I’m not bashing large sample sizes here or saying that hypothesis testing is unimportant (it is!), but rather emphasizing caution and clarity in our reporting.

Further, there’s another important lesson here. Sometimes, no matter how cleverly we choose our models or carefully we conduct our analysis, the explanatory power of a regression is going to be limited because you simply don’t have enough data. I don’t mean depth of data (i.e. sample size), but rather the breadth of the data (i.e. categories of information). For instance, personal income is clearly going to be an important factor in determining the dollar amount of a given political contribution. We don’t, however, have that kind of information about donors. Does that mean I should have just thrown away the regression and called it a day? Of course not, because obviously partial effects can be estimated fairly precisely with very large sample sizes, even with relatively large error variance. Again, the lesson is to be judicious in your interpretation and reporting of results.

I also noted that I thought my gender wage gap adjustment was fairly conservative. What I did was simple; for all contributions in the dataset made by females, I calculated an “adjusted” contribution amount by dividing the actual contribution amount by 0.77. This implicitly assumes that if women were paid equally for equal work, they would contribute more overall dollars, but at their current ratio of donations/income. In other words, their marginal propensity to donate would be constant as income increases. In fact, I think this is probably false in reality, and women (and men, for that matter) would instead demonstrate an increasing marginal propensity to donate with increased income, and therefore I should have increased the contribution amounts by even more than I did. I haven’t, however, read any study that provides a reliable estimate of a marginal propensity to donate, and therefore decided it best to keep things simple.

I already asked you to reach out and tell me what you’re interested in knowing, but I’ll double down here: I would love to hear from you and get your input so that the next blog post will reflect our community members’ input! So shoot me an email me at martin [at] actblue [dot] com.

Our crack tech team has been busting out the new features right and left lately. And now we’ve got one more: the ability to brand your pages without our assistance.

A few years ago we used to only allow brandings for our biggest clients. Then we opened it up to anyone, but you had to contact us, send over your logo file and background color and we’d get it up for you. Now you can do it yourself immediately and create as many different ones as you want to use on a page by page basis. It’s all part of our quest to bring our best and most awesome tools to everyone however big or small.

Just login and go to your admin panel and select that new option on the left navigation menu: “Branding”. If we haven’t set one up for you then just click “create new”. And then you’ll see a page that looks like this:

topbranding

Create a name for the branding for internal use. And decide if you want to make this the default branding for all your pages. You can have multiple brandings and use them on a page by page basis, or stick with one for all of your pages.

Click on the > beside the brand settings to expand it so you see this:

expandedview

And since not everyone knows their background hex color value off the top of their head, there is a color chooser that pops up when you click in the “Background Color” field. Choose your color and it’ll fill in the appropriate color code for you.

colorchooser

You can swap brandings on a page on the fly in the edit tab. It’ll let you select any one of the branding’s you’ve added.

And remember to watch that dashboard for notification of all the new features that we roll out.

A million dollars in one day, on a Sunday in June. Who would have thought that was possible? But man the last week in June was crazy, wasn’t it? And it turned into a huge day for thousands of campaigns and organizations on Sunday thanks to 26,285 donors. It was our first million dollar day of the year and somehow we don’t think it’ll be our last.

Our team was busy working all weekend, but frankly they’re happy to. It’s our little part to help keep this country moving forward.

Here are the toplines for the month, and it was our biggest one of the year!

The amazing ActBlue donor community chipped in (an understatement) $9,052,454.81 from 186,139 individual donations in June, bringing the total for the quarter up to $21.8 million and our grand total up to $395 million. That means that $400 million – and a celebration – is right around the corner!!!

This is how it compares to previous Junes:

junehistoric

The first thing we noticed was that the average donation amount actually went up, bucking our historical trend. So we dug into our numbers. It turns out that federal max-out donors in Q2, those folks giving $2,600 for a primary or $5,200 for the cycle, increased 75% from Q1. What’s that mean? Well, it looks like a bunch of federal candidates and committees are getting an early start building small dollar communities and reaching out to their early max out donors 17 months out. Frankly that should scare the crap out of the Republicans.

Donors contributed to 1,219 different committees last month, which is way up from 862 committees during June of the last off-year (2011). More candidates are starting to fundraise earlier from more donors. More. More. More! No seriously, it’s great to see and our team has been working hard adding dozens of newly declared candidates each month.

A full $4.2 million of the money you gave came in during the last week, in part because of the political events like Wendy Davis’s brave filibuster, the historic Supreme Court rulings and the End of Quarter deadline that mobilized and inspired all of you. Here are our day by day totals for the month. Check out that spike at the end!

junechart

Half of all contributions in June (51.5%) were made by ActBlue Express users! That’s definitely something to celebrate. That puts the count of our amazing ActBlue Express users up to 582,132 and growing.

By giving to candidates and organizations early in the election cycle, you’re helping them to build the foundation for 2014. With your support they’ll have the resources they need to mount strong campaigns and mobilize supporters. So thanks for doing your part to build a Democratic momentum for 2014!

We want to make it easy for campaigns to see their ROI on acquisition, so we’re launching a new tool for conversion tracking. For those who don’t understand that gobbledygook: it’s a way for people running online ads to track how well they’re doing.

Candidates and groups can now embed tracking codes on both the contribution form and the thank you page to track both those landing on the form and those who convert. And you’ve got the option to put the tracking snippets on a form by form basis, or set a default tracking setup for your committee which can then be opted-in on each contribution form.

The safest and easiest option for most committees will be to only set up tracking on contribution forms that are specifically made as an endpoint for an ad campaign. This ensures that the contribution forms that you use for email and website traffic load as fast and stable as possible.

To put tracking codes on a specific contribution form, first make sure you are logged in as the owner of that form. Then navigate to the “edit” tab for that contribution form:

And then look for the “Advanced Options” section:

If you have a campaign default set (see below for directions), you can select to use it here. To set up codes just for this contribution form click the radio button “Edit”:

Paste your code snippets in the correct fields. If your ad system allows you to pass a conversion amount, you can do so with the merge field {AMOUNT}. (Make sure to only do this on the conversion tracking page and not the landing page.)

We’ll replace that token with the amount of the contribution when the thanks page renders. This allows you to track the monetary value of a click from within your advertising platform.

To set a default tracking code for your committee, first visit your campaign dashboard. On the left side of the screen click on “Settings”. Now look for the “Advanced Options” section:

Paste your code snippets in the correct fields. If your ad system allows you to pass a conversion amount, you can do so with the merge field {AMOUNT}.

Once you have saved your committee default tracking code, you can select the radio button to use it on any of your contribution forms. See above for specific instructions.

That’s it. So go forth and track people (not in a creepy NSA way).

And here’s a page we’ve set up with the instructions that you can bookmark.

Follow

Get every new post delivered to your Inbox.

Join 27 other followers