Follow The Money, A Handbook

Searching for Patterns

Once your database is finished - you've categorized as many contributions as you can, and you've assigned "unknown" codes to all the rest - you're ready to begin compiling what you've got and digging up the patterns that stand out in bold relief. Just because the database is done, these patterns won't necessarily be obvious. Through the process of osmosis, you'll no doubt have picked up some interesting nuggets - an unexpectedly large concentration of funds from companies you might not have suspected, major bundling operations to specific politicians, etc. But the overall patterns of dollars going to the state legislature, for example, still need to be fished out of the data. To pull the trends out - and to find material for the stories you'll do later - there are a number of steps you can take.

Calculate totals for every category. This is a logical first step, and one that is guaranteed to show you things you'd never have known without doing all the research. To calculate category totals all you do is sort the database by the category code and have the computer generate totals. Save these totals as a separate file. What you'll have is a list like this:

A0000 Agriculture $8,000
A1000 Crop production & basic processing $83,039
A1100 Cotton $500
A1200 Sugar cane & sugar beets $12,300
A1400 Vegetables, fruit and tree nuts $41,925
A1500 Wheat, corn, soybeans and cash grain $10,050
A1600 Other commodities (incl rice, peanuts, honey) $6,839
A1300 Tobacco & Tobacco products $65,850

The next step is to aggregate these totals by industry and sector. Each category code - A1000, A1100, etc. - has an industry code associated with it. In the Center's category database (outlined here), this code is named "catorder" and it consists of one letter and two numbers. The industry called Crop Production & Basic Processing, for, example, is coded A01, and it includes not just A1000, but A1100, A1200, A1400, A1500 and A1600 as well. Note that it does not include A1300 - tobacco is classified as an industry in its own right. The A0000 code, which is a catchall category for agriculture-related contributors that you can't put anywhere else, carries a catorder of A11 (miscellaneous agriculture).

When you total up the list above, here's what you come up with:

A01 Crop production & basic processing $154,653
A02 Tobacco & tobacco products $65,850
A11 Miscellaneous agriculture $8,000

Do the same for all your categories, and you'll have compiled totals for approximately 100 industry and interest groups. The overall patterns will begin falling in place now, but 100 categories is still too many to illustrate in a simple graph that will show you at a glance which are the top contributors. To do that, you'll generate totals for each of 12 main sectors - Agriculture, Construction, Health, Labor, etc.

Just as each category has an industry or "catorder" attached to it, so it also has a sector attached to it. All the categories shown in the sample above fall within the Agriculture sector. Look here to see the sectors for each category code and you'll see how they all fit into place.

When you've generated totals for each sector, you'll have a list like this (the totals here are from the 1992 federal elections):

Agriculture $24,892,124
Communications/Electronics $21,232,464
Construction $15,246,489
Defense $8,328,760
Energy & Natural Resources $21,341,235
Finance, Insurance & Real Estate $71,091,876
Health $31,710,239
Lawyers & Lobbyists $44,058,744
Miscellaneous Business $38,478,007
Transportation $18,989,690
Labor $43,299,597
Ideological/Single-Issue $29,331,914

This information is compact enough to turn into a chart. You can do this in any spreadsheet program on your computer, or in a stand-alone graphics program. DeltaGraph on the Macintosh is an outstanding program for creating charts and graphs - and your paper's graphics department probably already has a copy.

But don't wait for the final publication of your stories to create a set of charts. Graphing the summary data is an excellent idea at this stage, even though you're not ready yet to put together the final graphics for your story. Unless you possess extraordinary powers of symbolic intuition, rows and columns of numbers, commas and dollar signs are not as effective in communicating patterns as a simple bar chart. The numbers, after all, are only symbols that represent patterns in reality. The chart is a direct, visual representation of those patterns. It's much easier to grasp intuitively - an important consideration when you're poring through piles of data trying to figure out what's significant.

Here's how the sector totals above show up as a graph:

Even this simple graph is very revealing. We see immediately that the Finance/Insurance/Real Estate sector is the leading source of campaign funds, far ahead of any other sector. Labor and Lawyers head the second tier of top givers, and Defense stands out as the financial runt of the litter. But there's a lot more information you can also find out from the data you've already compiled. Two of the most important elements that you can examine, and chart, are the breakdown between funds that came from PACs versus individuals (or PACs versus corporations, unions, and individuals, if your state allows all those groups to contribute), and the breakdown in contributions to Democrats and Republicans.

To get this information you'll have to go back to your original database and generate new totals. The first one is based on that single-character field that listed a code for the type of contribution - I for individual, P for PAC, etc. Sort the database by contribution type, then by category, and generate new category totals for PACs, individuals, etc. Aggregate the separate categories into industries and sectors, and generate a new chart. The chart below takes the same data we looked at above, and highlights how much came from PACs versus individuals in each sector.

Immediately we have new insights. You can see, for example, that the great majority of contributions from lawyers and lobbyists comes from individual contributors, not PACs. The opposite is the case with labor donations - nearly all are delivered through political action committees. That's significant, it's worth a story, and it suggests a whole new line of questioning you can undertake when you begin doing interviews.

More revelations are in store when you break down the sector totals by party. To get to that point, though, youÕve first got to add a new field to the database - the party affiliation of each candidate. If you've already gathered this information and put it into a separate database of all candidates, it's a relatively easy matter to merge the two databases together and update your main database. All you need is a single field that's common to both databases, like a candidate ID. With most database programs, updating a field in one database with information from another is a relatively straightforward task.

When you've got the party information attached to each contribution, sort the database by party, then by category, and generate new totals. Graph them, again using the 1992 federal election data, and your new chart provides a whole set of insights:

The overwhelmingly Democratic tilt of labor unions now stands out dramatically. Lawyers and lobbyists also clearly favor Democrats by a wide margin, as do ideological and single-issue groups. Most of the other sectors split their dollars fairly evenly between the parties. The chart, and the numbers that go into it, once again suggest a whole new series of questions you'll want to pursue.

In fact, every chart you do, every summary total you generate, every way you look at the data - raises new questions and brings new insights. Some will be dramatic, others will be subtle. All bear further investigation. Looking at the data won't necessarily give you answers to all your questions, rather it will suggest the questions you ought to raise - of candidates, funders and political gurus alike.

Though the examples above deal with sector totals, you'll want to generate the same data, and many of the same exploratory charts, for every industry and category. Once you've generated totals at the category level, it's easy to aggregate the numbers into industries and sectors.

Here are some suggestions for other ways of looking through your database to find potentially interesting patterns:

Calculate totals from business vs. labor vs. ideological/single-issue groups. Labor is a sector by itself. So is Ideological/Single-Issue. Aggregate the ten business-related sectors (Agriculture to Miscellaneous Business) and you'll have the total for business contributors. When you total the numbers this way, you'll probably find the same pattern that's evident at the federal level - namely, that most contributions come from business groups. Unions and ideological groups may also be big contributors, but not when compared with the combined total of all business categories.

Find out which industries are the biggest supporters of the Democrats and Republicans. If you generate a list of the leading industries giving to candidates from each party, you will almost certainly find that some industries show up at or near the top of both parties' chief supporters. That means heavy political clout, no matter who wins at the polls - a red flag that ought to provoke a closer look at the industry's legislative agenda, and what it's received for its bipartisan investment.

Calculate the industries that are most heavily partisan. This is simply a percentage for each industry - what proportion went to Democrats versus Republicans. Labor unions, and a few ideological categories, will almost certainly top the most-partisan list for Democrats. But what other industries give heavily to them in your state? Likewise, which industries most strongly support Republican candidates?

Calculate the fastest growing and fastest declining industries. Obviously, this is one trend you won't be able to spot until you've got more than one election cycle to look at. But when you do, it will provide you with a very important bit of information. Most industries at the federal level are surprisingly consistent in their contributions from year to year. But if there's heavy political action on the horizon, look for an industry's contributions to soar. That's what happened to health care contributions in 1992, as Congress prepared to consider massive changes to the nation's health and insurance system. The National Rifle Association also greatly stepped up its giving in 1992 - a sign the NRA was trying to shore up its defenses against a rising tide of gun control legislation. If an industry - or an individual company or PAC - dramatically increases its giving from one election cycle to the next, you can be sure that something is afoot.