The Beginning for the American Church

The Pieces Are Finally All On the Board; Let the Games Begin

Regular readers, or Twitter followers, know that I’m a Lutheran, specifically part of the Lutheran Church-Missouri Synod, the more conservative of the two largest Lutheran denominations in the US. A series of recent conversations with friends made me wonder what trends in religious affiliation look like in the U.S. Plus, there’s been no shortage of recent discussion about the future of Christianity, a topic that interests me very much. Sure, there are lots of “snapshots” of religion, especially in the last 20–40 years, but I wanted something more detailed and with a longer frame of reference. So I decided to build a complete annual dataset for every religious group in America as far back as I could get data, which turns out to be 1925.

Note: for readers uninterested in American religious demography, this post will be boring. For readers who are not theologically conservative/traditional/evangelical/orthodox Christians, this post will be abrasive at times, but please understand, if you’re coming here from that background, you’re eavesdropping on an in-house conversation. My intended audience today is Christians, mostly of the theologically conservative variety.

My first step was to use a resource anybody who works with religion data knows well: ARDA- the Association of Religion Data Archives. They have collected every available estimate of denominational membership for hundreds religious groups from 1925 to 2010. Now, each denomination has data for different years; none have data for all years. So you can’t just drop the data into a spreadsheet and call it a day. To be able to get interesting summary results, you have to come up with estimates or imputations for empty years. Plus, 2010 was 7 years ago now. So I had to look up more recent estimates for every religious group, or come up with plausible extrapolations.

The end result is a spreadsheet that looks something like this, where yellow values reflect imputations:

Even then, there are other shortcomings. Some denominations just BAM appear, with thousands (or millions!) of members. In the vast majority of those cases, it’s due to name changes or mergers/schisms in denominations, so I can build a time series of the pre-existing denominations to get a consistent estimator. But in some cases, I can’t, so I have to just come up with plausible backwards-looking extrapolations of membership.

The problem is even more fraught, however. ARDA data is almost exclusively groups that are either longstanding in the U.S., or Christian. So Muslims, Buddhists, Hindus, Jains, Zoroastrians, Sikhs, etc, are all ignored. Many New Age movements are uncounted. So I have to come up with estimates for those groups. Well, in 2010 the decennial religion census did survey these groups, which is nice, but it’s mostly a survey of adherency, whereas the ARDA data is institutional membership. Institutional membership can be greater than adherency, but is usually lower. Using a range of adherency surveys as well as some plausible adjustment factors to account for the adherency/membership gap, I came up with estimates for groups not included in ARDA.

Finally, it turns out “membership” has an unstable meaning. Some denominations have redefined membership at various times. Some denominations will only count adult membership, while others count kids too. Some only count regular attendees as members, some don’t. So the relationship between “adherency” and “membership” is unstable across denominations and time. In other words, granular comparisons of groups are a crapshoot unless you work to account for these differences!

So the dataset I’ve compiled is not the best dataset for estimating how many people believe in Wesleyan doctrines. It is not the best dataset for estimating how many people will call themselves Presbyterian. Rather, my dataset should approximate the institutional size and strength of American religious movements since 1925.

This matters. Adherency is important for society, as are specific beliefs. But much of the social impact of religions is via institutions and group membership. Institutions support schools, charities, NGOs, political activity, etc. It seems likely that attending non-members are less likely to be active participants in these activities.

While my data isn’t the end-all be all of religiosity in America, it is probably a good indicator of the social influence of religious groups.

And, crucially, to my knowledge, the dataset I’ve built is the most complete and up-to-date such dataset in existence, well, anywhere. Rigorous academics will be skeptical of it because I did tons of freestyle fill-in-the-blank, which is fair. But that’s exactly what we do when we compare decennial census rounds: even though censuses are on different years, we compare them together by extrapolating into a common year. All I’ve done is do this consistently across all groups and years, to allow us to get ballpark estimates of major groups.

A note on data access before we get to results: I am happy to share headline data, and if you have specific questions about denominations, then I’m happy to discuss or share a bit at a time. But this data has actually taken a lot of work to put together, and I intend to make heavier use of it in the future, and I’m still hoping to improve it. As such, I won’t be sharing the complete denomination-level dataset. I know, I know, that’s kind of a dick move on my part. But again, if you’re interested in a specific denomination or group, I’d love to share the bit you’re interested in! I just don’t want anybody pirating my work without attribution. So if you’re thinking, “Huh, I wonder what he estimates for my denomination?” just ask, I’ll share! But if you’re thinking, “Gee, I’d love to get that whole dataset and use it for this other project I’m doing…” sorry dude. I’m not game for that just yet.

Finally, this is a blog post on a personal blog. This is not an academic publication. My data almost certainly has errors. My hope is that in publicizing, people will ask me questions that lead to me finding some of those errors and improving on them.