Getting Your Foot Into Economic History Research
Economic history sits in an awkward middle ground between disciplines that don't always talk to each other. You need the quantitative rigor of economics, the archival patience of historians, and the ability to defend your work against reviewers from both sides who will criticize you for different reasons. I learned this the hard way after spending six months on a paper about colonial tax collection in 19th-century India, only to have it rejected by a history journal for "insufficient engagement with the historiography" and an economics journal for "identification strategy is unclear." Eventually it found a home in Explorations In Economic History after I restructured the introduction to lead with the economic question rather than the archival narrative. The journal covers everything from pre-industrial economies to contemporary development patterns, so your first decision is figuring out where your work fits and whether it's actually going to resonate with their readership. The editors tend to favor papers that make a clear causal argument supported by either novel archival evidence or clever use of existing data. Pure descriptive work or papers that are primarily historical narrative without an explicit economic mechanism tend to get desk-rejected quickly. When I was building my research pipeline, I realized that citing the journal properly matters more than most people think. If you're submitting work inspired by or engaging with their published articles, you need to reference Explorations In Economic History correctly in your bibliography. The standard format runs something like: author(s). year. title. Explorations In Economic History volume(issue): pages. DOI if available. Getting this wrong looks careless to editors who see hundreds of submissions.
The practical workflow I settled on for processing the literature was straightforward but tedious. I'd download the current issue, flag anything within two years of my topic area, and then trace backward through every citation chain for five years. That gave me a map of the intellectual territory without falling into the trap of only reading the most famous papers, which tend to be cited to death and miss the newer debates happening in the margins.
The Methodology Trap Nobody Warns You About
Most early-career researchers in this field come from an economics background and try to force historical questions into modern econometric frameworks. The problem is that the data simply wasn't collected the way modern statistical infrastructure assumes. Price series from the 1700s don't have the same measurement error structure as CPI data. Population counts from parish records are incomplete in ways that don't follow clean missing-at-random assumptions. Your standard regression will technically run, but the confidence intervals are essentially decorative if you haven't thought through what the underlying data generation process actually was. I ran into this specifically when trying to use a difference-in-differences approach on English wage data across parliamentary enclosures. The parallel trends assumption looked fine on the surface, but once I dug into the actual enclosure Acts, I found that the treatment and control groups were systematically different in soil quality and market access from the start. The "pre-trend" looked parallel only because I was using aggregated county-level data that smoothed over enormous within-county variation. Switching to a matching approach based on observable geographic and agricultural characteristics fixed the identification problem, but it cost me three extra months of work and cut my sample size by about forty percent. There is also a quiet expectation that you engage with cliometric methods even if your paper doesn't use them directly. Reviewers from this tradition tend to skim for whether you've at least considered counterfactuals and selection bias. You don't need a structural model to publish here, but dismissing causal inference entirely will raise eyebrows. A paragraph acknowledging these concerns and explaining why your approach is still valid under the circumstances goes a long way.
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Working With Archival Data
The single biggest time sink in economic history research is data acquisition. Digital repositories have made massive improvements, but the good stuff is still scattered across local archives, private collections, and databases that require physical visits. My advice is to spend one week upfront mapping exactly where every variable you need lives before you write a single word of analysis. I wasted about ten weeks on a project that turned out to depend on a ledger series held in a municipal archive that had no digitization plan and required a researcher appointment four months in advance. Once you have the data, the transcription and cleaning phase is where most projects either succeed or die. Handwritten records from before the 19th century are not going to cooperate with simple OCR. I started using a combination of Transkribus for the older documents and manual spot-checking for the ones that mattered most to my identification strategy. The process of cleaning a single ledger with 3,000 entries usually takes me about two hours if the handwriting is decent and four to six hours if it is not. Budget accordingly.
Writing for Two Audiences
This is the part that breaks most people. Economics-trained researchers write introductions that assume familiarity with instrumental variables and event study diagrams. History-trained researchers expect contextual depth that fills three pages before you even state your research question. Explorations In Economic History readers fall somewhere in between, but the journal skews toward the economics side. Lead with the question and the answer, then provide the historical context as support rather than as the main event. The literature review should do double duty. It needs to establish the historical facts that underpin your argument while simultaneously showing you know the relevant economic theory. This is harder than it sounds because the literature you need to cite comes from completely different journals with different conventions. I keep a running Zotero library split into sub-libraries for economic theory, historical context, and methodological approaches, and I merge them only when drafting the actual sections.
Common Rejection Patterns
The most common reason papers get rejected from this journal is that the contribution is unclear. Editors can tell within the first two pages whether you actually know what your paper is doing or whether you are just presenting data and hoping the implication is obvious. State your contribution explicitly in the introduction, ideally in a standalone paragraph near the end of it. The second most common issue is weak identification. This is especially prevalent among papers that rely heavily on institutional arguments without empirical support. A story about how a particular legal system influenced economic outcomes is interesting but insufficient unless you can show measurable consequences. The third is scope mismatch. Papers that try to cover too much territory, like "the evolution of markets from Rome to the present," rarely satisfy anyone. Narrow and deep beats broad and shallow every time in this field. If your paper gets rejected, read the reviewer comments carefully before resubmitting elsewhere. Reviewers in economic history tend to be thorough and usually point out the same structural problems regardless of which journal they are reviewing for. Addressing their concerns genuinely will make your next submission stronger, not just a fix for a single journal.
Practical Resources That Actually Help
The EconLit database is essential but underused for historical work. Most people search it like a modern economics database and miss half the relevant material. Try combining subject headings with date ranges and searching for terms like "economic conditions," "commercial history," and "trade" alongside your geographic and temporal focus. The Historical Abstracts database is also worth cross-referencing, though it covers less of the quantitative side. For data sources, the Maddison Project Database gives you macro-level GDP and population estimates that are useful for framing arguments even if you are working at a much finer granularity. The European Historical Economics Society maintains a helpful resource list. National statistics offices in several countries have digital archives going back centuries, and the UK National Archives has increasingly digitized its economic records. China Statistical Yearbook historical backfiles are accessible through academic institutions and useful for anyone working on pre-modern Chinese economic data. The Journal of Economic History and the Review of Economic Studies also publish relevant work, though their standards are higher and acceptance rates lower. If your paper feels like it might be on the weaker side, starting with a regional journal or a specialized economics history venue can build your publication record before you target the top tier.
Building a Sustainable Research Practice
The field moves slowly. A well-done archival project takes one to two years from conception to publication. This means you need to manage multiple projects at different stages simultaneously so that slow data collection on one doesn't leave you with nothing to write for two years. I typically keep a current paper in submission, one in revision, one in the data collection phase, and one in the early ideation stage. This way something is always advancing even when one project hits a wall. Collaboration helps enormously. Finding a co-author with complementary skills, like someone stronger in archival research if you are quantitative-focused or vice versa, cuts your workload and improves the paper. I collaborated with a historian who had spent five years studying Spanish colonial trade records while I handled the econometric framework. The resulting paper was significantly better than either of us could have produced alone, and the review process was smoother because each reviewer felt their side of the discipline was represented. The most important thing is to pick questions that genuinely interest you because the work is harder than it looks on paper. You will spend more time wrestling with messy data and defending your methods than you will on the actual theoretical contribution. If the question isn't compelling to you, the process will grind you down. Pick something you would want to read about even if no journal ever published it.
