The demographic framework is a powerful and influential approach for guiding decisions across a range of sectors. Practitioners are compelled to always explore new ways of using mathematics, principles, techniques, best practices, and existing data. While some research teams may toss around ‘demographics’ as a broad noun to refer to a few characteristics, but the Museum's research looks at it as a part of its research design on the same essential level as the scientific method or hypothesis testing.
Applied demography is characterized by a tense relationship between scholarly precision and explanatory power. There are many industries where applied demography may be used, such as businesses, non-profits, health systems, or public sector organizations. This tension is borne in similar ways by these different industries, but there are some dissimilarities. The main differences are in the constraints and imperatives of the client or employer.
Someone using the demographic framework for a business will identify and study demographic groups in pursuit of understanding human behaviors and forecasting trends in order to make more informed and competitive business decisions. This is largely the same as using the demographic framework in other industries. However, there are discrepancies in business demography versus, for example, public sector demography. The latter requires factoring in added standards and regulations as well as political and cultural implications. While a business might shy away from socially divisive issues such as gay rights, trans rights, or abortion, a government office will need to be attentive to policies on such issues even if these policies are at odds with the local community’s own attitudes.
It is important to explore more deeply how the demographic framework differs in how it is implemented in various industries. When looking more closely at differences between business demography and public sector demography (an arbitrarily chosen comparison, but a useful one nonetheless), an example of something much more relevant to the public sector versus businesses would be the United Nations’ Sustainable Development Goals (SDGs). With more public sector offices incorporating SDGs into their research and decision-making, different departments and even different countries can build off of one another’s progresses and learnings. If an office excels at implementing SDGs, they can accrue important ‘soft power’ both domestically and internationally. On the other hand, SDGs are not of major importance to the profit motive of a business unless it is working with the public sector or turns to the SDGs as indicative of future trends.
SDGs are one example, but it is not just about different norms. Sometimes, businesses and the public sector may be at odds and this will influence how demography is used. Both the private and public sector are bound by regulations, but in different ways and a practitioner must be a true connoisseur of the latest developments in their particular field. A business demographer may recommend that a company change its approach to e-cigarette marketing in light of possible regulation and increasingly negative perceptions of their industry, but avoid any substantial research into underage use of their products. Meanwhile, a public sector demographer may apply the demographic framework specifically to better understanding underage use of such products. This is a particularly extreme example where two applied demographers may have similar tools, similar principles, similar data sets, and still be on drastically different ends of the same regulatory framework and with very different imperatives with respect to their client. A professional demographer who has become specialized in their field must have a strong ability to maximize explanatory power, but also talentedly navigate the complexities of what ‘success’ actually means for their client. Business demography, public sector demography, and so forth differ the most in where this talent for navigation will take respective practitioners despite being under the large umbrella of applied demography.
Applied demography and basic demography
In addition to comparing and contrasting between various types of applied demography, it is also worth exploring applied demography versus basic demography. While basic demography (a.k.a academic demography) is hallmarked by scholarly acuity that oftentimes circumscribes explanatory power or recommendations, we find that applied demography uses principles, tools, approaches, data, and findings from basic demography to achieve the explanatory power required to make informed decisions.
While the ‘risk’ of making bad conclusions goes through peer review in basic demography by considering methodology, sample sizes, and statistical significance (among other tools), the level of ‘acceptable risk’ does not vary anywhere near as widely as in applied demography. A client will usually be the one to set how much ‘risk exposure’ they are willing to take by setting limits on time, money, and human resources. They may commission a market research report and pay the minimum amount possible, resulting in explanatory power that may be incomplete but which meets whatever threshold they are comfortable with for making some major (or minor) decision. Alternatively, a business might forego bespoke reports altogether and purchase a monthly subscription to a data analytics platform they use in a possibly clumsy way. In this case, they decided that the risk of misusing the platform and making bad decisions is worth the cost savings compared to more involved techniques such as focus groups. They may also blend bespoke and off-the-shelf solutions according to their specific business needs. A minor seasonal product may require little product research besides figuring out how to time the release, while a flagship product may go through in-depth focus groups before it ever reaches shelves.
Regarding the difference between applied and basic demography, the harshness of the negative exigencies (money and time) are the most significant. There exists a tension between less and more, and this is a consistent aspect of applied demography as opposed to academic demography. Working with nonprofits requires diligent fiscal discretion, encouraging him to make the most of limited resources to guide decision-making. Similarly, working in marketing and statistics requires trade-offs. Statistics require columnization and sanitization of vast amounts of data for processing, and inability to get consistent granularity results in ‘lowest common denominator’ decisions arising. If all demographic groups have less granular data available, while only some demographic groups have more granular data available, the less granular data is what must be used for comparative statistical analysis across various groups (while in basic demography, all data should be up to similar standards). Similarly, working in marketing with PRIZM means these same challenges in addition to looking at what demographic groups will actually be useful for clients. There is a strong business incentive to provide the most granular data only for the most profitable and targeted demographic groups, while leaving other demographic groups less researched although this limits the opportunity for comparative analysis.
Usefulness of the demographic framework
There are three ways to explore the demographic framework as integral to research: the universality of demographic concepts such as looking at rates; the universality of demographic norms such as parsing by age, sex, and other demographic characteristics; and the competitiveness of using the demographic framework to achieve more explanatory results with more impact.
Consider one hypothetical example to better understand these three perspectives. Perhaps Snapchat has rough age and location data on its users based on self-reported and device information. This data set is not particularly precise nor rigorous, and data potentially involving minors probably did not go through any ethics review. The company can use the demographic framework to look at census data and see the census areas where its user base’s age distributions are markedly distant. Applied demography gives the researcher great latitude on what ‘markedly distant’ can mean, and ultimately the outcome of the research will not be widely peer reviewed — in fact, the report will likely be totally proprietary. However, it benefits tremendously by relying on peer reviewed census data alongside its own user-generated data. Having a background in demography and easily working with census data would be a great advantage.
There are three ways to look at this hypothetical example of the relationship between applied demography and basic demography. One view emphasizes that whether looking at birth rates or conversion rates, we are looking at rates when trying to understand how much different demographic groups use Snapchats. Having a background in understanding and forecasting birth rates would equip the professional with a strong foundation in recognizing good data, sloppy data, and common pitfalls to avoid. Another perspective points out that looking at data by age — and the fact age is a major demographic characteristic already in census data — is a great example of the universality of demographic characteristics. Lastly, the third perspective sheds light on how essential it is to have a strong background in demography to professionally complete — and communicate — a fairly straightforward report on ages of Snapchat users compared to overall communities.
Agreement with basic demography
These views and the issues raised by the various examples given above are not at all in disagreement with scholarly views on whether applied demography is its own discipline or not. It seems that the professional practice of applied demography — though not necessarily the essential concepts — is indeed very different from basic demography. The prioritization of client needs and the mandate for effective and incisive outcomes has, over time, resulted in a separate but related field that has its own norms and best practices. While a strong value for statistical significance is irreplaceable in the academic context, in the applied context it would often be preferable to encounter weaker statistical significance but under the stewardship of an experienced professional who knows the industry well and can provide the fastest and most economical appraisal of the how the client should face the future.
Ultimately, these perspectives on applied demography versus academic demography fit within the conceptualization promulgated by Swanson (1996). Academic demography sets forth certain standards and must adapt its environment — through time, further research, and more money — to fit these standards. In academic demography, a softness around a certain result must be resolved or the entire research may fail at the peer review stage. Applied demography, on the other hand, is characterized by a “coupling” that occurs. An applied demographer must couple their vision for what data sources are ideal, with a review of what data sources are available or realistically established. An applied demographer must couple their vision for answering an inquiry, with a research approach that is adapted to the specific needs of their stakeholders. Even considerations of formatting, visualizations, and tone must be coupled to the gaze of the intended audiences, and focus on communicating in line with the skills they have or lack. Murdock (2008) describes applied demographers as requiring a strong technical expertise and theoretical basis, which supports practitioners’ views that, in essence, it takes a tremendous amount of experience to make information that is oftentimes very complicated look so easy — and, additionally, to be both trustworthy and impactful.