Why study diginomics?
Let’s say you want to build/fund the production of digital goods (e.g. database or data ecosystem) – or improve an entire segment of the economy be that pharmaceutical or “big tech”.
This stuff is somehow different from e.g. a bakery or making steel. Digital goods present specific issues (not found in our old school economy) around production and funding. These include:
- Costless copying: marginal cost is not a good guide to pricing. Open access is possible, as shown by many Open Source software projects.
- Hyper complex value chains. Aggregation is often central: data is usually valuable aggregated and can be thousands or even millions of “suppliers” (think of openstreetmap or carbon emissions in consumer goods).
- Potentially significant fixed costs of production: Even if creation/production of individual item can be close to zero eg. data stream from a single IoT device or data produced as a side product of existing activity, “quality production” is expensive (aggregated, consolidated, unified data). Think of a working software product, for example.
- Gap between private and social (or even sectoral) cost-benefit equations around data production/sharing: incentives aren’t “baked in” like they are for saleable physical goods (excuse the pun) - Adam Smith butcher and baker parable breaks down. Even in cases of private benefit, these can be subtle and not apparent to key decision-makers (e.g. efficiency gains in ways that aren't apparent upfront). All this results in a disinclination to invest.
These features of digital goods makes it (uniquely) hard to “do” economics properly in this area, and especially to design business and economic models which work. For example, you don’t need a special session on how to run a bakery vs another type of business. There is a reason the European Commission is not involved in funding bakeries, but they are involved in databases: the former are far more amenable to “standard” models of private sector provision that (at least mostly) work for both producers and consumers.
Producers of digital goods can face many struggles. For example, databases that struggle to earn enough revenue and so disappear, or have repeated sustainability crises, or key data sources that become monopolized and thus very expensive.
People who are funding or building data systems often create things that aren’t sustainable. This is particularly true in multi-stakeholder environments (e.g. firms sharing data across a supply chain, public environmental databases etc) as coordination in general, and particularly coordinated funding, is especially hard here due to collective action problems.
Most would be producers don’t know much specialist economics or mechanism design, which hinders their ability to come up with sustainable solutions. This lack of knowledge means that people tend to focus on tech vs the economics; people often spend more time designing the structure of their data than they do thinking about the funding of their data (and its distribution).
If I want to produce digital goods (or just want them to exist and be produced by others) what options do I have for resourcing [incentivizing] the production and distribution of digital goods – and which should I choose?
Resourcing requires a business model (even if you’re non-profit) and these have the following building blocks:
- Revenue model: how you earn revenue
- Legal structure: how the entity is organized and incorporated
- Supplier remuneration mechanism: how suppliers are incentivised to contribute (i.e. by providing new datasets or updates)
You can find more information on the options for each of these on the concepts page.
Using build blocks effectively requires you to understand the fundamentals of the digital economy. This is where diginomics comes in.