The Answer is, it depends.
You can monetize the value of trash kept out of a landfill. You can build a willingness-to-pay model for recycling services in Cairo and run the numbers until they hold up under scrutiny. You can do all of that and still leave things out. In fact, you always leave things out.
I know because I have done this work. Years ago, I conducted a social cost-benefit analysis in Egypt for a social enterprise in solid waste management and recycling. We scoped it carefully. We monetized what we could. And at the end, the analysis was defensible but incomplete, because that is what these analyses always are. Later in my career I conducted many analyses with a kind of SCBA-light methodology because the full methodology takes months and the underlying valuations are always contestable.
This is not a confession of failure. It is a description of reality. And it matters because across philanthropy, government, and the social sector, there is an honest and reasonable desire to know whether the money is working. The question is whether the tools we reach for actually answer that question, or whether they just make us feel like we have answered it.
The Toolkit Has Real Limits
Social cost-benefit analysis (SCBA) and social return on investment (SROI) both require you to monetize outcomes. What is housing someone worth in dollar terms? Fifty thousand in avoided shelter costs? Thirty thousand? Eighty thousand? Two analysts can produce wildly different numbers from the same program data. That is not a flaw in either analyst. It is a feature of a methodology that asks you to assign dollar values to things that do not naturally have them.
The incentive structure compounds the problem. When your continued funding depends on demonstrating impact, and the methodology lets you adjust assumptions, the pressure to produce impressive numbers is real. I have seen it. Funders end up preferring whatever is easiest to monetize rather than whatever produces the most meaningful change, and the measurement tail wags the program dog.
Quasi-experimental evaluation sits at the other end of the spectrum. It can isolate a program’s effect with genuine rigor. On a USDA trade program in Egypt, I managed a team from NORC that designed a quasi-experimental approach using commodity-level trade data, comparing export trajectories of program-targeted commodities against similar ones to isolate the program’s contribution within a volatile trade environment. The methodology was sound. It was also expensive, time-consuming, and required a specialized team. You cannot run that kind of analysis across a portfolio of twenty investments. It answers one question very well, but it cannot be your operating system.
What Cost-Effectiveness Gets Right
Cost-effectiveness analysis sidesteps the monetization problem entirely. It keeps outcomes in their natural units. How many people did you house, and what did it cost per person? The dollars stay on the spending side, not the value side. You never have to argue about what housing is “worth.” You just know what it costs to produce.
This is genuinely useful for funders comparing programs that pursue the same outcome. But cost-effectiveness can mislead if you strip out the context. Program A costs more per family housed, but rents in that geography have doubled over the past two years. Program B looks efficient on paper, but that reflects a favorable housing market, not better program design. The number is clean. The story underneath it is not.
Pair cost-effectiveness with real understanding of conditions, and you get intelligence that actually improves allocation decisions. Use it alone, and you get a spreadsheet that rewards luck.
Value for Money at Different Scales
Here is where it gets practical. Not every investment needs the same level of analysis, and pretending otherwise wastes time and money on both sides.
Julian King, a New Zealand-based evaluator, has developed what he calls the Value for Investment (VfI) approach, and it offers a useful way to think about this. VfI reframes value for money as a question about good resource use rather than a question that can only be answered with dollar figures. Instead of requiring you to monetize everything, it uses evaluative rubrics, transparent criteria, and multiple sources of evidence (qualitative and quantitative) to make structured judgments about whether an investment is creating enough value. The approach treats policies and programs as investments in value propositions, with the potential to create social, cultural, environmental, and economic value, not just financial returns. It is not another method. It is a system for guiding the use of existing methods, matched to the question and the context.
This matters because the funder’s real question is rarely “what is the precise social return on this dollar?” It is usually something more like: Is this working? Should we keep funding it? What would make it work better? A small community grant does not need a quasi-experimental design. It needs a clear logic model, honest reporting on what happened and what did not, and a conversation about what was learned. A multi-million-dollar systems change initiative probably does need a more rigorous approach, but even then, the question should drive the method, not the other way around.
Those questions can be answered at a fraction of the cost of a full SCBA, if you are willing to accept that the answer will be judgment-informed rather than mathematically precise. King’s VfI framework makes that judgment explicit and transparent rather than hidden behind a veneer of false precision.
The Answer Is That It Depends
I am not ideological about methodology. If there is something better and simpler that gives funders what they need to make good decisions, I want to find it. But after years of doing this work across different contexts, countries, and scales, I have come to believe that the tension between rigor and practicality is not a problem to be solved. It is the work itself.
The honest answer to “which methodology should we use?” is that it depends. It depends on the question you are trying to answer, the resources you have, the stakes involved, and what decisions the analysis will actually inform. That is not a dodge. It is the most rigorous thing I can tell you.
The danger is not in choosing a less-than-perfect methodology. The danger is in choosing a methodology because it looks rigorous rather than because it answers the question you actually need answered. Or in demanding a level of precision that the underlying data cannot support. Or in treating measurement as a substitute for judgment rather than an input to it.
The best evaluation I have seen did not always use the most sophisticated methods. It used the right methods for the situation, reported honestly about limitations, and produced findings that someone actually used to make a better decision. That is a lower bar than “prove this worked.” It is also a more honest one.
I think that tension, the pull between wanting certainty and accepting that good judgment is the best we can offer, is actually healthy. It keeps us honest. It keeps us asking whether the measurement is serving the mission or the other way around.
The work is learning to sit with that tension rather than resolving it prematurely with a number.
Anthralytic is a strategy and evaluation studio helping mission-driven teams clarify and amplify their impact.
