In March 2002 Ireland began charging shoppers 15 cents for every plastic bag at the till. Reports at the time, from the government and the press, said use fell by about 90 percent within weeks. Notice what did not happen: no inspector visited homes, no one was jailed, and nobody forbade bags. A small price, collected by the shop, changed what millions of people did without thinking about it.
Now picture the opposite. A national programme is announced to create jobs in a poor district. Years later the money has been spent on meetings and approvals, and almost no jobs exist. Nobody lied, nobody stole. Every office that had to agree did agree, slowly and partly. A good idea met a long chain of people who each had other priorities.
Public policy as a subject asks four things about any such case. Why did this problem get attention when others did not? What tool did the government choose, and why that one? Did the plan survive contact with the people who had to carry it out? And did it work, which means: compared with what would have happened anyway?
Textbooks draw policy as a loop: a problem is noticed, put on the agenda, a solution is designed, a decision is made, the decision is carried out, and the results are evaluated, which sends you back to the start. The idea goes back to Harold Lasswell's 1956 account of the "decision process" in stages, and nearly every introduction to public policy still opens with some version of it.
Its value is that it gives you questions to ask at each point. Its weakness is that real policy does not happen in order. Charles Lindblom argued in 1959 that officials do not survey all options and pick the best; they "muddle through", making small changes from where they already are. Solutions often exist before problems: a team has a programme in a drawer and waits for a crisis to attach it to. The cycle is a checklist, not a law of nature.
The case is Ireland's 2002 plastic bag levy. The stages are the textbook ones; the case details are simplified. Sources differ on exact usage figures, so treat "about 90 percent" as the figure reported at the time.
Without JavaScript: Ireland's 2002 levy moved from visible litter, to a design choice (a fee at the till rather than a ban), to implementation by shops, to a reported fall of about 90 percent in use, and a later increase of the fee after use crept up.
Thousands of real problems exist at any time. Only a few get a minister's, a legislator's or the public's attention. John Kingdon, in Agendas, Alternatives, and Public Policies (1984), described three streams flowing separately. The problem stream: indicators, crises, a disaster on the news. The policy stream: ideas floated by experts and officials, most of which go nowhere. The politics stream: the national mood, elections, lobbying, a change of government. When the three meet, a "policy window" opens, and a policy entrepreneur who has a solution ready can push it through. Windows close again, often within months.
A second account, by Frank Baumgartner and Bryan Jones in Agendas and Instability in American Politics (1993), says policy is mostly stable and then changes suddenly, a pattern they call punctuated equilibrium. Attention is scarce, so a problem can be ignored for years and then, when its image changes (from "a medical matter" to "a threat to children", say), move from one committee to a very different one.
A metaphor, not a measured model: the threshold of 6 is invented to make the idea playable. Kingdon himself describes the streams as largely independent and the window as partly chance.
Play with it. A loud crisis with no ready solution gets hearings and hand-wringing; a ready solution with no crisis waits in a drawer. The practical lesson is Kingdon's: people who want change prepare the policy stream in the quiet years, so that when a window opens they are not drafting from scratch.
Once a problem is on the agenda, the government chooses a tool. Political scientists group them roughly as: regulation (a rule, a ban, a standard), taxes and prices (change what a choice costs), spending and subsidies (pay for a service or reward a behaviour), information (warnings, labels, campaigns) and nudges (changing the default or the way choices are shown, without banning anything). Each acts on a different lever, and the same goal can be pursued with any of them. To cut sugar intake, Mexico taxed sugary drinks from January 2014 at one peso per litre; a study in the BMJ estimated purchases fell about 6 percent over 2014 and about 12 percent by December relative to earlier trends. The UK chose a levy on manufacturers' sugary drinks, in force from April 2018, which pushed producers to reformulate. Others rely on labels or school-meal rules.
The core economic point about price instruments is that a tax lets each firm or person choose where to cut, and so reaches the same target more cheaply than a uniform rule. Suppose three factories must together cut pollution by a fixed amount, and cutting costs each of them a different amount. A uniform standard makes each cut the same share, including the one for whom it is dearest. A tax makes everyone cut until the last unit costs the same as the tax, so the cheap cutters cut more. Try it.
Three factories whose cost of the next unit cut rises in proportion to how much they have already cut: slopes 4/r, 4 and 4r (r is the slider). Costs are in arbitrary money units. Textbook model; real regulators do not know these slopes, which is the standard argument for prices and tradable permits.
With r = 1 the factories are identical and the two tools cost the same. As the spread grows, the tax pulls ahead. The caveats matter as much as the result: the model assumes the regulator can set the right tax without knowing costs (it cannot, so it adjusts), and it ignores who pays. A flat tax on fuel hits a poor household harder, which is why many carbon taxes come with rebates. Nudges are the cheapest instrument and the most oversold. A well-known 2003 study by Eric Johnson and Daniel Goldstein found that countries where people must opt out of organ donation reported far higher consent than those where they must opt in. Later work cautions that consent is not donation: hospital systems and family permission matter as much as the default.
Jeffrey Pressman and Aaron Wildavsky's 1973 book Implementation (subtitled "How Great Expectations in Washington Are Dashed in Oakland; or, Why It's Amazing That Federal Programs Work at All") followed a federal job-creation scheme in Oakland, California. Their lesson: a programme that needs agreement from many separate offices has many chances to fail. Each "clearance" is a point where someone can delay or veto. If each has a high chance of agreement, the chain still frays, because probabilities multiply.
A simple multiplication that assumes the offices decide independently. Real offices are not independent, and later scholars argued that coordination and bargaining can raise the odds. Pressman and Wildavsky's own point was the warning, not the exact number.
A second source of gaps is the person at the counter. Michael Lipsky's Street-Level Bureaucracy (1980) argued that teachers, police officers, caseworkers and nurses make policy in practice: they have more cases than time, so they ration attention and bend rules. Policy on paper is what the capital writes. Policy in practice is what thousands of workers do when the line outside is long. Good designers ask: who has to do this, with what time, and what happens if they simply cannot?
The evaluator's central question is about a world nobody saw: what would have happened without the policy? This unseen outcome is the counterfactual. Comparing a group before and after a policy is the obvious test, and it is often badly wrong, because things change on their own: the economy grows, a disease wanes, a school year ends.
Mexico's Progresa programme, begun in 1997, gave poor families cash if their children went to school and clinics. Because it could not reach every community at once, the government drew lots for the first wave: of 506 communities in the evaluation, about 320 got the money first and about 186 waited (sources differ by one). Surveys every six months compared the two groups. Enrolment, especially in secondary school, rose in the treated communities. The programme was renamed Oportunidades in 2002 and later Prospera, and cash-transfer schemes with conditions spread to many other countries, Brazil's among them. The 2019 economics Nobel prize, shared by Abhijit Banerjee, Esther Duflo and Michael Kremer, honoured this style of experimental evaluation of anti-poverty policy.
Invented, illustrative numbers: both groups start at 60 percent enrolment and both are hit by the same background trend. Real estimates carry sampling error, which this toy leaves out.
Set the trend to +5 and the effect to 4. A naive before-and-after comparison claims a 9-point success. The comparison group reveals that 5 of those points would have come anyway. Set the trend negative and a working programme looks like a failure. The lesson is the one Progresa's designers acted on: a policy is judged against a counterfactual, and the best counterfactual is a comparison group chosen by chance. The methods piece (Research Methods in Political Science, 35/18) takes this much further.
"Evidence-based policy" means choosing policies because rigorous evaluations found they work. Governments have built institutions for it: in the UK, the What Works Network was launched in 2013 as a set of centres that synthesise evidence for areas such as crime, education and ageing. A cautionary example is "Scared Straight", a programme that takes at-risk teenagers into prisons to frighten them. It was popular and seemed obviously sensible. Systematic reviews of randomised trials found it did not reduce offending and, on average, probably increased it. Evidence overturned common sense.
The approach has limits, and its own defenders list them. Published results overstate. Journals prefer significant findings, and small studies that happen to land high are the ones that get published. Stefano DellaVigna and Elizabeth Linos (Econometrica, 2022) compared 126 trials run by two US "nudge units" with 26 academic papers: the academic papers averaged an effect of 8.7 percentage points on take-up, and the units' own trials 1.4 points. Publication bias, they estimated, explains much of the gap. Other researchers disagree on how large the true effect of nudges is: one 2022 meta-analysis found a moderate effect, and a reanalysis argued that after correcting for publication bias there is no clear evidence of any. What worked there may not work here. A result from Kenyan schools or a New Haven neighbourhood tells you about that place; whether it carries to Lagos, Lima or Leeds depends on how the programme works and who delivers it. Angus Deaton and Nancy Cartwright made this argument forcefully in 2018. Even the famous Kenyan school-deworming study (Miguel and Kremer, 2004) has been re-examined and argued over by later researchers, with reviews disagreeing about its size. And what is measured gets gamed. Where a number becomes the target, people optimise the number.
600 simulated trials of a programme that raises take-up from 10 percent. Only trials that are positive and significant at the usual 5 percent level count as "published". Illustrative simulation with a fixed random seed.
With a true effect of 1 point and 400 people per group, the few trials that clear the bar overstate the truth by several times. Make the trials bigger and the exaggeration fades. This is why evaluators ask for pre-registered trials, large samples and replications, and why a single dramatic study is a reason for interest, not a policy.
Policy is a chain: a problem gets attention, a tool is chosen, many people carry it out, and someone asks what would have happened anyway. Each link can break, and each can be examined. Read any new policy by asking which link you are being asked to take on trust.
Try this: this week, take one policy in the news, a tax, a ban or a scheme, and write down its goal, its instrument, who has to carry it out, and what comparison would show whether it worked.
Sources. Policy cycle: Lasswell, The Decision Process (1956); Lindblom, "The Science of Muddling Through", Public Administration Review (1959). Agendas: Kingdon (1984); Baumgartner and Jones (1993). Ireland's levy: introduced 2002 at 15 cents, a drop of about 90 percent reported by the government at the time (BBC News, 2002 and other reports; per-person figures differ between sources, and critics note bags are reused as bin liners). Mexico's soda tax: Colchero et al., BMJ (2016), about 6 percent fall in 2014 and about 12 percent in December 2014. UK Soft Drinks Industry Levy: announced 2016, in force 6 April 2018. Nudges: Johnson and Goldstein, Science (2003); DellaVigna and Linos, Econometrica 90(1), 2022, 81-116 (1.4 vs 8.7 points; 126 trials vs 26 papers); the Mertens et al. meta-analysis and the Maier et al. correction, PNAS (2022). Implementation: Pressman and Wildavsky (1973); Lipsky (1980). Progresa: renamed Oportunidades 2002; evaluation of 506 communities, about 320 treatment (J-PAL and Attanasio, Meghir and Santiago, Review of Economic Studies); 2019 Nobel to Banerjee, Duflo and Kremer. What Works Network: launched 2013 (UK government). Scared Straight: Cochrane systematic review of randomised trials (Petrosino and colleagues). Deaton and Cartwright, "Understanding and misunderstanding randomized controlled trials", Social Science & Medicine (2018). Miguel and Kremer, Econometrica (2004). Sections on the nudge-unit and trial simulation are the author's own illustrations. This piece describes how policy works; it is not advice on any particular policy.