{"id":627,"date":"2019-12-05T18:55:51","date_gmt":"2019-12-05T17:55:51","guid":{"rendered":"https:\/\/blogx.ethz.ch\/energy\/?p=627"},"modified":"2022-10-12T16:57:18","modified_gmt":"2022-10-12T14:57:18","slug":"randomized-field-experiments","status":"publish","type":"post","link":"https:\/\/blogt.ethz.ch\/energy\/randomized-field-experiments\/","title":{"rendered":"Can randomized field experiments help close the energy efficiency gap? \/ K\u00f6nnen randomisierte Feldstudien helfen, die Energieeffizienzl\u00fccke zu schliessen?"},"content":{"rendered":"\t\t
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By Nina Boogen<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Die deutsche Version des Textes finden Sie weiter unten.<\/em><\/p>

Some time ago economists appropriated a method from medical research called randomized controlled trials, which has become the \u201cgolden standard\u201d to detect causal effects of a policy intervention. Could they also assist in closing the energy efficiency gap? In this blog article, I argue why they could, but also why they are not a universal remedy.<\/b><\/p>

\u00a0<\/b><\/p>

Next Tuesday (10th of December) the 2019 Nobel Prize in Economics<\/a> will be officially awarded to three empirical researchers that introduced randomized controlled trials (RCT) as a method in economic policy evaluation, specifically in the field of development economics. One of the winners, Esther Duflo, is only the second woman to win this prize after Elinor Ostrom. The work of Esther Duflo, Michael Kremer and Abhijit Banerjee have \u201c<\/span>dramatically improved our ability to fight poverty in practice<\/span><\/i>\u201d, reported the Nobel committee. Further, they state that \u201c<\/span>in just two decades, their new experiment-based approach has transformed development economics<\/span><\/i>\u201d. Evidence-based impact evaluation research has been there for a long time in order to help policymakers design more effective policy instruments. In the early 1990s, the so-called \u201cRandomistas\u201d \u2013 advocates of RCTs, such as the three winners \u2013 took the idea of clinical trials in medicine and adapted it to real-world economic decision-making situations studied in economics.<\/span><\/p>

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But how does an RCT actually work?<\/b><\/h3>

The fundamental problem of measuring the causal impact of a policy instrument is that we need to somehow find or construct a so-called counterfactual (see Figure 1). The counterfactual is the situation that would have happened if the policy had not been there, so something that does not exist in the real world and is thus not observable. An RCT can resolve this issue by simply randomizing study participants: randomization aims at constructing a group that proxies the behaviour of the treated had they not been treated.<\/span><\/p>

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Figure 1: <\/em><\/span>The fundamental problem of economic impact evaluation is to measure the difference between the actual outcome (shown in blue) and a counterfactual that is never observed (shown in red). Impact evaluation methods thus somehow always need to construct the counterfactual in order to estimate the intervention\u2019s impact.<\/span><\/i><\/p>

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Dina Pomeranz explains how this works in her article<\/a> aimed at non-economists: \u201c<\/span>The goal of RCTs is to create an ideal comparison group by design from the beginning of the intervention. Study participants are randomly assigned to either receive the treatment or be in the comparison group.\u201d <\/span><\/i>Such a random assignment ensures that individuals in the treatment and control group are, on average, the same with respect to socio-economic factors, for example. Researchers can therefore exclude the possibility that the measured effect is due to a systematic difference between the treatment and control group.<\/span><\/p>

Esther Duflo and her team designed a pilot program where small cash transfers were made to parents of school-aged children<\/a> in poor, rural communities in Morocco. The economists chose 320 school sectors in the five poorest regions of Morocco, where 260 of them were randomly selected to be in the treatment group and thus to participate in a two-year pilot education support program. The other 60 sectors were consequently assigned to the control group with no program participation. In all school sectors, they measured the school attendance rate. The program resulted in a large increase in school attendance among children whose parents could participate in the program. Thus, the pilot program was effective in promoting education.<\/span><\/p>

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Investigating casual effects in energy efficiency research<\/b><\/h3>

In the last decade, RCTs have become a popular method in other fields of economics, too. In energy economics they are especially useful for situations where one wants to study how individuals decide to invest in energy-using durables. In a <\/span>recent article, <\/a>Hunt Allcott and Christopher Knittel investigated whether car buyers are poorly informed about and inattentive to fuel economy. To do this, they selected car dealers of a well-known automobile brand in the US and had research assistants ask around 2,000 potential buyers whether they wanted to participate in a survey. The consumers were asked some socio-economic questions and then randomly allocated to be informed about the fuel costs of the cars they were most closely considering (i.e., treated group) or not (i.e., control group).\u00a0 <\/span><\/p>

In a similar manner, but with a different design, our research group at the Centre for Energy Policy and Economics<\/i> (<\/span>CEPE<\/a>) also implemented a randomized experiment within the H2020 project PENNY<\/a> with around 600 Swiss participants (see Figure 2 for the design of the experiment). In contrast to the US study, we visited people in the treatment group in their homes and provided them afterwards with tailored information on the monetary savings potential on energy cost they could realize if they adopted new, energy-efficient appliances.<\/p>

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Figure 2: Experimental design of the PENNY experiment.<\/span><\/i><\/em><\/span><\/p>

\u00a0<\/span><\/i><\/em><\/span><\/p>

What now: Does information provision affect the purchase decision?<\/b><\/h3>

After one year, we went back to the households and asked them if and what appliances they had bought within the past 12 months. And indeed, people who received the information on monetary savings did buy more energy-efficient appliances. Thus, our experiment shows that some Swiss households did lack knowledge about the energy costs of using appliances and that providing them with tailored information about it is an effective strategy to induce an increase in efficiency of the stock of appliances.<\/span><\/p>

Allcott and Knittel, however, did not find any effect of the information they provided to the participants in the treatment group, as they did not buy more fuel-efficient cars. They provide two interpretations for this result: First, while their interventions did draw attention to fuel economy for a short time, the information they provided was not useful or the consumer soon forgot it. And second, imperfect information and inattention do not have a significant systematic effect on vehicle markets.\u00a0<\/span><\/p>

Are these now conflicting results from research? No, the two studies are done in completely different settings and circumstances. In the Allcott and Knittel study, the information was provided once at the selling location, while in our case, the information was provided in people\u2019s homes. The energy durables in question and its market were also clearly different: one group was making a decision about purchasing a new car, while the other was considering buying a new home appliance. Finally, there are US study participants in one study and Swiss in the other. The context of a study matters.\u00a0<\/span><\/p>

\u00a0<\/span><\/p>

Not a universal remedy<\/b><\/h3>

A randomized controlled trial is a good thing: it helps inform policymakers what program to implement or how to improve existing programs. If it is conducted properly, it estimates the causal effect in an accurate and unbiased way. Simply said, it measures what it tries to measure very well. Researchers call this internal validity.\u00a0<\/span><\/p>

But nothing comes without flaws in the real world: even the \u201cgold standard\u201d method has its limitations<\/a>, that also the three Nobel Prize winners are aware of. As just mentioned, context matters, study results may not transfer to different goods in different contexts. Researchers call this external validity, which might not be given in all cases in RCTs. Another Nobel Prize winner, Angus Deaton even says in a scientific article<\/a> from 2018 that \u201cresearchers put too much trust in RCTs<\/i>\u201d. And lastly, while an RCT can tell you a lot of things about how to make the world a better place, it may cost a whole lot of money to actually do this research.\u00a0 So, in some cases, randomized experiments can point policymakers towards good interventions to help close the energy efficiency gap, and in some other cases, other methods are better fits.<\/p>

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Cover photo by\u00a0Chrissie Kremer<\/a>\u00a0on\u00a0Unsplash<\/a>.<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Keep up with the Energy Blog @ ETH Zurich on Twitter @<\/em>eth_energy_blog<\/a>.<\/p>

Suggested citation: Boogen, Nina. \u201cCan randomized field experiments help close the energy efficiency gap?\u201d, Energy Blog @ ETH Zurich, ETH Zurich, December 5, 2019,\u00a0<\/span><\/i><\/em>https:\/\/blogt.ethz.ch\/energy\/randomized-field-experiments\/<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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If you are part of ETH Zurich, we invite you to contribute with your findings and your opinions to make this space a dynamic and relevant outlet for energy insights and debates. Find out how you can contribute<\/a> and contact the editorial team here<\/a> to pitch an article idea!\n<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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K\u00f6nnen randomisierte Feldstudien helfen, die Energieeffizienzl\u00fccke zu schliessen?<\/h1>
\u00a0<\/div>

Vor einiger Zeit haben sich \u00d6konomen eine Methode aus der medizinischen Forschung angeeignet, die randomisierte Feldstudien genannt wird und zum “goldenen Standard” geworden ist, um kausale Wirkungen eines Politikinstrumentes zu messen. K\u00f6nnten sie auch dazu beitragen, die Energieeffizienzl\u00fccke zu schliessen? In diesem Blogartikel argumentiere ich, inwiefern dieser Ansatz helfen k\u00f6nnte, aber auch, warum er kein Allheilmittel ist.<\/b><\/p>

\u00a0<\/b><\/p>

Am n\u00e4chsten Dienstag (10. Dezember) wird der<\/span> Wirtschaftsnobelpreis<\/a> 2019 offiziell an drei empirisch Forschende verliehen, die randomisierte Feldstudien (englisch: Randomized Controlled Trials (RCT)) als Methode zur Wirkungsanalyse von Reformen und Interventionen eingef\u00fchrt haben, insbesondere im Bereich der Entwicklungs\u00f6konomie. Eine von ihnen, Esther Duflo, ist erst die zweite Frau, die diesen Preis nach Elinor Ostrom gewinnt. Die Arbeit von Esther Duflo, Michael Kremer und Abhijit Banerjee habe “<\/span>unsere F\u00e4higkeit, Armut in der Praxis zu bek\u00e4mpfen, drastisch verbessert\u201d<\/span><\/i>, berichtete das Nobelkomitee. Weiter hiess es, dass “<\/span>ihr neuer experimenteller Ansatz in nur zwei Jahrzehnten die Entwicklungs\u00f6konomie ver\u00e4ndert habe.<\/span><\/i>” Evidenzbasierte Wirkungsanalysen gibt es schon seit l\u00e4ngerem, insbesondere um Entscheidungstr\u00e4ger*innen bei der Entwicklung von effektiveren politischen Instrumenten zu unterst\u00fctzen. In den fr\u00fchen 1990er Jahren nahmen die sogenannten “Randomistas” \u2013 Bef\u00fcrworter*innen von randomisierten Feldstudien wie die drei diesj\u00e4hrigen Nobelpreistragenden \u2013 die Idee von klinischen Studien in der Medizin auf und passten sie an reale wirtschaftliche Entscheidungssituationen an, die in der Entwicklungs\u00f6konomie untersucht werden.<\/span><\/p>

Aber wie funktioniert ein RCT eigentlich?<\/b><\/h3>

Das Hauptproblem der Messung der tats\u00e4chlichen kausalen Wirkung eines Politikinstrumentes besteht darin, dass man eine sogenannte \u201ckontrafaktische\u201d Situation finden oder konstruieren muss (siehe Abbildung 1). Der Kontrafakt ist der hypothetische Zustand, der sich ergeben h\u00e4tte, wenn keine Intervention stattgefunden h\u00e4tte. Also eine Situation, die in der Realit\u00e4t gar nicht beobachtet werden kann. Ein RCT kann dieses Problem beheben, indem es die Teilnehmer der Studie randomisiert: Die Randomisierung zielt darauf ab, eine Gruppe zu bilden, die sich m\u00f6glichst wie Behandelte verh\u00e4lt, wenn diese gar nie behandelt worden w\u00e4ren. <\/span><\/p>

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Abbildung<\/span><\/i> 1: Das grundlegende Problem von \u00f6konomischen Wirkungsanalysen besteht darin, die Differenz zwischen dem tats\u00e4chlichen Ergebnis (blau dargestellt) und einer nie beobachteten kontrafaktischen Situation (rot dargestellt) zu messen. Wirkungsanalysen m\u00fcssen also irgendwie immer einen Kontrafakt konstruieren, um die kausale Wirkung der Intervention absch\u00e4tzen zu k\u00f6nnen.<\/span><\/i><\/em><\/span><\/p>

\u00a0<\/span><\/i><\/p>

Wie das funktioniert, erkl\u00e4rt Dina Pomeranz in ihrem Artikel<\/a> f\u00fcr Nicht-\u00d6konom*innen: “<\/span>Ziel von RCTs ist es, von Beginn der Intervention an eine ideale Vergleichsgruppe per Design zu schaffen. Die Studienteilnehmer werden nach dem Zufallsprinzip entweder der Behandlungs- oder der Vergleichsgruppe zugeordnet.<\/span><\/i>” Eine auf Zufall basierende Zuordnung stellt sicher, dass die Personen in den beiden Gruppen im Durchschnitt gleich sind, z.B. in Bezug auf sozio\u00f6konomische Faktoren. Wenn dies zutrifft, kann ausgeschlossen werden, dass der gemessene Effekt auf einen systematischen Unterschied zwischen Behandlungs- und Kontrollgruppe zur\u00fcckzuf\u00fchren ist.<\/span><\/p>

\u00a0<\/span><\/p>

Esther Duflo und ihr Team entwickelten ein Pilotprojekt, bei dem Eltern von schulpflichtigen Kindern, die in armen l\u00e4ndlichen Gemeinden in Marokko lebten, einen <\/span>kleinen finanziellen Betrag bezahlt erhielten<\/a>. Die \u00d6konom*innen w\u00e4hlten rund 320 Schulsektoren in den f\u00fcnf \u00e4rmsten Regionen Marokkos aus, wobei 260 von ihnen nach dem Zufallsprinzip in die Behandlungsgruppe zugeteilt wurden und so an diesem zweij\u00e4hrigen Pilotprogramm zur Bildungsf\u00f6rderung teilnahmen. Die anderen 60 Schulsektoren wurden folglich in die Kontrollgruppe eingeteilt, also die Gruppe, die nicht am Programm teilnahm.\u00a0 Die Forschenden massen in allen Sektoren, wie h\u00e4ufig die Kinder in die Schule gingen. Das Programm f\u00fchrte zu einer starken Zunahme der Anwesenheit in der Schule der Kinder, deren Eltern an diesem Programm teilnehmen konnten. Somit trug die finanzielle Unterst\u00fctzung zur F\u00f6rderung der Bildung bei.<\/span><\/p>

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Untersuchung kausaler Effekte in der Energieforschung<\/b><\/h3>

Solche randomisierten Feldstudien wurden in letzter Zeit auch von anderen Bereichen der Wirtschaftsforschung \u00fcbernommen. In der Energie\u00f6konomie sind sie\u00a0 insbesondere n\u00fctzlich f\u00fcr Forschungsfragen, in denen man untersuchen m\u00f6chte, wie Individuen entscheiden, in energieverbrauchende G\u00fcter zu investieren. In einem k\u00fcrzlich publizierten <\/span>Artikel<\/a> untersuchen<\/span> Hunt Allcott und Christopher Knittel, ob Autok\u00e4ufer*innen schlecht informiert oder unachtsam bez\u00fcglich des Energieverbrauchs sind. Dazu w\u00e4hlten sie H\u00e4ndler einer bekannten Automobilmarke in den USA aus. Forschungsassistierende befragten rund 2\u2019000 potenzielle K\u00e4ufer an den Verkaufsstellen der Autoh\u00e4ndler, ob sie an einer Umfrage teilnehmen wollten. Den interessierten Personen wurden einige sozio\u00f6konomische Fragen gestellt. Danach wurde nach dem Zufallsprinzip entschieden, ob sie \u00fcber die Energiekosten der Fahrzeuge, an denen sie interessiert gewesen waren, informiert wurden (Behandlungsgruppe) oder nicht (Kontrollgruppe).\u00a0<\/span><\/p>

In \u00e4hnlicher Weise aber mit einem anderen Design haben wir in unserer Forschungsgruppe am <\/span>Centre for Energy Policy and Economics<\/span><\/i> (<\/span>CEPE<\/a>) im Rahmen des H2020-Projekts<\/span> PENNY<\/a> mit rund 600 Schweizer Haushalten ein solches Experiment durchgef\u00fchrt (siehe Abbildung 2 f\u00fcr das Design des Experiments).\u00a0<\/span><\/p>

Im Gegensatz zur US-Studie haben wir die teilnehmenden Haushalte in der Behandlungsgruppe besucht und ihnen anschliessend einen Bericht mit massgeschneiderten Informationen abgegeben. Dem Bericht k\u00f6nnen sie entnehmen, wie viel Geld sie bei den\u00a0 Energiekosten sparen k\u00f6nnten, wenn sie neue, energieeffizientere Ger\u00e4te anschaffen w\u00fcrden.<\/span><\/p>

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Abbildung<\/span><\/i> 2: Design des PENNY-Experiments.<\/span><\/i><\/em><\/span><\/p>

\u00a0<\/span><\/i><\/em><\/span><\/p>

Was nun: Beeinflusst die zus\u00e4tzliche Information die Kaufentscheidung?<\/b><\/h3>

Nach einem Jahr fragten wir die Haushalte, ob und welche Haushaltsger\u00e4te sie im letzten Jahr gekauft hatten. Tats\u00e4chlich kauften Teilnehmende, die unseren Bericht \u00fcber das Einsparpotential erhalten hatten, energieeffizientere Haushaltsger\u00e4te. So zeigt unser Experiment, dass in einigen Schweizer Haushalten wohl zu wenig Wissen \u00fcber die Energiekosten bei der Nutzung von Haushaltsger\u00e4ten vorhanden ist und dass die Bereitstellung von solchen massgeschneiderten Informationen eine effektive Strategie ist, um eine Effizienzsteigerung des Ger\u00e4tebestands zu erwirken.<\/span><\/p>

Im Gegensatz dazu fanden Allcott und Knittel keine Wirkung ihrer zus\u00e4tzlichen Informationsbereitstellung: Die Studienteilnehmer*innen kauften keine energiesparenderen Autos. Die beiden Autoren liefern zwei m\u00f6gliche Erkl\u00e4rungen daf\u00fcr: Erstens, w\u00e4hrend ihre Intervention die Aufmerksamkeit f\u00fcr kurze Zeit auf den Energieverbrauch lenkte, waren die von ihnen bereitgestellten Informationen eventuell nicht n\u00fctzlich, und\/oder der Verbraucher vergass sie schnell wieder. Eine zweite Erkl\u00e4rung w\u00e4re, dass unvollst\u00e4ndige Informationen und Unaufmerksamkeit keine signifikanten systematischen Auswirkungen auf die Fahrzeugm\u00e4rkte haben.<\/span><\/p>

Sind das nun widerspr\u00fcchliche Forschungsergebnisse? Nein! Die beiden Studien wurden unter unterschiedlichen Rahmenbedingungen und Umst\u00e4nden durchgef\u00fchrt: In der Studie von Allcott und Knittel wurden die Informationen am Verkaufsort bereitgestellt, w\u00e4hrend in unserem Fall die Informationen zu Hause bereitgestellt wurden. Auch die zwei untersuchten energieverbrauchenden Haushaltsg\u00fcter und deren M\u00e4rkte sind nicht vergleichbar: Die eine Gruppe traf eine Entscheidung \u00fcber den Kauf eines neuen Autos w\u00e4hrend die andere Gruppe \u00fcberlegte, ein neues Haushaltsger\u00e4t zu kaufen. Auch der kulturelle Hintergrund ist nicht zu vergessen: die eine Studie wurde in der Schweiz, die andere in den USA durchgef\u00fchrt.\u00a0 Fazit ist demnach: Der Kontext einer randomisierten Feldstudie ist entscheidend.\u00a0<\/span><\/p>

\u00a0<\/span><\/p>

Kein Allheilmittel<\/b><\/h3>

Eine randomisierte Feldstudie ist eine gute Sache: Sie hilft den Politiker*innen dabei, zu entscheiden, welche Programme sie umsetzen oder wie sie bestehende Programme verbessern k\u00f6nnen. Wenn sie richtig durchgef\u00fchrt wird, misst sie den kausalen Effekt auf eine genaue und unvoreingenommene Weise. Einfacher ausgedr\u00fcckt: Was sie zu messen versucht, misst sie sehr gut. Forschende nennen das interne Validit\u00e4t.\u00a0<\/span><\/p>

Aber in Realit\u00e4t ist nichts perfekt: Auch die “Goldstandard”-Methode hat ihre<\/span> Grenzen<\/a>, denen sich auch die drei diesj\u00e4hrigen Nobelpreistragenden durchaus bewusst sind. Wie bereits erw\u00e4hnt, ist der Kontext einer Wirkungsanalyse massgebend, Studienergebnisse sind teilweise nicht auf verschiedene G\u00fcter in verschiedenen Kontexten \u00fcbertragbar. Forschende nennen dies externe Validit\u00e4t, die f\u00fcr randomisierte Studien m\u00f6glicherweise nicht in allen F\u00e4llen gegeben ist. Angus Deaton, ebenfalls Nobelpreistr\u00e4ger, sagt in <\/span>einem wissenschaftlichen Artikel<\/a> aus dem Jahr 2018: “<\/span>Forscher setzen zu viel Vertrauen in RCTs<\/span><\/i>“. Und schlussendlich, obwohl solche Studien viel dar\u00fcber erz\u00e4hlen k\u00f6nnen, wie die Welt zu einem besseren Ort gemacht werden k\u00f6nnte, ben\u00f6tigen sie auch ein grosses Forschungsbudget. Nichtsdestotrotz k\u00f6nnen randomisierte Experimente in vielen F\u00e4llen helfen, die Energieeffizienzl\u00fccke zu schliessen. In anderen F\u00e4llen sind wiederum andere Methoden besser geeignet.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Bleiben Sie auf dem Laufenden beim Energy Blog @ ETH Z\u00fcrich auf Twitter\u00a0 @<\/em>eth_energy_blog<\/a>.<\/em><\/p>

Empfohlene Zitierweise: Boogen, Nina. \u201cK\u00f6nnen randomisierte Feldstudien helfen, die Energieeffizienzl\u00fccke zu schliessen?\u201d, Energy Blog @ ETH Zurich, ETH Zurich, Dezember 5, 2019,\u00a0<\/em>https:\/\/blogt.ethz.ch\/energy\/randomized-field-experiments\/<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Wenn Sie Teil der ETH Z\u00fcrich sind, laden wir Sie dazu ein, mit Ihrem Wissen dazu beizutragen, diesen Blog zu einem gelichzeitig dynamischen und relevanten Ort f\u00fcr Einblicke und Diskurse in der Energieforschung zu machen. Erfahren Sie hier<\/a>, wie Sie die Redaktion kontaktieren k\u00f6nnen, wenn Sie eine Idee f\u00fcr den n\u00e4chsten Blogbeitrag haben!<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\tTwitter<\/span>\n\t\t\t\t\t\t<\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\tLinkedin<\/span>\n\t\t\t\t\t\t<\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\tEnvelope<\/span>\n\t\t\t\t\t\t<\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Some time ago economists appropriated a method from medical research called randomized controlled trials, which has become the \u201cgolden standard\u201d to detect causal effects of a policy intervention. Could they also assist in closing the energy efficiency gap? In this blog article, I argue why they could, but also why they are not a universal remedy. 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