Location: UK

The Cost of Climate Change Policy: The Case of Coal Miners

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The phasing out of coal is considered a key component of the upcoming energy transition. While environmentally appealing, this measure will have a devastating effect on those working in the coal industry. Using the dissolution of the UK coal industry under Margret Thatcher as a natural experiment, we estimate the long run costs of being displaced as a coal miner. We find that within the first year of displacement, earnings fall by 80-90 percent, relative to the earnings of a carefully matched blue-collar manufacturing worker, while the wages of miners who find alternative employment fall by 40 percent. The losses are persistent and remain significant fifteen years after displacement. Our results are considerably above the estimates provided by other studies in the job displacement literature and may serve as a guide for policy makers when aiming for a just energy transition.

The Coal Mining Industry and Global Warming

According to the recent IPCC report, limiting global warming to 2 degrees Celsius requires a near complete and rapid elimination of coal in the global use of energy. Such a drastic measure is bound to have devastating effects on anybody economically linked to and dependent on the coal industry. Our back-of-the-envelope calculation suggests that the closure of the currently 2300 active industrial coal mines would translate into more than 5 million displaced coal miners. In Figure 1 we plot the spatial distribution of coal mines, indicating the locations of the upcoming displacements globally.

Figure 1. Location of industrial coal mines. The seven biggest producers and exporters of coal are marked in green.

Source: SNL Energy Data Set produced by S&P Global.

In a new paper (Rud et al., 2022), we estimate the average loss in the earnings of coal miners who have been displaced following one of the most notorious labor disputes of the 20th century: the dissolution of the coal sector in the UK. When Margaret Thatcher came into power many of the mines were unprofitable (Glyn, 1988). Considering the mines to be ripe for closures, the UK government publicly announced the closure of 20 mines in 1984. After additional information on further closures reached the press, the Union of Miners called for a general strike. The strike lasted for nearly a year and ended with a devastating defeat of the miners. From 1985 and onwards, the closure of mines proceeded at such an incredible pace that the dissolution of the UK coal industry is considered the most rapid in the history of the developed world (Beatty and Fothergill, 1996). As shown in Figure 2, the closures resulted in an equally rapid displacement of miners, from 250 000 employed miners in 1975 to less than 50 000 by 1995.

Figure 2. Coal Mining Employment in the UK 1975-2005

Note: The number on employed miners is collected from National Coal Board (1970-1993) and used in Aragon et al., (2018). The percent of employment shown on the right axis was calculated from the New Earnings Survey, the main data source used in this paper.

The Effects of UK Coal Mine Closures on Miners

At the heart of our empirical analysis is the New Earnings Survey, a longitudinal dataset covering 1 percent of the UK population since 1975. For the period 1979-1995 (marked in gray in Figure 2), among the 25-55 years old and those who were employed by the same mine for at least two consecutive years, we identify 2152 miners who experienced a final separation from a mine. In our baseline specification, these miners are matched to a single manufacturing worker using a large array of observables such as age, gender, hours worked, pre-separation employment and earnings, geographical administrative unit (county), as well as whether their respective wage was determined in a collective agreement. By the nature of the exercise we are unable to match on industry and instead match on detailed occupational information. A variety of other matching procedures suggest our results are robust.

In Figure 3 we plot the estimated differences in the evolution of earnings and wages for four years before, and fifteen years after displacement. The coefficients are estimated conditional on time and individual fixed effects. Due to the normalization of the dependent variable, the estimates should be interpreted as the percentage change relative to pre-displacement values. In Panel A of Figure 3 we show that hourly wages and weekly earnings conditional on employment drop by around 40 percent in the year after displacement and recover only slowly. It should be noted that the losses in earnings conditional on employment are not driven by changes in hours since the two series are close to identical.

In Panel B of Figure 3 we show the effect on earnings taking into account the losses of those who have not been successful in finding alternative employment in another industry. To get to these results we need to make some assumptions since the New Earnings Survey neither includes earnings information on the self-employed, nor on those who are active in the informal sector. Many other studies in the job displacement literature share similar data limitations, so we follow their approach in dealing with these. On the one hand, we assume zero individual earnings for periods without any observed labor earnings in the data, as assumed by Schmieder et al. (2022) and Bertheau et al. (2022). This assumption does not appear too strong since there is some evidence suggesting that ignoring the self-employed only marginally affects the results (Upward and Wright, 2017; Bertheau et al., 2022). On the other hand, we complement our results with an approach inspired by Jacobson (1993) where we keep only individuals who experience positive earnings within four years after displacement. The latter approach provides a more conservative estimate of displacement costs by assuming zero earnings only for individuals who eventually return to work.

Figure 3. The hourly wage and earnings conditional on employment (Panel A), and overall earnings costs of final displacement from a mine (Panel B).

Note: We plot the coefficients of the estimated panel data model with time and individual fixed effects and distributed leads and lags. ”Earnings: come back” refers to the treatment group where we only include those who have positive earnings at some point four years after job loss, and impute periods without employment as zeros. ”Earnings: all zeros” refers to the treatment in which we replace the earnings of any miner with a zero if the miner is not observed for any year, without restrictions.

Interpreting all periods of missing information as zeros, we find the initial losses to be around 90 percent of pre-displacement earnings within the first year after separation, while the more conservative estimates are only slightly lower at around 80 percent in the short run. In the long run, the losses are persistent and remain significantly depressed even fifteen years after displacement. Over the fifteen years after displacement these numbers amount to the miners losing on average between 4 to 6 times of their pre-displacement earnings. This implies that miners only receive 40-60 percent of the present discounted counterfactual earnings.

Our estimates are considerably above those provided by studies in the job displacement literature that focus on mass layoffs. Couch and Placzek (2010), for instance, report initial losses to amount to about 25-55 percent, while Schmeider et al. (2022) find initial earnings losses to be around 30-40 percent. Davis and Wachter (2012) estimate the long-run effects based on US data and find the present discounted earnings losses to be on average 1,7 times the workers’ pre-displacement earnings.

The large estimated individual costs to the displaced miners are likely due to a combination of at least two reasons. First, the complete collapse of the sector forces displaced miners to reallocate and search for another job in other industries, and likely other occupations. Since coal mining is a highly specialized occupation, this greatly reduces miners’ ability to transfer the accumulated human capital to another activity (Beatty and Fothergill, 1996; Samuel, 2016). Second, most coal miners are employed in remote and rural areas where mining is often the main employer, something which remains an issue for current miners around the world (see Figure 1). This feature reduces local economies’ capacity to absorb displaced miners after a mine closure and, due to the need to relocate, greatly increases workers’ job searching costs.


While it is important to globally transition away from the excessive use of fossil fuels, we should keep in mind the devastating effects such transition will end up having on some groups. And while coal miners are particularly vulnerable to the upcoming energy transition, the ramifications do not stop there. Individuals employed in industries linked to the coal industry are likely to also be affected by its dissolution. Moreover, individuals employed in industries providing local services, such as retail stores, restaurants and pubs are likely to experience a significant drop in demand. Thus, the impact of coal mine closures on coal dependent communities typically goes far beyond the displacement of miners (Aragon et al., 2018). The closure of mines will lead to spikes in local unemployment, often unregistered (“hidden”), as well as an exodus of the population. Estimating and accounting for these effects is important if we aim to provide a just energy transition for all.

Attempts have been made to foster economic recovery of affected communities. Regeneration policies have included re-training of local workers, support of small and medium-sized businesses, and investments in local infrastructure, among others. However, their success has been limited and former mining communities remain among the poorest in the UK (Beatty et al., 2007). Preparing a set of policies which will have the capacity to reduce the costs of the transition, as not to repeat the devastating experience of UK coal miners and their communities, is an important task ahead of current policy makers.


Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Money Laundering: Regulatory or Political Capture?

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Danske Bank has recently been accused of having laundered more than 200 billion Euros through its Estonian branch. The size of the scandal has reinvigorated the discussion over lax enforcement by regulators and poor bank compliance with anti-money laundering laws. In this brief, we concisely review some recent cases of poor regulatory and political behaviour with respect to these matters, focusing in particular on the UK, whose financial system seems to have become a main hub for this type of financial misconduct.

A widespread phenomenon

The size of the recent money laundering scandal at Danske Bank, involving more than 200 billion Euros, has surprised many. Money laundering is a widespread issue in an increasingly complex world where financial transactions are many and instantaneous, while oversight slow and limited (Radu 2016). According to the United Nations Office on Drugs and Crime, an estimated $800 – $2 trillion is laundered every year (United Nations Office on Drugs and Crime). The source of laundered money is often from corruption, crime and drug cartels (as with the HSBC scandal, see below). Attempts to blow the whistle on these illegal transactions have gotten several people killed, especially in Russia (The Daily Beast, October 2018).

Malta’s Pilatus bank recently had its license revoked by the European Central Bank after its chairman was charged with money laundering (Reuters, October 2018). The investigative reporter Daphne Caruana Galizia was killed in a car bomb in October of 2017 in Malta (The Guardian, October 2017). She was leading the Panama Papers investigation into corruption in the country and had accused Pilatus bank of processing corrupt payments (The Guardian, November 2018). In Sweden, some banks have recently been criticized for insufficient actions against money laundering. Experts at the regulator recommended extensive sanctions, but upper management stopped them (Svenska Dagbladet, December 2018). In November, Deutsche Bank’s headquarters in Frankfurt were raided by prosecutors in a money laundering investigation (BBC, November 2018).

Back to Danske Bank. Its Estonian branch was recently accused of having laundered money, amounting to over 200 billion Euros of suspicious transfers (Financial Times, November 2018). In 2011 the Estonian branch accounted for 0.5% of Danske Bank’s assets, while generating 12% of its total profits before taxes. In 2013, 99% of the profits in the branch came from non-residents. Many of the non-resident customers are believed to be from Russia and other ex-soviet states (Forbes, September 2018). The alleged money laundering came to light due to the whistleblower Howard Wilkinson, who headed Danske Bank’s market trading unit in the Baltics from 2007 to 2014. Surprisingly, his anger over these transactions was not primarily aimed at top management in Copenhagen, or failure of rank and file employees to follow protocol in customer acquisition, but against the UK, who he claimed is “the worst of all” when it comes to combating money laundering (Financial Times, November 2018). In fact, the UK institutions seem to have been at the very heart of the scandal (ibid):

“Mr Wilkinson’s emails to Danske executives in 2013 and 2014 highlighted how UK entities were “the preferred vehicle for non-resident clients” at the heart of the scandal.”

In an address to European Union Lawmakers, he said (Reuters, November 2018):

“The role of the United Kingdom is an absolute disgrace. Limited liability partnerships and Scottish liability partnerships have been abused for absolutely years”.

Regulatory or political capture?

The increasingly central role that the UK appears to be playing as a hub for financial crime is perhaps not new or surprising. The UK has indeed come to be widely recognized as one – though certainly not the only – main hub for these illegal transactions (see e.g. Radu 2016, p.15). The UK’s National Crime Agency estimates 93 billion GBP of tainted money is flowing into Britain annually (Financial Times, September 2018).

And according to the classic theory of regulatory capture (Stigler, 1970), it is to be expected that a large, wealthy and highly concentrated sector such as the UK financial industry, will be able to capture regulatory institutions and lead them to act more in its favour than in that of the (national or international) community. However, besides being a concentrated source of special interests, the financial sector also represents a large share of the UK economy. It could be the case, therefore, that the capture goes all the way up to the political system and the government (as in Becker 1983, and Laffont, 1996). So, is it the alleged crime-friendly environment in the UK financial system linked more to problems of regulatory capture, or to deeper political capture?

Already in 2004 there were worrying signs of possibly deep political capture.  At the time, Paul Moore, a senior risk manager at Halifax Bank of Scotland (HBOS), raised concerns about the bank’s risk taking and was subsequently fired by the executive James Crosby. Crosby then proceeded to become Deputy Chairman at the Financial Services Authority (FSA). HBOS then collapsed during the financial crisis of 2008 and merged with Lloyds bank, leading to one of the most concentrated banking systems in the world (the top 5 banks have 85% of the UK banking market). Many took this to substantiate Moore’s claim that the bank had been taking excessive risks. During Prime Minister’s question time in the House of Commons, David Cameron commented on then Prime Minister Gordon Brown’s decision to appoint Crosby to the FSA:

“Sir James Crosby, the man who ran HBOS and whom the Prime Minister singled out to regulate our banks and to advise our Government, has resigned over allegations that he sacked the whistleblower who knew that his bank was taking unacceptable risks.” (cited in Dewing and Russell 2016, p.165)

A suggestive episode directly involving politicians and money laundering is the case of HSBC, with headquarters in London. HSBC avoided criminal prosecution in the US and entered into a deferred prosecution agreement with the DOJ in 2012 (Department of Justice, December 2012). HSBC was found to have violated U.S. Anti-Money Laundering and Sanctions Laws by laundering billions of dollars linked to Mexican drug cartels, groups in Iran and Syria, and groups linked to terrorism. While HSBC apparently had systems to flag suspicious transactions, employees were told to disregard red flags (Garrett 2014, p.201). The case led to a 2016 House Committee report entitled “too big to jail” that was extensively used against the Democrats by the Trump presidential campaign (Committee on Financial Services, 2016).

The report states that on the 10th of September 2012 UK Chancellor George Osborne (the UK’s chief financial minister) wrote a letter to Federal Reserve Chairman Ben Bernanke (with a copy transmitted to then Treasury Secretary Timothy Geithner). In the letter, Chancellor Osborne insinuated that the U.S. was unfairly targeting UK banks by seeking settlements that were higher than comparable settlements with U.S. banks. He also worried about what criminal sanctions against HSBC would imply for financial stability. Criminal charges could also lead to a revoked license, making the bank unable to do business in the US (Financial Times, July 2016). HSBC was eventually ordered to pay a 1.9 billion dollar fine, while another whistleblower claims that the money laundering still went on (Huffington Post, August 2013).

The FSA also appeared much more concerned about criminal sanctions against HSBC than with money laundering for the bloodiest drug cartel in history (estimated to be responsible for several tenths of thousands of murders). In fact, the house committee report states that “The FSA’s Involvement in the U.S. Government’s HSBC Investigations and Enforcement Actions Appears to Have Hampered the U.S. Government’s Investigations and Influenced DOJ’s Decision Not to Prosecute HSBC” (p.24).

Things have not improved more recently. In 2013 the FSA was split up into the Financial Conduct Authority and the Prudential Regulation Authority (FCA & PRA). In 2014 the FCA & PRA came out with a note requested by the British parliament on whether financial incentives for whistleblowers should be introduced in the UK. These financial incentives, or reward programs, are used extensively in the US in tax, procurement, and securities. The FCA & PRA came out strongly against rewards in their seven-page note, yet do not cite a single piece of evidence (PRA and FCA, 2014). Most importantly, the note contains important factual misstatements about available evidence on their effectiveness that were easy to check at the time of the report (Nyreröd & Spagnolo 2017, National Whistleblower Center 2018). Nor was the note amended when one of us repeatedly communicated the mistakes to the agencies. This suggests persistent and deep regulatory capture. Consistent with this interpretation is the sanctioning behavior of UK regulators.

A blatant recent example is the ridiculous fine against CEO of Barclays Bank Jes Staley. He ordered his security team to unveil the identity of an uncomfortable whistleblower, going so far as to request video footage of the person who bought the postage for the letter. Yet, the FCA & PRA decided to just fine him £642 000 – a small fraction of his pay package that year (Reuters, May 2018). When Moore was asked about the fine he replied that “it is a very clear sign to whistleblowers not to bother” (Reuters, April 2018).


Is this regulatory capture, or political capture? The impressive list of consistent cases of regulatory slack and of political complacency suggests both, at least in the case of the UK. But the problem of regulatory capture in the case of financial crimes goes way beyond the somewhat extreme case of the UK. In all jurisdictions financial misbehavior has recently only led to settlements between regulators and the infringing financial institution, with settlement payments way too low to generate (financial stability concerns, and) deterrence effects. Banking regulators appear mainly concerned about banks’ health and profitability, so that large financial institutions have not only become too big to fail, but also too big to jail, and now even too big to fine, at least to the appropriate extent (Spagnolo 2015). All this even though the financial crime has been that actively supporting through money laundering criminal organizations that killed tenths of thousands of innocent people.


Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Energy Demand Management: Insights from Behavioral Economics

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It has long been recognized that consumers fail to choose the cheapest and most efficient energy-consuming investments due to a range of market and non-market failures. This has become known as the ‘Energy Efficiency Gap’.  However, there is currently a growing interest in terms of understanding on how consumers make decisions that involve an energy consumption component, and whether the efficiency of their decisions can be improved by changing the market incentives and governmental regulation. Meeting this interest, the most recent SITE Energy Talk was devoted to Demand Side Management.  SITE invited Eleanor Denny, Associate Professor of Economics at Trinity College Dublin, and Natalya Volchkova, Assistant Professor at the New Economic School (NES) in Moscwo and Policy Director at the Center for Economic and Financial Research  (CEFIR) to discuss the Demand Side Management process. The aim of this brief is to present the principles of Demand Side Management and discuss a few implemented programs in Europe, based on the discussions  during this  SITE Energy Talk.

For the last two decades, climate change policies have mostly been focused on the energy supply side, constantly encouraging new investments in renewables. But reducing energy demand may be as effective. Indeed, Denny and O’Malley (2010) found that investing 100MW in wind power is equivalent, in terms of emissions, to a decrease in demand of 50MW. Hence, there is a clear benefit of promoting energy saving. This has been the central point of different Demand Side Management (DSM) programs that may diversely focus on building management systems, demand response programs, dynamic pricing, energy storage systems, interruptible load programs and temporary use of renewable energy. The goal of these programs is to lower energy demand or, at least, smoothen the electricity demand over the day (i.e. remove peak-hour segments of demand to off-peak hours) as illustrated in Figure 1.

Figure 1 – Smoothing electricity demand during the day

A behavioral framework

DSM encompasses initiatives, technologies and installations that encourage energy users to optimize their consumption. However, the task does not seem easy, given the well-documented energy efficiency gap problem (e.g. Allcott & Greenstone, 2012 or Frederiks et al., 2015): consumers do not always choose the most energy efficient investments, despite potential monetary saving. One reason why might be that energy savings per se are not enough to trigger investment in energy efficient solutions or products. As Denny mentioned in her presentation, consumers will invest when the total  private benefits are higher than the costs of investment. This trade-off can be summarized by the following equation:

This equation illustrates that any DSM design should take into account both non-monetary benefits and consumers’ time preferences. The non-monetary benefits, such as improved comfort, construction and installation time, but also warm glow (i.e. positive feeling of doing something good) or social comparison, may play a major role. Moreover, the consumers’ time preferences (reflected here by the discount rate ) are also crucial in the adoption of energy efficient products. In particular, if consumers have present biased preferences, they would rather choose a product with a lower cost today and greater future cost than the reverse (i.e. higher cost today with lower future cost). Since energy-efficient products often require higher upfront investment, consumers that are impatient for immediate gains, may never choose energy efficient products.

Ultimately, it is an empirical (and context specific) question when and why DSM programs can reduce the energy efficiency gap. We describe below some DSM programs that have been implemented and discuss their impact.

Smart meters, a powerful DSM tool

A common DSM program is the installation of smart meters, which measure consumption and can automatically regulate it. The adoption of smart meters allows real-time consumption measures, unlike traditional meters that only permitted load profiling (i.e. periodic information of the customer’s electricity use).

Figure 2 – Energy Intensity in Europe

As illustrated in Figure 2, many European countries have implemented smart meter deployment programs. Interestingly, most of those countries have a relatively high level of energy efficiency (proxied by the energy intensity indicator of final energy consumption). On the contrary, in the Balkans and non-EU Eastern Europe countries, which fare poorly on the energy intensity performance scale, no smart meter rollout programs seem to be implemented.

Following the European Commission (EC) directive of 2009 (Directive 2009/72/EC), twenty-two EU members will have smart meter deployment programs for electricity and gas by 2020 (see Figure 2).  These programs are targeting end-users of energy, e.g. households that represent 29% of the current EU-28’s energy consumption, industries (36.9%) and services (29.8%) (EEA). With this rollout plan, a reduction of 9% in households’ annual energy consumption is expected.

The situation across the member states is however very different. Spain was one of the first EU countries to implement meters in 1988 for industries with demand over 5MW. All the meters will be changed at the end of 2018. 27 million euros for a 30-year investment in smart meter installations is forecasted (EC, 2013). Sweden started to implement smart meter rollout in 2003 and 5.2 million monthly-reading meters were installed by 2009. Vattenfall, one of the major utilities in Sweden, assessed their savings up to 12 euros per installed smart meter (Söderbom, 2012). Similarly in the United Kingdom, the Smart Metering Implementation Programme (SMIP) is estimated to bring an overall £7.2 billion (8.2 billion euros) net benefit over 20 years, mainly from energy saving (OFGEM, 2010). In general, smart metering has been effective, but its effectiveness may diminish over time (Carroll et al, 2014).

From smart-meter to real-time pricing

The idea of real-time pricing for electricity consumers is not new. Borenstein and Holland (2005) and Joskow and Tirole (2006) argue that this price scheme would lead to a more efficient allocation, with lower deadweight loss than under invariant pricing.

By providing detailed information about real-time consumption, smart meters enable energy producers to adopt dynamic pricing strategies. The increasing adoption of smart meters across Europe will likely increase the share of real-time-pricing consumers, as well as the efficiency gains. With the digitalization of the economy, it is likely that smart metering will grow. Indeed, Erdinc (2014) calculates that the economic impact of smart homes on in-home appliances could result in a 33% energy-bill reduction, due to differences in shift potential of appliances.

In 2004, the UK adopted a time-of-use programme called Economy 10, which provides lower tariffs during 10 hours of off-peak periods – split between night, afternoon and evening – for electrically charged and thermal storage heaters. The smart time-of-use tariffs involving daily variation in prices were only introduced in 2017.

Likewise, France’s main electricity provider EDF, implemented Tempo tariff for 350,000 residential customers and more than 100,000 small business customers. Based on a colour system to indicate whether or not the hour is a peak period, customers can automatically or manually monitor their consumption by controlling connection and disconnection of separate water and space-heating circuits. With this program, users reduced their electricity bills by 10% on average.

In Russia, the “consumptions threshold” program discussed by Natalya Volchkova, gave different prices for different consumption thresholds. But it seems that the consumers’ behaviour did not change. This might be due to the thresholds being too low, and an adjusted program should be launched in 2019.

Joskow and Tirole (2007), argue that an optimal electricity demand response program should include some rationing of price-insensitive consumers. Indeed, voluntary interruptible load programs have been launched, mainly targeting energy intensive industries that are consuming energy on a 24/7 basis. These programs consist of rewarding users financially to voluntarily be on standby. For instance, interruptible programmes in Italy apply a lump-sum compensation of 150,000 euros/MWh/year for 10 interruptions and 3000 euros/MW for each additional interruption (Torriti et al., 2010).

Nudging with energy labelling

Energy labelling has been also part of DSM. Since the EC Directives on Ecodesign and Energy Labelling (Directives 2009/125/EC and 2010/30/EU), energy-consuming products should be labelled according to their level of energy efficiency. For Ireland, Eleanor Denny has tested how labelling electrical in-home appliances may affect consumers’ decisions, like purchasing electrical appliances or buying a house. First, Denny and co-authors have nudged buyers of appliances, providing different information regarding future energy bills saving. They find that highly educated people, middle income and landlords are more likely to be concerned with energy-efficiency rates, rather than high-income people.

In another randomized control trial, Denny and co-authors manipulate information on the energy efficiency label for a housing purchase. In Ireland, landlords are charged for energy bills even when they rent out their property. The preliminary findings are that landlords informed about the annual energy cost of their houses are willing to pay 2,608 euros for a one step improvement in the letter rating – the EU label rating for buildings ranges from A to G – compared to the landlords that do not receive the information (see CONSEED project).

Similar to the European Directive, the 2009 Russian Energy efficiency law includes compulsory energy efficiency labels for some goods and improvements of the building standards (EBRD, 2011). Volchkova and co-authors run a randomized controlled experiment on the monetary incentives to buy energy efficient products. In 2016, people in the Moscow region received a voucher with randomly assigned discounts (-30%, -50% or -70%- for the purchase of LED bulbs. Vouchers were used very little, irrespective of the income. It seems that consumption habits and not so much monetary rewards were the main driver of LED bulb purchase.

How can DSM be improved?

Any demand response program requires some demand elasticity. For example, smart meters and dynamic pricing only improve electricity consumption efficiency if demand is price elastic. As Jessoe and Rapson (2014) show, one should provide detailed information (e.g. insights on non-price attributes, real-time feedback on in-home displays) to try to increase demand elasticity. Hence it seems that  the low adoption of energy efficient goods is partly due to a lack of information or biased information received by the consumers. First, it is difficult for many to translate energy savings in kWh in monetary terms. Second, many consumers focus on the short-term purchase cost and discount heavily the long run energy saving. These information inefficiencies can, in principle, be diminished by private actors and/or governmental regulation. Denny mentioned the possibility of displaying monetary benefits on labels in consumers’ decision-making in order to improve energy cost salience. For instance, in the US or Japan, the usage cost information is also displayed in monetary terms. Moreover, lifetime usage cost (i.e. cost of ownership) should also be given to the customers since it has been shown that displaying lifetime energy consumption information has significantly higher effect than presenting annual information  (Hutton & Wilkie 1980; Kaenzig 2010).

Summing up, DSM programs, including those with a behavioral framework, are an important tool for regulators, households and industries helping to meet emissions reduction targets, significantly decrease demand for energy and use energy more efficiently.


  • Allcott, Hunt ; Greenstone, Michael. 2012. “Is There an Energy Efficiency Gap?”, Journal of Economic Perspectives, 26 (1): 3-28.
  • Borenstein, Severin; Holland, Stephen. 2005. “On The Efficiency Of Competitive Electricity Markets With Time-Invariant Retail Prices”, Rand Journal of Economics, 36(3), 469-493.
  • Carroll, James; Lyons, Seán; Denny, Eleanor. 2014. “Reducing household electricity demand through smart metering: The role of improved information about energy saving,” Energy Economics, 45(C), 234-243.
  • Denny, Eleanor; O’Malley, Mark. 2010. “Base-load cycling on a system with significant wind penetration”, IEEE Transactions on Power Systems 2.25, 1088-1097.
  • Erdinc, Ozan. 2014. “Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households”, Applied Energy126(C), 142-150.
  • European Bank for Reconstruction and Development. “The low carbon transition”. Chapter 3 Effective policies to induce mitigation (2011).
  • European Commission. Electricity Directive 2009/92. Annex I.
  • European Commission. Ecodesign and Energy Labelling Framework directives 2009/125/EC and 2010/30/EU.
  • European Commission. “From Smart Meters to Smart Consumers”, Promoting best practices in innovative smart metering services to the European regions (2013).
  • European Commission. “Benchmarking smart metering deployment in the EU-27 with a focus on electricity” (2014).
  • European Environment Agency. Data on Final energy consumption of electricity by sector and Energy intensity.
  • Frederiks, Elisha R.; Stenner, Karen; Hobman, Elizabeth V. 2015. “Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour”, Renewable and Sustainable Energy Reviews, 41(C), 1385-1394.
  • Hutton, Bruce R.; Wilkie, William L. 1980. “Life Cycle Cost: A New Form of Consumer Information.” Journal of Consumer Research, 6(4), 349-60.
  • Jessoe, Katrina; Rapson, David. 2014. “Knowledge is (less) power: experimental evidence from residential energy use”, American Economic Review, 104(4), 1417-1438.
  • Joskow, Paul; Tirole, Jean. 2006. “Retail Electricity Competition, Rand Journal of Economics, 37(4), 799-815.
  • Joskow, Paul; Tirole, Jean. 2007. “Reliability and Competitive Electricity Markets”, Rand Journal of Economics, 38(1), 60-84.
  • Kaenzig, Josef; Wüstenhagen, Rolf. 2010. “The Effect of Life Cycle Cost Information on Consumer Investment Decisions Regarding Eco‐Innovation”, Journal of Industrial Ecology, 14(1), 121-136.
  • OFGEM. “Smart Metering Implementation Programme” (2010).
  • Söderbom, J. “Smart Meter roll out experiences”, Vattenfall (2012).
  • Torriti, Jacopo; Hassan, Mohamed G.; Leach, Matthew. 2010. “Demand response experience in Europe: Policies, programmes and implementation”, Energy, 35(4), 1575-1583.

Project links

Eleanor Denny and co-authors’ European research projects:

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.