Are the Benefits of Electrification Realized Only in the Long Run? Evidence from Rural India

 

I have a new working paper coauthored with my master's student Suryadeepto Nag on the impact of rural electrification in India. Surya did his master's at IISER in Pune with me as his supervisor. He visited Canberra over the last Southern Summer. This paper is based on part of Surya's thesis.

The effect of providing households with access to electricity has been a popular research topic. It's still not clear how large the benefits of such interventions are. Is electricity access an investment that generates growth? Or is it more of a consumption good that growing economies can afford? Researchers have used traditional econometric methods on secondary data (observational studies) and also carried out field experiments, such as randomized controlled trials (RCTs), to try to answer this question.

Experimental methods have generally found smaller and less statistically significant results than observational studies have. Is this because experiments are more rigorous? Or because observational studies usually measure impacts over a longer period of time? It's likely that it takes time for people to make use of a new electricity connection. They will need to save and buy appliances. Effects on children's education will take an especially long time to come to fruition.

We carry out a meta-analysis of 16 studies previously reviewed by Bayer et al. (2020):

We assigned each positive impact (for example on income or on education) found in a study the score of 1 and each negative impact a -1 and then averaged over all the impacts. The graph shows this "positiveness of impact" compared to the time households had been connected to electricity. While observational studies found more positive impacts than experimental studies, there is also a positive correlation between duration of connection and positiveness of impact (and between duration of connection and being an observational study). Regression analysis shows that only duration of connection is statistically significant. 

But this small sample of studies can't be that conclusive, so we then carry out our own analysis to test whether impacts increase over time.

Using three waves of Indian household surveys from 1994-95, 2004-5, and 2011-12, we quantify the impacts of short-term (0-7 years) and long-term (7-17 years) electricity access on rural household well-being. These surveys tracked the same households over time. We don't know exactly when a household was connected, just whether it was already connected in 1994-95 or whether it got connected between the other surveys. We do know when villages were connected.

We use a difference in differences regression that is weighted using "inverse propensity scores". This is supposed to compensate for the fact that households are not actually connected randomly to the grid. If, for example, poor households are less likely to get connected, we overweight them in the sample. In our main analysis, we exclude households that were already connected in 1994-95 so that the control group only includes households that were not connected by 2011-12.*

We find that long-term electricity access increases per capita consumption and education, and reduces the time spent by women on fuel collection (compared to the control group). The effect of short-term connection is smaller and statistically insignificant. We find no significant effects on agricultural income, agricultural land holding, and kerosene consumption. 

Here is our main table of results:

The long-term impact on consumption is really very big – 18 percentage points more than the control group over a 7 year period. The effect on education is 0.4 of a year relative to the control group.

We did some robustness tests – using different weights and including the households connected before 1994-95 as "very long-term connections". The results roughly hold up, though the weighting isn't ideal in either case.

We think our results show that experimental studies really need longer term follow-ups before coming to conclusions.

* The recent research on differences in differences shows that many past studies used inappropriate control groups.

Video from Arndt-Corden Seminar

Today, I gave a seminar in the Arndt-Corden Department of Economics Seminar Series, titled: "Electricity Markets with Speculative Storage and Stochastic Generation and Demand." We hope to post a working paper soon. In the meantime, here's the video * of my seminar: 

 * Introduction and question time deleted

Prepaid Metering and Electricity Consumption in Developing Countries

I've written an Energy Insight policy brief for the EEG Programme with my PhD student Debasish Das on prepaid metering and its effect on electricity consumption.

The bottom line is that consumers who are switched to prepaid metering significantly reduce their electricity consumption. 

Debasish is working on a study of the effect of prepaid metering in Bangladesh and some preliminary results are in this paper. This graph shows the estimated difference in monthly electricity consumption between consumers in two areas of Dhaka, Bangladesh around the time that one group was switched to prepaid metering:

 

 

Electricity consumption in the treated group fell by 17%. This graph didn't make it into the final version of the paper, because it was deemed to be too mathy. Debasish has a very large dataset that he obtained from the Bangladesh electric utilities. He's still working on getting this into a usable form. But hopefully we will have some more results soon.

Designing Future Electricity Markets: Evolution Rather than Revolution

We have a new working paper on designing wholesale electricity markets with high levels of zero marginal cost intermittent energy sources. The paper reports on the findings of a session on this topic at the Future of Electricity Markets Summit that was held in November last year in Sydney. Gordon Leslie led the writing with contributions from myself, Akshay Shanker, and Mike Hogan. The session itself featured papers by Paul Simshauser, Mike Hogan, Chandra Krishnamurthy (a paper coauthored with Akshay and myself), and Simon Wilkie.

Imagine the future where fossil fuel generation has disappeared and generation consists of intermittent sources like solar and wind. The cost of producing more electricity with these technologies is essentially zero – the marginal cost of production is zero – once we have invested in the solar panels or wind turbines. But that investment is costly. How would markets for electricity work in this situation? The naive view is that introductory economics tells us that the price in competitive markets, like many wholesale electricity markets (e.g. Australia's National Electricity Market – the "NEM"), is equal to marginal cost. If renewables have zero marginal cost, then the price would be zero. So, an "energy only market" where generators are paid for the electricity they provide to the grid won't be viable and we need a total redesign.

But the consensus in our session was that zero marginal cost generation doesn't mean that energy only markets can't work. Marginal cost of production isn't as important as marginal opportunity cost. Hydropower plants have a near zero marginal cost of production but if they release water when the price of electricity is low, they forego generating electricity when the price is high. Releasing water has an opportunity cost. Mike Hogan pointed out that the same is true of reserves more generally. There will also be electricity storage including pumped hydropower as well as batteries and other technologies in the future. The Krishnamurthy paper focused in particular on the role of storage. The price these receive should also be determined by opportunity cost. Even without storage and reserves there should be a market equilibrium with a non-zero price as long as there is sufficient flexibility of demand for electricity:

Intermittent renewables supply to the market however much power, Rt, that they can generate. They can't supply unlimited power at zero marginal cost... The market clearing price is p, where the demand curve, Dt, crosses the vertical supply curve. Price does not equal marginal cost of production. Of course, the quantity and price is continually fluctuating. But, if, on average, the revenues are insufficient to cover the costs of investment, generation capacity will shut down and exit the market until long-run equilibrium is restored. Storage has the effect of making the supply curve more elastic like the St supply curve in this graph:

In this example, storage lowers the price and increases the quantity of electricity consumed. This is when storage is discharging electricity. When it is charging, it will raise the price relative to the case with no storage. Storage has the effect of reducing volatility.

This is of course very simplified. The real world is more complicated. The paper shows that increasing intermittent generator penetration increases the importance of adequately pricing scarcity and all network constraints and services. Wind generation tends to colocate in the best wind resource areas overloading the gird. Locational marginal pricing pricing is required to deliver investment incentives for the right technologies to locate at the right locations to efficiently maintain a stable and reliable electrical network as discussed by Katzen and Leslie (2020). The NEM currently only has a spot market that schedules generation for each 5 minute interval. A day-ahead market could help in valuing the provision of flexible generation and storage. The current charges for transmission costs in the NEM also disincentivize grid-scale storage. Electricity users pay for these costs. This makes sense when the user is an end-user. But storage is treated as a customer and also pays these charges.  So "fresh electricity" where only one set of charges is paid has a cost advantage over stored electricity.

In conclusion, electricity markets need to evolve to provide the correct incentives to generation and storage. A total rethink isn't needed.

Two New Working Papers

We have just posted two new working papers: Technology Choices in the U.S. Electricity Industry before and after Market Restructuring and An Analysis of the Costs of Energy Saving and CO2 Mitigation in Rural Households in China.

The first paper, coauthored with Zsuzsanna Csereklyei, is the first to emerge from our ARC funded DP16 project.  Our goal was to look at the factors associated with the adoption of more or less energy efficient electricity generating technologies using a detailed US dataset. For example, combined cycle gas turbines are more energy efficient than regular gas turbines and supercritical coal boilers are more efficient than subcritical. Things are complicated by the different roles that these technologies play in the electricity system. Because regular gas turbines are less energy efficient but have lower capital costs they are mainly used to provide peaking power, while combined cycle turbines contribute more to baseload. So comparing combined cycle gas to subcritical coal makes more sense as a test of how various factors affect the choice of energy efficiency than comparing the two types of gas turbine technologies.

Additionally, some US regions underwent electricity market reform where either just wholesale or both wholesale and retail markets were liberalized, while other regions have retained integrated regulated utilities, which are typically guaranteed a rate of return on capital. Unless regulators press utilities to adopt energy efficient technologies there is much less incentive under rate of return than under wholesale markets to do so.

The graph shows that following widespread market reform at the end of the 20th Century there was big boom in investment in the two main natural gas technologies. More recently renewables have played an increasing role and there was a revival of investment in coal up to 2012. These trends are also partly driven by the lagged (because investment takes time) effects of fuel prices:

We find that electricity market deregulation resulted in significant immediate investment in various natural gas technologies, and a reduction in coal investments. However, market deregulation impacted less negatively on high efficiency coal technologies. In states that adopted wholesale electricity markets, high natural gas prices resulted in more investment in coal and renewable technologies.

There is also evidence that market liberalization encouraged investments into more efficient technologies. High efficiency coal technologies were less negatively affected by market
liberalization than less efficient coal technologies. Market liberalization also resulted in increased investment into high efficiency combined cycle gas. In summary the effect of liberalization is most negative for the least efficient coal technology and most positive for the most efficient natural gas technology.

The second paper is based on a survey of households in rural China and assesses the potential for energy conservation and carbon emissions mitigation when energy saving technologies are not fully implemented. In reality, appliances do not always survive for their designed lifetime and households often continue to use other older technologies alongside the new ones. The effect is to raise the cost of reducing energy use and emissions by a given amount. The paper computes marginal abatement cost curves under full and partial implementation of the new technologies.

The graph shows the marginal abatement cost curve for rural households in Hebei Province, scaled up from the survey and our analysis. Full-Scenario is the curve with full implementation of new technologies and OII-Scenario is with actual partial implementation. This analysis does not take into account any potential rebound effect of energy efficiency improvements.

The first author, Weishi Zhang, is a PhD student at the Chinese University of Hong Kong. She contacted me last year about possibly visiting ANU, and I supported her application for a scholarship to fund the visit (which unfortunately she didn't get), because I thought her research was some of the more interesting research on Chinese energy use and pollution that I had seen. I helped write the paper (and responses to referees in our revise and resubmit).

Mid-Year Update

It's the first official day of winter today here in Australia, though it has felt wintry here in Canberra for about a week already. The 1st Semester finished last Friday and as I didn't teach I don't have any exams or papers to grade and the flow of admin stuff and meetings seems to have sharply declined. So, most of this week I can just dedicate to catching up and getting on with my research. It almost feels like I am on vacation :) Looking at my diary, the pace will begin to pick up again from next week.

I'm working on two main things this week. One is the Energy for Economic Growth Project that has now been funded by the UK Department for International Development. I mentioned our brainstorming meeting last July in Oxford in my 2015 Annual Report. I am the theme leader for Theme 1 in the first year of the project. In the middle of this month we have a virtual workshop for the theme to discuss the outlines for our proposed papers. I am coauthoring a survey paper with Paul Burke and Stephan Bruns on the macro-economic evidence as part of Theme 1. There are two other papers in the theme: one by Catherine Wolfram and Ted Miguel on the micro-economic evidence and one by Neil McCulloch on the binding constraints approach to the problem.

The other is my paper with Jack Pezzey on the Industrial Revolution, which we have presented at various conferences and seminars over the last couple of years. I'm ploughing through the math and tidying the presentation up. It's slow going but I think I can see the light at the end of the tunnel! This paper was supposed to be a key element in the ARC Discovery Projects grant that started in 2012.

In the meantime, work has started on our 2016 Discovery Projects grant. Zsuzsanna Csereklyei has now started work at Crawford as a research fellow funded by the grant. She has been scoping the potential sources of data for tracing the diffusion of energy efficient innovations and processing the first potential data source that we have identified. It is hard to find good data sources that are usable for our purpose.

There is a lot of change in the air at ANU as we have a new vice-chancellor on board since the beginning of the year and now a new director for the Crawford School has been appointed and will start later this year. We are also working out again how the various economics units at ANU relate to each other... I originally agreed to be director of the Crawford economic program for a year. That will certainly continue now to the end of this year. It's not clear whether I'll need to continue in the role longer than that.

Finally, here is a list of all papers published so far this year or now in press. I can't remember how many of them I mentioned on the blog, though I probably mentioned all on Twitter:

Bruns S. B. and D. I. Stern (in press) Research assessment using early citation information, Scientometrics. Working Paper Version | Blogpost

Stern D. I. and D. Zha (in press) Economic growth and particulate pollution concentrations in China, Environmental Economics and Policy Studies. Working Paper Version | Blogpost
 
Lu Y. and D. I. Stern (2016) Substitutability and the cost of climate mitigation policy, Environmental and Resource Economics. Working Paper Version | Blogpost

Sanchez L. F. and D. I. Stern (2016) Drivers of industrial and non-industrial greenhouse gas emissions, Ecological Economics 124, 17-24. Working Paper Version | Blogpost 1 | Blogpost 2

Costanza R., R. B. Howarth, I. Kubiszewski, S. Liu, C. Ma, G. Plumecocq, and D. I. Stern (2016) Influential publications in ecological economics revisited, Ecological Economics. Working Paper Version | Blogpost

Csereklyei Z., M. d. M. Rubio Varas, and D. I. Stern (2016) Energy and economic growth: The stylized facts, Energy Journal 37(2), 223-255. Working Paper Version | Blogpost

Halkos G. E., D. I. Stern, and N. G. Tzeremes (2016) Population, economic growth and regional environmental inefficiency: Evidence from U.S. states, Journal of Cleaner Production 112(5), 4288-4295. Blogpost

Explaining the Decline in Australian Electricity Use

Some interesting graphs in the Business Spectator that help explain the recent decline in electricity use in Australia. Uptake of rooftop solar by consumers is a big part of the story as official electricity use doesn't include self-generation. Here is the graph for South Australia, which is the most dramatic:



The decline in electricity closely matches the solar intensity curve over the day.

U.S. Electric Supply and Grid

Some great maps of U.S. electricity supply and grid from NPR's website. The map above shows all power stations by size. Other maps show the share of different power sources by state (e.g. Vermont is the most nuclear state in the Union). Most importantly the transmission lines are mostly not where the best locations for alternative energy are (with the exception of some areas of the southwest near Los Angeles. Not surprisingly the latter are a hotbed of solar investment. Australia's situation is similar from what I have heard.

Interfuel Substitution and the Costs of Climate Change Policy

A meta-analysis is an analysis of existing empirical studies rather than another original primary study. The aim of a meta-analysis is to find out what is the average size of some parameter or effect in the existing literature - for example the average estimated damage from climate change or the elasticity of demand for gasoline - and what are the factors that cause there to be differences between the various studies.

The interfuel elasticity of substitution indicates how hard it is to replace one fuel with another when, for example, the price of one fuel goes up.* My meta-analysis of 46 empirical studies of this issue finds that at the level of individual industries it is probably not so hard to substitute between fuels (the elasticity of substitution is greater than one). At least some of the models, for example the G-Cubed model, used to assess the costs of climate change policy assume that it is a lot harder than this.

As far as I understand, this should mean that they overestimate the costs of climate change policy. The harder it is to replace fossil fuels with electricity or coal with natural gas the costlier it should be to adjust to a carbon tax or cap and trade scheme. But how sensitive are their results to the values of parameters such as the interfuel elasticity of substitution? Is there any research on this out there? (Yes, please point me to it!) Or is this a topic I should include in my research agenda? In fact I've been surprised in the past (back when Mick Common and I were debating ABARE in the run up to Kyoto) that the estimated costs of climate change policy aren't higher given the low substitutability assumptions built into these models.

* It's more complicated than that of course :)

Delays for Clean Coal

Interesting article by Gregg Easterbrook on delays in implementing more energy and carbon efficient approaches to electricity generation from coal. It sounds like perverse regulation is getting in the way of what the market is willing to do...