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Showing posts with label Development Economics. Show all posts
Showing posts with label Development Economics. Show all posts

Wednesday, July 26, 2023

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.

Wednesday, October 19, 2022

What Changed in the World Bank's Adjusted Net Saving Measure?

In August, I showed that using the World Development Indicators' current Adjusted Net Saving (ANS) data there is no relationship between ANS and the share of mining rents in GDP. I now know the main reason why this relationship appeared to change but I don't know yet why the World Bank made the changes that they did. 

In 2006 and earlier, the World Bank measured mineral and energy depletion using mining rents – the difference between mining revenues and the cost of production not including a return to the resource stock. This is based on Hartwick's Rule – resource rents should all be invested in produced capital in order to achieve sustainability. 

In recent years, they have used a different method. First, they estimate the net present value of resource rents (assuming that they remain constant in the future) using a 4% discount rate. Then they divide that amount by the number of years, T, that they assume the resource would last. The ratio of the current rent to this quantity is given by:

So, for example, if the resource has an expected 30 year lifetime then resource depletion is about 58% of current rents. Energy depletion for Saudi Arabia is around 1/3 of reported rents. This would imply that the lifetime of the resource is around 70 years.* This could explain in general why adjusted net saving is now estimated to be much higher for resource rich countries than it used to be.**

What I don't know yet is why they made this change. I haven't been able to find a rationale in the relevant World Bank publications. It is similar to but different from the El-Serafy (1989) method of measuring depletion. According to El-Serafy, the ratio of depletion to rent should be (1/(1+r))^(T+1). For a 30-year life span and a 4% discount rate, this is equal to 30%.

* The notes downloaded with the WDI data say that the lifetime is capped at 25 years. But this isn't mentioned in the relevant reports and makes the gap between rents and depletion harder to explain.

** There are a lot of other issues with assuming that the lifetime of a resource equals the expected lifetime of reserves and that rents will not change over time. There are also apparent inconsistencies between the stated methods and the results...

Thursday, August 11, 2022

Do Mining Economies Save Too Little?

I'm currently teaching Agricultural and Resource Economics for the first time. This week we started covering non-renewable resources focusing on minerals. One of the topics I covered is the resource curse. One of my sources is van der Ploeg's article "Natural Resources: Curse or Blessing?" published in the Journal of Economic Literature in 2011. In the paper, he reproduces this graph from a 2006 World Bank publication that apparently uses 2003 data from the World Development Indicators:

Genuine saving – now known as "adjusted net saving" – is equal to saving minus capital depreciation and various forms of resource depletion with expenditure on education added on. The idea is to measure the net change in all forms of "capital" in an economy. Mineral and energy rents are the pre-tax economic profits of mining. They are supposed to represent the return to the resource stock. The graph tells a clear story: Countries whose GDP depends heavily on mining tend to have negative genuine saving. So, they are not adequately replacing their non-renewable resources with other forms of capital. Van der Ploeg states that this is one of the characteristics of the resource curse.

Preparing for an upcoming tutorial on adjusted net saving and sustainability, I downloaded WDI data for recent years for some mining intensive countries, expecting to show the students how those countries still aren't saving enough. But this wasn't the case. Most of the mining economies had positive adjusted net saving. So, I wondered whether they had improved over time and downloaded the data for all available countries for 2003:


I've added a linear regression line.* There seems to be little relationship between these variables. The correlation coefficient is -0.017. Presumably, this is because of revisions to the data since 2006.

* I dropped countries with zero mining rents from the graph. The three countries at  top right with positive adjusted net saving are Saudi Arabia, Kuwait, and Libya. Oman and then does Democratic Republic of Congo have the next highest levels of mining rents and negative adjusted net savings.

Tuesday, December 21, 2021

Estimating the Effect of Physical Infrastructure on Economic Growth

I have a new working paper coauthored with Govinda Timilsina of the World Bank and my PhD student Debasish Das. It is a panel data study of the effect of various forms of infrastructure on the level of GDP. 

Compared to existing studies, we use more recent data, include new types of infrastructure such as mobile phones, and provide separate estimates for developing and developed countries. We find larger effects than most previous studies. We also find that infrastructure has a larger effect in more recent years (1992-2017) than in earlier years (1970-1991), and the effects of infrastructure are higher in developing economies than in industrialized economies. The long-run effects seem to be much larger than the initial impact. We also tried to estimate the effect of infrastructure on the rate of economic growth. Controlling for the initial level of GDP per worker we found a null result. So, we can't say that having more infrastructure means a more rapid rate of economic growth.

Getting good quality data that is comparable across countries is really a problem in this area of research. Many types of infrastructure only have data available for a few years. The ones that have more panel-like data often suffer from differences in definition across countries – such as what is a road or a motorway – or unexplained jumps in individual countries. So, our results are subject to a lot of measurement error.

Our main analysis uses data on five types of infrastructure – roads, railways, electric generation capacity, fixed line telephones, and mobile telephones*:

Following some previous research, we aggregate the individual types of infrastructure using principal component analysis. We use two principal components. One factor seems to be related to transport infrastructure and the other to electricity and telecommunications. Still, we can recover estimates of the effect of each individual type of infrastructure.

Also following some previous research, we use the Pooled Mean Group estimator to estimate a dynamic panel regression model. This allows us to test for the weak exogeneity of the explanatory variables, allowing us to give the results a somewhat causal interpretation.

The table shows the percentage change in GDP per worker for a 1% change in each infrastructure type. Getting standard errors for these estimates would be rather tricky.** Interestingly, the PMG estimates are mostly much larger than the static fixed effects estimates. Static fixed effects can be expected to converge to a short-run estimate of the effects while PMG should be a better estimate of long-run effects. Fixed effects also tends to inflate the effects of measurement error

Maybe the most innovative thing in the paper is that we plot the impulse response functions of GDP with respect to a 1% increase in each of the two main types of infrastructure:

PC1 is electricity and communications and PC2 transport infrastructure.*** Long-run effects of infrastructure are much larger than the short-run effects. In the short run, transport infrastructure even has a negative impact.

* Note that the graphs show the country means of these variables, while we actually use the deviations from those means over time in each country

** We only estimate the GDP-infrastructure relationship, but I think we would need time series models for each of the explanatory variables in order to sample from those models' residuals in a bootstrapping procedure. Bootstrapping is needed because we first carry out the principal components analysis and then estimate the PMG model in a second stage. These elasticities are combinations of the parameters from those two models.

*** We could get a confidence interval for these impulse response functions if we assume that the explanatory variables in the PMG model are deterministic as this analysis assumes...


Thursday, October 21, 2021

Energy and Development

The first of two book chapters for Elgar encyclopedias I recently wrote.

What is the Role of Energy in Economic Activity?

The economic system must operate within the constraints determined by the laws of physics and human knowledge of technology. Production, including household production, requires energy to carry out work to convert materials into desired products and to transport raw materials, goods, and people. The second law of thermodynamics implies that energy cannot be recycled and that there are limits to how much energy efficiency can be improved. Therefore, energy is an essential factor of production, and continuous supplies of energy are needed to maintain existing levels of economic activity as well as to grow and develop the economy (Stern, 1997). The first law of thermodynamics states that energy cannot be created and so energy (and matter) must be extracted from the environment. Also, energy must be invested in order to capture useful energy (Hall et al., 1986). Before the Industrial Revolution, economies depended on energy from agricultural crops and wood as well as a smaller amount of wind and waterpower, all of which are directly dependent on the sun (Kander et al., 2015). This is still largely the case in the rural areas of the least developed countries. While solar energy is abundant and inexhaustible, it is very diffuse compared to concentrated fossil fuels. This is why the shift to fossil fuels in the Industrial Revolution relaxed the constraints on energy supply and, therefore, on production and growth (Wrigley, 1988).

How Does Energy Use Change with Economic Development?

Figure 1 shows that energy use per capita increases with GDP per capita, so that richer countries tend to use more energy per person than poorer countries. The slope of the logarithmic regression line implies that a 1% increase in income per capita is associated with a 0.8% increase in energy use per capita. As a result, energy intensity – energy used per dollar of GDP – is on average lower in higher income countries. These relationships have been very stable over the last several decades (Csereklyei et al., 2016). Energy intensity in today’s middle-income countries is similar to that in today’s developed countries when they were at the same income level (van Benthem, 2015).

Figure 1. GDP and Energy Use per Capita 2018

Energy intensity has also converged across countries over time, so that countries that were more energy intensive in the 1970s tended to reduce their energy intensity by more than less energy intensive countries, and the least energy intensive countries often increased in energy intensity. Though data are limited to fewer and fewer countries as we go back further in time, these relationships also appear to hold over the last two centuries – energy use increased, energy intensity declined globally, and countries converged in energy intensity (Csereklyei et al., 2016). Though data is even more limited, it seems that the share of energy consumption expenditure and production costs also declines as countries develop (Csereklyei et al., 2016; Burke et al., 2018).

The mix of fuels used changes over the course of economic development. Figure 2 shows the average mix of energy sources in each of five groups of countries ordered by income per capita in 2018. In the lowest income countries in the sample (approximately below $5,000 per capita in 2017 purchasing power parity adjusted dollars), traditional use of biomass such as wood and agricultural waste dominates and oil use for transportation as well as electricity generation and other uses is the second most important energy source. As we move to richer countries, the relative role of biomass declines radically, and first oil and then natural gas and primary electricity increase in importance. Note that biomass use per capita in the richest quintile (above $40,000 per capita) is actually greater than in the lowest quintile, as total energy use increases with income. The ways in which this biomass is used will of course be quite different. Higher quality fuels are those that provide more economic value per joule of energy content by being converted more efficiently, being more flexible or convenient to use, and by producing less pollution. We would expect that lower income households would be more willing to tolerate the inconvenience and pollution caused by using lower quality fuels to produce energy services. So as household income increases, we would expect households to gradually ascend an “energy ladder” by consuming higher quality fuels and more total energy. Recent studies often find a more ambiguous picture where multiple fuels are used simultaneously as modern fuels are added to the use of traditional fuels (Gregory and Stern, 2014). 

Figure 2. Fuel Mix and Development 2018

 

In 2016, approximately one billion people remained without access to electricity at home (International Energy Agency, 2017). Around 85% of these people lived in rural areas. There has been rapid progress in electrification in recent years with both grid expansion and the spread of off-grid systems (Burke et al., 2018; Lee et al., 2020). Due to the complexity and costs of electricity-sector management and constrained and weak institutions, power supply is usually less reliable in developing countries than in developed countries (Figure 3) and electricity theft is also more common (Burke et al., 2018). Best and Burke (2017) found that countries with higher levels of government effectiveness have achieved greater progress in providing access to reliable electricity. Industry and other electricity consumers, therefore, often rely on self-generation of electricity, but this is a costly solution (Fingleton-Smith, 2020). 

 Figure 3. Electricity Reliability and Development 2017

 

Does Energy Use Drive Economic Growth?

Economic growth refers to the process that results in increasing GDP per capita over time while development refers to a broader range of indicators including health, education, and other dimensions of human welfare. However, GDP per capita is highly, although not perfectly, correlated with broader development measures (Jones and Klenow, 2016) and so it is worth considering what the role of energy is in economic growth.

Mainstream economic growth models largely ignore the role of energy in economic growth and focus on technological change as the long-run driver of growth. On the other hand, there is a resource economics literature that investigates whether limited energy or other resources could constrain growth. By contrast, many ecological economists believe that energy plays the central role in driving growth and point to the switch traditional energy sources to fossil fuels as the cause of the industrial revolution (Stern, 2011). 

To reconcile these opposing views, Stern and Kander (2012) modified Solow’s neoclassical growth model (Solow, 1956) by adding an energy input that has low substitutability with capital and labor. Their model also breaks down technological change into those innovations that directly increase the productivity of energy– energy-augmenting technical change and those that increase the productivity of labor – labor-augmenting technical change. In this model, when energy is superabundant the level of the capital stock and output are determined by the same functions of the same factors as in the Solow model. But when energy is relatively scarce, the size of the capital stock and the level of output depends on the level of energy supply and the level of energy-augmenting technology. Therefore, in the pre-industrial era and possibly when energy was scarce – and possibly in developing countries today – the level of output was determined by the supply of energy and the level of energy augmenting technology. Until the industrial revolution, output per capita was generally low and economic growth was not sustained (Maddison, 2001). After the industrial revolution, as energy became more and more abundant, the long-run behavior of the model economy becomes more and more like the Solow growth model. If this model is a reasonable representation of reality, then mainstream economists are not so wrong to ignore the role of energy in economic growth in developed economies where energy is abundant, but their models have limited applicability to both earlier historical periods and possibly to today’s developing countries. McCulloch and Zileviciute (2017) find that electricity is often cited as a binding constraint on growth in the World Bank’s enterprise surveys. Energy is expensive relative to wages in developing countries. The price of oil is set globally, and the share of electricity in costs or expenditures can be very high in middle income countries (Burke et al., 2018).

Electricity and Development

Access to energy and electricity, in particular, is a key priority for policymakers and donors in low-income countries. For example, the United Nations’ Sustainable Development Goal 7 targets universal access to modern energy by 2030. Electrification can allow poor households to have easy access to lighting for evening chores or studying and power for phone charging and for a range of new small business activities, both on and off the farm (Lee et al., 2020). Electricity access allows a reallocation of household time, especially for women, away from obtaining energy, for example by collecting firewood, and towards more productive activities. Electricity could also provide health benefits by allowing deeper wells, refrigeration, reduced exposure to smoke etc. (Toman and Jemelkova, 2003).

The micro-level effect of electrification is a growing area of empirical research (Lee et al., 2020). While micro studies typically suggest positive impacts of electrification on income and other development outcomes, more recent quasi-experimental approaches such as randomized controlled trials typically find a smaller impact for electrification than earlier studies did (Lee et al., 2020). Estimates of the effect of electricity infrastructure on economic growth are typically small. One of the best studies (Calderón et al., 2015) estimates the elasticity of GDP with respect to electricity generation capacity as 0.03 (Burke et al., 2018).

Lee et al. (2020) argue that providing poor households with access to electricity alone is not enough to improve economic and noneconomic outcomes in a meaningful way. Complementary inputs are needed, which will accumulate very slowly. Imagination and role models are also important in understanding how to exploit electricity to develop businesses (Fingleton-Smith, 2020). When electricity becomes available in rural areas of sub-Saharan Africa, it is often not used to power agricultural or other productive activities (Bernard, 2012). Institutions are also vital for attaining broad-based benefits from electricity in developing countries. Many developing countries have reformed their electricity sectors during the last few decades, mostly towards market liberalization and corporatization. These efforts have only been partially successful in promoting efficient pricing and greater electricity access (Jamasb et al., 2017). Studies assessing the economic effects of these reforms are scarce. The effects on economic growth seem positive, while the effects on poverty are mixed (Jamasb et al., 2017). In this context, technology transfer and development finance will be critical for increasing the use of electricity in developing countries (Madlener, 2009).

Burke et al. (2018) examined electrification success stories - countries that, from a low level of economic development, have now achieved near-universal electricity access as well as relatively high levels of electricity use. These countries are South Korea, China, Thailand, Vietnam, Egypt, and Paraguay. The first four are well-known development success stories too. Paraguay has abundant hydroelectricity and both Paraguay and Egypt have had relatively strong economic growth. Egypt has been less successful in providing a reliable electricity supply. The most successful countries in increasing access in Sub-Saharan Africa have been South Africa and Ghana, which both suffer from unreliable electricity, which constrains economic activity.

References

Bernard, T., 2012. Impact analysis of rural electrification projects in Sub-Saharan Africa. World Bank Research Observer 27(1): 33–51.

Best, R., and P. J. Burke, 2017. The importance of government effectiveness for transitions toward greater electrification in developing countries. Energies 10(9): 1247.

Burke P. J., D. I. Stern, and S. B. Bruns, 2018. The impact of electricity on economic development: a macroeconomic perspective. International Review of Environmental and Resource Economics 12(1): 85–127.

Calderón, C., E. Moral-Benito, and L. Servén, 2015. Is infrastructure capital productive? A dynamic heterogeneous approach. Journal of Applied Econometrics 30: 177–198.

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.

Fingleton-Smith, E., 2020. Blinded by the light: The need to nuance our expectations of how modern energy will increase productivity for the poor in Kenya. Energy Research & Social Science 70: 101731.

Gregory, J. and D. I. Stern, 2014. Fuel choices in rural Maharashtra. Biomass and Bioenergy 70: 302–314.

Hall, C. A. S., C. J. Cleveland, and R. K. Kaufmann, 1986. Energy and Resource Quality: The Ecology of the Economic Process. New York: Wiley Interscience.

International Energy Agency, 2017. Energy Access Outlook 2017: From Poverty to Prosperity. World Energy Outlook Special Report.

Jamasb, T., R. Nepal, and G. R. Timilsina, 2017. A quarter century effort yet to come of age: a survey of electricity sector reform in developing countries. Energy Journal 38(3): 195–234.

Jones, C. I., and P. J. Klenow. 2016. Beyond GDP? Welfare across countries and time. American Economic Review 106(9): 2426–2457.

Kander, A., P. Malanima, and P. Warde, 2014. Power to the People: Energy in Europe over the Last Five Centuries. Princeton University Press.

Lee, K., E. Miguel, and C. Wolfram, 2020. Does household electrification supercharge economic development? Journal of Economic Perspectives 34(1): 122–144.

Maddison, A., 2001. The World Economy: A Millennial Perspective. Paris: OECD.

Madlener, R., 2009. The economics of energy in developing countries. In: L. C. Hunt and J. Evans (eds.), International Handbook on the Economics of Energy, Edward Elgar.

McCulloch, N., and D. Zileviciute, 2017. Is electricity supply a binding constraint to economic growth in developing countries? EEG State-of-Knowledge Paper Series 1.3.

Solow, R. M., 1956. A contribution to the theory of economic growth. Quarterly Journal of Economics 70: 65–94.

Stern, D. I., 1997. Limits to substitution and irreversibility in production and consumption: a neoclassical interpretation of ecological economics. Ecological Economics 21: 197–215.

Stern, D. I., 2011. The role of energy in economic growth. Annals of the New York Academy of Sciences 1219: 26–51.

Stern, D. I., and A. Kander, 2012. The role of energy in the industrial revolution and modern economic growth. Energy Journal 33(3): 125–152.

Toman, M. A., and B. Jemelkova, 2003. Energy and economic development: An assessment of the state of knowledge. Energy Journal 24(4): 93–112.

van Benthem, A. A., 2015. Energy leapfrogging. Journal of the Association of Environmental and Resource Economists 2(1): 93–132.

Wrigley, E. A., 1988. Continuity, Chance, and Change: The Character of the Industrial Revolution in England. Cambridge: Cambridge University Press.

Tuesday, October 12, 2021

How Large is the Economy-Wide Rebound Effect in Middle Income Countries? Evidence from Iran

 


We have a new working paper in our rebound effect series. Previous papers reviewed the literature on the economy-wide rebound effect, estimated the economy-wide rebound effect for the United States, and estimated it for some European countries (as well as the United States). The new paper is about Iran. This is a middle income country with a resource intensive and quite regulated economy. Is it a lot different to the developed economies we have already looked at?

The rebound effect is large in Iran too. A major difference between Iran and the developed economies is that energy intensity has been rising in Iran:

 

Total energy use tripled from 1988 to 2017, which is the sample period used in our econometric analysis (quarterly data):


The econometric model is the same as that used in the US paper that is now published in Energy Economics, except we only use the distance covariance method for the independent component analysis in this paper. The next figure shows the estimated impulse response functions of energy, GDP, and the price of energy to energy efficiency, GDP, and price shocks:

The top left panel shows the rebound effect. Initially, there is a large drop in energy use, but this diminishes over time. We estimate that the rebound is 84% after six years. The confidence interval is wide and includes 100%.

On the other hand, the GDP shock has large positive effects on energy (top middle panel) and GDP (middle). These are similar in size. By contrast, in the US, the effect on energy is much smaller than on GDP. This seems to be "why" energy intensity falls in the US but rises in Iran.

In this paper we also conduct a forecast error variance decomposition:

This shows how much each of the shocks explain each of the variables at different time horizons. Energy efficiency shocks explain most of the forecast error variance in the first few quarters after a shock. But over time, the GDP shock comes to explain most of the forecast error variance. This is why I argue that the relative GDP shocks are what drives energy intensity.

The paper is coauthored with Mahboubeh Jafari at Shiraz University and Stephan Bruns at University of Hasselt.




 


Tuesday, November 17, 2020

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.

Thursday, December 28, 2017

The Impact of Electricity on Economic Development: A Macroeconomic Perspective

I have a new working paper out, coauthored with Paul Burke and Stephan Bruns. The paper is one of those commissioned for the first year of the Energy for Economic Growth program, which is funded by the UK Department for International Development and directed by Catherine Wolfram.  The paper was actually completed in January 2017, but there has been a lot of delay in getting approval for publication. The project and the paper focuses on the role of electricity in economic development in Africa and South Asia, the two regions of the world where electricity is least accessible.

Access to and consumption of electricity varies dramatically around the world. Access is lowest in South Sudan at 4.5% of households, while consumption ranges from 39 kWh per capita – this includes all uses of electricity not just household use – in Haiti to 53,000 kWh per capita in Iceland – driven by aluminum smelting. Consumption is 13,000 kWh per capita in the US. Electricity use and access are strongly correlated with economic development, as theory would suggest:


Access and consumption have increased strongly in many poorer countries in recent years – will this have beneficial effects on development? The specific questions that DFID asked us to answer were:

• How serious do electricity supply problems have to be in order to constitute a serious brake on economic growth?

• To what degree has electrification prolonged or accelerated economic growth?

• What can be learned from the development experience of countries that have invested successfully in electrification?

In principle, it should be easier to find evidence for causal effects using more disaggregated micro level data as some variables can more easily be considered exogenous, and randomized trials and other field experiments are possible. On the other hand, growth is an economy-wide, dynamic, and long-term process with effects that cannot usually be captured in micro studies. Therefore, macroeconomic analysis is also needed. Our paper is complemented by a paper covering the microeconomic aspects of these questions.

Despite large empirical literatures – such as that on testing for Granger casuality between electricity use and economic growth – and suggestive case evidence, we found few methodologically strong studies that establish causal effects for electricity use, access, infrastructure, or reliability on an economy-wide basis. The best such study that we found is a paper by Calderon et al. in the Journal of Applied Econometrics. But this paper actually tests the effects of an aggregate of different types of infrastructure on growth.

We propose that future research focuses on identifying the causal effects of electricity reliability, infrastructure, and access on economic growth; testing the replicability of the literature; and deepening our theoretical understanding of how lack of availability of electricity can be a constraint to growth.

Thursday, April 27, 2017

How Accurate are Projections of Energy Intensity?

A new short working paper about how accurate projections of future energy intensity are. It's an extension of comments I made at Energy Update 2016 here at the ANU.

Energy intensity is one of the four factors in the Kaya Identity, which is often used to understand changes in greenhouse gas emissions. It is one of the two most important factors together with the rate of economic growth. The 2014 IPCC Assessment Report shows that less than 5% of models included in the assessment project that energy intensity will decline slower than the historic rate under business as usual:*


Is this likely? In the paper, I evaluate the past performance of the projections implied by the World Energy Outlook (WEO) published annually (except in 1997) by the International Energy Agency (IEA). The following graph shows the average annual difference between the projected and actual rate of change in energy intensity in subsequent years** for each WEO since 1994:


Positive errors mean that energy intensity declined slower than projected in the following years while negative errors mean it declined faster. So, for example, the error of -0.4% for 2000 means that over the years 2001-2015, on average energy intensity declined by 0.4% a year faster than was projected in the 2000 WEO.

It turns out that these errors are strongly negatively correlated (r = -0.8) with the error in projecting the rate of economic growth, which IEA outsources. Csereklyei et al. (2016), similarly, find that reductions in energy intensity tend to only occur in countries with growing economies. If we divide and multiply the growth rate of energy intensity g(E/Y) by the growth rate of GDP g(Y) we get the following identity:

The first term on the right hand side can be seen as the elasticity of energy intensity with respect to GDP.*** The following graph plots the elasticity as projected and as subsequently realized for each WEO:


The two seem to have tracked each other quite well. But there is a complication. The 1994 to 96 WEOs only projected future energy use up to 2010. 2010 is the only recent year when global energy intensity actually increased. This end point reduces (in absolute value) the actual elasticities for these three WEOs. From 1998 on, the difference between the projected and actual rate of change in energy intensity is calculated up to 2015. But through the 2011 WEO, 2010 is one of the years in the projection period. From 2012, 2010 is no longer include in the projection period and there is a sharp step down in the actual elasticity over the projection period. I think that the elasticities for 2012-16 probably under-estimate the true long-run elasticities and that the relatively stable values from 1998-2011 are more representative of what the future elasticities will be over the full projection horizon to 2030 or 2040.

If that is the case, then the projected elasticity of -0.6 in the 2016 WEO probably over-estimates the the elasticity that will be realized in the long run. Why would this be the case?

Early WEOs largely modeled energy intensity trends based on historical trends. This is not the case for recent WEOs. Over time, the IEA has endogenized more variables in their model of the world energy system and included more and more explicit energy policies. It is likely that the model under-estimates the economy-wide rebound effect. It's also possible that energy efficiency policies are not implemented as effectively as expected.

As part of our ARC funded DP16 project, we hope to contribute to improving future projections of energy intensity by empirically estimating the economy-wide rebound effect.

* The light grey area indicates the projections between the 95th and 100th percentile of the range for the default scenario.
** The base year for each WEO is 2-3 years before the publication date. Therefore, we can already assess the 2015 and 2016 WEO's.
*** We can use the identity to decompose the projection errors:


Over time the contribution of errors in the projected growth rate has increased relative to the contribution from errors in the elasticity. But I think that if we revisit this experiment in 2030 we will find a larger contribution from errors in the elasticity for what are currently recent issues of the WEO.

P.S. 23 June 2017

The paper is now published in Climatic Change.

Wednesday, March 11, 2015

Kander et al. Paper on National Greenhouse-Gas Accounting in Nature Climate Change

Astrid Kander and coauthors at Lund and the University of New South Wales have a paper in Nature Climate Change that proposes a new way to account for embodied carbon in trade that improves on existing measures of consumption based emissions. The collaboration with UNSW was sparked when Astrid gave a presentation at Crawford School in 2012 on the topic, which was attended by Tommy Wiedmann who was then at CSIRO but moved soon after to UNSW. Astrid was visiting ANU to work on our ARC project.

The most common way to compute carbon emissions is based simply on where the emissions are produced. These are called production based emissions (PBA). It is often argued though that this approach overly penalizes countries that export emissions intensive goods and makes countries that import these goods look like their emissions are low when they benefit from emissions intensive production elsewhere. Consumption based emissions (CBA) count all the emissions produced by a country's consumption wherever in the world the goods consumed were produced. Usually, developed countries look more carbon intensive and developing countries less carbon intensive on this basis than when using production based emissions. The following Figure from Kander et al. shows that in the European Union and the USA consumption based emissions exceed production based emissions and vice-versa in China:



But if developed countries tried to produce all their imported goods at home, it is likely that their production techniques would be less emissions intensive than those in the countries that they are importing from. So, consumption based emissions accounting gives a biased view of how much developed countries have managed to reduce emissions by offshoring production. Also, if consumption based emissions were used to apportion world responsibility for reducing emissions the only strategy an importer would have to reduce emissions accounted this way is to stop importing and produce domestically which might not be economically efficient, while the exporter has no incentive to cut these emissions.

However, accounting for emissions embodied in imports based on how much carbon would be emitted if they were produced in the importing country will underestimate total global emissions and so if we want a system of apportioning emissions fairly and usefully for global climate policy purposes it is not so useful.

Kander et al.'s approach deals with the incentive issue. They measure embodied emissions in imports in the same way as conventional CBA. However, they account for exports using the world average emissions intensity for the given good to deduct emissions from exporters instead of deducting the actual emissions produced. This reduces the emissions total for exporters who produce in a low emissions intensive way and increase the emissions of emissions intensive exporters compared to CBA. These technology adjusted consumption based (TCBA) emissions do sum to world total emissions. All exporters now have an incentive to reduce their exports emissions intensity if they were held responsible for their TCBA emissions. The resulting TCBA per capita emissions are shown in the map below and the graphs above.

On this basis emissions per capita in Europe are even less than production based emissions while in the USA they are similar to consumption based emissions. Australia also doesn't look too good on the map. On the other hand, in China TCBA emissions are intermediate between CBA and PBA emissions. The strong performance of Europe is because they have lower than average emissions intensity for the products they export. The latter means that world average emissions for those products is deducted from Europe's balance but their actual emissions for producing those products is lower than that.

The biggest "winners" are Austria, Ireland, and Belgium, which look much more emissions intensive under CBA than under PBA but much less emissions intensive under TCBA.

Astrid discusses the rationale for their approach further in this news article.

Tuesday, October 14, 2014

Environmental & Resource Economics Acceptance Rate 2013

Just got an e-mail from the journal noting that they got just over 600 submissions in 2013. They have already reached that level at this point of 2014. Back in 2004 they got less than 200 submissions. This is a common trend at many journals including Ecological Economics, where I am an associate editor. The email from ERE also notes that their impact factor is now 1.7 and this too has risen over time (0.6 in 2005). One of the reasons for that is the increased coverage of economics journals in the Journal Citation Reports. The number covered doubled in the last decade and that increases the opportunity to get cited in outlets covered by the JCR. One thing, ERE don't report on is their acceptance rate, but it is easy to calculate. According to the JCR they published 83 articles in 2013. Therefore, the acceptance rate is 13%.

I previously reported that the acceptance rate at ERE was 21%. So, this is a big drop. This is also a common trend.

Saturday, September 13, 2014

Fuel Choices in Rural Maharashtra Accepted for Publication

My paper with former masters student Jack Gregory was just accepted for publication in Biomass & Bioenergy. Jack is just starting his PhD at University of California, Davis.

This paper went through one of the more tortuous paths to publication, largely because we didn't have any sort of price data, which made the paper less interesting to economics journals. This is where we sent it: World Development (submitted 27 Jan 2012, desk reject, too narrow case study), Environment and Development Economics (referee review), Energy Policy (referee review), Energy (desk reject, too economics focused), Biomass and Bioenergy (revise and resubmit and accept). More than 2.5 years to get it published. But that's not very unusual...

Sunday, August 17, 2014

Jakob et al. (2012) Revisited

A recent paper by Jakob et al. (2012) finds that there is decoupling between growth in energy use and growth in GDP in developed countries. The authors regress the first differences between five year period means of log per capita energy use on the same transformation of GDP per capita separately for panels of OECD and non-OECD countries. They have 21 OECD and 30 non-OECD countries between 1971 and 2010. They estimate that the elasticity in developing countries is 0.631 (standard error = 0.167) and in developed countries -0.181 (0.343).

I was curious why these results are very different from those in our stylized facts paper where we find a stable monotonic relationship between energy use and PPP GDP per capita over the 1971-2010 period for 99 countries (75 non-OECD, 24 OECD) with an elasticity of around 0.70. Obviously, Jakob et al.'s method is different, their sample is smaller, and they also use market exchange rates. So, I re-estimated their model using our dataset. I find that the elasticity in developing countries is 0.395 (0.081) and in OECD countries 0.479 (0.078). This is in line with our stylized facts results. The numbers are lower probably due to using differences and country and time fixed effects.

In supplementary material, Jakob et al. report that when they use PPP GDP data from the World Development Indicators the elasticity estimates are 0.626 (0.180) and -0.353 (0.474) for non-OECD and OECD countries respectively. I would have doubted that the differences are mostly due to the different source of PPP data  - we used the Penn World Table - but our OECD sample only includes three countries omitted by Jakob et al. So, this will need further investigation.

Reference

Jakob, M., M. Haller, and R. Marschinski (2012). “Will History Repeat Itself? Economic Convergence and Convergence in Energy Use Patterns.” Energy Economics 34: 95–104.

Thursday, April 17, 2014

Chapter 5 and the Summary for Policy Makers

Chapter 5 was one of the main chapters of the Working Group III 5th Assessment Report at the centre of the controversy this week on so-called censorship of the Summary for Policy Makers (SPM). The SPM is an executive summary of the report for the IPCC member governments. Those member governments get to dictate what points from the underlying report get included in this summary and how they are "spun". However, there is also a Technical Summary that is written entirely by the researchers responsible for the main report. The material from Chapter 5 that was in the draft SPM but eliminated in the plenary meeting in Berlin referred to emissions from specific groups of countries. This blogpost provides a quick overview of the deleted figures, some of which are still in  the Technical Summary.

The first graph breaks down emissions by broad global regions:

The developed countries are represented by the members of the OECD as it stood in 1990 (since then Mexico, Korea, Czech Republic etc. have joined). Eastern Europe and the former Soviet Union are designated "Economies in Transition" and the developing world is broken down into Asia (importantly including China and India), Latin America, and the Middle East and Africa. The left-hand panel shows emissions year by year since the Industrial Revolution and also breaks them down into energy and industrial and land use related emissions. The former continue to increase but the latter appear to have peaked. Since the 1970s, the majority of growth in energy and industrial emissions has come from developing countries and particularly Asia. In an attempt to better represent the historical responsibilities of each group of countries the right-hand panel shows the cumulative historical emissions of greenhouse gases by region.* China and particularly India have campaigned to get historical contributions to global warming better-acknowledged. But the results of our analysis show that less than half of the cumulative emissions now come from the developed countries as a whole (more when only energy and industrial emissions are considered). This, presumably, isn't the message that developing country delegates wanted to see.

The next controversial figure breaks down total and per capita greenhouse gas emissions by country income groups:


The leftmost panel shows total emissions which increased everywhere due to population growth. But they particularly increased in upper middle income countries (which includes China). The total emissions from this group are now almost equal to that from the high income countries. On a per capita basis, emissions were flat in the developed world and declining in the poorest countries (as emissions from land use declined). They rose in the middle income countries. The figure does, however, also show that in all developing country groups per capita emissions remain much below those in the developed countries.

The final deleted figure deals with emissions embodied in trade:


Looking at the emissions generated in producing imports and exports, the developed countries and economies in transition ("Annex B") import more "embodied" emissions than they export. The opposite is true of the developing countries ("Non Annex B"). Emissions that include the net emissions embodied in trade are termed "consumption emissions" in contrast to the "production emissions" that are the total emissions emitted within a country and are the usual way of calculating emissions.** These numbers are derived using input-output modelling. The results are often used to argue that developed countries have reduced their emissions by offshoring production to developing countries, which is a controversial question. But properly answering this question is more complicated than this. They are also used to claim that developed countries are responsible for their consumption emissions rather than their production emissions. But both importers and exporters gain from this trade. Because of these controversies I can understand the decision to drop the discussion and figure from the SPM.

* These do not directly correspond to the amounts of gases in the atmosphere. A large fraction of annual carbon dioxide emissions are absorbed by the ocean, vegetation etc. and methane only survives for an average of 11 years in the atmosphere before being oxidised to carbon dioxide and water. So, I am not very enthusiastic about treating cumulative emissions of carbon dioxide equivalent greenhouse gases as an indicator of historical responsibility.

** Economists would usually use the term "production emissions" to refer to emissions from production activities  and "consumption emissions" to refer to emissions by consumers. This initially caused some communication problems among researchers from different disciplines in our chapter team.


Thursday, April 10, 2014

The Motorcycle Kuznets Curve

My colleague Paul Burke has a new paper with the intriguing title "The Motorcycle Kuznets Curve". Motorcycle usage peaks in middle income countries. Population density helps increase motorcycle usage. I guess country fixed effects deal with the climate.


Sunday, January 5, 2014

Harvard MIT Atlas of Economic Complexity

This is an interesting effort to assess the complexity of production and the level of local production knowledge across the countries of the world. The index of economic complexity is derived from the diversity and ubiquity of the goods which countries export. The rich data available on world trade is the strength of the indicator but also its weakness. It doesn't take into account of course any of the sophistication a country might have on the service side of the economy or in non-tradables. Australia ranks very badly. Based on the index the Zimbabwean and Australian economies have the same level of sophistication. Australia's complexity has also declined as minerals have increasingly dominated exports over time. With the upcoming demise of Holden and Ford, Australia is going to look even less sophisticated. Obviously, the Australian economy doesn't produce as wide a range of sophisticated products as the major industrial exporters. Still, it does seem that it has more sophisticated knowledge than the developing economies it ranks with in this analysis.

Saturday, September 28, 2013

Capital in the Penn World Table 8.0

This is another tricky issue with the new Penn World Table (PWT 8.0). In principle it is easy to compute a capital series if we know the level of investments each year, have estimates of the depreciation rate and the initial capital stock. The latter is the most difficult to obtain and cross-country datasets make essentially arbitrary decisions to estimate these starting stocks. The usual approach is to assume that the economy is in the steady state of the Solow model and compute the initial stock from the current level of investment, some growth rate of the economy or capital stock and the rate of depreciation. We are using that for the paper we are writing on the stylized facts of energy and growth. PWT 8.0 instead assumes that all countries had a capital/GDP ratio of 2.6 expressed in units of the local currency in the first year that data is available for that country, which could be anywhere from 1950 to 1990... There is some rationale for this. A regression analysis shows that there is no relation between the level of GDP and capital/GDP ratios in 2005 (Because of depreciation capital stocks in 2005 are not that sensitive to the initial values) and the average is about 2.6.

The interesting thing is that they have separate price series for each country for (output side) GDP (pl_gdpo) and for capital stock (pl_k). These show that in developing countries capital is much more expensive relative to output than it is in the US and other developed countries. This means that a common ratio of 2.6 translates into a real capital/GDP ratio where capital and GDP are both aggregated using US prices that varies across countries and is lower in developing and higher in developed countries. You can compute this as CK/CGDPO. Also, this will mean that there is an extra term in a cross-country Solow growth model which is the capital/output price ratio:



where Y is GDP, K capital, delta is the depreciation rate, s is the saving rate, and pY/pK is the ratio of output to capital prices. In developing countries saving buys less new capital stock per Dollar than it does in developed countries. This would be another reason in the Solow framework for why developing countries are poorer than developed countries. At least, that's what I'm understanding at the moment.

Here are the three different capital-output ratios for China:


The blue line is the ratio at international prices and the red line at constant national prices. These are equal by construction in 2005. The green line is the nominal ratio of dollar values of capital and GDP. This is equal to 2.6 in 1952. The blue line shows the strong capital deepening in China since the late 1980s. The other series do not indicate any capital deepening at all. The discrepancy between the blue and green lines is easy to explain. The price of capital/output relative to the US ratio has fallen from 2.77 in 1952 to 0.74 in 2011 (capital cost 1.39 times the US price in 1952 and 0.46 times in 2011 while output's price changed from 0.5 to 0.61 times the US level). By assumption capital and output have the same price in the US.

So what does the red "constant national prices" series mean? It will deviate from the blue line to the extent that the prices of different types of capital deviate in the country in question from the international price vector. It seems that the two lines tend to track each other much better in developed countries than developing, though India is a clear exception to that rule. For example, if structures are relatively undervalued in China (as would make sense as structures are non-traded) and the capital deepening in China is heavily driven by structures (as the data in this article by Wang and Szirmai support) then the red line will show a much slower increase in capital per unit of GDP than the blue line. 

Thursday, September 26, 2013

Penn World Table 8.0

The new version of the Penn World Table - version 8.0 - has recently been made available and is now hosted at University of Groningen in the Netherlands. An NBER working paper by Feenstra et al. describes what is new in PWT 8.0.

The new edition of the dataset introduces several new measures of GDP and the working paper is mostly devoted to discussing them as well as the relationship between PPP exchange rates (relative to market exchange rates) and the level of income known as the Penn or Balassa-Samuelson Effect.

GDP is now given both in terms of the output side and the expenditure side. The difference between these is that real output side GDP (RGDP(O)) deflates expenditure on final goods (the standard macro-economic C+I+G - consumption, investment, and government expenditure), exports (X), and imports (M) using separate deflators:


The expenditure side real GDP (RGDP(E)) uses only the final output deflator to deflate the GDP. Feenstra et al. argue that the former expresses better the real production level in each country and the latter the standard of living in each country. Previous versions of the Penn World Table used the expenditure side measure only. The difference between the two measures is due to the terms of trade. Countries with relatively expensive exports and relatively cheap imports will have living standards (RGDP(E)) that are higher than their real productive capacity (RGDP(O)).

GDP is also given in "current" and "constant" prices. This terminology is confusing because usually current prices mean prices not adjusted for inflation and constant prices mean adjusted for inflation. Here constant prices mean the reference prices from a given benchmark year -  in the current version 2005 - and current prices mean using the reference prices from each year though these are adjusted for US inflation. These differ because the reference prices change over time. The constant price series RGDP are better for comparisons across time while the current price series CGDP can be used to compare countries at a single point in time.

Finally, there is also an RGDP(NA) series that uses the growth rates in each country's own national accounts to extrapolate GDP in that country in years other than the benchmark year. National accounts growth rates were used exclusively in previous versions of the Penn World Table. This series can differ substantially from the RGDP(E) series as is shown by this graph for India:

According to RGDP(E) living standards in India fell from 1975 to 1985 while according to India's own national accounts they rose. Which is right? Well, it depends what you want to measure. The change in RGDP(E) measures the change in relative living standards across countries while that in RGDP(NA) measures the change in real expenditure weighted according to the budget shares in the country in question. They differ because budget shares differ across countries. RGDP(E) will also grow faster than RGDP(NA) in a country experiencing an improvement in the terms of trade as, for example, Australia did in the years up to 2009 due to the mining boom.

PWT 8.0 also includes capital stock, human capital, and total factor productivity series. The former was included for some countries in some previous versions but not version 7. The latter are both new.

So, all this sounds more complicated than using The Economist's Big Mac Index or previous versions of the PWT. The User Guide gives a less technical guide on how to use the data.


Monday, July 29, 2013

Travel Cost Study: Lake Nakuru National Park, Kenya

I was interested to see a working paper on pricing Lake Nakuru National Park, which I recently visited.  They find that the revenue maximizing price for foreign visitors is $1500 compared to a fee of $75 currently. It is hard to imagine it could be this high given the existence of substitutes, though obviously some foreign visitors would be able to pay it. For comparison our whole trip for four days to Lake Nakuru and Maasai Mara cost less per person than than this suggested entrance fee for one day at one national park.


Friday, September 7, 2012

Fuel Choices in Rural Maharashtra

I have a new working paper up coauthored with former masters student Jack Gregory. The paper analyses data from two tribal area villages in Maharashtra State. Jack organized and supervised the survey when he worked for the NGO WOTR in India a few years ago. The surveyed was intended to provide data on greenhouse gas emissions and so had some shortcomings as an information source for understanding energy demand and the choice of fuel. Still, we thought it was worthwhile presenting this information to a wider audience.

We found that there were really big differences between the villages. In the village of Purushwadi income had a big effect on energy use, but there was little relationship in the nearby village of Kohane. Unfortunately, we don't have any explanation for this difference. In Purushwadi the relationship between energy use and income was also different than that found in national surveys.

The national surveys show fairly constant use of biomass across income groups and increased use of modern fuels in the upper half of the income distribution in rural India (Khandker et al., 2012):



The numbers refer to income deciles. Unfortunately, we don't have data on electricity use, though we do have data on electricity connections. Here is the data for per capita energy use that we do have by income quintile in each village (P = Purushwadi, K = Kohane):



It seems to me that research in this area is mostly about coming up with common patterns but understanding more about the differences between villages might be also of interest.

Besides this, our modelling shows modest support for the energy ladder or rather "energy stacking" hypothesis. Energy stacking implies that rural households continue to use traditional fuels but add more and more of the modern ones as their income rises. Also we find that using higher quality energy sources reduces energy use, ceteris paribus. We also find that household size, stove ownership, and season influence rural energy choices. However, the effects of improved stoves are small and not consistent across the villages. This fits with recent evidence for modest or even perverse impacts of improved stoves.

Here are some pictures to give you an idea of what is involved in measuring energy use in rural India:

(a) Measuring rice with a 5 kg basket scale; (b) Measuring a headload of branches with a 25 kg hanging scale; (c) Measuring kerosene with a 200 ml graduated cylinder

Лучший частный хостинг