metadata: authors: 0: author: Costolanski,Peter
1: author: Elahi,Raihan
2: author: Iimi,Atsushi
3: author: Kitchlu,Rahul
count: Ethiopia
docna: 0: docna: Impact evaluation of free-of-charge CFL bulb
            distribution in Ethiopia
docty: Policy Research Working Paper
subtopic: Science of Climate Change,Climate Change and Health,Climate Change and Environment,Energy Consumption,Energy Policies & Economics,Energy Conservation & Efficiency,Environment and Energy Efficiency,Energy Demand,Energy and Environment,Energy and Mining,Global Environment
teratopic: Environment,Macroeconomics and Economic Growth,Energy
abstracts: cdata!: Electricity infrastructure is one of the
            most important development challenges in Africa. While more
            resources are clearly needed to invest in new capacities, it
            is also important to promote energy efficiency and manage
            the increasing demand for power. This paper evaluates one of
            the recent energy-efficiency programs in Ethiopia, which
            distributed 350,000 compact fluorescent lamp bulbs free of
            charge. The impact related to this first phase is estimated
            at about 45 to 50 kilowatt hours per customer per month, or
            about 13.3 megawatts of energy savings in total. The overall
            impact of the compact fluorescent lamp bulb programs, thanks
            to which more than 5 million bulbs were distributed, could
            be significantly larger. The paper also finds that the
            majority of the program beneficiaries were low-volume
            customers -- mostly from among the poor -- although the
            program was not targeted. In addition, the analysis
            determines the distributional effect of the program: the
            energy savings relative to the underlying energy consumption
            were larger for the poor. The evidence also supports a
            rebound effect. About 20 percent of the initial energy
            savings disappeared within 18 months of the program's completion.
geo_reg_key: 517191
repnme: repnme: Impact evaluation of free-of-charge CFL
            bulb distribution in Ethiopia
display_title: Impact evaluation of free-of-charge
            CFL bulb distribution in Ethiopia
listing_relative_url: /research/2013/03/17428953/impact-evaluation-free-of-charge-cfl-bulb-distribution-ethiopia
topicv3: Energy and Extractives m1326595 507,Climate Change m1326829 567,Energy Efficiency m1326852 533,Power and Electricity Sector m1327094 515,Environment, Natural Resources and Blue
            Economy m1327391 566
publishtoextweb_dt: 2013-03-01T00:00:00Z
historic_topic: Environment,Macroeconomics and Economic Growth,Energy
no_of_pages: 24
author_keys: 0: author_key: 435369
1: author_key: 428011
2: author_key: 914707
3: author_key: 1127100
model_extractions: WPS6383 Policy Research Working Paper 6383 Impact Evaluation Series No. 87 Impact Evaluation of Free-of-Charge CFL Bulb Distribution in Ethiopia _Peter Costolanski Raihan Elahi Atsushi Iimi Rahul Kitchlu_ The World Bank Africa Region Sustainable Development Department March 2013
Policy Research Working Paper 6383

# **Abstract**

Electricity infrastructure is one of the most important development challenges in Africa. While more resources are clearly needed to invest in new capacities, it is also important to promote energy efficiency and manage the increasing demand for power. This paper evaluates one of the recent energy-efficiency programs in Ethiopia, which distributed 350,000 compact fluorescent lamp bulbs free of charge. The impact related to this first phase is estimated at about 45 to 50 kilowatt hours per customer per month, or about 13.3 megawatts of energy savings in total. The overall impact of the compact fluorescent lamp bulb programs, thanks to which more than 5 million bulbs were distributed, could be significantly larger. The paper also finds that the majority of the program beneficiaries were low-volume customers— mostly from among the poor—although the program was not targeted. In addition, the analysis determines the distributional effect of the program: the energy savings relative to the underlying energy consumption were larger for the poor. The evidence also supports a rebound effect. About 20 percent of the initial energy savings disappeared within 18 months of the program’s completion.

This paper is a product of the Sustainable Development Department, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at aiimi@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**IMPACT EVALUATION OF FREE-OF-CHARGE CFL BULB DISTRIBUTION IN ETHIOPIA**

Peter Costolanski, Raihan Elahi, Atsushi Iimi,<sup>†</sup> Rahul Kitchlu<sup>¶</sup> Key words: Impact evaluation; energy efficiency program; fixed-effect least-squares, fixedeffect quantile regression.

JEL Classification: C33; C36; L94.

> † Corresponding author.

> ¶ We would like to express our special thanks to the Ethiopian Electric Power Corporation (EEPCO) and to the World Bank Country Office in Ethiopia for their close collaboration in data collection for this analysis. We also thank Luis Andres and other Bank colleagues for their insightful suggestions.
- 2 -

# **I. INTRODUCTION**

1. Electricity infrastructure is one of the most important development challenges in Africa. Access to electricity is very limited across the continent. Globally, it is estimated that roughly 1.3 billion people live without access to electricity (IEA 2011). In Sub-Saharan Africa, about 585 million people, or approximately 70 percent of the total population in the region, are still living without access to power. In rural areas in particular, the electrification rate is still as low as 14 percent. In addition, many of those with nominal access to electricity face frequent, if not chronic, power outages.

2. Significant resources are required to meet the existing infrastructure deficiencies. While the region’s annual spending needs are estimated at US$40.8 billion in the power sector, only US$11.6 billion is currently available (World Bank 2010). In Africa, new investment in the energy sector is generally expensive. An additional 1 MW of installed capacity would cost between US$0.3 and US$0.5 million per annum (Figure 1). Improving energy efficiency is among the important measures required to meet these gaps.

3. In theory, energy efficiency is an inexpensive win-win proposition, particularly when supply capacity is severely constrained, as it is in many African countries.<sup>1</sup> While end users can reduce their energy spending, power utilities can avoid costly new investment in developing their capacity. At the same time, manufacturers and vendors of energy-efficient technology can profit from the sale of energy goods or services. Moreover, energy efficiency can contribute to mitigating the effects of global warming. Africa is not a major CO<sup>2</sup> emitter at present; the region only accounts for some 3 percent of total CO<sup>2</sup> emissions, while about 15 percent of the world’s total population resides there. However, among other developing regions, Africa is projected to be among the most vulnerable to climate change and possible extreme events.

4. One of the most important development efforts currently under way in Africa is the Lighting Africa Initiative, which aims to provide power access to 250 million people by 2030. The initiative seeks to promote private sector participation in the regional home appliance market to encourage the widespread distribution of energy-efficient goods and equipment, such as compact fluorescent lamp (CFL) bulbs, as well as the development of renewable energy sources, such as solar, wind and micro hydro power. Thanks to this initiative, roughly 3.8 million people have already received new access to clean, safe lighting as of 2012.<sup>2</sup> 5. A number of residential CFL bulb distribution programs have been implemented all over the world, including in the Philippines, Rwanda, Thailand, Uganda and Vietnam (e.g., World Bank 2006; ESMAP 2009). Recently, Ethiopia carried out a series of CFL bulb distribution programs. From an engineering point of view, these programs clearly contribute

> 1 In practice, there are various institutional challenges to promoting the adoption of energy efficiency measures, e.g., long payoff periods and market failure to finance upfront large investment (see Singh et al. 2010, for example).

> 2 <u>http://www.lightingafrica.org/</u>
- 3 -

to energy conservation. For instance, the CFL program in Vietnam, in which a total of 1 million CFL bulbs were distributed at favorable prices, is estimated to have reduced peak demand by 31MW (ESMAP 2009).<sup>3</sup> This figure is calculated based on the wattage differentials between old and new lamps. There is similar discussion in the literature on other energy-efficiency goods and equipment, such as agricultural pumps (e.g., Garg et al. 2011).

6. From an economic point of view, however, these engineering calculations may overlook at least three important aspects related to the impact of a CFL bulb program. First, the distribution of new bulbs does not automatically lead to their actual use. People may or may not use them as expected. In Vietnam, the original assumption was that a 75W incandescent lamp would be replaced with a 20W CFL. However, many households replaced old CFLs with new CFLs. As a result, the impact on the peak power demand turned out to be smaller than the engineering estimate had anticipated (ESMAP 2009).

7. Second, engineering calculations cannot capture the distributional impact that the CFL program might have. Many CFL bulb distribution programs are implemented on a voluntary participation basis. Thus, it remains open to argument who the major beneficiaries ultimately are, and exactly how they benefit from these programs. Notably, the ways of using electricity vary significantly between the rich and the poor segments of the population. In developing countries, lighting is the most basic power demand among the poor. But the potential savings from CFL bulbs may not be as significant for the rich, who use electricity for many other purposes.

8. Finally, related to the above, people’s behavior can be changed by the energy efficiency program. On one hand, the CFL bulb distribution—either free of charge or at deep discounts—could raise awareness with respect to energy efficiency and global warming, motivating people to conserve more energy on other occasions. For example, the Brazilian temporary electrification rationalization is found to have had a long-term effect. The temporarily limited electricity supply encouraged customers to purchase more energyefficient home appliances, leading to long-term energy savings even after the supply constraint eased (Costa 2012). On the other hand, rebound or backfire effects may occur, as observed in many cases related to energy-efficient technologies. For instance, a more fuelefficient car may motivate people to actually drive more, not less.<sup>4</sup> 9. The current paper casts light on these issues by evaluating the energy-saving impact of a free CFL distribution program in Ethiopia. To remove possible self-selection bias, the fixed-effects model is applied for the panel data. To investigate the distributional impact, a two-step fixed-effects quantile regression (Canay 2011) is also used. The following sections are organized as follows: Section II briefly explains recent developments in the Ethiopian

> 3 The average wattage of the old lamps was 58W. These were replaced with 20W CFL bulbs. With a usage factor taken into account, it is estimated that 31MW of peak demand was reduced.

> 4 Evidence shows that the short- and long-term rebound effects of vehicle fuel efficiency are 4.5 percent and 22 percent, respectively (Small and van Dender 2007). The literature reports even larger rebound effects of about 50-60 percent (Frondel and Vance 2010; Frondel, Ritter, and Vance 2012). In a much broader context, the rebound effect for various GHG abatement actions is estimated at 34 percent in the UK (Druckman _et al_ . 2011).
<!-- Start of picture text --> OR ——SSSSSSSeeeeeeSeeeeeeeeeeeeeseseseSeeF<br>=4<br>3S 07 Trade<br>=> expansion; _<br>5 s 0.6 | Trade stagnation<br>a3<br>&=EG 05<br>iss 0.32 }<br>Bz 02<br>38<br>8 g 0. |<br>So<br>Zogo |<br>> = + se 2 e © @ RS<br>3Se.Ns & &we we e< >er seOS ss<<br><!-- End of picture text -->
<!-- Start of picture text --> 7000<br>6000 5866<br>5163<br>5000<br>4000 3763<br>3363<br>3000<br>2000 1787<br>10000 ia899 |<br>2006 2007 2008 2009 2010 2011<br><!-- End of picture text -->
- 6 -

# **III. THE PROGRAM TO BE EVALUATED AND OUR DATA**

15. The current impact evaluation focuses on the first phase of the CFL bulb distribution program, implemented in 2009. In collaboration with EEPCO, the electricity consumption data were collected in Addis Ababa’s Bole-Kazanchis Service Area. This service center covers 13,000 customers in total; about 3,500 customers benefited from the program and received CFL bulbs (most exchanged four, as mentioned above).

16. In our sample, 2,000 customers were randomly selected from a group of CFL beneficiaries (i.e., treatment group). Another 2,000 customers were also randomly selected from a group of customers who did not benefit from the program (i.e., control group). Each customer’s monthly energy consumption and bill data were collected from January 2007 to August 2012. The sample period was intended to cover several years prior to and following implementation of the program, so that the program impact could be clearly distinguished from the trend component of the electricity demand. In our sample, 60 percent of beneficiaries received CFL bulbs in June 2009, and the rest received them in July 2009. The lengthy follow-up period allows for examination of the long-term effect of the program on people’s energy use.

17. According to engineering calculations, it is estimated that about 7.2 MW of energy could be saved by the first CFL program (Table 2). The differentials between old incandescent bulbs and new, efficient CFLs are 29W to 80W. Assuming that everything else remains constant, in theory, the demand for electricity would drop by 7.2 MW immediately after implementation of the program. This represents approximately 1 percent of EEPCO’s installed capacity. Recall that the first phase was implemented as a pilot and scaled up in the second and third phases. Under the same assumptions, the second phase is expected to reduce energy consumption by as much as 90 MW. This would be equivalent to nearly 10 percent of the total power generation capacity in Ethiopia in 2009.

18. It may appear that this initiative would contribute significantly to energy conservation. However, its actual impact remains to be examined further. At first glance, electricity consumption indeed dropped among beneficiaries of the program (Figure 3). But consumption by non-beneficiaries also dropped following its implementation. Thus, the program’s net impact remains questionable. Moreover, statistics also suggest that, in spite of the program, demand for electricity has been increasing over time, a fact that must also be taken into account. It is likely that demand for electricity tends to increase and become diversified as the economy grows. This is because current levels of consumption are very low and the demand is for primitive purposes. This raises the question as to whether the shortterm impact of this type of initiative could be different from its long-term impact.

19. Figure 3 also indicates that levels of electricity consumption among the treatment group (i.e., program beneficiaries) are systematically different from those of the control group (i.e., non-beneficiaries). Thus, the problem of self-selection must be taken into account in the program’s evaluation. Ultimately, the voluntary CFL bulb distribution program benefited the
<!-- Start of picture text --> 450<br>400 + =<br>250+<br>——Non-beneficiaries<br>——CFL beneficiaries<br>SSBSBSSSSBSSSSSSSSXAaAHSaAa<br>SFFSGRSFFSGRSFFSERSFFSRSFSHRZS<br><!-- End of picture text -->
- 8 -

participation and outcome. This will cause self-selection bias. In the CFL bulb context, some segments of the population may be more aware of environmental issues than others. Those who are more concerned about global warming and energy efficiency are perhaps more likely to adopt new energy-efficient technologies, such as CFL bulbs, and curb their use of energy. If this is the case, _CFLit_ is correlated with people’s unobservable preferences, which are included in the error term _εit_ . As a result, the program effect captured by coefficient _α_ would likely be negative, regardless of the program.

22. To mitigate this problem, the fixed-effects model is used (e.g., Holl 2004; Khandker et al. 2009; Khandker and Koolwal 2011). Recall that our sample data are almost perfectly balanced for about 4,000 customers over the 67-month period (from January 2007 to July 2012). Thus, the individual- and time-specific fixed effects are included ( _ci_ and _ct_ ). These fixed effects will eliminate unobserved individual characteristics and time effects, mitigating the risk of self-selection bias.

23. An alternative approach may be to assume some deterministic structure on time trend while keeping the individual fixed effects in the model. The time trend can be included in the quadratic form in time _t_ . Given the fact that our sample data are monthly, a set of monthly dummy variables is also included ( _SAt_ ). Normally, electricity demand experiences seasonality from month to month.

24. An advantage of this specification is that it allows one to explicitly examine the effect of supply constraints. Despite the fact that the installed capacity has significantly increased over the past few years, the country recently faced a chronic power shortage. This is partly because of the rapidly growing electricity demand and partly due to Ethiopia’s dependency on hydropower for electricity generation. The available capacity is highly dependent on weather conditions (e.g., Engida et al. 2011). For example, available capacity was among the lowest during the summer of 2009 (Figure 4). Thus, the total amount of electricity generated (GWh), _Qt_ , is included in the equation. Since individual power consumption represents a tiny fraction of total power production, this supply constraint is assumed to be exogenous.<sup>5</sup> 5 Our empirical results are found to be unchanged regardless of whether or not the supply constraint is included.
<!-- Start of picture text --> 700<br>600<br>500 | A<br>iV<br>400 aA ee<br>300 I\ }\ aN,<br>200 _h— —\/ Vy<br>100<br>0<br>wo& wo¢ Lied$$ le§ o¢ oO§¢ n§ aF§ oSFFo CoalF CalFFow<br>s 2S ws 2S &® SB & 3S &§ B & 2 S<br><!-- End of picture text -->
<!-- Start of picture text --> 0.045<br>0.040 —<br>0.035 }<br>= es<br>= 0.030 i<br>5 —<br>fp 0.025 ;<br>ws '<br>3!<br>@0.020 -<br>$0015 —<br>2<br>ao<br>0.010<br>0.005<br>0.000<br>0 100 200 300 400 500 600 700<br>Electricity consumption (kWh)<br><!-- End of picture text -->
- 11 -

predicted values, the actual bill payment is regressed on a constant term and the quantity of consumption: because _D j_ = _Billij_ − _MPj_ ∗ _kwhij_ . _Billij_ is the amount of bill payment for customer _i_ , who is faced with _j_ th block marginal rate. Thus, the marginal price and Nordin’s _D_ can be instrumented by<sup>γˆ</sup> 2 _j_ and<sup>γˆ</sup> 1 _j_ , respectively (Hewlett 1977). Note that<sup>γˆ</sup> 2 _j_ and<sup>γˆ</sup> 1 _j_ are blockspecific predictions estimated from a separate regression for each _j_ .

30. To estimate Equations (3) and (3’), the analysis uses the fixed-effects instrumental variable (FE IV) regression using Equation (4). This will remove the potential self-selection and simultaneity bias and provide a consistent estimate of the program impact, _α_ . To assess the long-run impact of the CFL program, the lagged CFL impact is considered in the following specification: where _CFLit-s_ takes one if household _i_ received CFL bulbs _s_ months before, and zero otherwise. For instance, if _s=6_ , _αs_ will capture the impact of using CFL bulbs at least for six months, because _CFLit-s_ is set at zero for 6 months after program implementation. By the same token, the long-term impact will be examined for _s_ = ,1  ,24 .

31. Finally, a semi-parametric technique of quantile regression is also used to estimate the distributional impacts of the program. Quantile regression has the great advantage of capturing potential differences in the response of the dependent variable at different points. In our context, the program impacts can be different between low- and large-volume consumers. In addition, quantile regression can be more efficient than least squares estimators, if the error term is not normal (e.g., Buchinsky 1998; Koenker and Hallock 2001).

32. A two-stage fixed-effects quantile regression (2SFEQR) is applied for panel data. The 2SFEQR technique can provide an unbiased estimate as _T_ increases (Canay 2011). A monte carlo simulation shows that with _T=20_ , possible bias would be less than 0.04 percent. At the first stage,<sup>_c_ˆ</sup> _i_<sup>and</sup><sup>_c_ˆ</sup> _t_<sup>are estimated through the standard IV fixed-effects OLS regression. By</sup> subtracting<sup>_c_ˆ</sup> _i_<sup>and</sup><sup>_c_ˆ</sup> _t_<sup>from</sup><sup>_kwh_, quantile regression is then performed on</sup> ˆ ˆ _kwhit_ = _kwhit_ − _ci_ − _ct_ at the second stage. Five quantiles are examined: 0.1, 0.25, 0.5, 0.75 and 0.9.
- 12 -

# **V. MAIN ESTIMATION RESULTS AND POLICY IMPLICATIONS**

33. First of all, the fixed-effects OLS regression is performed. The estimated CFL bulb impact is found to be negative (Table 3). But this could be potentially biased, as discussed above. Importantly, the coefficient of the marginal price turned out to be positive when both individual and time-fixed effects are included. This contradicts economic theory. It occurred because the endogeneity between price and quantity has not yet been properly solved. Under the increasing block tariff schedule, higher block rates are applied to larger-volume consumers. The positive coefficient captured this correlation, not true price elasticity of demand.

34. To control for endogeneity, the instrumental-variable fixed-effects (IV FE) regression is used. The unbiased impact of the CFL bulb distribution is estimated at about 45-50 kWh. When both individual and time-fixed effects are included, the coefficient of _CFL_ is estimated at -45. When putting a structure on the time component, the coefficient is about -50. Both are statistically significant. Therefore, it can be concluded that the program reduced electricity consumption by 45-50 kWh per customer.

35. This magnitude of impact seems to be consistent with the engineering calculations. Assuming that people use CFL bulbs 10 hours every day, the estimated coefficient can be translated to about 150 watts of energy savings. Since most program beneficiaries traded in four bulbs, the implied energy savings are 38 watts per bulb. This is broadly consistent with the wattage differentials between an old and new bulb (i.e., 40W and 11W, or 60W and 15W). In total, the program is estimated to bring 13.3 MW of energy savings to Ethiopia.<sup>10</sup> This investment seems to be quite cost effective, since the total cost of purchasing CFL bulbs (ignoring the bulb distribution or program administrative costs) is only about US$0.5 million.<sup>11</sup> 36. Apart from the CFL impact, the estimated results also suggest that the energy demand per household would increase by 3 kWh every month. This means that the demand grows by 1.4 percent per month, which is significant. At the same time, however, evidence indicates that the demand increase would likely diminish over time. The coefficient of _T_<sup>_2_</sup> is significant and negative. Therefore, the electricity demand would increase with time in a concave fashion, as normally expected. The supply constraint partly explains the electricity consumption as well. The coefficient of _Q_ is positive, as expected. It means that people cannot use as much electricity as they want, when the supply capacity is low. This is simply because the limited capacity would likely lead to chronic power outages.

37. The electricity price elasticity is estimated at -0.29 or -0.26. This is relatively low but consistent with the existing literature. While the classical literature shows the elasticity would range from -1 to -2 (e.g., Taylor 1975; Wilder and Willenborg 1975), more recent

> 10 The estimated energy savings are 37.9 watts per bulb. This is multiplied by the total number of CFL bulbs distributed, i.e., 350,000.

> 11 It is assumed that a CFL bulb costs US$1.50.
- 13 -

literature supports a more modest range of elasticity, from -0.1 to -0.4 (e.g., Espey and Espey 2004; Bernstein and Griffin 2005; Reiss and White 2005). Our result seems reasonable because many households in Ethiopia are still using electricity for basic living needs. Therefore, the demand tends to be inelastic, even if the price is changed. Nonetheless, the price elasticity is statistically significant, indicating the importance of the price incentives to control for electricity demand.

38. The elasticity with respect to the Nordin’s _D_ is a mirror of income elasticity. Estimated elasticity is relatively high, at about 0.77, regardless of specifications. This must reflect the fact that the demand for electricity has been growing strongly in developing countries such as Ethiopia. As the economy grows, the electricity demand increases rapidly. This reconfirms the continuous need for electricity capacity expansion and the importance of demand-side management.

**<u>Table 3. Fixed-effects OLS and IV estimation results</u>**

||FE O|LS|FE O|LS|FE|IV|FE|IV|
|---|---|---|---|---|---|---|---|---|
|_CFL_|-35.30|(2.70) ***|-39.90|(2.34) ***|-45.54|(0.99) ***|-49.97|<br>(0.94) ***|
|_MP_|89.30|(28.82) ***|-123.89|(22.33) ***|-295.94|(13.17) ***|-269.83|(13.09) ***|
|_D_|-10.76|(0.23) ***|-11.46|(0.22) ***|-14.54|(0.06) ***|-14.37|<br>(0.06) ***|
|_Q_|||0.22|(0.01) ***|||0.16|<br>(0.01) ***|
|_T_|||3.28|(0.20) ***|||3.36|<br>(0.11) ***|
|_T_<sup>2</sup>|||-0.02|(0.003) ***|||-0.02|(0.002) ***|
|_SA_2|||-0.18|(0.99)|||-0.41|<br>(1.14)|
|_SA_3|||1.26|(1.23)|||0.81|<br>(1.15)|
|_SA_4|||-0.33|(1.12)|||-1.51|<br>(1.18)|
|_SA_5|||-5.75|(1.19) ***|||-7.48|<br>(1.20) ***|
|_SA_6|||14.65|(1.47) ***|||15.10|<br>(1.20) ***|
|_SA_7|||4.89|(1.27) ***|||5.70|<br>(1.26) ***|
|_SA_8|||-8.70|(1.12) ***|||-5.06|<br>(1.32) ***|
|_SA_9|||18.26|(1.29) ***|||14.96|<br>(1.22) ***|
|_SA_10|||17.49|(1.30) ***|||15.35|<br>(1.20) ***|
|_SA_11|||-3.46|(1.07) ***|||-1.72|<br>(1.21)|
|_SA_12|||-2.88|(1.05) ***|||-2.29|<br>(1.20) *|
|constant|-32.40|(10.37)***|-25.05|(9.26)***|47.99|(4.85)***|-10.41|<br>(6.32)*|
|Obs.|266836||262858||258747||254964||
|No. of groups|3998||3998||3993||3993||
|Fixed effects:|||||||||
|Individual|Yes||Yes||Yes||Yes||
|Time|Yes||No||Yes||No||
|F stat|168.31||555.11||||||
|Wald stat|||||1.2E+06||1.2E+06||
|Implied elasticity:|||||||||
|_MP_|0.09|(0.03) ***|-0.12|(0.02) ***|-0.29|(0.01) ***|-0.26|<br>(0.01) ***|
|_D_|0.57|(0.01)***|0.61|(0.01)***|0.77|(0.003)***|0.76|(0.003)***|
- 14 -

39. To assess the distributional effect, the two-stage fixed-effects quantile technique is applied for Equation (3). Note that, for computational simplicity, the two endogenous variables— _MP_ and _D_ —are also replaced by their predicted values using the results of the first-stage FE IV regression. The results are broadly consistent with the above FE IV estimation results. However, there are marked differences in the program impact among different quantiles in terms of energy consumption. The CFL bulb impact is estimated at -48 kWh per month for low-volume first quantile customers (Table 4). On the other hand, the estimated impact is much larger (in absolute terms) for larger-volume consumers. The coefficient is -87 for fifth quantile customers. Thus, large-volume power consumers—who are most likely the rich—have the highest energy savings impact of the CFL bulb distribution program. The estimated coefficient can translate into 73 watts of energy savings per bulb, which is consistent with the highest differential between an inefficient and a new bulb (i.e., 100W and 20W).<sup>12</sup> 40. In relative terms, however, the program impact is larger for low-volume power users— presumably, the poor. For first quantile customers, the estimated reduction in power use accounts for 95 percent of their total power consumption (Figure 6). By contrast, the relative impact to total power consumption is much smaller for large-volume power consumers; the savings represent only about 15 percent of their total power consumption.

41. The price and income elasticities also vary among different quantile consumers. Price elasticity is the lowest among the first quantile. As discussed, this reflects the fact that electricity demand by the poor is mostly to service basic needs. This demand must, of necessity, be price-inelastic. As household income increases, however, customers may have more flexibility in energy consumption. For instance, they could purchase more energyefficient home appliances. Therefore, price elasticity is higher for the rich. One resulting policy implication is that price incentives in the tariff, such as increasing block rates, are particularly important to managing the electricity demand of large-volume customers.

42. By contrast, income elasticity is higher for low-volume customers. This is a natural result because electricity is not a luxury good but rather a basic human need. Therefore, the power demand is not likely to increase in response to income growth. Rather, elasticity decreases from 1.8 to 1.2 when power consumption increases.

12 It is assumed that the average household uses CFL bulbs 10 hours per day.
<!-- Start of picture text --> 120<br>m Energy savings (kWh)<br>100 @ Energy savings relative to total<br>power consumption (%)<br>80<br>60<br>40<br>20<br>0<br>0.1 0.25 05 O75 09<br>Quantile<br><!-- End of picture text -->
LO L2 L4 L6 L8 L410 L12 L14 L146 L.18 L.20 L.22 L.24 0 <!-- Start of picture text --> 5<br>a —Coef- = 425.E.<br>1)S45 -=<s25.£.<br>ou<br>oO<br>@ -20<br>b-)<br>wg -25<br>[=]<br>> -30<br>2 _.<br>a -eorTM “ee<br>fdwi a7 a7 SO-or0 7 eer e ee=NN<br>450<br>-50<br><!-- End of picture text -->
**- -** **<u>Table 5. Long term effects of the program: IV Fixed effects regression</u>**

||L.0|L.1|L.2|L.3|L.4|L.5|L.6|L.7<br>|L.8<br>|L.9<br>|L.10|L.11|L.12|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_CFLL.t_|-45.54 ***|<br>-44.98 ***|<br>-44.15 ***|<br>-43.66 ***|<br>-42.33 ***|<br>-41.05 ***|<br>-39.95 ***|<br>-38.96 ***|-38.46 ***|-37.64 ***|-37.42 ***|<br>-37.64 **|*<br>-37.44 ***|
||(0.99)|(0.98)|(0.97)|(0.98)|(0.99)|(1.00)|(1.01)|(1.02)|(1.02)|(1.03)|(1.03)|(1.04)|(1.05)|
|_MP_|-295.94 ***|-284.53 ***|-277.74 ***|-270.21 ***|-265.20 ***|-267.35 ***|-260.50 ***|-257.36 ***|-261.22 ***|-279.34 ***|-303.08 ***|-301.12 **|* -294.31 ***|
||(13.17)|(13.32)|(13.38)|(13.69)|(13.95)|(14.33)|(14.59)|(14.89)|(15.14)|(15.41)|(15.66)|(15.94)|(16.25)|
|_D_|-14.54 ***|<br>-14.57 ***|<br>-14.64 ***|<br>-14.74 ***|<br>-14.84 ***|<br>-15.01 ***|<br>-15.11 ***|<br>-15.25 ***|-15.41 ***|-15.67 ***|-15.96 ***|<br>-16.10 **|*<br>-16.23 ***|
||(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.07)|(0.07)|(0.07)|(0.07)|(0.07)|
|Constant|47.99 ***|242.25 ***|240.38 ***|238.05 ***|235.80 ***|233.77 ***|231.32 ***|229.04 ***|227.88 ***|227.60 ***|228.12 ***|226.70 **|* 224.37 ***|
||(4.85)|(2.54)|(2.52)|(2.55)|(2.56)|(2.60)|(2.62)|(2.64)|(2.65)|(2.67)|(2.68)|(2.70)|(2.72)|
|Obs.|258747|254793|250864|246919|242983|239073|235159|231235|227328|223422|219510|215605|211707|
|No. of||||||||||||||
|groups<br>Fixed effects|3993<br>:|3989|3988|3988|3988|3988|3987|3987|3987|3987|3985|3984|3984|
|Individual|<br>Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Pseudo R2|1.2E+06|1.2E+06|1.3E+06|1.2E+06|1.2E+06|1.2E+06|1.2E+06|1.2E+06<br>|1.1E+06<br>|1.1E+06<br>|1.1E+06|1.1E+06|1.1E+06|
||L.13|L.14|L.15|L.16|L.17|L.18|L.19|L.20<br>|L.21<br>|L.22<br>|L.23|L.24||
|_CFLL.t_|-36.66 ***|<br>-36.19 ***|<br>-36.01 ***|<br>-35.33 ***|<br>-34.93 ***|<br>-35.42 ***|<br>-35.55 ***|<br>-36.14 ***|-36.93 ***|-37.11 ***|-38.38 ***|<br>-37.31 **|*|
||(1.06)|(1.07)|(1.08)|(1.09)|(1.11)|(1.11)|(1.13)|(1.16)|(1.18)|(1.20)|(1.23)|(1.27)||
|_MP_|-287.19 ***|-288.03 ***|-277.49 ***|-267.88 ***|-259.38 ***|-249.48 ***|-245.60 ***|-247.57 ***|-256.39 ***|-265.48 ***|-264.65 ***|-272.45 **|*|
||(16.59)|(16.93)|(17.32)|(17.69)|(18.09)|(18.10)|(18.45)|(18.75)|(19.04)|(19.32)|(19.69)|(20.11)||
|_D_|-16.37 ***|<br>-16.54 ***|<br>-16.62 ***|<br>-16.72 ***|<br>-16.79 ***|<br>-16.78 ***|<br>-16.80 ***|<br>-16.85 ***|-16.93 ***|-17.00 ***|-17.03 ***|<br>-17.14 **|*|
||(0.07)|(0.07)|(0.07)|(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|(0.09)||
|Constant|221.65 ***|219.85 ***|217.50 ***|214.85 ***|212.78 ***|211.74 ***|210.96 ***|211.11 ***|212.03 ***|212.73 ***|212.98 ***|212.53 **|*|
||(2.75)|(2.77)|(2.08)|(2.84)|(2.88)|(2.86)|(2.89)|(2.92)|(2.95)|(2.97)|(3.01)|(69.48)||
|Obs.|207804|203887|199968|196054|192132|188280|184437|180592|176770|172931|169119|165270||
|No. of||||||||||||||
|<br>groups|3983|3983|3981|3980|3980|3980|3980|3980|3980|3980|3980|3979||
|Fixed effects|:|||||||||||||
|Individual|<br>Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes||
|Time|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes||
|Pseudo R2|1.1E+06|1.1E+06|1.1E+06|1.0E+06|1.0E+06|1.0E+06|1.0E+06|9.9E+05<br>|9.6E+05<br>|9.4E+05<br>|9.1E+05|8.8E+05||
# **VI. CONCLUSION**

46. Electricity infrastructure is one of the most important development challenges in Africa, where access remains very limited. In Sub-Saharan Africa, about 585 million people, or roughly 70 percent of the total population in the region, are still living without access to power. Both significant resources and active measures are required to address the existing infrastructure gap, and promoting energy efficiency is among the most important initiatives in this respect. It is an inexpensive, win-win proposition. While end users can reduce their energy spending, power utilities can avoid costly new investments in developing their capacity. Furthermore, energy efficiency can contribute to mitigating global warming.

47. Ethiopia has been implementing a series of energy-efficiency programs at the end user level to manage the increasing demand for electricity and to conserve energy. The current paper evaluates the impact of the first CFL bulb distribution program implemented in JuneAugust 2009. In total, 350,000 CFL bulbs were distributed free of charge nationwide in exchange for old incandescent light bulbs. The evaluation focuses on one of the service centers in Addis Ababa, Bole-Kazanchis Service Area, in which roughly 3,500 customers received CFL bulbs in the framework of this program.

48. The evidence suggests that this initiative mostly benefited the poor. Although the program was not targeted but rather distributed CFL bulbs on a voluntary basis, program participants seem to have been self-selected. The majority of beneficiaries were relatively low-volume customers, presumably from the poorer segments of society. This is because lighting is the most important electricity demand among the poor.

49. The evidence clearly shows that the CFL bulb distribution program contributed significantly to conserving energy in Ethiopia. The program is estimated to have saved 45-50 kWh per customer, bringing the country13.3 MW of energy savings in total. This investment is highly cost effective, since the total cost of purchasing CFL bulbs (ignoring other necessary costs) is about US$0.5 million.

50. It was also demonstrated that the program had a distributional effect. In absolute terms, large-volume power consumers—i.e., the rich—received the largest energy savings from the program. Relative to underlying electricity consumption, the program’s impact is larger for low-volume power users. For the poorest (i.e., first quantile customers), the estimated reduction in power use accounted for 95 percent of their total power consumption.

51. Another important finding is that there seems to have been a rebound effect on the CFL bulb distribution program. Approximately 20 percent of the initial energy savings disappeared 18 months following the program’s implementation. This is not surprising because estimated income elasticity is high. As the economy grows, demand for electricity is increasing. In addition, price elasticity was determined to be moderate in general but high for high-volume users. Hence, to promote energy efficiency over the long term, it is important to integrate the CFL bulb distribution program with other energy-efficiency measures and pricing incentives, such as increasing block tariffs. Depending on changing electricity
- 19 -

demand, other energy efficiency programs will be needed, and pricing will be an effective policy instrument to motivate the rich to rationalize their electricity use.