rafmacalaba/datause-encoder-data huggingface dataset:
específicos (=outputs), con los que se consiguen resultados deseados (=outcomes), en un contexto (=environment) dado: <!-- Start of picture text --> Recursos Productos Resultados<br>Funciones Procesos<br>(Inputs) (Outputs) (Outcomes)<br><!-- End of picture text --> Los contextos social, económico, político, ecológico y biológico determinan el entorno que afecta poderosamente a los recursos, funciones, procesos, productos y resultados. Poner énfasis en los efectos del contexto conduce a considerar dinámicas complejas e iterativas entre los elementos de un sistema, en lugar de asumir relaciones mecánicas, secuenciales o lineales.

La Teoría de Sistemas se ha mostrado útil para explicar relaciones y procesos conceptualmente complejos de sistemas sociales que operan en un contexto dado, incluidas las dinámicas y relaciones de los sistemas sanitarios contemporáneos<sup>11</sup> .

En este Informe, se sugiere un marco de sistemas simplificado, a modo de herramienta descriptiva que permite captar las dinámicas y relaciones básicas entre los diferentes componentes de la respuesta al VIH/SIDA en Colombia. No se sugiere, por tanto, como modelo de herramienta de causalidad o predictiva, sino como un mapa de análisis e investigación sobre una serie de aspectos de la eficiencia en la implementación, interrelacionados entre sí. El marco se ha demostrado útil para permitir al equipo

> 11World Health Organization (2000) _World Health Report 2000._ Health systems: improving performance. Geneva, World Health Organization; Murray CJL and Frenk J (2000) A framework for assessing the performance of health systems. _Bulletin of the World Health Organization_ 78(6):717-731; Black N and Gruen R (2005) Understanding Health Services, Maidenhead, UK: Open University Press; Shakarishvili G (2009) _Building on Health Systems Frameworks for developing a common approach to health systems strengthening_ . Paper prepared for the World Bank, Global Fund Fight AIDS, Tuberculosis and Malaria and GAVI Alliance, Technical Workshop on Health Systems Strengthening, Washington DC,June 25-27, 2009.

14
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 4: Regional log Formal Earnings Premia - 1992-2010 <!-- Start of picture text --> 2.3<br>2<br>1.7<br>1.4<br>1.1<br>0.8<br>0.5<br>0.2<br>-0.11992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-0.4<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the change in regional log formal earnings premium from 1991 to the year listed on the x-axis, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with state fixed effects and pre-trend control. Positive estimates imply larger earnings declines in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

43
# _b) Vietnam 's export structure and resource endowment in comparative perspective_

Table 13 compares Vietnam's resource endowments with those of a number of other countries in East and South East Asia. It shows that Vietnam has a high endowment of skilled labor and relatively little land. In 1990, the latest year for which data is available for all countries listed in table 14, Vietnam's workers had an estimated average of 6.28 years of education. This was close to the regional average and was above the levels of China, Malaysia, Thailand and Indonesia. It shows Vietnam's impressive effort to build up human capital. In addition, table 13 shows that Vietnam's availability of land relative to its adult population is low. In 1990, Vietnam had more workers per square kilometer of land than most other countries in the region, including the four countries mentioned above. Such resource endowments form the basis of a very strong comparative advantage in manufactures.

Looking at table 13, it is clear, however, that much of Vietnam's comparative advantage is still untapped. In 1997, manufactures accounted for only 36.7% of Vietnam's total exports. This is the lowest share of all the East and South-East Asian countries listed in table 13. In China, for example, manufactures represented 86.7% of exports, while in the whole of East Asia they made up on average 83% of total exports. Even a decade ago, all these countries (except Indonesia) had more manufacturing goods in their exports than Vietnam in 1997. Most relevant, however, is a comparison between Vietnam and the Philippines. The Philippines has an endowment of resources similar to that of Vietnam, but its exports in 1997 consisted of 87.6% of manufactures. This suggest that Vietnam is well within its potential and that there is scope for Vietnam to significantly increase manufacturing exports in the near future.

**Table 13: Export structure and resource endowments**

|Country|Share of m<br>in total exp|anufactures<br>ort revenues|Average years<br>of education|Square km of land<br>per 100 workers in|
|---|---|---|---|---|
||1990|Latest year*|in 1990|1990|
|Vietnam<br>|n.a|36.7|6.28|0.75|
|China|72.9|86.7|5.9|1.14|
|Hong Kong|95.8|95.6|9.2|0.02|
|Indonesia|35.7|55.4|4.6|1.60|
|Korea|92.8|88.1|9.9|0.31|
|Malaysia|54.8|78.5|6.0|3.00<br>|
|Philippines|67.5|87.6|6.9|0.80|
|Singapore|72.6|86.9|5.9|0.03|
|Taiwan, China|92.7|94.3|8.0|0.24|
|Thailand|60.5|73.3|5.6|1.36|
|Average<br>(excluding<br>Vietnam)|71.7|82.9|6.88|0.94|

* All latest year data is from 1997. The only exceptions are Singapore for which data is for 1998 and Taiwan for which it refers to 1996

_Source:_ For all countries, except Vietnam: export data is from COMTRADE. Manufactures refer to categories 5-8 less 68 of the Standard International Trade Classification (SITC); Education and population data are from Barro and Lee (1996) and land 13
**Table 9.** IEAs, Distance, and the Direction of FDI flows. Hurdle negative binomial regressions

||(|17)|(1|8)|(|19)||(20)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|logit|NB|logit|NB|Logit|NB|Logit|NB|
||All sample|All sample|All sample|All sample|SS|SS|SS|SS|
|_GDPpcj_|0.276|-0.117|0.504**|0.334|0.483*|0.195|0.191|-0.138|
||(0.210)|(0.296)|(0.253)|(0.269)|(0.256)|(0.334)|(0.221)|(0.243)|
|_Populationj_|0.623***|0.669***|0.416***|0.799***|0.396***|0.409*|0.680***|0.305**|
||(0.103)|(0.141)|(0.109)|(0.172)|(0.138)|(0.235)|(0.145)|(0.135)|
|_Distanceij_|-0.0003***|-0.0002**|-0.0003***|-0.0002**|-0.0003***|-0.00065|-0.0003***|-0.0006*|
||(6.60e-05)|(8.85e-05)|(6.67e-05)|(9.92e-05)|(7.59e-05)|(0.00045)|(7.63e-05)|(0.000330)|
|_Tradeij_|4.86e-08*|-1.54e-08*|8.44e-08**|-1.93e-08*|1.87e-07***|-1.18e-08|5.82e-08|-6.65e-09|
||(2.89e-08)|(8.51e-09)|(3.63e-08)|(1.10e-08)|(6.90e-08)|(1.47e-08)|(6.11e-08)|(9.93e-09)|
|_Comlang ij_|1.844***|3.108***|1.931***|2.877***|1.666***|6.771|1.383***|7.346|
||(0.371)|(0.656)|(0.401)|(0.629)|(0.549)|(12.80)|(0.486)|(10.02)|
|_Pol Stabilityj_|0.518**|0.660***|||||0.666**|0.462*|
||(0.229)|(0.249)|||||(0.268)|(0.248)|
|_SSij_|0.349|-0.697|0.415|-0.0758|||||
||(0.648)|(0.691)|(0.636)|(0.813)|||||
|_PTAij_|0.460|1.006*|0.124|1.429**|0.141|2.194*|1.078*|0.798|
||(0.424)|(0.544)|(0.440)|(0.722)|(0.734)|(1.321)|(0.653)|(0.825)|
|_BITij_|0.355|0.424|0.674|-0.0943|0.104|2.728|-0.399|4.780|
||(0.482)|(0.567)|(0.543)|(0.669)|(0.851)|(13.78)|(0.745)|(10.75)|
|_BIT*Dist*SS ij_|0.00018*|0.00019**|0.00014*|0.00021**|||||
||(8.22e-05)|(8.23e-05)|(8.32e-05)|(8.77e-05)|||||
|_PTA*Dist*SS ij_|9.21e-05|-0.000118|0.000109|-0.000184|||||
||(7.65e-05)|(8.53e-05)|(7.67e-05)|(0.000116)|||||
|_BIT*Dist ij_|||||0.00017*|0.00065|0.0002**|0.00049|
||||||(8.72e-05)|(0.000464)|(8.19e-05)|(0.000335)|
|_PTA*Dist ij_|||||0.000118|-0.000359|1.98e-05|-0.000184|
||||||(9.31e-05)|(0.000233)|(9.04e-05)|(0.000165)|
|_Gov qualityj_|||-0.275|2.141|-2.927|0.305|||
||||(1.294)|(1.960)|(1.866)|(2.032)|||
|Constant|-16.20***|-11.58***|-15.01***|-20.59***|-13.41***|-21.10|-16.38***|-4.155|
||(3.162)|(4.412)|(3.529)|(5.190)|(3.463)|(14.54)|(3.192)|(3.334)|
|Home FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Sector FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Observations|2,508|2,508|1,852|1,852|1,291|1,291|1,919|1,919|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 24
# **Table 7**

**Drivers of Gross Capital Inflows: INDUSTRIAL COUNTRIES, 1980‐99 vs. 2000‐17** _Dependent Variable: GROSS Inflows, Total and By Type (% GDP) Sample: 22 countries, 1980‐2017 (annual) Estimation method: Instrumental Variables (IV)_

|Variables|Total<br>1980-99|Total<br>2000-17|FDI<br>1980-99|FDI<br>2000-17|PI<br>1980-99|PI<br>2000-17|OI<br>1980-99|OI<br>2000-17|
|---|---|---|---|---|---|---|---|---|
|_Domestic Factors_|||||||||
|GDP Growth|-0.00406|0.0172|-0.000112|0.00318|-0.00353|0.0135|-0.00297|0.000606|
||(0.0118)|(0.0162)|(0.00581)|(0.00721)|(0.00821)|(0.00918)|(0.00738)|(0.0126)|
|GDP per capita|2.244***|-0.278|0.255*|-0.174*|1.235***|0.0167|0.701***|-0.127|
|_(in logs)_|(0.315)|(0.235)|(0.150)|(0.104)|(0.212)|(0.133)|(0.196)|(0.182)|
|CPI Inflation|-0.0102*|0.0211*|-0.00192|-0.00511|-0.00625*|0.00722|-0.00235|0.0166*|
||(0.00553)|(0.0119)|(0.00257)|(0.00527)|(0.00349)|(0.00671)|(0.00345)|(0.00922)|
|Primary Balance|-0.00761**|0.0155***|0.00126|-0.000970|-0.00451**|0.00624***|-0.00426**|0.0107***|
|_(% of GDP)_|(0.00342)|(0.00355)|(0.00159)|(0.00157)|(0.00225)|(0.00200)|(0.00213)|(0.00275)|
|Exchange rate flexibility|-0.0128*|0.00650|0.000860|-0.00255|-0.0115**|0.00496|-0.00267|0.00421|
||(0.00707)|(0.00979)|(0.00328)|(0.00435)|(0.00464)|(0.00554)|(0.00441)|(0.00760)|
|Investment Profile|-0.0273***|0.0232***|-0.00608|0.000570|-0.00645|0.0121**|-0.0127**|0.00938|
||(0.00915)|(0.00869)|(0.00425)|(0.00386)|(0.00600)|(0.00492)|(0.00571)|(0.00674)|
|Trade Openness|0.437*|-0.461***|0.106|-0.0847|0.194|-0.112|0.138|-0.287**|
|_(% of GDP, logs)_|(0.248)|(0.152)|(0.118)|(0.0675)|(0.166)|(0.0860)|(0.155)|(0.118)|
|Financial Openness|-0.0123|0.306***|-0.0160**|0.00110|-0.00231|0.108***|-0.00340|0.171***|
|_(Chinn‐Ito Index)_|(0.0173)|(0.0339)|(0.00777)|(0.0148)|(0.0110)|(0.0189)|(0.0108)|(0.0263)|
|_External Factors_|||||||||
|Foreign Growth|-0.000306|-0.0127|0.00497|0.00275|0.00245|0.000869|-0.00317|-0.0148**|
||(0.0140)|(0.00817)|(0.00664)|(0.00361)|(0.00938)|(0.00460)|(0.00874)|(0.00634)|
|VIX Index|-0.0519|-0.226***|0.0264|0.000584|-0.0660**|0.00189|-0.00242|-0.215***|
||(0.0473)|(0.0791)|(0.0218)|(0.0352)|(0.0308)|(0.0448)|(0.0295)|(0.0613)|
|US Economic Policy|0.00403***|0.000156|0.000600|-0.000358|0.00327***|-6.35e-05|-8.47e-05|0.000494|
|Uncertainty|(0.000991)|(0.000500)|(0.000444)|(0.000222)|(0.000626)|(0.000283)|(0.000618)|(0.000388)|
|Commodity Terms of|1.688|-0.841|0.172|0.0626|1.411|-0.419|0.163|-0.375|
|Trade|(1.478)|(0.601)|(0.658)|(0.267)|(0.931)|(0.340)|(0.922)|(0.467)|
|Oil Price|0.221***|-0.0955|-0.0290|-0.0312|0.117**|0.0997**|0.0984**|-0.173***|
|(in logs)|(0.0773)|(0.0722)|(0.0351)|(0.0320)|(0.0494)|(0.0408)|(0.0482)|(0.0560)|
|<br>Minerals and Metals|0.0442|0.156*|-0.0381|0.0979**|0.0561|-0.122**|-0.00900|0.191***|
|Prices (in logs)|(0.132)|(0.0918)|(0.0627)|(0.0406)|(0.0884)|(0.0517)|(0.0824)|(0.0712)|
|Observations|162|392|171|394|172|394|162|392|
|Number of cnum|20|22|21|22|21|22|20|22|

_Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1_ 25
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA irrespective of their level of consumption. Komives and others (2005) compare the average price per m<sup>3</sup> of IBT, IBT with fixed fee, and IBT with fixed fee and minimum consumption charges, and find that lowvolume consumers under the latter two-tariff regimes bear the burden of higher prices. Second, the arrangement of the block’s size and price is important, particularly that of the first block. If the first block is wide, it not only allows leakage of the implicit subsidy to the nonpoor, but also leads to a higher price per m<sup>3</sup> for the low-volume consumers in the band.

In only a few countries in Africa do small consumers pay the lowest price. Among the 45 utilities in the sample, the effective price increases with rising consumption in 27 utilities (table 8). In the majority of utilities, high-end consumers pay more than low-end or average consumers. But inequity is more prevalent at the lower end of the consumption—among households consuming between 4 and 10 m<sup>3</sup> /month. In almost three-fourths of cases, the consumers with water intake at the survival level pay equal or higher than the average consumers.

The tariff structures in Cote d’Ivoire, Malawi (BWB), and Benin most favor small consumers. In SODESI in Cote d’Ivoire, consumers at the survival level pay only $0.04/m<sup>3</sup> , which increases to about $0.53/m<sup>3</sup> for large-volume consumers with intakes of 40 m<sup>3</sup> . In Benin, average consumers pay 1.5 times more than small consumers, and large consumers pay more than twice the small consumers. The Ethiopian utilities, WASA in Lesotho, ELECTRA in Cape Verde, and DAWASCO in Tanzania also strongly favor the small consumer. Mozambique, Malawi (CRWB and LWB), Madagascar, and Tanzania (DUWASA) are the countries where the tariff strongly favors large consumers. In Mozambique, small consumers must pay 60 percent more than mid-level consumers, and more than 35 percent compared to large consumers. In Madagascar, small consumers have to pay 41 percent more than mid-level consumers and 30 percent more than large ones.

In 16 utilities, the effective tariffs of small consumers are higher than those of average consumers. This difference is pronounced in the case of five utilities in Mozambique. Since these utilities have a minimum threshold of 10 m<sup>3</sup> , the small consumer whose water intake is about 4 m<sup>3</sup> pays on average about $0.57 more than those consuming 10 m<sup>3</sup> and about $0.40 more than those consuming 40 m<sup>3</sup> . Therefore, it is not economical for households to consume less than the minimum threshold, as they end up paying more for consuming less water (Figure 12).

**Figure 12. Utilities charging higher effective prices to small consumers**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>Greater than 1<br>40%<br>Equal to 1<br>30%<br>Less than 1<br>20%<br>10%<br>0%<br>Ratio of Price at 4m3 & Price at 10m3 Ratio of Price at 4m3 & Price at 40m3<br>Percentage of Utilities<br><!-- End of picture text --> _Source:_ AICD WSS Survey Database, 2007.

20
# **Table 1: Governing law and listing distribution of dollar-denominated emerging market -** **<u>central government debt (1990 2015)</u>**

|||**Governing ju**|**risdiction**|||
|---|---|---|---|---|---|
|**Listing location**||U.K. law|US law|Other|Total|
|Luxembourg|# of Issues|98|305|32|435|
||_Volume ($bn.)_|_51.9_|_243.2_|_34.2_|_329.3_|
|Luxembourg plus U.S.|# of Issues|1|7|0|8|
||_Volume ($bn.)_|_0.5_|_8.3_|_0_|_8.8_|
|Luxembourg plus others|# of Issues|4|42|4|50|
||_Volume ($bn.)_|_1.3_|_26.6_|_1.9_|_29.8_|
|United States|# of Issues|0|2|0|2|
||_Volume ($bn.)_|_0_|_0.8_|_0_|_0.8_|
|United Kingdom|# of Issues|12|9|0|21|
||_Volume ($bn.)_|_12.9_|_8.6_|_0_|_21.5_|
|Ireland|# of Issues|7|1|1|9|
||_Volume ($bn.)_|_8.5_|_0.6_|_0.7_|_9.8_|
|Singapore|# of Issues|5|16|3|24|
||_Volume ($bn.)_|_2.8_|_17.9_|_5.5_|_26.2_|
|Other countries|# of Issues|2|1|9|12|
||_Volume ($bn.)_|_0.4_|_0.5_|_4.9_|_5.8_|
|Unreported|# of Issues|11|87|13|111|
||_Volume ($bn.)_|_3_|_54.1_|_8.1_|_65.2_|
|**Total**|# of Issues|140|470|62|672|
||_Volume ($bn.)_|_81.2_|_360.5_|_55.2_|_496.9_|

Source: Authors’ calculations based on Dealogic data. Excludes bonds issued by tax havens and offshore financial centers.

**Figure 3: Volume of dollar-denominated central government debt issued under U.K. and**

**U.S. governing laws**

<!-- Start of picture text --> $ billions<br>50<br>45 U.K. Law<br>40<br>U.S. Law<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: Authors’ calculations based on Dealogic data.<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> 5
between July and September, while precipitation is scarce during the rest of the year. As a result, malaria transmission is higher in the period from July to December, with a peak in September and October, following the rainy season.

A two-arm cluster-randomized controlled trial (using a post-test only design) was used to evaluate the impact of IRS on malaria infection prevalence. Effectiveness was measured as a single difference between treatment and control groups. One hundred and sixteen (116) villages in Gash Barka were selected for the study. Fifty-eight (58) villages were randomly assigned to the treatment group and 58 villages were randomly assigned to serve as the control group. A geographic buffer was used to insure that treatment and control villages were at least 5 km apart. The NMCP verified the distance between treatment and control villages, and villages that were within 5 km from another were replaced by the closest village at least 5 km apart. In addition, further replacements were made in a few cases where the originally chosen village had moved and could not be found or reached. Again, the closest eligible village was chosen as a replacement.<sup>7</sup> In each treatment village, the intervention involved the control of adult mosquito populations using IRS with the insecticide dichlorodiphenyltrichloroethane (DDT), which is recommended by the Eritrean NMCP. During the months of June-July 2009, dwellings were sprayed according to the manufacturer’s recommended guidelines. The spraying targeted all households to ensure a minimum coverage of 80 percent, as recommended by the World Health Organization (WHO). Treatment and control villages received similar levels of ITNs, LHM and case management, per existing NMCP guidelines and policy. Further details on the study design and intervention are available in Keating et al. (2011).

# **3 Data**

A household survey was conducted in October 2009,<sup>8</sup> which corresponds to the period right after the peak of the malaria season. Only one person per household was interviewed 7This procedure is documented in detail in the Online Supplementary Material that describes the procedures followed for treatment allocation.

> 8A baseline survey was not collected because of budgetary constraints. Appendix C provides a detailed description of the data and of all the variables used in this paper.

7
Summary statistics for the sample are given in table 4. The first projects entered the pipeline in December, 2003 and the sample period ends in our analysis in August 2009, so the first and last rows of the table represent results from partial years. The table gives the number of projects entering the pipeline each year, the additional CERs the new projects are expected to provide annually by 2012 and by 2020, and the accumulated stock of annual project CERs for both periods.<sup>10</sup> A quick examination of the table reveals that the expected flow of CERs from the pipeline corresponds to the high end of early predictions of market size reported in table 3. The table also shows that the number of projects has grown each year, although the rate of growth stalled slightly in 2008. Not all projects that enter the pipeline are successful; some projects are withdrawn by the project developer or rejected by the Board. The last column of the table indicates that about 3-14 percent of the 2012 CERs entering the pipeline are withdrawn or rejected. Averaging masks some of the variation in the scale and frequency with which CERs entered the pipeline. Figure 1 shows the build-up of the introduction of new projects and the stock of CERs against a daily scale.

# <u>Applied diffusions models</u>

As discussed, the purpose of this paper is to estimate the CDM diffusion patterns and compare ex ante estimates of CDM market size with predictions from the diffusion models. In particular, we use information about projects entering the CDM pipeline and combine it with an assumption that the adoption of CDM project methodologies follows a path typical of new technologies. We consider two types of models, a symmetric and an asymmetric model.

Following Feder and Umali (1993), the general form of the logistic model for CDM adoption is where, in our case, _ct_ denotes the accumulated expected flow of amount of CO2 to be abated * through existing CDM pipeline projects at time _t_ , and where _c_ denotes the overall saturation point, and where _β_ is a parameter measuring the rate of adoption. With time, the

> 10 The UNEP/Risoe data reports total CERs by 2012 and 2020. Annual values are simply these total divided by 5 and 10 respectively. This is done to correspond to model projects, which, by convention, are stated as CERs produced annually.

9
receipt of payments for greenhouse gas emissions reductions from adopting the improved stoves can influence the adoption decisions of individual households by lowering the net cost of the stoves.

# **2. Literature on Improved Stove Attributes**

Several previous studies have examined the effects of traditional cookstoves and analyzed how to effectively encourage adoption of improved cookstoves. Most studies have found that traditional stoves have high nonmonetary costs, because of high fuelwood consumption and associated air pollution emissions. Duflo, Greenstone and Hanna (2008) found that in rural India, there was a high correlation between using a traditional stove and having symptoms of respiratory illness. Parikh (2008) observed connections among gender, energy use, and health in the Himachal Pradesh region in India. Certain groups suffer from the negative effects of traditional stoves and fuels more than others. Survey data provide evidence that women generally walk the most to collect fuel, they lose potential work days, and they suffer from physical stress from the long and often strenuous walks. Parikh (2008) also found that girls below age 5 as well as females age 30 to 60 show more symptoms of respiratory illness than do males of similar ages, because women and female children are the ones who spend the majority of their days inside their smoky homes.

Simon, Bumpus and Mann (2012) explored possibilities of “win‐win” programs for climate protection and economic development and found that distributing lower‐emitting stoves and using carbon finance could support both objectives in a poor country. These programs set both local (ex. economic development and improvement of children’s health) and global (mitigating climate change) goals, and results showed that economic development success was linked directly to environmental improvements.

There are, however, various challenges for stove adoption, as not everyone in developing countries is willing to pay for or wants to use such new technologies (Lewis and Pattanayak 2012, Bensch et al. 2015, Hanna et al. 2016). Previous efforts to encourage people to adopt and use new stoves in Ethiopia have met with limited success. The government has tried to promote clean stove technology, and though adoption rates have steadily increased over time, the large‐scale adoption of stoves necessary to achieve climate benefits has not occurred. Beyene and Koch 5
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