Draft 2 ∗ and H´ectorM. N´u˜nez 1,2,3 Abstract (CIDE), Mexico : Q42, Q48, Q51. : Residential renewable electricity demand; just energy Aguascalientes, Mexico Via a discrete choice experiment, this paper documents that resi- JEL Classification Keywords Ad´anL. Mart´ınez-Cruz Center of Economic Research (CER-ETH), ETH Zurich, Switzerland dential electricity consumers in Aguascalientes,pay Mexico, a are premium willing to forgreen jobs. renewable energies These asFederal results administration well are has particularly redirected as priorities timely fortion from because that an the the energy was creation transi- expected current of toenergy boost sovereignty. renewable Concerns energies regarding toraised the this by pursuing national prioritization of and haveeconomic international been inefficiency stakeholders and due to itsmate its change implications goals. potential for These the concernsbegin have achievement on only of how intensified cli- as Mexico discussions shouldpaper’s face findings the open post-coronavirus the recession.just door energy This transition to together discuss with whethershould the boosting a be of combination part renewable of energies oftime a a that strategy Mexico to deals reach with energy a post-coronavirus sovereignty at world. the same transition; post-coronavirus policy;crete energy choice sovereignty; experiment. Mexico; dis- 3 Renewable Electricity and Green Jobs in Stated Willingness to Pay for Residential E-mail addresses: [email protected] (A. Mart´ınez-Cruz) and hec- Department of Economics, Centro de Investigacion y Docencia Economicas ∗ 2 Department of Forest Economics and Centre for Environmental and Resource 1 Economics (CERE), Swedish University of Agricultural Sciences (SLU), Sweden [email protected] (H.M. Nu˜nez) Draft 1 Since 2018, a change in Federal administration has led to new priorities. This change in priorities has been received with concerns from both na- 1 Introduction As part of the global commitment to tackleapproved climate change, in Mexico’s 2012 Congress the Generalcrafting Law of on a Climate comprehensive long-term Change national which climate policy.2015’s mandates Following Energy the suit, Transition Law aimsadopting to transform clean Mexico’s energy energy technologies sectorstrategies and by for targets, the electricity including sector decarbonization (SENER, ). 2018b In particular, the focus is now on energyreflects sovereignty the —which is intention a of concept bringing that thecontrol energy (Gross, industry back2019 ). under Consequently, government withoutagreements backing off or from contracted international commitments, thehas current implemented Federal administration measures that effectivelyin slow general, down and the the energy promotion transition stances of of renewable such measures energies are in theico’s particular. cancellation fourth of Two what clean in- would energy have been auction,plants Mex- and which primarily the run modernization on ofIn coal, existing addition, fuel power public oil, remarks and from naturalconception Mexico’s gas against president, (Davis renewable seem). 2019 energies to —in suggestbuilt referring a with to pre- support a wind from poweradministration, Federal plant the and president State stated governments that duringpollution, this and a plant it previous only is produces an benefits exampleto to of private consumers owners in visual who USA sell the (Infobae, energy ). 2020 tional and international stakeholdersplications because for of climate its change potential goals,the negative its uncertainty im- it potential generates economic, (Davis inefficiency,2019; ceso, and Gonzalez, 2020; ; 2019 Stromsta, , Nava 2019).; 2020 Pro- sions Such concerns begin have on only how intensified Mexico as should discus- face the post-coronavirus recession (see Draft 1 2 Assuming an exchange rate of 19.22 MXP/USD which was the average closing price In this context, two policy-relevant findings are highlighted in this docu- Strictly speaking, these findings hold for consumers responding our sur- This paper’s findings arise from the implementation of a Discrete Choice 1 in 2019 (Macrotrends, 2020). , ; 2020 Casta˜neda-Morales Levy, 2020). ment. First, residential electricity consumersthat in they Aguascalientes are City willing report to(2018 pay MXP) a for bimonthly an premium increase uptricity in mix. to the Second, 23 share consumers Mexican are of pesos alsoup renewable willing to energy to in MXP pay a their 59 bimonthly elec- a for premium policy the that creation achieves of1,000 a green new 30% jobs. green biomass jobs share According wouldin together to generate Aguascalientes with our equivalent benefits to the findings, among MXP residential creation 63 consumers (USD of 3.28) on a bimonthlyvey basis. in Aguascalientes city, and further research isor necessary other to scale claim representativeness. national Withthis this paper’s caveat in findings mind, highlight we antion believe opportunity that may that be the overlooking current whenmakers administra- pursuing can energy generate sovereignty. societal That benefits is,boosts through policy renewable a energies policy and that encouragesin simultaneously fossil that fuel-based workers industries currently transition employed to jobsdustries. in The renewable latter energy-based goal in- is atthat the has core been of a put just forward energy towhose transition guide jobs, —a the concept income, designing and of livelihoods strategies are thatable at aid pathways risk those , (Rosemberg as).2017 the In worldfindings section7, pursues may we sustain- inform discuss a how just this energy paper’sis transition, also and emphasize desirable that to this overcomethis policy sense, the our post-coronavirus results may recession help infor other regions Mexico. the in challenge the In of world as overcomingachieving policy the energy guidance recession transitions. while maintaining the goal of Draft 3 The price attribute is presented as an increase in respondents’ self-reported The rest of this manuscript is organized as follows. Section2 summarizes Experiment (DCE) that presents respondentsternative to is the four status alternatives. quo option, Oneof and four al- the attributes. other three The are first described attribute—solar, refers in biomass, to terms the and source a of 50/50 renewable mix energy age of of both. renewable energy The (from second any attribute20%, source) is and in percent- 30%. current electricity mix Theand —10%, 2,000— third which attribute are described is as numbersector. new of jobs created green in jobs the renewable —100, energy 1,000, bimonthly electricity bill —5%, 20%, and 40%.increase Importantly, the in hypothetical bill is not explainedin electricity to tariff respondents —which as would be resulting unrealistic fromincreasing in an this block context increase tariff because of scheme the incomes Mexico. from Instead, an the opt-in hypothetical extra increase feepensate (holding for tariffs an unchanged) increase that in wouldenergies. the com- share of electricity generated with renewable previous DCE studies that have documented stated preferencesrenewable for energy. residential 3 Section describes the currentits Mexican energy implications policy for and the promotion ofthe renewable theoretical energies. and Section4 empiricaldescribes strategiesexplains supporting the design our of analysis. ourreports5 Section DCE, descriptive describes statistics. the data collectionand Section6 strategy, welfarereports and estimates, econometric and specifications discussesin how previous these studies. estimates Section7 comparediscusses tocurrent how this those energy paper’s policy findings can of inform study Mexico. and further Section8 researchreports needed. the limitations of this Draft Researchers have im- 2 4 . Oerlemans et al.(2016) carry out a litera- synthesizing studies Vignette scenarios resemble alternatives presented by DCE in the sense that they are The four meta-analyses differ in the welfare measures under analysis. In terms of empirical methods, these synthesizing studies report that CV 2 2 Previous studies There is a well-established literature onnewable stated electricity. preferences Findings for residential from re- thisone literature literature have review been (Oerlemans consolidated et in et al., al., 2016), and2015; fourSoon meta-analyses2016). and (Ma Through Ahmad, this2015; section, whenSundtwe referring call and to them Rehdanz, these five2015; studies, Pokhrel ture altogether, review of 57 studies implementingto a estimate contingent WTP valuation (CV) for residential protocol renewable electricitytifying —with areas a of focus potential on iden- improvement to deliver more reliable estimates. Also, the number andthe subset subsets of largely intersect. primary studiesexpressedMa et as are al.(2015) perpetual different analyze payment —although 142 per29 WTP primary kWh estimates studies. (in(2016)Pokhrel 2006 studies USD),monthly 99 2008 and WTP USD reported estimates per in measured household, as and obtained Ahmad(2015) from focus 21 on primary 124 studies. WTPUSD estimatesSoon per expressed household, as monthly reported 2013 (2015) in analyze 30 85 primary WTP studies. estimatespressed reported eitherSundt by as 18 and monthly 2010 primary Rehdanz USD studies, per and household ex- or US-cents perand kWh. DCE are the twofor most residential common renewable methods energy. collectingchotomous stated CV choice and preferences studies open-ended questions have to predominantly elicit2016). WTP used (Oerlemans di- et More al., recently, vignetteerature. designs A have vignette made study their(vignettes) that uses way are into short shown this descriptions to lit- ofabout respondents these in situations scenarios order or (Atzm¨ullerand to Steiner, persons elicit2010). their judgments Draft In the context of studies 3 5 The term publication bias refers to the systematic preference (from either authors Results highlighted by the meta-analyses include i) residential consumers’ In terms of geographical coverage, all five synthesizing studies report that Publication bias seems to not be present in the literature documenting 3 designed to experimentally vary thetics levels (or of attributes). theoretically important vignette characteris- or journals or both)between to the select outcome publications variable thatproject. and report the statistically explanatory significant variable associations of main interest in a research estimating stated WTP for residentialthat renewable authors electricity, it and is journals important estimates report but not also zero-WTP only and positive, negativeare WTP statistically estimates theoretically significant —as feasible these values as well.studies, Ma Focusing et on al.(2015) differentstatistics and subsets that ofSoon suggest primary lack and of Ahmad)(2015 publication report bias. Begg’s test WTP for renewable electricity differswith depending solar and on wind the energies being source more of valued(Ma than energy et hydropower and al., — biomass 2015; Sundt and Rehdanz, higher2015 ); WTP ii) than metropolitan their residents rural report counterparts (Soonand and Canada, Ahmad residents2015); report iii) higher U.S. WTP than residents in other parts of the plemented vignette designs toity explore mixes household’s (that preferences may for include(Rehdanz electric- et renewable al., energies)2017 ), in and post-Fukushima residentialenergies consumers’ Japan and preferences supply for reliability renewable in2019). Germany and Great Britain (Merk et al., stated preferences for residential renewable energyin have been the studied U.S., mostly Canada,(Sundt some and EU Rehdanz, ; 2015 countries,Soon China, andand South, Ahmad in2015). Korea, general Latin less and America, developed Japan countries Africa, of are the studies. regions with the least number stated WTP for residential renewable electricity. Draft 6 Ma et al.(2015) cautions against benefit 4 5 estimated obtained on a different population that, under transferring The impact from studies’ design is an undesirable result —as ideally the instrument In this paper’s context, benefit transfer refers to estimating WTP for residential renew- The meta-analyses reach different conclusions in two realms. First, while Our analysis contributes to evaluating the case of stated preferences for 4 5 with which data isHowever, an collected advantage of is ainstrument expected meta-analysis is is to itself that be and researchers can uninformative take learn it of how into the important considerationable the resulting accordingly. electricity estimates. for ais population carried for out whichspecific by no assumptions, primary is data deemed is informative available. of the Such unstudied estimation population. Soon and Ahmad(2015) report that DCECV studies studies, yield the lower estimates opposite than direction(2016) is and reportedSundt by andMa Rehdanz(2015). etconclusion Second, al.(2015); with theyPokhrel differ respect in to terms thecorresponding of reliability their meta-regressions. of benefit transfers based on their world (Soon and Ahmad, 2015; Sundtsocioeconomic and characteristics (e.g. Rehdanz, income,2015); electricity iv) consumption respondents’ level)knowledge and about renewable energies are relevantWTP to (Ma explain heterogeneity et in al. , 2015; Rehdanz, 2015);, Pokhrel and2016 ; v)Soon characteristics of and thevariations, Ahmad study in itself2015; the statisticallySundt explain WTP and (Ma2015; etSundt and al., Rehdanz, ; 2015 2015). , Pokhrel 2016 ; Soon and Ahmad, transfer because their analysis yields thatweigh characteristics more of than the study other design factors(2016) when documents explaining that variation a in performs unit WTP. Pokhrel several value meta transfer analytic with strategies. pursue income the adjustmentSoon transfer out- and of) Ahmad(2015 benefits dothat —deeming not their it transfers controversial. are With)Rehdanz(2015 the unreliable report caveat when a median dealing percentage withWTP error biomass, per of household 21%Sundt per for and month the –anranges. transfer error of that falls within previous reported renewable energies in a less developed country. The focus on the Mexican case Draft 7 3 Energy policy3.1 in Mexico Energy Transition: 2013-2018 Mexico’s previous administration (2013-2018) signed important international environmental agreements and pushed ancost energy to reform promote at a a competitive highelectricity market and political across fuel the markets, with whole renewable supplyvironmental energy chain one goals in of of the the Mexico priorities. were En- the set greenhouse to gases (GHG) an emissions unconditional respect 22% toand a reduction specifically baseline a of scenario 31% by GHG 2030, emissions reductionsector by by the the generation same electricity year (updatedConcurrently, 2018 Mexico “General has Law committed on to Climate anand Change”). energy following transition during years. the last Inthe 2015, Mexican government through committed to the 30% Energy of35% clean by Transition electricity 2024 Law by (Mexican 2021 (ETL), 2015 –and “Energyincreased Transition the Law”). production As of a clean electricity result,from at Mexico a 2014 rate to of 4.8% 2018, annualwhich average and 6.5% corresponds reached to a efficient 23.1% co-generationremaining and share nuclear 16.6% power into to and the the renewable electricity sourcespower mix increased (SENER, by of 2019, only 1.0%).2018d over thethe same Renewable electricity period, supply while decreased its contribution fromto to 18% the to reduction 16%. in hydropower Theseof generation. other two However, sources issues the (i.e. relate installed wind, capacity matically geothermic, increased. biomass, solar, For and instance, biogas)up solar has from dra- and 461 biogas MW installed and capacityrespectively 89 went (SENER, MW2018e). in Their 2017bioenergy potential to is estimated 1647 still MW installed quite and capacity large, 217 will solar MW be and in 11,661 2018, MW and 1,478 MW also stems from the fact thatenergy current sovereignty administration over is climate changing and priorities clean to energy goals. Draft 8 In intersection with the clean energy goals, ETL has promoted natural Finally, ETL aims to promote a competitive market across all the supply The following transition steps should allow private companies to compete by 2032, respectively SENER(2018b). Ittion is in worth Mexico noticing is that larger solar(Hancevic than isola- et 5, al. 2017). kWh/m2/day, one of the largest worldwide gas (NG) use in newventional power plants plants under the as argument well that as NG isand modernization cleaner price of than other existing is fossil con- significantly fuels lower.emission power This plants, transition like coal-fired includes power plants. toof From shut an view, aggregated down point it high is expectedfrom closed new down green plants, jobs but at toFor regional instance, be level coal-fired this able power does to plants not compensate in necessarilyare the hold. job expected states to losses of be Coahuila retired and byties, Guerrero 2030, since which plants will and affect coal directly minesin local represent the the communi- region. most important source of jobs chain in the electricity and fuel markets.been In to the let former private case, companies the enter first toThe step power previous has market administration and implemented compete three with successful CFE. GW auctions of to add energy 7 (about 6%solar, of wind, the and projected geothermal installed(SENER, to capacity2019). the in In system 2032) addition in from topromote the the auctions, the following there development 15 are of to two renewable other 20Clean schemes energies. years Energy to Certificates, First, which the wereshares introduction created of of clean to energy ensure market. increasingprojects Second, annual for promotion of the distributed residential generation progress sector. in the country The and latter aof massive alternative electricity program companies has can (see made threat financial small Hancevic viability et, al. 2019). in the rest oftariff the scheme supply would chain, have including been the required to retail let market. private retailers A offer change a in com- Draft 9 petitive portfolio to consumers with astance, large one variety scheme of that characteristics. includes For shares in- ofsold. renewable as However, such part change of will the face electricity tial a electricity high consumption obstacle. in As Mexico mentioned, is residen- through heavily a and horizontally increasing subsidized blockonly tariff pay (IBT) 46% schedule. of thedistribution total On and cost average, retail of households costs. the service Reducingical –i.e., this costs generation, subsidy transmission, would for imply any high administration,IBT polit- schedule so unchanged. the As one expected in previoushave turn maintained and it current usually untouched. administrations maintains the 3.2 New energy sovereignty:New administration 2019- (2019-2024), which was electedjority, in has 2018 changed with radically a vast tonow ma- on new energy sovereignty priorities. – aiming In togovernment control bring particular, the through energy the the industry focus monopoly backand under publicly is decreasing owned imports company of refined (CFE) fuelsof (Gross, the2019). new One direction of has the been firstfederal to signals hold government up argues the that fourth auction renewableneeds indefinitely. energy from The is cheap fossil too fuel expensivebenefits power because some to few connect private companies to, (e.g. the2020).; 2019 M´ejiaand grid Salda˜na Infobae, Although and becuase it only hasit committed is to expected comply to withimportant the do signal clean it has energy been using to goals, more investbuild significant NG new resources fossil than both fuel based in renewable plants, the energies. toand CFE renew to to some Another postpone of the some current powerplants. shutting plants, down Fossils plans, have including also thegovernment been coal-fired to boosted power the by oil important company investments (PEMEX) from and the the building of a major oil Draft 6 Mexico.pdf 10 de Gobierno See report from president L´opez Obrador after his first year of ad- Nevertheless, electric sovereignty is in question still. NG is the main fuel The city of Aguascalientes is the capital of the state with the same name, 6 ministrationgb.cdn.gob.mx/informe/Informe in “Primer Informe de Gobierno” at https://framework- used for electricity generation inmore Mexico than 60% (68%). of the Mexico NGthe currently required U.S., imports to and satisfy domestic the demand(SENER, electricity mainly2018c, sector from 2019). claims more Thisin than trend is the 50% expected coming of to years that remain (SENER, of amount at2018c ). NG a consumption As similar are explained level explainedand above, in cheaper the fuels part high in by levels recent the2018d). years transition and toward the cleaner low NG price in the US3.3 (SENER, Renewable energies inMexico Aguascalientes government follows anicipalities federal have system certain under autonomy which toexclusive competence states legislate of in and the matters mu- federation that andsources. to are spend For not autonomously this the some case, re- support states renewable and energies, municipalities and therefore may green taketives jobs includes further in but steps their are regions. to not Incen- plants limited and/or to their property tier tax companies, exemptionsat training the human to states capital renewable universities, waste on recovering energy and recycling,versely, issues they among others. cannot take Con- direct measures to avoid power plants’ close downs. it has an area ofsemi-arid 385 region, km2 with and a aboutand high 1 a million potential medium-high residents. and potential for an It biomassAguascalientes installed is (IRENA, state located capacity2018; isSENER, in home for,d). 2018a for a solar twoto 250 the MW solar grid parks and already will connected host about additional 570 MW (SENER, 2019). The refinery, which is expected to be ready by 2023. Draft (1) can be expressed as mutually exclusive alternatives, ij U J . The individual is assumed to know 11 , ..., J 2 ij ,  + = 1 ij j x 0 β . Thus, from the researcher’s point of view and once a and = ij U ij  + , ..., I 2 ij , chooses the alternative that provides him/her with the highest V i = = 1 i ij U for Whereas the federal government seems to slow down renewable energy ij his/her own utility function with certainty.fully The observe researcher, each however, cannot linear indirect utility function is assumed, utility. An individual’s indirectU utility from each alternative is denoted as 4 Theoretical and4.1 empirical approaches Random Utility Model The Random Utility Model (RUM) provides theoretical supportical to analysis the empir- of discrete choice experimentspoint (see of, Train 2009). the RUM The is departure that, when faced to state is estimated to havefrom an urban annual solid power waste generation and potential wastewater,region of which is (SENER, 1350 a).2018a TJ high In potential for addition,encourage a the renewable small local energy government further, is for committedsources training instance, to at by state universities, promoting incentivizing human PVand solar re- launching parts a companies, new waste processing facilityuses. for power generation and other progress in the country, othersteps groups forward such as to state support governmentsClimate it could Change. take and CFE to can comply buytration the will this realize ETL energy that and and consumers are eventually General willing federalthe to Law best adminis- pay of on for our it. knowledge Unfortunately, there to studies is to not support any such source policy of recommendations.an information Hence, interesting or our piece previous study to would do be so. individual Draft , 1 (3) (2) X max i U + 1) M is a ( ij x ] (4) is the price preference parameter. } p iJ β 12 } follow a type I extreme value distribu- iJ , where ij , ...... , V 1 column vector representing the alternative-  2 i max i X U ,V 1 i ’s WTP; and alternative-specific attributes and the alternative- , ...... , U +1) V i 2 i { M M ,U 1 chooses i max [ i is a ( U  { p β E max i β U as defined in equation (2). A researcher can only make state- max ) = is individual = i = is the negative of the marginal utility from income. i is the component observed by the researcher; max i max i p U β U represents the purely random heterogeneity that the researcher is ij ( max i V WTP E WTP U ij  Under the assumption that Under the assumptions embedded in equation (1), a researcher cannot The willingness to pay (WTP) for the alternative associated to the highest If an individual chooses the alternative associated to the highest utility, , i.e. ij tion, the expected maximum utility can be calculated through the logsum ments in terms of expected utilities which are calculated over the error term where observe Under the assumption that indirectincome, utility is linear in attributes, including where i.e., utility is expressed as the monetary value of the utility derived from column vector denoting specific intercept, and the preferencesand for the alternative-specific attributes; then the individual unable to observe. specific intercept; Draft (7) (6) has been added q ’s utility is expressed i )) (5) after ∆ b ij q V ( is marginal, and q )) exp J =1 j X max,b i ) situations —where after implies a . The expected CV can be expressed a . U 8 ln ( q  13 − E changes in a non-marginal fashion across is the level of ) a ij q − in equation (5) and, because such a change q V ) ( q ) ij + ∆ V q p exp b ( max,a i β β q q p U J q =1 ( j β β X = exp  ∆ ) and an after ( − b a E ln − J ( ( =1 q j X p p β β 1 1 ) = ln i ]) = − − q = 1 —i.e. when the change in = ) = q [∆ ) = i i max i i.e. is the marginal utility from CV CV MWTP U 7 ( ( ( q (    β E E E E . Introduce the change in b Pioneer derivations of the logsum formula were independently developed by Ben-Akiva Further details can be found in Haab and McConnell(2002). The marginal willingness to pay (MWTP) can be derive from equation Equation (6) reduces to the WTP for a marginal change across alterna- Accordingly, statements involving welfare measures are made in expected q 8 7 as follows to occurs across all alternatives, factor it Equation (7) can be interpreted asattribute the that ratio changes of and the the marginal negative utility of from the the marginal utility from income. all alternatives -i.e. (1973) and (1973). McFadden (5) as follows. Assume attribute tives when ∆ as where formula, terms. For a beforechange ( in the available alternatives—, thetion expected variation value (CV) of due the to compensa- the change in individual Draft (8) 9 is distributed according to a type ] ] ij j j  6= 6= 14 k k ) can be obtained via a conditional logit ik ∀ ∀ is expressed as follows p x 0 ik ik ˆ j β β   ij e x 0 + + J β ∈ e and ij ik k V q P ˆ β − > V = ik ij  ik V + > V e ij ij J ij V ∈ V  e [ [ k chooses alternative P P r P r i = = = ij P A third limitation is that a CL is not fitted to capture correlation over time, (Train A conditional logit (CL) specification faces two limitations to model em- The random parameters logit (RPL) results from adapting the CL model The RPL probabilities are the integrals of standard logit probabilities 9 2009). pirical discrete choice data, (Train 2009).atic variation First, (i.e. a CL taste variation can that representbut is system- not related to random observed taste characteristics) linked variation to (i.e. observed characteristics). Second, differences thebilities in estimation implies of proportional tastes the substitution that CL across proba- cannotmore alternatives realistic —more be patterns flexible, cannot be fitted with a CL model. to incorporate non-systematic heterogeneity inproportional preferences substitution and across discard alternatives. the highly The flexible model RPL that turns can outFadden approximate and to, any Train be2000). random utility a model (Mc- over a density of parameters. That is, keeping in mind equation (8), a RPL 4.2 Econometric model Empirical estimations of the parametersexpected required MWTP in (i.e. the calculation of the I extreme value distribution.individual Under this assumption, the probability that econometric specification. Thethe departure same point as of to establish this theunder empirical theoretical discrete expectations model choice of is modelling the welfare —i.e. measures Draft (9) , with the weights given ) as the mixing function. β β ( f ’s with β 15 dβ ) β ( f ik x 0 β ij e x 0 ). In statistical terms, the weighted average of several J β β ∈ e ( k f P Z ) is a density function. The RPL probability is a weighted average = β ( ij f P The first attribute refers to the source of renewable energy —solar, biomass, The second attribute in our DCE refers to the share of renewable energy 5 Survey methods5.1 and data Discrete choice experiment 1 Table lists the four attributes of ourtheir discrete corresponding choice levels. experiment The (DCE) included and attributessen and closely levels resembling have designs been reported cho- inbefore previous embarking studies, on and the were gathering piloted of final data. or a 50/50 mix. WTP forto residential renewable vary energy depending has been on documented stated the WTP is source higher of for solar thehydropower. and These renewable wind empirical energies energy. results and have lower beenconsolidating In for reported biomass the particular, both and relevant by literature documents (Ma2015), et and al., by individual2015; studiesSundt focusing,et and for al., Rehdanz, instance,2012), on Danish Spaniard (Yang (Gracia and et Italian al., consumers2016), (Vecchiato and American, Tempesta ( Borchers; 2015 etCicia al., et al. , 2007), ). 2012 in current electricity mixunder —10%, the 20% light or of 30%. thethe Mexican ETL These Energy mandates Transition values that Law are 10.9% (ETL). relevant of Specifically, electricity consumption in Mexico comes by the density functions is called a mixedthe logit function. function evaluated at Consequently, different a RPL is a mixture of where of the logit formula evaluated at different values of is a model whose choice probabilities can be expressed in the following form Draft . This attribute takes green jobs 16 The inclusion of source as well as share of renewable energy is key for this The third attribute in our DCE refers to the number of new jobs cre- Similar to Amador et(2013), al. our DCE presents the price attribute as from clean energies for large consumers.generation In in addition, Mexico 30% must of come total from electricity clean energies by 2021. study’s purpose. Respondents may beable electricity willing because to they pay value renewable a energiesHowever, premium regardless it their for is source. renew- also possible thaterences this for premium a arises specific from source respondents’consumers of pref- have renewable preferences energy. for A a third largersame alternative share is time of that that renewable they electricity atboth attach the attributes, a our premium DCE to is athree designed hypotheses specific to holds. empirically source.Borchers test et al.(2007), which ByGraciaet one et including al.(2016) of al.(2012), and those areYang instances ofshare previous of renewable studies energy that as also attributes include in their source DCE. and ated in the renewable sector —which we call values 100, 1,000, or 2,000.attribute Strictly of speaking, the the electricity creation service. ofare jobs altruistic This is in attribute not this aims an respectfunction to —i.e. of explore whether the whether respondents’ number indirect of utilityspondents’ jobs is utility that positively a the depends on renewable number sectorthe of may adoption green create. of jobs, renewable then If energies pursuing may re- yieldof a less double greenhouse emissions dividend per —generation KWh andby the residential creation of consumers. jobs A that new are valued jobsin attribute DCE has studying previously stated been preferences included forBergmann residential et renewable al., electricity2006; (e.g. Soli˜noet al., ; 2012 Yoo and Ready, ). 2014 an increase in respondents’ self-reported bimonthly electricity billand —5%, 40%. 20%, When incorporated in theresponding empirical hypothetical analysis, bimonthly electricity we bill calculate by the applying cor- change the to percentage the self-reported bill. The hypothetical increase in bill comes from Draft 17 The scenarios of our DCE were designed according to a orthogonal main In terms of range of age, respondents of our DCE are younger than an opt-in extra fee (holding tariffsincrease unchanged) in that the would share compensate of for electricity an generatedwe with deem renewable more energies realistic —which in a contextsubsidized where in tariffs Mexico. are heavily and horizontally effects strategy (see Aizaki, 2012). Thewhich were DCE presented contains to nine respondents. choice sets, Eachtives choice all described set of in includes terms three of alterna- fourrespondents attributes, were and asked a status to quo choose alternative.1 one The illustrates alternative a in choice each set. choice set. Figure 5.2 Data collection Face-to-face implementation of our DCE wasto conducted November, 2019. through September Respondents were approached randomlysuch in as public shopping spaces malls and thecity. main We made square sure in that downtown of respondentscity Aguascalientes that were adults contribute to residing paying in the Aguascalientes electricity bill. 5.3 Descriptive statistics Once missing values have beenspondents. dropped, our2 Table samplereports is mean, composedimum standard of of deviation, 199 variables minimum re- describing and respondents’ max- teristics. and To his/her put household’s our charac- its sample’s last characteristics in column context, the table2 official(see reportsINEGI, statistics).2018 in for In household comparison headsAguascalientes, to in our official Aguascalientes statistics sample of is household composedversus heads by 26%), in and a a higher smaller share proportion of of females married (42% people (44% versushousehold 58%). heads in Aguascalientes. The proportion of respondents that are Draft 18 A bigger proportion of our respondents report higher incomes at the Two averages at the household level in our sample are almost identical to In comparison to the official statistic, a smaller proportion of respondents younger than 30 years old is higher inportion our of sample (43% respondents versus between 12%). 30 Theidentical and pro- to 40 the years official proportion old of in householdsample our heads sample includes (22% versus is smaller 21%). almost shares Our of(16% respondents versus i) 23%); between ii) 40 between to 50older 50 to than years 60 60 old years years old old (14% (6% versus versus 20%); 24%). and iii) household level in comparisonlevel. to While official the statistics official at(MXP) proportion is the for 83%, household income this head belowMXP proportion 8,000 8,000 is Mexican and 39% pesos MXP in 15,000,is our the 33%; sample; official and for statistic for income isour income 10% between sample’s above and is MXP our 28%. sample’s 15,000, The thecloser proportion official to of number the respondents is official with 7% a statisticversus 41%. full-time and for jobs Around household 72% is heads of our inof respondents Aguascalientes report the affording —48% electricity the bill full amount —whichthe implies electricity that bill a hold part-time share jobs. of respondents affording official numbers. In our sample,that around is owned 70% by of a respondents householdmembers live member is in and 3.93. a the house average These number numbers ofrespectively. in household the official statistics are 69% and 3.91, pay electricity on bimonthly basisaverage self-reported —61% electricity bill in is our MXP 455 sampleaverage —on hypothetical versus a bimonthly bimonthly 89%. basis. bill The implied The 659. by The the average self-reported DCE bill scenarios is455 is bigger versus than MXP MXP the 306—, and official a statistic two-tailed —MXP t-testthe rejects difference the null between hypothesis these that numbers isa p-value zero of with 0.0019 99% that of corresponds to confidence a —with t-test statistic of 3.15. We highlight Draft 19 The first specification (I) in table3 has been estimated on the entire work- At the individual level, our sample is composed by a smaller share of ing sample (199 respondents). Consideringof that household 3.93 members, we is have the re-estimated averageincludes the number RPL respondents on whose a households sample that haverespondents). only less This than is eight the memberscheck second (195 the specification sensitivity (II) of the reportedthird parameter in associated specification table3. to (III) the To excludes pricebill attribute, 33 falls the choice below sets or inrespondents above which —which the the are 1% not hypothetical tails.specification the in same The3 table as resultingis in estimated sample on specification contains data (II). 195 from The 191 respondents fourth that pass 6 Results 6.1 Econometric specifications 3 Table reports estimates from sixcations. random In parameter all logit six (RPL)parameters with specifications, specifi- exception of normal the distributions parameterwhich associated are is to assumed assumed the price fixed. for attribute all that our sample is similarcharacteristics to such official as statistics number of when household it membersis comes and owned to whether by the household’s a house household member. married people; and bigger sharesbelong of females, to and higher younger respondents incomesadults who households. that contribute As (at our leastnot necessarily partially) respondents household to heads, of the paying interest differences the in are are individuals’ electricity not characteristics bill unexpected. and The higherincome self-reported electricity imply bill that and our household’s average sample one is in composed Aguascalientes. In by section8,differences households we in richer discuss terms than implications of of external the these validity of our results and conclusions. Draft 20 We highlight five features from table3. First, point estimates of all but Features 1 and 4 of table3 suggest that a few point estimates depend The variation in those parameters is associated to the exclusion of (upper (II)+(III) exclusion criteria. The fifthsets specification in (IV) which excludes the 193 hypothetical choice billa falls sample below with or 177 above thesample respondents. 5% of tails, 174 The leaving respondents sixth resulting specificationcriteria. from implementing is (II)+(IV) estimated exclusion on a the price parameter are similarparameters across but specifications. the one Second, associated allcally to estimated significant. 20% Third, of the renewable levelparameter energy of statistical remains are significance the statisti- of same each across estimated is specifications always for above all a attributes 95% —andviation confidence. it of most Fourth, parameters point are estimates similarbeing of across the specifications standard —the standard de- exceptions deviations for2,000 parameters new associated green jobs. with biomasssolar, Fifth, and and standard 30% deviations of associated renewablefications. energy to are We status deem statistically quo, features significant 2 acrossin and speci- 3 our as DCE evidence are that relevantthat all to unobserved attributes respondents; heterogeneity included is and at features placeconditional 4 —and logit and RPL (CL) should 5, specifications. be as preferred evidence to on the composition ofestimated the in sample a under range analysis. that-5.59 goes (sixth The specification from price in -2.46 table3) parameter (first —withrespectively. is specification standard Estimates errors of in of the3) table standard 0.27 deviation to and ofto 0.46, the biomass parameter associated go from a0.33 statistically that insignificant is 0.23 significant (first at specification)estimates 95% to of of a the confidence standard (sixth deviation specification). ofgreen jobs the Similarly, go parameter from associate a statistically to insignificant 2,000at 0.19 new 99% to a confidence. 0.37 that is significant Draft 21 When it comes to the sign of specific parameters, we highlight that the and lower) tails of theprice hypothetical parameter, bill’s point estimates distribution. remainentire For similar sample the across and specifications case specifications on of that the the 8 exclude members; i) households ii) with 1% morejump tails; than from and -2.46 (on iii) entire thesedropped sample) (fifth two to specification); criteria -5.54 and happens together. the point whenhouseholds estimate the with However, becomes more 5% than -5.59 a tails 8 when members are the areSimilarly, also the excluded (sixth standard specification). deviation ofsensitive the to parameter associated the to exclusion biomassparameter of is associated the to 5% 2,000 tails. newtails green are jobs The excluded is and standard insignificant remains deviation unless significanttails. of in the specifications 1% the that drop the 5% negative sign of the status quo parametercurrent implies that situation respondents in dislike the terms ofmix renewable as energies. reference Taking category, the theimplies solar-biomass positive that sign solar of isbiomass the preferred parameter solar over implies energy the that parameter mix; thedering mix and of preferences is the consistent preferred with negative over results signet biomass from al., previous of —an2012; studies or- the Gracia (e.g. etCicia al., of2012; Vecchiato electricity and from, Tempesta 2015). renewable sources Taking 10% of as reference the category, parameter the associated positive to sign share of 30% 30% from is renewable preferred sources overreference a implies category, share the that of positive a 10%. signs Taking2,000 of 100 new parameters new green jobs, green associated respectively, jobs to imply as that 1,000of respondents and prefer 1,000 the and creation 2,000 green jobs over the creation of 100 jobs. Draft 10 22 The smaller estimates are direct consequence of the higher absolute value of the price We focus our attention on the most conservative welfare estimates —i.e. 10 parameter yielded by thesimilarity corresponding among specification. pointhigher estimates From absolute equation of value (7), of all and the but given price the the parameter results price in parameter, a it smaller MWTP. can be seen that a Thus respondents’ loss in utility forvalued remaining at in MXP the 899 bimonthly. status When quoWTP it situation is comes is to MXP solar 34 energy,interval respondents’ on includes the a zero, bimonthly and basis theat zero —a a null 10% WTP hypothesis of can whose significance. onlywhen 95% be it Respondents confidence rejected comes report to a biomassand bimonthly energy solar loss in energies. of comparison The MXP to WTP 51 astatistically for 50/50 significant. a mix share of In of biomass contrast, 20%74 of the on renewable WTP bimonthly energy for basis. is a not is The share WTP MXP of for 40 30% the on is creation bimonthlygreen MXP basis; of jobs. 1,000 and new MXP green 59 jobs for the creation of6.3 2,000 new Robustness checks We have also estimatedsix conditional samples logit than (CL) those specifications ofappendix. the on RPL the Parameter specifications same estimates reported from in table3 thesein CL the specifications are reported 6.2 Welfare estimates For each attribute in our DCE,to we pay have (MWTP) estimated —expressed the(MXP). marginal as 4 Table willingness reports bimonthly, six 2018 sets of thousand MWTP estimates Mexicaninterval. and their pesos 95% Each confidence set oftable3. MWTP estimates corresponds to one specification in those that imply theare smallest reported MWTP in for the all lastthat attributes. column excludes the of 5% table4, These tails and estimates and arise the from households with the more specification than 8 members. Draft 23 10. MWTP for a 1% increase in the share of renewable energy is . 0 p < The corresponding CL specifications and their MWTP estimates are re- In addition, we have estimated six RPL specifications that assume share MXP 5 which implies a WTP oftwice MXP as 150 for much a as share the ofin 30% MXP which 1,000 74 is new around reported green in jobs table4.close, is respectively MXP MWTP to 30, for the or MXP an for 40 increase 2,000 and jobs MXP is 59 MXP reported 60, inported which table4. in are tables A5 remain and similarA6. to those previously Orders discussed. of magnitude and significance levels in table A1, and theA2. corresponding Due MWTP to estimates are limitationsonly reported of be the in used CL table to (see check section4),of that estimates the magnitude from welfare than a estimates those CL are arisingof around can from the table a sameA2, order RPL. we Focusing highlightyielded on that by the most the last MWTP column RPL estimatesthe specification, are status with similar quo exception to —which those ofthe the the RPL. CL one Similarly yields associated to a to welfareloss third estimates in utility of from from the the the RPL, value statusbiomass table resulting quo (MXPA2 (value from 46); atreports MXP no a 298, statisticallyand bimonthly) positive significant and MWTP MWTP from for for solar aand energy share the (MXP creation 46), of of a 20%; 1,000 share (MXP of 31) 30% and (MXP 2,000 85), (MXP 63)of new renewable green energy jobs. and number ofThe new parameter green jobs estimates are from continuousestimates these variables. are RPL reported and in their tables tention correspondingA3 on welfare and theA4, last respectively. columnestimates of Focusing in table our comparisonA4, at- to we those highlightloss in the in table4. utility similarity is For in estimated the point at casecase MXP of of 751 status (versus biomass, quo, MXP the 899 the loss in isSolar4). table valued is at For the MXP MXP 54 30 (versus (versuswith MXP MXP 51). 34) MWTP for —and it is statistically significant only Draft and the corresponding tariff is signifi- 24 Demanda de alto consumo Correspondingly, electricity consumption is inferred by inverting 11 vious studies If annual consumption exceeds 3000 Kwh, the household is reclassified as a high- Once electricity consumption is inferred, estimated bimonthly WTP is 5 Table reports WTP per kWh under nine scenarios that we deem of 11 cantly higher than tier-3high-consuming tariff. category. We are not able to identify any of our respondents in the consuming household ( the tariff formula. added to the self-reported bimonthly bill,the and inferred the consumption. total amount This is estimation divided ofconsumers by WTP know per and kWh keep assumes that in i) to mind deciding the three-tier electricity block consumption; tariff ii)tax when consumers amounts it pay and comes attention not to topremium before- after-tax on amounts; top of and their iii)levels. current had bills, consumers they paid would a not change consumption public policy interest. Allenergy, nine and scenarios the corresponding assume WTP a is 30%reported calculated share in based last on of column MWTP renewable of numbers table4.achieved A with first biomass scenario energy assumes (I). that Noticescenario this that implies share estimation the is of subtraction WTP of under MXP this 51 from MXP 74 which is the utility 6.4 This paper’s results in comparison to thoseTo in put pre- this paper’spapers’ welfare estimates, estimates it is in convenient to context carryper with out kWh comparisons respect under in specific terms to of scenarios. WTP previous electricity Thus, bill, based we on first the retrieve after-tax electricity consumption, self-reported kWh. measured in In terms Aguascalientes, of consumers faceAt a the time three-tier of block the increasing survey, first tariff. to tier charge 150 was kWh; MXP 0.823 the to second consumptionkWh tier up and charged u to MXP 280 0.996 kWh; to and the280 consumption third Kwh. tier above charged 150 2.912 for consumption over Draft 10. The . 0 p < Then5 table reports the 12 25 Assuming an exchange rate of 19.22 MXP/USD which was the average closing price A 30% share of renewable electricity generated through biomass is valued For the three scenarios involving solar energy under the assumption that 12 in 2019 according to Macrotrends(2020). that 30% renewable share produces —thefrom subtraction is biomass due energy to in the comparison desutility second to and a third 50/50 scenarios biomass assume (I) andand and, solar respectively, 2,000 mix. the new creation The green of 1,000 jobs.of The renewable fourth energy scenario assumes is thatfor reached the this with 30% solar source share energy ofinto but energy consideration no that, (II). according premium This to is estimates noMWTP in paid premium for the solar last assumption column energy aims of is table4, to statistically take significant only with bimonthly WTP as a proportionreported of and hypothetical. the In average its bimonthly last billWTP two columns, per —both table5 kWh self- reports —in bimonthly 2018 MXP and USD (cents), respectively. at USD 1.20 on aconsumers’ bimonthly stated basis. benefits When are 1,000 USD new 3.28;added, and green stated when jobs benefits 2,000 are are new created, estimated green at jobsrespectively USD are 5%, 4.27. 14%, These and numbers 18% represent, ofand the 12% average of self-reported the bill; hypothetical or bill. 3%,are 10%, equivalent, In respectively, terms to of 3.09 USD cents, per 4.33 cents, kWh, and these 4.93 numbers cents. no premium is paid for thisfor energy the source, 30% the share; bimonthly and WTP USD is 5.93 USD (USD 3.85 6.92) when 1,000 (2,000) are added. fifth and sixth scenarios assumeand (II) 2,000 and, new respectively, green the jobs. creation Theof of seventh renewable 1,000 scenario energy assumes is that reached the withis 30% solar paid share energy for and this a source premium(III) of of energy and, MXP respectively, (III). 34 the The creation eight of5 and 1,000first and ninth reports 2,000 scenarios the new assume bimonthly green WTP jobs. under—both each Table in of the 2018 nine describes MXP scenarios and USD, respectively. Draft 26 +10] interval reported by Ma et al.(2015). , 10 − Our per kWh welfare estimates fall well within the range of values re- In terms of monthly household WTP, our estimates fall well below USD 7 Public policyThe implications national business sector has expressedegy concerns pursuing about energy the sovereignty Federal due strat- to itspetitiveness negative of implications for the the energy com- sectorsectors and (Gonzalez, potential2019 ; spillovers, toNava other2020).ternational economic stakeholders This and apprehension discussions isof have shared eight been developed by hold countries in- among toconcerns diplomats explore to the the best Federal administration way (Proceso to, 2020). communicate such Scholars and ana- In temrs of cents per kWh,respectively. these numbers When translate a premium into for 4.68, solar 5.98, energybecomes and is 6.52, USD added, 5.62, the USD bimonthly WTP 7.70, andper USD kWh, 8.69, respectively. these In numbers terms are of equivalent WTP to 5.74, 6.99, and 7.59,ported respectively. by previous studies. The studiesprovide summarizing useful the information previous to literature carrythat out a a majority comparison. ofMa WTP et valuesSundt(2015) al. falling and find between Rehdanz(2015) -10 report cents an andkWh +10 average estimates of cents (3.09) and 3.18 for cents. 30% share Ourto produced lowest the with per average biomass reported is not by onlyourSundt highest close and bimonthly(2015), Rehdanz per kWh but estimate alsosolar, (7.59) along for both with 30% fall share within produced the with [ 13.13 (average) and USD 11.67(2015). (median) reported Our by lowestSundtand monthly and our household Rehdanz WTP highest is isWTP equivalent USD should to not 4.34. 0.60 be surprising cents, Thisdeveloped as countries relative most and of magnitude Mexico previous in has studiesmiddle only the have income. recently focused household being on classified as upper Draft 27 These concerns have become stronger as discussions begin on how to over- In this context, it is reasonable to highlight the estimations suggesting In a similar vein, findings from this study suggest that a policy simultane- lysts have also expressed that theelectricity planned companies renovation may of be state-owned too oil costly, and andfrom in achieving addition climate may deviate change Mexico goals2019). , (Davis 2019; Stromsta, 2019; Martin, come the post-coronavirus, recession2020; (Casta˜neda-Morales , Levy 2020). The volatility in oil prices makeriskier investments —as in state-owned reflected companies by even theand its re-evaluation oil of company. the However, credit inannouncement face ranking from of the for the federal Mexico evidence, government, such itcurrent as would everyday administration be new would naive to follow think anyjust that policy energy recommendations transition. toward Hence, a othertake groups steps such as forward state to governments support could ance it. partially This the slowing kind down of of initiatives the would energy counterbal- transition. that Mexico could increase theboost health its of economy its by population pursuing andet a al.(2019) simultaneously strong report climate that policy. Mexico For could5 instance, save billionFlores more in than public 25,000 health costs livesemissions and by —as 2030 USD it if has it committed pursues under mitigationof the of Paris such greenhouse Agreement. reductions Three-quarters wouldtransport arise and from industry. addressing three sectors —electricity, ously boosting renewable energies and the creation ofthe green support jobs would of i) have residential electricitythe consumers; post-coronavirus ii) recession; and help iii) incurrently provide recovering employed a from in just fossil-fuel transition based forpasses industries. workers policies A that just support transition workerslivelihoods encom- and are at families risk as whose the jobs, world pursues2017). incomes, sustainable pathways and , (Rosemberg Draft 28 Our findings suggest that a policy achieving a 30% share of renewable A policy encouraging re-training of individuals to transition to the re- When designing a policy that boosts both renewable energies and creation biomass energy and the creationtricity of consumers at 1,000 USD new 3.28 green onto jobs bimonthly basis the is —2.1 creation valued of of by which the correspond elec- ministration jobs. provides USD To 195 put on this a number29 monthly basis in years to context, old people the who between Federal 18 enrollwere ad- and in going training to learn programs skills (STPS, renewable that2020). sector, facilitate a If his/her household the participation in1% in trainee our the of sample energy the would USD contribute withEquivalently, 195 the around that contribution the from Federal195 100 administration on transfer households a to would bimonthly basis. a cover trainee. the USD newable energy industry wouldcoronavirus also recession. support As many the peoplepensation is recovery during expected from the to re-training the loss period their would post- helpworkers jobs, in transition a avoiding to com- that displaced informal sectortor jobs. is A undesirable transition because to i) the—negatively it informal impacting would sec- not only decrease newcomers wages but inhold also an the those informal informal who job; permanently sector and ii) itindividuals implies and a loss the in State returns from havethe investments done that informal on sector specific —a skills waste from thattransition which are to a not the recovery is useful informal difficult in sector oncedo because workers not it return has to been the documented formal that sector they easily (Levy, 2020). of green jobs, it is important to keepjobs in mind differs that depending the potential on number of the new Renewable Energy source Agency(2015) of estimates energy. thatof while For solar instance, one photovoltaic GigawattInternational power (GW) can create onbiomass average power 8,250 can new create jobs, 31,000 one new GWis jobs. of that Another jobs element created to by keep biomass in energy mind generation are mostly permanent jobs Draft pay for the creation of new actually 29 We want to highlight that the justification of a policy as the one described 8 Limitations andThis further paper’s research findings comethis with study’s four external validity. caveats. We cannotuniverse A claim of first that residential consumers our limitation in results refers Aguascalientes holdthat City. to for our While the sample we highlight is similarcharacteristics to such official as statistics number when of it membersby comes and a to whether household households’ the member, house we issample also owned is recognize composed by that a at smallerof the share females, individual of younger, level married and people; our cause and higher our bigger incomes respondents shares are respondents. adults that contribute We (at least argue partially) that, to paying be- green jobs or even fortion an is increase particularly in pertinent renewable in energy timesevidence share. of that, post-coronavirus This in recession. clarifica- developed There countries, is publica support transition of to policies a encouraging renewable energypayment of portfolio a has actually premium translated foring into green post-coronavirus the electricity recession, however, (e.g. prioritiesKnapp of residential etMexico consumers may al., in have2020). changed —at Dur- least temporarily—to and expect it them may to be monetarily unrealistic contributeever, to this the temporary creation condition should of not greenand take jobs. the long-term How- focus goals away from such medium- astimately, the the reduction slow of down greenhouse of emissionspolicy climate and, is change. ul- reasonable not Thus, onlyin a on the just this grounds energy document of transition but theinternational also stated societies. benefits in reported terms of wider benefits to the Mexican and but those created by solar energy generation are in general temporaryhere ones. does not need tosures rely as the on willingness the of interpretation consumers of to estimated welfare mea- Draft 30 The implication from this caveat is that further research needs to be A second caveat refers to the absence in our DCE of attributes that The implication from not including trade-offs in terms of environmental the electricity bill, these differencesstatistics with should respect not to be household surprising. headsthe official However, external this validity argument of does our not sample. clarify done to learn whetherfuture our explorations results of hold residential at electricitymind demand, a an we issue wider suggest that scale. will to helpviewing keep official clarify In in household the heads carrying representativeness may out of not provide findings:holds’ a energy-related clear inter- decisions. picture about In house- ongoing work, Charlier(2020) and argue Martinez-Cruz that there is evidenceliteratures from suggesting both that engineering household and heads’ psychological are characteristics not and necessarily preferences the most importantenergy ones consumption when and it efficiency. comes to a household’s capture potential negative externalities or outcomestion arising of from electricity the with genera- renewable energy.tal In benefits some associated contexts, the with environmen- transitionsources from may have fossil trade-offs fuels in to termstal renewable of impacts. energy non-negligible Previous negative DCE environmen- studiescontext have of documented landscape these impacts, impacts trade-offs on inBotelho the the fauna et and al., flora, and; 2018 noiseKoseniusalready (e.g. and built, wind Ollikainen power2013). plant, Mexico’sthat In president his the has administration context recently will of emphasized prioritize an when the considering potential the granting environmental of damages permits(Infobae, to2020). build energy renewable projects externalities is that welfare estimates reportedonce these in externalities this are study incorporated. may This bewith paper, smaller a however, specific does project not deal to whichThus a we clear negative deem externality appropriate can be to attached. abstract from negative externalities in this Draft 31 Also, a few previous studies have explore consumers preferences in con- A third caveat of this study refers to the possibility that Mexican resi- A fourth caveat of this study is that, while it documents unobserved application. texts in which higher sharesservices of in renewable terms energies of may stability implyherent due lower-quality in to the the renewable variability in energies thethis (Longo respect, electricity et we flow highlight al., in- that2008; the existenceMerknewable of et energy a trade-off al., and between stability).2019 share has of recently In re- beenElliston(2018). challenged by AlsoDiesendorf and for the(2018)Pardo have Mexican proposed case, a transitionVidal-Amaro strategy and tohigh a Sheinbaum- as system 75% with a of share renewable as deem energies appropriate in to the abstract electricity portfolio. fromservices. potential Thus changes we in also the stability of the dential consumers in general, and respondentshave of changed our their DCE in priorities particular due may bility to implies the that coronavirus further pandemic. researchto This is which possi- need preferences on have shifted; i)rary and whether or ii) and permanent. whether the such While direction changes thisworth are study noting tempo- that has the referred possibility to of thein a Mexican terms permanent of case, change renewable in it energy societal is and priorities across climate the change world. policies may have occurred heterogeneity in preferences viadoes random not parameters explore the logit factors specifications, associatedthis with it paper such heterogeneity. has The been focus on of exploringenergies whether and a green simultaneous boost jobs of ispreferences. renewable justified based Future on research residential willnomic consumers’ focus characteristics, average and on knowledge documenting and whetherexplain attitudes the socioeco- towards variation climate in change preferences. 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Master’s thesis, Norwegian University of Life Sciences, for green electricity: A review of thesources contingent of valuation error. literature and its CRE quehttps://www.elfinanciero.com.mx/economia/concamin-advierte-sobre- buscaproyecto-de-la-cre-que-busca-modificar-reglas-sobre-suministro-electrico. modificar reglasAccessed: 2020-04-06. sobre suministro electrico. cessed: 2020-01-28. energias-renovables-son-demasiado-caras-bartlett/1341235 Journal of applied Econometrics 15 supply security and green electricity:britain. Evidence from germany and great masiado caras: Bartlett. Rehdanz, K., C. Schr¨oder,D. Narita, and T. Okubo (2017). Public pref- Proceso (2020). Revelan reuni´on de diplom´aticos de ocho Pokhrel, K. R. (2016). Consumer willingnes to pay for renewable energy: a Oerlemans, L. A., K.-Y. Chan, and J. Volschenk (2016). Willingness to pay Nava, D. (2020). Concamin advierte sobre proyecto de la McFadden, D. and K. Train (2000). Mixed mnl models for discrete response. Merk, C., K. Rehdanz, and C. Schr¨oder(2019). How consumers trade off M´ejia, X. and I. E. Salda˜na (2019). Energ´ıas renovables son de- Draft Renewable . Secretar´ıa . Secretar´ıade . Accessed: 2020- . Secretar´ıade En- . Accessed: 2019-12-06. , 798–806. , 877–887. 38 Energy Policy 41 https://stanleycenter.org/publications/pab/ Prospectiva del Sector Electrico 2018-2032 Prospectiva de Gas Natural 2018-2032 Reporte de Avance de Energ´ıasLimpias Primer Semestre Prospectiva de Energias Renovables 2018-2032 Programa de Desarrollo del Nacional Sistema 2019- El´ectrico https://dgel.energia.gob.mx/azel/ . Secretar´ıade Energ´ıa. . Secretar´ıade Energ´ıa. https://jovenesconstruyendoelfuturo.stps.gob.mx/.04-06. Accessed: 2020- RosembergPABStrengtheningJustTransition417.pdf climate governance. limpias. 03-11. de Energ´ıa. and Sustainable Energy Reviews 44 analysis on the willingness-to-pay for renewable energy use. 2018 2033 electricity with forest biomass: Consistency and paymentin timeframe choice effects experiments. erg´ıa. Energ´ıa. SENER (2018b). STPS (2020). Programa Jovenes Construyendo el Futuro. Rosemberg, A. (2017). Strengthening just transition policies in international SENER (2018a). Atlas nacional de zonas con alto potencial de energ´ıas SENER (2018c). Soon, J.-J. and S.-A. Ahmad (2015). Willingly or grudgingly? a meta- SENER (2018d). SENER (2018e). SENER (2019). Soli˜no,M., B. A. Farizo, M. X. V´azquez,and A. Prada (2012). Generating Draft , 1–8. . Cambridge , 521–531. Energy Economics 51 , 101–114. 39 Energy Policy 97 , 168–179. Discrete choice methods with simulation Energy Economics 42 (1), 47–66. Energy 88 Journal of Sustainable Development of Energy, Water and Envi- energy technology. electricity: A meta-analysis of the literature. ronment Systems 6 renewable energy source: Preference for electricity productsof when the renewable share energy increases. der AMLO.mexico-set-to-miss-clean-energy-target-under-amlo. https://www.greentechmedia.com/articles/read/woodmac- Accessed: 2020-04-06. contracts including renewable energy:experiments. A marketing analysis with choice from fossil fuels tosystem. renewable energy sources in the mexican electricity university press. Train, K. E. (2009). Yoo, J. and R. C. Ready (2014). Preference heterogeneity for renewable Sundt, S. and K. Rehdanz (2015). Consumers’ willingness to pay for green Yang, Y., H. S. Solgaard, and W. Haider (2016). Wind, hydro or mixed Stromsta, K. (2019). WoodMac: Mexico Set to Miss Clean Energy Target Un- Vecchiato, D. and T. Tempesta (2015). Public preferences for electricity Vidal-Amaro, J. J. and C. Sheinbaum-Pardo (2018). A transition strategy Draft 40 Figure 1: Example of a choice set Figures Draft a b 41 Variable Mean Std.Dev. Min. Max. Aguascalientes 1 if female 0.417 0.494 0.000 1.000 0.260 1 if married 0.442 0.498 0.000 1.000 0.580 1 if full-time job 0.477 0.501 0.000 1.000 0.410 1 if 30 to1 40 if years 40 old to1 50 if years 0.216 50 old to 60 years 0.156 old 0.413 0.136 0.364 0.000 0.343 0.000 1.000 0.000 1.000 1.000 0.210 0.230 0.200 1 if older than 60 years old 0.065 0.248 0.000 1.000 0.240 1 if younger than 30 years old 0.427 0.496 0.000 1.000 0.120 Attributes Levels Number of household members 3.930 1.890 1.000 15.000 3.910 Table 1: Attributes and levels in DCE 1 if bill is paid on bimonthly basis 0.613 0.488 0.000 1.000 0.890 1 if monthly income up to 8K (MXP) 0.392 0.489 0.000 1.000 0.830 1 if monthly income above 15K (MXP) 0.276 0.448 0.000 1.000 0.070 1 if affords full amount of electricity bill 0.719 0.451 0.000 1.000 – 1 if house is owned by a household member 0.704 0.458 0.000 1.000 0.690 Household’s characteristics Respondent’s characteristics Self-reported electricity bill (thousand MXP) 0.455 0.667 0.004 7.500 0.306 1 if monthly income between 8K and 15K (MXP) 0.332 0.472 0.000 1.000 0.100 Source of renewable energy% of renewable energyNew in green current jobs electricity (new mix% jobs increase in in renewable self-reported energy bimonthly sector) electric bill 100, 1,000, and 10%, 2,000 20%, and 30% 5%, 20%, and 40% Solar, biomass, and mix (50/50) Source: INEGI(2018). A two-tailed t-test rejects at 99% the null hypothesis that the difference between sample mean and population parameter is zero —p-value is 0.0019 for a t-test statistic of 3.15. Hypothetical bimonthly electricity bill (thousand MXP) 0.659 0.920 0.008 7.875 – b a Table 2: Sample’shousehold heads summary in statistics Aguascalientes (n=199), and official statistics for Tables

Draft

otne nnx page next on Continued

il(huadM eo)(.7)(.8)(.2)(.4)(.5)(0.459) (0.456) (0.343) (0.324) (0.287) (0.273) pesos) MX (thousand bill

yohtclbmnhyeetiiy-2.465 electricity bimonthly Hypothetical -5.592 -5.545 -3.720 -3.420 -2.652

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

009)(.71 001)(.76 006)(0.0804) (0.0763) (0.0736) (0.0712) (0.0701) (0.0695)

f200nwgenjobs green new 2,000 if 1 0.308 0.313 0.328 0.335 0.353 0.329

c ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

000)(.72 001)(.74 007)(0.0775) (0.0770) (0.0734) (0.0718) (0.0712) (0.0705)

f100nwgenjobs green new 1,000 if 0.196 0.211 0.209 0.226 0.220 0.224

c ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗

oe rmrnwbesources renewable from comes 007)(.93 003)(.93 008)(0.0997) (0.0982) (0.0943) (0.0933) (0.0903) (0.0877)

b

f3%o lcrct 0.407 electricity of 30% if 1 0.413 0.425 0.417 0.413 0.423

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

oe rmrnwbesources renewable from comes 000)(.73 001)(.78 005)(0.0768) (0.0757) (0.0738) (0.0714) (0.0713) (0.0700)

b f2%o lcrct 008 005 006 009 001 -0.0643 -0.0615 -0.0490 -0.0368 -0.0353 -0.0288 electricity of 20% if 1

42

002)(.74 003)(.71 008)(0.0818) (0.0782) (0.0761) (0.0732) (0.0714) (0.0727)

-0.284 -0.269 -0.251 -0.236 -0.243 -0.235 biomass if 1

∗∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗ a

003)(.86 005)(.89 004)(0.0955) (0.0948) (0.0879) (0.0851) (0.0856) (0.0831)

fsolar if 1 0.192 0.206 0.210 0.217 0.204 0.188

a ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗

071 068 064 074 066 (0.687) (0.686) (0.724) (0.654) (0.678) (0.781)

fsau u pin-4.891 option quo status if 1 -5.028 -4.969 -5.401 -4.937 -5.101

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

Mean

(II) (III) (IV)

(l)

hmembers hh 8 > %tails 1% (II)+(III) %tails 5% (II)+(IV)

d d

niesample Entire

Excluding Excluding Excluding al :Rno aaee oi pcfiain nsae choices stated on specifications Logit Parameter Random 3: Table

Draft

otne nnx page next on Continued

I 86837. 70835. 3823318.1 3388.2 3653.3 3760.8 3776.2 3886.8 AIC

l-984-831-854-816-691-1644.1 -1679.1 -1811.6 -1865.4 -1873.1 -1928.4 ll

bevtos76 0073 8869 6287 6390 6888 7032 7020 7164 Observations

epnet 9 9 9 9 7 174 177 191 195 195 199 Respondents

010 017 017 010 014 (0.104) (0.144) (0.130) (0.127) (0.147) (0.130)

.9 .6 0.228 0.163 0.195 jobs green new 2,000 if 1 0.371 0.256 0.275

c ∗∗∗ ∗ ∗∗ ∗

009)(.0)(.0)(.1)(.1)(0.112) (0.114) (0.118) (0.107) (0.107) (0.0991)

.57002 .40002 .890.0150 0.0899 0.0620 0.0420 0.0127 0.0577 jobs green new 1,000 if 1

c

oe rmrnwbesources renewable from comes 008)(.0)(.0)(.0)(.0)(0.110) (0.107) (0.104) (0.103) (0.101) (0.0982)

b

f3%o lcrct 0.829 electricity of 30% if 1 0.951 0.947 0.940 0.906 0.867

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

oe rmrnwbesources renewable from comes 019 010 010 018 016 (0.158) (0.176) (0.128) (0.130) (0.140) (0.139)

b

f2%o lcrct .48009 .70014009 0.0645 0.0899 0.154 0.0790 0.0995 0.0418 electricity of 20% if 1

43

016 020 028 017 020 (0.144) (0.230) (0.137) (0.248) (0.220) (0.196)

fbiomass if 1 .3 .1 .5 0.288 0.150 0.116 0.237 .1 0.333 0.214

a ∗∗ ∗∗

005)(.98 009)(.99 011 (0.106) (0.111) (0.0969) (0.0896) (0.0948) (0.0956)

fsolar if 1 0.847 0.843 0.779 0.734 0.743 0.715

a ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

062 056 049 063 072 (0.614) (0.732) (0.623) (0.489) (0.536) (0.632)

fsau u pin4.528 option quo status if 1 5.162 4.827 4.700 4.087 4.093

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

SD

(II) (III) (IV)

(l)

hmembers hh 8 > %tails 1% (II)+(III) %tails 5% (II)+(IV)

d d

niesample Entire

Excluding Excluding Excluding al :Rno aaee oi pcfiain nsae choices stated on specifications Logit Parameter Random 3: Table Draft

44

trfr obt oe n pe al ftedsrbto fhpteia iotl lcrct bill. electricity bimonthly hypothetical of distribution the of tails upper and lower both to refers It

d

eeec aeoy 0 e re jobs. green new 100 category: Reference

c

eeec aeoy 0 feetiiycmsfo eeal sources. renewable from comes electricity of 10% category: Reference

b

eeec aeoy 05 obnto fslradbiomass. and solar of combination 50/50 category: Reference

a

tnaderr nparentheses. in errors Standard 10, . 0 < p 05, . 0 < p 001. . 0 < p

∗ ∗∗ ∗∗∗

I 99937. 83735. 4973419.3 3489.7 3755.8 3863.7 3879.1 3989.9 BIC

(II) (III) (IV)

(l)

hmembers hh 8 > %tails 1% (II)+(III) %tails 5% (II)+(IV)

d d

niesample Entire

Excluding Excluding Excluding al :Rno aaee oi pcfiain nsae choices stated on specifications Logit Parameter Random 3: Table

Draft

10. . 0 < p with rejected be can hypothesis null Zero

a

pe on .9 .8 .4 .3 .9 0.092 0.096 0.137 0.145 0.186 0.199 Bound Upper

oe on .6 .6 .5 .5 .3 0.030 0.037 0.051 0.055 0.065 0.068 Bound Lower

,0 e re os0150180060000040.059 0.064 0.090 0.096 0.118 0.125 jobs green new 2,000

pe on .4 .4 .0 .0 .6 0.069 0.069 0.104 0.107 0.140 0.144 Bound Upper

oe on .2 .2 .2 .2 .1 0.013 0.013 0.023 0.021 0.028 0.024 Bound Lower

,0 e re os0000000010010000.040 0.040 0.061 0.061 0.080 0.080 jobs green new 1,000

pe on .5 .4 .8 .6 .1 0.114 0.116 0.169 0.182 0.243 0.254 Bound Upper

oe on .9 .9 .6 .6 .4 0.040 0.044 0.065 0.069 0.095 0.097 Bound Lower

0 frnwbeeeg .6 .5 .2 .1 .7 0.074 0.077 0.112 0.121 0.159 0.165 energy renewable of 30%

pe on .4 .4 .3 .2 .1 0.017 0.017 0.028 0.033 0.043 0.048 Bound Upper

oe on 008-.6 002-.5 008-0.038 -0.038 -0.052 -0.052 -0.066 -0.068 Bound Lower

0 frnwbeeeg 002-.1 001-.1 001-0.011 -0.011 -0.013 -0.011 -0.013 -0.012 energy renewable of 20%

pe on 002-.4 000-.3 003-0.024 -0.023 -0.030 -0.030 -0.043 -0.042 Bound Upper

oe on 017-.4 012-.0 007-0.080 -0.077 -0.109 -0.112 -0.148 -0.157 Bound Lower

ims 005-.9 009-.6 009-0.051 -0.049 -0.068 -0.069 -0.091 -0.095 Biomass

45

pe on .5 .4 .1 .0 .7 0.069 0.072 0.106 0.117 0.147 0.150 Bound Upper

oe on .0 .1 .1 .0 .0 0.000 0.003 0.009 0.013 0.011 0.007 Bound Lower

oa .7 .7 .6 .5 .3 0.034 0.037 0.056 0.063 0.077 0.076 Solar a

pe on 121-.4 107-.1 065-0.633 -0.625 -1.016 -1.007 -1.342 -1.271 Bound Upper

oe on 294-.8 199-.7 122-1.208 -1.212 -1.973 -1.979 -2.687 -2.904 Bound Lower

ttsqo-.8 193-.4 142-.9 -0.899 -0.896 -1.452 -1.443 -1.923 -1.984 quo Status

(II) (III) (IV)

(I)

WPfor MWTP hmembers hh 8 > (II)+(III) tails 1% (II)+(IV) tails 5%

niesample Entire

Excluding Excluding Excluding

3 table in reported specifications Logit Parameter Random from resulting al :Mria ilnns opy(iotl,21 huadMxcnpss n 5 ofiec intervals confidence 95% and pesos) Mexican thousand 2018 (bimonthly, pay to willingness Marginal 4: Table

Draft

il n v suigta W eanucagd X e W r acltdudrec scenario. each under calculated are KWh per MXP unchanged, remain kWh that assuming iv) and bill;

saddt h self-reported the to added is ) 5 table in column (first WTP bimonthly scenario, given a for iii) inferred; are kWh consumed ii)

ae natrtxsl-eotdbl,i osmr r rtasge otercrepnigtariff; corresponding their to assigned first are consumers i) bill, self-reported after-tax on based , 6.4 section in described As

apema fipidbmnhyWPprkh suigcnue W eanucagdi nseai si place. in is scenario an if unchanged remain kWh consumed assuming kWh, per WTP bimonthly implied of mean Sample

b

). 2020 , Macrotrends (see 2019 in price closing average the was which MXP/USD 19.22 of rate exchange an Assuming

a

II ,0 e re os1786 .702 .67.59 1.46 0.25 0.37 8.69 167 jobs green new 2,000 + (III)

II ,0 e re os1877 .302 .46.99 1.34 0.22 0.33 7.70 148 jobs green new 1,000 + (III)

0 hr rmslr(I)1856 .401 .05.74 1.10 0.16 0.24 5.62 108 (III) solar from share 30%

suiga3 X rmu o solar for premium MXP 34 a Assuming

I)+200nwgenjb 3 .202 .012 6.52 1.25 0.20 0.29 6.92 133 jobs green new 2,000 + (II)

I)+100nwgenjb 1 .302 .711 5.93 1.14 0.17 0.25 5.93 114 jobs green new 1,000 + (II)

0 hr rmslr(I 438 .601 .04.68 0.90 0.11 0.16 3.85 74 (II) solar from share 30%

suign rmu o solar for premium no Assuming 46

I ,0 e re os8 .701 .209 4.93 0.95 0.12 0.18 4.27 82 jobs green new 2,000 + (I)

I ,0 e re os6 .801 .008 4.33 0.83 0.10 0.14 3.28 63 jobs green new 1,000 + (I)

0 hr rmboas()2 .000 .305 3.09 0.59 0.03 0.05 1.20 23 (I) biomass from share 30%

suigdstlt f5 X rmbiomass from MXP 51 of desutility Assuming

cnro 21 X)USD MXP) (2018 Scenarios MP45 MP69 21 X)UD(cents) USD MXP) (2018 659) (MXP 455) (MXP

a a

iotl T efrpre yohtclprkWh per hypothetical self-reported WTP Bimonthly

b

vrg iotl bill bimonthly average

spooto of proportion As

4 table of column last in reported estimates on based al :Bmnhywligest a o eeal nrysaernwbesuc/re osseais—calculated scenarios jobs source/green share/renewable energy renewable for pay to willingness Bimonthly 5: Table Draft 47 Appendix

trfr obt oe n pe al ftedsrbto fhpteia iotl lcrct bill. electricity bimonthly hypothetical of distribution the of tails upper and lower both to refers It

d

eeec aeoy 0 e re jobs. green new 100 category: Reference

c

eeec aeoy 0 feetiiycmsfo eeal sources. renewable from comes electricity of 10% category: Reference

b

eeec aeoy 05 obnto fslradbiomass. and solar of combination 50/50 category: Reference

Draft a

001. . 0 < p 05, . 0 < p 10, . 0 < p parentheses. in errors Standard

∗∗∗ ∗∗ ∗

I 47647. 33145. 9953875.3 3949.5 4256.8 4363.1 4375.3 4477.6 BIC

I 42542. 38340. 8543821.3 3895.4 4202.1 4308.3 4320.5 4422.5 AIC

l-233-122-161-030-997-1902.6 -1939.7 -2093.0 -2146.1 -2152.2 -2203.3 ll

bevtos76 0073 8869 6287 6390 6888 7032 7020 7164 Observations

epnet 9 9 9 9 7 174 177 191 195 195 199 Respondents

il(huadM eo)(.4)(.5)(.8)(.9)(.9)(0.395) (0.395) (0.298) (0.285) (0.250) (0.241) pesos) MX (thousand bill

yohtclbmnhyeetiiy-2.178 electricity bimonthly Hypothetical -4.708 -4.706 -3.243 -3.024 -2.309

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

003)(.63 004)(.63 007)(0.0683) (0.0677) (0.0653) (0.0645) (0.0643) (0.0636)

f200nwgenjobs green new 2,000 if 1 0.269 0.270 0.278 0.280 0.300 0.294

c ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

005)(.64 006)(.64 000)(0.0707) (0.0702) (0.0674) (0.0667) (0.0664) (0.0657)

f100nwgenjobs green new 1,000 if 1 0.137 0.145 0.138 0.147 0.140 0.145

c ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗

oe rmrnwbesources renewable from comes 001)(.68 002)(.67 005)(0.0656) (0.0651) (0.0627) (0.0620) (0.0618) (0.0612)

b

f3%o lcrct 0.409 electricity of 30% if 1 0.400 0.392 0.411 0.414 0.406

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

oe rmrnwbesources renewable from comes 007)(.64 008)(.64 001)(0.0726) (0.0719) (0.0694) (0.0686) (0.0684) (0.0677) b

48 f2%o lcrct oe 000 008 003 002 008 -0.0826 -0.0789 -0.0620 -0.0532 -0.0488 -0.0405 comes electricity of 20% if 1

006)(.66 007)(.66 001)(0.0717) (0.0712) (0.0686) (0.0678) (0.0676) (0.0669)

-0.218 -0.214 -0.199 -0.192 -0.203 -0.196 biomass if 1

a ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗

000)(.63 001)(.63 004)(0.0652) (0.0646) (0.0623) (0.0616) (0.0613) (0.0607)

fsolar if 1 0.217 0.229 0.232 0.238 0.223 0.230

a ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

011 012 012 014 018 (0.119) (0.118) (0.114) (0.112) (0.112) (0.111)

fsau u pin-1.211 option quo status if 1 -1.403 -1.373 -1.307 -1.268 -1.244

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

(IV) (III) (II)

(I)

(II)+(IV) (II)+(III) members hh 8 > %tails 5% tails 1%

d d

niesample Entire

Excluding Excluding Excluding al 1 odtoa oi pcfiain nsae choices stated on specifications Logit Conditional A1: Table

pe on .9 .8 .4 .3 .9 0.097 0.098 0.136 0.144 0.187 0.197 Bound Upper

oe on .6 .6 .5 .4 .3 0.035 0.036 0.047 0.051 0.063 0.066 Bound Lower

,0 e re os0140170020060040.063 0.064 0.086 Draft 0.092 0.117 0.124 jobs green new 2,000

pe on .2 .2 .9 .8 .6 0.062 0.060 0.089 0.092 0.125 0.128 Bound Upper

oe on .0 .0 .0 .0 .0 0.001 0.000 0.004 0.002 0.006 0.004 Bound Lower

,0 e re os0030030060050000.031 0.030 0.045 0.046 0.063 0.063 jobs green new 1,000

pe on .6 .5 .9 .7 .1 0.119 0.117 0.177 0.191 0.251 0.268 Bound Upper

oe on .2 .2 .9 .8 .5 0.058 0.057 0.088 0.095 0.120 0.129 Bound Lower

0 frnwbeeeg .8 .7 .3 .2 .8 0.085 0.083 0.127 0.137 0.176 0.188 energy renewable of 30%

pe on .4 .4 .3 .2 .1 0.014 0.014 0.025 0.030 0.041 0.047 Bound Upper

oe on 003-.8 004-.6 008-0.049 -0.048 -0.063 -0.064 -0.083 -0.083 Bound Lower

0 frnwbeeeg 009-.2 008-.1 007-0.018 -0.017 -0.019 -0.018 -0.021 -0.019 energy renewable of 20%

pe on 005-.3 003-.2 008-0.019 -0.018 -0.024 -0.023 -0.035 -0.035 Bound Upper

oe on 017-.5 010-.0 006-0.077 -0.076 -0.105 -0.110 -0.151 -0.157 Bound Lower

ims 000-.8 004-.6 005-0.046 -0.045 -0.061 -0.064 -0.088 -0.090 Biomass

pe on .7 .5 .2 .1 .7 0.077 0.079 0.116 0.125 0.159 0.171 Bound Upper 49

oe on .4 .4 .3 .3 .2 0.018 0.021 0.033 0.037 0.043 0.049 Bound Lower

oa .0 .9 .7 .7 .4 0.046 0.049 0.071 0.079 0.097 0.106 Solar

pe on 046-.2 035-.2 026-0.241 -0.236 -0.324 -0.335 -0.424 -0.436 Bound Upper

oe on 072-.9 059-.0 030-0.368 -0.360 -0.506 -0.529 -0.695 -0.722 Bound Lower

ttsqo-.5 059-.1 043-.9 -0.298 -0.292 -0.403 -0.419 -0.539 -0.556 quo Status

(II) (III) (IV)

(I)

WPfor MWTP hmembers hh 8 > (II)+(III) tails 1% (II)+(IV) tails 5%

niesample Entire

Excluding Excluding Excluding

A1 table in reported specifications Logit Conditional from resulting al 2 agnlwligest a bmnhy 08tosn eia eo)ad9%cndneintervals confidence 95% and pesos) Mexican thousand 2018 (bimonthly, pay to willingness Marginal A2: Table

Draft

otne nnx page next on Continued

0.876 0.824 0.759 0.751 0.743 0.702 solar if 1

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ a

045 056 040 040 054 (0.398) (0.574) (0.490) (0.470) (0.506) (0.475)

fsau u pin3.731 option quo status if 1 4.457 4.179 4.108 3.932 3.855

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

SD

il(huadM eo)(.7)(.8)(.2)(.4)(.5)(0.465) (0.458) (0.342) (0.326) (0.286) (0.279) pesos) MX (thousand bill

yohtclbmnhyeetiiy-2.544 electricity bimonthly Hypothetical -2.618 -3.458 -3.652 -5.537 -5.699

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

006)(.36 007)(.36 008)(0.0439) (0.0389) (0.0376) (0.0371) (0.0366) (0.0365)

0.183 0.174 0.159 0.158 0.152 0.147 (thousands) jobs green New

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ c

0053 0054 0051 0054 0058 (0.00625) (0.00568) (0.00544) (0.00531) (0.00524) (0.00503)

frnwbeenergy renewable of % 0.0242 0.0238 0.0246 0.0243 0.0254 0.0229

b ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

002)(.79 003)(.78 000)(0.0832) (0.0806) (0.0728) (0.0733) (0.0719) (0.0724) 50

fbiomass if 1 -0.234 -0.239 -0.233 -0.240 -0.271 -0.278

a ∗∗ ∗∗∗ ∗∗ ∗∗∗ ∗∗∗ ∗∗∗

002)(.86 004)(.81 002)(0.0998) (0.0928) (0.0861) (0.0849) (0.0846) (0.0822)

fsolar if 1 0.140 0.198 0.198 0.215 0.190 0.173

a ∗∗ ∗∗ ∗∗ ∗∗ ∗∗

068 063 061 067 072 (0.600) (0.712) (0.677) (0.681) (0.653) (0.638)

fsau u pin-4.348 option quo status if 1 -4.595 -4.777 -4.192 -4.801 -4.603

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

Mean

(III) (II) (IV)

(l)

hmembers hh 8 > (II)+(III) tails 1% %tails 5% (II)+(IV)

d d

niesample Entire

Excluding Excluding Excluding

e re osatiue r continuous are attributes jobs green new al 3 admPrmtrLgtseictoso ttdcocs—suigta frnwbeeeg and energy renewable of % that —assuming choices stated on specifications Logit Parameter Random A3: Table

Draft

trfr obt oe n pe al ftedsrbto fhpteia iotl lcrct bill. electricity bimonthly hypothetical of distribution the of tails upper and lower both to refers It

d

. A3 table in reported specifications in variable continuous a assumed is jobs green New

c

. A3 table in reported specifications in variable continuous a assumed is energy renewable of %

b

eeec aeoy 05 obnto fslradbiomass. and solar of combination 50/50 category: Reference

a

001. . 0 < p 05, . 0 < p 10, . 0 < p parentheses. in errors Standard

∗∗∗ ∗∗ ∗

I 98036. 82035. 4923392.0 3479.2 3750.2 3842.0 3861.0 3958.0 BIC

I 82338. 76537. 4493317.8 3404.9 3675.0 3766.5 3785.5 3882.3 AIC

l-902-818-823-865-614-1647.9 -1691.4 -1826.5 -1872.3 -1881.8 -1930.2 ll

bevtos76 0073 8869 6287 6390 6888 7032 7020 7164 Observations

epnet 9 9 9 9 7 174 177 191 195 195 199 Respondents 006)(.4)(.99 016 021 (0.0700) (0.201) (0.106) (0.0989) (0.140) (0.0866)

51

e re os(thousands) jobs green New .2 .76013017006 0.256 0.0867 0.117 0.113 0.0766 0.121

c ∗∗∗

0059 0067 0066 0073 0068 (0.00705) (0.00608) (0.00713) (0.00656) (0.00647) (0.00589)

frnwbeenergy renewable of % 0.0663 0.0564 0.0576 0.0578 0.0551 0.0522

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ b

013 016 013 012 021 (0.309) (0.201) (0.182) (0.193) (0.196) (0.153)

fbiomass if 1 .4 .9 .2 .4 .8 0.172 0.287 0.143 0.225 0.191 0.245

a

007)(.94 005)(.98 013 (0.0999) (0.103) (0.0958) (0.0950) (0.0954) (0.0875)

(III) (II) (IV)

(l)

hmembers hh 8 > (II)+(III) tails 1% %tails 5% (II)+(IV)

d d

niesample Entire

Excluding Excluding Excluding

e re osatiue r continuous are attributes jobs green new al 3 admPrmtrLgtseictoso ttdcocs—suigta frnwbeeeg and energy renewable of % that —assuming choices stated on specifications Logit Parameter Random A3: Table

Draft

eonl yohsscnb eetdwith rejected be can hypothesis null Zero 10. . 0 < p

a

pe on .9 .9 .7 .6 .4 0.047 0.048 0.069 0.072 0.093 0.093 Bound Upper

oe on .3 .3 .2 .2 .1 0.016 0.018 0.025 0.026 0.033 0.033 Bound Lower

e re os(huad)0090090070050020.030 0.032 0.045 0.047 0.059 0.059 (thousands) jobs green New

pe on .1 .1 .1 .1 .0 0.007 0.007 0.011 0.011 0.014 0.015 Bound Upper

oe on .0 .0 .0 .0 .0 0.003 0.003 0.004 0.004 0.005 0.006 Bound Lower

frnwbeeeg .10090070070050.005 0.005 0.007 0.007 0.009 0.01 energy renewable of %

pe on 008-.4-.3-.3-.2 -0.025 -0.021 -0.03 -0.03 -0.04 -0.038 Bound Upper

oe on 013-.5 013-.0 007-0.084 -0.077 -0.108 -0.113 -0.153 -0.153 Bound Lower

ims 002-.9 00 007-.4 -0.054 -0.049 -0.067 -0.07 -0.093 -0.092 Biomass

pe on .4 .40170120070.063 0.067 0.102 0.107 0.14 0.148 Bound Upper

52

oe on .2 .1 .0 .1005-0.001 0.005 0.01 0.009 0.013 0.021 Bound Lower

oa .8 .7 .5 .5 .3 0.030 0.036 0.055 0.058 0.075 0.082 Solar a

pe on 117-.5 082-.4 059-0.526 -0.569 -0.842 -0.822 -1.259 -1.117 Bound Upper

oe on 245-.7 193-.3 105-1.013 -1.085 -2.032 -1.963 -2.578 -2.485 Bound Lower

ttsqo-.2 183-.4-.8 088-0.751 -0.808 -1.389 -1.34 -1.833 -1.723 quo Status

(II) (III) (IV)

(I)

WPfor MWTP hmembers hh 8 > (II)+(III) tails 1% (II)+(IV) tails 5%

niesample Entire

Excluding Excluding Excluding

A3 table in reported specifications Logit Parameter Random from resulting al 4 agnlwligest a bmnhy 08tosn eia eo)ad9%cndneintervals confidence 95% and pesos) Mexican thousand 2018 (bimonthly, pay to willingness Marginal A4: Table

trfr obt oe n pe al ftedsrbto fhpteia iotl lcrct bill. electricity bimonthly hypothetical of distribution the of tails upper and lower both to refers It

d

. A5 table in reported specifications in variable continuous a assumed is jobs green New

c

. A5 table in reported specifications in variable continuous a assumed is energy renewable of %

b

Draft biomass. and solar of combination 50/50 category: Reference a

001. . 0 < p 05, . 0 < p 10, . 0 < p parentheses. in errors Standard

∗∗∗ ∗∗ ∗

I 58141. 38847. 9853915.1 3988.5 4273.6 4378.8 4416.9 4518.1 BIC

I 46847. 37643. 9783874.5 3947.8 4232.6 4337.6 4375.7 4476.8 AIC

l-224-118-128-103-979-1931.3 -1967.9 -2110.3 -2162.8 -2181.8 -2232.4 ll

bevtos73 0276 9465 6347 6450 6924 7068 7092 7236 Observations

epnet 9 9 9 9 7 174 177 191 195 195 199 Respondents

lcrct il(huadM eo)(.3)(.4)(.8)(.9)(.9)(0.392) (0.391) (0.293) (0.281) (0.247) (0.238) pesos) MX (thousand bill electricity

yohtclbmnhy-2.148 bimonthly Hypothetical -4.601 -4.602 -3.173 -2.963 -2.274

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

002)(.33 003)(.39 005)(0.0354) (0.0351) (0.0339) (0.0335) (0.0333) (0.0329)

e re os(thousands) jobs green New 0.130 0.130 0.137 0.137 0.148 0.145

c ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

0034 0037 0039 0033 0034 (0.00337) (0.00334) (0.00323) (0.00319) (0.00317) (0.00314)

frnwbeenergy renewable of % 0.0223 0.0222 0.0228 0.0227 0.0216 0.0221

b ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ 005)(.65 007)(.67 000)(0.0704) (0.0700) (0.0677) (0.0670) (0.0665) (0.0659)

53

-0.223 -0.219 -0.214 -0.207 -0.210 -0.203 biomass if 1

∗∗ ∗∗ ∗∗ ∗∗ ∗∗ ∗∗ a

009)(.61 000)(.62 003)(0.0639) (0.0634) (0.0612) (0.0606) (0.0601) (0.0595)

fsolar if 1 0.202 0.213 0.214 0.220 0.213 0.219

a ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

011 013 013 015 018 (0.130) (0.128) (0.125) (0.123) (0.123) (0.121)

fsau u pin-0.907 option quo status if 1 -1.082 -1.057 -0.991 -0.951 -0.942

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗

(IV) (III) (II)

(I)

(II)+(IV) (II)+(III) members hh 8 > %tails 5% tails 1%

d d

niesample Entire

Excluding Excluding Excluding

osatiue r continuous are attributes jobs al 5 odtoa oi pcfiain nsae hie asmn ht%o eeal nryadnwgreen new and energy renewable of % that —assuming choices stated on specifications Logit Conditional A5: Table

Draft

pe on .9 .9 .7 .6 .4 0.049 0.049 0.067 0.072 0.092 0.097 Bound Upper

oe on .3 .30050030080.017 0.018 0.023 0.025 0.03 0.032 Bound Lower

e re os(huad)0010070060030020.031 0.032 0.043 0.046 0.057 0.061 (thousands) jobs green New

pe on .1 .1 .1 .10060.007 0.006 0.01 0.011 0.014 0.015 Bound Upper

oe on .0 .0 .0 .0 .0 0.003 0.003 0.005 0.005 0.007 0.007 Bound Lower

frnwbeeeg .100 .0 .0 .0 0.005 0.005 0.007 0.008 0.01 0.01 energy renewable of %

pe on 004-.3 006-.2 008-0.019 -0.018 -0.026 -0.026 -0.035 -0.034 Bound Upper

oe on 011-.5 016-.1 008-0.079 -0.078 -0.111 -0.116 -0.155 -0.161 Bound Lower

ims 004-.9 00 008-.4 -0.048 -0.048 -0.068 -0.07 -0.092 -0.094 Biomass

pe on .60180170170040.072 0.074 0.107 0.117 0.148 0.16 Bound Upper

oe on .50050070020010.018 0.021 0.032 0.037 0.045 0.05 Bound Lower 54

oa .0 .9 .7 .6 .4 0.044 0.046 0.067 0.074 0.094 0.102 Solar

pe on 033-.9 024-.3-.7 -0.176 -0.172 -0.23 -0.234 -0.299 -0.303 Bound Upper

oe on 054-.5 044-.0 027-0.303 -0.297 -0.409 -0.424 -0.559 -0.574 Bound Lower

ttsqo-.2 044-.2 032-.3-0.235 -0.23 -0.312 -0.321 -0.414 -0.423 quo Status

(II) (III) (IV)

(I)

WPfor MWTP hmembers hh 8 > (II)+(III) tails 1% (II)+(IV) tails 5%

niesample Entire

Excluding Excluding Excluding

A5 table in reported specifications Logit Conditional from resulting al 6 agnlwligest a bmnhy 08tosn eia eo)ad9%cndneintervals confidence 95% and pesos) Mexican thousand 2018 (bimonthly, pay to willingness Marginal A6: Table