Balancing the scales: gendered impacts and policy responses to oil price shocks in Spain

Economic Systems Research ISSN: 0953-5314 (Print) 1469-5758 (Online) Journal homepage: www.tandfonline.com/journals/cesr20 Balancing the scales: gendered impacts and policy responses to oil price shocks in Spain Eva Alonso-Epelde, María Victoría Román de Lara, María Moyano-Reina, Xaquín García-Muros, Manuel Tomás, Mikel González-Eguino & Iñaki Arto To cite this article: Eva Alonso-Epelde, María Victoría Román de Lara, María Moyano-Reina, Xaquín García-Muros, Manuel Tomás, Mikel González-Eguino & Iñaki Arto (20 Feb 2025): Balancing the scales: gendered impacts and policy responses to oil price shocks in Spain, Economic Systems Research, DOI: 10.1080/09535314.2024.2445718 To link to this article: https://doi.org/10.1080/09535314.2024.2445718 © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. View supplementary material Published online: 20 Feb 2025. Submit your article to this journal Article views: 938 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=cesr20 ECONOMIC SYSTEMS RESEARCH https://doi.org/10.1080/09535314.2024.2445718 Balancing the scales: gendered impacts and policy responses to oil price shocks in Spain Eva Alonso-Epelde a,b, María Victoría Román de Lara a, María Moyano-Reina a, Xaquín García-Muros a,c,d, Manuel Tomás a,b, Mikel González-Eguino a,b,dand Iñaki Arto a aBasque Centre for Climate Change (BC3), Scientific Campus of the University of the Basque Country, Leioa, Spain; bDepartment of Economic Analysis, University of the Basque Country (UPV/EHU), Bilbao, Spain; cMassachusetts Institute of Technology (MIT), MIT Joint Program on the Science and Policy of Global Change, Cambridge, MA, USA; dIkerbasque, Basque Foundation for Science, Bilbao, Spain ABSTRACT This paper analyses Spanish households’ vulnerability to oil price shocks from a gender perspective and explores different compensatory policies to reduce it. The methodological approach combines an input–output price model and a microsimulation model based on the Household Budget Survey. The paper studies the impacts of the recent surge in oil prices using four scenarios based on different alleviation measures (discounts on fuels, subsidies for public transport, and a combination of the two). Gender implications are analyzed considering the gender of the household reference person and the household feminization degree. The results show that oil price shocks have a more significant impact on those households with greater mobility needs, dependence on private transport, and less accessibility to public transport. Among the policy responses, subsidizing public transport would be the most effective policy from an economic point of view and the fairest from a gender and social justice perspective. ARTICLE HISTORY Received 30 September 2023 In final form 17 December 2024 KEYWORDS Oil price shock; vulnerability; gender; distributional impacts 1. Introduction Transportationisacritical aspectof dailylife,andaccesstotransportationservicesisessential for individuals to participate fully in social and economic activities (Cass et al., 2005; Coote & Percy, 2020;Kenyonetal.,2003;Lucas,2019). In the last decades, transportation systems have experienced a relevant transformation thanks to the availability of cheap (fossil) fuels and motorized vehicles. Motorization of transport has played an important role in configuring the current complex supply chains and the spatial design of human settlements, with the proliferation of sparse cities and investments in infrastructure to connect them. CONTACT Eva Alonso-Epelde [email protected] Basque Centre for Climate Change (BC3), Scientific Campus of the University of the Basque Country, Building 1, 1st floor, Sarriena s/n, 48940 Leioa, Spain; Department of Economic Analysis, University of the Basque Country (UPV/EHU), Avenida Lehendakari Aguirre 83, 48015 Bilbao, Spain Supplemental data for this article can be accessed online at https://doi.org/10.1080/09535314.2024.2445718. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 2E. ALONSO-EPELDE ET AL. Research has found that modern transportation systems have different implications for women and men as they are also influenced by gender roles (Grieco, 1995;Jonesetal., 1983; Loukaitou-Sideris, 2020;Rosenbloom&Burns,1994; Sánchez de Madariaga, 2013; Turner & Fouracre, 1995; Turner & Grieco, 2000). Traditional work/family-related gender roles have contributed to shaping the differences between women’s and men’s transportation needs and preferences. For example, women are more likely to make multiple stops on short trips for both work and family care tasks (Gordon et al., 1989;Guiliano,1979; Hanson & Johnston, 1985;Ng&Acker,2018;Pickup,1985;Rosenbloom&Burns,1994; Rutherford & Wekerle, 1989;SilveiraNetoetal.,2015).Womenalsotendtorelymoreon non-motorizedtransportandpublictransportation since they are more affordable (Adeel et al., 2017;Hortelanoetal.,2021; Tran & Schlyter, 2010). However, despite these differences in transportation habits and needs between men and women, the gender dimension hasbeenignoredinthedesignoftransportpoliciesoverthepastdecades 1(Levy, 2015; McDowell, 1983). Women have to invest more money, time, and effort in meeting their mobility needs, which means that many are in a situation of multiple vulnerabilities, due to either a lack of accessibility or the affordability of transport goods and services. Access to services, such as healthcare or education, and opportunities, for instance, employment or cultural events, mightbelimitedwhenthecostoftransportationaccountsforahighproportionofpeople’sdisposableincome(Kuttler&Moraglio,2021). Therefore, high transportation prices can reinforce transport/energy poverty, structural poverty, and social exclusion (Lucas, 2012). This is especially acute for women since they tend to have lower incomes and increases in transportation costs have a greater impact on their ability to afford to meet some basic needs. However, studies have found that several strategies can be implemented to reduce gender inequality in transport, such as investing in public transportation and favoring transport policies that consider women’s specific needs and preferences (Levy, 2015; Loukaitou-Sideris, 2020). One key characteristic of the current transport system is its heavy reliance on oil. According to the International Energy Agency (Brand et al., 2025; Kanaboshi et al., 2021; Yu et al., 2022), more than 90% of the world’s transportation is fueled by oil.2Recent events, such as the Russia–Ukraine war and the post-pandemic economic recovery, have tightened the oil market, leading to a surge in energy prices. This has brought about asymmetric impacts on households both between and within countries (Guan et al., 2023). In the short term, the energy crisis has hit households mainly through two channels. On the one hand, families have faced higher energy bills (direct impact). On the other hand, the prices of non-energy goods have risen due to the increased cost of intermediate inputs that rely on fossil fuels (indirect impact). Thus, if costs are passed on in final prices, the level and structure of households’ consumption expenditure shape the distributional effects on society. Several studies have investigated the unequal impact of oil price shocks on households’ welfare. Kpodar and Liu (2022) analyzed a rich database of retail fuel prices to observe the 1In recent years, the gender–transport link has begun to be studied in greater depth (Casado-Díaz et al., 2023; Farré et al., 2023; Le Barbanchon et al., 2021), but the gender approach has not yet been fully integrated in a transversal way into this research area. 2https://www.iea.org/data-and-statistics/data-product/world-energy-balances ECONOMIC SYSTEMS RESEARCH 3 changesinconsumerpriceinflation.Theyfoundthat,asfuelpricesincrease,familiesexperience a decline in purchasing power, but the resulting distributional impact on society is progressive. Guan et al. (2023) combined a multi-regional input–output model and household consumption surveys to assess the direct and indirect impacts of the energy crisis on households in 116 countries. Using a set of energy price scenarios, they confirmed that the cost of living has risen sharply for households worldwide. They observed that the pressure of increased prices is unevenly distributed in society, with many households at risk of beingpushedintopoverty.Tianetal.(2024), using similar techniques and data, assessed the susceptibility of older people to energy price hikes in developed countries compared with younger groups. They concluded that older people have larger per capita direct and indirect energy footprints, making them more vulnerable than younger groups in times of energy crisis. In this context, however, little attention has been paid to how oil price shocks affect men and women. Previous research has pointed out that the consumption habits of men and women differ considerably, influencing their energy dependency and environmental footprint(CarlssonKanyamaetal.,2021;Osorioetal.,2024). Indeed, women tend to rely more on domestic energy because they are often responsible for care tasks within their homes (Eurostat, 2024;Finch&Groves,2022). They also typically have less access to private vehicles, so they rely on public transportation more often (Casado-Díaz et al., 2023; Næss, 2008). Additionally, due to their lower incomes, women are more likely to face energy poverty and encounter financial barriers to the adoption of more sustainable technologies (Borza, 2012). Therefore, it is necessary to conduct gender-based analyses of distributional impacts to implement inclusive public policies that help to close the gender gap in times of energy crisis and during the transition to a sustainable economy (Alonso-Epelde et al., 2024). To fill this gap, we analyzed the extent to which energy price spikes and measures to buffer them affect men and women differently. The case study is based on the recent global surge in oil prices experienced in 2022. In particular, we studied the impact of increased prices on Spanish households and the effect of various policy packages implemented by the Spanish Government to mitigate it. After the price of gasoline and diesel surpassed the barrier of e2/liter in 2022 (Pérez, 2022), the Spanish Government applied a transitory universal discount of 20 euro cents per liter to transport fuels at the pump (Jefatura de Estado del Gobierno de España, 2022). In parallel, the central government, in collaboration with other regional and local administrations, subsidized public transport. Four scenarios were therefore considered to assess the direct and indirect impacts of the oil price shock and compensation schemes such as subsidies for oil consumption, public transport, and a combination of the two. Since gender underrepresentation in research is particularly pronounced in modeling studies (van Soest et al., 2019), especially in multisectoral-multiregional frameworks, this paper contributes to closing this gap by integrating gender as both a dimension of analysis and an outcome of an input–output (I–O) model. In fact, our metrological approach combines an I–O price model and a microsimulation model based on Spain’s Household Budget Survey (HBS). This allowed us to account for the short-term effects of an oil price shock and the policy measures that aim to compensate affected industries’ final consumers before they adjust their behavior (Guan et al., 2023). 4E. ALONSO-EPELDE ET AL. Furthermore, similar to Osorio et al. (2024), in studying carbon footprint inequalities, gender-based distributional impacts were analyzed by classifying households according to their feminization degree, that is, their share of female members. These results were compared with those found by applying the standard method used in the previous literature, which classifies households into two groups according to the gender of the person of reference (the person who makes the largest contribution to the household budget). We used both criteria and compared the results to check whether the classification method conditioned our findings. To the best of our knowledge, this has not been considered previously in studying the distributional impacts of price shocks. Therestofthepaperisstructuredasfollows:Section2describesthemethodologyand the scenarios designed for the case study, Section 3 analyzes and discusses the results, and Section4summarizesthestudy’smainconclusionsandmakesrecommendationsforfuture research. 2. Methodology and data In this research, we linked an I–O price model with a microsimulation model based on the HBS to calculate the direct and indirect impacts of the increase in oil prices on Spanish households. The price model calculates how changes in the costs of specific industries in the economy affect the prices of final goods and services, assuming that sectors translate all their additional costs into prices (pass-through of 100%). In this case, the change in cost is a shock to the price of crude oil, which is imported and affects all the sectors of the Spanish economy that use this product as input in their production processes. This shock produces a cascading effect of rising costs across the Spanish economy that is finally reflected in the prices of different goods and services. The effect of the new (higher) prices was later introduced into the microsimulation model to determine how much that change affects the expenditures of different households. The results were aggregated depending on the gender of the reference person of the households and the degree of feminization of the households to discern whether the gender of its members significantly changes the impact of higher prices. 2.1. The price model The starting point of the model was the product-by-product I–O table published by the Spanish National Statistics Institute for the last year available, that is, 2015 (INE, 2015b), including the undisclosed CPA-COICOP and valuation matrices (trade and transport margins and net taxes on products) elaborated by the Spanish National Statistics Institute. According to the data in the I–O table, the output of each industry is equal to the sum of its consumption of goods and services from other industries (domestic and imported) and its primary inputs (net taxes on products, compensation of employees, other net taxes on production, and gross operating surplus)3: x=iZ+iM+v(1) 3Bold-faced lower-case letters are used to indicate vectors, bold-faced capital letters indicate matrices, and italic lowercase letters indicate scalars (including elements of a vector or matrix). Subscripts indicate industries. Vectors are columns by definition, and row vectors are obtained by transposition, denoted by a prime (e.g.x). Diagonal matrices are denoted as(e.g., ˆ x). ECONOMIC SYSTEMS RESEARCH 5 where xis a column vector containing the output at basic prices for each of the products (64 in the Spanish National Accounts);vis the column vector of primary inputs’ by-product (64 rows ×1column);Zis the domestic intermediate consumption matrix (64 rows ×64 columns); Mis the imported intermediate consumption matrix (64 rows ×64 columns); and iis a summation vector of ones (64 rows ×1column). The matrix of technical coefficients Acan be derived from matrix Zand vector x,dividingeachelementzij bytheoutputofproductj,thatis,xj,and,giventhatZ=Aˆx,expression (1) can be written as follows: x=iAˆx+iM+v(2) Multiplying both sides of Equation 2 by ˆx−1produces i=iA+c(3) where c=[iM+v]ˆx−1is the vector of the coefficients of primary inputs and intermediateimportsperunitofoutput.Equation3representsthecoststructureofeachofthe industries (i.e. the unit cost of production, including the cost of domestic intermediate inputs per unit of output and the cost of primary inputs and intermediate imports per unit of output). In the I–O model, prices equal the cost of production, meaning that they are also equal to one. This is because one of the characteristics of this model is that the units of measurement of output are defined as the quantity of each product that can be bought for one monetary unit. This implies that all prices are equal to one at the initial point in time so that they can be interpreted as price indices. Therefore, the following expression can be defined: p=pA+c(4) where pisthevectorofpricesinthebaseyear.Fromthisexpression,weobtained the expression of the price model, which allowed us to determine the new prices from exogenous values of the primary inputs: p=c(I−A)−1=cL(5) where (I−A)−1or ListheoutputmultipliermatrixorLeontiefinversematrix.Eachelement lij denotes the increase in output of product idue to the unit increase in the demand for product j. Often, and in this exercise, the transposed model is used, expressing prices as column vectors, as follows: p=Lc(6) Given certain exogenous values of the primary inputs ¯ c,newpricescanbecalculated simply as ¯ p=L¯ c(7) In this case, the vector ¯ cis the primary input after the price shock. The calculation isexplainedindetailinsectionScenarios. The change in prices was calculated as the difference between the new and the original price, p. At this point, we had the changes in prices at basic prices and at the product level (classification of products by activity (CPA)), which are in the original I–O table. However, 6E. ALONSO-EPELDE ET AL. to introduce these prices into the microsimulation model, they needed to be converted into the purchasers’ prices and COICOP consumption category (classification of individual consumption by purpose), as explained by Cazcarro et al. (2022). We departed from the expenditure in the final consumption of households in the domestic I–O table, eD i,and in the import table, eM i,byproduct,i, and the price increase for each product and scenario, s.Elementiof the vector of expenditure es iis: es i=eD ips i+eM i(8) Then, we calculated the margins paid mps ias mps i=es irmpi(9) where rmpiis the ratio of margins paid. This ratio was obtained from the margins and taxes table of the Spanish National Statistics Institute4as the margins paid among the total (domestic and imported) expenditure in the final consumption of households, ei,by product. The received margins, mrs i,werecalculatedas mrs i=rmri i mps i(10) where rmriis the ratio of margins received. This ratio was also obtained from the margins andtaxestableasthemarginsreceivedbyproductwerebetweenthetotalmarginsreceived, aggregating all products. Then, we calculated the net taxes on products as. ts i=(es i+mps i−mrs i)tri(11) where triis the tax rate, also obtained from the margins and taxes table as the ratio between the net taxes and the sum of expenditure and margins, by product. The new expenditure at purchasers’ prices is: es i=es i+mps i−mrs i+ts i(12) This vector of expenditure by product at purchasers’ prices was then multiplied by a correspondence matrix containing 64 rows (products) and 54 columns (consumption categories) to distribute the expenditure by product iinto the COICOP consumption categories, c. The change in prices of each scenario sby COICOP category cwas obtained as. ps c=(es c/e0 c)−1 (13) where e0 cis the expenditure in consumption category cin the baseline scenario, that is, the original expenditure before price shocks are introduced. 4This table is confidential. We used the table of 2017 and adjusted the margins and net tax ratios to make them consistent with the 2015 table by using elevation factors. The proportions of expenditures at the product level in both years were used to adjust the margins received. Then, the ratio between the aggregate margins received in the two years was used to adjust the margins paid. The ratio between the total taxes before adjustment and the total taxes from the I–O table was used to adjust taxes on products by products. With adjusted margins and tax margins, the tax ratios adjusted to the year 2015 were calculated. ECONOMIC SYSTEMS RESEARCH 7 2.2. The household microsimulation model The microsimulation model was constructed with the microdata from the Spanish HBS for 2015 provided by the INE (2015a). The HBS provides information about households’ expenditure on goods and services and socio-economic and demographic characteristics. It contains information at two levels: one for households and their expenditures and the otherforhouseholds’members.TableA1offersanoverviewofthedifferentconsumption categories considered in our micro model and provides the COICOP categories of each of them. In addition, other information on the HBS was harmonized and adjusted to provide an adequate database for the simulation. As mentioned earlier, to capture the gender implications, we used two different approaches: the gender of the household’s reference person and the degree of feminization of the household. Although the HBS already includes the gender of the reference person among its variables, the second indicator was calculated using the information on the gender of household members. Thus, we calculated the percentage of household members who are women over 14 years old (this age was used because it is considered the age at which individuals begin to have decision-making capacity). Then, we divided the households into five groups based on the share of women: 0–20%, 20% – 40%, 40% – 60%, 60% – 80%, and 80% – 100%. We adopted this approach, proposed byOsorio et al. (2024), within the framework of households’ carbon footprints to capture all the dimensions of the intra-household consumption behavior. Although the HBS covers a representative sample of the population and provides a very detailedimageofhouseholds’annualconsumption, theaggregatecostsofthesurvey arenot aligned with the principles and data of the National Accounting, which builds its macroeconomic aggregates based on more complete sources of information. Therefore, before simulatingthescenarios,theHBSdatawereadjustedtomakethemconsistentwiththe macroeconomic dimension. Two adjustments were carried out: (i) the HBS population was scaled to be consistent with the reference population of the National Accounts; and (ii) the HBS consumption data were scaled according to the final consumption per energy good of the National Accounts. ThemodelsimulatesthechangesinspendingintheCOICOPcategories,multiplying the changes in prices from the price model described above by the current levels of spending on the different products consumed by the households that are part of the HBS dataset. Thus, the model reflects the direct impacts of the scenarios described in section 2.3before assuming any changes in behavior related to the new prices. In other words, the microsimulation model does not reflect the different types of households’ reactions to price changes. To carry out a ‘behavioral’ impact study, it would be necessary to collect the direct responses of consumers (through the price elasticities of the demand for goods) and the induced reactions (through cross-elasticities and income elasticities). However, in the case of energy goods and transportation services, the prices of which vary more widely than those of other consumption categories in the proposed scenarios, these effects are known to be small in the short term since households do not easily change their behavior as far as energy consumption is concerned (Guan et al., 2023; Labandeira et al., 2017). 8E. ALONSO-EPELDE ET AL. The results derived from the microsimulation model are presented as the relative impact (%)onthetotalequivalentconsumptionexpenditure. 5The relative impact, es h,showsthe additional cost that household hwould assume in proposed scenario sin relative terms (in percentage) compared with the initial household expenditure, and it is calculated as: es h=cec,h(1+ps c)−cec,h cec,h ×100 (14) Here, ec,hrefers to the total spending on each consumption category, c,consumedbyeach of the households, in the baseline scenario and ps cis the price increase by consumption category and scenario obtained with the price model. Additionally, a Laspeyres-type Consumer Price Index (CPI) by household type was calculated for each scenario to analyze the asymmetric impact of the different price shocks by households’ type. We calculated first the Laspeyres-type price index for each household and then the average for the households in each category of analysis. CPIs h=cps c,h×q0 c,h cp0 c,h×q0 c,h ×100 (15) where ps cis the price of consumption category cin scenario sfor household h,p0 crefers to the price of the same category but before any price shock, and q0 cstands for the weight of each consumption category cin the basket of goods and services of household hin the base year. 2.3. Scenarios 2.3.1. Reference scenario: oil price shock with no mitigation measures For the RefScen_Price Shock scenario, we departed from the Spanish I–O table of intermediate imports for 2015. We computed the new vector of primary inputs after introducing a shock in theoilpriceof100%.That is, we assumed thatthecrudeoilpriceduplicated, which is in line with the increase for the European Brent crude oil prices (EIA, 2023)6between January 2021 (US$54.7) and the peak price in 2022 during the summer (measured as the monthly average from April to August, US$110.6). To reflect this, we duplicated the value of imports of ‘coke and refined petroleum products’ in all sectors of the economy and the value of imports of ‘extractive industries’ in the sector ‘coke and refined petroleum products.’ The latter flow is formed by imports of crude oil. Then, we computed the new primary inputs accounting for the increased value of imports. This new vector of primary inputs was introduced into the I–O price model to obtain the increase in prices for all goods and services: by CPA product at basic prices and by COICOP categories at purchasers’ prices. The increase in prices was then introduced into the microsimulation model in two steps to distinguish between (i) the effect due to the increase in the price of transport fuel bought 5Equivalent consumption expenditure was used instead of income as it is considered a better proxy for permanent household income since it fluctuates less in the long run (Goodman & Oldfield, 2004). The equivalent spending was calculated based on household spending relativized by the modified OECD equivalence scale, thus considering the economies of scale generated in households according to their size. The modified OECD scale values 1 for the reference person in the household, 0.5 for other people aged 14 or over, and 0.3 for other people under 14 years of age. 6https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RBRTE&f=M ECONOMIC SYSTEMS RESEARCH 15 Figure 2. Welfare impact by gender and quintile. households are the ones that dedicate a greater share of their expenditure to road transport goodsandservices,and,second,theyarealsothemainconsumersofairandmaritime transport services (the price of which is also increased by 11.6% and 6.2%, respectively, in the reference scenario). Regarding the mitigation policies, in man-headed households, the middle classes still suffer the greatest decrease in their income, regardless of the compensation policy that is implemented. However, in woman-headed households, the impacts become progressive in the Policy_Public Transport and Policy_Fuels & Public Transport scenarios, with the 16 E. ALONSO-EPELDE ET AL. highest-income households being the most affected. High-income woman-headed householdsdonotbenefitfromthepricereductionofpublictransportsincetheytendtouse privatetransportinalargerproportionandareaffectedtoagreaterextentbytheincreasein the price of air and maritime transport services. Likewise, it is worth noting the significant improvement for Q1 woman-headed households, especially in the Policy_Public Transport scenario (which rises from – 2.9% without measures to – 1.4% with the public transport subsidy). This is because woman-headed households tend to be concentrated in the lower part of the income distribution and, in addition to spending a very small proportion of their income on fuel, they are the main users of public transport, so they will benefit to a greater extent from the decrease in the price of road transport services. Moreover, the Policy_Public Transport and Policy_Fuels & Public Transport scenarios distribute mitigation impacts similarly, but devoting the entire cost of the compensation policy to subsidizing public transport (Policy_Public Transport) is more beneficial in all cases, regardless of the quintile and the gender of the reference person in the household, than dividing the subsidy between public transport and fuels for sectors (Policy_Fuels & Public Transport). The Policy_Fuels scenario, in addition to being the least beneficial for almost all households, is the one that most mitigates the impact on upper-class households. Regarding the analysis of compensation policies by feminization degree, in mixed households, the lower–middle classes would continue to suffer a greater decrease in their welfare, while, in the most and least feminized households, compensation policies would become progressive, with high-income households being the most affected. Even though, in the Policy_Public Transport and Policy_Fuels & Public Transport scenarios, the mitigation of the price shock would be distributed similarly across the quintiles in each feminization degree, devoting the full cost of the policy to subsidizing public transport (Policy_Public Transport) remains the most beneficial option for all households. In fact, the public transport subsidy is especially progressive for the most and least feminized households (since higher-income households are the ones that dedicate a greater proportion of their income to the consumption of fuel for private transport) and particularly beneficial for low-income households in FD4. This is because low-income households in FD4 spend significantly less on electricity (so they are not as affected by the increase in electricity prices) and because they dedicate a large share of their income to the consumption of road transport services. Conversely, the Policy_Fuels scenario is the least beneficial for the poorest households regardless of the feminization degree. Likewise, the Policy_Fuels scenario mitigates the impact on high-income households to a greater extent in the most and least feminized households, while, in mixed households, it benefits the middle classes proportionally more. 3.2.3. Intersectional analysis of the distributional impacts Beyond income, other sociodemographic characteristics, such as the location of the household, the type of family, or the characteristics of the reference person (age, gender, migration background, studies, etc.) are highly relevant when analyzing the distributional incidence of oil price shocks (Flues & Thomas, 2015). Figure A2 shows the average impact of the price shock on welfare according to multiple household characteristics. The results show the relevance of mobility patterns in explaining the impact of the different scenarios. The less affected households would be those that have lower mobility needs or use public transport more, such as the elderly or adults living alone, households ECONOMIC SYSTEMS RESEARCH 17 with a reference person who is not studying or is working without a contract, and householdslocatedinurbanareas.Conversely,themostaffectedhouseholdswouldbethosewith more complex mobility patterns due to multiple factors: (i) the location, whichexplains the greater reliance on private cars and less accessibility to public transport, such as rural or semi-urban areas; (ii) the composition, with large families with children having greater mobility needs; (iii) the working conditions, such as households headed by individuals with temporary or part-time contracts with greater commuting needs; or (iv) the migration background, such as households with a reference person who was born in a non-European country. As mentioned earlier, distributional analysis that follows an intersectional approach is essential for looking at the complexity of the world and for analyzing and understanding how gender intersects with other socio-economic categories. Thus, in the following sections,wewillexploretheroleofsomekeysocio-economiccharacteristicsfromagender perspective, aiming to highlight the relevance of the intersectionality of different socio-economic characteristics. 3.2.3.1. Household rurality level. Given that rural households are the most affected by a price shock, and in line with the debate on the impact of the energy transition on rural households, it is important to analyze the distributional impact of price shocks and mitigation policies by location and gender (Figure 3). Rural households have greater energy needs and depend more on fuel for private transport (Creutzig et al., 2020;Robinson& Mattioli, 2020; Shammin et al., 2010;Tomásetal.,2020,2023; Wiedenhofer et al., 2013), whichexplainswhy,regardlessofgender,ruralhouseholds aremore affectedbytheincrease in fuel prices (RefScen_Price Shock). The greater effect on rural households can lead to situations of transport poverty that can limit both their access to key activities, such as education, work, or healthcare, and their right to participate fully in society (AlonsoEpelde et al., 2023;Kenyonetal.,2003). At the same time, man-headed households are more affected by an oil shock than woman-headed households in all locations, providing further evidence of the importance of the role of gender in explaining private transport consumption over other socio-economic characteristics. In urban and rural areas, the most affected households are mixed families (FD2–FD4), especially more masculinized ones (FD2), and the least affected are the ones with the highest feminization degree (FD5). In semi-urban areas, the degree of feminization of the household significantly influences the welfare impacts of the oil price shock: the more feminized the household, the lower the impact. The greater dependence of rural and semi-urban households on private transport also impliesthattheybenefitmorewhenfuelsubsidiesareintroduced(Policy_Fuels). This scenario would also mitigate the effect on man-headed households to a greater extent, irrespectiveoftheareainwhichtheyreside.Inthecaseofruralhouseholds,fuelsubsidies (Policy_Fuels) would mitigate the negative impact on the least feminized mixed families (FD2) to a greater extent. However, feminized households would benefit more from public transport subsidies regardless of their level of rurality (Policy_Public Transport and Policy_Fuels & Public Transport), showing once again that, even in rural areas, gender plays a relevant role in mobility behavior. This result highlights the increased vulnerability of women living in rural settings as the lack of accessible public transport that adapts to their 18 E. ALONSO-EPELDE ET AL. Figure 3. Welfare impact by gender and rurality. Note. The labels on the left represent the level of rurality of the household’s area of residence. For more details, see the variable ZONE in Table A5 in Annex A. needs and the incapacity to afford the cost of private transport often limit their access to key activities, such as some jobs that could improve their economic situation. In semi-urban areas, the more masculinized the household, the greater the capacity of thefuelsubsidytomitigatethenegativeimpactofanoilpriceshock.However,whenthe level of feminization is higher in semi-urban areas, the benefit of fuel subsidies is lower and thepublictransportsubsidy(Policy_Public Transport and Policy_Fuels & Public Transport) best compensates feminized households. Access to public transport is greater in semiurban areas than in rural areas, and therefore the users of these services (mostly women) would benefit more from public transport subsidies. 3.2.3.2. Age of household reference person. The age of the reference person also plays a fundamental role in the welfare of households. In general, households with younger reference persons are more affected by the oil price shock (Figure A2). Figure 4shows the welfare impacts according to the gender and age of the reference person. Noting the difference between woman-headed and man-headed households by age, it is apparent that an oil priceshockhasasimilarimpactonyoungpeople,while,inadultandelderlyhouseholds, the difference between the two genders increases (being higher in households with an older reference person). This is because the consumption patterns of men and women are very similar among young people. At the same time, the consumption differences increase with age and are especially accentuated among the elderly. In fact, elderly people living alone ECONOMIC SYSTEMS RESEARCH 19 Figure 4. Welfare impact by gender and age of the household reference person. (primarily women) are the least affected by the oil price shock (RefScen_Price Shock) since their mobility needs are low and most of them use public transport (many might not have a driver’s license). Regarding the household feminization degree, as previously mentioned, the welfare loss is greater for middle-aged mixed households (with greater mobility needs due to work and care loads). However, in households with a younger reference person, the more masculinized households (FD1–FD3) are the most affected, showing once again that, even though consumption patterns among young people are more similar between men and women, gender and home configuration play a relevant role in mobility. Regarding compensation measures, subsidizing public transport continues to be the measure with the greatest potential to mitigate the loss of welfare in all households (Policy_Public Transport and Policy_Fuels & Public Transport scenarios), except for the most masculinized middle-aged households (FD1 and FD2), for which the fuel subsidy (Policy_Fuels)wouldbemorebeneficial.Thesehouseholdsnotonlyuseprivatetransport regularly but also dedicate a high proportion of their income to air transport services. 3.2.3.3. Migration background of the household reference person. Finally, the analysis of the intersection between gender and migration background is of particular interest. Firstly, race, gender, and social class play an important role in maintaining power relations in society and in increasing the risk of vulnerability (Barnett, 2003;Davis,1983)butalso because households with a reference person who was born in a non-European country are among those most affected by the initial price shock (Figure 4)andbecomeamongthe 20 E. ALONSO-EPELDE ET AL. Figure 5. Welfare impact by gender and country of birth of the household’s reference person. least affected after the introduction of transport subsidies (see Figure A1 Policy_Public Transport Scenario). Figure 5reports the welfare impacts of the scenarios by nationality and gender. As Figure 5shows, the shock affects households with a migration background (not national) more. This is because their spending on transport goods and services is significantly higher (between 9.4% and 12.4% of their income) than those born in Spain (8.9%). These differences are mainly because these households dedicate a high proportion of their income to air transport services (between 1.6% and 2% of their income compared with 0.4% of national households) to visit family and friends in their countries of origin, and the price has increased considerably (+11.6%). Moreover, they tend to be concentrated in the lowest part of the income distribution, especially households from non-European countries (48% of which are concentrated in quintile 1). From a gender perspective, the households most affected by the price shock (RefScen_Price Shock) are the most masculinized households (FD1 and FD2) in which the referencepersoncomesfromaEuropeancountrythatdoesnotbelongtotheEUandmixed families (FD2 and FD3) with a migration background outside Europe. Likewise, the most affected households headed by men are those with a reference person who comes from a non-European country, while, in the case of those headed by women, it is those with a reference person who comes from an EU country. Although, in both cases, households with a reference person who was born in a non-EU country dedicate a higher proportion of their income to transport goods and services, woman-headed households are proportionally less affected due to their high use of public transport (non-EU woman-headed households ECONOMIC SYSTEMS RESEARCH 21 spend 4% of their income on public transport, while non-EU man-headed households spend only 2.5%). This also explains why woman-headed households from non-European countries benefit particularly from public transport subsidies (Policy_Public Transport and Policy_Fuels & Public Transport). The Policy_Public Transport and Policy_Fuels & Public Transport scenarios demonstrate that well-designed climate or energy policies would not only not exacerbate the existing inequalities but also have the potential to redistribute the effects so that the most vulnerable groups suffer less. Devoting the entire cost of the compensation policy to subsidizing public transport (Policy_Public Transport) would mitigate the adverse effects of the price shock not only on one of the most vulnerable groups (households headed by people born in non-European countries) but also on the poorest households (Q1) led by immigrant womenfromnon-Europeancountries,whichwouldslightlyimprovetheirwelfare (+0.7%) by reducing their expenditure on transport goods and services. This is mainly due to two points: (i) they are barely affected by the increase in fuel prices for private transport since many households with these characteristics do not have frequent access to their own vehicles and therefore spend a very low percentage of their income on fuels (3.3% while the average for households amounts to 7.2%); and (ii) they are the main beneficiaries of aid for public transport because they dedicate 4.7% of their income to the consumption of road transport services (while the household average is 1.4%). 3.3. Limitations The results of this study are limited by the assumptions of the standard I–O model and its linkage with microdata from surveys of households. In this regard, the limitations of this exercise are similar to previous studies, like that by Guan et al. (2023). The model assumes fixed technologies; therefore, there is no reaction to the price increase in terms of energy efficiency or substitution. The model also assumes that households do not react to the changes in prices (i.e. it captures the ‘overnight’ effect). However, these limitations can be considered minor given that the aim is to analyze short-term scenarios and, especially in the case of transport fuel, substitution is limited (short-term elasticities for transport fuels are typically around -0.20). The second limitation of the I–O model arises from the assumption of industry and product homogeneity. As also explained by Guan et al. (2023), the allocation of all products andservicestoalimitednumberofproducttypesinthepricemodelintroducesuncertainty into the estimation of the indirect price effect. This means, for instance, that, if the crude oil price shock studied in this exercise was translated differently into the various derived oil products, and, hence, to the different sectors using them, that effect would not be captured by the model. Homogeneity also applies to products consumed by different households, this previously being recognized as a limitation (like the quality effect reported by Pottier (2022), for instance), given that the income level of households influences the type of goods consumed, something that might trigger non-homogeneous indirect price shock effects. The model also assumes a complete full pass-through of the (net) increase in costs to final consumers. Inthecaseanalyzedhere, whilethe increase in crudeoilpricesispassedon to consumers, fuel stations have been argued to have captured that part of the fuel subsidies (Hidalgo et al., 2022;Moral,2023). In this regard, our study might have overestimated the effect of fuel subsidies. 22 E. ALONSO-EPELDE ET AL. There are several limitations related to the link between the I–O price model and the microdata from the HBS. First, there is an aggregation bias derived from the limited number of products covered by the I–O table (64 products). Second, the HBS does not differentiate between domestic and imported goods. Thus, we assumed that all households consume the same share of domestic and imported goods. 4. Conclusions This paper analyzed the vulnerability of Spanish households to oil price shocks from agenderperspective.Theresultsshowthatoilpricesurgeshaveagreaterimpacton those households with greater mobility needs, greater dependence on private transport, less accessibility to public transport, or less sustainable consumption patterns. Among them, households headed by men, mixed households (especially those made up of couples with children) that are more masculinized, middle-income households, households residing in rural areas, and households with a young reference person are prominent. However, research has shown that the lack of affordability of private transport makes women (whose incomes are generally lower than those of men) choose to use public transport to carry out their daily activities. This is worrying when the lack of access to affordable and accessible transport translates into limited access to key activities that allow women to developfreelyinsociety.Althoughitseemsthatanoilpriceshockmayaffectmenmore because they are the main consumers of transport goods and services (especially fuel for private transport), women tend to be exposed to situations of hidden poverty in which many, even with the prices before the price shock, cannot afford to consume this type of goods. Among the policies proposed to reduce the impact of oil price shocks, we find that subsidizing public transport would be not only the most beneficial measure for the environmentandthemosteconomicallyeffectivebutalsothefairestfromagenderand social justice perspective. Besides, the fact that these subsidies are implemented through direct aid to households is especially beneficial since only 33% of the policy cost reaches final consumers when the subsidies are also distributed to sectors. Fuel subsidies are the instrument that least mitigates the effects of oil prices and have been widely criticized for discouraging the decarbonization of the transport sector and favoring high-income over low-income households (Van Dender et al., 2022). However, fuel subsidies are the most effective in reducing the impact on rural households. Considering that rural households are the most affected not only by oil prices but also by decarbonization policies (e.g. fuel and CO2taxes), in the short term, it is necessary to deploy compensatory measures while seeking alternatives to reduce their dependence on oil, such as promoting electric vehicles and public transportation or improving their accessibility to essential services. In fact, in this paper, we only explored short-term interventions, like subsidizing public transport. More structural interventions, which are beyond the scope of this paper, like reducing mobility needs, as proposed by the idea of the 15 minute city, are moving in this direction. Regarding the methods explored to carry out the analysis from a gender perspective, we viewed the degree of feminization of the household and the reference person in the household as two complementary methods. Considering the differences based on the reference person in the household within households with the same degree of feminization ECONOMIC SYSTEMS RESEARCH 23 allowed us to identify, albeit in a limited way, the existence of power relations within families. However, more research is needed in this area to identify and quantify inequality and power relations within households. The current oil-fueled transport system poses challenges in terms of energy dependence and environmental sustainability. For example, transport emissions represent around 25% of the European Union’s total greenhouse gas emissions and are one of the main sources of air pollutants (European Environment Agency, 2022). Thus, the last Intergovernmental Panel on Climate Change (IPCC) assessment report once again highlighted the need for a profound energy system transformation at the speed and scale needed to achieve the Paris Agreement goals (Rama et al., 2022). In this context, the European Union is currently working on the ‘Fit for 55’ package, which includes new energy taxation rules for transport fuel and an emission trading system for buildings and road transport (ETS2) (European Commission, 2021a,2021b). These policies will contribute to reducing the emissions but at the same time increase the price of fossil-fueled transport modes (as in the case of oil price shocks), hitting some specific population groups hard (e.g. vulnerable and lowand middle-income households) and aggravating existing inequalities (Böhringer et al., 2022; Feindt et al., 2021;OECD,2011;Piketty&Saez,2014). As we showed, implementing mitigation or compensatory measures is essential to reduce the vulnerability to transport of the most vulnerable groups (including women) and to guarantee a fair energy transition, preventing or mitigating the social rejection of climate and energy policies. In this regard, the method and results can help to address the gender and distributional dimension of the ‘Fit for 55’ package, which is a key concern for the European Union (Clancy et al., 2022; European Commission, 2022). Finally, the study showed how well-designed climate or energy policies not only may not exacerbate existing inequalities but also may have the potential to redistribute the positive impacts of just transition policies to benefit the most vulnerable groups to a greater extent. However, this will require an ex-ante impact analysis of climate and energy policies from a gender and social justice perspective and a precise diagnosis of the needs and possibilities of the most vulnerable groups. Having participatory processes and co-creating measures can be very beneficial in implementing fairer policies and ensuring their political and social viability. Notes 1. In recent years, the gender–transport link has begun to be studied in greater depth (CasadoDíaz et al., 2023; Farré et al., 2023;LeBarbanchonetal.,2021), but the gender approach has not yetbeenfullyintegratedinatransversalwayintothisresearcharea. 2. https://www.iea.org/data-and-statistics/data-product/world-energy-balances 3. Bold-faced lower-case letters are used to indicate vectors, bold-faced capital letters indicate matrices, and italic lower-case letters indicate scalars (including elements of a vector or matrix). Subscripts indicate industries. Vectors are columns by definition, and row vectors are obtained by transposition, denoted by a prime (e.g.x). Diagonal matrices are denoted as(e.g., ˆx). 4. Thistable isconfidential. Weusedthe tableof2017 and adjusted themarginsandnet tax ratios to make them consistent with the 2015 table by using elevation factors. The proportions of expendituresattheproductlevelinbothyearswereusedtoadjustthemarginsreceived.Then,the ratio between the aggregate margins received in the two years was used to adjust the margins paid. The ratio between the total taxes before adjustment and the total taxes from the I–O table 24 E. ALONSO-EPELDE ET AL. was used to adjust taxes on products by products. With adjusted margins and tax margins, the tax ratios adjusted to the year 2015 were calculated. 5. Equivalent consumption expenditure was used instead of income as it is considered a better proxy for permanent household income since it fluctuates less in the long run (Goodman & Oldfield, 2004).Theequivalentspendingwascalculatedbasedonhouseholdspendingrelativized by the modified OECD equivalence scale, thus considering the economies of scale generated in households according to their size. The modified OECD scale values 1 for the reference person in the household, 0.5 for other people aged 14 or over, and 0.3 for other people under 14 years of age. 6. https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RBRTE&f=M 7. We used the following conversion factors: 3.27∗10−5TJ/liter for gasoline and 3.63∗10−5TJ/liter for diesel (PGRWEB). 8. The price shocks for the complete list of products are available in Annex A. On the one hand, Table A2 shows the results of the price model, that is, the change in prices after the application of the input–output price model for each product at the CPA (classification of products by activity) level. On the other hand, Table A3 shows the change in prices at the COICOP consumption category level after applying the microsimulation model to the results obtained by the price model. 9. Note that, comparing the shares of consumption of each sector of coke and refined petroleum products between the input–output tables (INE, 2015b) and those reflected in the PEFA (Eurostat, 2022),therearesignificantdifferencesbetweenthetwosectors(tocalculatetheseshares, we aggregated the following products in the PEFA use tables: P10, P11, P14, P15, P16, P17, P18, P19, P20, and P21). The share of the coke and petroleum refinery sector accounts for 54% of the intermediary consumption in the PEFA and only 22% in the IO tables, while the electricity sector accounts for 6% of the consumption in the PEFA and 31% in the IO tables. Therefore, despite the existence of electric thermal centrals in Spain, especially in the Canary Islands, we believe that the IO tables might, to a certain extent, overestimate the amount of consumption of refined petroleum products in the electricity sector. 10. The figures showing the CPIs for each scenario can be found in Annex A (Figure A1). In addition, Table A4 lists the CPI for each household type and each scenario. 11. Welfare impacts were measured through the expenditure change of the households. Expenditure was used because it is considered to be a good proxy for permanent household income (Goodman & Oldfield, 2004). 12. The average number of members per household in FD1 and FD5 is 1.2 and 1.4, respectively. 13. The discount for the sectors (e2,149 M) is distributed as follows within the price model: (a) households e708 M, (b) private non-profit institutions e21 M, (c) general government e209 M, (d) gross capital formation e217 M, (e) gross fixed capital formation e212 M, (f) changes in inventories and acquisitions less disposals of valuables e5M,and(g)exportse994 M. 14. The equivalent spending quintiles were calculated based on household spending relativized by the modified OECD equivalence scale, thus considering the economies of scale generated in households based on their size. The modified OECD scale assigns values of 1 for the reference person in the household, 0.5 for other people aged 14 or over, and 0.3 for other people under 14 years of age. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This research has received funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement No 101069880 – AdJUST, Advancing the understanding of challenges, policy options and measures to achieve a JUST EU energy transition.