Urban Households’ Investment Behaviour Towards Home Appliances in India: Do Gender and Neighbourhood Effect Matter?

Abstract

This study assesses the influence of women’s bargaining power and the peer or neighbourhood effect on urban households’ investment behaviour towards home appliances, specifically refrigerators and air conditioners. The impact of women’s bargaining power has been demonstrated in different domains, including household expenditure/investment decisions, technology and clean fuel adoption, children’s education, and so on. Since women primarily operate household appliances, for example, refrigerators, we argue that the former could impact investment decisions related to appliances. Additionally, we study the neighbourhood effect as the acquisition and choice of appliances, vehicles, and new technologies and the reduction of household energy consumption heavily rely on information from the neighbours. This communication dynamic reduces perceived social and technological uncertainties related to the product. Therefore, we hypothesise that household appliance ownership accelerates when there is increased use in the neighbourhood. Analysis based on data from the Indian Human Development Survey (IHDS, 2005 & 2012) shows that women’s education and property ownership are positively related to refrigerator and air conditioner ownership. The neighbourhood effects on the same have also been found to be positive and significant. This study, in sum, provides new insights into the factors that influence urban households’ investment behaviour towards appliances, and in terms of policy implications, these findings could be useful in the promotion of energy-efficient home appliances as we are aiming for net zero emission in the decades to come.

Keywords

Investment decision home appliance gender peer effects urban households India

Introduction

The rapid growth in appliance ownership worldwide has caught the interest of policymakers and researchers attempting to analyse the key factors contributing to the increase. Many studies consider household and residence-related characteristics such as family income, household size, educational accomplishments of the household head, age of household head, type of dwelling, access to electricity etc. (Dhanaraj et al., 2018; O’Doherty et al., 2008; Leahy & Lyons, 2010; Ramakrishnan et al., 2020; Rao & Ummel, 2017; Sakah et al., 2019). While these studies emphasize household heterogeneity, appliance ownership also depends on gender preferences and power relations within the household (Dhanaraj et al., 2018; Mohapatra & Simon, 2017; Richmond & Urpelainen, 2019). According to intra-household bargaining theory, a household does not function as a single unit because men and women possess different natures, preferences, responsibilities and power relationships, all of which affect the welfare of the household (Pachauri & Rao, 2013). The theory mainly focuses on the impact of women’s preferences and their control of resources in the household (Doss, 2013). Studies in developing countries have already looked at how women’s preferences and their bargaining power affect the variables such as household expenditures, choice of clean fuel, children’s education, fertility, health outcomes, and other economic decisions (Duflo, 2003; Mohapatra & Simon, 2017; Schmidt, 2012). The findings of these studies imply that greater bargaining power allows women to steer the allocation of resources in their preferred direction, improving household welfare (Duflo, 2003; Mohapatra & Simon, 2017; Schmidt, 2012). Moreover, women get more benefits from home appliances (such as air conditioners and refrigerators) as these increase their productivity, give them more time to engage in activities outside the home, and relieve them from the heat-related effects of indoor chores. This indicates the possibility that the higher their bargaining power, the more likely they are to be involved in appliance purchasing decisions. However, there has not been much progress in understanding how power dynamics and personal preferences affect household welfare, especially on ownership of appliances. Thus, in this article, we study a less-explored explanation of appliance ownership, i.e., the contribution of women’s bargaining power to the ownership of home appliances. We hypothesize that when women have high bargaining power, they influence investment decisions.

In addition, the influence of neighbours on appliance ownership is also examined in this study, as households living in similar areas tend to adopt similar perspectives, attitudes, and behaviours as their neighbours (Brown, 2014). This is called imitation behaviour in social learning theory, that is, households imitate each other in choosing similar products in their social network (Du et al., 2022). Imitation behaviour leads to the neighbour or peer effect and has been observed in technology adoption studies that examine the purchase of hybrid or electric vehicles, flexi fuel vehicles, cooking stoves, solar photovoltaics, energy-efficient products etc. (Mau et al., 2008; Palm, 2017; Wolske et al., 2020). These studies highlight the influence of the neighbourhood, quantified in terms of the increased likelihood of adoption within a geographical area (Mau et al., 2008; Palm, 2017; Wolske et al., 2020). Following previous research, we hypothesize that ownership of appliances increases as the number of households that own home appliances in the neighbourhood increases. Understanding the influence of the neighbourhood effect is required for more insightful research and policy formulations to promote sustainable energy solutions. For instance, the role of the peer effect has been studied in developed countries such as the USA and European Union for household energy conservation; however, there is a lack of studies in developing countries like India (Bahinipati et al., 2022). Therefore, this study aims to understand the influence of neighbourhood effect on household appliance ownership, for example, refrigerators and air conditioners.

In this study, the India Human Development Survey (IHDS, 2005 and 2012) household-level data is used to examine the effects of gender differences in intra-household resource distribution and neighbourhood effect on ownership of home appliances in urban India. In India, appliance ownership and corresponding electricity demand are expected to grow by 5–7.8% annually because of rapid urbanization, rising incomes, increased electrification, and social aspirations between 2015 and 2030 (Ramakrishnan et al., 2020). Several studies have also predicted that ownership and energy use for various appliances such as refrigerators, air conditioners, televisions, and fans will increase significantly by 2030 (McNeil & Letschert, 2007; Parikh & Parikh, 2016). Moreover, the IHDS report also reveals a dramatic increase in ownership for high-energy-consuming appliances such as refrigerators and air conditioners from 34.5% and 1.7% in 2005 to 49.1% and 5.1% in 2012, respectively. This calls for examining the factors that caused the change; however, the studies are limited or absent for India. Given this backdrop, the article investigates the impact of women’s bargaining power, the neighbourhood effect, and household and building characteristics on the ownership of refrigerators and air conditioners using a logit (logistic) model. The article is organized as follows: The section ‘Background’ discusses related literature highlighting the relevance of gender and neighbourhood effects as determinants of appliance ownership; in the section ‘Methods’, the research methods and data are explained; the section ‘Results and Discussion’ presents the findings and policy implications; the limitations of this study and recommendations for future research are given in the ‘conclusion’ section.

Background

In traditional economics and policymaking, the household is viewed as a single decision-making unit where all family members have the same utility function and decisions are made based on the preferences of one member and not influenced/affected by who in the house earns the income or owns any assets (Arthur-Holmes & Busia, 2020; Doss, 2013; Haddad et al., 1997; Schmidt, 2012). This is called a unitary household model.

Recent studies, however, have rejected the unitary model of intra-household resource allocation, which assumes that the bargaining power of an individual member does not affect household production and consumption decisions (Dhanaraj et al., 2018; Doss, 2013; Haddad et al., 1997). Empirical evidence finds men and women have different preferences; they use their resources and spending patterns differently within the household. As a result, their bargaining power impacts key household outcomes differently (Duflo et al., 2003; Schmidt, 2012). Collective models accommodate such intra-household preferences and allow bargaining power to play an essential role in purchasing decisions (Schmidt, 2012). In this article, we build on the collective model by considering each household member’s preferences and relative economic resources rather than considering the household as a single economic actor. The bargaining models focus on how women’s bargaining power gives them greater power to influence intra-household resource allocation and increases participation in household decision-making (Doss, 2013; Duflo, 2003). A substantial body of literature has already examined the role of women’s bargaining power on household outcomes such as children’s education, the well-being of women, health, savings, expenditure, choice of technology, and purchases (Afoakwah, 2020; Doss, 2013; Mohapatra & Simon, 2017; Wang, 2014). However, the impact of women’s bargaining power on appliance ownership has not received much attention so far in the literature. Women prioritize home appliances more than men because it gives them more leisure time and comfort and allows them to enter the job market (Bose et al., 2022). This prima facie justifies the assumption that those women having more bargaining power at home will influence appliance ownership. But the empirical analysis of the impact of women’s bargaining power on appliance ownership requires reliable measures, such as women’s earned and unearned income from pension benefits, remittances, and interest from capital. But these variables involve endogeneity issues because incomes influence and are influenced by the decision of how many hours one works, which is frequently determined jointly with household consumption decisions (Kibe, 2017). Thus, using income as a proxy is problematic in explaining the bargaining effect. To avoid the endogeneity issue, studies have instead focused on gender-linked indicators, where any improvement in them will signal an increase in bargaining power. For example, studies in other domains use land rights or asset ownership, education, involvement in family decision-making, and autonomy to analyse their impact on household outcomes (Acharya et al., 2010; Frempong & Stadelmann, 2017; Mohapatra & Simon, 2017; Schmidt, 2012; Wang, 2014).¹ The education level of women has been widely used in literature as a proxy for bargaining power as it enables them to make life choices because of knowledge, skills, and resources acquired and can also serve as a proxy for their employability (Frempong & Stadelmann, 2017; Samarakoon & Parinduri, 2015). Further, evidence suggests more years of schooling among women lead to decreased child labour, better health outcomes for children, more job opportunities, and increased appliance ownership (Breierova & Duflo, 2004). Dhanaraj et al. (2018) used education as a proxy for women’s bargaining power and found that the likelihood of appliance ownership is higher for women who have completed their tertiary level of education.

Apart from education, evidence from various studies also reinforces that access to credit or property rights, information, and transport are the other measures that can be used as proxies for women’s bargaining power. Doss (2018) found that the distribution of property rights between men and women affects household expenditure decisions. When women hold property rights, it decreases their share of work, strengthens their decision-making power over durable goods, and reduces the consumption of tea and alcohol (Wang, 2014). According to Seebens (2011), ownership of land assets is an important indicator of bargaining power as it serves as a ‘credible threat’ that women will keep/use after leaving the household. Another measure of women’s intra-household bargaining power is their involvement in household decision-making and freedom to move short distances without asking permission. For example, Agarwal (1997) states that the bargaining power distribution within the household is revealed through ‘who participates in the decision-making and about what’. Hence the participation of women in decision-making reflects their greater bargaining strength. Moreover, restricting women’s mobility also impacts their access to goods and services and economic empowerment (Choudhuri & Desai, 2020); thus, it can be used as a proxy for women’s bargaining power.

In addition to the above, this study aims to inquire about neighbourhood effects because, according to social learning theory, adopting a particular technology affects other groups of people through a learning process or peer effect (Wolske, 2020). Research in domains like agriculture, solar energy, and the automobile sector provides evidence of the impact of neighbours/peers on technology purchases (Tessema et al., 2016; Zhu & Liu, 2013). The impact of neighbours is called the spatial neighbourhood effect, where individuals/households learn from their neighbours whether to adopt a new technology or not. It operates through interaction among individuals/households in the same geographical region. Studies on alternative fuel vehicle purchases show spatial clustering, where users are more likely to adopt alternative fuel vehicles when their exposure is high in the surrounding area (Mau et al., 2008; Zhu & Liu, 2013). Moreover, the information gained from observing the purchases of neighbours reduces the perceived social and technological uncertainties related to the product (Adjemian et al., 2010). The theoretical model for such neighbourhood effect explicitly models the contagion or spatial effects and captures the spatial interdependence across the neighbourhood (Noonan et al., 2013; Zhu & Liu, 2013). While the literature on the neighbourhood effect shows positive outcomes on the ownership of new vehicles, and solar photovoltaic technologies, discussions on its impact on appliance ownership have been sparse. Research on behavioural theories concerning household energy consumption behaviour and energy efficiency investments shows the involvement of a strong social component, that is, being influenced by others in household behaviour is more powerful than cost considerations (Bahinipati et al., 2022; Wolske et al., 2020). Therefore, we argue that household appliance ownership accelerates when appliance use is increasingly visible in the neighbourhood areas.

Methods

Data Set

The home appliances in our study include specifically air conditioners and refrigerators. We have used Indian Human Development Survey data, a nationally representative, multi-topic survey that conducted two rounds of surveys in 2005 (IHDS-1) and 2011–2012 (IHDS-2). We have created panel data by matching urban household IDs across both rounds. Our sample size is restricted to 22,146 urban households in the case of panel data and 13,442 urban households for the second round of the IHDS.

We model the probability of owning an appliance as a function of women’s bargaining power, the neighbourhood effect, household characteristics, and residence characteristics using second-round IHDS survey and panel data. We estimate using logit (logistic) regression for IHDS-2 and random effects logistic model for panel data. The model specifications for IHDS-2 (Equation 1) and panel data, consisting of both IHDS-1 & 2, (Equation 2) are given below:

\begin{array}{l} Y_{i} = {[l o g \frac{p r (O w n = 1)}{1 - p r (O w n = 0)}]}_{i} = β_{i} + θ_{1} P r o p e r t y O w n e r s h i p_{i} + θ_{2} D e c i s i o n_{i} + θ_{3} E d u c a t i o n_{i} + \\ θ_{4} P e r m i s s i o n_{i} + θ_{5} H o u s e h o l d S i z e_{i} + θ_{6} P e r c a p i t a m o n t h l y c o n s u m p t i o n_{i} + \\ θ_{7} O c c u p a t i o n_{i} + θ_{8} R e l i g i o n_{i} + θ_{9} C a s t e_{i} + θ_{10} B u i l d i n g t y p e_{i} + θ_{11} E l e c t r i c i t y_{i} + \\ θ_{12} N u m b e r o f r o o m s_{i} + θ_{13} N e i g h b o u r h o o d e f f e c t_{i} + θ_{14} D e c i s i o n_{i} * E d u c a t i o n_{i} + S t a t e_{i} + ε_{i} \end{array}

Equation 1:

\begin{array}{l} Y_{i t} = {[l o g \frac{p r (O w n = 1)}{1 - p r (O w n = 0)}]}_{i t} = β_{i t} + θ_{1} P r o p e r t y O w n e r s h i p_{i t} + θ_{2} D e c i s i o n_{i t} + θ_{3} E d u c a t i o n_{i t} + \\ θ_{4} P e r m i s s i o n_{i t} + θ_{5} H o u s e h o l d S i z e_{i t} + θ_{6} P e r c a p i t a m o n t h l y c o n s u m p t i o n_{i t} + θ_{7} O c c u p a t i o n_{i t} + \\ θ_{8} R e l i g i o n_{i t} + θ_{9} C a s t e_{i t} + θ_{10} B u i l d i n g t y p e_{i t} + θ_{11} E l e c t r i c i t y_{i t} + θ_{12} N u m b e r o f r o o m s_{i t} + \\ θ_{13} N e i g h b o u r h o o d e f f e c t_{i t} + θ_{14} D e c i s i o n_{i t} * E d u c a t i o n_{i t} + Y e a r_{t} + S t a t e_{i} + ε_{i t} \end{array}

Equation 2:

Y_i and Y_it are the binary dependent variables describing the ownership of refrigerators and air conditioners for the ith households, and t is the period. θ_i and θ_it in Equations (1) and (2) are the parameters to be estimated. ε_it is the random effect that follows a normal distribution.

Dependent and Independent Variables

The variable ownership of a refrigerator or an air conditioner is a binary dependent variable describing whether household i owns a refrigerator and air conditioner. We have included four gender-related variables: education, mobility, women owning property in their name, and decision-making power to study their relative influences on the ownership of refrigerators and air conditioners (see Table 1).² The first variable is women’s educational qualifications because it allows them to allocate resources within households, enables them to have a say in decision-making (e.g., purchase of appliances and jewellery), and has agency in making choices (Samarakoon & Parinduri, 2015). We found that appliance ownership was higher among women who had completed their graduation than those with primary and secondary education (Figure 1). The IHDS-2 shows that 81% and 13% of graduated women have refrigerators and air conditioners. Thus, the educational status of women as a proxy of bargaining power has been shown to affect appliance ownership positively. The second variable is decision-making authority, which studies whether the subject has a say in purchasing expensive items such as refrigerators and air conditioners. Since men and women have different preferences, women are expected to exercise greater authority in purchasing decisions, especially when buying home appliances, improved cooking stoves or selecting clean fuel (Choudhuri & Desai, 2020; Schmidt, 2012). In many communities, cultural and social norms may restrict a woman’s mobility and impair her development, empowerment, and access to goods and services (Choudhuri & Desai, 2020). So the autonomy/mobility of women within the community is the third indicator of women’s bargaining power we include in our study. To test the impact of women’s autonomy on appliance ownership, we constructed a binary variable that takes the value zero if a woman visits any of the following locations without the permission of other household members: a friend’s house, hospital, grocery store, or anywhere a short distance away. The last indicator of women’s bargaining power is whether the woman holds any property rights. Economic theories propose that women’s access to land strengthens their financial security and improves their bargaining power within the household (Agarwal, 1994). Mishra and Sam (2016) empirically found that land ownership improves women’s bargaining power which is ultimately reflected through their involvement in household purchases.

Table 1.

Summary Statistics.

Description of the Variables	Type of the Variables	IHDS-2 (2012)		IHDS-1 & 2
Description of the Variables	Type of the Variables	N	Mean (SD)	N	Mean (SD)
Dependent Variables
Ownership of refrigerator	Dummy	13434	0.504 (0.500)	22108	0.412 (0.492)
Ownership of air conditioner	Dummy	13429	0.048 (0.213)	21074	0.029 (0.169)
Independent Variables
Household-related Variables
Household size	Continuous	13442	5.282 (2.333)	22146	5.479 (2.489)
Per capita monthly consumption expenditure (ln)	Continuous	13441	10.17 (0.653)	22126	8.465 (1.798)
Occupation of household head	Categorical	13435	2.263 (0.955)	22142	2.245 (0.950)
Religion	Dummy	13442	1.777 (0.415)	22146	1.771 (0.419)
Caste	Categorical	13401	1.879 (0.758)	22108	2.507 (0.631)
Building-related Variables
House type	Categorical	13391	2.011 (0.326)	21977	1.931 (0.418)
Access to electricity in hours	Continuous	13102	17.747 (6.283)	21287	17.843 (6.334)
Number of rooms	Continuous	13428	2.926 (1.733)	21923	2.844 (1.698)
Neighbourhood Effect
Percentage of households own refrigerator in a district	Continuous	13442	50.427 (23.428)	22146	41.469 (23.71)
Percentage of households own air conditioner in a district	Continuous	13442	1.244 (1.021)	22146	0.896 (0.953)
Gender-related Variables
Female owns property	Dummy	12935	0.193 (0.394)	20753	0.177 (0.381)
Female’s education: Graduation and above	Dummy	13435	0.218 (0.412)	22068	0.181 (0.385)
Female’s involvement in the purchase decision of appliances	Dummy	13383	0.795 (0.404)	21159	0.757 (0.428)
Permission to travel anywhere	Dummy	13407	0.944 (0.231)	21176	0.860 (0.346)

Note: SD: Standard Deviation.

Figure 1.

Refrigerator and Air Conditioner Ownership by Female Education Level for IHDS-1 and IHDS-2.

While we included indicators of women’s bargaining power, there is a high possibility that some indicators depend on another’s values. For example, women’s decision-making authority and education may be positively correlated, as educated women are more likely to meet their livelihood needs without depending upon their spouses (Acharya et al., 2010). Therefore, explaining the main effects of the indicators on appliance ownership would be incomplete without considering the statistical significance of the interaction between women’s decision-making authority and their educational qualifications. We address the problem by including an interaction term that the relationship between owning an appliance and a woman’s decision-making authority is different for women with graduate education than women with primary and secondary levels of education. Lastly, we have included the spatial neighbourhood effect,³ where we hypothesize that the higher the adoption of an appliance in the neighbourhood area, the higher the probability of a household purchasing the appliance.

To improve the precision of our estimates, possible confounders have been identified from the literature and included in the model. Table 2 provides details of the control variables used in the study. These have been divided into (a) household and (b) residence characteristics. Under household characteristics, we control the number of persons in the family because it can positively affect appliance ownership when the family’s total income increases due to household size (Leahy & Lyons, 2010; Lyons et al., 2010; Sakah et al., 2019). We used logarithmic per capita monthly expenditure to proxy for households’ affordability. Evidence suggests that income is a predominant driver of appliance ownership (Dhanaraj et al., 2018; O’Doherty et al., 2008; Ramakrishnan et al., 2020; Richmond & Urpelainen, 2019; Sakah et al., 2019).

We assume that household heads working in business/salaried/other professions are more likely to own an appliance because of their accessibility to credits than those engaged in the agricultural sector. We control religion as religious norms, customs, and beliefs influence consumption behaviours. For example, Rao and Ummel (2017) demonstrate that the likelihood of owning a refrigerator is high among the Sikh community because of their high milk consumption. Similarly, we include caste as ownership of appliances and assets allows lower caste people to redefine their social status (Ramakrishnan, 2020). Ramakrishnan et al. (2020) argue that beyond a certain threshold of income, households in emerging countries become more concerned about their relative income levels than their absolute income levels. They strive to adhere to the average consumption standards of their neighbourhood or social circle, among whom the ownership of appliances is a status marker. Thus, pursuing higher social status and recognition has led to the ownership of appliances. The literature review finds that building characteristics such as the number of rooms, access to electricity, and type of house are also crucial in determining home appliance ownership.

Results and Discussion

The cross-sectional and panel data analysis may feature multi-collinearity problems. We calculated the variance inflation factor (VIF) to check for multi-collinearity. The mean value of the VIF for all the independent variables is calculated as 1.28 for cross-sectional data and 1.25 for panel data, respectively. The calculated values are between 1.14 and 6.82, suggesting the absence of a multi-collinearity problem. We also used clustered standard error to account for the situation in which observations within each group are not independently and identically distributed.

Tables 2 and 3 report the step-wise regression results for Equations 1 and 2; the columns 1, 2 and 3 in both tables show the estimations based on cross-sectional data (i.e., IHDS 2) and the columns 4, 5 and 6 in both tables report the analysis based on panel data (i.e., IHDS 1 and IHDS 2). The results show that women graduates are more likely to own refrigerators and air conditioners by 1.09 and 1.10 percentage points (see columns 6 of Tables 2 and 3). Samarakoon and Parinduri (2015) also find that women who complete one more year of education and senior high school are more likely to own household appliances by 5% and 22%, respectively. Thus, education as a proxy for women’s bargaining power is a significant determinant of appliance ownership. But women’s decision-making authority and the interaction variable have an insignificant effect on appliance ownership. Women who take permission to travel short distances are seen to increase air conditioner ownership by 0.01 and 0.4 percentage points at a significance level of 5%, but this has no significant effect on refrigerator ownership (see columns 3 and 6 in Tables 2 and 3). The relationship between air conditioner ownership and women’s autonomy implies that women who do not have autonomy are more likely to own air conditioners, which is difficult to explain in the current context. The results of our study partially support the findings and observations of Choudhuri and Desai (2020), although they did not find a significant relationship between women’s autonomy and fuel choice. But they observed that women do not actively participate in fuel selection as their role in the household is limited to ‘the boundary of the house’, and they are not exposed to the domain of budgetary allocation. Property ownership by women has a positive relationship with appliance ownership at a significance level of 1 and 10%. It confirms the previous findings that women’s property rights support purchasing female-centric products at home. Thus, women who derive intra-household bargaining power through their education level or through owning property rights influence air conditioners and refrigerators ownership.

Table 2.

Marginal Effects of Refrigerator Ownership.

	IHDS II			IHDS I & II
	(1)	(2)	(3)	(4)	(5)	(6)
Gender-related Variables
Female owns property	0.085*** (0.131)	0.038*** (0.009)	0.010 (0.009)	0.665*** (0.092)	0.325*** (0.069)	0.209*** (0.060)
Female involvement in the purchase decision of appliances	−0.011 (0.012)	0.005 (0.009)	0.005 (0.010)	−0.138 (0.086)	−0.112 (0.084)	−0.033 (0.080)
Female education: Graduation & above	0.360*** (0.016)	0.170*** (0.012)	0.121*** (0.011)	2.563*** (0.164)	1.332*** (0.097)	1.086*** (0.072)
Female: Permission to travel anywhere	0.039 (0.043)	0.020 (0.030)	0.020 (0.022)	−0.252* (0.153)	−0.182 (0.148)	−0.040 (0.126)
Household-related Characteristics
Household size		0.035*** (0.002)	0.020*** (0.002)		0.219*** (0.021)	0.142*** (0.018)
Per capita monthly consumption Expenditure (ln)		0.289*** (0.011)	0.220*** (0.010)		2.160*** (0.113)	1.805*** (0.105)
Household Head’s Occupation
Business		0.176*** (0.013)	0.145*** (0.012)		1.440*** (0.968)	1.287*** (0.107)
Salaried		0.199*** (0.012)	0.151*** (0.011)		1.501*** (0.092)	1.261*** (0.010)
Others		0.199*** (0.020)	0.155*** (0.020)		1.504*** (0.124)	0.286*** (0.133)
Religion and Caste
Hindu		−0.007 (0.012)	−0.020* (0.010)		−0. 143 (0.099)	−0.293*** (0.099)
OBC		−0.046*** (0.013)	−0.022*** (0.011)		−0.407** (0.159)	−0.257* (0.142)
SC & ST		−0.071*** (0.011)	−0.050*** (0.010)		−0.436*** (0.150)	−0.291** (0.141)
Building-related Variables
Bungalow and shared walls			0.009 (0.045)			0.442** (0.204)
Flat			0.051 (0.041)			0.785*** (0.190)
Access to electricity (in hours)			0.002** (0.001)			0.017** (0.007)
Number of rooms			0.047*** (0.003)			0.336*** (0.029)
Neighbourhood Effect
Percentage of households own refrigerator in a district			0.006*** (0.000)			0.050*** (0.002)
Observations	12834	12786	12426	20603	20554	19515
Pseudo R²	0.16	0.32	0.37
YEAR				YES	YES	YES
STATE	YES	YES	YES	YES	YES	YES

Notes: Standard errors in parentheses are clustered standard errors.

***p < .01, **p < .05, *p < .1.

Studies on neighbourhood effects are a growing area of research because of their massive impact on individual, social, and economic outcomes (Lupton, 2003). Zhu and Liu (2013) believe that households in some regions share similar socio-economic and demographic characteristics and are more likely to have similar preferences. Also, due to the power of societal influence, families are more likely to buy the same appliances as their neighbours. This article studied how the concentration of refrigerator and air conditioner ownership in the district affects a household’s likelihood of owning the same or similar appliances. In line with previous works, the study finds a statistically significant and positive relationship between the neighbourhood effect and the ownership of air conditioners and refrigerators. The result confirms the impact of societal influence on households.

The household size is directly related to appliance ownership, as ownership increases with the number of household members. Per capita monthly expenditure increases the probability of owning a refrigerator and an air conditioner. The results show that households earning an income from business and other sources are likelier to own an air conditioner than salaried households. However, all occupation types show a positive and significant relationship with refrigerator ownership. Regression estimates for appliance ownership reveal that caste and religion have significant negative effects. Across appliance categories, the probability of ownership decreases if a household belongs to the Other Backward Class category (OBC) or the Scheduled Caste (SC) and Scheduled Tribe (ST) categories, confirming prevailing hierarchies in society. Hindus are found to have a negative relationship with appliance ownership than other religions, including Christianity, Muslim, Sikh, Jain, and others. Ganesan and Vishnu (2014) reveal that the average per capita income of Christian and other religious households is higher than that of Hindu households, possibly explaining the negative relationship.

We find that the availability of electricity significantly affects only refrigerator ownership. With every hour of access to electricity, owning a refrigerator is shown to increase by 0.2 and 1.7 percentage points (see columns 3 and 6 of Table 2). However, access to electricity does not have a significant relationship with air conditioner ownership. This implies that households with quality electricity supply prefer to own a refrigerator first (Richmond & Urpelainen, 2019). Moreover, air conditioners in India are still a luxurious commodity for middle-class households and are used seasonally. This could also explain the insignificant relationship between the two. In addition, the results also indicate a positive and highly significant relationship between house type, the number of rooms, and ownership of appliances. The study includes three categories of houses: slums and chawls, bungalows and shared buildings, and flats. It indicates that living in bungalows and flats increases the probability of owning air conditioners by more than 1% because such homes provide spaces and other conducive physical settings. Thus, housing type is a significant determinant for larger electrical appliances like air conditioners and refrigerators.

Table 3.

Marginal Effects of Ownership of Air Conditioner.

	IHDS-II			IHDS I & II
	(1)	(2)	(3)	(4)	(5)	(6)
Gender-related Variables
Female owns property	0.030*** (0.007)	0.016*** (0.006)	0.007 (0.005)	0.730*** (0.123)	0.435*** (0.161)	0.270 (0.185)
Female involvement in the purchase decision of appliances	−0.000 (0.006)	0.000 (0.005)	−0.000 (0.005)	−0.046 (0.208)	0.035 (0.212)	0.156 (0.225)
Female education: Graduation & above	0.096*** (0.007)	0.040*** (0.005)	0.030*** (0.004)	2.160*** (0.152)	1.267*** (0.151)	1.103*** (0.148)
Female: Permission to travel anywhere	0.011 (0.009)	0.009 (0.009)	0.019** (0.008)	0.104 (0.290)	0.169 (0.307)	0.448** (0.322)
Household-related Variables
Household size		0.006*** (0.000)	0.002*** (0.000)		0.199*** (0.026)	0.129*** (0.029)
Per capita monthly consumption Expenditure (ln)		0.056*** (0.005)	0.048*** (0.002)		1.898*** (0.140)	1.747*** (0.145)
Household Head’s Occupation
Business		0.037*** (0.010)	0.028** (0.010)		1.419*** (0.406)	1.110** (0.431)
Salaried		0.020** (0.007)	0.012 (0.008)		0.899** (0.370)	0.573 (0.404)
Others		0.022*** (0.008)	0.013 (0.009)		1.021*** (0.382)	0.723 (0.448)
Religion and Caste
Hindu		0.001 (0.006)	−0.006 (0.006)		0.280 (0.217)	−0.032 (0.222)
OBC		−0.023*** (0.005)	−0.018*** (0.005)		0.160 (0.260)	0.141 (0.268)
SC & ST		−0.027*** (0.006)	−0.019*** (0.006)		−0.453* (0.266)	−0.379 (0.278)
Building-related Variables
Bungalow and shared walls			0.031*** (0.005)			0.997** (0.449)
Flat			0.040*** (0.007)			1.358*** (0.453)
Access to electricity (in hours)			0.000 (0.000)			0.005 (0.012)
Number of rooms			0.009*** (0.000)			0.260*** (0.039)
Neighbourhood Effect
Percentage of households own air Conditioner in a district			0.048*** (0.002)			1.704*** (0.160)
Observations	12704	12656	12298	19384	19332	18321
Pseudo R²	0.17	0.28	0.38
Year				YES	YES	YES
State	YES	YES	YES	YES	YES	YES

Notes: Standard errors in parentheses are clustered standard errors.

***p < .01, **p < .05, *p < .1.

Conclusion

Due to rapid urbanization, rising incomes, and rising temperatures, there is a rise in the ownership of appliances in urban India. Understanding the factors that drive home appliance ownership is crucial for moving down to a low-carbon path. The present study examines the influence of women’s bargaining power and the neighbourhood effect on ownership of air conditioners and refrigerators and yields a few strategic conclusions and policy implications using household-level survey data. This study can be seen as the starting point for a gender-based investigation into energy-related choices.

In this study, the complex nature of intra-household power relations is proxied by a range of non-labour income measures that determine bargaining power. The results of the current study, which align with the theory, suggest that women’s bargaining power impacts the ownership of household appliances, particularly refrigerators and air conditioners. The study also looked at the neighbourhood effect as a determinant of appliance ownership and found that appliance ownership increases when there have been more purchases of home appliances in the neighbourhood. We took household and residence characteristics (except for caste and religion) as explanatory variables and found that they have a highly positive and significant relationship with ownership of refrigerators and air conditioners in urban India. Monthly consumption spending per person continues to impact appliance ownership significantly. Household size, building type, and the number of rooms are the most consistent indicators that increase the probability of owning refrigerators and air conditioners.

The study results imply that women’s bargaining power explains appliance ownership. Therefore, policymakers should not follow a unitary approach using the household as a unit to target; instead, they should consider intra-household bargaining power and decision-making processes to control the growth of appliances and encourage the adoption of energy-efficient appliances. Moreover, in current practice, the gender perspective is absent from Indian energy policies, leading to an ineffective energy policy. Shrestha (2021) states that women can add to realizing energy-related SDGs by adopting sustainable energy practices at the household level, as they are the primary energy users. Therefore, energy policies in India need to incorporate a gender mainstreaming approach to reduce carbon intensity in the environment and lower the country’s energy demand. This can be accomplished by strengthening women’s bargaining power by educating them about clean energy, increasing their involvement in energy technology, and offering subsidies to purchase energy-efficient refrigerators and air conditioners. The neighbourhood effect emphasizes that households are like to follow their neighbours’ preference patterns, which policymakers can consider while formulating energy-saving policies. For instance, feedback-based techniques or community programmes can be used to target households and check the growth of appliances. However, further investigation is needed to explore the impact of women’s bargaining power and neighbourhood effect across different energy-using appliance categories in an integrated manner for conclusive results.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

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