Accept or Pay? A Study of the WTA–WTP Disparity Due to Airborne Lead Pollution

Abstract

This research aims to evaluate the economic valuation of lead air pollution in the AA.HH. Virgen de Guadalupe, Callao, Peru. A survey was conducted with 182 residents, focusing on air quality, temporary exposure to pollution, aesthetic appreciation of the environment, willingness to accept (WTA) economic compensation, and willingness to pay (WTP) to mitigate lead air pollution. The data were analyzed using the logit model through STATA16 software. The results revealed that 62.09% of respondents were willing to accept economic compensation, while 56.04% expressed willingness to pay for reducing lead pollution. Furthermore, it was determined that the average monthly WTP is PEN 62.48 (USD 16.47), amounting to PEN 1,201,865.28 (USD 329,103.16) annually for the entire population of 1603 residents. In contrast, the average monthly WTA per resident is PEN 153.59 (USD 40.48), totaling PEN 2,954,457.24 (USD 809,010.16) annually due to lead air pollution. The study concluded that environmental perception, awareness, the desire to maximize present utility, and uncertainty about the future significantly influence the economic valuation of lead air pollution in this community.

1. Introduction

As the global economy grows, the increase in consumption and production activities has generated serious consequences, with air pollution standing out as a significant negative externality of these processes [1]. Air quality has become a critical environmental issue and a constant concern in numerous cities around the world [2]. A recent study revealed that India has been particularly affected, with approximately 1.7 million deaths attributed to air pollution, accounting for nearly 18% of the country’s total mortality [3]. These figures add to alarming global statistics, where it is estimated that 7 million people worldwide have died due to environmental air pollution.

The World Health Organization (WHO) considers air pollution a serious threat to public health, and a growing body of research supports this claim by demonstrating that atmospheric pollutants pose a considerable risk to both human health and economic development [4]. Among these pollutants, airborne lead stands out due to its impact, recognized for its harmful, cumulative, and permanent effects on children’s neurological development [5], as well as its serious environmental repercussions through metal deposition in soils and plants [6]. Lead absorption by plants generates phytotoxicity, which negatively impacts humans as this metal is transferred through the food chain [7]. The ingestion of lead-contaminated food can cause learning disabilities in children, neurotoxicity, kidney disease, and cancer [8]. Additionally, lead exposure in adults is associated with a wide range of health issues, including nephropathy, peripheral neuropathy, encephalopathy, hearing loss, and cognitive impairment [9].

Lead air pollution is a critical public health problem in several regions of Peru, severely affecting both the population and the environment [10]. One of the most emblematic cases is La Oroya, one of the most polluted cities in the world, where the Metallurgical Complex, which has been in operation for decades, has emitted enormous amounts of lead, contaminating local soil [11]. Studies conducted in the area have shown alarming levels of lead in children’s blood, irreversibly affecting their cognitive and neurological development [12]. Despite state interventions and international demands, the effects of decades of toxic emissions continue to represent a direct threat to the health of residents, particularly in the most vulnerable communities.

A particularly severe case of lead pollution is found in the port of Callao, where atmospheric emissions of this heavy metal are primarily attributed to the transportation and storage of minerals [13]. Residents near the mineral concentrate deposits have reported an increase in respiratory and neurological diseases, exacerbated by continuous exposure to contaminated dust [14]. The child population is the most affected, with studies revealing elevated blood lead levels, directly linked to learning and developmental problems [15]. These cases highlight how insufficient environmental regulation and a lack of effective measures to control pollution have led to serious public health consequences in Peru [16]. Industrial facilities dedicated to the smelting and extraction of lead are the primary sources of these emissions [17]. In this context, the population residing in the Virgen de Guadalupe settlement, in the Mi Perú district, has been particularly impacted, with blood lead levels reaching up to 2.466 µg/dL [18], underscoring the urgent need for corrective action.

In the economic and decision-making fields, valuing goods and services is an essential task that affects both consumers and producers. Two common approaches used to understand how people value these elements are the models of Willingness to Pay (WTP) and Willingness to Accept (WTA). WTP refers to the maximum amount an individual is willing to pay for a good or service, while WTA represents the minimum compensation, they would be willing to accept to give up that good or service [19]. The disparity between these approaches has been the subject of intense debate in the academic literature. The binomial models of WTP and WTA provide a solid theoretical framework for examining how people perceive and assign value to different products and services. Although these two approaches seem like two sides of the same coin, research has consistently revealed a discrepancy between the valuations obtained through WTP and WTA. This discrepancy raises crucial questions about how we perceive the value of things we own or wish to acquire and how this influences our economic decisions [20].

In recent decades, WTP and WTA methods have been essential for valuing non-market goods and services in various contexts, such as public health, the environment, transportation, and energy. Research on Willingness to Pay (WTP) has been widely used to assess improvements in air quality, especially in developing economies affected by high levels of pollution. Zhang [21] investigated the relationship between PM2.5 pollution and WTP in China, finding that a 100 μg/m³ increase in PM2.5 levels raised WTP by 84.1%, with an average of USD 57.9 annually. Ghanem [22] observed in Egypt that residents in highly polluted areas showed a significantly higher WTP to reduce the risks of respiratory diseases compared to less affected areas. Moon [23] applied a similar approach in South Korea, where WTP for air quality improvement amounted to USD 87.5 per household annually, highlighting the role of risk perception in payment decisions. In Indonesia, Imstanto [24] found that citizens were willing to pay 1.2% of their annual income to reduce atmospheric pollution levels in Jakarta. Pu [25] also reported that urban air quality improvement in China could increase WTP by up to 30% in areas with moderate to severe pollution levels. Sánchez-García [26] analyzed WTP in Spain, where respondents were willing to pay between 50 and 100 euros annually to mitigate air pollution in highly populated urban areas. Studies in Vietnam, conducted by Van [27], and in other emerging economies such as India and Mexico, show that WTP is positively correlated with household income and environmental awareness. These investigations highlight the importance of WTP as a tool to assess the economic value of air quality improvements. Its application has provided critical information for the formulation of effective public policies in various contexts.

Similarly, Willingness to Accept (WTA) has been widely used to measure the compensation required by communities in response to infrastructure projects or significant environmental impacts. Son [28] applied the contingent valuation method to estimate the WTA of residents near hydrogen plants in South Korea, finding that the average compensation required was KRW 7.8 million (USD 6037) annually to accept the construction of a 5 MW plant 1 km away. In a similar study, Zhang [29] evaluated WTA in China for compensation related to pollution impacts in industrial areas, discovering that residents demanded 20% to 35% more compensation when perceived health risks were involved.

Other studies, such as those by Drichoutis [30], addressed the disparity between WTP and WTA, showing that consumers tend to overvalue losses compared to gains, which reflects loss aversion bias. Liu [31] applied these methods in ecosystem valuation, where WTA was significantly higher than WTP, highlighting the difficulty of reaching consensus on environmental compensation. Feo-Valero [32] analyzed asymmetries in freight transport preferences, showing disparities between WTP and WTA for attributes such as transit time and service frequency. Additionally, studies like those by Villanueva [33] and Sindermann [19] applied WTP and WTA in the context of transportation and urban sustainability, showing user preference for more ecological mobility options, albeit with higher associated costs. Hadush [34] emphasized the importance of using these methods in valuing water resources in Africa, noting a significantly higher WTA among local communities, reflecting their resistance to accepting compensation for the use of these resources. Amoah [35] applied these methods in the analysis of sustainable tourism, showing that tourists are willing to pay more for destinations with ecological practices, while WTA for giving up certain comforts was considerably lower.

WTP and WTA methodologies offer a robust quantitative approach to analyzing the perception and valuation of non-market goods and services, providing key data for informed decision-making. The disparity between WTP and WTA has been widely recognized in economic literature, and understanding it is essential for unraveling the underlying motivations in individual and collective decision-making. Research by Zhang [28] and Son [29] has highlighted the importance of understanding the differences between these methodologies to improve the acceptance of projects that affect public health and the environment, particularly in high-risk contexts. Despite the extensive use of WTP and WTA methods in environmental economics, there remains a gap in applying both approaches simultaneously to lead air pollution in contexts characterized by high vulnerability and complex socio-environmental challenges. Most existing studies focus on either one valuation method (WTP or WTA) or address broader air contaminants without explicitly examining lead—a pollutant with irreversible neurological and ecological impacts. Moreover, limited research has explored how factors such as aesthetic perceptions, awareness levels, and uncertainty about health risks jointly shape individuals’ willingness to pay or accept compensation. This study fills that gap by providing a detailed comparative analysis of WTP and WTA in a Peruvian community severely impacted by airborne lead. By integrating both economic measures, our research offers novel insights into how residents balance immediate economic benefits against long-term health concerns, thereby contributing fresh evidence to the ongoing debate on the WTA–WTP disparity.

This study not only explores in depth the causes of this discrepancy but also analyzes its implications for policy formulation, especially in the context of environmental problems. Evaluating this disparity is crucial for integrating climate variables and phenomena that impact ecosystem services such as air quality. Therefore, it is necessary to study the perception and valuation of residents in Virgen de Guadalupe, in the Mi Perú district, Callao region, using the WTP–WTA approach applied to airborne lead pollution, which is the subject of this research. The results obtained will not only allow a better understanding of the local community’s willingness to accept compensation or pay for environmental improvements but will also contribute to designing more equitable and effective policies for managing air pollution.

2. Materials and Methods

2.1. A Description of the Case Study

The present research is quantitative in nature and follows a non-experimental design, allowing for a descriptive approach to the phenomenon under study [36]. The population analyzed consists of 1603 residents in the Virgen de Guadalupe settlement, located in the Mi Perú district, located at coordinates 11°51′42″ S and 77°7′18″ W (Figure 1). This settlement borders the “El Paraíso” settlement, sectors H and E, the Ventanilla hills, and the Ventanilla industrial park (Figure 1). Methodologically, probabilistic sampling was employed using the simple random method, given the homogeneity of the target population. To calculate the sample size, the corresponding formula was applied [37]. After performing the calculation, it was determined that the appropriate sample for surveying in the AA.HH. Virgen de Guadalupe community would consist of 470 individuals, based on the following eligibility criteria: (1) individuals not residing in the AA.HH. Virgen de Guadalupe community were excluded; (2) minors were excluded from the total population; and (3) surveys were conducted in the morning and afternoon, coinciding with working hours, primarily for safety reasons. Following these selection criteria, a total of 182 individuals were surveyed in the AA.HH. Virgen de Guadalupe community.

2.2. Technique and Instrument

The technique used for this research was a survey, through which quantitative measurements of a wide variety of objective and subjective characteristics of the population were obtained [37]. This technique allowed for the accumulation of a significant amount of data in a short period of time, meaning that a larger number of individuals could be surveyed quickly. The instrument used in this research was the questionnaire, which is a modality of the survey technique, consisting of a systematic set of written questions, presented on a form, that are related to the study hypotheses and, therefore, to the research variables and indicator.

This study was conducted in the Virgen de Guadalupe “Asentamiento Humano” (AA.HH.), located in the Carabayllo district of Lima, Peru. According to the latest census by the Instituto Nacional de Estadística e Informática of 2017, the population in this area is 1603. In order to determine the sample size, a formula was applied that assumed equal probability for success and non-success (p = 50%, q = 50%), with a 95% confidence level (Z_α) and a 5% margin of error (d = 0.05), resulting in an initial sample size of 470 individuals.

However, the actual number of respondents was reduced to 182 for several reasons. First, inclusion and exclusion criteria ruled out individuals who were either not residents of the AA.HH. Virgen de Guadalupe community or were under the age of majority. Moreover, surveys were mainly administered during morning and afternoon hours to ensure both participant safety and interviewer availability. While this approach allowed for more secure data collection, it limited the opportunity to reach the estimated sample of 470, thus contributing to the smaller final sample size.

Data were gathered through a questionnaire divided into seven sections. These sections included the following: (1) screening questions to confirm residency and legal age; (2) inquiries regarding social perception and socioeconomic characteristics; (3) items on ecological perception related to lead air pollution; (5) questions concerning temporal perception, covering daily habits, healthcare costs, and work absences due to lead-related health issues; (6) items addressing the Willingness to Accept (WTA) a monthly economic compensation, should mitigation measures not be promptly implemented; and (7) items on Willingness to Pay (WTP) a monetary contribution for effectively reducing airborne lead.

The questionnaire underwent expert validation. Three professional specialists in environment, cultural management, and project evaluation assessed the relevance, clarity, and coherence of each item. Two experts categorized the instrument as “Applicable” immediately, while the third deemed it “Applicable after corrections”. Following the recommended adjustments, the overall validity surpassed the 85% threshold, fulfilling the criteria set by the experts. Regarding reliability, the Cronbach’s Alpha statistic was employed using SPSS (version 25) to assess the internal consistency of the questionnaire. The resulting value of 0.723 exceeded the recommended minimum of 0.50 for social studies, underscoring the robustness of the questionnaire as the primary data collection tool.

Data collection for this study took place in June and July of 2023, offering a cross-sectional snapshot of prevailing economic conditions, environmental awareness, and pollution levels in the AA.HH. Virgen de Guadalupe community. It is important to recognize that the willingness to pay (WTP) and willingness to accept (WTA) estimates presented here may vary over time as local economic circumstances evolve, public education efforts enhance awareness of lead pollution risks, and regional policies alter the pollution burden. Consequently, longitudinal studies or periodic follow-up surveys could provide insights into how such factors—shifting employment opportunities, cost-of-living adjustments, or community-level mitigation strategies—might influence the economic valuation of environmental improvements in the future. By acknowledging these temporal dynamics, policy-makers and stakeholders can develop more adaptive and effective interventions to address lead air pollution.

2.3. Methods

The theoretical model is based on a development by Hanemann in 1984 [38], which originates from the availability of environmental quality (h) and income (y) as a function of utility. However, this utility is unobservable and individual, which allows for random data with a certain probability distribution. Therefore, total utility will have an unobserved component that is independently and identically distributed with a mean of zero. To analyze the change in utility from U₀ to U₁, we must consider the maximum willingness to pay (WTP) for an improvement in the quantity or quality of an ecosystem resource, or the minimum willingness to accept (WTA) compensation for giving up the quantity or quality of an ecosystem service. Although the equations used to estimate willingness to pay (WTP) and willingness to accept (WTA) are mathematically similar, the difference between the two lies in the individual’s reference state and the contextual framework of the change being assessed. WTP is used when the individual does not own the environmental good or service, and an estimate is made of how much they would be willing to pay to obtain it or avoid its loss. In contrast, WTA is applied when the individual already owns the good or service and an assessment is made of how much they would demand as compensation to give it up or accept its deterioration. This distinction is fundamental in economic valuation studies, since the perception of property rights and the endowment effect often generate significant asymmetries between the two values. The theoretical models are presented below.

$$V_1\left(h_1,y-WTP:s\right)+{\epsilon }_1\ge V_0\left(h_0,y:s\right)+{\epsilon }_0$$

(1)

$$V_1\left(h_1,y-WTA:s\right)+{\epsilon }_1\ge V_0\left(h_0,y:s\right)+{\epsilon }_0$$

(2)

Thus, the probability is given by the following equation:

$$P_0=\mathrm{P}\mathrm{r}[V_1\left(h_1,y-WTP:s\right)+{\epsilon }_1\ge V_0\left(h_0,y:s\right)+{\epsilon }_0]$$

(3)

$$P_0=\mathrm{P}\mathrm{r}[V_1\left(h_1,y-WTA:s\right)+{\epsilon }_1\ge V_0\left(h_0,y:s\right)+{\epsilon }_0]$$

(4)

Theoretically, the models are similar, with the same procedures used to find the parameters of interest; therefore, their application depends on the research context, the type of good or service—in this case, whether the good offers a positive or negative externality—the implementation of public policies that allow for the estimation of the costs individuals are willing to accept to undertake a given action, the availability of data, and the ease of application the method.

3. Results

3.1. Social Perception—Socioeconomic Characteristics

The respondents living in the AA.HH. Virgen de Guadalupe community, whose ages ranged from 18 to 28, 29 to 59, and 60 and above, represented 16.48%, 67.58%, and 15.93% of respondents, respectively (

Table 1. Description of socioeconomic characteristics.

Items	%
a. Age Range
18 to 28	16.48
29 to 59	67.58
60 and above	15.93
Total	100
b. Gender
Female	60.44
Male	39.56
Total	100
c. Educational Level
No formal education	2.2
Primary	8.24
Secondary	63.74
Higher education	25.82
Total	100
d. Occupation
Employed	61.54
Student	4.4
Unemployed	6.04
Homemaker	23.08
Other	4.95
Total	100
e. Length of Residency
Less than 1 year	4.95
1 to 3 years	2.75
3 to 5 years	3.85
5 to 7 years	8.24
More than 7 years	80.22
Total	100
f. Income Level (in soles)
Less than 850	19.78
851–1200	51.1
1200–1800	20.88
1801–2500	4.95
More than 2501	3.3
Total	100

a). Additionally, it was found that many respondents were women, comprising 60.44%, while 39.56% were men (Table 1b). Furthermore, 63.74% of the respondents had attained a secondary education level, 25.82% had reached higher education, 8.24% had completed only primary education, and 2.2% had no formal education (Table 1c). It was also revealed that 61.54% of respondents had a paid job, 23.08% identified as homemakers, 6.04% were unemployed, 4.4% identified as students, and 4.95% had another occupation not previously mentioned (Table 1d). On the other hand, it was shown that most participants had established a prolonged residence, with 80.22% having lived in the area for more than 7 years. Additionally, 8.24% had lived in the area for 5 to 7 years, 4.95% had resided there for less than 1 year, 3.85% had lived there for 3 to 5 years, and 2.75% had resided in the area for 1 to 3 years (Table 1e). Lastly, it was observed that participants reported different income levels: 51.1% had an income between PEN 851 and PEN 1200, 20.88% had an income between PEN 1200 and PEN 1800, 19.78% had an income of less than PEN 850, 4.95% had an income between PEN 1801 and PEN 2500, and 3.3% had an income above PEN 2501 (Table 1f).

3.2. Ecological Perception

It was observed that 58.24% of the surveyed residents in the AA.HH. Virgen de Guadalupe community had extensive knowledge about the diseases caused by lead. Additionally, 30.22% reported having a moderate level of knowledge on the subject, while 10.99% indicated limited knowledge, and 0.55% stated that they were completely unaware of lead-related diseases (

Table 2. Description of ecological perception.

Items	%
a. Knowledge of lead-related diseases
A lot	58.24
Regular	30.22
Little	10.99
None	0.55
Total	100
b. Lead contamination reduction measures
Prevention and control	19.78
Improvement of industrial structures	31.87
Better environmental supervision	6.59
Pharmacological treatments	5.49
Industrial relocation	75.82
Total	100
c. Do you smoke?
Yes, very frequently	4.4
Yes, frequently	5.49
Yes, sometimes	5.49
No	90.11
Total	100
d. Are you exposed to smoking circumstances?
Yes, very frequently	6.04
Yes, sometimes	9.34
No	84.62
Total	100
e. How many hours do you spend outdoors each day?
<1 h	9.34
1–3 h	24.18
3–5 h	15.93
5–8 h	17.03
>8 h	33.52
Total	100
f. Impact on mood
No	2.75
Yes	97.25
Total	100
g. Medical consultation cost
PEN 0	8.24
PEN 1–99	25.82
PEN 100–499	35.16
PEN 500–1200	30.77
Total	100
h. Cost of work loss
PEN 0	4.95
PEN 1–499	12.09
PEN 500–999	48.35
PEN 1000–1200	24.18
Over 1200	10.44
Total	100

a). Furthermore, 75.82% of participants opted for industrial relocation as a measure to reduce lead pollution, 10.99% chose prevention and control, 6.59% selected increased environmental supervision, and 3.3% chose improving environmental structures and pharmacological treatments as alternatives to mitigate lead contamination (Table 2b). It was also observed that 90.11% of respondents stated they did not smoke, 5.49% smoked occasionally, and 4.4% smoked frequently (Table 2c). Additionally, 84.62% of participants indicated they were not exposed to secondhand smoke, 9.34% reported occasional exposure, while 6.04% indicated they were frequently exposed (Table 2d). Moreover, 33.52% of respondents indicated they spent more than 8 h outdoors each day, 24.18% stated they spent between 1 and 3 h, and 17.03% reported spending between 5 and 8 h outdoors. Furthermore, 15.83% spent 3 to 5 h outdoors, and 9.34% reported spending less than 1 h outdoors (Table 2e). Additionally, 97.25% believed that people’s mood was being affected by lead pollution, while 2.75% believed it was not affecting people’s mood (Table 2f). Additionally, 39.01% of participants estimated that medical consultation costs would range between PEN 500 and PEN 1200, 32.42% believed the cost would be between PEN 100 and PEN 499, 20.33% of respondents believed it would cost more than PEN 1200, 7.69% estimated the cost would be between PEN 1 and PEN 99, and 0.55% believed medical care would cost PEN 0 (Table 2g). Lastly, 46.7% of respondents considered that the cost of job loss would range between PEN 500 and PEN 1200, 29.12% believed it would exceed PEN 1200, 19.23% believed it would range between PEN 100 and PEN 499, and 4.95% estimated it would be between PEN 1 and PEN 99 (Table 2h).

3.3. Aesthetic Perception

It was observed that among the surveyed individuals residing in AA.HH. Virgen de Guadalupe, 98.35% considered factory emissions to be the primary source of pollution. Additionally, 1.1% attributed it to vehicle emissions, and 0.55% indicated that solid waste was the main cause (

Table 3. Description of aesthetic perception.

Items	%
a. Source of Pollution
Vehicle emissions	1.1
Solid waste	0.55
Factory emissions	98.35
Total	100
b. Degree of Impact
High	73.63
Moderate	24.73
Low	1.65
Total	100
c. Well-being
High	67.58
Moderate	28.02
Low	3.85
None	0.55
Total	100

a). Furthermore, 73.93% of participants believed that air pollution significantly affects the natural landscape, 24.73% considered the impact moderate, and 1.65% thought it was minimal (Table 3b). Finally, 67.58% of respondents stated that the alteration of the natural landscape greatly impacts people’s well-being, 28.02% considered the impact moderate, 3.85% perceived it as minimal, and 0.55% believed that changes to the natural landscape had no effect on people’s well-being (Table 3c).

3.4. Willingness to Accept (WTA) and Willingness to Pay (WTP)—Data Description

According to the context of the research, it was observed that 62.09% of the respondents residing in the AA.HH. Virgen de Guadalupe community were willing to accept monthly monetary compensation for lead contamination, while 37.91% were not (

Table 4. Description of WTA and WTP.

Items	%
a. Willingness to Accept
No	37.91
Yes	62.09
Total	100
b. How much would you be willing to accept in PEN per month?
21–50	0.88
51–80	4.39
81–100	14.04
101–300	17.54
300 or more	63.16
Total	100
c. Willingness to Pay
No	56.04
Yes	43.96
Total	100
d. How much would you be willing to pay in PEN per month?
Less than 20	40.24
21–50	25.61
51–80	12.2
81–100	7.32
101–300	8.54
300 or more	6.1
Total	100

a). Furthermore, 63.16% of the respondents were willing to accept monthly compensation greater than PEN 300 for lead contamination, 17.54% were willing to receive between PEN 101 and PEN 300, 14.04% were willing to receive between PEN 81 and PEN 100, 4.39% were willing to receive between PEN 51 and PEN 80, and 0.88% were willing to receive between PEN 21 and PEN 50 (Table 4b). Additionally, 56.04% of respondents expressed a willingness to pay monthly to mitigate lead contamination, while 43.96% were not willing to do so (Table 4c). Moreover, 40.24% of respondents were willing to pay between PEN 5 and PEN 20 per month to help reduce lead contamination, 25.61% were willing to pay between PEN 21 and PEN 50 per month, 12.2% were willing to pay between PEN 51 and PEN 80 per month, 8.54% were willing to pay between PEN 101 and PEN 300 per month, 7.32% were willing to pay between PEN 81 and PEN 100 per month, and 6.1% were willing to pay more than PEN 300 per month. Lastly, 75.82% of the respondents indicated that the factors influencing their decision to pay were due to the belief that lead contamination should be addressed by polluting companies and the government, 8.24% indicated that it was due to lead levels, 7.69% attributed it to salary level, 6.04% believed the government would not be able to achieve the goals to reduce contamination, and 2.2% believed that everyone is responsible for the contamination (Table 4d).

Consequently, it is observed that the personal impact on those affected by pollution may lead them to accept compensation, as they experience immediate negative effects on their health and quality of life, with no prospects for improvement in the future. On the other hand, those who do not feel affected are more willing to pay to mitigate the problem, as they still have expectations for their future. However, the disparity in the models does not exclude the perception of responsibility, nor the levels of awareness and commitment, which suggests an expectation of action to address the problem.

3.5. Willingness to Accept (WTA) and Willingness to Pay (WTP)—Models

3.5.1. Coefficients and Significance of the Variables Influencing WTP and WTA

Table 5. Logit model coefficients and fit statistics for willingness to pay (WTP) and willingness to accept (WTA).

Variable	WTP (Willingness to Pay)	WTA (Willingness to Accept)
mWTP	0.012 **
mWTA		0.011 ***
I. Social Perception
P4	0.412	0.895 **
P6	−0.226	−0.846 **
P9	0.172	0.368
II. Ecological Perception
P11	0.013	−0.736
III. Temporal Perception
P12	−2.117 ***	−2.606 ***
P14	0.278 **	0.567 **
IV. Aesthetic Perception
P19	0.612 *	1.255 ***
_cons	−1.1	−2.2
N	182	182
Log-likelihood	−109.087	−89.225
chi2	31.466	63.112
AIC	236.174	196.45
BIC	265.01	225.287

p < 0.10 *, p < 0.05 **, p < 0.01 ***.

shows the results of the interactions developed by the STATA16 software when running the logistic model (logit) to find the coefficients of each indicator that could have the greatest effect, indicating the best overall measure of goodness-of-fit. It is assumed that the model selected and identified based on this criterion has good performance in terms of prediction. As shown in Table 5, two models are proposed with different indicators in order to estimate and understand how the explanatory indicators, such as social, economic, and environmental indicators, affect the outcome. Regarding the joint significance of the logistic models, it is observed that the relationship between the model coefficients and the probability of willingness to pay (WTP) or willingness to accept (WTA) is statistically significant at a 5% significance level.

Based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) indices, the model with the greatest consistency is the willingness to accept model. This is reflected in the fact that residents prioritize their current consumption and their inability to change their present situation. The decision to accept compensation is grounded in the economic theory of intertemporal consumption, which is reflected in preferences for obtaining greater utility and the uncertainty regarding the future.

3.5.2. Statistical Description of the Best Model

Table 6. Statistical description of the best model.

Variable	Obs	Mean	Standard Deviation	Min	Max
mWTP	182	225.03	70.724	35.5	300
mWTA	182	46.04	50.825	12.5	300
P4	182	1.99	0.571	1	3
P6	182	3.13	0.643	1	4
P9	182	2.21	0.929	1	5
P11	182	0.79	0.408	0	1
P12	182	0.90	0.299	0	1
P14	182	3.41	1.403	1	5
P19	182	1.28	0.486	1	3

shows the description of the coefficients for each indicator that has a significant effect on the WTP and WTA models. The indicators that present the strongest effect in the model are socioeconomic characteristics (age, education level, and income), air quality indicators (measures to reduce pollution), indicators of temporary exposure to pollution (smoker and leisure hours), and indicators of aesthetic appreciation and environmental beauty (the effect of pollution on the landscape).

Given the above, these indicators were included for the specification of the model. The WTP model is specified as follows:

$$\begin{array}{cc}Prob (WTP⁄x)& =\beta \_0+\beta \_1\ast MDAP+\beta \_2\ast P4+\beta \_3\ast P6+ \beta \_4\ast P9+ \beta \_5\ast P11+\beta \_6\ast P12\\ & +\beta \_7\ast P14+\beta \_8\ast P19 +\epsilon \end{array}$$

(5)

Meanwhile, the linear form of the WTA model is specified as follows:

$$\begin{array}{cc}Prob (WTA⁄x)& =\beta \_0+\beta \_1\ast MDAA+\beta \_2\ast P4+\beta \_3\ast P6+ \beta \_4\ast P9+ \beta \_5\ast P11+\beta \_6\ast P12\\ & +\beta \_7\ast 14+\beta \_8\ast 19 +\epsilon \end{array}$$

(6)

3.5.3. Logit Model Estimates with Variables Influencing WTP and WTA

In

Table 7. Relationship of independent indicators to WTP and WTA.

Description	Variables	WTP		WTA
		Relationship	Results	Relationship	Results
Willingness to Pay Amount	mWTP	+ positive	Higher offers on the amount willing to pay increase the probability that people are willing to pay	positive
Willingness to Accept Amount	mWTA	positive		positive	Higher offers on the amount willing to accept increase the probability that people are willing to accept
Age	P4	positive	Older individuals have a higher probability of being willing to pay (↑ P4—mWTP ↑)	positive	Older individuals have a higher probability of being willing to accept (↑ P4—mWTA ↑)
Education Level	P6	negative	Higher education levels decrease the probability of being willing to pay (↑ P6—mWTP ↓)	negative	Higher education levels decrease the probability of being willing to accept (↑ P6—mWTA ↓)
Income	P9	positive	Higher income increases the probability of being willing to pay (↑ P9—mWTP ↑)	positive	Higher income increases the probability of being willing to accept (↑ P9—mWTA ↑)
Measures to Reduce Pollution	P11	positive	People who believe that improvements and relocation of industrial areas reduce pollution have a higher probability of being willing to pay (↑ P11—mWTP ↑)	negative	People who believe that improvements and relocation of industrial areas reduce pollution have a lower probability of being willing to accept (↑ P11—mWTA ↓)
Smoking Status	P12	negative	Non-smokers have a lower probability of being willing to pay (↑ P12—mWTP ↓)	negative	Non-smokers have a lower probability of being willing to accept (↑ P12—mWTA ↓)
Leisure Time	P14	positive	More leisure time increases the probability of being willing to pay (↑ P14—mWTP ↑)	positive	More leisure time increases the probability of being willing to accept (↑ P14—mWTA ↑)
Effect of Pollution on the Landscape	P19	positive	Higher levels of pollution affecting the landscape increase the probability of being willing to pay (↑ P19—mWTP ↑)	positive	Higher levels of pollution affecting the landscape increase the probability of being willing to accept (↑ P19—mWTA ↑)

, it can be observed that the price indicator or willingness to pay amount (WTPA) has a positive sign. This indicator has a direct relationship with WTP, meaning that a higher offer regarding the average amount individuals are willing to pay increases the likelihood that people will be willing to pay to mitigate air pollution caused by lead. Additionally, the odds ratio of the WTPA indicator coefficient is 1.0125, which shows that the probability of residents in the Virgen de Guadalupe settlement, located in the Mi Perú district, accepting to pay for a reduction in lead air pollution increases by 1.0125.

Table 8. ODDs ratio of the WTP and WTA models.

Description	Variables	Modelo DAP		Modelo DAA
		Odds Ratio	Results	Odds Ratio	Results
Age	P4	1.5097	Individuals with a higher age are 1.509 times more likely to be willing to pay compared to others.	2.4474	Individuals with a higher age level are 2.44 times more likely to be willing to accept compared to others.
Educational Level	P6	0.7979	Individuals with a higher educational level are (1/0.7979 = 1.25) 1.25 times less likely to be willing to pay compared to others.	0.4291	Individuals with a higher educational level are (1/0.4291 = 2.33) 2.33 times less likely to be willing to accept compared to others.
Income	P9	1.1878	Individuals with a higher income level are 1.18 times more likely to be willing to pay compared to others.	1.4448	Individuals with a higher income level are 1.44 times more likely to be willing to accept compared to others.
Measures to Reduce Pollution	P11	1.0135	Individuals who believe that better and relocated industrial reduction will reduce pollution are 1.02 times more likely to be willing to pay compared to others.	0.4790	Individuals who believe that better and relocated industrial reduction will reduce pollution are (1/0.4790 = 2.08) 2.08 times less likely to be willing to accept compared to others.
Smoker	P12	0.1204	Non-smokers are (1/0.1204 = 8.30) 8.30 times less likely to be willing to pay compared to others.	0.0738	Non-smokers are (1/0.0738 = 13.54) 13.54 times less likely to be willing to accept compared to others.
Free Time	P14	1.3205	Individuals who spend more free time are 1.32 times more likely to be willing to pay compared to others.	1.7638	Individuals who spend more free time are 1.76 times more likely to be willing to accept compared to others.
Effect of Pollution on Landscape	P19	1.8442	Individuals who believe that air pollution sources alter the natural landscape are 1.8 times more likely to be willing to pay compared to others.	3.5066	Individuals who believe that air pollution sources alter the natural landscape are 3.50 times more likely to be willing to accept compared to others.

shows the results of the ODDs ratio regarding the WTP and WTA models. The indicator for the amount willing to accept (WTA) has a positive sign. This indicator has a direct relationship with WTA, meaning that the higher the offer for the amount willing to accept, the higher the probability that people are willing to accept compensation for the adverse consequences of lead air pollution. On the other hand, the odds ratio for the WTP coefficient indicator is 1.0107, which indicates that the probability of the residents of the AA.HH. Virgen de Guadalupe community, in the Mi Perú district, accepting compensation for the negative consequences of lead air pollution increases by 1.0107.

3.5.4. Calculation of Willingness to Pay (WTP)

The mean as a measure of welfare in the WTP model is given when the variation in utility is zero, meaning the individual will be indifferent between making the payment and receiving the environmental quality improvement, which will lead to a higher level of welfare, or not making the payment and perceiving the initial utility [20].

Table 9. Coefficients and average of the explanatory variables.

Variable	Coefficient (b)	Average (x)	b * x
mWTP	0.0124	1.9945	0.8215
P4	0.4119	1.9945	0.8215
P6	−0.2258	3.1319	−0.7070
P9	0.1721	2.2088	0.3802
P11	0.0134	0.7912	0.0106
P12	−2.1170	0.9011	−1.9077
P14	0.2780	3.4121	0.9487
P19	0.6121	1.2802	0.7836
_cons	−1.1032	−	−1.1032

shows the coefficients and averages of the models. Using the coefficients from Table 9, the WTP calculations based on the model yield a positive value. Likewise, this calculation shows that the average WTP is PEN 62.48 per inhabitant per month, resulting in an annual amount of PEN 1,201,865.28 for the total population of 1603, with an exchange rate equivalent to USD 329,103.16. This amount represents the willingness to pay to mitigate and reduce lead air pollution in the AA.HH. Virgen de Guadalupe community.

3.5.5. Calculation of Willingness to Accept (WTA)

Analyzing whether the individual accepts a change in utility, from the utility level U_0 to the utility level U_1, the hypothetical model also allows for determining the minimum willingness to accept monetary compensation for a reduction in the quality of the resource (for example, moving from clean, odorless air to air with unpleasant odors) [20]. Using the coefficients from

Table 10. Coefficients and averages of the explanatory variables.

Variable	Coefficients (b)	Mean (x)	b * x
mWTA	0.0107	1.9945	1.7851
P4	0.8950	1.9945	1.7851
P6	−0.8460	3.1319	−2.6494
P9	0.3680	2.2088	0.8127
P11	−0.7360	0.7912	−0.5824
P12	−2.6060	0.9011	−2.3483
P14	0.5675	3.4121	1.9362
P19	1.2547	1.2802	1.6062
_cons	−2.2003	−	−2.2003

, the WTA calculations through the model show a positive value. Additionally, this calculation shows that the average WTA is PEN 153.59 per inhabitant per month, amounting to an annual total of PEN 2,954,457.24 for the 1603 inhabitants, with a corresponding exchange rate of USD 809,010.16. This amount represents the compensation the population is willing to accept for air pollution by lead in the AA.HH. Virgen de Guadalupe community.

3.6. Model Adjustments

Predictions for the WTP and WTA Model

In

Table 11. Prediction matrix for the WTP and WTA Model.

Classified	WTP			WTA
	(D)	(~D)	Total	(D)	(~D)	Total
+positive	36	16	52	95	27	122
−negative	44	86	130	18	42	60
Total	80	102	182	113	69	182
Classified	≥0.5	≥0.5		≥0.5	≥0.5
True D defined as WTP ≠ 0
Sensitivity		Pr (+D)	45.00%		Pr (+D)	84.07%
Specificity		Pr (−~D)	84.31%		Pr(−~D)	60.87%
Positive predictive value		Pr (D+)	69.23%		Pr (D+)	77.87%
Negative predictive value		Pr (~D−)	66.15%		Pr (~D−)	70.00%
False + rate for true ~D		Pr (+~D)	15.69%		Pr(+~D)	39.13%
False − rate for true D		Pr (−D)	55.00%		Pr (−D)	15.93%
False + rate for classified +		Pr (~D+)	30.77%		Pr (~D+)	22.13%
False − rate for classified −		Pr (D−)	33.85%		Pr (D−)	30.00%
Correctly classified			67.03%			75.27%

, the adjustment of the estimated model for WTP and WTA is presented. Regarding WTP, out of the 182 respondents in the Virgen de Guadalupe settlement in the Mi Perú district of the Constitutional Province of Callao, 67.03% were correctly classified. Additionally, of the 130 respondents who expressed willingness to mitigate lead air pollution, 69.23% were correctly classified by the model, and of the 52 respondents who expressed unwillingness to pay, 67.03% were correctly predicted by the model. As for WTA, it was observed that 75.27% were correctly classified, and of the 122 respondents who expressed willingness to accept, 77.87% were correctly classified by the model, while the 60 respondents who expressed unwillingness to accept were correctly predicted by the model at a rate of 75.27%.

4. Discussion

Table 12 shows a comparison between different studies on Willingness to Pay (WTP) and Willingness to Accept (WTA), revealing significant disparities both in the reported amounts and in the contexts and methodologies applied. A preliminary analysis of WTP in international studies shows large variations in amounts depending on the type of pollution and the socioeconomic conditions of the populations studied. For example, in the study conducted in the Metropolitan Region of Chile for the recovery of green areas, the WTP was USD 44,375.88 per person annually, reflecting a high level of concern for air quality and environmental recovery in a middle-income country. In contrast, in the Bajío Industrial Region of Mexico, the WTP to reduce atmospheric pollution was USD 9,861,136.84 annually, an extremely high figure justified by the industrial impact in the region and the size of the affected population. This study reflects how industrial pollution tends to generate a higher willingness to pay due to direct risks to health and the environment.

In Bosa, Colombia, the WTP was considerably higher, reaching USD 103,081.45 annually, which could be explained by the high level of urbanization and severe air pollution in the city of Bogotá. In national studies, such as the one conducted in Juliaca, Peru, the WTP was USD 40,869,036.33 annually, reflecting concern over high levels of air pollution in rural and urban Andean areas [39]. In this current study on lead pollution in AA.HH. Virgen de Guadalupe, Callao, the WTP was USD 329,103.16 annually, highlighting the population’s concern about the effects of lead on health, particularly in a community affected by industrial activities, as shown by this study.

As for studies conducted in China and South Korea, WTP remained relatively low compared to other countries: USD 57.9 per household/year in Beijing for a reduction in PM_2.5 [21] and USD 87.5 per household/year in Seoul [23]. This difference may be due to the high population density in these countries, which dilutes the total amount that everyone is willing to pay. Additionally, in the case of Egypt, the study showed that WTP is significantly higher in more polluted areas, reaching USD 5000 annually [22], reinforcing the idea that risk perception and direct exposure are key factors in valuing air quality.

On the other hand, WTA also presented significant variations, particularly in a study by Zhang [29] in China, where the compensation required by residents in industrial areas affected by atmospheric pollution was 20% to 35% higher than usual amounts [29]. This finding suggests that, when it comes to accepting compensation, people tend to overestimate the losses they might suffer, which creates a significant disparity between WTP and WTA. This discrepancy may be related to the loss aversion effect, where individuals value what they already own or are exposed to losing more than the potential benefits of environmental improvement. In the case of the study by Woojin [28] in South Korea, the WTA to accept a hydrogen plant nearby was KRW 7.8 million annually (equivalent to USD 6037) [28], a value that clearly shows how perceived risk impacts the compensation required by the population.

Finally, the disparity between WTP and WTA is a recurring phenomenon in all the studies analyzed. In general, people require significantly higher compensation (WTA) to accept exposure to risks or the deterioration of environmental quality, compared to what they are willing to pay (WTP) to improve or prevent such risks. This difference is clearly observed in studies by Zhang [29] and Woojin [28], where WTA considerably exceeds WTP, reinforcing the theory of loss aversion and the subjective perception of environmental risk. This finding has important implications for public policy, as it suggests that traditional approaches based solely on WTP may underestimate real social demands and the compensation necessary to ensure the acceptance of projects that impact the environment and public health.

An important aspect to consider in the interpretation of WTP and WTA results is the potential influence of social desirability bias and hypothetical bias. Since these values are obtained from surveys and contingent valuation studies, it is possible that respondents tend to overestimate their willingness to pay (WTP) for environmental improvements or, in contrast, to inflate their willingness to accept trade-offs (WTA) due to the perception that a higher response may influence public policy decision-making. Social desirability bias may lead participants to declare amounts higher than their true willingness to pay to project a more pro-environmental or public health-committed image, without this translating into an actual intention to pay.

Furthermore, hypothetical bias is a key limitation in studies using declarative valuation methods, since respondents do not face a real decision with financial consequences, which could lead to responses that do not accurately reflect their behavior in a market context. This bias is especially relevant when the amounts reported in WTP or WTA are significantly high, as observed in some of the studies analyzed. The absence of a verification mechanism or incentives for responses to reflect real decisions may artificially inflate the figures obtained. Therefore, future research could benefit from the use of experimental approaches or preference revelation mechanisms closer to real market situations, to reduce the gap between stated intention and effective behavior in relation to environmental valuation.

Table 12. Comparisons of WTP and WTA obtained from different studies.

Category	Location	Title of the Study	Type of Pollution	Willingness (WTP/WTA)	Local Currency	Equivalent in USD	Methodology	Year of Study	Reference
International	Metropolitan Region, Chile	Valuation of Air Quality and Green Areas	Air quality (green area recovery)	44,375.88 dollars per year	USD	44,375.88	Contingent valuation method	2020	[40]
International	Bajío Industrial Corridor, Mexico	Valuation of the Impact of Atmospheric Pollution	Atmospheric pollution	166,451,905 million pesos	MXN	9,861,136.84	Contingent valuation method	2021	[41]
International	Bosa, Colombia	Valuation of Air Pollution in Bogotá	Air pollution	412,266.422 million pesos	COP	103,081.45	Contingent valuation method	2022	[42]
National	Metropolitan Area of Juliaca, Peru	Valuation of Air Pollution in Juliaca	Air pollution	1,465,136.61 million soles	PEN	40,869,036.33	Contingent valuation method	2017	[39]
National	AA.HH. Virgen de Guadalupe, Callao, Peru	Study on Airborne Lead Pollution	Air pollution by lead	1,201,865.28 million soles	PEN	329,103.16	Contingent valuation method	2023	This study
International	Beijing, China	Willingness to Pay for PM2.5 Reduction	PM2.5 pollution	57.9 dollars per household/year	CNY	57.9	Discrete choice method	2020	[21]
International	Seoul, South Korea	Valuation of Air Quality in Seoul	Air pollution	87.5 dollars per household/year	KRW	87.5	Contingent valuation method	2021	[23]
International	Egypt	Evaluation of the Impact of Air Pollution in Egypt	Air pollution	Higher WTP in polluted areas	EGP	5000	Contingent valuation method	2023	[22]
International	China	Impact of Pollution in Industrial Areas	Atmospheric pollution	35% higher compensation	CNY	-	Contingent valuation method	2023	[29]

Table 12. Comparisons of WTP and WTA obtained from different studies.

Category	Location	Title of the Study	Type of Pollution	Willingness (WTP/WTA)	Local Currency	Equivalent in USD	Methodology	Year of Study	Reference
International	Metropolitan Region, Chile	Valuation of Air Quality and Green Areas	Air quality (green area recovery)	44,375.88 dollars per year	USD	44,375.88	Contingent valuation method	2020	[40]
International	Bajío Industrial Corridor, Mexico	Valuation of the Impact of Atmospheric Pollution	Atmospheric pollution	166,451,905 million pesos	MXN	9,861,136.84	Contingent valuation method	2021	[41]
International	Bosa, Colombia	Valuation of Air Pollution in Bogotá	Air pollution	412,266.422 million pesos	COP	103,081.45	Contingent valuation method	2022	[42]
National	Metropolitan Area of Juliaca, Peru	Valuation of Air Pollution in Juliaca	Air pollution	1,465,136.61 million soles	PEN	40,869,036.33	Contingent valuation method	2017	[39]
National	AA.HH. Virgen de Guadalupe, Callao, Peru	Study on Airborne Lead Pollution	Air pollution by lead	1,201,865.28 million soles	PEN	329,103.16	Contingent valuation method	2023	This study
International	Beijing, China	Willingness to Pay for PM2.5 Reduction	PM2.5 pollution	57.9 dollars per household/year	CNY	57.9	Discrete choice method	2020	[21]
International	Seoul, South Korea	Valuation of Air Quality in Seoul	Air pollution	87.5 dollars per household/year	KRW	87.5	Contingent valuation method	2021	[23]
International	Egypt	Evaluation of the Impact of Air Pollution in Egypt	Air pollution	Higher WTP in polluted areas	EGP	5000	Contingent valuation method	2023	[22]
International	China	Impact of Pollution in Industrial Areas	Atmospheric pollution	35% higher compensation	CNY	-	Contingent valuation method	2023	[29]

5. Conclusions

The economic valuation of lead air pollution in the AA.HH. Virgen de Guadalupe community is crucial for understanding how residents perceive and assess the detrimental effects of pollution on their quality of life. This study reveals that while residents expect industrial relocation or improvements to reduce air pollution, they show lower willingness to pay (WTP) for mitigation efforts; however, they are more inclined to accept monetary compensation (WTA) for the negative impacts of pollution. Additionally, residents with higher incomes exhibit a greater propensity both to pay for environmental improvements and to accept compensation for adverse health and environmental effects.

The application of binary choice models (logit) was instrumental in quantifying monetary values that can inform decision-making processes in the public and private sectors. Findings indicate that the average monthly WTP stood at PEN 62.48 per inhabitant, summing to an annual total of PEN 1,201,865.28 for the population of residents. Nevertheless, environmental awareness did not significantly influence the amount that individuals were willing to pay to mitigate lead air pollution.

Similarly, the logit model for willingness to accept (WTA) compensation revealed an average monthly WTA of PEN 153.59 per inhabitant, corresponding to an annual sum of PEN 2,954,457.24 for the entire population. Again, environmental awareness was not a significant predictor of the compensation individuals would accept for lead pollution.

Taken together, these insights can directly inform local policies and interventions by highlighting how economic incentives, compensation structures, and industrial relocation or improvement programs can be designed to address community concerns. For instance, the gap between WTP and WTA underscores the importance of transparent risk communication and stronger public engagement efforts, which may foster increased willingness to invest in preventive measures. Additionally, authorities could tailor compensation schemes or enforcement strategies to the socio-economic profiles identified in the study, ensuring that policies are both equitable and effective in mitigating the persistent issue of lead air pollution in vulnerable communities like AA.HH. Virgen de Guadalupe.

These findings underscore the significance of integrating both willingness to pay (WTP) and willingness to accept (WTA) considerations when formulating strategies to address environmental challenges, as well as the need for targeted policies that reflect the specific socio-economic dynamics of affected communities. This research supplies a clear monetary framework that can guide future public policies and compensation strategies for mitigating lead air pollution in vulnerable communities like AA.HH. Virgen de Guadalupe. Nonetheless, the relatively smaller sample size of 182 participants—constrained by daytime-only data collection in a high-risk area—represents a limitation for the generalizability of our findings. We therefore recommend that future studies expand the sample frame, consider data collection during extended hours or weekends (where security permits), and refine inclusion criteria to capture a broader range of perspectives. Such measures would enhance both the representativeness of the results and their applicability to evidence-based policy-making in similarly at-risk communities.