3. Valuation of the Health Effects

The phenomenon of adverse gender ratio whereby male members exceed female members in Punjab is also reflected in our selected sample. Out of the total number of households surveyed in our sample, the ratio of male to female was approximately 55–45 % in all the three villages selected (Table 3.3). Population in working age out of total population of all the surveyed households was 70 %. About 28 % of the surveyed household members in the three villages were illiterate or educated up to primary level only. The highest (64 %) households were educated up to secondary level while only 7 % had higher education above secondary level. 67 % household members were self-employed in farming while 14 % were wage earners and 11 % were involved in formal or informal salaried work.

The average size of holdings was around 7 acres among the selected villages out of which 5.4 acres were irrigated (Table 3.4). On an average, leased-in area was more than leased-out area among the selected households, except in Ajnauda Kalan where leased-out area exceeded leased-in area. The cropping intensity was around two crops in a year among all the selected households. On an average, household assets valued at Rs. 3.5 lakhs that included ownership of tractor, submersible pump set, milch and non milching animals and animal house (Table 3.5). Economics of farming is worked out in Table 3.6.

In addition to rice and wheat grown during kharif and rabi seasons, some area was also devoted to green fodder crops like jowar and bajra during the kharif season and barseem during the rabi season. There were some miniscule examples of one or two farmers growing mustard (oilseed), moong (pulse crop), sugarcane and maize. The input-output table is worked out for the major crops of rice, wheat and green fodder. Among these three crops, returns were highest in wheat, followed by rice and green fodder while the latter was mostly produced for domestic animals and the crop was not sold in the market. Irrigation, labour, machine cost and plant protection were the major items of cost of production in rice and wheat while seed was the second significant item in the case of green fodder. The net returns per acre were around 14 thousand for rice and 16.5 thousand for wheat. However, while calculating cost of production, we have not included the rental value of own land, interest paid on fixed capital and depreciation value of implements and therefore value of net returns is exaggerated.

Table 3.7 presents the details of total amount of stubble (by-product) generated on the field, its various uses and various alternatives adopted by the farmers to dispose of the stubble for the two main crops of paddy and wheat. On an average, total amount of stubble generated for paddy and wheat per acre was around 23 and 19 quintals, respectively. Out of this in the case of paddy, more than 85 % was burnt in the open field and less than 10 % was incorporated using rotavator while rest of 8 % was used for other purposes. In the case of wheat, 77 % of the total amount was used as fodder for animals while 9 % was incorporated and around 11 % was burnt. The reason for burning high amount of paddy stubble in the open field was non availability of any machine which can be used to collect the crop remains after the combine harvest.⁴ Rotavator was used by around 10 % of the households to incorporate rice remains after combine harvest but it could be used only after partly or fully burning the field. On the other hand, in the case of wheat, 90 % of households used reaper driven by tractor after combine harvest to collect the wheat remains that were used as fodder for animals. Among the selected households, less than 7 % in wheat and 2 % in paddy harvested manually to collect the stubble from the field. More than 80 % households in wheat used reaper to collect wheat residue to use it as fodder. In the case of paddy, around 8 % households indicated using machine (rotavator) to remove crop remains from the field while around 90 % households fully burnt the field to remove the crop residue (Table 3.8).

Providing a reasoning why the majority of the households were burning rice residue, around 41 % households indicated that there was shortage of time period between harvesting of paddy crop and sowing of wheat crop. A majority, 48 % indicated that burning was more economical and only around 8 % opined that they were indulging in burning because there was shortage of labour force for manual removal of the residue (Table 3.9). Shortage of time in wheat sowing after harvest of paddy is further confirmed from the data shown in Table 3.10. Among the households who burnt the field the average time of sowing interval was 13 days while households who incorporated stubble using rotavator/zero drill, the average interval was 15 days. The average interval went up to 18 days among those households who used manual or other methods of stubble removal. In the case of wheat, however, there was sufficient time of 46–48 days for removal of stubble before the transplantation of paddy putting no additional compulsion on the farmers to burn the field for removal of crop residue. According to 82 % of the selected farmers, the easiest and quickest way of paddy stubble removal was burning while 14 % indicated incorporation using rotavator (Table 3.11). Not only the majority of farmers were of the opinion that burning was the quickest way of stubble removal but they were also convinced that this method of stubble management was ensuing them the maximum crop yield (Table 3.12).

A few farmers who incorporated the stubble of paddy, they used either rotavator or zero drill machines. The happy seeder machine (new invention for incorporating rice stubble) was not yet adopted by any selected farmers (Table 3.13). However, although farmers were convinced that burning was not harming the level of crop yield but they pointed out that burning of field added extra cost to the production because of top soil getting affected by the burning. Some of these farmers observed changes in colour of the top soil on the surface of the land after burning. Farmers indicated that in comparison to incorporation, burning required, on an average, 20–50 kg of extra chemical fertilizer that added Rs. 250–300 per acre extra cost of production (Table 3.14). The farmers who burnt the field (fully or partly) to clear the wheat stubble used 169 kg of urea in the next crop of paddy while those who incorporated or adopted other means used 145 and 148 kg of urea, respectively. Similarly, those farmers who burnt paddy field, used added amount of Di-Amonia Phosphate (DAP) to recapture the nutritive lost in the fire in comparison to those who incorporated or removed stubble manually (Table 3.16). Higher expenses are not only in terms of higher fertilizer but also in terms of higher irrigation requirement by those who burn their field to clear the stubble as indicated by Table 3.15. In paddy the total cost of irrigation was Rs. 2,220 for those who burn their filed in comparison to Rs. 2,000 for the farmers who incorporated the wheat stubble. In the case of wheat, per acre irrigation cost was Rs. 941 for those who burned the field but slightly less, Rs. 907 for those who removed stubble by other means. However, in wheat the cost of irrigation was higher for those who incorporated probably because of additional irrigation requirement for stubble fixation.

As mentioned in the beginning of the chapter, air pollution leads to respiratory diseases like eye irritation, bronchitis, asthma etc., increasing individuals’ disease mitigation expenses and also affecting ones’ working capacity. In addition, open burning in the field affects life of animals, birds and other insects below and above the earth. Burning at times also causes poor visibility and increases the incidents of road accidents. Our household survey shows that paddy stubble burning among our selected villages leads to air pollution and several other problems. There was no conclusive evidence of smoke caused by stubble burning affecting the health or productivity of the milk producing animals (Table 3.17). On the other hand, a significant numbers of households indicated that smoke caused loss to the vegetation in the field and it also led to accidents taking place on the road during the peak of stubble burning that happens in the months of October and November every year. To our question whether households were aware of harmful effects of residue burning, more than 90 % selected households indicated yes but almost none of them was taking any preventive measures to escape from smoke disease before the beginning of the harvest season.

Irritation in eyes and congestion in the chest were the two major problems faced by the majority of the household members (Table 3.18, Fig. 3.1). Respiratory allergy, asthma and bronchial problems were the other smoke related diseases which affected household members in the selected villages. Almost 50 % of the selected households indicated that their health related problems get aggravated during or shortly after harvest when crop stubble burning is in full swing during the months of October, November and December. In the peak season, affected families had to consult doctor or use some home medicine to get relief from irritation/itching in eyes, breathing problem and similar other smoke related problems. On an average, the affected members suffered at least half a month from such problems and had to spend Rs. 300–500 per household on medicine (Table 3.19). In addition there were few examples where a family member had to be hospitalized for three to four days and additional expenditure was incurred. Table 3.20 presents the total medical expenses incurred due to health problem caused by crop stubble burning. Members suffering from smoke related chronic and non-chronic diseases observed that their problem becomes acute and the severity increases during the time of crop stubble burning. On an average, households spent around more than a thousand Rupees on the non-chronic respiratory diseases like coughing, difficulty in breathing, irregular heartbeat, itching in eyes decreased lung function etc., during the year 2008–2009.⁵ However, out of this total expenditure, around 40–50 % was spent during the months of October and November during the time of crop stubble burning. There was an additional cost in terms of household members remaining absent from work due to illness (Table 3.20).

Some respondents pointed out that Punjab government from time to time advises farmers not to set their field on fire. It is advertised in the local newspapers to make people aware about the adverse effects of crop stubble burning. Some respondents pointed out that District Commissioner directed gram panchayats to prevent stubble burning (Table 3.21). The administration even makes such announcements by loud speaker in the villages. However, no documentary or road shows were organized in this regard. Similarly, those farmers who incorporate stubble instead of burning it were not provided with any incentive from the administration. Although farmers were not aware about the invention of happy seeder which can provide alternate to burning, significant majority of them showed interest to buy such machine if the Punjab government gives sufficient financial support for such machine given the price of happy seeder exceeds Rs. 1 lakh in the market. The farmers indicated that if at least half of the price is born by the government for happy seeder they would be interested to buy the happy seeder machine.

Air quality affects the utility of individuals and an economic value exists. There are several ways to capture this economic value, viz., dose-response, revealed preferences and contingent valuation methods. The dose-response method assumes a relationship between air quality and morbidity (and/or mortality). It puts a price tag on air quality without retrieving people’s preferences for the good. But, such type of mechanical relationship of the dose response function does not take into account consumer behavior. The revealed preference methods assume that the consumers are aware of the costs/benefits of air quality and are able to adjust their behavior to reveal their preferences. This necessitates the need to have estimates of willingness to pay (WTP) or willingness to accept (WTA) on the basis of a consumer choice models aimed at measuring the strength of association between health effects and contaminated air quality.

Suppose an individual maximizes his/her utility through expenditure on marketed goods and services, X.⁶ The utility depends not only on X but also on the state of health, H of an individual which is affected by the level of air quality (non-marketed good). It is further assumed that the contaminated air quality, P is beyond the control of individuals, but individuals can at least partially reduce its effects through incurring defensive expenditure, D. The utility function is defined as:where U_X > 0, U_XX < 0, H_D > 0, H_P < 0, H_DD < 0, H_PP < 0.

The state of health affects individual’s work performance and hence the wage income. Moreover, it is also possible that the contaminated air quality make the individual so sick as to be completely incapacitated. During the time the individual is under this condition, he/she is absent from work and loses the wage income completely. Therefore ‘sick time’, S can also be assumed to be the function of defensive expenditure and contaminated air quality,where S_D < 0, S_P > 0, S_DD > 0, S_PP > 0

The Eq. (3.1) is maximized subject to the following constraints:

The time constraint is:where W is the work time and T is the total time available. The income (resource) constraint is:where, mS is the medical expenses which are assumed proportional to illness, S, I denotes non-wage income and w is referred as wage rate.

The Lagrangian of the problem is:

The first-order optimization conditions are:

Using the envelope theorem, Harrington et al. (1989) obtain the individual willingness to pay (WTP) as:and the marginal loss of social welfare (SW) associated with individual responses to deterioration in air quality, therefore, is:

Equation (3.8) shows that the cost of illness caused by the contaminated air can be grouped into five categories. The term direct disutility is very subjective and it is very difficult to find its monetary value. The second term, the lost work productivity measures the value of loss caused by the illness due to lower work productivity. This loss is caused when the sick person is present for work but is not able to work with his/her full productivity. The third term measures the loss in social welfare due to illness absence of individuals from work. The last two terms measure the expenses individual have to incur for defensive and mitigating activities due to contamination of air quality. In rural areas during survey we could not get figures on the defensive activities of individuals, therefore we measures only two values: medical expenses and value of lost time during illness. Thus our measure of social loss due to contaminated air provides the lower bound of the value.

To get the estimates of social welfare loss due to contaminated air in terms of health damages, we estimate the following two equations consisting of demand function for medical expenses (mS) and the workdays lost due to illness (S):andwhere:

Note that the dependent variable in Eq. (3.9) is a censored variable, i.e., the dependent variable is zero for corresponding known values of independent variables for part of the sample. Therefore, we use Tobit model for estimating the demand for mitigating activities:where mS_i refers to the probability of the ith individual incurring positive medical expenditure and x _i denotes a vector of individual characteristics, such as assets, age, sex, education, pollution parameter etc.

In Eq. (3.10) the dependent variable is a count of the total number of workdays lost due to air pollution related illness by an individual during the particular period; therefore, there are zeros for many observations. In this case Poisson regression model is appropriate as it considers the predominance of zeros and the small values and the discrete nature of the dependent variable. The least square and other linear regression models do not take into account these features. The Poisson regression model can be stated as follows:

This equation is non-linear in parameters; therefore, for estimation purpose by taking its natural log we convert it into an equation which is linear in parameters. Note that the Poisson regression model is restrictive in many ways. For example, the assumption that the conditional mean and variance of y _i , given x _i are equal, is very strong and fails to account for over dispersion.⁷ Table 3.22 gives the descriptive statistics of the variables used in the estimation of the models.

The welfare loss in terms of health damage due to increase in the concentration of particulate matters from paddy straw burning in the ambient environment can be estimated in terms of increase in the medical expenditure on mitigating activities and the opportunity cost of workdays lost and are presented in Table 3.25.

To get the estimates of welfare loss in terms of increased medical expenditure we need to obtain the marginal effects. The marginal effects in the case of Tobit estimation could be computed by taking partial derivatives of mitigating expenditure equation with respect to PM₁₀ and multiplying it by the probability of the dependent variable taking the non-zero values. If the ambient PM₁₀ level is reduced from the level observed during the harvesting period of rice in rural Punjab to the safe level (i.e., a reduction of 207 µg/m³ since the safe level defined under NAAQS is 100 µg/m³ for the 24 h average), the estimated reduction in medical expenditure turns out to be Rs. 2.17 for the months of October and November for a representative person.

Total rural population projected for October 2008 based on Census 2001 is 1,083 thousand and 16,839 thousand for the district of Patiala and the state of Punjab, respectively. Extrapolating this welfare loss for the entire rural population of Patiala and Punjab, it is estimated as Rs. 2.35 million and Rs. 36.52 million, respectively.

To get the marginal effects of reduction in PM₁₀ level on workdays lost, we differentiated partially the reduced form equation of workdays lost with respect to PM₁₀. The Poisson estimates show that 1 µg/m³ increase in PM₁₀ results in a marginal loss of 0.0000946 days for a representative individual in these two harvesting months. If the PM₁₀ level is reduced from the current level to the safe levels during rice harvesting period, the estimated gain in workdays is 0.03. In monetary terms, the loss in terms of workdays lost for a representative individual is estimated to be Rs. 2.35 and for rural Patiala district and rural Punjab state it turns out to be Rs. 2.54 million and 39.57 million, respectively assuming a wage rate of Rs. 120 per day.⁹

The total monetary loss (due to lost workdays and increased medical expenditures) caused in terms of health damages due to increase in ambient PM₁₀ level beyond the safe level for the rural areas of Patiala district and Punjab state is estimated as, Rs. 4.89 million and Rs. 76.09 million, respectively. These losses should be considered the lower bound of health damages caused by the increased air pollution level in rural Punjab. These estimates could be much higher if expenses on averting activities, productivity loss due to illness, monetary value of discomfort and utility could also be considered. There is additional monetary cost of burning to the farmers in terms of additional fertilizer, pesticides and irrigation as was shown by the survey results discussed in section 4. One also has to add into the above cost the losses of soil nutrient, vegetation, bio-diversity and accidents caused because of low visibility.