Читать книгу Extreme Events and Climate Change - Группа авторов - Страница 33

We are interested in estimating the impact of heat on agricultural output. If we were to use the heat index value as an independent variable directly impacting output, we are likely to encounter endogeneity problems due to the feedback between production and labor and vice versa. Results of an ordinary least squares (OLS) estimation using the heat index value as an independent variable directly against output may then yield spurious results. To correct for the endogeneity problem, we estimate the impact of heat on agricultural production using a two‐stage least squares (2SLS) instrumental variable (IV) method. We hypothesize that extreme heat affects agricultural productivity via impacts on labor employed in each crop. To capture this effect, we first estimate the impact of heat on labor requirements via the following specification:

(3)

The specific model for 3 is:

(3.1)

In these equations, D₁, D₂ are defined as the number of days that the heat index exceeds specific HI value thresholds for any given month during the harvesting season such that 95 ≤ HI < 100 andHI₂ > 100. Thus, for any given crop, D₁ is the number of days that the heat index exceeded values between 95 and 100^OF during any given harvesting month. D₂ is interpreted similarly for values exceeding 100^OF. X_Ct and X_At are defined as the cost of production and the harvested acreage at year t respectively. Finally, L_it denotes the crop labor requirement for each crop i at time t. The second stage of the specification shows the relationship between the estimated impacts of heat on labor on agricultural output:

(4)

The specific model for 4 is:

(4.1)

We estimate Eqs. (3.1) and (4.1) using a panel‐fixed effect approach. Results for both stages of the estimation are specified in Table 2.3. Prior to estimation, we tested for endogeneity for both crops for all variables. Results of the endogeneity test reject the null hypothesis of “all variables in the model are exogenous” with values obtained of p < 0.01. Panel A shows the results for the first stage of the estimation, Eq. (3.1), and panel B indicates the results for Eq. (4.1). In addition to melons and onions, we estimated the model for grapes and almonds. Although the directional results were in tandem with the literature previously mentioned, the estimations did not yield significant statistical results.

Table 2.3 Instrumental Variable (IV) Estimation, First and Second Stages (Eqs. 3.1 and 4.1).

Panel A: First Stage, Eq. 3.1	Onions	Melons
Variable	Coefficient	Coefficient
Cost	0.9 (0.027)	0.86 (0.043)
Acreage	0.03 (0.022)	0.12 (0.041)
Number of Days D1, (95oF–100oF)	0.015 (0.014)	0.03 (0.001)
Number of days D2 (>100oF)	0.02 (0.001)	0.005 (0.002)
Constant	−1.21	−1.16
Panel B: Second Stage, Eq. 4.1	Onions	Melons
Variable	Coefficient	Coefficient
Labor	−0.05 (2.064)	0.037 (1.89)
Cost	0.05 (1.965)	−0.04 (1.64)
Acres	1.03 0.123)	0.015 (0.371)
Constant	−6.53 (3.437)	15.37 (2.95)
Within R2	0.9	0.9

Panel A shows that for melons, as the number of days above the heat index increases the crop labor requirement also increases; this is true for both heat index buckets. That is, as the heat index increases, the total labor required to harvest melons also increases. The sign is positive for cost and acreage harvested, as expected. The results for melons in Panel A are all significant. Panel B shows that as the labor requirement for melons increases total output also increases: a 1% increase in the crop labor requirement (i.e., labor employed) results in an increase in output of 3%. This result is deceiving: the reason that the crop labor requirement is increasing is due to a higher heat index. That is, in order to achieve higher output producers must “overcompensate” for the negative impact of heat on labor. This would obviously entail a cost to the farmer and potentially lower financial margins.

The results for onions are somewhat different. Panel A shows that as the number of days in each of the heat index buckets increases the crop labor requirements also increase. For example, a 1% increase in the number of days in the 95^oF–100^oF bucket increases the crop labor requirement by 0.15%. The impact of an increase in the number of days in the highest heat index bucket is even higher: a 1% increase in the number of days above 100^oF results in a 2% increase in the crop labor requirement for onions. The signs for cost and acreage are also positive and as expected: as acreage harvested increase, labor costs also increase, and the same is true for capital costs. Panel B shows the result of estimation of Eq. (4.1) for onions. Results show that increases in the crop labor requirement results in a negative impact on production. That is, as more labor is employed, the impact of heat on production has reached a value such that no matter how much labor you employ to compensate for the increases in heat, its impact on productivity is negative for all ranges of the heat index values considered in the analysis. Results in Panel B for onions are all statistically significant. This result is consistent with other results that have used linear models to estimate the impact of heat on rice workers in India (Sahu et al., 2013) and productivity of workers in industrial (indoor) settings in the absence of air conditioning (Somanathan et al., 2018). Our method improves on those other studies in that, at least relative to work on rice workers in India, we use an economic model to estimate the impact of heat on labor productivity within an economic production framework and not just an ad hoc relationship between harvest rates and heat.

The results reported here also add a policy dimension that transcends the health impact of heat on workers and agricultural production. For example, our estimation procedures show that the impact of heat is crop specific. In fact, for onions we have seen that despite the impact of heat in the counties under analysis, onion production has not diminished. One likely reason for this is that onions are a crop that has been gradually becoming more mechanized over time, enabling farmers to overcome the negative impact of heat. That is not the case for melons, for which harvesting is still done by hand.