1. Particulate Pollution and Productivity of Pear Packers
Study looks at the relationship between changes in environmental quality and worker productivity
Focuses on the level of a particular pollutant, particulate matter, and worker productivity in a specific pear packing plant
Particulate matter (PM2.5 ) is produced naturally by such events as forest fires, and by activities such as fossil fuel combustion in power plants and cars

2. Health Effects of Particulate Matter
If inhaled deep into lungs, PM2.5 impairs respiratory function
It can also enter the bloodstream where it can cause cardiovascular problems
Exposure to high levels of particulate matter may lead to such events as heart and asthma attacks

3. The paper examines whether exposure to PM2
The paper examines whether exposure to PM2.5 at fairly low levels negatively affects worker productivity
More generally, the paper examines whether pollution (specifically PM2.5) impairs an economy’s capacity to produce goods/services
The paper uses data from a pear packing firm.
The firm employs people to wrap harvested pears in tissue by hand and pack them tightly in boxes.

4. Employee compensation was directly related to how many boxes of pears they packed per day
Productivity is directly measured by observing the employees’ daily compensation
Data on PM2.5, measured in micrograms, is taken from local air monitoring stations
The pear packing takes place indoors, but unlike other pollutants, PM2.5 does not dissipate indoors

7. Paper uses linear regression to estimate the relationship:
Worker Productivity = f(environmental variables)
The unit of observation is the individual worker each of whom is observed daily over a two year interval

8. Table 3, model 1 estimates the linear inverse relationship between PM2
Table 3, model 1 estimates the linear inverse relationship between PM2.5 and productivity Model 3 in table 3 accounts for differences in marginal PM2.5 effects by using a series of dummy variables representing different ranges of PM2.5 levels

10. Primary results in model 1
A one unit change in PM2.5 reduces predicted hourly earnings by $0.041.
The relationship equals an elasticity of approximately 0.06 around the mean of the variables
meaning a 10% increase in PM2.5 leads to a 0.6% decrease in earnings.
Relationship is unlikely to be linear. Why?

11. Model 3 using dummy variables imply
Hourly earnings are 6 cents lower on days when PM2.5 is micrograms compared to when the PM2.5 is less than 10 micrograms
Hourly earnings are 53 cents lower on days when PM2.5 is micrograms compared to when the PM2.5 is less than 10 micrograms
Hourly earnings are $1 lower on days when PM2.5 is micrograms compared to when the PM2.5 is less than 10 micrograms
Hourly earnings are $1.85 cents lower on days when PM2.5 is greater than 25 micrograms compared to when the PM2.5 is less than 10 micrograms

12. Notes: The article is especially concerned with the magnitude of effects. Why? What if the workers affected by the pollutant simply don’t show up on the more polluted days? Is it legitimate to use the paper’s sample to say something about general effect of PM2.5 on worker productivity?