Ví dụ về việc sử dụng Allcott trong Tiếng anh và bản dịch của chúng sang Tiếng việt
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Adapted from Allcott(2011), figure 8.
Figure 4.8: Heterogeneity of treatment effects in Allcott(2011).
Allcott(2015) provides a careful theoretical and empirical treatment of site selection bias.
(2007) found differences between heavy and light users, Allcott(2011) found that there were also differences within the heavy- and light-user group.
Allcott and Rogers(2014) found that slightly more people receiving the Home Energy Reports upgraded their appliances.
In a first set of experiments involving600,000 households from 10 different sites, Allcott(2011) found that the Home Energy Report lowered electricity consumption.
For example, recall that Allcott(2011) showed that Home Energy Reports caused people to lower their electricity usage.
In a first set of experiments involving 600,000 households served by10 utility companies around the United States, Allcott(2011) found the Home Energy Report lowered electricity consumption by 1.7%.
Allcott(2015) speculated that this decline happened because, over time, the treatment was being applied to different types of participants.
Further, in subsequent research involving eight millionadditional households from 101 different sites, Allcott(2015) again found that the Home Energy Report consistently lowered electricity consumption.
Allcott(2015) argues that a major source of this pattern is that sites with more environmentally-focused customers were more likely to adopt the program earlier.
Then, in later chapters, I will tell you about researchers who used call records from mobile phones(Blumenstock, Cadamuro, and On 2015)and billing data created by electric utilities(Allcott 2015).
Together, these 111 experiments- 10 in Allcott(2011) and 101 in Allcott(2015)- involved about 8.5 million households from all over the United States.
In fact, as we will see later in this chapter, researchers have already used home power meters to measure outcomes in experiments about energyconsumption involving 8.5 million households(Allcott 2015).
(2007) and Allcott(2011) shows that the Opower experiments had a smaller estimated treated effect than the original experiments by Schultz and colleagues(1.7% versus 5%).
In chapter 4, we saw how the Opower experiments combined the readymade electricity measurement infrastructure with a custommade treatment to study the effects ofsocial norms on the behavior of millions of people(Allcott 2015).
First, Allcott(2011) used the large sample size(600,000 households) to further split the sample and estimate the effect of the Home Energy Report by decile of pre-treatment energy usage.
In Chapter 4, we saw how the OPower experiments that combine the ready-made electricity measurement infrastructure with a custom-made treatment to study the effects ofsocial norms on behavior at a massive scale(Allcott 2015).
In January, Hunt Allcott, of New York University, and Luca Braghiere, Sarah Eichmeyer and and Matthew Gentzkow, of Stanford University, published results of the largest such experiment yet.
In chapter 4, we saw how the Opower experiments combined the readymade electricity measurement infrastructure with a custommade treatment to study the effects ofsocial norms on the behavior of millions of people(Allcott 2015).
First, Allcott(2011) used the large sample size(600,000 households) to further split the sample and estimate the effect of the Home Energy Report by decile of pre-treatment energy usage.
In fact, as we will see later in this chapter, researchers have already used home power meters to measure outcomes in experiments about social norms andenergy consumption involving 8.5 million of households(Allcott 2015).
According to Hunt Allcott, an associate professor at NYU who was one of the researchers involved in the study, participants said Facebook is important to them and generally a positive thing in their lives.
For example, using a balance check, Allcott(2011) found some evidence that randomization was not implemented correctly in three of the Opower experiments(see table 2; sites 2, 6, and 8).
In a follow-up study, Allcott and Rogers(2014) partnered with a power company that, through a rebate program, had acquired information about which consumers upgraded their appliances to more energy-efficient models.