Question 149·Hard·Cross-Text Connections
Text 1
Economist Jae Lee and colleagues analyzed millions of smartphone location pings to track daily visits to thousands of small, consumer-facing businesses in 20 cities over three years. In cities that raised their local minimum wage above $15 during the study, foot traffic and the number of operating establishments remained stable relative to similar cities without such increases. Lee concludes that the wage hikes did not reduce employment in the affected service sectors.
Text 2
Data scientist Elena Morales argues that visit counts are a weak indicator of employment. A shop can keep customer traffic steady while cutting worker hours, shifting tasks to self-service kiosks, or consolidating roles; likewise, a stable number of open stores does not reveal whether each store employs fewer people. Morales contends that payroll or hours-worked data would be needed to assess employment effects of minimum wage changes.
Based on the texts, how would Morales (Text 2) most likely characterize Lee’s conclusion in Text 1?
For cross-text questions, first identify the main claim or conclusion in Text 1, then pinpoint how Text 2 responds to that claim: does it support it, qualify it, or challenge it? Zero in on the specific reason given in Text 2 (for example, a flaw in the data, method, or assumptions) rather than relying on your own opinions or outside knowledge. When you look at the answer choices, quickly eliminate any that (1) get the attitude wrong (support vs. challenge) or (2) introduce ideas not mentioned in either text. Choose the option that best matches both the tone of Text 2 toward Text 1 and the exact criticism or support stated in the passages.
Hints
First, nail down Lee’s main claim
Look back at the last sentence of Text 1: what conclusion does Lee reach about employment based on the foot-traffic and establishment data?
Focus on Morales’s specific objection
In Text 2, what does Morales say is missing or weak about using visit counts and number of stores to study employment? What kind of data does she say you really need?
Match both attitude and reason
Ask yourself: is Morales agreeing with Lee, adding a side point, or challenging his conclusion? Then check which answer choice gives both that attitude and a reason that clearly appears in Text 2, without shifting to a different issue.
Step-by-step Explanation
Identify Lee’s conclusion in Text 1
Lee uses smartphone location data to track visits to businesses and the number of operating establishments. He sees that in cities with higher minimum wages, these measures stay stable compared with similar cities. From that, he concludes that "the wage hikes did not reduce employment in the affected service sectors." So his main move is: stable visits + stable store counts → no reduction in employment.
Understand Morales’s main criticism in Text 2
Morales says that visit counts are a weak indicator of employment. She gives several ways employment could fall even if visits and open stores look the same:
- Stores can cut worker hours.
- Stores can shift tasks to self-service kiosks.
- Stores can consolidate roles, meaning fewer workers do more jobs. She also says you would need payroll or hours-worked data to properly measure employment effects. So her problem is with what Lee measures and what he thinks that data can prove.
Decide how Morales would view Lee’s reasoning
Morales is not saying that Lee’s result is impossible; she’s saying his data cannot actually prove what he claims. That means she thinks his conclusion about employment goes too far for the type of evidence he has. She would likely describe his claim as based on insufficient evidence, especially because visit counts and store counts do not show whether staffing or hours have changed.
Match Morales’s view and reason to the answer choices
Now compare each option to Morales’s actual critique:
- The correct option says Lee’s conclusion is too strong for his evidence, specifically because foot-traffic data can’t show whether businesses reduced staffing or hours—this directly echoes Morales’s point that visits and store counts don’t reveal employment levels.
- The other options shift the focus to consumer demand or to using payroll/hours data to explain changes in visits or to establish causation for foot traffic, which is not Morales’s critique. Therefore, the best answer is: As overconfident, because foot-traffic data cannot show whether businesses reduced staffing or hours to adjust to higher wages.