Daycare fines

Description

Researchers tested the deterrence hypothesis which predicts that the introduction of a penalty will reduce the occurrence of the behavior subject to the fine, with the condition that the fine leaves everything else unchanged by instituting a fine for late pickup at daycare centers. For this study, they worked with 10 volunteer daycare centers that did not originally impose a fine to parents for picking up their kids late. They randomly selected 6 of these daycare centers and instituted a monetary fine (of a considerable amount) for picking up children late and then removed it. In the remaining 4 daycare centers no fine was introduced. The study period was divided into four: before the fine (weeks 1–4), the first 4 weeks with the fine (weeks 5-8), the entire period with the fine (weeks 5–16), and the after fine period (weeks 17-20). Throughout the study, the number of kids who were picked up late was recorded each week for each daycare. The study found that the number of late-coming parents increased significantly when the fine was introduced, and no reduction occurred after the fine was removed.

Usage

daycare_fines

Format

A data frame with 200 observations on the following 7 variables.

center

Daycare center id.

group

Study group: test (fine instituted) or control (no fine).

children

Number of children at daycare center.

week

Week of study.

late_pickups

Number of late pickups for a given week and daycare center.

study_period_4

Period of study, divided into 4 periods: ⁠before fine⁠, ⁠first 4 weeks with fine⁠, ⁠last 8 weeks with fine⁠, ⁠after fine⁠

study_period_3

Period of study, divided into 4 periods: ⁠before fine⁠, ⁠with fine⁠, ⁠after fine⁠

Source

Gneezy, Uri, and Aldo Rustichini. "A fine is a price." The Journal of Legal Studies 29, no. 1 (2000): 1-17.

Examples

library(dplyr)
library(tidyr)
library(ggplot2)

# The following tables roughly match results presented in Table 2 of the source article
# The results are only off by rounding for some of the weeks
daycare_fines |>
  group_by(center, study_period_4) |>
  summarise(avg_late_pickups = mean(late_pickups), .groups = "drop") |>
  pivot_wider(names_from = study_period_4, values_from = avg_late_pickups)

daycare_fines |>
  group_by(center, study_period_3) |>
  summarise(avg_late_pickups = mean(late_pickups), .groups = "drop") |>
  pivot_wider(names_from = study_period_3, values_from = avg_late_pickups)

# The following plot matches Figure 1 of the source article
daycare_fines |>
  group_by(week, group) |>
  summarise(avg_late_pickups = mean(late_pickups), .groups = "drop") |>
  ggplot(aes(x = week, y = avg_late_pickups, group = group, color = group)) +
  geom_point() +
  geom_line()