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Can Patience Account for Subnational Differences in Student Achievement? Regional Analysis with Facebook Interests. (2023). Woessmann, Ludger ; Sancassani, Pietro ; Kinne, Lavinia ; Hanushek, Eric A.
In: CESifo Working Paper Series.
RePEc:ces:ceswps:_10660.

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  14. Crucially for our analysis, the sample is representative at the regional level for 19 of the 20 regions in Italy (Falorsi, Falzetti, and Ricci (2019)). The exception is Trentino-Alto-Adige, where only students in the autonomous municipalities of Bolzano and Trento are tested. The difference between the lowest and highest performing region in Italy in eighth-grade math amounts to roughly three quarters of a standard deviation, equivalent to the average learning of almost three school years.
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  33. Panel B: waves 2003-2018; students with both parents not born in the country where the student attends school; including 180 fixed effects for each residencecountry by wave cell. Control variables: Panel A: student gender, age, and migration status; imputation dummies; and wave fixed effects; Panel B: student gender, age, dummy for OECD country of origin, imputation dummies. Robust standard errors adjusted for clustering at the country level (migrant analysis: country of origin) in parentheses. Significance level: *** 1 percent, ** 5 percent, * 10 percent. Data sources: PISA international student achievement test, 20002018; Falk et al. (2018); own elaboration of Facebook data.
    Paper not yet in RePEc: Add citation now
  34. Putnam, Robert D. (1993). Making Democracy Work: Civic Traditions in Modern Italy. Princeton, NJ: Princeton University Press.
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  35. Significance level: *** 1 percent, ** 5 percent, * 10 percent. Data sources: PISA student achievement test, 2012; own elaboration of Facebook data.
    Paper not yet in RePEc: Add citation now
  36. Sunde, Uwe, Thomas Dohmen, Benjamin Enke, Armin Falk, David Huffman, Gerrit Meyerheim (2022). Patience and Comparative Development. Review of Economic Studies 89 (5): 28062840.
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  38. Table A11: Patience and Reading Achievement: Analysis of U.S. States 4 PCs 7 PCs 10 PCs (1) (2) (3) Patience 0.228*** 0.141* 0.227** (0.074) (0.077) (0.103) Wave fixed effects Yes Yes Yes Observations 153 153 153 States 51 51 51 R2 0.385 0.375 0.396 Notes: Dependent variable: NAEP eighth-grade reading test score in all NAEP waves 2015-2019. Least squares regressions with wave fixed effects. Unit of observation: state-wave combination. Col. 1-3 use the patience measure computed with 4, 7, and 10 principal components (PCs), respectively. Regressions control for risk-taking computed with the equivalent number of PCs. Robust standard errors adjusted for clustering at the state level in parentheses. Significance level: *** 1 percent, ** 5 percent, * 10 percent. Data sources: NAEP mathematics achievement test, 2015-2019; own elaboration of Facebook data.
    Paper not yet in RePEc: Add citation now
  39. Thorson, Kjerstin, Kelley Cotter, Mel Medeiros, Chankyung Pak (2021). Algorithmic Inference, Political Interest, and Exposure to News and Politics on Facebook. Information, Communication & Society 24 (2): 183-200.
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  40. Woessmann, Ludger (2010). Institutional Determinants of School Efficiency and Equity: German States as a Microcosm for OECD Countries. Journal of Economics and Statistics 230 (2): 234-270. Figure 1: Word Cloud of Facebook Interests Notes: Word cloud of 1,000 Facebook interests with largest worldwide audience. Larger font sizes indicate larger audience. Figure 2: Measure of Patience Derived from Facebook Interests for Italian Regions and U.S. States A. Italian Regions B. U.S. States Notes: The figures show maps of the Facebook-derived measure of patience obtained with 4 PCs for Italian regions (Panel A) and U.S. states (Panel B), respectively. Each color corresponds to a decile of the distribution of patience within each country. Darker colors denote higher levels of patience.

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