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A researcher’s guide to the Swedish compulsory school reform. (2020). Nix, Emily.
In: Working Paper Series.
RePEc:hhs:ifauwp:2020_014.

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  12. Average education of coworkers LOUISE, Statistics Sweden, 1985--2012 Described in detail in the main text (see Section 4).
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  22. CPI Statistics Sweden CPI is the deflation variable used to deflate monthly income and wages in the data. Throughout, I deflate the monthly income/wage variables so they are given in 2012 SEK. Bartik shocks Statistics Sweden Occupation ranking by interactions O*Net See Tables D.3 and D.4 Table D.3: Conversion from O*NET Categories to SSYK Categories: Part 1
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  23. D Data Appendix Table D.1: Variable Descriptions Variable Source Notes Income RAMS, Statistics Sweden, 1985–2012 Described in detail in the main text (see Section 4).
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  24. DIAMOND, R. (2016): “The Determinants and Welfare Implications of US Workers’ Diverging Location Choices by Skill: 1980-2000,” American Economic Review, 106(3), 479– 524.

  25. Firm ID RAMS, Statistics Sweden, 1985–2012 The firm ID comes in two levels: the firm ID and the workplace ID. I use the workplace ID for the main analysis, but also have used firm×worker fixed effects in robustness checks.
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  26. FRIEDRICH, B., L. LAUN, C. MEGHIR, AND L. PISTAFERRI (2019): “Earnings Dynamics and Firm-Level Shocks,” Working Paper 25786, National Bureau of Economic Research.

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  31. HERKENHOFF, K., J. LISE, G. MENZIO, AND G. M. PHILLIPS (2018): “Production and Learning in Teams,” Working Paper 25179, National Bureau of Economic Research.

  32. In columns 5–7, I repeat the exercise in columns 1--3 but add in measurement error on own schooling. As in Angrist (2014), this biases the estimates of peer effects (the coefficient on average education×municipality) upward. This demonstrates the purely mechanical positive effect (driven by measurement error) we expect to get when estimating peer effects. I now draw your attention to the estimates with fixed effects in columns 4 and 8. In column 4, I estimate a fixed effects specification without measurement error. In column 8, I estimate the fixed effects specification with measurement error. In contrast to the original regression, the introduction of measurement error now biases the coefficient downward, as we would normally expect. 42I use average education in the municipality instead of average education in the firm because this variable makes my table more directly comparable to Angrist’s table.
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  33. JACKSON, C. K., AND E. BRUEGMANN (2009): “Teaching Students and Teaching Each Other: The Importance of Peer Learning for Teachers,” American Economic Journal: Applied Economics, 1(4), 85–108.

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  46. Municipality LOUISE, Statistics Sweden, 1985–2012 There are currently 290 current municipalities in Sweden. However, there have been important revisions over time, which I accounted for when constructing the data.
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  48. People employed in the primary sector who had local wage settlement Wages Landkomm, Statistics Sweden, 1985–2011 Public employee wages at the county council level. People employed in the county councils whose wages were governed by county councils’ general provisions of the collective agreement for civil servants Wages Stat, Statistics Sweden, 1985–2011 Governmental public employee wages.
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  49. People employed in the state sector who received state-regulated wages Table D.2: Variable Descriptions Variable Source Notes Married LOUISE, Statistics Sweden, 1985–2012 All years past 1990 Male LOUISE, Statistics Sweden, 1985–2012 Education LOUISE, Statistics Sweden, 1985–2012 Only available in relatively coarse categories.
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  53. To estimate the results in this paper, I implement the user-written Stata command a2reg, which estimates the model as described in Abowd and Kramarz (1999).44 I estimate the problem in two parts. First, I run a regression of log wages on dummies for year, county×year, industry×year, married, and number of children. I then save the residuals from this regression. Next, I use a2reg to estimate a regression of residualized wages this period on average education in the firm last period. Note that a2reg requires all variables to be non-missing. Thus, after the first step above, I drop all observations with missing values of either average education of coworkers last period, workplace, or residual wage this period.
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  54. To obtain standard errors, a2reg requires a user written bootstrap. I thus also program a bootstrap that runs over the entire procedure.45 44Amine Ouazad, Program for the Estimation of Two-Way Fixed Effects, available at http://personal.lse.ac.uk/ouazad/, 2007. 45I produced the standard errors using 50 bootstraps.
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  55. To show that this approach works using the data, I have replicated Table 3 from Angrist (2014). My columns 1–3 and 5–7 estimate identical regressions to those in Angrist’s paper. Specifically, in the first column I estimate the effect of a college degree on wages. In the second column, I estimate the effect of average education at the municipality level on wages.42 In the third column, I estimate the effect of both average education and given worker’s own schooling (college degree or not) on wages.
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  56. Worker ID RAMS, Statistics Sweden, 1985–2012 Wages Arb, Statistics Sweden, 1985–2011 Private employee wages for firms with over 50 employees. Includes people who had hourly wages and were employed at companies/organizations in the private sector Wages Tjm, Statistics Sweden, 1985–2011 Private official wages for firms with over 500 employees. Includes people who had a monthly salary and worked at a company/organization in the private sector Wages Kommun, Statistics Sweden, 1985--2011 Public employee wages at the local level.
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