<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Magne Mogstad | Macro Paper Warehouse</title><link>https://macropaperwarehouse.com/authors/magne-mogstad/</link><description>Magne Mogstad</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://macropaperwarehouse.com/authors/magne-mogstad/index.xml" rel="self" type="application/rss+xml"/><item><title>Firm Responses and Wage Effects of Foreign Demand Shocks with Fixed Labor Costs and Monopsony</title><link>https://macropaperwarehouse.com/papers/firm-responses-and-wage-effects-of-foreign-demand-shocks-with-fixed-labor-costs-and-monopsony/</link><guid>https://macropaperwarehouse.com/papers/firm-responses-and-wage-effects-of-foreign-demand-shocks-with-fixed-labor-costs-and-monopsony/</guid><description>&lt;p&gt;&lt;strong&gt;Research Question.&lt;/strong&gt; The paper asks three related questions in the context of Belgium, a small open economy: (1) What do firms&amp;rsquo; responses to demand shocks reveal about their cost structures? (2) What are the worker and wage impacts of foreign demand shocks? (3) How sensitive are the aggregate wage effects of foreign demand shifts to firms&amp;rsquo; cost structures and imperfect competition in the labor market?&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Data.&lt;/strong&gt; The analysis combines administrative micro-data from Belgium for 2002–2014, provided by the National Bank of Belgium. The linked dataset covers 995,739 firm-year observations from private, non-financial firms with at least one FTE employee, and integrates: (a) a Business-to-Business (B2B) VAT transactions registry capturing all annual domestic firm-to-firm sales above €250; (b) customs records and intra-EU declarations for imports and exports at the 8-digit product level; (c) annual accounts containing data on sales, labor costs, intermediate inputs, capital, and firm characteristics; and (d) employer-employee matched data from the Belgian social security administration (BCSS) for a random sample of 500,000 workers in firms with 10 or more FTE employees, covering 2003–2014.&lt;/p&gt;</description></item><item><title>Selection in Surveys: Using Randomized Incentives to Detect and Account for Nonresponse Bias</title><link>https://macropaperwarehouse.com/papers/selection-in-surveys-using-randomized-incentives-to-detect-and-account-for-nonresponse-bias/</link><guid>https://macropaperwarehouse.com/papers/selection-in-surveys-using-randomized-incentives-to-detect-and-account-for-nonresponse-bias/</guid><description>&lt;p&gt;This paper addresses nonresponse bias in surveys — the distortion that arises when survey participants differ systematically from nonparticipants in ways that correlate with the survey&amp;rsquo;s outcomes of interest. The authors develop and apply methods to detect and correct for nonresponse bias using randomized financial incentives embedded in the survey design itself.&lt;/p&gt;
&lt;p&gt;The empirical application is the &amp;ldquo;Norge i Koronatid&amp;rdquo; (NiK) survey, conducted by Statistics Norway in April–May 2020 to study the immediate labor market consequences of Norway&amp;rsquo;s COVID-19 lockdown. The NiK survey has two features that make it unusually well-suited for studying nonresponse bias: (1) it is linked to full-population administrative data, providing a verifiable ground truth for the entire Norwegian adult population; and (2) survey invitees were randomly assigned to one of five financial incentive levels (0%, 1%, 5%, 7%, or 10% probability of receiving a 1,000 NOK prepaid card), generating exogenous variation in participation rates. The final sample of 10,000 randomly drawn adults achieved a 47.4% participation rate.&lt;/p&gt;</description></item><item><title>When is TSLS Actually LATE?</title><link>https://macropaperwarehouse.com/papers/when-is-tsls-actually-late/</link><guid>https://macropaperwarehouse.com/papers/when-is-tsls-actually-late/</guid><description>&lt;p&gt;This paper asks: when does two-stage least squares (TSLS) with covariates actually estimate a local average treatment effect (LATE) — a non-negatively weighted average of causal effects for compliers only? The authors show that the answer is: almost never in practice.&lt;/p&gt;
&lt;p&gt;The paper&amp;rsquo;s central theoretical result (Theorem 1) is that a linear IV estimand is weakly causal — meaning it cannot have the wrong sign relative to all underlying treatment effects — if and only if the IV specification has &amp;ldquo;rich covariates,&amp;rdquo; defined as the condition that the linear projection of the instrument onto the covariates, L[Z|X], equals the true conditional mean E[Z|X] at every covariate value. Saturated specifications (nonparametric covariate control) always satisfy rich covariates. Outside of two special cases — saturated covariates or an instrument that is mean-independent of covariates — rich covariates is an implicit parametric assumption that can fail.&lt;/p&gt;</description></item></channel></rss>