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Mr. Francesco Caselli, Mr. Francesco Grigoli, Mr. Damiano Sandri, and Mr. Antonio Spilimbergo

3 a g e g r o u p i a , g × ( ∑ p = 0 P γ p s , h l o c k j , t − p + ∑ p = 0 P ψ p s , h l n Δ c a s e s i , t − p ) + ε i , g , a , t + h ( 3 ) The specification features interaction terms between the lockdown stringency index and age group dummies a g e g r o u

International Monetary Fund. Western Hemisphere Dept.

state s in cohort c (e.g. the cohort that was 0–4 years old in 1993 would be c=1993). All regressions include fixed effects for each 6-digit industry i , state s and wave t . They further include age group dummies D AG for each industry-state pair which are intended to capture average life-cycle dynamics. The set of fundamentals X i,s,t can vary by industry, state and wave (year). The latter are computed based both on data from the Mexican Economic Census and the SIMBAD database which includes a range of socioeconomic indicators with variation at the

Christian Saborowski and Florian Misch

average life cycle growth across firms with different characteristics. The objective is to identify potential distortions that are correlated with the significant heterogeneity in firm-level lifecycle growth that we observe. We begin by estimating simple average life cycle dynamics as a benchmark. The specification we estimate is shown in Equation 1 where Q f,t is the cumulative growth rate in the number of employees of firm f between birth and wave t and D AG is a set of age group dummies. Note that we do not include a constant in this regression. Moreover

International Monetary Fund. Western Hemisphere Dept.
This Selected Issues paper on Mexico documents the composition, trends, and labor market implications of informality using data from the National Employment Survey (ENOE). Over half of the employed population has informal contractual relationships in Mexico both at formal and informal firms. Informality is found to be associated with lower levels of pay—even when accounting for worker composition differences—and lower wage growth over the life cycle. Policy drivers of this market duality, including minimum wage policy, are discussed. The results suggest that informality tends to select workers with lower earnings potential and limits their development. Informality indeed tends to be more prevalent among younger and less educated workers, for which better paid jobs are harder to come by. Moreover, it appears to lead workers toward a path of limited earnings and perhaps skill growth potential. Future labor market reforms should take a holistic approach that addresses both distributional concerns and formality barriers. One alternative is to reduce dependence on payroll taxes that are biased toward formal salaried workers while transitioning toward a social insurance system that provides good-quality services for all, irrespective of their salaried/nonsalaried status.
Cian Ruane
After impressive growth in the 2000s, China's productivity has more recently stagnated. We use firm-level data to analyze productivity and firm dynamism trends from 2003 to 2018. We document six facts that together show a decline in China’s business dynamism. We show that (i) the revenue share of young firms has declined, (ii) the life-cycle growth of young firms relative to older incumbents has slowed, (iii) weaker life-cycle growth can be explained by slower productivity growth and weaker investment in intangibles, (iv) younger and smaller firms are more capital constrained than their older and larger counterparts, (v) the responsiveness of capital growth to the marginal product of capital has declined, and (vi) large productivity gaps between SOEs and private firms persist. We find that business dynamism is weaker in provinces where SOEs account for a larger share of the capital stock. Our results suggest that declining private business dynamism is an important factor in explaining China's sluggish TFP growth and that SOE reform could boost productivity growth indirectly by stimulating business dynamism.
Cian Ruane

groups; 1–2, 3–5, 6–10, 11–15 and 16+ (omitted). FE st is a full set of sector year fixed effects. We set k = 3 in order to focus on the medium-run growth dynamics of firms and fitting better with our broad age categories. Finally, because we are interested in how firm’s life-cycle growth dynamics have changed over time, we interact the age group dummies with dummies for the two time periods of interest: 2003–2010 and 2011–2018. 2 Figure 4 shows the results. Similarly to what has been documented in other countries, we find that average firm growth decreases with

Christian Saborowski and Florian Misch
This paper examines the variation in life cycle growth across the universe of Mexican firms. We establish two stylized facts to motivate our analysis: first, we show that firm size matters for development by illustrating a close correlation with state-level per capita incomes. Second, we show that few firms grow as much as their U.S. peers while the majority stagnates at less than twice their initial size. To gain insights into the distinguishing characteristics of the two groups, we then econometrically decompose life cycle growth across firms. We find that firms that have financial access and multiple establishments and that are formal, part of diversified industries and located in population centers can grow at sizeable rates.
Mr. Francesco Caselli, Mr. Francesco Grigoli, Mr. Damiano Sandri, and Mr. Antonio Spilimbergo
Lockdowns and voluntary social distancing led to significant reduction in people’s mobility. Yet, there is scant evidence on the heterogeneous effects across segments of the population. Using unique mobility indicators based on anonymized and aggregate data provided by Vodafone for Italy, Portugal, and Spain, we find that lockdowns had a larger impact on the mobility of women and younger cohorts. Younger people also experienced a sharper drop in mobility in response to rising COVID-19 infections. Our findings, which are consistent across estimation methods and robust to a variety of tests, warn about a possible widening of gender and inter-generational inequality and provide important inputs for the formulation of targeted policies.
Mr. Marcello M. Estevão and Ms. Nigar Nargis

; Age group dummies: if 15 <= age < 25 then gl = 1, else gl = 0; if 25 <= age < 55 then g2 = 1, else g2 = 0; if 55 <= age then g3 = 1; else g3 = 0. Educational categories: JH = Junior-high school diploma; TD = Technical diploma; BAC - Baccalaureat; BAC+2 -BAC + 2 years of education; BAC+4 = BAC+ minimum of 4 years of education. Control group: Male, single, part-time, no-school diploma, interaction between g3 age group and education variables, industrial sector, and journalists, entertainers and artists. R eferences Abowd , John , and T

Mr. Roberto Cardarelli, Hippolyte W. Balima, Chiara Maggi, Mr. Adrian Alter, Jérôme Vacher, Matthew Gaertner, Olivier Bizimana, Azhin Abdulkarim, Karim Badr, Shant Arzoumanian, Mahmoud Harb, Mariam El Hamiani Khatat, Ms. Priscilla S Muthoora, and Aymen Belgacem

sector, 0 otherwise. Public administration Dummy equals 1 if the worker is in the public sector, 0 otherwise. Age group dummy (15–24) 25–34 Dummy equals 1 if the worker is aged 25–34, 0 otherwise. 35–54 Dummy equals 1 if the worker is aged 35–54, 0 otherwise. 55–64 Dummy equals 1 if the worker is aged 55–64, 0 otherwise. 65+ Dummy equals 1 if the worker is aged 65+, 0 otherwise. Education dummy (No education) Primary Dummy equals 1 if the worker has a primary education, 0 otherwise