This article develops the uniform asymptotic theory for local projection (LP) regression when the true lag order of the model is unknown and potentially infinite. The theory allows for varying degrees of persistence in the data, growing response horizons, and general conditionally heteroskedastic martingale-difference shocks. Based on the theory, we make two main contributions. First, we show that LPs can achieve semiparametric efficiency at a given horizon under classical assumptions on the data, provided that the controlled lag order diverges. Thus, the commonly perceived efficiency loss of LPs can become asymptotically negligible with many controls. Second, we propose LP-based inference procedures for (level and cumulated) impulse responses that possess robustness properties not shared by existing methods. Inference methods using two distinct standard errors are considered. The uniform validity for the first method depends on a zero fourth-order cumulant condition on shocks, while that of the second holds more generally for conditionally heteroskedastic martingale-difference shocks. We propose a bootstrap procedure that improves finite-sample performance and extend the standard error construction to structural responses.

This article combines personnel records of the U.S. government with census data to study how exposure to greater female representation at work can persistently reduce intergenerational gender gaps in labour market outcomes. Exploiting city-by-department variation in the sudden expansion of female employment during World War I, we find that daughters of civil servants exposed to female co-workers are more likely to work later in life. This intergenerational effect operates through exposed fathers and extends beyond the public sector, reducing the earnings gap by 12%. Consistent with a broader shift in attitudes towards working women, exposure to female co-workers also made male civil servants more likely to marry working women. We show that cities exposed to larger increases in female federal workers saw persistently higher female labour force participation in both the public and the private sector. Increasing gender representation within the public sector can thus have broader labour market implications.

We model an organization as a hierarchy of managers erected on top of a technology (here consisting of a collection of plants). In our framework, the role of a manager is to take steps to reduce the adverse consequences of shocks that affect the plants beneath him. We argue that different organizational forms give rise to different information about managers' performance and therefore differ according to how effective incentives can be in encouraging a good performance. In particular, we show that, under certain assumptions, the M-form (multi-divisional form) is likely to provide better incentives than the U-form (unitary form) because it promotes yardstick competition (i.e. relative performance evaluation) more effectively. We conclude by presenting evidence that the assumptions on which this comparison rests are satisfied for Chinese data.

Bureaucracies are configured differently to private sector and political organizations. Across a wide range of civil services entry is competitive, promotion is constrained by seniority, jobs are for life and retirement occurs at a fixed age. This implies that older entering officers, who are less likely to attain the glittering prize of reaching the top of the bureaucracy before they retire, may be less motivated to exert effort. Using a nationwide stakeholder survey and rich administrative data on elite civil servants in India we provide evidence that: (i) officers who cannot reach the senior-most positions before they retire are perceived to be less effective and are more likely to be suspended and (ii) this effect is weakened by a reform that extends the retirement age. Together these results suggest that the career incentive of reaching the top of a public organization is a powerful determinant of bureaucrat performance.

We study the impact of large, successful manufacturing plants on other local producers in China, focusing on “Million-Rouble Plants” built in the 1950s during a brief alliance with the U.S.S.R. The ephemeral geopolitical situation and the locations of allied and enemy airbases provide exogenous variation in plant siting. We find a boom-and-bust pattern: Counties hosting these plants were 80% more productive than control counties in 1982 but 20% less productive by 2010. This decline reflects the performance of local establishments, which exhibit low productivity, limited innovation, and high markup. Specialization hindered spillovers, preventing the emergence of new clusters and local entrepreneurship.

In this article, we use micro data on both trade and production for a sample of large Chinese manufacturing firms in the footwear industry from 2002 to 2006 to estimate an empirical model of export demand, pricing, and market participation by destination market. We use the model to construct indexes of firm-level demand, marginal cost, and fixed cost. The empirical results indicate substantial firm heterogeneity in all three dimension with demand being the most dispersed. The firm-specific demand and marginal cost components account for over 30% of market share variation, 40% of sales variation, and over 50% of price variation among exporters. The fixed cost index is the primary factor explaining differences in the pattern of destination markets across firms. The estimates are used to analyse the supply reallocation following the removal of the quota on Chinese footwear exports to the EU. This led to a rapid restructuring of export supply sources on both the intensive and extensive margins in favour of firms with high demand and low fixed costs indexes, with marginal cost differences not being important.

How do social networks influence the decision to migrate? Prior work suggests two distinct mechanisms that have historically been difficult to differentiate: as a conduit of information, and as a source of social and economic support. We disentangle these mechanisms using a massive “digital trace” dataset that allows us to observe the migration decisions made by millions of individuals over several years, as well as the complete social network of each person in the months before and after migration. These data allow us to establish a new set of stylized facts about the relationship between social networks and migration. Our main analysis indicates that the average migrant derives more social capital from “interconnected” networks that provide social support than from “extensive” networks that efficiently transmit information.