Agnes
Norris Keiller

I study treatment effect estimation when treatment events have persistent effects and can be experienced more than once. Natural disasters, job loss and health shocks are examples of such treatments. I show that the effect of a total treatment trajectory can be recovered under assumptions similar to those commonly invoked in single-event settings using suitably flexible TWFE models. Decomposing the total trajectory effect into portions attributable to distinct event occurrences, however, requires further assumptions. I propose an assumption similar to conditional parallel trends, imposing it on the growth of event-specific effects rather than on untreated outcomes. Combined with a linear-in-parameters model of effect growth, this assumption enables a sequential imputation estimator that consistently estimates the dynamic effects of each event occurrence and that can accommodate heterogeneity in effects according to observable event attributes, such as intensity. I demonstrate that several intuitive TWFE models fail to recover interpretable treatment effect parameters in the multi-event setting and illustrate the sequential imputation estimator's favourable performance using Monte Carlo simulations.

Climate change is making natural disasters more frequent, yet little is known about the capacity of firms to withstand such disasters and adapt to their increased frequency. We examine this issue using the latest wave of the World Management Survey (WMS) that includes new questions on firms’ climate change perceptions and adaptation behavior. Combining this with geocoded data on natural disasters and previous WMS waves, we create a panel spanning 8,000 firms across 33 countries and three decades that shows exposure to disasters decreases growth inputs, outputs and firm survival. More importantly, firms with structured management practices are more resilient, suffering much smaller drops in jobs and capital. To understand the mechanisms behind this resilience, we use the new WMS climate questions to show better managed firms have more accurate perceptions of climate-related risks to their businesses. Such firms are also more likely to have implemented measures to adapt to climate change both overall and in response to their perceived climate risk. Other aspects of firm organisation, such as decentralisation, also help protect against disasters, but their adaptation behaviour is not well-targeted. These results show that improving management is one way to help protect economies from climate change shocks.

Standard methods for estimating production functions in the Olley and Pakes (1996) tradition require assumptions on input choices. We introduce a new method that exploits (increasingly available) data on a firm’s expectations of its future output and inputs that allows us to obtain consistent production function parameter estimates while relaxing these input demand assumptions. In contrast to dynamic panel methods, our proposed estimator can be implemented on very short panels (including a single cross-section), and Monte Carlo simulations show it outperforms alternative estimators when firms’ material input choices are subject to optimization error. Implementing a range of production function estimators on UK data, we find our proposed estimator yields results that are either similar to or more credible than commonly-used alternatives. These differences are larger in industries where material inputs appear harder to optimize. We show that TFP implied by our proposed estimator is more strongly associated with future jobs growth than existing methods, suggesting that failing to adequately account for input endogeneity may underestimate the degree of dynamic reallocation in the economy.

When labour market competition is imperfect, positive industry (and firm) productivity shocks can be passed through to workers in the form of higher wages. We document how the UK auto industry, following a period of decline, experienced a four-decade-long productivity boom. There was a thirteen-fold increase in real output per worker between 1980 and 2018, compared to a four-fold increase in manufacturing. Greater foreign ownership, tougher competition and improved industrial relations all likely played a role. The greater use of intermediate inputs (outsourcing) and growing capital intensity account for most of this growth, but we estimate that TFP still grew three times as fast in the auto industry than the rest of manufacturing. Examining whether this productivity increase has been shared with employees, we find that auto workers experienced far stronger hourly wage growth than workers in the rest of manufacturing. After controlling for individual fixed effects, the auto wage premium relative to the rest of manufacturing doubled from 8% in the 1980s to 17% in the 2010s. Interpreted through the lens of a rent sharing model, we estimate that most of the wage increase (63% in the baseline case) can be accounted for by the auto productivity boom. In contrast, the bargaining power of UK auto workers seems to have fallen. If worker power had held up at the 1980s level, the wage premium would have been about 38% higher in the 2010s.

When firms sell in multiple markets, estimates of markups from the demand-side will generally diverge from estimates based on the supply-side (e.g. via production functions). The empirical examination of the importance of this fact has been hampered by the absence of market-specific cost data. To overcome this, we show production markups can be expressed as the revenue-weighted average of demand-based markups across markets (and products). This highlights that a divergence in demand-based and production-based markups is due to the revenue shares and markups across foreign and domestic markets, factors that can be assessed with readily available trade data. Using data from auto firms producing in the UK, we show production-based markups increased between 1998 and 2018 whereas demand-based markups decreased. These trends can be reconciled by an increase in the markup that UK-based producers gained on their exports, which we corroborate using administrative trade data. We find that increases in production-based markups have been driven by exports, particularly to China where foreign brands command high markups.

We examine the distributional consequences of post-Brexit trade barriers on wages in the UK. We quantify changes in trade costs across industries, accounting for input–output links across domestic industries and global value chains. We allow for demand substitution by firms and consumers, and worker reallocation across industries. We document the impact at the individual and household level. Blue-collar workers are the most exposed to negative consequences of higher trade costs, because they are more likely to be employed in industries that face increases in trade costs, and are less likely to have good alternative employment opportunities available in their local labour markets. Overall, new trade costs have a regressive impact, with lower-paid workers facing higher exposure than higher-paid workers once we account for the exposure of other household members.

We study household income inequality in both Great Britain and the United States and the interplay between labour market earnings and the tax system. While both Britain and the US have witnessed secular increases in 90/10 male earnings inequality over the last three decades, this measure of inequality in net family income has declined in Britain while it has risen in the US. To better understand these comparisons, we examine the interaction between labour market earnings in the family, assortative mating, the tax and welfare-benefit system and household income inequality. We find that both countries have witnessed sizeable changes in employment which have primarily occurred on the extensive margin in the US and on the intensive margin in Britain. Increases in the generosity of the welfare system in Britain played a key role in equalizing net income growth across the wage distribution, whereas the relatively weak safety net available to non-workers in the US mean this growing group has seen particularly adverse developments in their net incomes.

In 2016, the UK voted to leave the European Union and growth in UK manufacturing investment ground to a halt. This paper uses administrative trade data to investigate the causal relationship between these events. We exploit firm-level customs data from 2005 onwards to quantify firms’ exposure to EU and non-EU trade in inputs and outputs. Focusing on investment as a forward-looking, dynamic outcome (since the UK did not leave the EU until 2021), we relate firms’ investment to their pre-referendum EU exposure. This analysis shows firms’ exposure to EU trade had a negative impact on investments post-referendum, especially in 2021. Estimated impacts are stronger for import exposure than for export exposure and there is some evidence of depressed investment from exposure to non-EU imports, likely due to the large depreciation in sterling that followed the vote. Had the UK voted to remain in the EU, these estimates imply manufacturing investment would have been over 7% higher, about £2.4 billion annually between 2016 and 2021