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 and severe, yet little is known about the capacity of firms to withstand such disasters and adapt to them. We examine these issues using the World Management Survey (WMS), which includes new questions on firms' climate change perceptions and climate adaptation behavior as well as management practices. Combining the WMS with geocoded data on natural disasters, we create a panel spanning almost 8,000 firms across 33 countries over three decades. Exposure to disasters is associated with lower firm survival and subsequent growth, but firms with structured management practices are more resilient, suffering smaller drops in jobs growth and lower exit risk. Exploring mechanisms, we show that firms with structured management practices have more accurate perceptions of climate-related risks to their businesses and are also more likely to have implemented measures to adapt to climate change. Our results are consistent with structured management practices contributing to firms’ resilience to climate shocks, highlighting management as a policy-relevant margin for mitigating the harmful consequences of climate change.

Standard proxy methods for estimating production functions in the tradition require assumptions on input choices. We introduce a new method that exploits (increasingly available) data on firms' expectations of their future output and inputs that allows us to obtain consistent production function parameter estimates while relaxing these input demand assumptions. In contrast to both proxy and dynamic panel methods like, our proposed estimator can be implemented on a single cross-section of data 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 panel 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 the share of cross-firm TFP dispersion accounted for by persistent productivity differences is substantially larger when calculated using parameter estimates from our proposed estimator.

To what extent do identical workers in an industry benefit from productivity growth in the same industry? We study this question in the context of the UK auto industry which experienced a thirteen-fold increase in output per worker between 1980 and 2018. Over the same period, auto workers saw substantially stronger hourly wage growth than workers in other manufacturing industries. Controlling for unobserved worker heterogeneity, the auto wage premium over other manufacturing workers doubled from 8% in the 1980s to 17% in the 2010s, while the manufacturing premium over the rest of the economy remained roughly stable at 5%. Interpreted through the lens of a rent sharing model, we estimate that 63% of the increase in the raw firm-level auto wage premium can be accounted for by strong productivity growth. In contrast, differential bargaining power between auto workers cannot account for these changes.

When firms sell in multiple markets, demand-side markup estimates from a single market need not align with supply-side estimates derived from production data that aggregate across markets. Empirical analysis of this divergence has been constrained by a lack of market-specific cost data. We address this by showing that production markups can be expressed as a revenue-weighted average of demand markups across markets and products, which allows the relationship between the two measures to be quantified using revenue shares and markups in domestic and foreign markets—information that is generally more accessible than cost data. Applying this approach to UK car manufacturers, we find that supply-side markups increased between 1998 and 2018, while demand-side markups declined. 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 evidence that increases in supply-side markups have been driven by exports, particularly to China where prices are high relative to other export markets. Our results highlight how international market exposure can drive aggregate markups and generate differences between supply- and demand-based measures of market power.

Norris Keiller, de Paula, and Van Reenen (2024) (NPR) propose estimating production functions using firms' subjective expectations of future output and inputs, data which are becoming increasingly available in surveys. This note compares their proposed estimator to traditional dynamic panel data (e.g., Blundell and Bond 2000) and proxy variable methods (e.g., Olley and Pakes 1996). While NPR allows for nonlinear productivity processes, we discuss commonalities with the former when those processes are linear. We note that NPR may be more robust to oligopolistic competition than the latter since it does not employ input demand relations to proxy for productivity.

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