Global action to slow biodiversity loss is critically needed but comes at substantial cost. In this article, we assess the effectiveness of local ballot measures for land-based conservation projects in the United States as a way to increase bird abundance, a key conservation indicator. Using a citizen science dataset of bird observations, we employ an abundance model to estimate relative abundance conditional on observer effort and exploit a sharp discontinuity in land-based conservation funding at the vote threshold to estimate plausibly causal effects. We find that an approved ballot measure has modest but significant impacts that accrue over ten years.

Robust empirical analysis of climate change impacts is essential for designing effective climate policies and facilitating adaptation to climate damages. However, identifying the causal impacts of climate change faces the fundamental challenge of endogeneity — we cannot observe the Earth under alternative climate futures, making it difficult to fully disentangle climate impacts from other potential drivers of key socioeconomic outcomes. In this chapter, we review the rapidly expanding set of empirical approaches for quantifying the causal impacts of climate change. We begin by outlining several core econometric methodologies that have been applied to climate-related questions across various geographies, sectors of the economy, and societal dimensions. We next discuss key lessons learned from several decades of climate impact studies, including methodological extensions to account for adaptation, allowing for non-linear effects, potential specification issues when using weather data for economic analysis, and frontier applications of climate impact methodologies and novel data sources.

Global agricultural commodity markets are highly integrated among major producers. Prices are driven by aggregate supply rather than what happens in individual countries in isolation. Estimating the effects of weather-induced shocks on production, trade patterns and prices hence requires a globally representative weather data set. Recently, two data sets that provide daily or hourly records, GMFD and ERA5-Land, became available. Starting with the US, a data rich region, we formally test whether these global data sets are as good as more fine-scaled country-specific data in explaining yields and whether they estimate similar response functions. While GMFD and ERA5-Land have lower predictive skill for US corn and soybeans yields than the fine-scaled PRISM data, they still correctly uncover the underlying non-linear temperature relationship. All specifications using daily temperature extremes under any of the weather data sets outperform models that use a quadratic in average temperature. Correctly capturing the effect of daily extremes has a larger effect than the choice of weather data. In a second step, focusing on Sub Saharan Africa, a data sparse region, we confirm that GMFD and ERA5-Land have superior predictive power to CRU, a global weather data set previously employed for modeling climate effects in the region.

Teacher coaching has emerged as a promising alternative to traditional models of professional development. We review the empirical literature on teacher coaching and conduct meta-analyses to estimate the mean effect of coaching programs on teachers’ instructional practice and students’ academic achievement. Combining results across 60 studies that employ causal research designs, we find pooled effect sizes of 0.49 standard deviations (SD) on instruction and 0.18 SD on achievement. Much of this evidence comes from literacy coaching programs for prekindergarten and elementary school teachers in the United States. Although these findings affirm the potential of coaching as a development tool, further analyses illustrate the challenges of taking coaching programs to scale while maintaining effectiveness. Average effects from effectiveness trials of larger programs are only a fraction of the effects found in efficacy trials of smaller programs. We conclude by discussing ways to address scale-up implementation challenges and providing guidance for future causal studies.