In recent times, the economic and social relevance of constitutions and potential determinants of their validity have been increasingly debated. However, we still know little about the relationships between the text of a constitution and constitutional compliance. Does the wording of a constitution matter in this context? In this paper, drawing on a sample of democratic countries, we apply econometric and machine learning tools to provide some insights on these issues. The results suggest that shorter texts and placing more emphasis on punishments for transgressions seem to be positively correlated with the compliance level. Regarding the precision of constitutional text, in turn, we find mixed evidence. Econometric modelling suggests no statistically significant relationship with constitutional compliance, and machine learning models instead indicate this feature as a potentially important determinant of constitutional compliance.

Livestock abortion is a source of economic loss for farmers, but its economic impact has not been estimated in many Low and Middle-Income Countries. This article presents an estimation methodology and estimates for the gross and net cost of an abortion based on a sample of livestock-owning households in three regions of northern Tanzania and market data. We then generate aggregate estimates of abortion losses across Tanzania. We estimate annual gross and net annual losses of about $263 Million (about TZS 600 billion) and $131 million (about TZS 300 billion), respectively.

Residential energy efficiency programs play an important role in combating climate change. More precise quantification of the magnitude and timing of energy savings would bring large system benefits, allowing closer integration of energy efficiency into resource adequacy planning and balancing variable renewable electricity. However, it is often difficult to quantify the efficacy of an energy efficiency intervention, because doing so requires consideration of a hypothetical counterfactual case in which there was no intervention, and randomized control trials are often implausible. Although quasi-experimental econometric evaluation sometimes works well, we find that for a set of energy efficiency rebate programs in Northern California, a naïve interpretation of econometric measurement finds that rebate participation is associated with an average increase in electricity consumption of 7.2% [4.5%, 10.1%], varying in magnitude and sign depending on the type of appliance or service covered by the rebate. A subsequent household survey on appliance purchasing behavior and analysis of utility customer outreach data suggest that this regression approach is likely measuring the gross impact of buying a new appliance but fails to adequately capture a counterfactual comparison. Indeed, it is unclear whether it is even possible to construct a suitable counterfactual for econometric analyses of these rebate programs using data generally available to electric utilities. We view these results as an illustration of a limitation of econometric methods of program evaluation and the importance of weighing engineering modeling and other imperfect methods against one another when attempting to provide useful evaluations of real-world policy interventions.

Extensive research on gender and politics indicates that women legislators are more likely to serve on committees and sponsor bills related to so-called “women's issues.” However, it remains unclear whether this empirical regularity is driven by district preferences, differences in legislator backgrounds, or because gendered political processes shape and constrain the choices available to women once they are elected. We introduce expansive new data on over 25,000 US state legislators and an empirical strategy to causally isolate the different channels that might explain these gendered differences in legislator behavior. After accounting for district preferences with a difference-in-differences design and for candidate backgrounds via campaign fundraising data, we find that women are still more likely to serve on women's issues committees, although the gender gap in bill sponsorship decreases. These results shed new light on the mechanisms that lead men and women to focus on different policy areas as legislators.

Using daily data on vaccinations, disease spread and measures of social interaction from Google Mobility reports aggregated at the country level for 112 countries, we present estimates of behavioural responses to the global rollout of COVID-19 vaccines. We first estimate correlates of the timing and intensity of the vaccination rollout, finding that countries which vaccinated more of their population earlier strongly tended to be richer, whereas measures of the state of pandemic or its death toll up to the time of the initial vaccine rollout had little predictive ability after controlling for income. Estimates of models of social distancing and disease spread suggest that countries which vaccinated more quickly also experienced decreases in some measures of social distancing, yet also lower incidence of disease, and in these countries, policy-makers relaxed social distancing measures relative to countries which rolled out vaccinations more slowly.

Mixed-methods designs, especially those in which case selection is regression-based, have become popular across the social sciences. In this paper, we highlight why tools from spatial analysis—which have largely been overlooked in the mixed-methods literature—can be used for case selection and be particularly fruitful for theory development. We discuss two tools for integrating quantitative and qualitative analysis: (1) spatial autocorrelation in the outcome of interest; and (2) spatial autocorrelation in the residuals of a regression model. The case selection strategies presented here enable scholars to systematically use geography to learn more about their data and select cases that help identify scope conditions, evaluate the appropriate unit or level of analysis, examine causal mechanisms, and uncover previously omitted variables.

The current study evaluated the uncertainty of beef cattle supply for slaughter due to the variable climate of Rio Grande do Sul, Brazil. The data included the numbers of cattle slaughtered, local live cattle prices, price and amount of exported beef, and the prices of the Brazilian beef futures market. Data were collected on the beef supply from January 1997 to March 2014. Climate data included El Niño (EN), La Niña (LN), South Atlantic Sub-tropical Dipole (SASD), Negative and Positive Atlantic Dipole (−AD and +AD), Tropical South and North Atlantic indices. Statistical analysis was performed by a multivariate regression of time series. It was observed that EN and SASD climatic variables increased the numbers of beef cattle slaughtered, with a 1 and 4-month lag, respectively. On the other hand, LN and -AD decreased the number of animals slaughtered, with 4 and 0 months’ lag, respectively, meaning that there was an immediate response to −AD, while there was a 4-month delay for LN. The amount of exported beef and live beef cattle prices were explained by the number of animals slaughtered in the state. Data suggested that the beef cattle market in RS was more strongly influenced by the occurrence of climate phenomena with LN and −AD than by economic variables such as the price paid to the producer for beef and the amount exported. The climate changes evaluated in the current study affect the livestock production system and consequently the beef market industry in Southern Brazil.

We study consumers’ motivations for buying organic food by analyzing their shopping baskets. Buying organic can be motivated by concern about sustainable development and/or self-interest (considerations related to health or product quality). Pro-social motivation is inferred from the presence of fair trade products in the consumer's basket; consumer self-interest is deduced from the presence of healthy and higher-quality products bearing special quality labels or certifications. Our results indicate that environmental motivation predicts organic food purchases better than health or quality considerations: the complementarity between organic and fair trade products is strongest. In addition, the household's socioeconomic background influences secondary motivations.