Numerous bark- and wood-infesting insects have been introduced to new countries by international trade where some have caused severe environmental and economic damage. Wood packaging material (WPM), such as pallets, is one of the high risk pathways for the introduction of wood pests. International recognition of this risk resulted in adoption of International Standards for Phytosanitary Measures No. 15 (ISPM15) in 2002, which provides treatment standards for WPM used in international trade. ISPM15 was originally developed by members of the International Plant Protection Convention to “practically eliminate” the risk of international transport of most bark and wood pests via WPM. The United States (US) implemented ISPM15 in three phases during 2005–2006. We compared pest interception rates of WPM inspected at US ports before and after US implementation of ISPM15 using the US Department of Agriculture AQIM (Agriculture Quarantine Inspection Monitoring) database. Analyses of records from 2003–2009 indicated that WPM infestation rates declined 36–52% following ISPM15 implementation, with results varying in statistical significance depending on the selected starting parameters. Power analyses of the AQIM data indicated there was at least a 95% chance of detecting a statistically significant reduction in infestation rates if they dropped by 90% post-ISPM15, but the probability fell as the impact of ISPM15 lessened. We discuss several factors that could have reduced the apparent impact of ISPM15 on lowering WPM infestation levels, and suggest ways that ISPM15 could be improved. The paucity of international interception data impeded our ability to conduct more thorough analyses of the impact of ISPM15, and demonstrates the need for well-planned sampling programs before and after implementation of major phytosanitary policies so that their effectiveness can be assessed. We also present summary data for bark- and wood-boring insects intercepted on WPM at US ports during 1984–2008.
We examine an intervention randomized at the village level in which female farmers invited to a single training session were randomly paired with farmers whom they did not know and encouraged to share new agricultural information throughout the growing season for a recently adopted cash crop. We show that the intervention significantly increased the productivity of all farmers except of those who were already in the highest quintile of productivity, and that there were significant spillovers in productivity to male farmers. networks suggests that the structure of a network and the roles of individuals within that network can have important implications in learning [Bandiera and Rasul, 2006, Bramoullé et al., 2014, Bramoullè et al., 2009, Bursztyn et al., 2014, Conley and Udry, 2001, 2010, Jackson and Golub, 2012. Perturbing the network structure is one method of understanding the mechanisms of learning, particularly when the reflection problem confounds network effects [Manski, 1993]. Field et al. [2013] is another recent study that exogenously perturbs new microfinance groups in Bangladeshi villages by varying the meeting frequency of these groups to understand the impact of network effects on loan repayment. BenYishay and Mobarak [2016] also examine information flows by perturbing village networks in Malawi. They study information diffusion by altering which member of the network received an incentive to spread information. They find that peer farmers in Malawi (average village members selected by a local focus group), when provided with a small incentive, are more effective at promoting adoption than lead farmers (leaders identified by the same community focus group), or government-employed extension workers. Where our study, Field et al. [2013], and BenYishay and Mobarak [2016] attempt to alter the structure of the network, other studies, such as Adelman [2013], Leonard [2007], Duflo and Saez [2014] and Marmaros and Sacerdote [2002] use natural variation in networks to identify network effects. Still others use other sources of variation to understand when and how networks can affect decisions. For example, Breza [2016] uses natural variation in loan repayment incentives to study the impact of a peer's repayment on an individual's timing of payments, and shows evidence of network effects, and Banerjee et al. [2013] exploit the natural random variation found in the network centrality of each individual who was initially exposed to their microfinance program to identify network effects.Where we differ from other research is in our focus on developing new network ties between females, specifically, between individuals who do not know each other well but who may have different sources of valuable information. These nascent and weaker connections may be more likely to propagate new information [Granovetter, 1974, 2005, Santos and Barrett, 2005 and may therefore be more useful to individuals (and the network as a whole) than expanding the raw size of the network. Weak ties may also better incorporate individual...
There is a chronic shortage of agricultural labor in the US. Although growers increasingly turn to guest‐worker programs to meet their labor needs, few regard immigrant workers as a viable long‐term solution. Many producers of labor‐intensive agricultural commodities regard mechanization as a clear long‐term solution, making the slow rate of adoption of mechanized harvesting equipment in the US an empirical puzzle. In this article, we demonstrate that wage‐setting farmers have an incentive to “overmechanize,” or employ more than the cost‐minimizing level of capital when capital and labor are substitutes, but “undermechanize” when labor and capital are technical complements. This outcome can cause agricultural labor problems to persist under complementarity. To assess the potential role of farm under investment in labor augmenting capital equipment, we examine labor market outcomes following the adoption of non‐autonomous harvesting aids on a large strawberry farm in Central California. We develop an econometric model of peer‐affected productivity that controls for the group performance of farm workers operating in crews and find that mechanical aids complement labor in strawberry production, a finding that helps explain not only the relative lack of mechanized harvesting in strawberry production but, more generally, the persistent productivity gap in agricultural industries. We examine the broader implications of our theory for the slow rate of adoption of mechanical harvesting technologies in US agriculture by comparing general wage trends across several labor‐intensive and non‐labor‐intensive industries in California.
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