In 2012, President Enrique Peña Nieto set the very ambitious goal of increasing Mexico’s investment in Science and Technology from the current 0.4 percent to 1 percent of GDP by the end of his mandate in 2018. This commitment has generated a strong debate on what policies should be adopted to support such an extraordinary effort. In this framework, the question on what policies might work for Mexico and how their effectiveness could be measured has become more crucial than ever.
Last June I had the opportunity and pleasure to contribute to such debate in two events organized by the Mexican Foro Consultivo Cientifico y Technologico (FCCyT) in Mexico City. The first event was a two days training on “Impact Evaluation of Science and Technology Programs” taught by Gustavo Crespi and myself. The training targeted an audience of public officials, graduate students, and academics who wanted to have a better understanding of how impact evaluation techniques can be (or have been) applied to policies aimed at promoting scientific production and business innovation. Gustavo’s and my presentations have been posted in the FCCyT website.
The second event was a one day workshop on the evaluation of science, innovation and technology policies. Key authorities and stakeholders from the Mexican National Innovation System participated in the event and contributed to a deep and lively discussion moderated by the FCCyT’s Director Gabriela Dutrénit. The discussion included presentations by Fred Gault (UN-Merit University), Chiara Criscuolo (OECD), Ximena Usher (ANII), and other experts – including myself – and covered topics such as the measurement of the effects scientific research funding, the implementation and evaluation of business innovation support programs, and the generation and management of micro-data for impact evaluations. My personal contribution focused on the IDB experience in evaluating funding for scientific research. Because, the entire event was broadcasted live on the web, all the presentations and videos have been posted in the FCCyT website.
By Leonardo Corral* and Heath Henderson**
Land inequality in developing countries has been found to hamper long-term economic growth and also mitigate the poverty-reducing effects of existing growth by limiting effective access to land by the rural poor. Further, given the often-observed inverse relationship between farm size and productivity, land inequality can adversely affect agricultural productivity.
While land allocation has historically been driven by inheritance and land reform initiatives, in recent decades markets for the rental or sale of land have become considerably more prominent. So, how might the rise of such private, market-led initiatives affect land inequality?
By Graham Watkins*
A month ago I was bumping along a dusty road in the Beni (Bolivia) from San Borja to San Ignacio de Moxos. Someone in the car said that this road had been named as the “worst road in the world.” That reminded me of a 36 hours trip on the Georgetown-Lethem road (Guyana). We only had to travel 60 kilometers in an old British Army Bedford truck, but the trip included a broken femur, eight hours in axle deep mud, and a 10 km walk in the middle of the night with singing nightjars.
These kinds of roads connect people through remote areas. The people who live along these roads suffer the negative consequences –hurtling minibuses, cultural and lifestyle change, reduced security, land use by outsiders, stream pollution, commercial fishing and hunting, and even floods when the roads block natural water flows. The challenge in building these roads is how to ensure that the road provides a service to all people rather than being a service for some and a burden for others. Here are three suggestions as to how to better address this challenge.
By: David Alfaro Serrano*
Proper estimation of the standard errors of the estimators of regression coefficients is important. These estimates are needed when analyzing statistical significance, which is the basis of the interpretation of the results of an econometric analysis. In impact evaluation practice, analysis of statistical significance is what allows the researcher to say whether there is evidence for the effectiveness of an intervention.
In this post I tell you something I discovered recently about the calculation of estimators’ standard errors: there are cases in which the correlation of the errors of the regression model can be ignored when calculating them. Moreover, these cases arise frequently with experimental data. Read more…
No doubt, we economists believe that incentives matter. Recently, it has been argued that incentives could be used to encourage desirable habits, such as getting regular exercise or having a healthy diet. This has become particularly important in Latin America given its demographic and epidemiological situation (Galiani and Weinschelbaum, 2014).
If the welfare created by consuming or undertaking some activity today depends on that same level in the past, or in more technical terms, if the marginal utility of consumption today is positively correlated with past consumption, then providing monetary incentives to undertake an activity could lead to us to do that same activity in the future, thus changing behavior. This hypothesis is known as “habit forming.” However, it has also been noted that granting of rewards can be counterproductive, as it provides an extrinsic motivation for a task or activity that can displace the intrinsic motivation. This hypothesis is known as “crowding-out”. This effect could be particularly important in the medium term, when economic incentives are withdrawn and the individual may have lost her initial motivation for the activity in question.