世界経済・社会統計 = World development indicators

https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

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Modern Applied Statistics With S

We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

Are natural resources a “curse” or a “blessing”? The empirical evidence suggests either outcome is possible. The paper surveys a variety of hypotheses and supporting evidence for why some countries benefit and others lose from the presence of natural resources. These include that a resource bonanza induces appreciation of the real exchange rate, deindustrialization and bad growth prospects, and that these adverse effects are more severe in volatile countries with bad institutions and lack of rule of law, corruption, presidential democracies, and underdeveloped financial systems. Another hypothesis is that a resource boom reinforces rent grabbing and civil conflict especially if institutions are bad, induces corruption especially in non-democratic countries, and keeps in place bad policies. Finally, resource rich developing economies seem unable to successfully convert their depleting exhaustible resources into other productive assets. The survey also offers some welfare-based fiscal rules for harnessing resource windfalls in developed and developing economies.

Natural Resources: Curse or Blessing?

This brief summarizes the results of a gender impact evaluation study, entitled Reintegrating and employing high risk youth in Liberia : lessons from a randomized evaluation of a landmine action an agricultural training program for ex-combatants, conducted between 2009 and 2011 in Liberia. The study observed the impact of landmine action an agricultural training program on ex-combatants and other high-risk youth on the individual level. The program has a large and significant impact on engagement in agricultural activities. The program had little impact on illicit activities like mining, but those who participated in the program spent fewer hours engaged in illicit activities. The program led to no significant changes in crime or aggression, but the participants are less likely to be interested in or mobilized into violence after the election in Cote d'Ivoire. Funding for the study derived from the United Nations Peace Building Fund, Yale University, Spanish Impact Evaluation Fund, and Italian Children and Youth Trust Fund.

/pdf/reintegrating-and-employing-high-risk-youth-in-liberia-24f8x7e1fu.pdf

Reintegrating and Employing High Risk Youth in Liberia: Lessons from a randomized evaluation of a Landmine Action an agricultural training program for ex-combatants

High-impact policies may not lead to support for the political party that introduces them. In 2008, Uganda’s government encouraged groups of youth to submit proposals to start enterprises....

/pdf/do-anti-poverty-programs-sway-voters-experimental-evidence-1yxyf11t5j.pdf

Do Anti-Poverty Programs Sway Voters? Experimental Evidence from Uganda

This paper tests the feasibility of local-level violence forecasting We apply standard prediction models to new data from 242 Liberian communities to investigate whether it is to possible to predict outbreaks of local violence with sensitivity and accuracy, even with limited data We first trained our models to predict communal, extrajudicial, and criminal violence in 2010 using 2008 risk factors We then made forecasts of violence in 2012, before collecting data Our model predicted up to 88% of actual 2012 violence This came at the cost of many false positives, for overall accuracy of 33 to 50% Policy-wise, states and peacekeepers could use such predictions to prevent and respond to violence The models also generated new stylized facts for theory to explain In this case, ethnic cleavages and power-sharing predicted violence, while economic variables typically did not We illustrate how forecasting can be widely more applied to micro-level conflict data

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2595004_code345560.pdf?abstractid=2497153

Predicting Local Violence

Policymakers can take actions to prevent local conflict before it begins, if such violence can be accurately predicted. We examine the two countries with the richest available sub-national data: Colombia and Indonesia. We assemble two decades of finegrained violence data by type, alongside hundreds of annual risk factors. We predict violence one year ahead with a range of machine learning techniques. Models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best. Rich socio-economic data also substitute well for these histories. Even with such unusually rich data, however, the models poorly predict new outbreaks or escalations of violence. "Best case" scenarios with panel data fall short of workable early-warning systems.

The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia

Civil wars have afflicted two-thirds of African nations, drawing up to a third of male youth into armed groups. Little is known, however, about the long term effects of military participation due to a lack of data and potential sample selection: recruits are usually self-selected and screened, and may also selectively survive. This paper presents new evidence on the causal impact of military participation using an original dataset collected by the author in northern Uganda. The large-scale, indiscriminate and forcible abduction of youth by Ugandan rebels provide arguably exogenous variation in exposure to conflict. Results suggest that the most prevalent effect of abduction is on human capital acquisition: abductees lose nearly a year of schooling on average. Combined with a greater incidence of injuries, this schooling loss leads to nearly a third lower earnings. Meanwhile, exposure to conflict seems to increase political participation: abductees are more likely to vote and twice as likely to be community leaders. Finally, the psychological impacts of war appear to be moderate and concentrated in a minority. These results run counter to the prevailing view that war primarily causes ‘psychosocial’ distress. Post-conflict policy implications are discussed.

Christopher Blattman

Papers

Reintegrating and Employing High Risk Youth in Liberia: Lessons from a randomized evaluation of a Landmine Action an agricultural training program for ex-combatants

Do Anti-Poverty Programs Sway Voters? Experimental Evidence from Uganda

Predicting Local Violence

The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia

The Consequences of Child Soldiering