We review the state of information and communication technologies (ICTs) and their impact on agricultural development in developing countries, documenting the rapid changes that have taken place over the past decade. Although there remains a wide gap in access between rural and urban areas, the spread of mobile phones in rural areas has led to important changes in the agricultural sector. We find that access to mobile phones has generally improved agricultural market performance at the macro level; however, impacts at the micro level are mixed. Evidence regarding the impact of market information systems (MIS) delivered through mobile phones on farm prices and income is limited, but the evidence points to strong, heterogeneous impacts. Similarly, the rollout of extension programs though ICTs is still in an early stage, and little research is available regarding such programs’ impacts.

A key question in development economics is whether nutritional deficiencies generate intergenerational poverty traps by reducing the earnings potential of children born into poverty. To assess the causal influence on human capital of one of the most widespread micronutrient deficiencies, supplemental iron pills were made available at a local health center in rural Peru and adolescents were encouraged to take them up via classroom media messages. Results from school administrative records provide novel evidence that reducing iron deficiency results almost immediately in a large and significant improvement in school performance. For anemic students, an average of 10 100mg iron pills over three months improves average test scores by 0.4 standard deviations and increases the likelihood of grade progression by 11%. Supplementation also raises anemic students' aspirations for the future. Both results indicate that cognitive deficits from iron-deficiency anemia contribute to a nutrition-based poverty trap. Our findings also demonstrate that, with low-cost outreach efforts in schools, supplementation programs offered through a public clinic can be both affordable and effective in reducing rates of adolescent IDA.

The growing use of ICTs around the world, particularly cellular phone technology, provides a significant development opportunity. Under certain situations, ICTs can improve rural households’ agricultural production, farm profitability, job opportunities, adoption of healthier practices, and risk management. All these effects have the potential to increase wellbeing and food security in rural areas of developing countries. Several challenges to effectively scaling up the use of ICTs for development remain, however. Taking advantage of the opportunities provided by ICTs depends on increased connectivity of marginalized population groups, the content and usefulness of the information provided through ICTs, and the capacity of households in rural areas to understand and act on the information that they receive. We need innovative ways to bring together the public and private sectors to ensure that the three Cs (connectivity, content, and capacity) are addressed as a whole.

This paper presents new experimental evidence on the role of price information in agricultural markets. For this purpose, I set up a Randomized Control Trial (RCT) in the central highlands of Peru. A group of farmers in randomly selected villages got access to detailed price information for the most relevant local crops in six regional markets through cell phone SMS.

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This paper analyzes the impact of the Strengthening Women Entrepreneurship in Peru (SWEP) program. SWEP trained female micro entrepreneurs in business management practices (such as accounting and marketing). The training, which was provided in 4-to 5-hour sessions, used soap operas and practical exercises specifically designed for the program. A field experiment was conducted among a group of micro entrepreneurs based in two Peruvian cities (Lima and Piura) to investigate whether SWEP had a positive impact on its beneficiaries. The results show that the program positively affected the adoption of business practices taught by the program. In particular, those who received the training were 4 to 6 percentage points more likely to assign themselves a fixed salary (rather than taking cash from their businesses based on personal needs) and 6 to 11 percentage points more likely to keep better records of potential business contacts. Some positive impacts were found on the adoption of bookkeeping practices (4 to 6 percentage points), although this result is not significant across all of the specifications. Although these changes in adoption rates were large compared with their baseline levels, they were rather small in absolute terms. Therefore, the study did not find any impact on average business performance, household expenditures, or women's empowerment in the household. Qualitative information suggests that micro entrepreneurs were satisfied with the training, but considered that many of the practices taught by the program were difficult to follow because of time constraints. . We are grateful to Miriam Vasquez and Giancarlo Cafferata from APRENDA; Ximena Querol, Director of the 10000 Women Program -Peru; and Sandra Darville, Claudia Gutierrez, and Carmen Mosquera from the IADB's Multilateral Investment Fund for their support in the evaluation of the SWEP program. USKAY EIRL facilitated the data collection. Yuri Soares, Miguel Almanzar, and anonymous reviewers from IADB provided insightful comments on a previous version of this paper. Mike Murphy provided research assistance with the data and the survey instrument. Maribel Elias and Luciana Delgado supervised all the logistical details of the field work. All errors remain our own. 1 Post estimators: Y i,t = βTreat i + ε it , for each t=1 and t=2. 2 Average post estimator: Y i,t=1,2 = βTreat i + θD t=2 + ε it . 3 ANCOVA (analysis of covariance) estimators: Y i,t = βTreat i + γY i,t=0 + ε it , for each t=1 and t=2. 4 Post estimators including baseline controls: Y i,t = βTreat i + δX i + ε it , for each t=1 and t=2. X i includes sector, city, age, head of household at baseline, whether the micro entrepreneur had received previous business training, and marital status. 5 Fixed effects estimator: Y it = β 1 Treat i D t=1 + β 2 Treat i D t=2 + θ 1 D t=1 + θ 2 D t=2

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