DETERMINANTS OF PRETERM BIRTH AMONG MOTHERS WHO GAVE BIRTH AT A REFERRAL HOSPITAL, NORTHWEST ETHIOPIA: UNMATCHED CASE- CONTROL STUDY

Preterm birth is described as babies that are born alive before the end of 37 weeks of pregnancy. About 15 million babies are born preterm annually and more than a million died due to complications. In developing nations, like Ethiopia, PTB is underreported and underestimated. Objective - to identify determinates of preterm birth among mothers who gave birth at Felege Hiwot Comprehensive Specialized Referral Hospital, Northwest Ethiopia. Unmatched case-control study design was conducted. The total sample size was 558 mothers 140 cases and 418 controls. Simple random sampling was used to select the study population. Data were coded and entered into Epidata, version 3.2, and was analyzed by using STATA version 14. Mothers with a history of antepartum hemorrhage (AOR 3.53, 95% CI 1.31-9.47), premature rupture of membrane (AOR 8.9, 95% CI 4.51-17.57), pregnancy-induced hypertension (AOR 3.65, 95% CI 1.78-7.51), history of multiple pregnancies (AOR 2.49, 95% CI 0.89-6.95), primigravida (AOR 0.16, 95% CI 0.03-0.97) and Primiparity(AOR 0.054, 95% CI 0.05-0.64) had statistically significant association with experiencing preterm birth. The odds of giving preterm birth were higher among women with antepartum hemorrhage, PIH, PROM, and multiple pregnancies, but lower among primigravida and primiparous mothers.

Welfare impacts of smallholder farmers' participation in multiple output markets: Empirical evidence from Tanzania

A relatively large body of literature has documented the welfare effects of smallholder farmers' participation in single-commodity output markets. However, limited empirical evidence is available when smallholder farmers participate in multiple-commodities output markets. We tried to fill this gap in the literature by estimating the impacts of smallholder farmers' contemporaneous participation in both maize and legume markets vis-à-vis in only maize or legume markets using household-level data from Tanzania. Applying a multinomial endogenous switching regression model that allows controlling for observed and unobserved heterogeneity associated with market participation in single-commodity and multiple-commodity markets, results showed that smallholder farmers' participation in both single-and multiple-commodity markets was positively and significantly associated with household income and food security. Moreover, the greatest benefits were obtained when farmers participated in multiple-commodity markets, suggesting the importance of policies promoting diversification in crop income sources to increase welfare and food security. Our findings also signal the complementary-rather than substitute-nature of accessing multiple-commodity markets for enhancing household livelihoods under a specialization strategy. Finally, important policy implications are suggested, from promoting and supporting public infrastructure investments to expanding road networks to reduce transportation costs, especially in remote communities, to enhance smallholder farmer access to profitable maize and legume markets in Tanzania.

Market participation, household food security, and income: The case of cowpea producers in northern Nigeria

This article evaluates the impact of cowpea market participation on household food security and income in northern Nigeria. Using household survey data from a representative sample of over 1,500 farm households and applying a combination of instrumental variable techniques and dose-response functions, we found that cowpea market participation had a statistically significant positive impact on household food security and income. Cowpea market participation increased food expenditure by 1.6% and household income by 0.7% with a 10 unit increase in the quantity of cowpea sold. These results underscore the importance of cowpea market participation for household food security and income improvement. We also found that selling cowpea to rural and urban traders significantly increased household income, food expenditure, and food security. Results show that selling cowpea to rural and urban traders increased household income by 17% and 13%, respectively. The results point to the need for an enabling policy environment and public infrastructure to enhance market participation of farmers and traders. Public infrastructure investments in the form of feeder road construction and maintenance in the distant villages are encouraged, which in the long run can translate into improved cowpea productivity and welfare of smallholder farmers.

Agricultural technology adoption and household welfare: Measurement and evidence

Previous studies on the adoption and impacts of improved crop varieties have relied on self-reported adoption status of the surveyed households. However, in the presence of weak variety maintenance and poorly functioning seed certification system, measurement errors in self-reported adoption status can be considerable. This paper investigates how such measurement errors can lead to biased welfare estimates. Using DNA-fingerprinting based varietal identification as a benchmark, we find that misclassification in self-reported adoption status is considerable, with significant false negative and positive response rates. We empirically show that such measurement errors lead to welfare estimates that are biased towards zero and substantially understate the poverty reduction effects of adoption. While the empirical evidence suggests attenuation bias, our theoretical exposition and simulations demonstrate that upward bias and sign reversal effects are also possible. The results point to the need for improved monitoring of the diffusion process of improved varieties through innovative adoption data collection approaches to generate robust evidence for prioritizing and justifying investments in agricultural research and extension.

Measuring the impacts of adaptation strategies to drought stress: The case of drought tolerant maize varieties

This study measured the impacts of drought tolerant maize varieties (DTMVs) on productivity, welfare, and risk exposure using household and plot-level data from rural Nigeria. The study employed an endogenous switching regression approach to control for both observed and unobserved sources of heterogeneity between adopters and non-adopters. Our results showed that adoption of DTMVs increased maize yields by 13.3% and reduced the level of variance by 53% and downside risk exposure by 81% among adopters. This suggests that adoption had a "win-win" outcome by increasing maize yields and reducing exposure to drought risk. The gains in productivity and risk reduction due to adoption led to a reduction of 12.9% in the incidence of poverty and of 83.8% in the probability of food scarcity among adopters. The paper concluded that adoption of DTMVs was not just a simple coping strategy against drought but also a productivity enhancing and welfare improving strategy. The results point to the need for policies and programs aimed at enhancing adoption as an adaptation strategy to drought stress in Nigeria and beyond.

Productivity and Welfare Effects of Nigeria's e-Voucher-Based Input Subsidy Program

In an attempt to go beyond the so called "smart" subsidies, Nigeria has embarked on a potentially innovative mobile phone-based input subsidy program that provides fertilizer and improved seed subsidies through electronic vouchers. In this article, we examined the productivity and welfare effects of the program using household-level data from rural Nigeria. The article employed instrumental variable regression approach to control for the potential endogeneity of the input subsidy program. Our results suggest that the program is effective in improving productivity and welfare outcomes of beneficiary smallholders. The size of the estimated effects suggests a large improvement in productivity and welfare outcomes. Moreover, the distributional effects of the program suggest no heterogeneity effects based on gender and farm land size. These results are robust to using alternative measurements of program participation. The benefit-cost ratio of 1.11 suggests that the program is marginally cost-effective. Overall, our results suggest that while improving average productivity is a good outcome for improving food security, improving the distributional outcome of the program by targeting the most disadvantaged groups would maximize the program's contribution to food security and poverty reduction.

Impacts of extension access and cooperative membership on technology adoption and household welfare

This paper examines the impacts of access to extension services and cooperative membership on technology adoption, asset ownership and poverty using household-level data from rural Nigeria. Using different matching techniques and endogenous switching regression approach, we find that both extension access and cooperative membership have a positive and statistically significant effect on technology adoption and household welfare. Moreover, we find that both extension access and cooperative membership have heterogeneous impacts. In particular, we find evidence of a positive selection as the average treatment effects of extension access and cooperative membership are higher for farmers with the highest propensity to access extension and cooperative services. The impact of extension services on poverty reduction and of cooperatives on technology adoption is significantly stronger for smallholders with access to formal credit than for those without access. This implies that expanding rural financial markets can maximize the potential positive impacts of extension and cooperative services on farmers' productivity and welfare.

Prediction of future uniform milk prices in Florida federal milk marketing order 6 from milk futures markets

This study assessed the accuracy of 3 methods that predict the uniform milk price in Federal Milk Marketing Order 6 (Florida). Predictions were made for 1 to 12 mo into the future. Data were from January 2003 to May 2007. The CURRENT method assumed that future uniform milk prices were equal to the last announced uniform milk price. The F+BASIS and F+UTIL methods were based on the milk futures markets because the futures prices reflect the market's expectation of the class III and class IV cash prices that are announced monthly by USDA. The F+BASIS method added an exponentially weighted moving average of the difference between the class III cash price and the historical uniform milk price (also known as basis) to the class III futures price. The F+UTIL method used the class III and class IV futures prices, the most recently announced butter price, and historical utilizations to predict the skim milk prices, butterfat prices, and utilizations in all 4 classes. Predictions of future utilizations were made with a Holt-Winters smoothing method. Federal Milk Marketing Order 6 had high class I utilization (85 +/- 4.8%). Mean and standard deviation of the class III and class IV cash prices were $13.39 +/- 2.40/cwt (1 cwt = 45.36 kg) and $12.06 +/- 1.80/cwt, respectively. The actual uniform price in Tampa, Florida, was $16.62 +/- 2.16/cwt. The basis was $3.23 +/- 1.23/cwt. The F+BASIS and F+UTIL predictions were generally too low during the period considered because the class III cash prices were greater than the corresponding class III futures prices. For the 1- to 6-mo-ahead predictions, the root of the mean squared prediction errors from the F+BASIS method were $1.12, $1.20, $1.55, $1.91, $2.16, and $2.34/cwt, respectively. The root of the mean squared prediction errors ranged from $2.50 to $2.73/cwt for predictions up to 12 mo ahead. Results from the F+UTIL method were similar. The accuracies of the F+BASIS and F+UTIL methods for all 12 fore-cast horizons were not significantly different. Application of the modified Mariano-Diebold tests showed that no method included all the information contained in the other methods. In conclusion, both F+BASIS and F+UTIL methods tended to more accurately predict the future uniform milk prices than the CURRENT method, but prediction errors could be substantial even a few months into the future. The majority of the prediction error was caused by the inefficiency of the futures markets to predict the class III cash prices.