The impact of R&D on factor-augmenting technical change – an empirical assessment at the sector level*

The aim of the paper is to quantify endogenous factor-augmenting technical change driven by R&D investments in a panel of 11 OECD countries over 1987–2007. This paper contributes to the scant empirical evidence on the speed, sources and direction of technical change for various sectors and production factors. Assuming cost-minimization behavior, a CES framework is used to derive a system of equations that is estimated by a GMM system estimator. The estimated factor-augmenting technology parameters show that in most sectors, technical change was labor-augmenting and labor-saving. Statistically significant effects of manufacturing and services R&D were found on factor-augmenting technical change (with the highest R&D elasticities found in the high-tech manufacturing and transport, storage and communication sectors). Whereas ‘in-house’ R&D stimulates total factor productivity, R&D spilled over to other sectors has a capital-augmenting effect accompanied by a higher use of labor. The results of this study provide a starting point for incorporating endogenous factor-augmenting technical change in impact assessment models aimed at broad policy analysis including economic growth, food security or climate change.     

Analysing Production Technology and Risk in Organic and Conventional Dutch Arable Farming using Panel Data

This paper compares the production technology and production risk of organic and conventional arable farms in the Netherlands. Just–Pope production functions that explicitly account for output variability are estimated using panel data of Dutch organic and conventional farms. Prior investigation of the data indicates that within variation of output is significantly higher for organic farms, indicating that organic farms face more output variation than conventional farms. The estimation results indicate that in both types of farms, unobserved farm‐specific factors like management skills and soil quality are important in explaining output variability and production risk. The results further indicate that land has the highest elasticity of production for both farm types. Labour and other variable inputs have significant production elasticities in the case of conventional farms and other variable inputs in the case of organic farms. Manure and fertilisers are risk‐increasing inputs on organic farms and risk‐reducing inputs on conventional farms. Other variable inputs and labour are risk increasing on both farm types; capital and land are risk‐reducing inputs.     

Farm-gate tomato price negotiations under asymmetric information

This article provides an empirical analysis of farm‐gate tomato price negotiations under asymmetric information. Regression models are estimated to analyze when and by how much sellers stick to their initial ask prices and what explains the variation in the initial ask–offer price spread. Detailed information on 66 farm‐gate tomato transactions and daily tomato wholesale price data from the central vegetable market in Addis Ababa are used for the analysis. Estimation results show that farmers are less committed to their initial ask price when the buyer speaks out the transaction price first, when their quality perceptions of the tomatoes being transacted differ from those of the buyers, and when their tomato farm is at a large distance from the main road. Sellers stick more to their initial ask price when they know that the central market price is high. The initial ask–offer price spread decreases when the buyer speaks out the initial negotiation price first, but increases in the difference in quality perception between buyer and seller, and in the quantity of tomatoes being transacted.     

Adoption and Impacts of Sustainable Agricultural Practices on Maize Yields and Incomes: Evidence from Rural Zambia

This paper uses a multinomial endogenous treatment effects model and data from a sample of over 800 households and 3,000 plots to assess the determinants and impacts of adoption of sustainable agricultural practices (SAPs) on maize yields and household incomes in rural Zambia. Results show that adoption decisions are driven by household and plot level characteristics and that the adoption of a combination of SAPs raises both maize yields and incomes of smallholder farmers. Adoption of improved maize alone has greater impacts on maize yields, but given the high cost of inorganic fertiliser that limits the profitability of adoption of improved maize, greater household incomes are associated rather with a package involving SAPs such as maize–legume rotation and residue retention.     

Market interdependence and volatility transmission among major crops

This article provides a comprehensive analysis of the dynamics of volatility across major agricultural commodities in the United States. Volatility interactions across markets may lower the effectiveness of diversification strategies to mitigate price risks and should be taken into account when analyzing the pricing behavior of different agricultural commodities. We follow a multivariate GARCH approach to evaluate the time evolution of conditional correlations and volatility transmission across corn, wheat, and soybeans price returns on a daily, weekly, and monthly basis. The period of analysis is from 1998 to 2012. The estimation results indicate a lack of lead‐lag relationships between corn, wheat, and soybeans price returns at the mean level. We find, however, important volatility spillovers across commodities, particularly at the weekly and monthly level. Wheat and corn seem to play a major role in terms of volatility transmission. Despite the supposed higher financial market integration of agricultural commodities, we do not observe that agricultural markets have become more interdependent in recent years.     

Unravelling the JPMorgan spoofing case using particle physics visualization methods

On 29 September 2020, JPMorgan was ordered to pay a settlement of $920.2 million for spoofing the metals and Treasury futures markets from 2008 to 2016. We examine these cases using a visualization method developed in particle physics (CERN) and the messages that the exchange receives about market activity rather than time-based snapshots. This approach allows to examine multiple indicators related to market manipulation and complement existing research methods, thereby enhancing the identification and understanding of, as well as the motivation for, market manipulation. In the JPMorgan cases, we offer an alternative motivation for spoofing than moving the price.