Bounded Learning Efficiency and Sources of Firm Level Productivity Growth in Colombian Food Manufacturing Industry

The measurement of productivity fluctuations has been the focus of decades-long interest. In addition to broad structural forces driving productivity changes, there is more recent interest in measuring and identifying the heterogeneous forces driving these changes. A major force is learning-by-doing which is used by economists to describe the phenomenon of productivity growth arising from the accumulation of production experience by a firm. This paper proposes a bounded learning concept with the learning progress function characterized by the degree of efficiency and the specification of the learning progress as a logistic function capturing both the slow start-up and the limit in learning progress. The inter-firm learning inefficiency is defined as the inability of a firm to reach the optimal plateau relative to the ‘best practice’ firm from the set of comparable firms. We further differentiate learning efficiency from the technical efficiency. The key contribution of this research is to provide a measure the firm’s movement along the learning progress curve and explain the existence of firm-level heterogeneity in learning. The time varying technical efficiency is estimated based on stochastic production frontier methods and firm-specific learning efficiency is disentangled using the residual of the production frontier (productivity).The model is then used to decompose the factor productivity growth into components associated with learning, scale, technical efficiency, technological change and change in allocative efficiency. This productivity growth decomposition provides useful information and policy level insight in firm-level productivity analysis. The major econometric issue in production function estimation is the possibility that there are some forces influencing production that are only observed by the firm and not by the econometrician. With firm input use being endogenous, inputs might be correlated with unobserved productivity shocks. The measure of technical efficiency by estimating the production frontier directly in presence of endogeneity of input choice can be biased in the sense that the measure of efficiency favors the firms employing higher levels of inputs. The Levinsohn and Petrin (2003) approach is extended to overcome this simultaneity problem in stochastic production frontier estimation to generate consistent estimates of production parameters and technical efficiency. The model is applied to plant-level panel data on Colombian food manufacturing sector. The dataset is unique longitudinal data on firms in the sense that it has information on both plant-specific physical quantities and prices for both outputs and inputs. In contrast to most of the existing literature which measure productivity by deflating sales by an industry-level price index, these data eliminate a common source of measurement error in production function estimation. Plant-level productivity growth decomposition and the contribution of learning effect are explored by estimating the production frontier and firm-specific learning efficiency.Colombian food manufacturing industry, Bounded learning-by-doing, Learning efficiency, Logistic differential equation, Technical efficiency, Firm-level productivity growth, Decomposition of productivity change, Endogeneity of input choice, Stochastic production frontier, Agribusiness, Industrial Organization, Production Economics, Productivity Analysis,

Endogeneity corrected stochastic production frontier and technical efficiency

A major econometric issue in estimating production parameters and technical efficiency is the possibility that some forces influencing production are only observed by the firm and not by the econometrician. Not only can this misspecification lead to a biased inference on the output elasticity of inputs, but it also provides a faulty measure of technical efficiency. We extend the Levinsohn and Petrin (2003) approach and provide an estimation algorithm to overcome the problem of endogenous input choice in stochastic production frontier estimation by generating consistent estimates of production parameters and technical efficiency. We apply the proposed method to a plant-level panel dataset from the Colombian food manufacturing sector for the period 1982–1998. This dataset provides the value of output and prices charged for each product, expenditures, and prices paid for each material used, energy consumption in kilowatt per hour and energy prices, number of workers and payroll, and book values of capital stock. Empirical results find that the traditional stochastic production frontier tends to underestimate the output elasticity of capital and firm-level technical efficiency. The evidence in this research suggests that addressing the endogeneity issue matters in stochastic production frontier analysis

Monitoring and Evaluation (M&E) in Systems Research: Experience from Africa RISING

United States Agency for International Developmen

Farmers’ willingness to pay for improved agricultural technologies: Evidence from a field experiment in Tanzania

Initiatives on the sustainable intensification of agriculture have introduced improved technologies tailored to farmers’ local conditions by trial demonstration with free provision of improved seeds and fertilizers. It is not clear, though, whether smallholder farmers would be willing to pay for these technologies, and what factors determine their informed demand. Using a contingent valuation experiment, combined with information at baseline from 400 households in Northern Tanzania, this study measured farmers’ willingness to pay (WTP) for hybrid maize seed and local inorganic fertilizer. Farmers’ WTP was estimated using a dichotomous contingent valuation model with follow-up. Results showed that the average WTP was 61% higher for hybrid maize seed and 15% lower for inorganic fertilizer than their average local market prices during the reference period, suggesting that farmers were willing to pay a premium for hybrid maize seed, while they did not seem to be interested in fertilizer purchase at current market price. Moreover, since improved access to extension services was found to influence farmers’ WTP positively, strengthening extension services could be a suitable policy intervention to increase farmers’ demand for improved technologies. On the other hand, farmers’ risk aversion negatively affected WTP for both technologies. This result suggests that encouraging risk reduction options, such as agricultural insurance, could be one policy recommendation for boosting farmers’ demand for improved agricultural technologies

A field study for assessing risk-contingent credit for Kenyan pastoralists and dairy farmers

Purpose: The purpose of this paper is to assess the feasibility of risk-contingent credit (RCC) by presenting an experimental and participatory game designed to explain the concept of RCC to Kenyan pastoralists and dairy farmers. The paper investigates the uptake potential of RCC through qualitative assessment of field experiments and focus groups. Design/methodology/approach: The paper presents a method of community engagement through a participatory game played in a series of Focus Group Discussions (FGDs). The paper also presents theoretical justification of RCC in credit market structure. Findings: The game effectively explains the concept and mechanism of RCC by reflecting local situation and production potential. Participatory exercises within focus group discussions indicate that there exists a strong interest and support for RCC. Research limitations/implications: The methodology described in this paper can be used in extension programs for promoting innovative rural microcredit in developing countries but should be modified according to the local production and associated weather and market risks. Originality/value: Micro-insurance and credit program delivery can be improved by the innovative approach of community engagement for explaining financial products

Bounded learning-by-doing and sources of firm level productivity growth in Colombian food manufacturing industry

This paper models the bounded learning concept with the learning progress function characterized by the degree of efficiency and the specification of the learning progress as a logistic function capturing both the slow start-up and the limit in learning progress. We differentiate learning efficiency from the technical efficiency. The endogeneity corrected stochastic frontier model is then used to decompose the factor productivity growth into components associated with technological change, technical efficiency, scale, and learning. This productivity growth decomposition provides useful information and policy level insight in firm-level productivity analysis. Empirical results based on plant-level panel data on the Colombian food manufacturing industry for the period 1982–1998 suggest that productivity growth not only stems from technical progress, technical efficiency change, and scale but also from significant learning effect. The relative importance of the productivity growth components provides perspective for efficient resource allocation within the firm

Addressing fractional dimensionality in the application of weather index insurance and climate risk financing in agricultural development: A dynamic triggering approach

Climate risk financing programs in agriculture have caught the attention of researchers and policy makers over the last decade. Weather index insurance has emerged as a promising market-based risk financing mechanism. However, to develop a suitable weather index insurance mechanism it is essential to incorporate the distribution of underlying weather and climate risks to a specific event model that can minimize intra-seasonal basis risk. In this paper we investigate the erratic nature of rainfall patterns in Kenya using CHIRPS rainfall data from 1983-2017. We find that the patterns of rainfall are fractional, both erratic and persistent which is consistent with the Noah and Joseph effects well known in mathematics. The erratic nature of rainfall emerges from the breakdown of the convergence to a normal distribution. Instead we find that the distribution about the average is approximately lognormal, with an almost 50% higher chance of deficit rainfall below the mean versus adequate rainfall above the mean. We find that the rainfall patterns obey Hurst law and the measured Hurst coefficients for seasonal rainfall pattern across all years range from a low of 0.137 to a high above 0.685. To incorporate the erratic and persistent nature of seasonal rainfall, we develop a new approach to weather index insurance based upon the accumulated rainfall in any 21-day period falling below 60% of the long-term average for that same 21-day period. We argue that this approach is more satisfactory to matching drought conditions within and between various phenological stages of growth

Feasibility of implementing a Risk-Contingent Credit (RCC) program in Zambia: Stakeholder engagement

Changes in frequency and intensity of climate and weather events are a key challenge to agricultural production among farmers in Zambia. Climate variability reduces farm productivity, which in turn contributes to household food insecurity, income variability, and reduced overall economic growth. Using improved technologies such as mechanization, improved seed varieties, irrigation, and fertilizer can improve climate resilience and farm production among smallholder farmers. However, in Zambia, as in many countries in sub-Saharan Africa, most famers lack sufficient access to credit to purchase these technologies. Limited access to credit is mainly attributed to lack of collateral, fear of losing collateral in case of a default, and low financial literacy among smallholder famers. Information asymmetry also makes it risky and expensive for lenders to serve smallholder farmers, thus they ration the quantity of credit offered and/or raise the interest rates making credit too expensive and inaccessible for millions of smallholder farmers. Bundling agricultural credit with insurance, commonly referred to as risk-contingent credit (RCC), provides a mechanism for addressing some of the credit access constraints faced by smallholder farmers in developing countries. RCC is a loan product that is bundled with an insurance component. RCC seeks to enhance long-term resilience to climate uncertainties by promoting optimal farm investment and productivity among smallholders through sustainable access to credit markets. Under RCC, qualifying smallholder farmers borrow funds for agricultural production from formal financial institutions such as banks and microfinance institutions with minimum collateral requirements. The borrower’s ability to repay the loan is linked to climate outcomes, which are highly correlated with farm productivity. An insurance company underwrites the climate risks (either in the form of drought or flood), such that if that underlying risk passes a certain threshold, the insurance is triggered and part or all of the borrower’s liability is transferred to the insurer. If the underlying risk remains below the threshold, the borrower repays the loan at the agreed upon interest rates and is also obligated to pay the insurance premium, as part of the loan repayment. Linking farmers’ loan repayment obligations to an underlying risk, as opposed to stringent collateral requirements, is expected to reduce the borrowing constraints faced by many poor farmers. At the same time, de-risking the lender by transferring a portion of risks to the insurance market is expected to promote credit supply, hence expanding the rural credit market