Technical Efficiency in African Agriculture: Is It Catching Up or Lagging Behind?

Mugera, Amin; Ojede, Andrew

doi:10.1002/jid.2907

Cited by 16 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various applications analyzing real data for various economic questions can be found in [22,42,52,18,21,34], to mention just a few.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Aggregation of Efficiency and Productivity: From Firm to Sector and Higher Levels

Zelenyuk¹

2020

Handbook of Production Economics

View full text Add to dashboard Cite

Here we consider various cases where researchers are interested in measuring aggregate efficiency or productivity levels or their changes for a group of decision-making units. These could be an entire industry composed of individual firms, banks, and hospitals or a region composed of sub-regions or countries, or particular subgroups of these units within a group, e.g., subgroups of public vs. private or regulated vs. non-regulated firms, banks, or hospitals within the same industry, etc. Such analysis requires solutions to the aggregation problemsome theoretically justified approaches that can connect individual measures to aggregate measures. Various solutions are offered in the literature, and our goal is to try to coherently summarize at least some of them in this chapter. This material should be interesting not only for theorists but also (and perhaps more so) for applied researchers, as it provides exact formulas and intuitive explanations for various measures of group efficiency, group scale elasticity, and group productivity indexes and refers to original papers for more details.

show abstract

“…Various applications analyzing real data for various economic questions can be found in [22,42,52,18,21,34], to mention just a few.…”

Section: Discussionmentioning

confidence: 99%

“…Intuitively, (37) says that the weight of a firm is the weighted average over all the output shares of this firm in its group, where the weights are the revenue shares of the industry for each output m in the total revenue of the industry. Next, use (36) and (34) to derive the weights for aggregating "between the sub-groups"…”

Section: Price-independent Weightsmentioning

confidence: 99%

Aggregation of Efficiency and Productivity: From Firm to Sector and Higher Levels

Zelenyuk¹

2020

Handbook of Production Economics

View full text Add to dashboard Cite

show abstract

“…The research employing DEA and MPI has been conducted at different levels of agricultural policy making from evaluating farms in a specific region to evaluating productivity of countries. Country level evaluations of agricultural productivity change work on macro-agriculture data-sets of sample countries varying between studies depending on the regional differences such as samples of African countries, [15,20,23,27,29], Asian countries [26,28], Middle East and North African countries [2] and European Union countries [8,19,31] or on the economic differences such as samples of least developed countries [10], developing countries [21], OECD countries [18] and industrialized countries [22].…”

Section: Introductionmentioning

confidence: 99%

The measurement of agricultural productivity change in OECD countries with Fuzzy data

Atıcı¹,

Ulucan²,

Bayar³

2018

RAIRO-Oper. Res.

View full text Add to dashboard Cite

In this paper, we aim to measure agricultural productivity change of 34 OECD countries between years 1990 and 2014. The methods employed are data envelopment analysis (DEA) and malmquist productivity index (MPI). DEA is a relative efficiency method in a production technology, whereas MPI is based on DEA to measure the changes in the production technology over time. Our challenge is the existence of missing data points over the years in the initial dataset, which correspond to approximately 9% of the data. Removing units, factors or years with missing data as commonly practiced in DEA, would cause loss of information and makes it very difficult to draw conclusions in such a macro-data. We present the idea of using averages of available data points for a given factor and average variations over the years in those data to produce intervals for the missing points and handle the problem without any dimension reduction in the available data. Fuzzy DEA approach is employed using the calculated factor-specific intervals followed by MPI calculations to conduct a productivity change analysis. We suggest and empirically illustrate that instead of narrowing down the scope of the analysis by excluding the points missing, applying fuzzy approaches is an option worth considering by which it can be possible to make the best out of the available information. The results of the analysis are interpreted with respect to years, countries, regions and economic size of the countries.

show abstract

“…The result of that development is the fact that African countries are exporting a huge quantity of agricultural products with limited or almost no added value, while a significant portion of imported products is represented by highly processed production with much higher unit/kilogram prices. The problem of Africa is its inability to improve and increase its home processing capacities and another problem of African food sector is the low level of agricultural production efficiency (Mugera, Ojede, 2014). Despite 24% of the world's agricultural land and 16% of arable land being in Africa gross production amounts to only 8% of world agricultural production (326 bn.…”

Section: Introductionmentioning

confidence: 99%

African Countries’ Agricultural Trade Value Chain Assessment Case study: Tanzania (Cashew nut exports)

Kment

Rajdlová

Kapila

2016

AOL

View full text Add to dashboard Cite

Sub-Saharan Africa lost its status as a net exporter of agricultural products in the early 1980s when prices for raw commodities fell and local production stagnated. Since then, agricultural imports have grown faster than agricultural exports. In order to get to the bottom of this critical issue, UNIDO in partnership with the AU, IFAD, AfDB, FAO, and UNECA, developed the African Agribusiness and Agro-Industries Development Initiative (3ADI). The major objective of the 3ADI is to increase private sector investment flows going into the agriculture sector in Africa by mobilizing resources for agribusiness and agro-industrial development from the domestic, regional or international financial systems. This formed the basis of research with the objective of assessing the value addition chain for some vital agricultural commodities in the 3ADI focus countries. UNIDO is developing several action plans in a few African countries -one of them is Tanzania. In the case of Tanzania, the findings show the potential in cashew nuts. The paper's main goal is to propose a plan or set of steps leading to the improvement of added value generation in the area of agricultural trade in Tanzania. The paper is focused on one commodity Cashew-nuts. Tanzania boosts high volumes of local supply of this commodity, which is the key prerequisite for the value addition chain through local processing. The results from the analysis prove significant economic losses related to the current structure of Tanzanian trade in cashew nuts. The main problem of the current cashew nut trade activities is the very low added value of exported cashew nuts. The paper analyses the structure of value added activities related to the cashew nut trade and proposes a plan for increasing the share of processed cashew nuts at a much higher unit price in comparison to raw cashew nuts. The simulated development in the cashew sector in Tanzania to the year 2030 is based on two expectations a 5% increase of evaluation of particular steps and a 5% growth of processed cashew nuts export volume resulting in significant growth of export incomes and provides an important material stimulating discussion related to the importance of the transformation of the export structure from unprocessed raw products to processed -finalized products.

show abstract

Technical Efficiency in African Agriculture: Is It Catching Up or Lagging Behind?

Cited by 16 publications

References 23 publications

Aggregation of Efficiency and Productivity: From Firm to Sector and Higher Levels

Aggregation of Efficiency and Productivity: From Firm to Sector and Higher Levels

The measurement of agricultural productivity change in OECD countries with Fuzzy data

African Countries’ Agricultural Trade Value Chain Assessment Case study: Tanzania (Cashew nut exports)

Contact Info

Product

Resources

About