Measuring the Agricultural Productivity Gap

2016 ◽  
Author(s):  
David Gomtsyan
Keyword(s):  
Agricultural Productivity ◽  
Productivity Gap

The Agricultural Productivity Gap *

2013 ◽  
Vol 129 (2) ◽  
pp. 939-993 ◽  
Author(s):  
Douglas Gollin ◽  
David Lagakos ◽  
Michael E. Waugh
Keyword(s):  
Developing Countries ◽  
Agricultural Productivity ◽  
Value Added ◽  
Household Survey ◽  
National Accounts ◽  
Data Value ◽  
Alternative Measures ◽  
Labor Inputs ◽  
Household Survey Data ◽  
Productivity Gap

Abstract According to national accounts data, value added per worker is much higher in the nonagricultural sector than in agriculture in the typical country, particularly in developing countries. Taken at face value, this “agricultural productivity gap” suggests that labor is greatly misallocated across sectors. In this article, we draw on new micro evidence to ask to what extent the gap is still present when better measures of sector labor inputs and value added are taken into consideration. We find that even after considering sector differences in hours worked and human capital per worker, as well as alternative measures of sector output constructed from household survey data, a puzzlingly large gap remains.

scholarly journals THE AGRICULTURAL PRODUCTIVITY GAP IN EUROPE

2015 ◽  
Vol 53 (4) ◽  
pp. 1807-1817 ◽  
Author(s):  
Wenbiao Cai ◽  
Manish Pandey
Keyword(s):  
Agricultural Productivity ◽  
Productivity Gap

scholarly journals Examining the Gender Productivity Gap among Farm Households in Mali

2020 ◽  
Author(s):  
A Singbo ◽  
E Njuguna-Mungai ◽  
J O Yila ◽  
K Sissoko ◽  
R Tabo
Keyword(s):  
Gender Equity ◽  
Gender Gap ◽  
Rural Women ◽  
Rural Areas ◽  
Agricultural Productivity ◽  
Endowment Effect ◽  
Female Farmers ◽  
Female Specific ◽  
Gender Productivity Gap ◽  
Productivity Gap

Abstract This paper decomposes the gender agricultural productivity gap and measures the factors that influence the gap between male and female agricultural plot managers in Mali. The Oaxaca–Blinder approach and the recentred influence function (RIF) decomposition methodology are applied to a nationally representative survey of Mali. The results show that the agricultural productivity of female plot managers is 20.18% lower than that of male plot managers. Additionally, while more than half (56%) of the agricultural productivity gap is influenced by female-specific structural disadvantages, 44% of the gap is due to an endowment effect. Socio-economic characteristics such as the educational level and age of the plot manager, environmental factors and agricultural production practices, i.e., the differential use of inputs (organic or inorganic fertiliser and improved seeds) and the use of hired female workers seem to affect the female-specific structural disadvantages. To reduce or close the gender productivity gap, the underlying causes of female-specific structural disadvantages must be addressed to enable female farmers to obtain the same returns as men. Traditional means of addressing the gender gap, such as providing education for women in rural areas and facilitating rural women’ access to extension services and improved seeds, can mitigate the endowment deficit. This paper highlights the need to develop a better understanding of the factors influencing the structural disadvantages faced by female farmers in Mali that could feed into the development of more effective policies to address the gender gap in agricultural productivity, improving productivity and gender equity and reducing poverty.

scholarly journals The Agricultural Productivity Gap

2013 ◽  
Author(s):  
Douglas Gollin ◽  
David Lagakos ◽  
Michael Waugh
Keyword(s):  
Agricultural Productivity ◽  
Productivity Gap

International differences in labor productivity: Role of capital, technological level and resource rent

2016 ◽  
pp. 67-93 ◽  
Author(s):  
A. Zaytsev
Keyword(s):  
Labor Productivity ◽  
Developed Countries ◽  
Resource Rent ◽  
Per Capita Gdp ◽  
Technological Level ◽  
Per Capita ◽  
Oil Sector ◽  
Population Ratio ◽  
Gdp Gap ◽  
Productivity Gap

Using level accounting methodology this article examines sources of per capita GDP and labor productivity differences between Russia and developed and developing countries. It considers the role played by the following determinants in per capita GDP gap: per hour labor productivity, number of hours worked per worker and labor-population ratio. It is shown that labor productivity difference is the main reason of Russia’s lagging behind. Factors of Russia’s low labor productivity are then estimated. It is found that 33-39% of 2.5-5-times labor productivity gap (estimated for non-oil sector) between Russia and developed countries (US, Canada, Germany, Norway) is explained by lower capital-to-labor ratio and the latter 58-65% of the gap is due to lower technological level (multifactor productivity). Human capital level in Russia is almost the same as in developed countries, so it explains only 2-4% of labor productivity gap.

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