For insurance markets (Ali and Deininger, 2015;

For the
past decades, development economists have been puzzled by the empirical
findings that suggest an inverse relationship between farm size and land
productivity in developing countries. Numerous studies have tried to explain
the puzzle and some studies have suggested that it is partly because of market
failure, that is, incomplete land, labor, credit and insurance markets (Ali and
Deininger, 2015; Assunçao and Braido, 2007; Kimhi, 2006; Lamb 2003). This
reasoning is generally intuitive considering agriculture in most developing
countries is still labor-intensive with not much mechanization taking place. As such, in the
presence of incomplete land and labor markets, households with small farms but
abundant family labor have an advantage, as they can apply more of the labor
resource towards achieving optimal productivity unlike those with large farms
but labor constrained.

 

Results
from other studies suggest that the inverse relationship could also be because
of measurement errors resulting from the use of self-reported yield data (Ali
and Deininger, 2015; Carletto et al., 2013). A recent study by Desiere and
Jolliffe (2018) also support this line of thought reporting that the inverse land
size–productivity relationship disappears when crop-cut estimates are used
instead of self-reported production data. There are still ongoing debates about
the approaches and type of data to use when establishing productivity measures
for such studies.  Hence, the need for
more research to provide further empirical explanation of the drivers of the
inverse land size-productivity relationship to guide land reform policies in developing
countries. For many developing countries like Malawi, it remains unclear to
policy makers if the solution to the prevailing food security challenges really
lies in small-scale agriculture.

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One
possible explanation for inverse relationship puzzle could be omitted variable effects.
For example, omitting the effect of heterogeneity of soil quality across farms
could be one possible reason for the inverse relationship. It is, therefore, important
to account for other key factors of production when developing productivity measures.
For instance, the scale of operations for smallholders and large farms is
different. As such, the required level of capital intensification, that is,
technology use or mechanization may vary depending on farm size. When capital
is limiting, as is the case for most developing countries, it is likely for
small farms to remain productive on average using labor-intensive techniques only
unlike large farms that require mechanization or advanced technology for
optimal productivity. Similarly, the level of skills required to manage a small
farm may not be the same as the level of skills required to manage a large farm.
The existence of some management knowledge and skills gap in most developing
countries, therefore, may likely put households with large farms at a disadvantage.
Similarly, it is important to note that most farms in Africa are in remote areas
where access to energy is poor. While poor access to energy may affect
operations of large farms that require mechanization for optimal productivity,
access to energy may not be a productivity issue for small farms.

 

This
study, therefore, will help advance literature on farm size and productivity
relationship by focusing on how these key factors of production influence productivity
across different farm sizes. For Malawi, a recent study by Deininger and Xia
(2018) also shows that smallholders are on average more productive that
estates. Considering that estates are expected to be more productive because of
economies of scale, it is crucial to understand factors that are hindering these
estates from realizing optimal productivity. As such, in this study we intent to
perform a comparative analysis of factors influencing productivity for smallholder
and estate farms.  

 

1.0  Research
questions

1)     
What
are the determinant of productivity for estate and smallholder agriculture?

2)     
Do
estates and smallholder farms require the same level of management knowledge
and skills for optimal productivity?

3)     
Do
estates and smallholder farms use or require same level of energy?

4)     
 Does access to energy affect estates and
smallholder’s productivity in the same way? 

5)     
Is
the level of capital intensification required (laborsaving technology use,
mechanization of operations) for smallholders and estates the same?  

6)     
What
are the factors hindering estates from realizing optimal productivity?

2.0  Data

We intent to use 4 waves of Integrated
Household Survey data for Malawi (panel data). Some of the variables of
interest for the analysis will include data on crop yields, land holding size,
household size (family labor), hired labor, technology use, type of technology,
education, management skills, farming experience, access to credit and farm
access to energy amongst other factors.