Understanding the relationship between agricultural growth, rural development and poverty reduction has become very difficult because of the lack of reliable data to describe the entire scenario in the subSaharan African region. Bart Minten’s (2008) paper, Agricultural Technology, Productivity, and Poverty in Madagascar, attempts to demonstrate the existing relationship of agricultural growth, rural development, and poverty reduction by quantitatively using a spatially-explicit dataset. The objective is to study how such adoption of agricultural technology and crop yield shape the food prices, real wages of unskilled workers and key welfare indicators of Madagascar. Rice industry in Madagascar was used as a proxy for staple crops in the study. With the poor performance of the country affected also by the long-term trend in the global market, this diminishes the hope for agriculture poverty reduction in the subSaharan African region. However, Madagascar illustrates how improved agricultural productivity can make a significant difference to address poverty and the low food supply.
Using a predictive model on the partial equilibrium analysis, the author explores how improved agricultural production technologies, thereby increasing crop yields affect the welfare of the identified population. The model operates under the framework where induce price change is affected by how the output responds to technical changes and how prices respond to induced changes in the output. That is, net buyers benefit from low prices while net sellers lose.
There were three data sources used in the paper to include household data survey conducted in 2001, the national population census of 1999 and other geographical secondary data sources. The socio-economic profile of geographically defined administrative units in Madagascar was used for analysis. The survey on the local communities despite the remoteness of some depends on the focus group representation. The author acknowledged the fact that there may be some recall bias for the questions laid out because it was only a one time survey. The 1993 population census data shows a trend on the differences in education levels, age groups and access to infrastructures of the people in Madagascar. To simulate the possible effects of poverty in Madagascar, biophysical attributes of communes were controlled. Information on spatially-explicit cartography of soil conditions, temperature, altitude, and rainfall patterns were used to see the spatial distribution of such parameter. Empirical analysis of collected data uses a robust multivariate regression technique.
Initial results showed the significant number of richest group of households in the net buyer’s category which suggested a positive link between household welfare and diversification into high-return non-agricultural activities. During harvest season, a significant number of farmers sell rice. However, these farmers buy back what they sold during the lean season. In the 2001 national survey data, 25% of agricultural plots were cultivated using wage labor during the past agricultural season. A significant statistical correlation between rural wages and head-count poverty indicates the strong inverse relationship between wage rates and the welfare indicators such as food security. About 70% of the population has an annual income which falls below poverty line. Results suggested that the risky situation of the poor in Fianarantsoa (poorest province in Madagascar) is a reflection of the significant difference in welfare indicators.
The study demonstrates strong empirical evidence which proposed that rice productivity diminishes extreme poverty in Madagascar for net rice buyers, net rice sellers and unskilled workers. It has been found that better agricultural performance is strongly correlated with real wages. Among the implications of better agricultural performance include rice profitability and better consumer prices for the staple food. Results further indicated that greater rice productivity surpasses local market price declines which benefit the net sellers. Higher rice yields also facilitates unskilled workers and net buyers by decreasing consumer food prices while improving real wages of unskilled workers. Indeed, cash crop production is significantly linked with improved welfare conditions.
Agricultural productivity has been noted to have a strong positive correlation with the adoption of improved agricultural technologies, access to agricultural extension, the availability of irrigation and market access. The direct and indirect effects on rice yields in rural Madagascar on the availability of irrigation and market access has been observed through induced technology adoption. The commune-level data suggested that the government should develop policies on technology adoption because it increases the potential of agricultural productivity to reduce poverty and food insecurity. While, improved agricultural technology is deemed the most effective method to address poverty and food insecurity, other factors such as improvement of rural transport infrastructure, irrigation systems, livestock herds preservation, physical security, literacy rates, land tenure security, and access to extension services must also be considered because all play a positive role in encouraging productivity growth and poverty reduction.
While the study was able to show a significant relationship in agricultural productivity and poverty reduction, similar studies were already undertaken which also illustrates agricultural productivity and technology adoption vis-à-vis poverty reduction. The approach however is a novel one as the study tries to be objective as possible. Empirical data are integrated with answers from survey forms, secondary data and questionnaires. Using multivariate analysis and regressions the author was able to generate a robust analysis in his study which is essential in predicting possible outcomes based on the present scenario in the agricultural industry of Madagascar. Since there are no models yet that will explain the dynamics on poverty, technology and agriculture, the model as proposed by the author can be very useful specially to the stakeholders because it lays a an initial framework of what has already been done and what has to be done in the future. There are certain gaps that have to be filled in his model because other factors in measuring poverty were not taken into consideration. Models are also limited to certain assumptions and in this study there might also be a need to categorized or classify the people in Madagascar according to economic status. Further, the model may require a series of validation so that it can also be used or applied in other regions facing similar situations with Madagascar.
Lessons Learned
The study was able to illustrate that geographical data such as the proximity of people from access points where technology, education, infrastructures and the like can be used as a proxy on the analysis of the relationship of poverty to productivity. In this study, we have learned that just by using secondary geographical information and maximizing the utility of this data we can transform it into an empirical data which allows us to look at relationships in a multidimensional space and how significant is this relationship in proving such a certain point. That is through the use of multivariate analysis and regression and modeling.
One of the common applications of modeling has been elucidated in the field of meteorology where weather patterns and climate changes are being predicted. While models may be limited to certain assumptions, the power of models in illustrating the relationship of poverty and productivity must not be disregarded. Using models as an approach to show the relationship of such study can also be very useful because it allows us to predict possible outcomes based on the current scenario and it is also helps us in decision making. Take the weather station storm warning signals for instance. The only challenge in this type of study is how to make the model more accurate because the nature of data in determining poverty incidence and agricultural productivity are relative to one’s own perspective of poverty and productivity.
Summary Outline: Agricultural Technology, Productivity, and Poverty in Madagascar
I. Objective
A. To illustrate how agricultural technology and productivity reduces poverty in Madagascar
II. Conceptual Framework
A. Real wage and employment rates are key determinants of the welfare of the subpopulation of the poor who depend in whole or in part on the unskilled labor market for income
B. the adoption of improved agricultural production technologies and increased crop yields affect the welfare of each of these subpopulations (buyers, sellers, unskilled workers) by means of partial equilibrium analytical model
III. Materials and Methods
A. Data sources: commune-level census conducted in 2001, the national population census of 1993, and geographical data from secondary sources
B. Use of remoteness index as proxy to infrastructure development and agricultural productivity based on the factor analysis of various isolation measures that were collected in the commune census
IV. Result and Discussion
A. General overview of the welfare of populations subjected to empirical analysis
B. Implications of agricultural productivity and poverty on price and wages
C. Adopting agricultural technology to improve agricultural performance
V. Conclusion
A. Communes that have higher rates of adoption of improved agricultural technologies etc., leading higher crop yields significantly benefits on better welfare indicators when controlling for geographical and physical characteristics.
B. Empirical evidence supports improved agricultural productivity to reduce the high poverty rates in rural Madagascar
C. Improved agricultural technology diffusion seems the most effective means of improving agricultural productivity and reducing poverty and food insecurity in rural Madagascar
A. Integration of commune-level census conducted in 2001, the national population census of 1993, and geographical data from secondary sources and the transformation of these data to empirical data
B. Use of spatial empirical data and robust statistics to analyze and relate poverty incidence to agricultural productivity
C. Use of a model as a predictive tool in identifying the role of technology in improving agricultural productivity and how improved agricultural productivity affects wages and prices which are among the many key factors in poverty
D. Paper provides a new way to make correlations on agricultural productivity and factors affecting such productivity
II. Weaknesses of the Paper
A. Relationships on productivity have been long established in various economic models; the information may be still relevant but lacks novelty
B. Model is still crude as there are other key factors that should be included to provide a holistic view of diminishing poverty incidence
C. The model needs to be tested and may require validation so that it could be applicable also to similar cases in other parts of the world
D. Poverty is also a social construct and therefore categorizing people into groups based on the data source can be dangerous also
III. Conclusion
A. The author was successful in showing the relationship between poverty and agricultural productivity using geographic and empirical data
B. Since the author claims that his model is the first one to illustrate the dynamics on poverty, technology and agriculture, the model can also be very useful to the stakeholders because it lays a an initial framework of what has already been done and what has to be done in the future
C. However, there are certain gaps that have to be filled in his model because other factors in measuring poverty were not taken into consideration
D. The challenge is to generate empirical data that will encompass other key factors which also affects agricultural productivity and poverty
Works Cited
Minten, Bart. “Agricultural Technology, Productivity and Poverty in Madagascar.” World Development, 36.5 (2008): 797–822. Print.
