Principal investigator: Viviane Pires Ribeiro
Authors: Paula Bustos, Bruno Caprettini and Jacopo Ponticelli
Location of the Intervention: Brazil
Sample Size: two cultures
Main theme: Agriculture
Main Variable of Interest: Technical change in agriculture
Type of Intervention: Analysis of the effects of agricultural productivity on structural transformation.
Methodology: Econometric model
There is a long tradition in economics of studying the relationships between agricultural productivity and industrial development. The study by Bustos et al. (2016), for example, provides direct empirical evidence on the effects of agricultural productivity on structural transformation. The authors isolate these effects by analyzing the introduction of genetically modified soybeans in Brazil. This technology allows farmers to employ fewer workers per unit of land to produce the same output, increasing labor productivity in agriculture. The study's results suggest that the technical change in soybean production was strongly labor-saving and led to industrial growth, as predicted by the model.
Evaluation Context
Over the last few decades, Brazilian agriculture has relied on two new agricultural technologies for soybean and corn cultivation. The first is the use of genetically modified (GM) seeds in soybean cultivation. The second is the introduction of a second corn harvest season during the same agricultural year, which requires the use of advanced cultivation techniques.
On the one hand, it is observed that the main advantage of transgenic soybean seeds compared to traditional seeds is that they are herbicide-resistant, which facilitates the use of no-till planting techniques. In other words, planting transgenic soybean seeds does not require soil preparation, as herbicide application selectively eliminates all unwanted weeds without harming the crop. As a result, transgenic soybean seeds can be applied directly to the residues of the previous harvest, allowing farmers to save on production costs, since less labor per unit of land is needed to obtain the same yield.
On the other hand, the introduction of a second corn harvest season can affect the demand for labor in the agricultural sector through intra-crop and inter-crop effects. The first effect is directly due to the introduction of a second harvest, which increases the demand for labor relative to the reference value of a corn harvest. The second effect is due to the expansion of corn cultivation into areas previously dedicated to less labor-intensive activities, which also tends to increase the demand for labor.
Intervention Details
Development literature has documented that the growth trajectory of most developed economies has been accompanied by a process of structural transformation. As economies develop, agriculture's share of employment falls, and workers migrate to cities to find jobs in the industrial and service sectors. In this context, Bustos et al. (2016) provide direct empirical evidence on the effects of technological change in agriculture on the industrial sector, studying the recent widespread adoption of new agricultural technologies in Brazil. The authors analyze the effects of adopting genetically modified soybean seeds (transgenic soybeans). This new technology requires less labor per unit of land to produce the same output. Thus, it can be characterized as a labor-increasing technological change. Furthermore, the authors study the effects of introducing a second corn crop (second-crop corn). This technique allows for two crops to be cultivated per year, effectively increasing land endowment. Therefore, it can be characterized as a land-increasing technological change. The simultaneous expansion of these two crops allows for an assessment of the effect of agricultural productivity on structural transformation in open economies.
The main data sources used in the study were the Agricultural Census, the Population Census, and the Global Agroecological Zones database of the Food and Agriculture Organization of the United Nations (FAO). Data from the Brazilian Annual Industrial Survey (PIA) were used for robustness checks. Additionally, the Demographic Census was used to construct measures of the sectoral composition of employment and average wages. More specifically, data from the last two census rounds (2000 and 2010) were used to observe the variables of interest before and after the legalization of transgenic soybean seeds. Furthermore, an exogenous measure of technological change in agriculture was obtained using estimates of potential soybean and corn yields in geographic areas of Brazil from the FAO-GAEZ database.
Methodology Details
To guide the empirical work, Bustos et al. (2016) constructed a simple model describing a small, two-sector open economy where technological change in agriculture can be influenced by factors. The model predicts that a Hicks-neutral increase in agricultural productivity induces a reduction in the size of the industrial sector as labor is reallocated to agriculture, as in classical open economy models. Similar results are obtained when technological change expands land. However, if land and labor are strong complements in agricultural production, technological change that increases labor reduces the demand for labor in agriculture and causes workers to be reallocated to manufacturing. In short, the model predicts that the effects of agricultural productivity on structural transformation in open economies depend on the factor bias of technological change.
The authors propose establishing the direction of causality using two sources of exogenous variation in the profitability of technology adoption. First, in the case of transgenic soybeans, since the technology was invented in the USA in 1996 and legalized in Brazil in 2003, the latter date is used as the source of variation over time. Second, since the new technology had a differential impact on yields depending on geographical and climatic characteristics, differences in soil suitability between regions are used as the source of cross-sectional variation. Similarly, in the case of corn, the timing of the expansion of second-crop corn and regional differences in soil suitability are explored.
Results
Initial analysis revealed that regions where soybean cultivation expanded experienced increased agricultural output per worker, reduced agricultural labor intensity, and expanded industrial employment. These correlations are consistent with the theoretical prediction that the adoption of agricultural technologies that increase labor reduces labor demand in the agricultural sector and induces the reallocation of workers to the industrial sector. However, causality can occur in the opposite direction. For example, increased productivity in the industrial sector could increase labor demand and wages, inducing agricultural companies to switch to less labor-intensive crops, such as soybeans.
The results suggest that municipalities where the new technology is predicted to have a greater effect on potential soybean yields experienced a greater expansion of the area planted with transgenic soybeans. These regions also experienced increases in the value of agricultural production per worker and reductions in labor intensity measured as employment per hectare. Furthermore, they experienced faster employment growth and wage reductions in the industrial sector. Interestingly, the effects of technology adoption are different for maize. Regions where the FAO predicted potential maize yields would increase most by switching from traditional to new technology did, in fact, experience a greater increase in the area planted with maize. However, they also experienced increases in labor intensity, reductions in industrial employment, and increases in wages.
Regarding the analysis of the service sector, a central characteristic is the distinction between two effects of agricultural technical change: the supply effect and the demand effect. In the case of the land-adding technical change, the first effect is generated by the increase in the marginal product of labor in the agricultural sector, which draws workers away from other sectors. The second effect is generated by the increase in income resulting from the agricultural technical change, which leads to an increase in demand for non-tradable services. Both effects lead to a reallocation of labor out of the manufacturing sector. However, when the technical change saves labor, the supply effect frees up agricultural workers. As a result, the net effect of agricultural technical change on industrialization depends on the relative strength of the supply and demand effects. Furthermore, the demand effect is driven only by increases in land rents. Thus, its strength depends on the extent to which landowners consume services in the region where their land is located. Empirical results imply that in regions most affected by labor-saving technological changes, this factor of production has been reallocated from agriculture to manufacturing, and not to services.
Lessons in Public Policy
The study conducted by Bustos et al. (2016) contributes to the debate on the effects of agricultural productivity on industrialization in open economies. The authors argue that these effects crucially depend on the factor bias of technological change. Thus, the study provides evidence that when technological change in agriculture is strongly labor-saving, as in the case of genetically modified soybeans, it can foster industrialization. When, instead, technological change is labor-biased, as in the case of the introduction of a second corn crop, agricultural productivity can hinder industrialization.
The different effects of technological change in agriculture documented for soybeans and corn indicate that the bias factor of technological change is a key determinant of the relationship between agricultural productivity and structural transformation in open economies. The technological change that increases land use, as in the case of second-crop corn, leads to an increase in the marginal product of labor in agriculture and a reduction in industrial employment. However, the technological change that increases labor, as in the case of transgenic soybeans, leads to a reduction in the marginal product of labor in agriculture and growth in industrial employment.
The estimates obtained by the study can be used to quantify the effect of agricultural technical change influenced by factors in structural transformation. In particular, Bustos et al. (2016) calculated the elasticity of sectoral employment shares to changes in agricultural productivity induced by technical changes in soybean production: a 1% increase in agricultural labor productivity leads to a 0,16 percentage point reduction in the agricultural employment share and a similar magnitude increase in the industrial employment share. These estimates can be used to understand the extent to which the observed differences in the speed of structural transformation among Brazilian municipalities can be explained by labor-saving technical changes in soybean production.
References
BUSTOS, Paula; CAPRETTINI, Bruno; PONTICELLI, Jacopo. Agricultural productivity and structural transformation: Evidence from Brazil. American Economic Review, vol. 106, no. 6, p. 1320-65, 2016.
