What are the effects of technical change in agriculture on the industrial sector?

Responsible researcher: Viviane Pires Ribeiro

Authors: Paula Bustos, Bruno Caprettini and Jacopo Ponticelli

Intervention Location: Brazil

Sample Size: Two cultures

Big topic: Agriculture

Variable of Main 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 carried out 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 soy 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 results suggest that the technical change in soybean production was strongly labor-saving and led to industrial growth, as predicted by the model.

Assessment Context

During the last decades, Brazilian agriculture has relied on two new agricultural technologies for the cultivation of soybeans and corn. The first is the use of genetically modified (GE) 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 in relation to traditional seeds is that they are resistant to herbicides, which facilitates the use of direct planting techniques. In other words, planting transgenic soybean seeds does not require soil preparation, as the application of herbicide selectively eliminates all unwanted weeds without harming the crop. As a result, transgenic soybean seeds can be applied directly to past harvest residues, allowing farmers to save on production costs as less labor is required per unit of land to obtain the same production.

On the other hand, the introduction of a second harvest season for corn may affect the demand for labor in the agricultural sector through intra-harvest and inter-crop effects. The first effect is directly due to the introduction of a second harvest that increases the demand for labor relative to the reference value of a corn harvest. The second effect is due to the expansion of corn 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 employment in the industrial and service sectors. In this context, Bustos et al. (2016) provide direct empirical evidence on the effects of technical 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 soy seeds (transgenic soy). This new technology requires less labor per unit of land to produce the same output. Thus, it can be characterized as a technical change that increases work. Furthermore, the authors study the effects of introducing a second corn harvest season (off-season corn). This technique allows two crops to be grown per year, effectively increasing land allocation. In this way, it can be characterized as a technical change to increase land. The simultaneous expansion of these two crops makes it possible to assess 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 Food and Agriculture Organization of the United Nations (FAO) Global Agroecological Zones database. To carry out robustness checks, data from the Brazilian Annual Industrial Survey (PIA) were used. In addition, 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) to observe the variables of interest before and after the legalization of GMO soy seeds. Furthermore, an exogenous measure of technological change in agriculture was obtained using estimates of potential yields of soybeans and corn in geographic areas of Brazil from the FAO-GAEZ database.

Methodology Details

To guide empirical work, Bustos et al. (2016) built a simple model that describes a small open two-sector economy in which technical 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 classic open economy models. Similar results are obtained when technical change expands the land. However, if land and labor are strong complements in agricultural production, technical change that increases labor reduces labor demand 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 factorial bias of technical change.

The authors propose to establish the direction of causality using two sources of exogenous variation in the profitability of technology adoption. First, in the case of transgenic soy, as the technology was invented in the USA in 1996 and legalized in Brazil in 2003, this last date is used as the source of variation over time. Second, as the new technology has had a differential impact on yields depending on geographic and climatic characteristics, differences in soil suitability between regions are used as the source of cross-sectional variation. Likewise, in the case of corn, the timing of expansion of second-crop corn and regional differences in soil suitability are explored.

Results

In a first analysis, it was found that the regions where the area cultivated with soybeans expanded experienced an increase in agricultural production per worker, a reduction in work intensity in agriculture and an expansion in industrial employment. These correlations are consistent with the theoretical prediction that the adoption of labor-increasing agricultural technologies 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, an increase in 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 expected to have a greater effect on potential soybean yields have experienced greater expansion of the area planted with transgenic soybeans. It is observed that these regions also experienced increases in the value of agricultural production per worker and reductions in labor intensity measured as employment per hectare. Additionally, they have experienced faster employment growth and wage reductions in the industrial sector. Interestingly, the effects of technology adoption are different for corn. Regions where potential FAO corn yields were predicted to increase most when switching from traditional to new technology actually experienced a greater increase in corn acreage. However, they also experienced increases in work intensity, reductions in industrial employment, and increases in wages.

In relation to the analysis of the services 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 technical change to increase land, the first effect is generated by the increase in the marginal product of labor in the agricultural sector, which removes workers from other sectors. The second effect is generated by the increase in income resulting from technical change in agriculture which leads to an increase in demand for non-tradable services. Both effects lead to a reallocation of labor away from the manufacturing sector. However, when 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 supply and demand effects. Furthermore, the demand effect is driven only by increasing land rents. Thus, its strength depends on the extent to which landowners consume services in the region where their land is located. The empirical results imply that in regions most affected by technical changes that save labor, this factor of production was reallocated from agriculture to manufacturing and not to services.

Public Policy Lessons

The study carried out by Bustos et al. (2016) contribute to the debate on the effects of agricultural productivity on industrialization in open economies. The authors argue that these effects depend crucially on the bias of technical change factors. Thus, the study provides evidence that when technical change in agriculture is strongly labor-saving, as in the case of genetically modified soybeans, it can foster industrialization. When instead technical change is labor biased, as in the case of the introduction of a second crop in corn, agricultural productivity can slow industrialization.

The different effects of technological change on agriculture documented for soybeans and corn indicate that the technical change bias is a key determinant of the relationship between agricultural productivity and structural transformation in open economies. The technical change of increasing land, in the case of second harvest corn, leads to an increase in the marginal product of labor in agriculture and a reduction in industrial employment. However, technical change that increases labor, in the case of transgenic soybeans, leads to a reduction in the marginal product of labor in agriculture and employment growth in the industrial sector.

The estimates obtained by the study can be used to quantify the effect of factor-influenced agricultural technical change on structural transformation. In particular, Bustos et al. (2016) calculated the elasticity of sectoral employment shares to changes in agricultural productivity induced by the soybean technical change: 1% increase in agricultural labor productivity leads to a 0.16 percentage point reduction in the agricultural employment share and a increase in the share of industrial employment of similar magnitude. These estimates can be used to understand the extent to which observed differences in the speed of structural transformation between Brazilian municipalities can be explained by labor-saving technical changes in soybeans.

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.