The world is experiencing a new technological revolution that can have profound implications for the nature of work. Currently, attention is particularly focused on the implication of the increased adoption of industrial robots. Multiple studies predict that especially developing countries are at risk to be negatively affected by robotization in the near future, in ways such as unemployment and greater income inequality. To a large extent, developing countries became integrated in the world economy through their involvement in global value chains (GVCs). The establishment of GVCs, in turn, were a direct result of an upward trend in offshoring. However, because both offshoring and industrial robots primarily affect routine and labor-intensive tasks, the labor-cost advantage of developing countries is quickly eroding and puts at risk their strong export position. Furthermore, in recent year world trade is indeed observed to slow down, which has further fueled concerns. This thesis aims to quantify how much the adoption of industrial robots in developed countries contributed to the decline in world trade experienced in recent years. We do this by regressing a measure of offshoring on the density of industrial robots and other control variables. Offshoring is quantified in terms of the offshoring index, which is constructed using two datasets by the Organization for Economic Co-operation and Development (OECD). To get data on the adoption of industrial robots, we use a dataset that has been made available by the International Federation of Robotics (IFR). The final unbalanced panel dataset consist of 29 countries, 15 industries and spans the period 1993-2015. The other factors that will be controlled for in the models include: labor intensity, wages, year-dummies, country-trends and industry-trends. Furthermore, we estimate the models using fixed-effects. Our regression estimates provide evidence for a negative and statistically significant relationship between the adoption of industrial robots and offshoring intensity. We estimate that if the density of industrial robots increases by 10% in OECD countries, then offshoring decreases by 0.29%. Furthermore, we investigate if the effects are particularly strong for certain industries. We find that the effect between robotization and offshoring particularly holds true for industries that have already robotized the most in relative terms. We conclude by discussing several policies that can help developing countries to tackle the disruptive effects of automation and safeguard their future economic development.