Increasing energy efficiency is discussed as an effective way to protect the climate, even though this is frequently associated with additional (investment) costs when compared to standard technologies. However, the investment costs of emerging energy-efficient technologies can be reduced by economies of scale and experience curve effects. This also brings about higher market penetration by lowering market barriers. Experience curves have already been analyzed in detail for renewable energy technologies, but are not as well documented for energy-efficient technologies despite their significance for energy and climate policy decisions. This work provides empirical evidence for effects of economies of scale and experience on the costs of energy-efficient electric motors. We apply a new methodology to the estimation of learning effects that is particularly promising for energy-efficient technologies where the very low data availability did not allow calculations of learning rates so far. Energy-efficient electric motors are a highly relevant energy technology that is responsible for about 55% of German electricity consumption. The analysis consists of three main steps. First, the calculation of composite price indices based on gross value added statistics for Germany which show the changes in cost components of electric motors over the period 1995 to 2006; second, an estimation of the corresponding learning rate which is, in a third step, compared with learning rates observed for other energy-efficient technologies in a literature review. Due to restrictions of data availability, it was not possible to calculate a learning rate for the differential costs of energy-efficient motors compared to standard motors. Still, we estimated a learning rate of 9% for “Eff2” motors in a period when they penetrated the market and replaced the less efficient “Eff3” motors. Furthermore, we showed the contribution of different effects to these cost reductions, like a reduction of material use per motor produced by 15% and an improvement of labor productivity of 43%.