Academic journal article Economic Inquiry

The Agricultural Productivity Gap in Europe

Academic journal article Economic Inquiry

The Agricultural Productivity Gap in Europe

Article excerpt

For 15 European countries over the 1970-2004 period we find large and persistent agricultural productivity gaps, the ratio of value added per hour in nonagriculture to that in agriculture. Comparing the gap in value added per hour to the wage gap between the two sectors suggests that value added in the data is mismeasured. We further find that, controlling for differences in gross domestic product per capita and institutions, the mismeasurement is positively related to self-employed share of hours in agriculture. Correcting for underreporting of self-employment income using our preferred correction factor reduces the measured agricultural productivity gap by 38%. These findings suggest that underreporting can account for a significant portion of the measured agricultural productivity gap. (JEL E01, 047, 052, Q10)

I. INTRODUCTION

The agricultural productivity gap, defined as the ratio of labor productivity in nonagriculture to that in agriculture, is as large as a factor 10 for countries in the bottom decile of the world income distribution (Caselli 2005). (1) Even though agriculture in poor countries is much less productive relative to nonagriculture, it employs most of the labor. At face value, this suggests that labor is misallocated; and moving labor out of agriculture to nonagriculture would lead to sizeable gain in aggregate output. (2)

The debate in the literature is over whether the measured agricultural productivity gaps reflect real productivity differences between agriculture and nonagriculture or merely measurement errors. For a set of (mostly developing) countries, Gollin, Lagakos, and Waugh (2014) construct measures of the agricultural productivity gap. They conclude that because micro data from household surveys lead to productivity gaps of similar magnitudes, measurement errors in national accounts are not likely to account for much of the productivity gap. (3) Herrendorf and Schoellman (2014) instead focus on agriculture productivity of U.S. states where, relative to developing countries, the quality of the data is less of a concern. They find that the agricultural productivity gaps for U.S. states, while large, are mainly because of undermeasurement of value added in agriculture. However, "whether this type of mismeasurement is equally at play in countries outside the United States, [...], is an open question" (Gollin, Lagakos, and Waugh 2014, 3).

In this article, we use EU-KLEMS data from 1970 to 2004 for 15 European countries (EU15) to measure the agricultural productivity gaps and examine the importance of mismeasurement in accounting for these gaps. (4) For these countries national accounts and labor force data are derived using the same statistical framework, and are of reasonably high quality. In addition, relative to the U.S. states examined by Herrendorf and Schoellman (2014) where the differences in the level of development or institutions are negligible, there are marked differences in gross domestic product (GDP) per capita and institutions across EU15 countries, especially in earlier periods.

We document significant agricultural productivity gaps for Europe. Measured as the ratio of nominal value added per worker in nonagriculture to that in agriculture, the average agricultural productivity gap is roughly a factor 2. The gap in nominal value added per worker is not because of more hours worked in nonagriculture. In fact, when measured as the ratio of value added per hour, the average productivity gap increases to 2.2. These agricultural productivity gaps we find for Europe are similar in magnitude to those documented by Herrendorf and Schoellman (2014) for U.S. states, and those by Gollin, Lagakos, and Waugh (2014) for developed countries in their sample. We provide two additional findings. First, the agricultural productivity gaps for Europe are persistent over the 35year period. Second, there are large differences in the agricultural productivity gap across countries in our sample: for the value added per hour measure it ranges from as low as 1. …

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