For a single …rm with a given volatility of total factor productivity at the gross output level (GTFP), the volatility of total factor productivity at the value added level (YTFP) increases with the share of intermediate goods in gross output. For a CobbDouglas production function in capital, labor and intermediate goods, YTFP volatility is equal to GTFP volatility divided by one minus the share of intermediate goods in gross output. In the U.S., this share is steadily around 0.6 for manufacturing and 0.38 for services during the 1960-2005 period. Thus, the same level of GTFP volatility in the two sectors implies a 55% larger YTFP volatility in manufaturing. This fact contributes to the higher measured YTFP volatility in manufacturing with respect to services. It follows that, as the services share in GDP increases from 0.53 in 1960 to 0.71 in 2005 in the U.S., GDP volatility is reduced. I construct a two-sector dynamic general equilibrium input-output model to investigate the role of the sectorial reallocation between manufacturing and services in reducing U.S. GDP volatility. Numerical results for the calibrated model economy suggest that the sectorial reallocation can account for almost a half of the 56% GDP volatility di¤erence between the 1960-1983 and the 1984-2005 periods. When the highly volatile period 1973-1983 is excluded, the sectorial reallocation alone is able to account for the entire di¤erence in GDP volatility between the 1960-1973 and the 1984-2005 periods.JEL Classi…cation: C67, C68, E25, E32.

I construct a two-sector general equilibrium model of structural change to study the impact of sectoral composition of gross domestic product (GDP) on cross-country differences in GDP growth and volatility. For an empirically relevant parametrization of sectoral production functions, an increase in the share of services in GDP reduces both aggregate total factor productivity (TFP) growth and volatility, thus reducing GDP growth and volatility. When the model is calibrated to the US manufacturing and service sector, the rise of the service sector occurring as income grows can account for a large fraction of the differences in per capita GDP growth and volatility between high-income economies and upper middle income economies. (JEL E23, E25, E32, L60, L80) T his article puts forth the idea that the composition of GDP represents an important channel in shaping both GDP growth and volatility. Cross-country evidence suggests that: (i) per capita GDP of high-income economies grows slower than that of middle income economies; (ii) high-income economies display lower per capita GDP volatility than middle income ones; and (iii) the share of services in GDP increases with income per capita. These facts together suggest that both the growth rate and the volatility of an economy might be related to its productive structure. To address this issue, I first report empirical evidence on the three observations above. Next, I present a two-sector general equilibrium model qualitatively consistent with such evidence. Finally, I use a calibrated version of the model to assess the importance of structural change between the broadly defined manufacturing and service sector for observed differences in per capita GDP growth and volatility between upper middle and high-income economies. Even in the most conservative

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. 2 Terms of use: Documents in EconStor may Non-technical summaryDuring the last decades, several emerging market economies have experienced a banking crisis together with a sudden stop. In most of these cases national governments and national central banks adopted several types of interventions to fight the crisis. In this paper we analyze an early example of twin-crisis, which occurred in the 1860s in Spain. The episode is particularly interesting due to the decentralized nature of the Spanish banking system of that time.We document how, at the beginning of the 1860s, Spain experienced a large and relatively quick inflow of foreign capital followed by a sudden stop in the middle of the decade. In the following years the Spanish economy was hit by a severe banking and economic crisis with GDP falling by more than 10% in 1868 and half of the Spanish banks going bankrupt in the years between 1865 and 1870.We argue that the main reason of the stop in capital inflows is the international financial crisis of 1864-66. This was a major crisis that affected most European economies and provoked the fall of one of the largest London banks, Overend, Gurney and Co., which was followed by the 'Black Friday' of May 1866, a massive financial panic in the City of London that quickly spread to other countries in Europe. We collect new empirical evidence supporting our claim: capital inflows were abruptly interrupted from 1864 to 1867, in line with a current account correction during the same period and two recessions, a softer one in 1865 and a more severe one in 1868. We document how the two main financial shocks, those of 1864 and 1866, were coincident with large financial panicsacross Europe.We also analyze the microeconomic behavior of individual banks of issue in facing the crisis. To this end we construct a database with information about the balance sheets of the banks of issue. We regard this as a natural experiment in which all banks operating under a common regulatory and economic environment face a common aggregate financial shock represented by the twin-crisis. We find that three out of twelve banks of issue existing before the crisis were liquidated due to it. In these cases the direct exposure to unprofitable railways projects or the involvement with investment banks that were bankrupted during the crisis are the main causes of the liquidation. In contrast, a number of banks of issue experienced an improvement in their balance sheet position during the crisis...

Using new home production data for the United States, we estimate a model of structural transformation with a home production sector, allowing for both non-homotheticity of preferences and differential productivity growth in each sector. We report two main findings. First, the estimation results show that home services have a lower income elasticity than market services. Second, the slowdown in home labor productivity, which started in the late 70s, is a key determinant of the rise of market services. Our counterfactual experiment shows that, without the slowdown, the share of market services would have been lower by 7.5 percent in 2010. (JEL D13, J24, L16)

We document that employment polarization in the 1980-2008 period in the U.S. is largely generated by women. For the latter, employment shares increase both at the bottom and at the top of the skill distribution, generating the typical U-shape polarization graph, while for men employment shares decrease in a similar fashion along the whole skill distribution. We show that a canonical model of skill-biased technological change augmented with a gender dimension, an endogenous market/home labor choice and a multi-sector environment accounts well for gender and overall employment polarization. The model also accounts for the absence of employment polarization during the 1960-1980 period, which is due to the flat behavior of changes in women's employment shares along the skill distribution, and can reproduce the different evolution of employment shares across decades during the 1980-2008 period. The faster growth of skill-biased technological change since the 1980s accounts for a substantial part of the employment polarization generated by the model. JEL Classification: E20, E21, J16.