3.2.1 Introduction
In this section, I disentangle the effects of increasing returns to scale in manufacturing. As discussed in Chapter 1, while the existence of IRTS in U.S. manufacturing during this period is still a debated issue, some studies point out that this was a crucial feature that promoted the development of U.S. economy during this period.42 But, to my knowledge, its quantitative
effects have never been evaluated and in this section I try to pursue this goal. Before I turn to the procedure and result, I discuss more about the IRTS assumption imposed in the model.
There are several ways to consider the economies of scale. For exam- ple, Johnston (1990), in investigating the possible impact of Civil War debt repayment, assumes that there existed learning effects from investments in U.S. production technology. More specifically, he postulates that invest- ments also generate industrial knowledges about how to manage and pro- duce more efficiently. This knowledges cannot be kept private but ‘spill over’ to other producers, thus creating positive externalities. In other words, he applies the famous mechanism emphasized by the endogenous growth liter- ature. According to this line of thought, the production function would look like the following,
Y =ALαK1−α+µ where 0< α <1 and µ >0
where Y is output, L is labour supply and K is capital. Obviously, µ > 0 creates the economies of scale as well as positive externalities. This kind of externality is called ‘non-pecuniary externality’ or ‘technological external- ity’. According to Ottaviano and Thisse (2000), this kind of IRTS and the externalities generated seem to be more reasonable to explain ‘geographi- cal cluster of somewhat limited spatial dimension such as cities and highly specialized industrial districts’.
On the contrary, the externality generated by the kind of IRTS consid- ered in the model is called ‘pecuniary externality’. The origin of externality is clearer in this case as it emphasizes the role of market interactions among consumers and firms whereas ‘non-pecuniary’ externality is perceived as a ‘black-box’. More specifically, the pecuniary externality is generated by the agglomeration force due to the backward and forward linkages which are
responses to changing prices and market conditions.43 Also Ottaviano and Thisse (2000) puts forward an argument that the pecuniary externality is more ideal to explain ‘inter-regional agglomeration such as the Manufactur- ing Belt in the U.S. and the Hot Banana in Europe’.
Finally, under the current assumption about the IRTS, it seems much easier to pin down the parameters that control the degree of IRTS and ex- ternality. For example, the value of the fixed cost, F, can be anything as it does not influence the aggregate variables. Andαm, the parameter that
determines the share of manufacturing intermediate usage and the agglom- eration effect, can be calibrated using input-output tables. On the other hand, at least, the process of determiningµdoes not look as simple as that as one needs to correctly identify the pure technology (or residual) shock from the spill-over effects.
3.2.2 Procedure and Results
The CRTS model from the previous chapter yields identical calibrated pa- rameters and benchmark equilibrium as the baseline IRTS model. In addi- tion the results generated from the CRTS model account for the data in 1913 quite closely and yield almost identical results as the baseline IRTS model.44 This gives an ideal condition to isolate the effects of IRTS. A simple way to do this is to feed in the shocks implied by the IRTS framework to the
CRTS model. Note that the changes in endowments are measured usinga priori informations so that they are identical under both frameworks. The measured changes in TFP for primary and services sector for Britain and the U.S. are identical as well, as they are assumed to have same functional forms. But the implied changes in the manufacturing TFP for all regions and the primary TFP for the rest of world and the trade costs take different values. The effects of IRTS can be isolated as the difference between the
43
see Fujita, Krugman and Venables (1999) for the details.
results generated by this exercise and that by the IRTS model. Table 31 below presents the result for U.S. economy.
The first two columns are restatements of the data and of the base- line equilibrium for the sake of comparison. The last column presents the simulation results. We can see that without IRTS, U.S. share of world manufacturing is reduced to 25.1% from 32.2%. Therefore IRTS contribute about 7 percentage point increase in the share. And without the IRTS the model only accounts for around 67% of increase in manufacturing output (6.52/9.74). So the remaining 33% can be attributed to the presence of IRTS. On the other hand, the primary sector grows about 7% without the IRTS in manufacturing (3.243−.043.04). This is natural because the U.S. loses its comparative advantage in manufacturing coming from the scale economy and consequently the comparative advantage shifts to the primary. This can be readily seen in the results for the trade in Table 32 below. Finally IRTS accounts for about 10% of the increase in real GDP.
Table 31: The effects of IRTS on U.S. economy
Data BL IRTS IRTS removed Share in world primary(%) 14.4 13.9 14.8
Share in world manufacture(%) 31.9 32.2 25.1
Share of LF in primary(%) 30 37 41 Share of LF in manufacture(%) 30 32 27 Ya,1913/Ya,1870 2.58 3.04 3.24 Ym,1913/Ym,1870 9.46 9.74 6.52 CF RGDP / BL RGDP - - 0.90 BL: baseline, RGDP: real GDP
The effects of IRTS in manufacturing are not small and they are more pronounced in manufacturing, as expected. But again I emphasise that these results are more meaningfulif the assumption of IRTS is valid.
Table 32: Implications of IRTS on U.S. trade
Data BL IRTS IRTS removed
Exports/GDP 0.06 0.04 0.05
manu. exports / total exports 0.50 0.61 0.19 manu. imports / total imports 0.44 0.32 0.74 BL: baseline simulation