Structural Change With Long-Run Income And Price Effects

Reviewed a few of the authors previous paper that were cited, this one from five years ago seemed most relevant (by 2/3 of them), pretty well cited (178 times) it takes a higher level view looking at the decline of agriculture, rise and decline of manufacturing and ongoing rise in services (Table 1) and finds that income effects are the main contributor to structural transformation

“…we turn to the analysis of the drivers of structural change. We use our model to decompose the within-country evolution of relative sectoral employment into income and price effects. We find that income effects are the main contributors to structural transformation. They account for over 73% of the within-country sectoral reallocation in employment predicted by the estimated model…..The empirical evidence suggests that the relationship between relative sectoral expenditure shares and income is stable,and the slopes of relative Engel curvesdo not level off rapidly as income grows.Using aggregate data from a sample of OECD countries,Figure 1 plots the residual (log) expenditure share in agriculture (Figure 1a) and services (Figure 1b) relative to manufacturing on the y-axis and residual (log) income on the x-axis after controlling for relative prices. The depicted fit shows that a constant slope captures a considerable part of the variation in the data and that it does not appear that the relationship levels off as aggregate consumption grows.…..If relative sectoral demand shows a strong and stable dependence on income, the demand channel can readily explain the reallocation of resources toward sectors with higher income elasticities. For instance, rising demand for services and falling demand for agriculture, when both are compared against manufacturing, may give rise to sizable shifts of employment from agriculture toward services….As discussed below, in this paper we complement this aggregate-level evidence with micro-level household data from the Consumption Expenditure survey (CEX) from the US and the National Sample Survey (NSS) from India. We analyze the relationship between relative shares and expenditure in these data, and show that sectoral differences in the estimated slopes do not level off and remain stable across households with different expenditure levels….This paper presents a tractable model of structural transformation that accommodates both long-run demand and supply drivers of structural change. Our main contributions are to introduce the nonhomothetic CES utility function to growth theory, show its empirical relevance and use its structure to decompose the overall observed structural change into the contribution of income and price effects. These preferences generate nonhomothetic Engel curves at any level of development, which are in line with the evidence that we have from both rich and developing countries. Moreover, for this class of preferences, price elasticities are independent from nonhomotheticity parameters, and they can be used for an arbitrary number of sectors. We argue that these are desirable theoretical and empirical properties. We estimate these preferences using household-level data for the U.S. and India, and aggregate data for a panel of 39 countries during the post-war period. We argue that nonhomothetic CES preferences provide a good fit of the data despite their parsimony. Armed with the estimated price and nonhomotheticity parameters, we then use the demand structure to decompose the broad patterns of reallocation observed in our cross-country data into the contribution of nonhomotheticities and changes in relative prices. We find that the majority of the within country variation is accounted for nonhomotheticities in demand. To conclude, we believe that the proposed preferences provide a tractable departure from homothetic preferences. They can be used in other applied general equilibrium settings that currently use homothetic CES and monopolistic competition as their workhorse model, such as international trade. These preferences can be nested in the same manner as homothetic CES. Also, as we discuss in Appendix A, it is possible to generalize nonhomothetic CES to generate variable nonhomotheticity parameters. These properties may be useful in some applications. Even in this case, nonhomothetic CES remains a local approximation (with constant nonhomotheticity parameters) and can be used to guide how the varying elasticities should be parametrized, e.g., by estimating nonhomothetic CES across sub-samples…..”