Vertical specialisation indicator based on supply-driven input-output model

“Import content of exports”, based on Leontief’s demand-driven input-output model, has been widely used as an indicator to measure a country’s degree of participation in vertical specialisation trade. At a sectoral level, this indicator represents the share of inter-mediates imported by all sectors embodied in a given sector’s exported output. However, this indicator only reflects one aspect of vertical specialisation – the demand side. This paper discusses the possibility of using the input-output model developed by Ghosh to measure the vertical specialisation from the perspective of the supply side. At a sector level, the Ghosh type indicator measures the share of imported intermediates used in a sector’s production that are subsequently embodied in exports by all sectors. We estimate these two indicators of vertical specialisation for 47 selected economies for 1995, 2000, 2005 using the OECD’s harmonized input-output database. In addition, the potential biases of both indicators due to the treatment of net withdrawals in inventories, are also discussed.

How serious is the omission of bilateral tariff rates in gravity?

In this paper, using the worldwide dataset of bilateral tariff rates, we explore how serious the omission of bilateral tariff rates in gravity is. Our findings are as follow. Firstly, the omission of bilateral tariff rates seems not to be so serious in terms of omitted-variable biases because the coefficients for the usual gravity variables do not change before or after their inclusion. Secondly, while the widely-used dummy variable of regional trade agreement could not play an alternative role in place of tariff rates, the inclusion of time-invariant pair fixed effects in addition to the time-variant importer fixed effects and exporter fixed effects accounts for the omission of tariff rates. The inclusion of those fixed effects makes the coefficient for bilateral tariff rates insignificant.

Impact of diagonal cumulation rule on FTA utilization : evidence from bilateral and multilateral FTAs between Japan and Thailand

In this paper, we empirically investigate the effect of diagonal cumulation on free trade agreement (FTA) utilization by exploring Thai exports to Japan under two kinds of FTA schemes. While the one scheme adopts bilateral cumulation, the other scheme does diagonal cumulation. Comparing trade under these two kinds of FTAs, we can examine the effect of diagonal cumulation without relying on not only the variation in cumulation rules across country pairs but also the variation across years. In short, our estimates do not suffer from biases from time-variant elements and country pair-specific elements. As a result, our estimates show around 4% trade creation effect of diagonal cumulation, which is much smaller than the estimates in the previous studies (around 15%).

Agglomeration and firm-level productivity : a Bayesian spatial approach

This paper estimates the impact of industrial agglomeration on firm-level productivity in Chinese manufacturing sectors. To account for spatial autocorrelation across regions, we formulate a hierarchical spatial model at the firm level and develop a Bayesian estimation algorithm. A Bayesian instrumental-variables approach is used to address endogeneity bias of agglomeration. Robust to these potential biases, we find that agglomeration of the same industry (i.e. localization) has a productivity-boosting effect, but agglomeration of urban population (i.e. urbanization) has no such effects. Additionally, the localization effects increase with educational levels of employees and the share of intermediate inputs in gross output. These results may suggest that agglomeration externalities occur through knowledge spillovers and input sharing among firms producing similar manufactures.

A note on three factor model of discounting

In Montiel Olea and Strzalecki (2014), authors have axiomatically developed an algorithm to infer the parameters of beta-delta model of cognitive bias (present and future biases). While this is extremely useful, it allows the implied beta to become very large when the response is impatient in the future choices relative to present choices, i.e., when there is a strong future bias. I modify the model to further exponentiate the functional form to get more reasonable beta values.