Econometric specification

The goal of the econometric analysis is to estimate the magnitude of the frictions facing sector level flows in labor markets across countries. Using the ECM model, I estimate the speed of adjustment parameter λ for the whole sample, and for different sectors and country groups. This parameter is the rate at which jobs flow from one sector to another, which is an implied measure of the magnitude of adjustment costs (i.e. frictions). A negative estimate of λ would imply converging productivity gaps across sectors, while a positive one would imply a diverging pattern whereby gaps grow even wider.

In the baselines specification, I estimate λ for the full sample first and then attempt to explore the heterogeneity across country groups and sectors by estimating it for the corresponding subsamples. Substituting equation (3.4) into (3.6), after log transforming the variables yields an ECM of labor reallocation dynamics:4

∆log Ni,c,t

Nj,c,t

!

=

Short Term Dynamics

z }| { β1∆log V Ai,c,t V Aj,c,t ! + β2∆log Pi,c,t Pj,c,t ! + β3∆Xi,c,t + λ log Ni,c,t−1 Nj,c,t−1 ! − " δ1log V Ai,c,t−1 V Aj,c,t−1 ! + δ2log Pi,c,t−1 Pj,c,t−1 ! + δ3Xi,c,t−1 #! | {z }

Long Term Dynamics

+ui,c,t

(3.10) where Xt includes controls for factors that may justify persistent deviation (i.e. error)

in observed employment shares from the efficient allocation described by equation (3.4), such as differences in human capital and the skill level of workers. I control for the human capital effects by including a (sector ×time) fixed effect, given the assumption discussed above of a constant γ across countries. The fixed effects also control for essential differences across sectors and countries (e.g. capital intensity). As mentioned earlier, both Ni,c,t−1

Nj,c,t−1

and V Ai,c,t−1

V Aj,c,t−1

exhibit similar trends that reflect the process of structural transformation as the shares of labor and value added grow in manufacturing and services at the expense of agriculture and other lower labor productivity sectors (Herrendorf,

4_{This econometric specification is motivated above as an equilibrium condition of the structural} problem facing labor force, and it is consistent with general specification of the ECM model discussed in Sargan (1964) and Alogoskoufis and Smith (1991): ∆yt= λ∆yt∗− λ(yt−1− yt−1∗ )

Rogerson, and Valentinyi (2013)).

Specifically, Xt includes world real GDP growth rate, growth rate of countries real GNP

per capita, population growth rate, a global linear trend, as well as constant and linear trend fixed effects: (sector × country) and (linear trend × sector × country); these ad- ditional controls make sure that the estimated rate of adjustment captures only country level market and institutional factors, and is not contaminated by the effects of demo- graphic and other global and sector specific trends or global fluctuations. β1, β2 and β3

are the short term elasticities, λ is the adjustment speed, and δ1, δ2 and δ3 are the long

term elasticities.

Equation (3.10) implies that labor moves every period to correct past deviations from the optimal values of employment shares (i.e. long term target), and to accommodate contemporaneous changes in these optimal allocations (short term dynamics). In all estimations, I always use the agriculture sector as sector j; such that employment and value added shares as well as sector price levels are normalized by the corresponding values for the agriculture sector.

The role of labor market frictions in labor reallocation

Next, I explore the answer to the second main question of the paper on the magnitude of the part played by labor market regulations in the process of structural transformation. I carry out this task by augmenting the baseline equation (3.10) to introduce a country level labor market indicator Rt via an interaction term with the rate of adjustment.

∆log Ni,c,t Nj,c,t ! = β1∆log V Ai,c,t V Aj,c,t ! + β2∆log Pi,c,t Pj,c,t ! + β3∆Xi,c,t+ λ1Gapi,j,c,t−1+

Labor market Interaction

z }| {

λ2{Rt−1× Gapi,j,c,t−1}}+βZt−1+ ui,c,t (3.11)

where,

Gapi,j,c,t ≡ log

Ni,c,t−1 Nj,c,t−1 ! − " δ1log V Ai,c,t−1 V Aj,c,t−1 ! + δ2log Pi,c,t−1 Pj,c,t−1 ! + δ3Xi,c,t−1 #! (3.12)

Zt is a vector of controls for other areas of regulations that could be correlated with

the state of labor market institutions. For that, I use the IMF structural reform indices for four key areas: capital flows, banking, domestic finance, and trade. In addition, I also include the other components of the labor market regulations when examining their interaction individually,

Zt≡ {R CapitalF lows t , R Banking t , RDomesticF inancet , R T rade t ,

RAvg.LaborCost_t , RAvg.payrolltax_t , RM inimumW ageM eanW ageRatio_t } (3.13)

λ2 is a measure of the contribution of Rt−1in explaining the differences across high-income

countries in the average pace of job flows across sectors. I also add another interaction term for low- and high-income country groups, to analyze whether the role labor market regulations plays changes for economies that are at different stages of development,

∆log Ni,c,t Nj,c,t ! = β1∆log V Ai,c,t V Aj,c,t ! + β2∆log Pi,c,t Pj,c,t ! + β3∆Xi,c,t+ λ1Gapi,j,c,t−1+

Labor market Interaction

z }| {

λ2{Rt−1× Gapi,j,c,t−1}+ λ3{DLowIncome× Rt−1× Gapi,j,c,t−1}+

βZt−1+ ui,c,t (3.14)

DLow_Income is a dummy for countries that belong to lower middle income and low income classification of the World Bank. λ3 measures how much λ2 changes for low-income

countries compared to high-income countries.