SEGURO SOCIAL DE SALUD
GERENCIA GENERAL 2014
IV. ÁMBITO DE APLICACIÓN
2. Disposiciones Específicas
Using Equation 3.12, it follows that ex-ante change in aggregate welfare can be expressed by
ˆ Wi = Y s ˆ λ−βis/θs iis · X g∈Gi Y ig Yi Y s ˆ π−βis/κig igs ! . (3.13)
Shocks affecting wis will have aggregate welfare effects through impact on both income and
sector-level prices. The second term of aggregate welfare is given by the weighted sum of the
group-level changes in welfare. Intuitionally, the second part of the right-hand side shrinks
as workers can more easily substitute for one another. In the limit, κig → ∞ implies identical
workers in a frictionless environment, at which point the group-level differences tend to zero
and the welfare gains are simplified to the Arkolakis et al. (2012) term, Q
sλˆ −βis/θs
iis .
A seemingly counterintuitive implication of this welfare expression is that aggregate gains
from inter-sectoral trade are strictly higher for κig < ∞ than the gains that arise in the limit
for κig → ∞. To illustrate why, suppose that country i consists of a single group, i.e.
Gi = 1, and is in full autarky. Then expenditure shares and income shares would be equal
in the original equilibrium, i.e. βis = πigs. When moving from autarky, ˆWi depends in part
uponQ
sπˆ −βis/κig
igs with ˆπigs = πigs0
βis for some new allocation π
0
igs. By rewriting this expression
as Q sπˆ −βis/κig igs = exp 1 κig P sβisln(βis/π0igs)
it can be seen that higher values of κig will
shrink the magnitude of the welfare gains. GRY’s Proposition 2 proves that this property
holds for Gi > 1.
To intuit why this property is true, consider the case where Gi = 1 and country i engages
in inter-sectoral trade, i.e. πigs 6= βis for some s. Suppose this country closes to trade and
returns to autarky, and we are interested in the total change in welfare for this transition, ˆ
WA
i . Finite κig introduces greater “curvature” to the PPF and makes it harder for the
economy to adjust as it moves to autarky while keeping expenditure shares (βis) constant.
In the limit for κig → ∞, the economy is able to shift most easily to its new labor allocations,
i.e. higher κig tends to shrink ˆWiA. If we define the gains from trade as GF Ti ≡ 1 − ˆWiA, it
is easy to see why opening to trade would yield higher aggregate welfare changes in country
4
Data
For the empirical analysis, I combine data from a few sources. As my setting is in Germany,
the most important source is labor market data at the regional and sectoral level. The IAB-
Establishment History Panel (BHP)17 includes the universe of all German establishments
with at least one employee subject to social security. These administrative data are restricted
to information relevant for social security,18but they annually cover around 2.7 million panel
observations from 1975 to the present for West Germany, and from 1992 for East Germany.
The data for regional employment by sector is available only from 1978 onward. Sector
information is based on NACE Rev. 1 classifications.
I use trade data from the United Nations Statistical Division (UNSD) Commodity Trade
(COMTRADE) database to measure import penetration in manufacturing between Ger-
many, China, and a group of countries economically similar to Germany between 1992-
2012.19 Trade flows are deflated to 2010 USD values using data from the World Bank. In
order to match the trade data and establishment data, I translate the SITC Rev. 3 product
codes from COMTRADE to ISIC Rev. 3 to NACE Rev. 1 at the two-digit level via the
correspondence tables provided by the UNSD and Eurostat RAMON.
In the counterfactual simulations, I use trade data from the World Input-Output Database
(WIOD). I use this second source of international trade flows to measure both manufacturing
and non-manufacturing flows in the world economy. The WIOD covers 40 countries, and a
model for the rest of the world for the period 1995-2011. Data for 35 sectors are classified
according to the ISIC Rev. 3, which I similarly correspond to NACE Rev. 1 at the two-digit
17This study uses the weakly anonymous Establishment History Panel (Years 1992 - 2014). Data ac-
cess was provided via on-site use at the Research Data Centre (FDZ) of the German Federal Employment Agency (BA) at the Institute for Employment Research (IAB) and/or remote data access. Data documen- tation by Alexandra Schmucker, Johanna Eberle, Andreas Ganzer, Jens Stegmaier, Matthias Umkehrer
(2018): Establishment History Panel 1975-2016. FDZ-Datenreport, 01/2018 (en), Nuremberg. DOI:
10.5164/IAB.FDZD.1801.en.v1.
18I.e. the structure of the workforce by wage, sex, age, German/non-German status, occupation in certain
classes, and a measurement of skill intensity.
19Specifically, the instrument group consists of Australia, Canada, Japan, Norway, New Zealand, Sweden,
level via the correspondence tables provided by the UNSD.
In order to avoid changes from the inclusion of East Germany, I use 1992 as the base
year. I use the period 1992-2002 to estimate the value of κig without the impact of Hartz,
and the period 2005-2012 to estimate the value of κig after the reforms.20 See Appendix 6
for more details on the construction of variables.
5
Estimation and Empirical Exercise
The ultimate goal is to study how the Hartz Reforms impacted the group-level changes in
welfare that stemmed from increased import penetration from China between 2005-2012. To
that end, I want to simulate the German economy before and after the Reforms’ effects on
the labor supply elasticities, κig. To do so, I need to estimate the labor supply elasticity in
the two periods, 1992-2002 in the pre-Hartz period vs. 2005-2012 in the post-Harz period.
This section details how I derive my estimates.
The estimation procedure is built from several pieces. First, I detail the construction of
the ADH instrument for trade penetration. I next establish the relevance of the instrument
for changes in employment shares in Germany, ˆπigs, which in turn affect the changes in
average group income per worker. I then demonstrate how to estimate κig from a structural
relationship in the model using the instrument. I present results under the assumption
κig = κ, and conclude by showing how the ˆκ estimate changes with the arrival of the Hartz
Reforms.
20For robustness, I consider 1993 as a possible base year, and 2002 and 2003 as the “start” of the Hartz
period. Although the measurement of κig has some small variation depending on the period used, results
5.1
Instrumental Variable Approachas an instrument for the above
import exposure, namely
In this section, I detail how to use data on Chinese import flows to serve as an instrument to
estimate κ through changes in sectoral labor allocations. An ideal estimating equation is one
where I can use variation in ˆπigs in order to estimate the κ parameter. To that end, define
the change in earnings per worker in group g by ˆyig ≡ ˆ Yig
ˆ
Lig. Using this definition together
with Equations 3.8 and 3.10 implies
ˆ yig = ˆwisˆπ −1/κ igs Aˆ 1/κ igs. (5.1)
Note that this equation holds for each sector s. The problem with taking this equation
directly to the data is the endogeneity between the change in employment share, ˆπigs, and
the change in average group level income per worker, ˆyig. In order to remedy this problem,
I need an instrument that affects group-level income through its effect on changes in labor
allocation, ˆπigs. I follow ADH in their focus on the effects of increasing manufacturing
import penetration from China on local labor markets.21 To define how groups of workers
are potentially affected by the rise of China during the time periods of interest, I construct
a measure of trade exposure at the sector level. In particular, I measure
∆ [import exposure]CHNst ≡ ∆IM
DEU←CHN st
LDEU,st