Working in progress. China’s Three Gorges Dam Town Resettlement Initiatives: Effects on Industrial Productivity Wenjie Wua, Minzhe Dub, Ning Zhangb, Bing Wangb*1
a. Heriot-Watt
University, Edinburgh, EH14 4AS, UK.
b. Department of Economics, Ji Nan University, No. 601 Huangpu Road, Guangzhou, 510632, China
Abstract This paper explores the impacts from China’s Three Gorges Dam (TGD) town resettlement initiatives on industrial productivity. Measures of plant‐level industrial productivity have been obtained from the Annual Industrial Firm Datasets from 1998 to 2007. Industrial plants in TGD flooded counties were subject to substantial funds for resettlement compared to those in adjacent non‐flooded counties. The results show that the TGD town resettlement initiatives have a positive effect on both total factor productivity and labor productivity. The resulting estimates further suggest the evidence of heterogeneous productivity effects on dispersal of plants in flooded counties that have been relocated into the designated new town centers relative to displaced plants that have been moved to other places. The analysis also finds that firm survivors, firm births and deaths have played a significant role in explaining the effects of the TGD town resettlement. Additional results quantify evidence of distributive effects on employment and wage per worker. Keywords: Productivity, Hydraulic dam infrastructure, China JEL Codes: D24, O13, P25 1 Corresponding author. 1 “The Three Gorges Dam project is a symbol of the superiority of the socialist system.” China’s
Former Premier Minister Peng Li (International Rivers Network, 2003)
1Introduction
China is the home for almost half of the world’s 45,000 largest dams2. The Three Gorges
Dam (TGD), locating in the Yangtze River (Figure 1), is the largest hydropower project built
in the history of the world3. The installation of super-power generators in the dam made the
total power capacity of the TGD even larger than the capacity of the Itaipu Dam in Brazil
(Government of China, 2006). This TGD project has been the dream of Chinese political
leaders for almost the entire 20th century, including Sun Yat-Sen, Mao Zedong and Deng
Xiaoping. As reported by the International Rivers Network (2003), the former Premier Minister
Peng Li has emphasized the TGD project as a “symbol of the superiority of the socialist system.”
Clearly dams can deliver socioeconomic benefits and losses for people and places (McCully
2001; World Commission on Dams, 2000; Singh 2002; Bhatia et al., 2008). Proponents argue
that dams are frequently proposed as a strategy for increasing water supply, flood control,
electricity power generation, poverty reduction and regional economic growth (Howe, 1968;
Eckstein 1971; Hussein 1981; Merrouche, 2004; Aleseyed et al, 2007; Duflo and Pande, 2007).
However, the complex and massive effort to relocate millions of displaced people and flooded
counties has caused a range of considerable debates over the human rights violations, and
economic impacts of TGD dams in the global society (Gleick, 2009; People’s Daily Online,
2007). Despite of intense policy and public interests, little is known about the extent of the
gains that result from the TGD town resettlement policy. Direct evidence of the dam-induced
town resettlement effects on industrial productivity using the quasi-natural experimental
approach is rare.
2 http://www.wsj.com/articles/SB119880902773554655 3 https://en.wikipedia.org/wiki/Three_Gorges_Dam 2 This paper is the first large scale empirical study to investigate the industrial productivity
consequences of this extraordinary TGD town resettlement policy that produced a plausibly
arbitrary design by exploiting the spatial differences in industrial productivity for plants in the
flooded counties relative to adjacent non-flooded counties. Based on the hydraulic engineering
design, the TGD reservoir can stretch around 600 kilometers upstream and reach a depth of
approximately 135 meters (xu shui wei) by 2003, with the potential to go up to 175 meters
(State Council, 1993). Due to terrain characteristics, however, this dam reservoir would flood
19 counties (districts) that are closing to the TGD reservoir along the Yangtze River 4. This
displacement itself can be regarded as exogenously determined. By using the State Council’s
allocated resettlement funds, these flooded counties’ town centers were reinforced to relocate
into designated areas for setting up new town centers before the reservoir started to rise in April
2003. The combined plant–level data file from the National Industrial Plant Surveys (Zheng et
al., 2015) is used to measure the total factor productivity and labor productivity (value-added
output per worker) of manufacturers to the State Council mandated regulations in the TGD
flooded counties.
Our investigation is structured in four stages. First, we examine the TGD town
resettlement effect on the targeted plants’ total factor productivity (TFP) and labor productivity.
The estimates suggest that flooded counties (relative to adjacent non-affected counties)
experienced significant gains on industrial productivity. Second, a quasi-natural experimental
research design based on this town resettlement initiative allows for a unique instrument to
evaluate the displacement consequences on industrial productivity of plants that have been
relocated to other places relative to plants that have been locked-in to resettle into the
designated areas5. Third, we confirm the presence of the heterogeneous effects as induced by
flooded counties include: Digui, Xingshan, Yiling, Badong, Peiling, Wanzhou, Yubei, Banan, Changshou, Wulong,
Zhong Xian, Kai Xian, Yunyang, Fengjie, Jiangjin, Wushan, Wuxi, Shizhu, Fengdu. See http://www.people.com.cn/GB/jinji/222/10814/10824/10835/20030529/1003396.html 5 Note that we are not able to follow individual workers in plants over time to explicitly incorporate the labor market
4 These
3 industry and locational-specific characteristics, such as how plant-level variations in age,
ownership, size and industry types influence productivity effects differently, and how countylevel variations in market potentials, as a key driver of agglomeration economies, generate
differential effects. Finally, we provide the decomposition analysis and sheds lights on the
channels at work underlying the observed effects. We identify the TGD town resettlement
effects on two channels— the external channel by choosing to exit and entry into the market
(plant births and deaths, new entries, relocation) and the internal channel by adjusting labor
and capital inputs and outputs (surviving plants).
The fundamental challenge in identifying the causal effects of place-based programs is
choosing appropriate control groups (Neumark and Kolko, 2010; Gibbons et al., 2011; Mayer
et al., 2012; Busso et al., 2013; Faggio, 2014; Einio and Overman, 2016). This study begins by
using a conventional difference-in-difference (DD) analysis at the plant level, controlling for
industry by year fixed effects, county by year fixed effects and plant-level fixed effects and
among others. We compare the industrial productivity among flooded counties with outcomes
among adjacent non-flooded counties during the same period. To further strengthen our
assessment, we adopt the boundary discontinuity (BD) approach that has intensively been
applied in the literature (Holmes, 1998; Neumark and Kolko, 2010; Duranton et al., 2011;
Gibbons et al., 2013; Lu et al., 2015; Keele and Titiunik, 2015). At its heart we focus on
identifying the discontinuity in treatment within and beyond the policy assignment boundary
at a narrow spatial margin on the basis of geographically coded information on firm location
and flooded county boundaries. We combine the DD and BD approaches into a unified
framework to separate the TGD town resettlement’s true effect on industrial productivity from
differencing the estimated effects on flooded counties and those on non-flooded counties at a
consequences of town resettlement designation that are worthwhile for further studies. See Walker (2013) for the detailed
discussion about transaction costs of sectoral relocations induced by the US environmental regulations on workers’ earnings.
We are, however, able to identify the plant address changes and compare the policy effects on the industrial productivity
between plants which remain in the newly resettled areas and plants which move to other places.
4 very close spatial margin. We use a series of analyses to investigate the robustness of the
findings, including experimenting with different control groups, placebo tests to verify the
parallel pre-trends assumption between treated areas and control areas.
This research is related to the empirical literature on the evaluation of localized economic
effects of hydraulic dam infrastructure programs. Worldwide, dams are built for increasing
irrigation and hydro-electricity output, and international organizations such as the World Bank
would help developing countries to undertake these investments with preferential policies for
supporting infrastructure development (World Bank, 1994; 2002). On the flip side, these dams
would also displace millions of people and flooding towns to be relocated, change agricultural
cropping patterns of arable land (World Commission on Dams, 2000). The distribution of the
costs and benefits of large dams across population groups, and, in particular, the extent to which
the rural poor have benefited, are issues that remain widely debated. Despite the importance of
dams, the empirical literature has mostly ignored the local economic effects of dams. Much of
it is concerned with variation in property values (e.g. Bohlen and Lewis, 2009) and macro
policy implications (e.g. World Bank, 1996), an issue not directly related to our work. For
economists, only a handful of papers had focused on evaluating the spatial economic effects of
large dams. Aleseyed et al (2007) examine significantly positive effects of 40 large dams
opened in the U.S. during the period 1975-1984 on county-level income, earnings, population,
and employment growth. Like other place-based infrastructure, dams might be typically
targeted to where there is perceived need, rather than randomly allocated across space. One
fundamental challenge is to separate out the effects of the supply of dams from other
determinants of economic performance. Duflo and Pande (2007) use the fact that river gradient
affects a district’s suitability for dams as the instrumental variable for the treatment status of
dams and estimates the effects of dam placement on agricultural production and poverty rates
across districts with dams and districts without dams in India. While previous studies have
5 focused on the effects of the supply of dams on aggregated economic outcomes in India and
the US, we look at the distributional effects of the dam-induced town resettlement on plantlevel industrial productivity at flooded Chinese counties, as a complementary inquiry.
The empirical strategy of this research is related to recent work on evaluating place-based
resettlement policies. Dams have potential economic impacts both locally – via reservoirs –
and regionally through town resettlement policies. For reservoirs, an increasing number of
reservoirs could bring recreational uses, irrigation, water and hydropower benefits for local
agricultural sectors and farmers’ quality of life across upstream and downstream places
(Aleseyed and Rephann 1994; Duflo and Pande, 2007). But none of previous studies have
examined whether and to what extent the dam-induced town resettlement policy can exert
differences in industrial productivity performance at flooded, and resettled counties. In the
developed countries, the situation is different where plants can get tax and financial incentives
for being included in certain place-based policies to stimulate local economic growth. For
example, recent evaluations of the US Enterprise and Empowerment Zone programmes have
documented strong evidence of the creation of jobs by providing financial incentives to plants
located in designated areas (Neumark and Kolko, 2010; Busso et al. 2013). There is a large
number of literature in dealing with that the impacts of various place-based policies on shaping
economic agglomerations in the American cities (Rossi-Hansberg et. al. 2010, Kline and
Moretti, 2013) and transaction costs of sectoral relocations (Walker, 2013). The UK
government has also implemented various relocation programmes such as the Lyons Review
to reduce the spatial disparities in income and employment patterns (Faggio and Overman,
2014; Faggio, 2014). Furthermore, corresponding to the private land ownership rights plants in
the developed countries can often get heavy compensation deals for being displaced by the
government to other areas for restarting their businesses. See also Thukral (1992) for daminduced land right compensations in India. In contrast, urban lands in the Communist China
6 are owned by the state rather than by individual plants, so in general flooded counties’ town
centers will be directly resettled in the designated areas with the support of State Council’s
resettlement funds and administrative orders, and the potential self-selection issue of location
was not as serious as it first seems in the United States and other developed countries. In terms
of methodological approaches, we rely on the previous literature dealing with the boundary
discontinuity design, particularly those examining the impacts of place-based policies on firm
outcomes such as firm exits and entry, employment, and agglomeration (Duranton et al., 2011;
Brülhart et al., 2012; Busso et al., 2013; Kline and Moretti, 2014).
Finally, our work is a complementary to the literature that investigates various aspects of
infrastructure investments on influencing China’s economic growth. While existing studies
have mostly focused on evaluating the city sizes and economic impacts of transport
infrastructure improvements (Au and Henderson, 2006; Banerjee et al., 2012; Zheng and Kahn,
2013; Faber, 2014; Baum-Snow et al., 2015), little is known about economic implications of
dams for industrial productivity and welfare. Our finding lends support to the viewpoint that
dam-induced town settlement policies play a heterogeneous role in the distributive
consequences of industrial productive gains over space.
China has acquired the know-how to build dam schemes through the TGD project, and
has begun exporting hydraulic dam infrastructure investments in not just its domestic markets
but also other low-income developing countries. Given the massive amounts of dam
infrastructure expenditures, it is important to assess the impacts of these dams on local
industrial productivity through careful evaluations rather than political lobbying conversations.
Our analysis provides the first rigorous assessment for such impacts in a large developing
country context and future work are encouraged to corroborate our results when longer-term
plant level data observations are available.
7 2EconometricModel
We seek to estimate the TGD town resettlement effects on local industrial productivity. Our
estimation adopts a variety of quasi-experiment approaches. The first approach is a
conventional strategy that uses the difference-in-difference (DID) method to fit the following
equation to the plant level data:
Y ijt     1  TGD ijt   2  Post t   3  TGD ijt  Post t  f  Z 1990 j * Tt   (1)
where Yijt is a measure of industrial productivity such as total factor productivity and labor
productivity, measured by plant i in county j in year t. TGDijt is a binary treatment indicator that
equals to one if a plant i in county j is located in the flooded counties in year t and is eligible
for assigning into the treatment group, and equals to zero if a plant i in county j is not to be
resettled but is located adjacent to treated counties. The rationale behind this is that, although
counties that are to be resettled are likely to differ in both observable and unobservable ways
from those treated counties, these differences can be minimized by focusing on close spatial
margins: non-flooded (non-resettled) counties that directly share the administrative borders
with treated counties are likely to be similar with treated counties in terms of pre-treatment
demographics characteristics, though their “treatment” statuses will be different. In the baseline
model specification, we can therefore define this comparison group (control counties) as being
closing to the treated counties and directly sharing the administrative border with the treated
counties. In addition, we adjust the selection criteria of comparison groups in different ways as
the robustness checks. Postt is a ‘policy-on’ time dummy that equals to one if a county j is
being resettled for the post-program period (year>2003). f is the county-fixed effect that
controls for place-specific unobserved factors that are fixed over time. Additional controls
include pre-treatment 1990 county demographic characteristics Z1990j interacted with
quadratic time trends Tt. The results are similar to the exclusion of adding these county
8 demographic characteristics. We include 4-digits industry codes by year fixed effects (industry
by year fixed effects, thereafter) to control for the potential variation in exposures to pollution
intensity levels and productivity shocks across industries. In addition, county by year fixed
effects are included in order to flexibly account for localized trends over time. The plant-level
fixed effects, and county by industry fixed effects are also included into empirical model
specifications to control for differences in permanent plant growth rates and other unobservable
industry and county characteristics that might be correlated with treatment status. 3 is the
parameter of interest. It captures the TGD town resettlement impacts on local industrial
productivity performance. This strategy makes use of before and after comparisons in industrial
productivity performance observed both before and after the policy was implemented so as to
explicitly address the concerns about differences in time-varying location characteristics across
treatment and control groups.
While this basic approach provides a useful guide for the empirical work, recent research
has constructed more reliable comparison groups in ways that try to address the nonrandomness of the policy placement problem when interventions are targeted places. The
availability of institutional details, such as boundary discontinuity; policy implementation
stages, and comparisons of policy recipients within the narrow margins, has stimulated a
number of economic studies on evaluating the place-based policy impacts (Glaeser and
Gottlieb, 2008; Gibbons et al., 2011; Mayer et al., 2012; Busso et al., 2013; Einio and Overman,
2016). In China, Chen et al. (2013) applied the regression discontinuity (RD) design to examine
the relationship between pollutants and human health by restricting the comparison to counties
that are locating in the north and south side of the Huai River. In our context, the completion
of the TGD project has flooded adjacent counties in the upstream direction due to terrain
characteristics and thus the boundaries of flooded counties can be regarded as exogenously
determined. We apply for the BD approach based on geographical distance to the flooded
9 county boundaries. Our BD approach follows Holmes (1998), Duranton et al.(2011) and
Gibbons et al. (2013) by restricting the analysis to a sample of areas within a close distance
margin from the discontinuity— the boundary of flooded counties. The BD-DD estimation
equation can be written as:
Y ijt   1  Boundary
ijt
 f  Z 1990 j * Tt   (2)
Where Yijt measures the industrial productivity of plant i in year t within a 5km buffer of
the boundary of flooded counties j. Boundaryijt is an indicator that equals to one if a plant i is
within a 5km buffer inside the boundary of flooded counties j with the TGD policy applied in
year t, and 0 otherwise. The control group here refers to the sample of plants that are located
outside the flooded county boundaries but are within the same distance buffer margin of the
boundary of flooded counties.
Several placebo tests have been conducted. First, in the baseline specifications, we use a
distance buffer of 5 kilometers as the threshold, but 2 kilometer and 10 kilometers are tested in
the robustness checks. Second, we examine whether there are significantly differences of
industrial productivity between treated areas and control areas in the pre-treatment period.
Third, we produce a placebo experiment that verifies the parallel pre-trends assumption at
treated areas and control areas in the absence of the treatment.
Methodologically, our BD-DD estimation involves calculating each plant’s distance from
the nearest TGD flooding boundary. The coordinates of each plant’s location are extracted from
the online mapping system (www.map.baidu.com). But the precise geocoding of TGD flooding
boundary is not known which prevents the measurement of the distance from each plant’s
location to the boundary directly. We instead apply the identification strategy proposed by
Duranton et al (2011) to exploit the distance-to-boundary indirectly. To determine whether a
plant is located within 5 kilometers of the boundary, we search within a spatial buffer of 5
10 kilometers of the plants in the treatment group. Figure 2 visualizes our identification principle
for treated and control plants near the flooding boundary. If plant A is located outside the
flooded counties and is found to be within 5 kilometers of plant B inside a flooded county, then
we can assign plant A into the control group (within 5 kilometers of the boundary). Following
a similar logic, if plant D is located outside a flooded county and plant C is within flooded
counties, and if plant C is found to be located within 5 kilometers of the plant D, then we can
assign C into the treatment group (within 5 kilometers of the boundary). We repeat this
calculation procedure until all plants that are eligible for our BD-DD regression exercises have
been chosen.
In our research design, a key innovation is that we see the TGD town resettlement program
as a policy shock for displacing industrial plants to other places, and this would confound the
estimates of the pooled treatment effects on industrial plant TFP and other outcomes. This
possibility is mitigated by the fact that the TGD town resettlement policy is not a sort of
enterprise zoning policy used for business purposes (Neumark and Kolko, 2010; Busso et al.
2013). The implementation of TGD town resettlement policy followed a state-guarantee
process (zheng fu bao gan) for compensating the mobility costs of industrial plants, and
enforced the treated plants to be relocated. As such, it is worthwhile to exploit the differences
between the displacement effects on industrial productivity of plants that have been relocated
to other places and the effects on industrial productivity of plants that have been locked-in to
relocate to designated new town centers. The equation can then be modified as:
Y ijt    1  Lockedin _ plants ijt   2  Displaced _ plants ijt  f  Z 1990 j * Tt   (3)
Where Lockedin_plantsijt represents the treatment effect of the TGD town resettlement
policy on industrial productivity outcomes for a plant i that has been locked-in to relocate to
the designated new town centers at the flooded county j in year t. Displaced_plantsijt represents
the treatment effect of the TGD town resettlement policy on industrial productivity outcomes
11 for a plant i that has been displaced from the flooded county j to other places in year t. As
before, equation (3) includes industry by year fixed effects, year by county fixed effects, and
plant level fixed effects and among others. We apply both of the conventional DD approach
and the BD-DD approach to do the estimation. In robustness checks, we also estimate the
equation (3) by using the weighted least squares, with the weights equal to the number of plants
in the respective county by year and the results are similar (see Appendix Table 2). This
approach offers the prospect of simultaneously estimating the effects of plants that are relocated
to designated new town centers and those that are relocated to elsewhere in the country.
This analysis is subject to the identifying assumption that for large-size industrial plants,
they are not likely to relocate their factories within a short time window because this would be
a very costly process, unless these factories are forced to be relocated by the State Council due
to the exogenous TGD construction. But our interpretation of the differences in the effect
between displacements to other places and displacement to designated counties could be
problematic if industrial plants have the willingness to relocate due to business expansions
rather than the cause of the State Council reinforcement. Thus, our estimates of the effect of
the TGD town resettlement program on industrial productivity would be biased upward or
downward. We acknowledge this issue. As a partial test for the underlying mechanisms, we
have conducted a wide range of sensitivity analyses including the important roles of firm births
and deaths, firm survivors to play in influencing the estimated effects. But as a baseline, our
exercises about whether displacement affected displaced plants on average are robust since the
displacement itself is exogenous.
3DataSourcesandSummaryStatistics
The data sources for fundamental characteristics used in industrial plant variables are
12 reported in the National Industrial Firm Surveys (NIFS) from 1998 to 2007, National Bureau
of Statistics of China (NBSC). The Chinese industrial firm data are similar to the Longitudinal
Research Database (LRD) complied by the U.S. Bureau of the Census. When one is reading
the results, it is important to note that the unique characteristic of the NIFS is the selection of
firm size threshold for “above-scale” industrial plants. All the state-owned industrial firms and
private industrial firms with annual sales of more than 500 million RMB in the manufacturing
sector are included in the NIFS. We acknowledge that this may lead to the slightly
inconsistency of sampling plant sizes over time if firm A that is in the survey one year may
experience the sale reductions next year, leading to the drop of its scale below the threshold
level next year. However, China’s manufacturing sectors have experienced fast growth during
the past ten years, and plants’ sale revenues are not likely to drop down significantly over this
period. Like existing studies that have used plant-level production information based on the
NISF data set (see a recent review by Zheng et al., 2015), we are pushed to assume that this
sampling issue won’t affect the robustness of the estimation results. Recent studies have shown
that the sampled industrial plants account for around 70% of the industrial employment, 90%
of the industrial outputs and 98% of the industrial exports (Brandt et al. 2012), suggesting the
representativeness of using NIFS in identifying the Chinese industrial market performance. As
compared to national economic census data, one unique feature of NIFS is that its fundamental
information on a plant’s address, employment, industrial outputs, and accounting variables
allow us to evaluate the plant level labor productivity (value-added output per worker, see
Dollar and Wolff, 1988), and total factor productivity (TFP) based on the Cobb-Douglas’s
classic exposition and Levinsohn and Petrin estimation methods (Levinsohn and Petrin, 2003;
See Appendix A for technical details).
To estimate the impact of the TGD town resettlement policy, the plant-level observation
data are collapsed to a plant level panel dataset, and an aggregated cross-sectional county-level
13 dataset. Additionally, we have used the 1990 population census information (a census period
that is before the construction of TGD project) to identify the pre-existing demographic
characteristics at the affected counties versus unaffected counties. We have merged the data
information to the geographic information system (GIS) so as to facilitate the BD-DD
identification strategy. To eliminate the effect of price factors, all nominal variables are deflated
to real variables by using GDP deflators for the year 1998.
Table 1 shows the summary statistics for differences in changes in plants’ industrial
productivity outcomes in the pre-treatment period, and provides the suggestive evidence on the
validity of our research design. In Panel A of Table 1, columns (1) and (2) report sample means
and column (3) conducts t tests for statistical differences in means between flooded counties
and control counties6. Column (4) reports the adjusted differences between means for these two
groups by using the propensity score matching method to reweight the observations to balance
the mean plant characteristics. The propensity score matching method7 was developed at least
since Rosenbaum and Rubin (1984) and has been applied successfully for non-experimental
causal studies in economics, statistics and other social science fields (Dehejia and Wahba,
2002). The key finding from here is that there are no significant differences between the group
means with respect to TFP, labor productivity during the pre-treatment period. In addition, the
adjustment for propensity score reweighting in key observable county demographics reduces
these differences but may still underestimate the importance of controlling for unobservable
characteristics in the following empirical implementation. Thus the main results below control
for these characteristics through various fixed effects. Panel B of Table 1 further stratifies the
6 Results are similar to the boundary discontinuity (BD) application. See the results in Appendix Table 1. 7 It
is worth noting that while not superior to various matching techniques, our approaches are still useful for two
reasons. First, compared with propensity score matching techniques it is more straightforward to account for
different trends in the vicinity of treated areas and assess the extent to which the identification is driven by the
data versus potentially unobserved extrapolations. Second, quasi-experimental approaches applied in this study
provide estimates without hard-to-measure assumptions involved in achieving consistent estimates like the
propensity score methods.
14 treated industrial plants into two sub-groups: locked-in plants and displaced plants. In most
cases, there are no substantial differences between locked-in plants and displaced plants in
flooded counties and plants in control counties in terms of pre-treatment industrial productivity.
These comparisons provide descriptive evidence on inferring the relationship between the
TGD town resettlement policy and industrial productivity between flooded and control
counties. But it appears that a simple comparison will not reveal the causal relationship, and
the subsequent analysis will need to adjust for comparison groups, as well as control for various
fixed effects such as plant-level fixed effects, industry by year fixed effects, and county by year
fixed effects so as to capture unobservable factors that may determine local industrial market
performance.
4Results
4.1 Baseline results
Table 2 reports the baseline results from the plant-level specification, with a specific focus
on estimating two outcome variables: total factor productivity (TFP), and labor productivity.
The inclusion of the set of controls is reported at the bottom of the table. Columns (1) and (2)
present the DD estimates. The model specification in column (1) includes industry by period
fixed effects, and county fixed effects. The model specification in column (2) further includes
a set of plant-level fixed effects so as to control for remaining differences in plant industrial
performance. Column (3) replicates the model specification in column (1) but presents the BDDD estimates. Column (4) is the preferred model specification with the plant-level fixed effects
in the BD-DD estimation.
Three resulting patterns emerge from this table. The first is that the implementation of the
TGD town resettlement policy has been beneficial to industrial plants’ TFP in flooded counties.
15 The magnitude of the TGD town resettlement impacts remain robust as various fixed effects
are included in the model specifications. In the results that are reported in the Appendix Table
2, there is some evidence that the policy had heterogeneous impacts on wage per worker and
employment number. We acknowledge the potential limitations of the interpretation of these
results. For example, while the most productive workers may remain in the same workforce ex
post, a fraction of less productive workers in the treated plants may now be without jobs, may
endure prolonged unemployment durations or may work in a different plant after the
implementation of the specific place-based program (Abraham and Medoff 1984; Gibbons and
Katz, 1991; Jacobson et al., 1993; von Wachter et al., 2009; Walker, 2011). We are not able to
test for these conjectures due to data limitations. When one is reading the results, it should be
noted that the estimated TGD town resettlement effects may not be able to reflect these
compositional changes in the workforce across industries. The second is the inclusion of plantlevel fixed effects leads to better modeling fits as marked by the increase in the R square
statistics (Table 2). These findings are consistent with the results of Greenstone (2002) and
other studies that suggest that adding the plant-level fixed effects is important to control for
unobservable plant features that may be related to the estimated policy regulation effects. The
third is that the BD-DD point estimates are larger and more positive than the OLS estimates,
pointing to the possibility that a simple comparison of productivity gains in flooded and nonflooded counties can lead to an underestimation of the TGD town resettlement effect.
The results from Table 3 demonstrate that the TGD town resettlement effects on industrial
plants’ TFP and other outcomes are distributed unevenly between locked-in plants and
displaced plants. In many specifications, we find significant effects on displaced plants’ TFP
levels and labor productivity, whereas there are less significant productivity effects on lockedin plants. In the results that are reported in the appendix tables, we also find the heterogeneous
impacts on wage per worker and employment number on locked-in plants relative to displaced
16 plants. In sum, the separation of these industrial productivity effects across locked-in plants
and displaced plants provide new insights about the differential displacement effects of the
TGD town resettlement policy on industrial productivity. We also explored the robustness of
the Table 2 and Table 3 results to weighted least square approaches and the results are similar
to this modification (See Appendix Table 2). Additionally, Appendix Table 3 explores the
displacement effects by using an adjusted triple-differencing version of the baseline model
specification in which an additional dummy indicator of displaced treatment status is included.
The overall estimated effect of being subject to the town resettlement program for displaced
plants is positive and statistically significant at conventional levels.
4.2 Robustness
In this subsection, we present several sensitivity analysis of the estimated productivity
effects as robustness checks. First, the BD-DD estimated impacts are based on a comparison
of control counties that are within 5km of the boundary. This allows us to restrict the focus onto
places with narrow spatial margins to control for the policy shock to industrial productivity that
may confound the resettlement assignments. However, some of neighboring areas that smaller
or larger than 5km buffer were also likely to be suitable for comparison. To the extent that this
is the case, it would be useful to examine the sensitivity of the estimated effects to changes in
distance bands. We report the estimates in columns (1)-(4) of Table 4 using two alternative
distance bands: 2 kilometer and 10 kilometers. Empirical results from these alternative distance
band samples are similar to the baseline specifications, suggesting that the estimates are not
sensitive to the selection of a particular distance band relative to the boundary.
Second, we present two tests for the robustness of our BD-DD estimates. The first test is
to compare the industrial productivity of plants that are located inside the flooded counties but
are within 5km of the boundaries with that of plants 5-10km within the boundaries. As these
plant groups have all been treated by the policy, any significant differences in their industrial
17 productivity performance would imply the estimation bias in the BD-DD analysis. The
estimates are reported in columns (5)-(6) of Table 4.The second test is to compare the industrial
productivity performance of plants that are located outside the flooded counties but are within
5km of the boundaries with that of plants 5-10km from the boundaries. We expect that there
are no significant differences between these groups because they are all located beyond the
treatment region of the TGD town resettlement program. The estimates are reported in columns
(7)-(8) of Table 4. We find almost all of these estimates are very small and insignificant. This
provides clear evidence about the validity of our identification strategy.
Finally, we present two placebo experiments that examine the pre-trends and verify the
parallel pre-trends assumption prior to the TGD town resettlement policy. First, we cannot
adopt a set of temporal differencing since the relocation time schedule for each flooded county
is not known in great detail. To simplify the analysis, we treat the town center relocations in
flooded counties as a single event because these resettlements have all been completed by 2003
when the dam is starting to fill up water. However, it is possible that some plants and flooded
counties may have completed their relocations in a year before 2003. If this is the case, then
we should observe the presence of treatment effects before the implementation of the TGD
town resettlement policy in 2003. Table 5 explores this possibility by using an adjusted version
of the baseline model specification in which indicators of treatment status are included as 1999,
2000, 2001, and 2002 respectively. Estimates from this table show no statistical significance,
suggesting that the estimated effects are robust to this potential threat to our identifying
assumption. Second, we modify the equation (1) by interacting our regressor of interest with a
set of year dummies from 1999 to 2007. The estimation results are reported in Table 6. Results
from Table 6 are in line with a causal interpretation of our benchmark findings. There are no
significant impact of the treatment status on local industrial productivity performance in years
before the implementation of the TGD town resettlement policy, but we see significant impacts
18 when the resettlement policy is implemented after 2003. Particularly, the significant impacts
on TFP, and labor productivity emerge in the later years of the policy implementation. One
credible explanation is that, the resettlement procedures for industrial plant relocations may
involve prolonged negotiations between plant owners, investors, developers and local
governments detailing required start-up financial support and compensation deals. So, even if
plants have been informed that the dam would start to fill up the water in 2003, it may take
some time for plants to rebuilt factories and set up the required costly equipment and other
conditions. These findings provide additional evidence in support of the dynamic effects of the
TGD town resettlement policy on industrial productivity.
4.3 Channels at work
This subsection examines the robustness of the results to the additional variation in our
empirical specifications and thus sheds lights on the underlying mechanisms.
China’s TGD town resettlement program reimburses the capital, equipment and land costs
for plants’ relocations at flooded counties, which has profound impacts on industrial
productivity performance. When facing this policy shock, plants in flooded counties can
respond along the internal channel at work by adjusting labor and capital inputs and outputs.
Plants can also respond along the external channel at work by choosing to enter into the flooded
counties (plants exist after 2003, but did not exist before 2003) and exit the flooded counties
(plants that existed before 2003, but not after 2003), and sort into these flooded counties after
the announcement of the TGD project in 1992 but before the 2003 town resettlement policy.
The compositional changes of plants in a county is then observed by the surviving plants
choosing to stay production there; plants that were not located in flooded counties in the pretreatment period but moved into the flooded counties in the post-treatment period; new entrants
and exiters that choose to enter or exit the flooded counties; and plants moving into flooded
counties during 1992 and 2002.
19 After the TGD town resettlement is applied, what are the changes in the composition of
the sets of plants located in the flooded counties relative to adjacent non-flooded counties? And
how does the change in composition affect the robustness of the productivity effects? To
investigate these potential mechanisms, we decompose the productivity effects into: (1) plants
that are newly-opened at flooded counties during 1992 and 2002; (2) plants that were not
located in flooded counties in the pre-treatment period but moved into the flooded counties in
the post-treatment period (“movers”); (3) surviving plants; (4) plant ownership switchers; and
(5) new entrants and exiters.
First, we examine if the baseline results are robust to the self-selection considerations.
Column (1) of Table 7 replicates the specifications in Table 2 and Table 3 but drops the newlyopened plants during 1992 and 2002. Column (2) of Table 7 excludes the sample of plants that
were not located in flooded counties in the pre-treatment period but moved into the flooded
counties in the post-treatment period. The rationale behind this is that, there is a potential selfselection concern that may exist among plants which relocated into the flooded counties after
the approval of the TGD project by the State Council in 1992, and/or after the TGD reservoir
started to fill up water in 2003. That said, some plants may simply move into in the flooded
counties in order to gain compensation deals and other preferential policies at flooded counties.
The results appear to be robust in terms of qualitative nature across model specifications and
thus corroborate our baseline findings. For the locked-in and displaced plants, however, the
estimates from Panel B of Table 7 appear to be larger in magnitude and more statistically
significant than the baseline estimates with the consideration of potential self-selection issues.
The second dimension of the decomposition analysis is to consider which plants survive
the TGD resettlement designation. The estimates in the previous section used the unbalanced
panel of plant samples, and therefore the estimated effects suggest that the TGD town
resettlement policy affects the mechanisms of plants exit and entry. Column (3) of Table 7
20 restricts the data sample to a balanced panel of plant*year observations. The estimates are
negative and significant. These results provide direct lights on the concern that restricting to
surviving plants throughout the study period may underestimate the effects of the TGD town
resettlement policy on industrial activity if plants experiencing the largest productivity gains
from the policy are likely to be new entries into the treatment areas.
The third dimension is to consider a number of plants that switch enterprise ownerships
during the time period. Some of the sampled plants may switch out of (or into) state-ownership
or private ownership due to the Chinese institutional transition since the late 1980s (Li, 1997;
Jefferson and Rawski, 1999). If these shifts are coincident with resettlement designations in the
plants’ counties, this could impact the estimated effects. Column (4) of Table 7 restricts the
focus on the sample of plants that remain in the same ownership category during the study
period. Finally, column (5) of Table 7 drops the entrants and exiters from the plant sample.
Doing these modifications shrink the sample size, but the results are similar with those
observed from the baseline model specification in terms of qualitative and quantitative nature.
Appendix Table 4 reports the decomposition results for other outcome variables. There is
some evidence that most of the TGD town resettlement effects on employment and wage per
worker are affected by changes in the composition of firms. These results consistent with recent
studies such as Criscuolo et al (2012) that document that the UK’s RSA place-based policy
effects on employment come from both of incumbent firms and net entry firms. But the
estimated effects associated with industrial outcome measures are also likely to be biased due
to the lack of output price information in the NIFS data set. For example, it is hard to predict
what would happen on existing TFP measures if some large industrial plants can have the power
in increasing the price of their output after the TGD town resettlement policy. It is also difficult
to predict the interaction effects between the TGD town resettlement policy on industrial
activity and other place-based policies such as the completion of new highway lines and
21 railway lines, or the reinforcement of new environmental regulations on industrial activity.
Furthermore, given China’s amazingly fast-growing industrial market during the treatment
period, it is possible that local entrepreneurs are overconfidence to the positive benefits that
the TGD town resettlement policy will bring in the short-run (e.g. the improved cargo
transportation capacity in the Yangtze River), and this may lead to a biased productivity gain
without considering potential agglomeration and spillover effects over space (Rosenthal and
Strange 2004, Arzaghi and Henderson 2008, Greenstone et al., 2010). We don’t have capacities
in testing these conjectures. Future studies should examine this potential channel at work.
4.4 Heterogeneous effects
This subsection examines the heterogeneity of the results to several variations in the
details of plant and county characteristics.
We consider four specific and observable plant-level characteristics: age, ownership, size
and industrial types. First of all some of these plants may have established in the centralplanned economy era whereas others may have established recently. The second dimension is
the variation in the enterprise ownership, and the third dimension is the variation in the size of
plants---measured by total employment number and the ratio of fixed asset value and total asset
value (fixed asset ratio), respectively. If the Chinese governments subsidized the state-owned
enterprises (SOE), larger-sized plants and elderly plants with a more sustained resettlement
compensation policy and if diminishing outputs have taken place unevenly across plants, then
we will observe that the heterogeneous productivity effects. If these differences are coincident
with industrial productivity performance in the plants, this could impact the estimated effects.
To test if the baseline results are sensitive to these dimensions, we stratify the plant sample by
using their state enterprise ownership status (state-owned enterprises or not), establishment
policy period (named as Old and Young in the column headings respectively), employment
number and fixed asset ratio respectively.
22 Empirical results from these adjusted samples are in columns (1)-(8) of Table 88. They
show comparative patterns in magnitude; however, the estimated effects are not all statistically
significant at conventional levels. The estimated productivity effects are consistently larger in
the elderly age plant groups than the younger age plant groups. Since state-owned plants
account for a small share of all plants, the larger non-state-owned plant sample helps to explain
the substantial estimated productivity effect. In addition, we find that there is the notable
improvement in the estimated productivity effects for both of large and small sized plants with
the larger employment number plants experiencing the most significant productivity
improvements. Finally, a higher fixed asset ratio plant that is enforced to make the resettlement
will have to reset up equipment that may be costly in enhancing the productivity in a short-run
term. As a result, higher fixed asset ratio plants would be expected to benefit less productivity
gains. Columns (7) and (8) from Table 8 show that this is indeed true.
So far we have focused on the variations in plant-level characteristics without considering
the tremendous heterogeneity in the county-level characteristics that may directly affect the
trade of industrial output over space. To investigate whether there are differential productivity
effects in counties with different levels of market access (drivers of agglomeration economies),
we construct an accessibility index based on a market potential approach that has been widely
applied in the empirical literature (Hanson, 2005; Gibbons et al., 2012; Donaldson and
Hornbeck, 2013; Zheng and Kahn, 2013). The market potential is defined as:
Ai 
iPi ij
1
(4)
where Ait is an index of market access in place i by railroads, and  ij is the transportation
cost along the minimum travel time route9 along the railroad network (excluding the high-speed
for other outcome variables are reported in Appendix Table 5.
We are indebted to Jingjuan Jiao and Jiaoe Wang for providing the minimum railroads travel time origin–destination matrix
data.
8 Estimates
9
23 rail lines) from place i to place j. Pj is the pre-treatment 1990 population in place j. The sampled
counties are then divided into two groups based on whether their index was above or below the
sample median.
The estimation results are shown in Table 8, with column (9) for counties with good
accessibility, column (10) for counties with poor accessibility. There are markedly differences
in the productivity effects between sampled plants that have been located at counties with good
and poor accessibility at the pre-treatment period. These results are not as rigorous as the main
analysis. But these findings suggest that market potentials, as a key driving force of
agglomeration economies, do not play significant roles in enhancing the productivity effects of
the TGD town resettlement policy in affected areas. Taken together, our results provide the
insights that the importance of industry and locational-specific characteristics in evaluating the
place-based (resettlement) policies depends on the specific empirical settings (Graham et al.,
2010; Criscuolo, et al., 2012; Combes and Gobillon, 2015; Briant et al., 2015).
5Conclusions
This paper provides the insight for spatial economic implications of the hydraulic dam
infrastructure construction in the Communist China. Since the early 20th century, the Chinese
leader Sun Yat-sen has envisioned the building of a dam across the Yangtze River10 to address
flooding problems in Yangtze River regions. The recent completion of the Three Gorges Dam
(TGD) infrastructure has marked the accomplishment of this Chinese dream. Using the
implementation of the TGD town resettlement policy as a quasi-experiment, we find that the
TGD town resettlement policy had exerted substantial effects on local industrial productivity
performance for plants in the flooded counties.
10 http://news.sina.com.cn/c/2006‐10‐23/113410303856s.shtml 24 We have reached several meaningful results. First, it led to the rise of plants’ industrial
productivity in flooded counties, but such effects vary substantially by industry-specific and
locational-specific characteristics. When longer run data is available, we expect to observe a
more nonlinear distributive consequence of the TGD town resettlement policy on local
industrial market performance. The local industrial productivity dynamics that prevailed during
the study period provide profound implications on: whether resettlement works; who are
winners and losers; and what works for whom (Neumark and Simpson, 2014). Although the
government relocates industrial plants into designated new town areas, local plants may not
truly benefit from the policy since place-based policies do seem to have poor performance at
reversing the depressed areas or generating heterogeneous agglomeration economies (Glaeser
and Gottlieb, 2008; Kline 2010; Faggio et al., 2015). In addition, we find that the
implementation of the TGD town resettlement policy had significantly heterogeneous impacts
on industrial productivity of plants that have been relocated to pre-determined new town areas
relative to plants that have been relocated to elsewhere in the country. Indeed, plants could
relocate themselves in places for achieving a new spatial equilibrium characterized by
rebalancing fundamental factors such as labor costs and capitals—that may lead to a prosper
prospect as evidenced by the creation of jobs, value-added per worker and asset growth. This
finding may help explain why there is the serious concern in scientific journals and policy
media channels that plants being displaced due to dam construction projects face long-term
risks of becoming less/more productive and are also threatened with homelessness, joblessness
and social marginalization (World Commission on Dams 2000, Li et al. 2001, Heggelund, 2004;
2006). These results provide direct implications in rethinking through the local multiplier
effects of place-based policy (Moretti, 2010) regarding productive social infrastructure
investments in developing countries.
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30 Figure list
Figure 1. Distribution of the Yangtze River in China 31 5 km
D
A B
5 km
C
Boundary
Figure 2. Identification of treated plants and control plants near the flooding boundary
32 Table list
Table 1. Industrial activity characteristics in the pre‐treatment period (DD) Variables
(1)
Flooded counties
(2)
Control counties
(3)
t-test
(1)vs(2)
(4)
Weighted t-test
(1)vs(2)
Panel A: Changes in plant-level industrial activity outcomes (1998-2003)
Total factor productivity (TFP)
0.012
0.004
-0.353
-0.29
Labor productivity (1000 yuan/person)
3.784
3.651
-0.081
Control
counties
-0.88
Flooded counties
Variables
(1)
Displaced plants
(2)
Locked-in plants
(3)
plants
(4)
t-test
(1)vs(3)
(5)
Weighted t-test
(1)vs(3)
(6)
t-test
(2)vs(3)
(7)
Weighted t-test
(2)vs(3)
0.004
-0.857
0.87
-0.147
0.33
Panel B: Changes in plant-level industrial activity outcomes (1998-2003)
Total factor productivity (TFP)
0.045
0.008
Labor productivity (1000 yuan/person)
12.277
2.676
3.651
-1.150
1.27
0.645
-0.64
Notes: Control counties are restricted to adjacent non-flooded counties that share borders with flooded counties. Column (4) from Panel A, and Columns (5) and (7)
from Panel B report the adjusted t-test differences weighted by 1990 population density, share of permanent residents, and residents with high school education
attainment level and above per every 10,000 person based on the propensity score matching method. *, **: significant at 10 percent, 5 percent respectively. Sources: 1
National Industrial Firm Surveys (NIFS).
33 Table 2. TGD town resettlement effects on industrial activity, baseline specifications
DD
DD
BD-DD
BD-DD
(1)
(2)
(3)
(4)
Panel A. TFP
TGD*post
0.158*** 0.298*** 0.118**
0.347**
(0.044)
(0.104)
(0.047)
(0.150)
Obs.
26149
24281
21286
19885
adj. R2
0.205
0.726
0.176
0.726
Panel B. Labor productivity
TGD*post
0.149*** 0.289*** 0.159***
0.223
(0.041)
(0.108)
(0.047)
(0.147)
Obs.
26347
24487
21438
20042
2
adj. R
0.321
0.705
0.290
0.696
Industry by year fixed effect
yes
yes
yes
yes
County fixed effect
yes
no
yes
no
County by year fixed effect
no
yes
no
yes
Plant fixed effect
no
yes
no
yes
Notes: This table reports the results from the estimation of the equations (1) and (2),
which involves the use of plants’ TFP levels and labor productivity indicators as
dependent variables (Panels A-B), and various sets of area and time fixed effects that are
noted in the row headings at the bottom of the table. TGD*post is a treatment indicator
variable for whether the plant is inside the flooded counties or not. Standard errors are
in parentheses. Standard errors are clustered by county-year. ***, ** and * denote
significance at the 1, 5 and 10% level, respectively.
34 Table 3. TGD town resettlement effects on industrial productivity, core specifications
DD
DD
BD-DD
BD-DD
(1)
(2)
(3)
(4)
Panel A. TFP
Effects on displaced plants
0.322*** 0.592*** 0.406*** 0.481***
(0.078)
(0.152)
(0.070)
(0.152)
Effects on locked-in plants
0.138*** 0.286*** 0.099**
0.170
(0.046)
(0.104)
(0.047)
(0.149)
Obs.
26149
24281
21286
19885
2
adj. R
0.205
0.726
0.177
0.726
Panel B. Labor productivity
Effects on displaced plants
0.271*** 0.519*** 0.238*** 0.323**
(0.057)
(0.154)
(0.066)
(0.149)
Effects on locked-in plants
0.134*** 0.279** 0.154***
0.092
(0.043)
(0.109)
(0.048)
(0.149)
Obs.
26347
24487
21438
20042
adj. R2
0.321
0.705
0.290
0.696
Industry by year fixed effect
yes
yes
yes
yes
County fixed effect
yes
no
yes
no
County by year fixed effect
no
yes
no
yes
Plant fixed effect
no
yes
no
yes
Notes: This table reports the treatment effects on displaced plants and locked-in plants
from the estimation of the equation (3), which involves the use of plants’ TFP levels and
other industrial productivity indicators as dependent variables (Panels A-B), and various
sets of area and time fixed effects that are noted in the row headings at the bottom of the
table. Standard errors are in parentheses. Standard errors are clustered by county-year.
***, ** and * denote significance at the 1, 5 and 10% level, respectively. See other notes
in the text.
35 Table 4. TGD town resettlement effects on industrial productivity: Robustness
Within 5km v.s. 5-10km
Alternative distance bands
Within 2km
Within 10km
Inside the boundary
Outside the boundary
TFP
Labor
TFP
Labor
TFP
Labor
TFP
Labor
productivity
productivity
productivity
productivity
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Panel A
TGD*post
Obs.
Panel B
Effects on displaced plants
0.247***
(0.058)
9786
0.253***
(0.053)
9849
0.111**
(0.044)
23490
0.146***
(0.043)
23658
-0.095
(0.082)
6258
-0.017
(0.076)
6294
-0.006
(0.076)
15497
-0.085
(0.077)
15629
0.675***
0.478***
0.395***
0.188***
0.117
0.168
0.083
0.085
(0.105)
(0.087)
(0.072)
(0.068)
(0.104)
(0.111)
(0.112)
(0.111)
Effects on locked-in plants
0.213***
0.235***
0.094**
0.143***
-0.156*
-0.071
-0.013
-0.098
(0.058)
(0.053)
(0.044)
(0.044)
(0.084)
(0.076)
(0.076)
(0.077)
Obs.
9786
9849
23490
23658
6258
6294
15497
15629
Note: Observations are at the county-year level. In columns 1-4, the regression samples consist of areas within 2 km and 10 km of the boundary as
reported in the column headings. In column 5-6, the regression sample includes plants inside the flooding boundary: areas within 5km versus those
within 5km to10 km distance ranges. In columns 7-8 the sample is composed of areas outside the zone. Industry-year fixed effects, county-year fixed
effects and plant fixed effects are included. Standard errors are in parentheses. The standard errors are clustered at the county-year level. ***, ** and *
denote significance at the 1, 5 and 10% level, respectively.
36 Table 5. TGD town resettlement effects on industrial activity: placebo treatment year tests
Panel A1. TFP
TGD*post
Obs.
Panel A2. Labor productivity
TGD*post
Obs.
Panel B1. TFP
Effects on displaced plants
Effects on locked-in plants
Obs.
Panel B2. Labor productivity
Effects on displaced plants
1999
(1)
2000
(2)
2001
(3)
2002
(4)
0.182
(0.208)
19885
0.147
(0.211)
19885
0.135
(0.160)
19885
0.194
(0.168)
19885
0.251
(0.238)
20042
0.177
(0.233)
20042
0.081
(0.169)
20042
0.084
(0.172)
20042
0.210
(0.222)
-0.081
(0.210)
19885
0.173
(0.220)
-0.088
(0.191)
19885
0.156
(0.169)
-0.004
(0.171)
19885
0.285
(0.181)
0.030
(0.161)
19885
0.262
0.185
0.079
0.113
(0.253) (0.240) (0.176) (0.188)
Effects on locked-in plants
0.160
0.108
0.096
0.032
(0.183) (0.232) (0.212) (0.185)
Obs.
20042 20042 20042 20042
Note: This table reports the results by using different placebo
treatment year (as noted by column headings). Observations are at
the county-year level. Industry-year fixed effects, county-year fixed
effects and plant fixed effects are included. Standard errors are in
parentheses. The standard errors are clustered at the county-year
level. ***, ** and * denote significance at the 1, 5 and 10% level,
respectively.
37 Table 6. TGD town resettlement effects on industrial activity: Tests for pre-trends
TFP
Labor productivity
(1)
(2)
0.124
0.200
(0.185)
(0.203)
TGD*year2000
0.169
0.313
(0.371)
(0.400)
TGD*year2001
0.050
0.174
(0.280)
(0.289)
TGD*year2002
-0.126
-0.058
(0.310)
(0.326)
TGD*year2003
0.211
0.220
(0.277)
(0.285)
TGD*year2004
0.216
0.131
(0.276)
(0.312)
TGD*year2005
0.457*
0.461*
(0.253)
(0.264)
TGD*year2006
0.512**
0.440*
(0.232)
(0.262)
TGD*year2007
0.429*
0.345
(0.251)
(0.274)
Obs.
19885
20042
Note: This table reports the placebo test results for the common pre-trend assumption.
Columns (1)-(2) report the estimation results using the TFP and labor productivity as
outcome variables, respectively. TGD is an indicator variable for whether the plant is
inside the flooded area or not. Year1999 (1999…2007) is an indicator variable for
whether the year is 1999 (1999…2007). Observations are at the county-year level.
Industry-year fixed effects, county-year fixed effects and plant fixed effects are included.
Standard errors are in parentheses. The standard errors are clustered at the county-year
level. ***, ** and * denote significance at the 1, 5 and 10% level, respectively.
Panel A
TGD*year1999
38 Table 7. TGD town resettlement effects on industrial productivity: A decomposition analysis
Excluding
Excluding
Survivors
Excluding
Excluding new
newly-opened
movers
ownership
entrants
and
plants 92-02
switchers
exiters
(1)
(2)
(3)
(4)
(5)
Panel A1. TFP
TGD*post
0.210***
0.150***
-0.138***
0.116**
0.130**
(0.062)
(0.048)
(0.050)
(0.045)
(0.052)
Obs.
18235
20464
4463
18706
18354
Panel A2. Labor
productivity
TGD*post
0.347***
0.184***
-0.114**
0.127***
0.182***
(0.054)
(0.049)
(0.050)
(0.048)
(0.052)
Obs.
18361
20612
4477
18847
18494
Panel B1. TFP
Effects on displaced plants
0.639***
0.439**
0.019
0.379***
0.474***
(0.120)
(0.182)
(0.102)
(0.074)
(0.081)
Effects on locked-in plants
0.190***
0.146***
-0.161***
0.095**
0.109**
(0.063)
(0.048)
(0.055)
(0.046)
(0.053)
Obs.
18235
20464
4463
18706
18354
Panel B2. Labor
productivity
Effects on displaced plants
0.382***
0.425***
0.022
0.236***
0.256***
(0.092)
(0.141)
(0.109)
(0.078)
(0.071)
Effects on locked-in plants
0.345***
0.181***
-0.133**
0.119**
0.178***
(0.055)
(0.049)
(0.053)
(0.049)
(0.053)
Obs.
18361
20612
4477
18847
18494
Notes: The total effect of the TGD town resettlement has been decomposed into differential effects due to
movers, entrants and exiters, survivors, and ownership switchers. Standard errors are in parentheses.
Standard errors are clustered by county-year. ***, ** and * denote significance at the 1, 5 and 10% level,
respectively. See other notes in Table 2 and Table 3.
39 Table 8. TGD town resettlement effects on industrial productivity: Heterogeneous effects
SOE
Non-SOE
Old
Young
Large-size Small-size High fixed Low fixed
asset ratio asset ratio
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Panel A1. TFP
TGD*post
Obs.
Panel A2. Labor productivity
TGD*post
Obs.
Panel B1. TFP
Effects on displaced plants
Effects on locked-in plants
Obs.
Panel B2. Labor productivity
Effects on displaced plants
Good MP
Poor MP
(9)
(10)
0.072
(0.085)
6008
0.100**
(0.050)
15230
0.082
(0.056)
15843
0.230***
(0.073)
5403
0.146***
(0.050)
10840
0.112*
(0.062)
10407
0.088*
(0.050)
10586
0.153**
(0.064)
10667
0.132*
(0.069)
13469
0.063
(0.062)
7769
0.087
(0.066)
6058
0.110*
(0.056)
15332
0.153***
(0.058)
15972
0.185***
(0.060)
5426
0.106**
(0.048)
10897
0.187***
(0.065)
10502
0.166***
(0.050)
10587
0.096
(0.069)
10816
0.209***
(0.068)
13578
0.071
(0.058)
7810
0.351***
0.367***
0.352***
0.561***
0.198**
0.367***
0.390***
0.455***
0.606***
(0.117)
0.052
(0.088)
6008
(0.078)
0.082
(0.051)
15230
(0.078)
0.064
(0.056)
15843
(0.211)
0.206***
(0.075)
5403
(0.094)
0.142***
(0.051)
10840
(0.098)
0.096
(0.063)
10407
(0.113)
0.072
(0.051)
10586
(0.099)
0.129**
(0.065)
10667
(0.170)
0.117
(0.071)
13469
0.342**
*
(0.089)
0.034
(0.062)
7769
0.151
0.200**
0.219***
0.411***
0.151*
0.308***
0.270***
0.206**
0.421***
0.146*
(0.105)
(0.084)
(0.072)
(0.141)
(0.077)
(0.114)
(0.101)
(0.087)
(0.135)
(0.082)
Effects on locked-in plants
0.083
0.104*
0.149**
0.168***
0.103**
0.180***
0.161***
0.088
0.202***
0.064
(0.067)
(0.056)
(0.059)
(0.060)
(0.048)
(0.065)
(0.050)
(0.069)
(0.069)
(0.058)
Obs.
6058
15332
15972
5426
10897
10502
10587
10816
13578
7810
Note: This table reports the heterogeneous results by different industry-specific and locational-specific characteristics (as noted by column headings). Observations are at the
area-year level. Columns (1) and (2) report the results for plants with state-owned enterprise (SOE) ownership and non-SOE ownership respectively. Columns (3) and (4) stratify
the sample by using whether plants are established before or after the 1980s. Columns (5) and (6) stratify the sample based on whether a plant’s employment number is above or
below the sample median level. Columns (7) and (8) stratify the sample based on whether a plant’s fixed asset to total asset ratio is above or below the sample median level. In
columns (9) and (10), counties with good market potentials (MP) are those with accessibility indices above (below) the sample median level: a higher MP index indicates better
railroad infrastructure accessibility. Industry by year fixed effects and county fixed effects are included. Standard errors are in parentheses and are clustered at the county-year
level. ***, ** and * denote significance at the 1, 5 and 10% level, respectively
40 Appendix A: Constructing the plant-level TFP measure
In this appendix we briefly explain the mathematical deductions for a conceptual
framework of measuring a manufacturer’s total factor productivity (TFP) level. We then justify
our specification for estimating the TFP measure based on the Levinsohn and Petrin methods
(Levinsohn and Petrin, 2003).
To motivate the empirical models, we start by considering that a manufacturing plant has
a Cobb-Douglas production function:
Yit  Ait  Lit  K it
(A1)
Where Yit is output for plant i at time t, which is the conceptual function of labor inputs,
Lit , and capital inputs, Kit . Ait is a Hicks-neutral technology shifter that can improve on a
plant’s total factor productivity through technological innovations and enhancing production
efficiency. By taking the natural logs, we transform the equation (A1) into a linear regression
function:
(A2)
ln Yit  a0    ln Lit    ln K it  it
Following Zheng et al (2015), we could define the dependent variable as the value added
output at the plant level. Labor inputs, Lit , is measured by the number of employees each year
per plant observation. Capital inputs, Kit , is measured by plant-level real value of fixed assets.
The NSIFs datasets include the information about the value of plants’ fixed capital stocks at
original purchase price, and their capital stock at original purchase prices less accumulated
depreciation. Following Zheng et al (2015), we acknowledge that these values are the sum of
nominal values and may not be consistent across time and firms.  it it is the error term. It has
two components: a white noise component, and a time-varying productivity shock. As
suggested by the literature, the sum of a0   it would be a proxy for the absolute TFP value，
41 and can be estimated by :
ln TFPit  ln Yit  ˆ  ln Lit  ˆ  ln Kit
(A3)
There are two serious limitations in the above equation. The first limitation is the
correlation between unobservable productivity shocks and the input factors. For example, it is
possible that changes in productivity shocks would lead to changes in input factors (Marschak
and Andrews, 1944). In practice, this means that if firm managers can observe some positive
productivity shocks, firm managers will then enhance capital and labor inputs. As such, this
would lead to biased estimates in the ordinary least square (OLS) estimation procedure. Second,
there is the endogeneity concern about plant sample selection. The rationale behind this is that,
plants can exit the market when they have experienced negative productivity shocks, and
therefore, the surviving plant samples may not be randomly selected. To address these issues,
Olley and Pakes (1996) proposed a semi-parametric estimation approach for estimating the
TFP measure.
The Olley-Pakes (OP) estimator has been widely applied in studying the firm TFP
performance. See recent applications in Zheng et al (2015) and among others. However,
Levinsohn and Petrin (2003) find two potential problems in the Olley-Pakes (OP) estimator:
First, the Olley-Pakes (OP) estimator solved the correlation problem between capital inputs
and the residuals but may not solve the correlation problem between labor inputs and the
residuals. Second, the possibility for observing “zeros” in the Olley-Pakes (OP) estimator may
lead to biased estimates. In light of precision issues, we rely on Levinsohn and Petrin estimator
for measuring the plant level TFP.
42 Appendix B: Descriptive statistics
Appendix Table 1. Industrial activity characteristics in the pre‐treatment period (3)
(4)
(1)
(2)
t-test
Weighted t-test
Variables
+5km
-5km
(1)vs(2)
(1)vs(2)
Panel A: Changes in plant-level industrial productivity outcomes (1998-2003)
Total factor productivity (TFP)
0.016
0.005
-0.475
0.09
Labor productivity (1000 yuan/person)
3.398
1.761
-0.920
0.18
+5km
Variables
(1)
Displaced plants
-5km
(2)
Locked-in plants
(3)
plants
(4)
t-test
(1)vs(3)
(5)
Weighted t-test
(1)vs(3)
(6)
t-test
(2)vs(3)
(7)
Weighted t-test
(2)vs(3)
0.005
0.448
-1.69
-0.719
0.45
Panel B: Changes in plant-level industrial productivity outcomes (1998-2003)
Total factor productivity (TFP)
0.020
0.023
Labor productivity (1000 yuan/person)
4.954
3.110
1.761
-0.507
-0.75
-0.811
1.14
Notes: The control area includes plants that are located outside the boundary but are within 5km distance buffer relative to the boundary (marked by -5km in the column
headings). The treatment area includes plants that are located inside the boundary but are within 5km distance buffer relative to the boundary (marked by +5km in the
column headings). Column (4) from Panel A, and Columns (5) and (7) from Panel B report the adjusted t-test differences weighted by 1990 population density, share
of permanent residents, and residents with high school education attainment level and above per every 10,000 person based on the propensity score matching method.
*, **: significant at 10 percent, 5 percent respectively. Sources: National Industrial Firm Surveys (NIFS) and 1990 county population census data.
43 Appendix C: Additional robustness checks and other outcome variables
Appendix Table 2. TGD town resettlement effects on industrial activity:
Weighted least square estimation
DD
DD
BD-DD
BD-DD
(1)
(2)
(3)
(4)
Panel A1. TFP
TGD*post
Panel A2. Labor productivity
TGD*post
Panel A3. Wage per worker
TGD*post
Panel A4. Employment
TGD*post
Panel B1. TFP
Effects on displaced plants
Effects on locked-in plants
Panel B2. Labor productivity
Effects on displaced plants
Effects on locked-in plants
Panel B3. Wage per worker
Effects on displaced plants
Effects on locked-in plants
0.156**
(0.067)
0.234**
(0.100)
0.153**
(0.060)
0.329**
(0.152)
0.169***
(0.062)
0.238**
(0.099)
0.170***
(0.063)
0.191
(0.141)
-0.004
(0.018)
-0.012
(0.032)
0.019
(0.020)
0.000
(0.082)
0.009
(0.039)
0.035
(0.046)
0.015
(0.044)
0.271***
(0.075)
0.408***
(0.096)
0.128*
(0.068)
0.439***
(0.159)
0.228**
(0.099)
0.551***
(0.085)
0.131**
(0.061)
0.467***
(0.150)
0.162
(0.159)
0.327***
(0.086)
0.151**
(0.062)
0.363**
(0.144)
0.234**
(0.100)
0.331***
(0.083)
0.161**
(0.064)
0.271*
(0.141)
0.094
(0.154)
0.052
(0.037)
-0.010
(0.018)
0.025
(0.066)
-0.014
(0.033)
0.079**
(0.039)
0.016
(0.020)
0.036
(0.094)
-0.046
(0.077)
Panel B4. Employment
Effects on displaced plants
0.222**
0.161**
0.387***
0.333***
(0.087)
(0.078)
(0.095)
(0.081)
Effects on locked-in plants
-0.015
0.031
-0.006
0.192**
(0.038)
(0.046)
(0.044)
(0.092)
Industry by year fixed effect
yes
yes
yes
yes
County fixed effect
yes
no
yes
no
County by year fixed effect
no
yes
no
yes
Plant fixed effect
no
yes
no
yes
Notes: This table reports the additional robustness results, which involves the use of plants’ TFP levels and
other industrial activity indicators as dependent variables. The results from this table are estimated by using
the weighted least squares, with the weights equal to the number of plants in the respective county. Standard
errors are in parentheses. ***, ** and * denote significance at the 1, 5 and 10% level, respectively.
44 Appendix Table 3. TGD town resettlement effects on industrial activity: A triple-differencing
specification
DDD
DDD
BD-DDD
BD-DDD
(1)
(2)
(3)
(4)
Panel A1. TFP
Displaced*TGD*post
0.192***
0.221**
0.363***
0.370***
(0.061)
(0.106)
(0.056)
(0.097)
26149
24281
21286
19885
Panel A2. Labor productivity
Displaced*TGD*post
0.173***
0.136
0.144***
0.263***
(0.045)
(0.115)
(0.052)
(0.100)
26347
24487
21438
20042
Panel A3. Wage per worker
Displaced*TGD*post
0.035
0.066
0.094***
0.104
(0.032)
(0.047)
(0.031)
(0.072)
27333
25476
22349
20941
Panel A4. Employment
Displaced*TGD*post
0.100
0.114*
0.330***
0.134*
(0.068)
(0.065)
(0.078)
(0.075)
27389
25520
22375
20957
Industry by year fixed effect
yes
yes
yes
yes
County fixed effect
yes
no
yes
no
County by year fixed effect
no
yes
no
yes
Plant fixed effect
no
yes
no
yes
Notes: This table reports the additional robustness results by using DDD, which involves the use of plants’
TFP levels and other industrial activity indicators as dependent variables. Displaced is a binary treatment
indicator that equals to one if a plant i in county j in the pre-period is displaced to another county k in the
post-period and zero otherwise. Standard errors are in parentheses. ***, ** and * denote significance at the
1, 5 and 10% level, respectively.
45 Appendix Table 4. TGD town resettlement effects on other outcome variables: A decomposition
analysis
Excluding
Excluding
Survivors
Excluding
Excluding new
newly-opened
movers
ownership
entrants
and
plants
switchers
exiters
(1)
(2)
(3)
(4)
(5)
Panel A1. Wage per worker
TGD*post
0.082***
0.017
-0.059**
0.006
0.019
(0.021)
(0.017)
(0.026)
(0.018)
(0.019)
Obs.
19168
21489
4577
19715
19343
Panel A2. Employment
TGD*post
-0.175***
-0.015
-0.022
0.009
-0.049
(0.046)
(0.036)
(0.029)
(0.039)
(0.040)
Obs.
19190
21512
4582
19741
19366
Panel B1. Wage per worker
Effects on displaced plants
0.243***
-0.016
-0.027
0.058
0.075**
(0.048)
(0.054)
(0.058)
(0.036)
(0.034)
Effects on locked-in plants
0.074***
0.017
-0.063**
0.002
0.016
(0.021)
(0.017)
(0.025)
(0.018)
(0.019)
Obs.
19168
21489
4577
19715
19343
Panel B2. Employment
Effects on displaced plants
0.400***
0.065
-0.013
0.264***
0.337***
(0.109)
(0.195)
(0.121)
(0.092)
(0.083)
Effects on locked-in plants
-0.203***
-0.016
-0.023
-0.012
-0.072*
(0.048)
(0.036)
(0.030)
(0.039)
(0.040)
Obs.
19190
21512
4582
19741
19366
46 Appendix Table 5. TGD town resettlement effects on other outcome variables: Heterogeneous effects
SOE
Non-SOE
Old
Young
Large-size Small-size High fixed Low fixed
asset ratio asset ratio
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Panel A1. Wage per worker
TGD*post
0.020
-0.012
-0.013
0.083***
0.044**
-0.018
-0.005
0.020
(0.028)
(0.022)
(0.023)
(0.024)
(0.020)
(0.026)
(0.022)
(0.028)
Obs.
6617
15681
16428
5883
11395
10915
11156
11163
Panel A2. Employment
TGD*post
0.000
0.007
-0.077*
0.093
0.050**
-0.030
-0.073
0.077
(0.058)
(0.042)
(0.042)
(0.059)
(0.021)
(0.031)
(0.045)
(0.059)
Obs.
6632
15692
16446
5891
11407
10929
11172
11173
Panel B1. Wage per worker
Effects on displaced plants
0.075
0.062
0.041
0.222***
0.152***
0.032
0.147**
0.059
(0.073)
(0.047)
(0.041)
(0.076)
(0.046)
(0.042)
(0.060)
(0.048)
Effects on locked-in plants
0.016
-0.017
-0.017
0.073***
0.037*
-0.021
-0.013
0.017
(0.029)
(0.022)
(0.022)
(0.025)
(0.020)
(0.026)
(0.022)
(0.028)
Obs.
6617
15681
16428
5883
11395
10915
11156
11163
Panel B2. Employment
Effects on displaced plants
0.273*
0.295***
0.205**
0.271
0.061
0.201***
0.192**
0.428***
(0.141)
(0.085)
(0.098)
(0.176)
(0.073)
(0.057)
(0.093)
(0.112)
Effects on locked-in plants
-0.019
-0.013
-0.096**
0.080
0.049**
-0.045
-0.087*
0.049
(0.062)
(0.042)
(0.042)
(0.062)
(0.021)
(0.031)
(0.045)
(0.058)
Obs.
6632
15692
16446
5891
11407
10929
11172
11173
47 Good MP
Poor MP
(9)
(10)
0.017
(0.023)
14215
-0.004
(0.029)
8087
-0.073
(0.049)
14231
-0.048
(0.059)
8097
0.059
(0.073)
0.015
(0.022)
14215
0.110**
(0.045)
-0.016
(0.029)
8087
0.370**
(0.151)
-0.087*
(0.050)
14231
0.252**
(0.105)
-0.080
(0.059)
8097
48