Internal Migration and Firm Growth: Evidence from China. * Clement Imbert Marlon Seror Yifan Zhang Yanos Zylberberg Preliminary and incomplete – do not circulate Abstract This paper provides some of the first empirical evidence on the role of internal migration in manufacturing growth, using Chinese data. We first identify shocks to rural livelihoods caused by variation in international agri- cultural prices and local climatic conditions. We then combine these shocks with a gravity model to predict yearly migrant inflow into each urban center. Finally, we use household survey data and a census of large firms to estimate the causal impact of migrant inflows on the urban economy. Preliminary re- sults suggest that by increasing labor supply, migration lowers labor costs and increases the profitability of manufacturing firms. JEL codes: D24; J23; J61; O15. 1 Introduction As countries develop, labour shifts from the traditional—agriculture—to the modern sector—manufacturing,—which implies migration from rural to urban areas (Lewis, 1954; Kuznets, 1964; Harris and Todaro, 1970). 1 Despite the fact that the movement * Imbert: Warwick University, [email protected]; Seror: PSE, [email protected]; Zhang: CUHK, [email protected]; Zylberberg: Bristol University, [email protected]. We are grateful to Gharad Bryan, Jon Temple, Christine Valente, Thomas Vendryes, Chris Woodruff for useful discussions and comments. We also thank participants in Bristol, CUHK and Warwick for helpful comments. The usual disclaimer applies. 1 In a recent study of the determinants of structural change in today’s developed economies, Alvarez-Cuadrado and Poschke (2011) propose an empirical exercise to distinguish “push” from “pull” models. In “labour push” models, rising productivity in agriculture releases labour, which in 1
Transcript
Internal Migration and Firm Growth: Evidence
from China.∗
Clement Imbert Marlon Seror Yifan Zhang
Yanos Zylberberg
Preliminary and incomplete – do not circulate
Abstract
This paper provides some of the first empirical evidence on the role of
internal migration in manufacturing growth, using Chinese data. We first
identify shocks to rural livelihoods caused by variation in international agri-
cultural prices and local climatic conditions. We then combine these shocks
with a gravity model to predict yearly migrant inflow into each urban center.
Finally, we use household survey data and a census of large firms to estimate
the causal impact of migrant inflows on the urban economy. Preliminary re-
sults suggest that by increasing labor supply, migration lowers labor costs and
increases the profitability of manufacturing firms.
JEL codes: D24; J23; J61; O15.
1 Introduction
As countries develop, labour shifts from the traditional—agriculture—to the modern
sector—manufacturing,—which implies migration from rural to urban areas (Lewis,
1954; Kuznets, 1964; Harris and Todaro, 1970).1 Despite the fact that the movement
∗Imbert: Warwick University, [email protected]; Seror: PSE, [email protected]; Zhang:CUHK, [email protected]; Zylberberg: Bristol University, [email protected] are grateful to Gharad Bryan, Jon Temple, Christine Valente, Thomas Vendryes, ChrisWoodruff for useful discussions and comments. We also thank participants in Bristol, CUHKand Warwick for helpful comments. The usual disclaimer applies.
1In a recent study of the determinants of structural change in today’s developed economies,Alvarez-Cuadrado and Poschke (2011) propose an empirical exercise to distinguish “push” from“pull” models. In “labour push” models, rising productivity in agriculture releases labour, which in
1
of labour is central in structural transformation, there is little empirical evidence on
its short- and medium-run impact on the urban manufacturing sector.
The objective of this paper is to estimate the causal impact of migration inflows
on urban labor markets and manufacturing firms in China. We use variation in
rainfall and world prices for agricultural commodities, combined with information on
cropping patterns and potential yields to construct exogenous shocks to agricultural
labour returns in each rural prefecture.2 We then combine these shocks with a
gravity model, which includes distance between rural origin and urban destination
and population at destination to predict migration inflows into each urban area.
Finally, we use these origin-based fluctuations to instrument immigrant inflows and
estimate their effect on the urban economy through the observation of workers and
firms.
China arguably offers the best context to study the role of migration in economic
development. The Chinese economy has experienced a remarkably rapid structural
transformation, with a sharp fall in the share of agriculture and a symmetric rise
in manufacturing and services for the last three decades. China’s agricultural em-
ployment share was about 70% in 1980 and is predicted to taper off at 24% in 2020
(ADB, 2014).3 At the same time, China has seen massive migration flows from rural
to urban areas. The stock of rural-to-urban migrants, i.e. the urban population with
a rural household registration or residence permit (hukou), rose from 46.5 million in
1982 to 205.6 million or 30.9% of the total urban population in 2010 (Chan, 2012).4
This rapid evolution allows us to study migration and manufacturing growth with
coherent data sources spanning a significant part of the structural transformation
period.
Our empirical strategy proceeds in three steps. In a first step, we construct shocks
to agricultural incomes. For this we collect geocoded grids (1km×1km) provided by
the Food and Agricultural Organisation (FAO). We multiply the 1990 harvested
area with a model-based measure of potential yield combining crop requirements
and soil characteristics to create a measure of expected output for each crop and
turn triggers industrialization (Gollin et al., 2002). In “labour pull” models, technological changeincreases productivity in manufacturing, which attracts workers out of agriculture (Herrendorf etal., 2013). In both narratives, migration—and thus structural change—is prompted by a produc-tivity gap between sectors.
2Prefectures are the second administrative division in China below the province (there wereabout 345 prefectures in 2005).
3In comparison, Alvarez-Cuadrado and Poschke (2011) find that it took 108 years on averagefor agricultural employment share to decline from 60% to 20% of the labour force in 12 of today’sdeveloped economies.
4This figure suggest that internal migration in China alone is of the same order of magnitude asinternational migration worldwide. In 2010 the stock of international migrants was an estimated222 million (United Nations, 2015).
2
prefecture. We then combine the expected output with two crop-specific shocks.
First, we isolate short-term fluctuations in international crop prices and transform
these price variations into variations in expected agricultural income (for a fixed
agricultural portfolio). Second, we interact the crop water requirement during the
growing season with monthly precipitation to create a yearly distance to ideal water
requirement in each prefecture. These origin shocks exhibit a large time-varying
volatility coming from the World demand and supply or rainfall cycles but also
large cross-sectional differences due to the wide variety of harvested crops across
China.
In a second step, we combine rural income shocks with a gravity model which uses
distance between rural origins and urban destinations and population at destination
to predict migration inflows into urban areas. Fluctuations in agricultural income
due to international prices and rainfall generate significant variations in outflows
from rural areas. An origin-specific agricultural portfolio 10% above its long-term
value (about 1 standard deviation) is associated with a 0.25 p.p. lower outmigration
incidence. Similarly, a 1 standard deviation increase in our measure of distance to
ideal water requirement is associated with a 0.18 p.p. lower outmigration incidence.
Both effects are very robust and generate economically significant variations in mi-
gration outflows (the average outmigration incidence is around 1.4 p.p.). We next
use a gravity model based on geographic distance and historical data on destina-
tion populations to transform these rural outflows into immigration inflows to urban
destinations. Our approach is similar in that respect to Boustan et al. (2010).
In a third step, we identify the causal impact of migrant inflows on the urban
economy. We first use an annual survey of urban households (Urban Household
Survey) and estimate the effect of migration on wages and employment for urban
“natives”. We find that migration inflows exert a downward pressure on urban wages
and crowd urban residents out of wage employment. The implied wage elasticity
with respect to migration is 0.15 to 0.28. As expected, the effects are stronger for
less educated workers, who are close substitutes for migrant labour. We next use
a yearly census of large firms from the National Bureau of Statistics (NBS) and
estimate the effect of migration on labour costs and profitability. We show that
migration inflows markedly reduce the wage rate (wage bill divided by employment)
and increase profitability (value added minus labour costs divided by revenues) for
urban firms. The effects are stronger for firms employing mostly unskilled labour.
Our (preliminary) findings contribute to different strands of the literature. First,
this paper contributes to the literature on structural transformation by estimating
the direct impact of rural-to-urban migration on the modern sector, using worker and
3
firm data. Our findings that migration decreases wages and increases profitability in
urban areas relate to “labour push models,” which generally imply that, by releasing
labour, labour-saving rising agricultural productivity may trigger industrialization
(Gollin et al., 2002; Alvarez-Cuadrado and Poschke, 2011; Bustos et al., 2015). Our
results may also complement Marden (2015), who finds that for an earlier period
of Chinese development (the 1990s), the increase in farm profits due to agricultural
reforms provided credit to finance non-agricultural sector growth.
Second, this paper relates to the nascent literature which uses firm-level data to
study how migrants’ labour supply is absorbed by the economy (Peri, 2012; Kerr et
al., 2015; Dustmann and Glitz, 2015).5 The context of our study is however very
different. Urban China has experienced massive flows of internal migrants and its
economy has been expanding at a very high rate, with a constant reallocation of
resources toward small, young and productive firms (Song et al., 2011).
Third, this paper relates to the literature on the effects of immigration on labour
markets (Borjas, 2003), and more specifically to studies that focus on internal mi-
gration. Boustan et al. (2010) study the labour market effects of changes in internal
migration in the US during the Great Depression. El Badaoui et al. (2014), Imbert
and Papp (2014), Kleemans and Magruder (2014) and Feng et al. (2015a) among
others study the labour market effects of migration in Thailand, India, Indonesia
and the United States, respectively.
Fourth, this paper contributes to the literature on the role of migration in shap-
ing economic development in China. Ge and Yang (2014) use wage decomposition
methods and a simple calibration to show that migration depressed unskilled wages
in urban areas by at least 20% throughout the 1990s and 2000s. Based on aggregated
data at the provincial level, De Sousa and Poncet (2011) find that migration helped
alleviate upward pressures on Chinese wages in 1995-2007. In contrast, Meng and
Zhang (2010) provide evidence of a modestly positive or zero effect of rural migrants
on native urban workers’ labour market outcomes, and Combes et al. (2015) put
forward a strong positive externality on local wages. Mayneris et al. (2014) consider
another type of shock to the labour market, an increase in legislated minimum wages.
As we do with migration, they assess the impact of the shock on firm outcomes in
China. To the best of our knowledge, our paper is the first microeconomic paper
to investigate and provide evidence of the effect of migration on firm outcomes, and
thus contributes to linking to empirics “push” models of rural outmigration fuelling
modern sector growth. It complements Facchini et al. (2015), who show that trade
5Giesing and Laurentsyeva (2015), provide evidence on the effect of emigration on firm outcomesin Eastern European countries.
4
shocks increase demand for labor in manufacturing and stimulate internal migration
(which is consistent to a “pull” model).6
Much attention has been given to the mechanisms and patterns of the Chinese
growth, and a large body of literature is devoted to migration in China. However,
while the role of rural-to-urban migration in fuelling economic growth finds a large
echo in the policy debate in China,7 the economic literature has given it much less
attention. For example, Song et al. (2011) focus on three main features of the
Chinese economic take-off—high output growth, with high and sustained returns
to capital, reallocation within the manufacturing sector from large state-owned en-
terprises (SOEs) to smaller private firms, and large savings invested abroad. Their
explanation relies on credit market imperfections, which force small productive firms
to save before growing at the expense of larger, less productive firms. Interestingly,
migration from rural areas may also help explain these stylized facts. Indeed, the
constant increase in labour supply of migrants, by moderating urban wage growth,
may have allowed firms to sustain high profits, accumulate internal savings and
The remainder of the paper is organized as follows. In Section 2, we describe
our three main data sources allowing us to create migration flows, labour market
outcomes and firm-specific outcomes. We also detail how we isolate exogenous vari-
ations at origin that impact migration flows. In Section 3, we describe our empirical
strategy, in particular how we generate synthetic migration flows thanks to our
agricultural productivity shocks and estimates of migration flows on urban labour
markets and firm outcomes at destination. We present our main results in section 4.
Section 5 concludes.
2 Data
This section presents the data we use and how we construct the main variable of
our analysis.8. We first present our two main sources of exogenous variation in
agricultural returns to labour, i.e. the price and yield shocks. We next present our
measures of migration flows and urban outcomes.
6Macroeconomic discussions of the link between migration and productivity can be found inAu and Henderson (2006), Au and Henderson (2007) and Tombe and Zhu (2015). These papersall focus on mobility restrictions, as do Bosker et al. (2012), an economic geography analysis, andVendryes (2011), a theoretical paper.
7See Meng and Zhang (2010) for a survey.8As we rely on many data sources, we describe them briefly below and provide a more detailed
discussion in the appendix.
5
2.1 Rural income shocks
In order to construct shocks to productivity of labor in agriculture, we combines
three types of information: potential agricultural output, international prices and
rainfall.
Potential Agricultural Output We construct the potential output for each crop
in each prefecture, by combining a measure of harvested area, and a measure of yield
both provided by the Food and Agriculture Organization (FAO).
First, we extract from the 1990 World Census of Agriculture the geo-coded map
of harvested area for each crop (in a 30 arc-second resolution, approximately 1km).
We then overlay this map with a map of prefectures, and we construct total harvested
area hc,o for a given crop c and a given prefecture o.9
Second, we use a measure of potential yield per hectare as computed in the Global
Agro-Ecological Zones (GAEZ) Agricultural Suitability and Potential Yields dataset.
The measure is model-based and uses information on crop requirements (e.g. the
length of yield formation period and the stage-specific crop water requirements), soil
characteristics (i.e. the ability of the soil to retain and supply nutrients) in order
to generate a potential yield for a given crop, and a given soil under 5 scenarios:
rain-fed (high/intermediate/low water input), and irrigated crop (high/intermediate
water input). For each crop c and prefecture o, we use information on whether it
was rain-fed or irrigated in 1990 to construct potential yield yic,o.10
The interaction between harvest area and potential yield hc,oyic,o is our mea-
sure of potential agricultural output for each crop in each prefecture in 1990. Fig-
ure 3 displays potential output hc,oyic,o for rice and cotton, and illustrates the large
geographic variation in agricultural portfolios. By construction, hc,oyic,o is time-
invariant. We next combine potential output at the prefecture level with two time-
varying shocks, international prices and rainfall shocks.
International price shock As a measure of exogenous changes in international
demand for crops, we use the World Bank Commodities Price Data (“The Pink
Sheet”).11. We consider prices in constant 2010 USD and per kg between 1980
and 2009 for the following commodities: banana, cassava, coffee, cotton, an index
9We collapse our analysis at the prefecture level to match migration data but agricultural shockscan be constructed at a 30 arc-second resolution over the whole country.
10The measure is given as a 30 arc-second resolution geo-coded map which we overlay withprefecture maps to generate the prefecture average.
11The data is freely available online at http://data.worldbank.org/data-catalog/commodity-price-data
6
of foddercrops, groundnut, maize, millet, potato, pulses, rapeseed, rice, sorghum,
soybean, sugar beet, sugar cane, sunflower, tea and wheat.12 These crops account
for the lion’s share of China’s agricultural production over the period of interest
(they represented 90% of total agricultural output in 1998 and 79% in 2007).13
We also collected producer prices, exports and production as reported by the FAO
between 1991 and 2013 for China (and other countries) to check that international
price variations translate into producer price variations.
In order to identify shocks in international prices, we use next a deviation from
long-term trend hpc,t by applying a Hodrick-Prescott (HP henceforth) filter on the
logarithm of nominal prices. The Appendix Figure A3 presents the series for three
crops, i.e. rice, bananas and groundnuts, and illustrates the magnitude of fluctua-
tions: The market value of rice production decreases by 40% between 1998 and 2001
and increases by 70% between 2007 and 2008. As shown in Figure A3, fluctuations
in prices are not pure transitory shocks but rather behave as an AR(1) process with
rare and large jumps. Hence our price shocks capture the equivalent of business
cycle fluctuations in international crop prices.
Finally, we transform fluctuations in World prices into an estimate of the value of
crop production for each year in each prefecture. In order to do this, we construct for
each prefecture o the value gap for the agricultural portfolio. We consider the crop-
specific deviations from long-term trend, {hpc,t}c, and weight them by a constant
weight equal to the expected share of agricultural revenue for crop c in prefecture o.
These shares are {hc,oyic,opc}c where hc,oyic,o is potential output in 1990 described
above and pc is a snapshot of international crop prices in 1980.
po,t =
(∑c
hc,oyic,opchpc,t
)/
(∑c
hc,oyic,opc
)(1)
The price shocks po,t exhibit some time-varying volatility coming from World demand
and supply, but there are also large cross-sectional differences. A prefecture is only
exposed to the variations in the prices of crops that it produces. The wide variety
of harvested crops across China guarantees a large cross-sectional variance in prices
po,t that will be exploited in our main empirical strategies. Panel A of Figure A4
shows price shocks po,1999−2000 in 1999 and 2000, just before farmers experienced a
crisis across China due to a strong decrease in the price of rice.
These shocks are likely to have a strong effect on outmigration. Indeed, fluc-
12We exclude from our analysis one crop, i.e. tobacco, for which (i) China has a dominant positionand directly influences the international prices and (ii) China National Tobacco Corporation, astate-owned enterprise, has a monopoly on cigarette production.
tuations in prices exhibit some persistence: prices follow a process that looks like
an AR(1). Accordingly, a negative shock does not only affect returns to labour in
the same year but also the following ones. This persistence helps us in triggering
migration outflows but will also introduce some auto-correlation in the resulting
immigration inflows to urban centers.
As the fluctuations in po,t entirely come from fluctuations in the World com-
modity prices, we need to assume that these prices are driven by supply shocks in
other exporting countries, demand fluctuations in importing countries or the World
agricultural market integration, but that these demand and supply fluctuations are
orthogonal to Chinese urban labour demand.14.
Rainfall shocks In our analysis, we use a second type of shocks to agricultural
income based on rainfall deficit during the growing period of each crop.
Our rainfall data is a monthly precipitation measure (0.5 degree latitude x 0.5
degree longitude precision) which covers the period 1901-2011 and mostly relies on
the Global Historical Climatology Network.15 Once collapsed at the prefecture level,
This provides us with a measure rao,m,t of rainfall for prefecture o in month m and
year t.
We refine this rainfall measure to account for the growing cycle of each crop,
i.e. (i) the harvest season and (ii) rainfall requirements. For a given year, there are
several sources of variation across Chinese prefectures in actual yields due to rainfall.
First, different locations receive different levels of rainfall. Second, exposure to
rainfall depends on the growing cycle of the different harvested crops (winter, spring
or summer/autumn crops). In addition, some crops are resistant to large water
deficits while others immediately perish with low rainfall. The large cross-sectional
variations in each year may come from (i) a direct effect of local rainfall, (ii) an
indirect effect coming from the interaction with the crop-specific growing cycle.
We rely on the measure rao,m,t of rainfall for prefecture o in month m and year t
and we construct for each crop a measure wrc of the minimum crop water require-
ment during the growing season Mc as predicted by the yield response to water.16
14One potential issue is that agricultural prices could have a direct effect on firms which useagricultural products as inputs. We test the robustness of our results by excluding these firmsfrom our analysis
15UDel AirT Precip data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado,USA, from their Web site at http://www.esrl.noaa.gov/psd/.
16http://www.fao.org/nr/water/cropinfo.html
8
We then generate
ro,t =
(∑c
(max{
∑m∈Mc
wrc − rao,m,t, 0}wrc
)αhc,oyic,opc
)/
(∑c
hc,oyic,opc
). (2)
This measure has a very intuitive interpretation. The quantity max{∑
m∈Mcwrc −
rao,m,t, 0} is the deficit between actual rainfall and the minimum crop water require-
ment wrc during the growing season. We then penalize this deficit with a factor α
capturing potential non-linearities in the impact of rainfall deficit. In our baseline
specification, this penalization parameter α will be set equal to 3.17 A high ratiomax{
∑m∈Mc
wrc−rao,m,t,0}wrc
would be associated with a bad harvest for the specific crop.
We then weight these ratios by potential output for each crop in each prefecture.
Panel B of Figure A4 displays rainfall shocks rao,1999−2000 in 1999 and 2000. As
expected, there is large year-to-year variation in rainfall availability. Also, for a
given year, because of differences in cropping patterns across prefectures, the spatial
auto-correlation of rainfall shocks is much lower than the correlation of rainfall
itself. While the exogeneity of rainfall shocks is not questionable, in order to use
it as instrument for rural to urban migration we need to assume that urban labor
demand is not directly affected by rainfall.18
We view price and rainfall shocks as complement, since they have different
strengths. On the one hand, price shocks reflect business fluctuations and will likely
have a stronger effect on rural to urban migration than rainfall shocks, which are
short-lived. On the other, rainfall shocks are idiosyncratic, which makes it more
likely for us to identify their immediate effect on migration flows.
2.2 Migration and urban outcomes
We now describe our measures of migration flows and workers and firms outcomes
in urban areas.
Migration flows In order to measure migration flows, we use a random 20% ex-
tract of the 1% Population Survey 2005, also called “2005 mini-census”. These data
are representative of the whole of China and contain data on occupation, industry,
income, ethnicity, education level and housing characteristics. Most importantly
for our purpose, the 2005 mini-census is the first to contain comprehensive data
on migration status. This can be determined thanks to information on household
registration type (agricultural or non-agricultural) and on the places of registration
17The results are robust to more conservative values for α, e.g. α = 1 or α = 2.18In the analysis, we test the robustness of our results by controlling for local rainfall shocks.
9
and of residence, which are available down to the prefecture level.19 Migrants are
further asked the main reason for leaving their place of registration and when they
did so. Because of their quality and degree of detail, the census data collected by the
National Bureau of Statistics are widely used in the literature (Combes et al., 2015;
Facchini et al., 2015; Meng and Zhang, 2010; Tombe and Zhu, 2015, inter alia).
Moreover, information on places of origin and residence can be combined with
retrospective data on the year that the respondent first left her place of registration
(censored above six years prior to the interview) in order to create a matrix of yearly
net migration flows across all Chinese prefectures between 1999 and 2005, as well
as to determine the migrant stock in 1999. There were about 345 prefectures (diji
qu/shi) in China over this period, home to 3.7 million people on average. Prefec-
tures are the third tier of government in China, below the central and provincial
governments, and the lowest level of government with accessible data on bilateral
migration flows.
Unlike most studies relying on census data, migration flows are directly observed
rather than computed as a difference of stocks. However, our measure of migration
has two limitations that must be borne in mind in the subsequent analysis. First,
since migration flows are reconstructed ex post, we expect some attrition, i.e. re-
turnees are not counted as former migrants but as agricultural hukou holders living
in their prefectures of registration. Second, the census does not record when the
respondent arrived at her place of residence but only when she left her place of
registration, hence we have to assume that the two happen in the same year. Some
migrants may have however resided in other urban centers in between (step migra-
tion). Return and step migration may dilute the effect of the shocks at origin on
migration flows.20
Figure 4 illustrates the rise in migrant flows between 2000 and 2005 as a share of
the total locally registered urban population, i.e. locally registered (at the prefecture
level) non-agricultural hukou holders. We consider only inter-prefectural migration
flows. The rising trend and the magnitude of migration flows is striking: In 2005, the
inflow of migrants from other prefectures was in excess of 6%, as against less than 2%
in 2000. Two interesting facts pertain to the composition of the incoming migrants.
First, between 78% in 2000 and 83% in 2005 of the yearly migrant inflow consist of
rural hukou holders, the remainder being accounted for by urban dwellers originating
19Unfortunately, information on the place of residence does not distinguish between rural orurban settings.
20The 2005 mini-census also contains information on the place of residence one and five yearsprior to the interview. In appendix B.1, we use these data to quantify return and step migration.The results suggest that return migration is substantial, but step migration negligible.
10
from other prefectures. Second, on average more than 78% of interprefectural rural-
urban migrations recorded over the period 2000-2005 involved the crossing of a
provincial border. We provide in the Appendix (B.1) a more detailed description of
migration flows and migrants.
Wages and employment In our analysis, we first study labour market outcomes
from the worker point of view, using household survey data. The household data
used to assess the link between migration and destination-specific labour market
outcomes come from the national Urban Household Survey (UHS) collected by the
National Bureau of Statistics.
The UHS is a nationally representative survey of Urban China that covers the
period 2002-2008. It is based on a three-stage stratified random sampling, whose
design is similar to that of the Current Population Survey in the United States (Ge
and Yang, 2014; Feng et al., 2015b). Its sample includes 18 provinces and 207 pre-
fectures.21 The data we use for our analysis are annual cross-sections, with a sample
size that ranges from 68,376 to 94,428 individuals (in 2002 and 2008 respectively).
Before 2002, the population covered by the UHS explicitly excluded the “floating
population” of agricultural hukou holders living in urban areas. Since 2002, all
households living in urban areas are eligible. However, sampling still ignores urban
dwellers living in townships and in the suburban districts of Beijing, Chongqing,
Shanghai, and Tianjin (Park, 2008). Rural-urban migrants, who are more likely to
live in peripheral areas of cities, are therefore under-represented. Our analysis is
thus restricted to the locally registered urban population.22
The UHS is a very rich dataset with detailed information on individual employ-
ment, income —including monthly wages, bonuses, allowances, housing and medi-
cal subsidies, overtime, and other income from the work unit—and household-level
characteristics. It also includes detailed data on household expenditures collected
using diaries—see Feng et al. (2015b) for more detail—. As our main income mea-
sure, we use monthly wages divided by a prefecture- and year-specific consumer
price index which we constructed ourselves using consumption data.23 We also
construct three employment outcomes: wage employment, unemployment and self-
employment (which also includes firm owners).24 Table A1 in the Appendix provides
21Although the 18 provinces capture much of China’s regional disparities, it must be noted thatthey may not constitute a faithful picture of China as a whole. The provinces are Beijing, Shanxi,Liaoning, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Shandong, Henan, Hubei,Guangdong, Chongqing, Sichuan, Yunnan, Shaanxi and Gansu.
22The UHS data also tend to oversample employees from state and collective enterprises, whereresponse rates are higher (Ge and Yang, 2014; Feng et al., 2015b).
23Statistical Yearbooks in China do not publish CPIs below the province level.24Working hours in the month preceding the survey were also recorded in UHS 2002-2006. How-
11
average summary statistics of key variables over the period 2002-2008.
Firms Our second piece of information on the urban economy comes from firm-
level data spanning 1998-2007 from the National Bureau of Statistics (NBS).25 The
NBS implements every year a census of all state-owned enterprises and all non-state
firms with sales exceeding 5 million RMB.26 It covers the industrial sector, which
is defined as mining, manufacturing and utilities In our analysis, we focus on the
manufacturing sector in which large firms are responsible for most of the production:
the NBS sample is responsible for 90% of gross manufacturing output.
The data are based on a standard firm survey and contain information on each
firm’s location, industry, ownership type, number of employees and a wide range
of accounting variables (e.g. output, input, value added, wage bill, fixed assets,
financial assets, etc.). In our preliminary analysis we consider two firm outcomes:
labor costs and profitability. As our measure of labor costs, we construct the wage
rate as the wage bill divided by the total number of employees. As our measure of
profitability, we use revenues divided by sales.
There is a number of caveat with using the NBS census. First, the 5 million
RMB threshold that defines whether a firm belongs or not to the NBS census was
loosely implemented. In effect, it is impossible to know the exact level of sales
before implementing the survey and some firms only entered the database several
years after having reached the sales cut-off.27 This truncation potentially introduces
a selection bias. For that reason, we restrict ourselves to the balanced panel of firms
over the period in most of our analysis, and we only resort to the unbalanced panel
as a robustness check.
Second, matching firms over time in the NBS is difficult because of frequent
changes in firm identifiers. In order to match “identifier-switchers,” we use the
fuzzy algorithm developed by Brandt et al. (2014), which uses slowly-changing firm
characteristics such as its name, address or phone number. While total sample size
ranges between 150,000 and 300,000 per year, we end up with 55,000 firms when we
limit the sample to the fully balanced panel between 1999 and 2005.
Third, although we shall use the terms “firm” and “enterprise” interchangeably
ever, as pointed out by Ge and Yang (2014), they vary within a very narrow range, which meansthat the UHS measure might understate actual variations in working hours. For this reason, wedo not use hours of work in our analysis.
25The following description borrows heavily from a detailed discussion in Brandt et al. (2014).26The average exchange rate over the period of interest was 8.26 RMB to the USD, so 5 million
RMB represent about $605,000.27Conversely, about 5% of private and collectively-owned firms, which are subject to the thresh-
old, continue to participate in the survey even if their annual sales fall short of the threshold.
12
in the remainder of the paper, the NBS data cover “firms” in the narrow sense
of “legal units” (faren danwei). Subsequently, different subsidiaries of the same
enterprise may be surveyed, provided they meet a number of criteria, including
having their own names, being able to sign contracts, possessing and using assets
independently, assuming their liabilities and being financially independent.
3 Empirical strategy
In this section, we first describe how we create exogenous rural-to-urban migration
flows based on our price and rainfall variations at origin. Our strategy closely follows
Boustan et al. (2010) to evaluate labour market effects of internal migration in the
United States.28 We then present the empirical strategy we use to estimate the
causal impact of migrant inflows on the urban sector.
3.1 Predicting rural-urban migration flows
Let Mo,d,t denote the migration flows between origin o (rural areas of a prefecture
o) and destination d (a “city,” i.e. urban areas in a prefecture d) in a given year
t = 2000, . . . , 2005, which we construct using retrospective questions of the 2005
mini-census.29 We construct the outmigration rate in year t, mo,t, by dividing the
sum of migrants who left o in year t by the number of adults who still reside in o,
which we denote with Ro. Formally, we have:
mo,t =
∑dMo,d,t
Ro
.
We also construct the probability that a migrant from o goes to d at time t, which
we denote with po,d,t =Mo,d,t∑dMo,d,t
.
For the sake of exposition, we describe our strategy for a given shock so,t to the
rural origin o in year t, which may either be a price shock or a rainfall shock.
In order to estimate the causal effect of migration inflow on urban destinations,
we need variations in migration flows that are unrelated to potential destination
outcomes. Our empirical strategy follows Boustan et al. (2010), and interacts two
sources of exogenous variation. First, we use price and rainfall variations as ex-
ogenous determinants of migration outflows in each rural prefecture. Second, we
28A similar approach is adopted by El Badaoui et al. (2014), Feng et al. (2015a) and Kleemansand Magruder (2014).
29A ”prefecture” comprises both urban and rural areas. There is some debate on how wellurbanisation is captured in Chinese data—see Chan (2007). Note that our results are not sensitiveto such a measurement issue since we assume, based on the literature and Census data on reasonsfor migration, that rural outmigrants settle in urban areas.
13
combine we use a gravity model which includes geographic distance between prefec-
tures and urban population in 1990 to allocate rural migrants to urban destinations.
This provides us with a prediction of migrant inflow in each urban area that is
exogenous with respect to urban outcomes.
Exogenous variations in migration outflows We first regress migration out-
flow from each rural area on shocks to agricultural income. Formally, we estimate
the following equation:
mo,t = β0 + βsso,t−1 + δt + νo + εo,t, (3)
where o indexes the origin, and t indexes time t = 2000, . . . , 2005. mo,t and so,t
denote is the outmigration rate and the shock at origin o in year t, respectively.
νo denotes for origin fixed effects and captures any time-invariant characteristics of
origins, e.g. barriers to mobility. We use 1990 population at origin as weight to
generate consistent outmigration predictions in the number of migrants.
As our measure of shock so,t, we use the average of rainfall or price shocks in
t − 1 and t − 2. A migration spell at date t = 2005 for instance corresponds to a
migrant worker who moved between October 2004 and October 2005. Hence, given
the timing of the growing cycle for most crops in our sample, migration spells in
period t are most likely to be impacted by rainfall and price variations in t− 1 and
before—especially if there are lags in the decision to migrate.30
Estimating equation 3 yields the predicted migration rate mo,t from origin o in
year t:
mo,t = β0 + β1so,t + νo + δt
where δt is the average of the time effect.31 We then multiply the migration rate by
rural population at origin Ro to compute predicted migration flows from o:
Mo,t = mo,t ×Ro
We present the estimation of equation (3) in Table 1. In the first two columns,
we report the estimates for the price variations with lags (column 1) and with lags
and forwards (column 2). The third and fourth columns display the estimates for
the rain variations, and the last two columns include both price and rainfall shocks.
30Incorporating contemporary price/rainfall shocks in the analysis does not change the results.We also estimate the same specification using forward shocks, i.e. the average of prices in t + 1and t+ 2, to show that shocks are not anticipated.
31We remove time variation from our predictions, in order to avoid correlation between ourmigration flows and destination trends in outcomes.
14
In Table 1 – columns 1 and 2, we see that migration flows are negatively correlated
with (lagged) price deviations from their long-term values. A price 10% above its
long-term value is associated with a 0.25 p.p. lower migration incidence, which is on
average around 1.3 p.p. in our sample. In order to better understand the magnitude
of this effect, let us normalize by the standard deviations of our variables. An
additional standard deviation in the price shocks decreases migration incidence by
0.18 standard deviations. This effect is thus economically large, and quite precisely
estimated. Figure 1 plots the residuals of outmigration (y-axis) against the residual
value of the prefecture-specific agricultural portfolio as predicted by international
prices (x-axis), once cleaned by prefecture and year fixed-effects. The relationship
is globally linear.
As shown in Table 1 – columns 3 and 4, migration flows are positively correlated
with rainfall deficits (see definition in section 2). A standard deviation increase in
rainfall deficits is associated with a 0.18 p.p. higher migration incidence. Figure 2
displays the relationship between outmigration and rainfall deficits, once prefecture
and year fixed effects are partialled out. The relationship seems linear.
As a robustness check, we test whether shocks are anticipated and find that
forward variations in rainfall or prices do not predict migration outflows (Column
2, 4 and 6 of Table 1). Finally, we include both types of shocks in the estimation.
As columns 5 and 6 of Table 1 show, price and rainfall shocks have independent
effects on migration outflows. The estimated coefficients on the lags and forwards
of our constructed shocks in the joint regression are similar to those in the separate
specifications.
Exogenous variations in origin-destination migration flows We next esti-
mate the following equation:
po,d = f(disto,d) + γPopd,1990 + µo + εo,d, (4)
where po,d is the share of migrants from prefecture o who went to prefecture d,
disto,d is the distance between o and d, f is a parametric function of distance and
Popd,1990 is the total urban population of prefecture d in 1990. Equation 4 yields
po,d, the predicted probability for migrants from prefecture o to go to prefecture
d based on distance, a fixed and exogenous characteristic of the pair (o, d), and
the attractiveness of d captured by its lagged population. The specifications are
weighted by Popd,1990.
We report the results of this estimation for three simple parametric specifications
15
in Table 2. In the first column, we use a linear specification in distance.32 In column
2, we add a quadratic term and we use the inverse of distance in column 3. As
apparent in this table, (i) distance is a very strong predictor of migration flows and
(ii) the last specification in column 3 generates a much better fit of the data. 33
Predicted migration flows Finally, we combine predicted migration outflows
(Equation 3) and predicted probabilities to come from each origin to each destination
(Equation 4) to predict migration inflows into each urban destination. Formally, we
compute :
Md,t =∑o 6=d
Mo,t × po,d, (5)
where Md,t are migration inflows in destination d in year t, Mo,t is predicted migration
outflow from origin o in year t and po,d is the predicted probability that a migrant
from o goes to d. In order to avoid that migration inflows are correlated with
destination outcomes, we exclude from Md,t immigration flows attributable to rural
areas of prefecture d.
This two-stage process yields synthetic migration inflows into prefectures of des-
tination that are exogenous with respect to destination outcomes. We first pro-
vide some intuition about the nature of these exogenous variations in Figures A6
(measure Md,t as predicted by price variations) and A7 (measure Md,t as predicted
by rainfall variations). We report these measures cleaned for cross-sectional time-
invariant factors in 2001 (left panels) and 2004 (right panels). As shown in Fig-
ure A6, there is some spatial auto-correlation in these measures arising from the
spatial auto-correlation of crop composition across prefectures and the transforma-
tion of outflows to inflows involving distance between prefectures. There is also
some auto-correlation across periods as international prices exhibit persistence in
their fluctuations. However, there are also large cross-sectional and time-varying
fluctuations that we can use for our analysis. Figure A7 illustrates cross-sectional
and time-varying fluctuations for the immigrant inflows measure as predicted by
rainfall variations.
In order to test whether our migration predictions are accurate, we regress the
actual migrant inflows observed in the mini-census data on the predicted immigrant
inflows. Table 3 reports the correlation between actual and predicted migration
rates. As Columns 1 and 3 show, the relationship is strong, positive and significant
32The distance between two prefectures o and d, po,d, is measured as the distance between thecentroids of o and d.
33This is confirmed by Figure A5 in Appendix which displays the average migration share toeach destination by distance from the origin.
16
with destination-fixed effects. It remains so after adding year fixed effects (Columns
2 and 4). The coefficient in both specifications is close to one. This suggests that,
as expected and by construction, our instrument successfully predict variation in
migration inflow between years for a given prefecture and across prefectures for a
given year, even if they do not explain most of the total variation in migration rates.
This baseline relationship between actual and exogenous variations in immigration
rates will serve as a first stage in our analysis to estimate the impact of migration
on urban labour markets and firm outcomes.
In a robustness check, we only keep migrants from different provinces and run
a similar exercise as in Table 3 (see Table A6). The predictive power of the syn-
thetic migration flows is not affected by the restriction to migration spells between
provinces. This feature is important because it allow us to separate the potential di-
rect effects of price or rainfall shocks on a province (through demand for non-tradable
goods for instance) from the indirect effects through the arrival of workers.
We now turn to the second stage of our analysis, which estimates how rural-to-
urban migration is absorbed by the urban modern sector.
3.2 Migration flows and labour market outcomes
In order to estimate the effect of migration on urban labour market outcomes, we
use employment and wage data from the Urban Household Survey.34
We estimate the impact of migration on labour market outcomes of individual i
in destination d in year t by regressing each outcome, which we denote with yi,d,t,
on predicted migration that year, Md,t, and a vector of individual characteristics Xi.
The vector Xi includes dummy variables for individual i’s marital status, gender,
education level (primary, lower secondary, upper secondary and tertiary), and age
(24-35, 35-44, 45-54 and 54-64). We also include seven occupation dummies in order
to better control for workers’ skills.35 In order to control for labour market conditions
at destination and aggregate fluctuations in labour market outcomes, we also include
destination and year fixed effects. The effect of Md,t on yi,d,t is estimated through
34Since UHS does not cover all prefectures, but only a representative sample of 18 provincesand 207 prefectures, we checked that our predictions and actual migration rates are indeed wellcorrelated within the UHS sample (Results available upon request).
35UHS occupation categories are “Head of organization,” “Professional skill worker,” “Staff,”“Commercial and service worker,” “Agriculture,” “Production operator,” “Soldier” and “Otheroccupations”. Since occupation itself may be an outcome of migration, we check that our resultsare robust to excluding it from the vector of controls.
17
Two-Stage Least Squares (2SLS) with Md,t as an instrument:{Md,t = b0 + bmMd,t + bxXi + ed + nt + ed,t
yi,d,t = β0 + βmMd,t + βxXi + ηd + νt + εi, (6)
and standard errors are clustered at the level of the prefecture of destination×year.36
3.3 Migration flows and firm outcomes
We next turn to the estimation of the effect of migrant inflows on firm outcomes.
One challenge with firm data is that some variables, e.g. size, are not station-
ary and these differential trends would not be captured by firm fixed effects. We
describe below our strategy when dealing with stationary variables: wage rate and
profitability (profits normalized by sales) and we describe in the appendix the em-
pirical strategy to deal with non-stationary variables.
We take advantage of the panel structure of the data and implement a 2SLS-FE
specification in which we regress the outcome of firm j in year t in urban prefecture
d on migration inflow in d, which we denote Md,t, using predicted migration Md,t as
instrument and including firm fixed effects ηj.{Md,t = b0 + bmMd,t + ej + nt + ed,t
yj,d,t = β0 + βmMd,t + ηj + νt + εj,t, (7)
with standard errors clustered at the level of the prefecture of destination×year.37
4 Results and discussion
In this section, we discuss our preliminary findings on the absorption of labour supply
in urban centers. We first analyze the impact on urban labour markets, which helps
identify the nature of the shock induced by immigant inflows at destination. We
then discuss preliminary findings on firm outcomes.
36Because the regressor of interest, the migration rate, is itself predicted, correct inference re-quires to bootstrap the first stage. The standard errors in the second stage are however correctlyestimated through 2SLS.
37Because the regressor of interest, the migration rate, is itself predicted, correct inference re-quires to bootstrap the first stage. The standard errors in the second stage are however correctlyestimated through 2SLS.
18
4.1 Effects of migration inflow on urban workers
In order to identify the shift in urban labour supply, we use repeated cross sections
from the National Urban Household Survey and consider the effect of migration
inflows on labour market outcomes of locally registered urban residents aged 15
to 64. In this exercise, we ignore the existence of heterogeneity between migrants
and “natives,” i.e. assume that they are perfectly substitutable. As Table A3 in
the Appendix shows, however, migrants are significantly less skilled than urban
workers.38 For this reason, we also estimate the change in labour market outcomes
for “natives” with primary education and lower secondary education only.
Table 4 presents our estimates of the effect of migration inflows on four outcomes:
wages, wage employment, unemployment and self-employment of urban residents.
The first column presents results from a simple OLS regression of each outcome
on the actual immigration rate. The second and third columns present 2SLS es-
timations, using rainfall and price shocks, respectively, as instruments for migrant
inflows.
We first consider the impact on urban wages. The OLS estimate is negative but
small: a 1 p.p. increase in the immigration rate is associated with a 0.09% decrease
in wages. The IV estimates are negative and larger in magnitude: If migrants are
attracted to cities that offer higher wages, OLS estimates should indeed be biased
upwards. Using rainfall and price shocks to predict migration, we find that a 1
p.p. higher immigration rate is associated with 0.17% - 0.22% lower wages. The
effects become larger when we focus our attention on urban residents with lower
secondary education or less, who are more likely to compete for jobs with migrants.
A 1 p.p. higher immigrant rate is associated with a 0.17% - 0.31% decrease in wages.
Overall, these estimates suggest that, once cleaned for the potential demand-driven
fluctuations, an influx of rural migrants depresses urban wages. Following Borjas
(2003) we can recover the elasticity of urban wages with respect to migration by
multiplying the coefficient by 1(1+m)2
, where m is the ratio of migrants to native.
In our context, the migration rate is about 5%, hence 1(1+m)2
≈ 0.90. The implied
wage elasticities from our estimates are between 0.15 and 0.28, which is lower than
Borjas’s (2003) own estimates (0.3− 0.4).
We next consider the effect of rural to urban migration on the status of active
urban residents (wage employment, self-employment or unemployment). The OLS
estimates are close to zero and mostly insignificant, as are the IV estimates using
price shocks as instrument. The IV estimates using rainfall shocks, however dis-
38See section B.1 in the Appendix for a systematic comparison between rural migrants and urbanresidents.
19
play significant decrease in wage employment: a one percentage point increase in
migration decreases wage employment of urban residents by 9 percentage point (the
average participation to wage employment is above 90%). Correspondingly, unem-
ployment and self-employment seem to increase (the effect on self-employment is not
significant). These results provide some evidence that employers substitute urban
workers with rural migrants, leaving urban residents unemployed or leading them to
become self-employed. However, these effects are not consistent across instrumen-
tation strategies.
Overall, our results confirm that the arrival of migrants shifts labour supply
downward. The estimated effect of migration on wages is relatively small, as com-
pared to those from the literature on international migration into developed coun-
tries (Borjas, 2003) and to other studies on internal migration in developing countries
which use a similar strategy (Boustan et al., 2010; El Badaoui et al., 2014; Imbert
and Papp, 2014; Kleemans and Magruder, 2014). One reason behind such pattern
could be that the labour market for urban residents is regulated with the existence
of minimum wages, while the labour markets for migrants are unregulated. The
marginal labour cost may thus drastically respond to the arrival of migrants when
the average labour cost (mostly driven by residents) remains quite high.
4.2 Effects of migration inflow on manufacturing firms
We now turn to the firm side and analyze the impact of exogenous changes in
migration inflows on labor costs and profitability.
In Table 5, we analyze specification 7 on the subsample of firms present from
1999 to 2005. We look at two “stationary” outcome variables: wage rates, which
are defined as total wage bill divided by total labour force, and profitability, which
is equal to total profits (value added minus wage bill) divided by total revenue.39
We take the logarithm of both variables. In column 1, we report the correlation
between these variables (at the end of period t) and migrant inflows during period t.
In column 2 (resp. 3), we use our migration flows as predicted by the rainfall (resp.
price) shocks to instrument actual movements from rural to urban areas. Note that
firms relying on migrants may be selected in terms of unobservable characteristics.
All regressions in Table 5 and the following thus include firm fixed effects to clean
for fixed firm-specific determinants of their reliance on migrant workers.
As shown in the top panels of Table 5 – column 1, the correlation between firm-
level wage rates and total migrant inflows is negative and significant. As expected
given the lower education level of migrants—see Table A3,—the effect is slightly
39We ignore here the ownership structure of the firm.
20
larger in absolute value when one restricts the analysis to firms that rely heavily
on unskilled labour, i.e. food manufacturing, beverage manufacturing, footwear,
wood processing, and textile. To interpret the size of these correlations, the within
standard deviation of migration flows is around .2, which implies that current mi-
gration flows higher by 1 within standard deviation would be associated with a small
0.03% decrease in wage rates. These results are however likely to be biased upwards
(towards zero) as migrants tend to settle in high-wage destinations.
Columns 2 and 3 address this concern thanks to the instrumental variable strat-
egy delineated in Section 3 and show a very different picture. Coefficients become
much larger in absolute value when migration flows are purged of the endogeneity
in migration decisions. A 1% higher immigration rate translates to 1.2% lower wage
rates when using rainfall as a source of exogenous variation and 0.6% lower when we
rely on price shocks. In standardised terms, a 1 within standard deviation increase
in the immigration rate yields a .25% (resp., .13%) drop in wage rates using the
rainfall- (resp., price-) based instrument. The range can be explained by two fac-
tors. First, the estimation based on rainfall tends to be noisier. Second, rainfall and
price shocks identify different local average treatment effects (LATEs). Whereas a
shortage of rainfall is likely to trigger distress migration, price fluctuations exhibit
some serial correlation and might thus lead rural dwellers relying on agriculture for a
living to update their expectations on returns to farming and engage in more planned
migration. We would therefore expect a stronger immediate impact of immigration
on urban labour markets when rainfall is exploited as a source of identification.
Finally, we can note that the effects are larger in magnitude—albeit imprecisely
estimated—when we focus on low-skill industries.
The bottom panels of Table 5 explore the effect of migrant inflows on firms’ prof-
itability, for the whole sample first and then focusing on low-skill sectors. Profitabil-
ity is positively and consistently affected by migration flows. The effect becomes
positive and significant when we implement our instrumental variable strategy.40
The effect of a 1 within standard deviation increase in the immigration rate iden-
tified thanks to international prices (resp., rainfall) on firms’ profitability is a .2%
(resp., 3%) rise in profitability for the whole sample. Firms that hire mostly low-
skilled workers enjoy a larger positive effect of immigration: +.3% (resp., +.4%).
We run a number of robustness checks to verify that our estimates are indeed cap-
40Note that the OLS effect is lower than the coefficients on the instrumented migration flowsand statistically indistinguishable from zero. One explanation is that opposite effects are at work:First, an influx of migrants enables firms to enhance their profits and grow; second, destinationsexperiencing migration flows have already experienced some economic growth with larger and moreestablished firms than in other regions, thereby attracting migrants through higher posted wages.
21
turing labour supply shifts induced by origin-driven fluctuations. All corresponding
tables are in the Appendix.
One concern could be that firms in cities rely on the provision of important crops
as intermediate inputs, and are directly affected by World crop prices as final good
producers (rice vinegar exporters for instance). One possible solution is to exploit
the differential flows between products and migrants with the latter moving much
farther than the former (migration costs are paid once while transportation costs are
paid continuously). Instead, we use the precise indicators of industries and perform
two robustness checks. First, in order to clean for the potential shortages in crop
provisions for some cities close to the fields, we exclude all firms potentially using
one of our crops as an input. These consist of—among others—food exporters and
part of the textile industry. We report the results of this analysis in Table A7. The
results are virtually unchanged compared to Table 5. We also control for the direct
effect of price and rainfall shocks in destination prefectures. These controls are built
based on equations 1 and 2, respectively, and lagged in order to match the way
migration shocks were created. The results, reported in Table A8, are imprecisely
estimated but confirm our findings that immigration depresses wages and boosts
firm productivity at destination.
Second, there may be some delay between the arrival of migrants and the result-
ing increase in firm factor use. Although this does not jeopardise our identification
strategy or the interpretation of the results, we provide in Table A9 the effects of
lagged shocks. Results are consistent with contemporary shocks.
The results of this section give some credit to our constructed migration flows:
migration shifts labour supply to the right, thereby decreasing wages and boosting
labour demand. In the next section, we look more precisely at this effect and better
identify which firms gain from the newly-available resources.
4.3 Reallocation of resources across firms [Work in progress.]
5 Conclusion
A key link in the chain of events between rural to urban migration and manufacturing
growth is the impact of migration on urban labor markets and firms. This paper
provides some of the first causal empirical evidence of this impact using Chinese
data.
We predict migrant inflows into urban areas based on shocks to agricultural in-
comes in rural origins and distance between prefectures of origin and destination.
These predictions are exogenous with respect to urban workers’ and firms’ environ-
22
ments, which allows us to tackle the issue of migrants self-selecting into buoyant
labour markets and provide causal estimates of the effect of migration on urban
outcomes. Using a representative survey of urban households, we find that migrant
inflows from rural areas have a negative effect on urban dwellers’ wages and—to
a lesser extent—employment. We next use a census of large firms and show that
migration decreases labor costs and improves profitability of manufacturing firms
This new piece of evidence brings together two main features of Chinese devel-
opment, massive internal migration and manufacturing growth despite severe credit
constraints (Song et al., 2011). By keeping labor costs low, rural-to-urban migration
may have allowed firms to accumulate larger profits, which were then reinvested to
finance future growth.
23
References
ADB, “The Declining Share of Agricultural Employment in the People’s Republic
of China: How Fast?,” ADB Economics Working Paper Series 419, Asian Devel-
opment Bank (ADB) November 2014.
Alvarez-Cuadrado, Francisco and Markus Poschke, “Structural Change Out
of Agriculture: Labor Push versus Labor Pull,” American Economic Journal:
Macroeconomics, July 2011, 3 (3), 127–58.
Au, Chun-Chung and J. Vernon Henderson, “How migration restrictions limit
agglomeration and productivity in China,” Journal of Development Economics,
2006, 80, 350–88.
and , “A numerical simulation analysis of (Hukou) labour mobility restrictions
in China,” Journal of Development Economics, 2007, 83, 392–410.
Borjas, George J., “The Labor Demand Curve Is Downward Sloping: Reexamin-
ing The Impact Of Immigration On The Labor Market,” The Quarterly Journal
of Economics, November 2003, 118 (4), 1335–1374.
Bosker, Maarten, Steven Brakman, Harry Garretsen, and Marc
Schramm, “Relaxing Hukou: Increased labor mobility and China’s economic
geography,” Journal of Urban Economics, 2012, 72, 252–66.
Boustan, Leah Platt, Price V. Fishback, and Shawn Kantor, “The Effect
of Internal Migration on Local Labor Markets:American Cities during the Great
Depression,” Journal of Labor Economics, October 2010, 28 (4), 719–746.
Brandt, Loren, Johannes Van Biesebroeck, and Yifan Zhang, “Challenges
of working with the Chinese NBS firm-level data,” China Economic Review, 2014,
30 (C), 339–352.
Bustos, Paula, Bruno Caprettini, and Jacopo Ponticelli, “Agricultural pro-
ductivity and structural transformation: evidence from Brazil.,” 2015. Forthcom-
ing American Economic Review.
Chan, Kam Wing, “Misconceptions and Complexities in the Study of China’s
Cities: Definitions, Statistics, and Implications,” Eurasian Geography and Eco-
nomics, 2007, 48 (4), 383–412.
, “Migrant and development in China: trends, geography and current issues,”
Migration and Development, 2012, 1 (2), 187–205.
24
Combes, Pierre-Philippe, Sylvie Demurger, and Shi Li, “Migration exter-
nalities in Chinese cities,” European Economic Review, 2015, 76 (C), 152–167.
Dustmann, Christian and Albrecht Glitz, “How Do Industries and Firms Re-
spond to Changes in Local Labor Supply?,” Journal of Labor Economics, 2015,
33 (3), 711 – 750.
El Badaoui, Eliane, Eric Strobl, and Frank Walsh, “The Impact of Internal
Migration on Local Labour Markets in Thailand,” EconomiX Working Papers
2014-12, University of Paris West - Nanterre la Defense, EconomiX 2014.
Evans, David S, “Tests of alternative theories of firm growth,” The Journal of
Political Economy, 1987, pp. 657–674.
Facchini, Giovanni, Maggie Y. Liu, Anna Maria Mayda, and Minghai
Zhou, “The impact of China’s WTO accession on internal migration,” December
2015.
Fan, Cindy C., China on the Move, Routledge, 2008.
Feng, Shuaizhang, Michael Oppenheimer, and Wolfram Schlenker,
“Weather Anomalies, Crop Yields, and Migration in the US Corn Belt,” March
2015.
, Yingyao Hu, and Robert Moffit, “Long Run Trends in Unemployment and
Labor Force Participation in China,” August 2015.
Ge, Suqin and Dennis Tao Yang, “Changes In China’s Wage Structure,” Journal
of the European Economic Association, 04 2014, 12 (2), 300–336.
Giesing, Yvonne and Nadzeya Laurentsyeva, “Brain Drain and Firm Pro-
ductivity: Evidence from the Sequential Opening of EU Labour Markets,” 2015.
Unpublished.
Gollin, Douglas, Stephen Parente, and Richard Rogerson, “The Role of
Agriculture in Development,” American Economic Review, May 2002, 92 (2),
160–164.
Harris, John R. and Michael P. Todaro, “Migration, Unemployment and De-
velopment: A Two-Sector Analysis,” The American Economic Review, 1970, 60
(1), pp. 126–142.
25
Herrendorf, Berthold, Christopher Herrington, and Akos Valentinyi,
“Sectoral Technology and Structural Transformation,” Technical Report 9386,
C.E.P.R. Discussion Papers March 2013.
Imbert, Clement and John Papp, “Short-term Migration and Rural Workfare
Programs: Evidence from India,” 2014. Manuscript.
Kerr, Sari Pekkala, William R. Kerr, and William F. Lincoln, “Skilled
Immigration and the Employment Structures of US Firms,” Journal of Labor
Economics, 2015, 33 (S1), S147 – S186.
Kleemans, Marieke and Jeremy Magruder, “Labor Market Changes in Re-
sponse to Immigration: Evidence from Internal Migration Driven by Weather
Shocks in Indonesia,” 2014. Manuscript.
Kuznets, Simon, Agriculture in Economic Development, McGraw-Hill Book Com-
pany, 1964.
Lewis, Arthur, “Economic Development with Unlimited Supplies of Labour,” The
Manchester School, 1954, 22 (2), 139–191.
Marden, Sam, “The agricultural roots of industrial development: “forward link-
ages” in reform era China,” 2015. Manuscript.
Mayneris, Florian, Sandra Poncet, and Tao Zhang, “The cleansing effect of
minimum wages. Minimum wages, firm dynamics and aggregate productivity in
China,” Core Discussion Papers 2014/44 October 2014.
Meng, Xin and Dandan Zhang, “Labour Market Impact of Large Scale Inter-
nal Migration on Chinese Urban ’Native’ Workers,” IZA Discussion Papers 5288
October 2010.
Park, Albert, China Urbanizes: Consequences, Strategies, and Policies, The World
Bank, 2008.
Peri, Giovanni, “The Effect Of Immigration On Productivity: Evidence From U.S.
States,” The Review of Economics and Statistics, February 2012, 94 (1), 348–358.
Song, Zheng, Kjetil Storesletten, and Fabrizio Zilibotti, “Growing Like
China,” American Economic Review, 2011, 101 (1), 196–233.
Sousa, Jose De and Sandra Poncet, “How are wages set in Beijing?,” Regional
Science and Urban Economics, 2011, 41 (1), 9–19.
26
Tombe, Trevor and Xiaodong Zhu, “Trade, Migration and Productivity: A
Quantitative Analysis of China,” June 2015.
United Nations, “Trends in International Migrant Stock: The 2015 revision.,”
Technical Report, United Nations, Department of Economics and Social Affairs
2015.
Vendryes, Thomas, “Migration constraints and development: Hukou and capital
accumulation in China,” China Economic Review, 2011, 722, 669–92.
27
A Figures and tables
Figure 1. Value of agricultural portfolio at origin and outmigration rates.
Notes: This Figure illustrates the relationship between the standardized value of the prefecture-specific agriculturalportfolio as predicted by international prices (x-axis) and outmigration (y-axis). We consider the residuals of allmeasures once cleaned by prefecture and year Fixed-Effects. For the sake of exposure, we group prefecture×yearobservations, create 100 bins of observations with similar price shock and represent the average outmigration ratewithin a bin. The lines are locally weighted regressions on all observations.
28
Figure 2. Rainfall deficits relative to water requirements at origin and outmigration rates.
Notes: This Figure illustrates the relationship between the standardized rainfall deficit relative to water requirementsfor the origin-specific agricultural portfolio (x-axis) and outmigration (y-axis). We consider the residuals of allmeasures once cleaned by prefecture and year Fixed-Effects. For the sake of exposure, we group prefecture×yearobservations, create 100 bins of observations with similar rainfall shock and represent the average outmigration ratewithin a bin. The lines are locally weighted regressions on all observations.
Figure 3. Potential output in China for rice and cotton (1990).
(a) Paddy rice. (b) Cotton.
Notes: These two maps represent the potential output constructed with 1990 harvested areas and potential yield(GAEZ model) in 1990 for 2 common crops in China, i.e. paddy rice (left panel), and cotton (right panel).
29
Figure 4. Evolution of migration rates between 1999 and 2005.
Sources: 2005 Mini-Census.
30
Table
1.
Mig
rati
on
flow
san
dpri
ce/ra
infa
llsh
ock
s(2
000-2
005).
Spe
cifi
cati
on
(3)
Mig
rati
onou
tflow
s(1
)(2
)(3
)(4
)(5
)(6
)
Pri
ceL
agsp[t−2,t−1]
-0.0
249*
**-0
.0196***
-0.0
213***
-0.0
143**
(0.0
036)
(0.0
059)
(0.0
037)
(0.0
060)
Pri
ceF
orw
ard
sp[t+1,t+2]
0.0
0889
0.0
130*
(0.0
079)
(0.0
074)
Rai
nfa
llL
agsr [t−
2,t−1]
0.0
617***
0.0
623***
0.0
518***
0.0
552***
(0.0
069)
(0.0
068)
(0.0
067)
(0.0
064)
Rai
nfa
llF
orw
ard
sr [t+
1,t+2]
-0.0
0629
-0.0
177**
(0.0
086)
(0.0
088)
Ob
serv
atio
ns
2,02
22,0
22
2,0
22
2,0
22
2,0
22
2,0
22
R-s
qu
ared
0.80
70.8
08
0.8
07
0.8
07
0.8
11
0.8
12
Ori
gin
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yea
rF
EY
esY
esY
esY
esY
esY
es
Rob
ust
stan
dar
der
rors
are
rep
orte
db
etw
een
par
enth
eses
.T
he
un
itof
ob
serv
ati
on
isan
ori
gin×
aye
ar
an
dth
ere
gre
ssio
nis
wei
ghte
dby
ori
gin
rura
lp
opu
lati
onin
1990
.M
igra
tion
outfl
ows
are
year
lyou
tflow
sn
orm
ali
zed
by
the
pre
fect
ure
’sru
ral
pop
ula
tion
in2005.
Pri
ce(r
esp
.R
ain
fall
)L
ags
are
defi
ned
asth
eav
erag
enor
mal
ized
pri
ced
evia
tion
s(r
esp
.ra
infa
lld
efici
ts)
inp
erio
dt−
1an
dt−
2.
Pri
ce(r
esp
.R
ain
fall
)F
orw
ard
sare
defi
ned
as
the
aver
age
nor
mal
ized
pri
ced
evia
tion
s(r
esp
.ra
infa
lld
efici
ts)
inp
erio
dt
+1
an
dt
+2.
See
sect
ion
2fo
ra
com
ple
ted
escr
ipti
on
of
the
pri
cean
dra
infa
lldefi
cit
con
stru
ctio
n.
31
Table 2. Distance and migration flows between origins and destinations (2000-2005).
Specification (4)
Migration flows (share) (1) (2) (3)
Distance do,d (1,000 km) -0.0116*** -0.0449***(0.000539) (0.00286)
Squared Distance d2o,d 1.04e-08***
(8.50e-10)Inverse Distance 1/do,d 9.424***
(0.757)Destination population (1,000), 1990 Popd,1990 0.943*** 0.956*** 0.949***
Robust standard errors are reported between parentheses. The unit of observation is an origin×adestination×a year. Migration flows (share) are the number of migrants going from origin o todestination d normalized by the total number of migrants from origin o. For the sake of exposition,we normalize distance do,d and destination population Popd,1990 by 1, 000.
Table 3. Comparison of actual and predicted immigration rate in urban areas (2000-2005).(1) (2) (3) (4)
Observations 2,028 2,028 2,028 2,028R-squared 0.812 0.875 0.813 0.879Year FE No Yes No YesDestination FE Yes Yes Yes Yes
Standard errors are clustered at the destination level and are reported between parentheses. ***p<0.01, ** p<0.05, * p<0.1. An observation is a destination×year. The immigration rate is thenumber of agricultural hukou holders from all origin prefectures who went to a destination prefec-ture d in a given year divided by population at destination. The independent variable correspondto Md,t as defined in equation 5. Regressions are weighted by total urban adult population atdestination.
32
Table 4. Effect of migration flows on wages earned by urban residents and unemployment proba-bility.
OLS 2SLS: rainfall 2SLS: priceEffect of migration inflows on ... (1) (2) (3)Real monthly wages -0.090*** -0.224* -0.170*
Standard errors are clustered at the prefecture/year level. The unit of observation is an individual.In the first two panels, the dependent variable is the log of wages deflated using a consumer priceindex computed by the authors using the UHS data. In the next six panels, the dependent variablesare dummies that take the value one if the individual works for wage, is unemployed or is self-employed. See section 3 for a complete description of the price- and rainfall -related migrationflows. All specifications include characteristics of the resident population (proportions by maritalstatus, gender, age group, education level, rural registration, and firm ownership for the wagespecifications) and log adult population, as well as year and prefecture fixed effects. The firststages are reported in Table A6.
33
Table 5. Effect of migration flows on wages and profitability using firm data.
OLS 2SLS: rainfall 2SLS: priceEffect of migration inflows on ... (1) (2) (3)Wages -0.168*** -1.221* -0.614**
Standard errors are clustered at the prefecture/year level. The unit of observation is a firm ×a year. In the top two panels, the dependent variable is the log of total wage bill divided bythe number of employees. In the bottom two panels, the dependent variable is the log of profitsdivided by revenues. See section 3 for a complete description of the price- and rainfall -relatedmigration flows. The first stages are reported in Table A6. Low skill indicates firms in sectorsemploying mostly low-skill workers (i.e. food manufacturing, beverage manufacturing, footwear,wood processing, and textile).
34
A Additional tables and figures
Figure A1. Share of return migrants by age.
Sources: 2005 Mini-Census.
Figure A2. Share of step migrants as a function of age and time since departure.
Sources: 2005 Mini-Census.
35
Figure A3. Price deviations from trends on International Commodity Markets 1998-2010 (blue:banana, red: rice, teal: groundnut).
Note: These series represent the Hodrick Prescott residual applied to the logarithm of internationalcommodity prices for three commodities: banana, rice and groundnut. For instance, the price ofrice can be interpreted as being 35% below its long-term value in 2001.
Figure A4. Price and rainfall shocks across Chinese prefectures in 1999/2000.
(a) Price shock. (b) Rainfall shock.
Notes: These two maps represent the standardized price shock po,t in 1999/2000 (left panel), and standardizedrainfall shock ro,t in 1999/2000 (right panel). Note that 1999/2000 corresponds to a pre-crisis period: in 2001, theprice of rice decreases which generates a very negative shock across China concentrated in rice-producing prefectures.
36
Figure A5. Origin-destination migration predictions—the role of distance.
Notes: Migration flows constructed with census data (2000-2005).
Figure A6. Measure Md,t of immigrant inflows to cities as predicted by prices in 2001 and 2004.
(a) 2001 (b) 2004
Notes: These two maps represent the quantities Md,2001 and Md,2004, where Md,t is the measure of immigrantinflows as predicted by price variations and the weighting distance matrix between origins and destinations.
37
Figure A7. Measure Md,t of immigrant inflows to cities as predicted by rainfall in 2001 and 2004.
(a) 2001 (b) 2004
Notes: These two maps represent the quantities Md,2001 and Md,2004, where Md,t is the measure of immigrantinflows as predicted by rainfall variations and the weighting distance matrix between origins and destinations.
Figure A8. Evolution of the share of private firms in the industrial sector.
Sources: 1998-2007 NBS above-scale firm data.
38
Table A1. Descriptive statistics from the UHS data (2002-2008).
Mean St. Dev.
Age 43.17 11.00Female 0.50 0.50Married 0.88 0.33Born in prefecture of residence 0.61 0.49
All variables except Age and Income are dummy-coded. The table displays averages over the period 2002-2008. Thesample is restricted to locally registered urban hukou holders aged 15-64.
39
Table A2. Descriptive statistics from the 2005 mini-census (1/2).
Count Share of total Std. Dev.resident urban
population
Rural migrants from another province 94,326 0.15 0.36Rural migrants from another prefecture 122,756 0.19 0.40
Count Percent CumulativePercent
Reason for moving
Work or business 100,670 82.01 82.01Follow relatives 6,474 5.27 87.28Marriage 5,783 4.71 91.99Support from relatives/friends 4,461 3.63 95.62Education and training 1,367 1.11 96.73Expropriation and relocation 603 0.49 97.22Job transfer 522 0.43 97.65Mission 498 0.41 98.06Recruitment 158 0.13 98.19Deposit household registration demand 142 0.12 98.31Other 1,956 1.59 99.90Missing 122 0.10 100.00
Count Percent CumulativePercent
Starting year of last migration spell
2005 25,968 21.18 21.182004 24,917 20.32 41.502003 17,893 14.59 56.092002 11,110 9.06 65.152001 7,468 6.09 71.242000 7,325 5.97 77.211999 or before 27,954 22.79 100.00“Rural migrants” are defined as inter-prefectural migrants with an agricultural hukou aged 15-64. “Total residenturban population” refers to the population in the prefecture that is either locally registered and holds a non-agricultural hukou or resides in the prefecture but holds an agricultural hukou from another prefecture. The samplein the middle and bottom panels is restricted to inter-prefectural rural migrants.
40
Table A3. Descriptive statistics from the 2005 mini-census (2/2).
Rural-urban Local urban Difference p-valuemigrants hukou
All variables except Age and Income are dummy-coded. Only the income of individuals who reported having a jobis considered. The sample is restricted to individuals aged 15-64. * p<0.01
41
Table A4. Descriptive statistics from the NBS firm-level data.Public sector Domestic Foreign
private sector private sector
Real capital stock 37539.69 20346.01 47592.38Sales revenue 63149.08 71267.68 167520.80Value added 18470.79 17106.11 40216.00Total wage bill 3695.91 2938.08 6613.63Total number of employees 340.20 216.93 318.76
All variables except “Total number of employees” are in RMB 1,000. The table displays yearly averages over theperiod 1998-2007.
Table A5. Correlation between crop international prices and local Chinese prices/production.
Robust standard errors are reported between parentheses. The unit of observation is a crop×ayear. The two regressions include time trends, and weighted by the average crop production shareover the period 1991-2010. Dependent and the main explaining variables are in logs.
Table A6. Comparison of actual and predicted immigration rate in urban areas (robustness checkwithout intra-province migration spells, 2000-2005).
Observations 2,028 2,028 2,028 2,028R-squared 0.807 0.861 0.807 0.863Year FE No Yes No YesDestination FE Yes Yes Yes Yes
Standard errors are clustered at the destination level and are reported between parentheses. ***p<0.01, ** p<0.05, * p<0.1. An observation is a destination×year. The immigration rate is thenumber of agricultural hukou holders from all origin prefectures who went to a destination prefec-ture d in a given year divided by population at destination. The independent variable correspondto Md,t as defined in equation 5. Regressions are weighted by total urban adult population atdestination.
42
Table A7. Effect of migration flows on wages and profitability using firm data – robustness check:industries linked with agriculture.
OLS 2SLS: rainfall 2SLS: priceEffect of migration inflows on ... (1) (2) (3)Wages -0.162*** -1.348* -0.663**
Standard errors are reported between parentheses and clustered at the prefecture×year level. Theunit of observation is a firm in a given year. In the top panel, the dependent variable is the log oftotal wage bill divided by the number of employees. In the bottom panel, the dependent variableis the log of profits divided by revenues. See section 3 for a complete description of the price- andrainfall -related migration flows.
Table A8. Effect of migration flows on wages and profitability using firm data – robustness check:controlling for shocks in the prefecture of destination.
OLS 2SLS: rainfall 2SLS: priceEffect of migration inflows on ... (1) (2) (3)Wages -0.139*** -1.264 -0.473
Standard errors are reported between parentheses and clustered at the prefecture×year level. Theunit of observation is a firm in a given year. In the top panel, the dependent variable is the log oftotal wage bill divided by the number of employees. In the bottom panel, the dependent variableis the log of profits divided by revenues. See section 3 for a complete description of the price- andrainfall -related migration flows.
43
Table A9. Effect of migration flows on wages and profitability using firm data – robustness check:lagged shocks.
OLS 2SLS: rainfall 2SLS: priceEffect of lagged migration inflows on ... (1) (2) (3)Wages -0.111*** 0.335 -0.629***
Standard errors are reported between parentheses and clustered at the prefecture×year level. Theunit of observation is a firm in a given year. In the top panel, the dependent variable is the log oftotal wage bill divided by the number of employees. In the bottom panel, the dependent variableis the log of profits divided by revenues. See section 3 for a complete description of the price- andrainfall -related migration flows.
44
B Data description
B.1 Migration flows and census
In this section, we provide some descriptive statistics about migrants and migration
flows.
Patterns of migration in the mini-census Table A2 displays the shares of
rural-to-urban migrants in the total urban population of prefectures. We define
rural-to-urban migrants as agricultural hukou holders who crossed a prefecture
boundary and belong to working-age cohorts (15-64).41
The upper panel of Table A2 distinguishes between inter-prefectural migrants
and those who left their provinces of origin. We see that inter-prefectural migrants
represented 19% of a prefecture’s total number of urban residents on average in 2005,
while inter-provincial migrants accounted for 15% of it, which reveals that a majority
(77%) of inter-prefectural migrations imply the crossing of a provincial boundary.
The middle panel presents the reasons put forward by inter-prefectural agricultural
hukou migrants for leaving their places of registration. A vast majority (82%) moved
away in order to seek work (“Work or business”), mostly as labourers, while all other
rationales attracted much smaller shares.42 When we look at the last migration
spell for these migrants (lower panel), we see that most inter-prefectural migrants
(56.46%) arrived in the three years before the survey, illustrating the acceleration of
migration in the early 2000s and potentially the selection bias generated by return
migration.43 We now investigate the extent to which return migration and step
migration affect our description of migration flows.
Return and step migration in the mini-census In this paper, we construct
annual migration flows between each prefecture of origin and destination by com-
bining information on the current place of residence (the destination), the place of
41Although data are not available, it is clear from the literature that rural-to-rural migration,represents a small share of outmigration from rural areas, not least because most of it is explainedby marriages, which usually give right to local registration (Fan, 2008; Chan, 2012). Only 4.7%of agricultural hukou inter-prefectural migrants in the 2005 mini-census reported having left theirplace of registration to live with their spouses after marriage.
42The only other reasons that display shares in excess of 1% are “Education and training,”“Other,” “Live with/Seek refuge from relatives or friends,” which Fan (2008) based on metadatafrom the Population Census Office dubs “Migration to seek the support of relatives or friends,”“Following relatives,” which should be understood as “Family members following the job transferof cadres and workers” (ibid.), and “Marriage”.
43Data on return migration are scarce. Chan (2012) highlights a “noticeable, though still small,but increasing amount of outmigration” from provinces that have been migration magnets sincethe early 2000s.
45
registration (the origin) and the year in which the migrant left her place of regis-
tration. We implicitly assume that all migrants who left the origin in year Y have
reached the destination that same year and stayed there. As discussed in section 2,
we may underestimate migration flows in year Y if some of the migrants who left in
year Y have gone back to their place of origin before the census (return migration).
We may also be mistakenly assigning the arrival of a migrant to year Y if instead
of directly going to destination she stopped on the way and only arrived some years
later (step migration).
In order to measure return and step migration, we use the information from the
2005 census about the province of residence in 2004 and 2000. Unfortunately, the
census does not report the prefecture of residence in 2004 and 2000. However, as
shown in Figure 4, a majority of rural to urban migrants go beyond province borders.
We first consider the extent of return migration. Among all migrants from rural
areas who lived in their province of registration in 2000 and who lived in another
province in 2004, we compute the fraction that had returned to their province of
registration by 2005. As Figure A1 shows, this share is not negligible: in a given year,
between 4 and 6% of rural migrants who have left their province of registration in the
last six years go back a year later. This fraction is higher for older migrants. Return
migration is hence an important phenomenon, which leads us to underestimate true
migration flows, and the effect of shocks on out-migration.
We next study the importance of step-migration. Among all migrants who lived
in their province of registration in 2000 and are living in another province in 2005,
we compute the fraction that lived in yet another province in 2004. As Figure A2
shows, only a minority of migrants have changed provinces of destination in the last
year. Step-migration is concentrated in the first year of migration and virtually zero
thereafter. One limitation of this approach is that we cannot measure step-migration
if it occurs within a province. With this caveat in mind, these results do suggest
that for most migrants we correctly assign the year of arrival at destination.
Comparison of urban dwellers by hukou status The UHS data are represen-
tative of urban “natives,” not of the urban population as a whole, and urban workers
differ significantly depending on their hukou status. As is usual with internal migra-
tion, we consider in the main specifications that migrants and “natives” are highly
substitutable. However, Chinese rural-to-urban migrants tend to be younger (and
thus less experienced) and less educated, which reduces their ability to compete with
urbanites for the same jobs.
Table A3 provides summary statistics on key characteristics of inter-prefectural
46
migrants and compares them with the locally registered urban population. It appears
that migrants and natives are statistically significantly different on most accounts,
the former being on average younger, less educated, more likely to be illiterate,
and more often single, and employed without a labour contract. Important facts
for the analysis that follows are that rural-to-urban migrants are overrepresented
in privately owned enterprises and in manufacturing and construction industries:
91% of them are employed in the private sector as against 42% of locally registered
non-agricultural hukou holders; and the share of rural-to-urban migrants working
in manufacturing and construction is 51% and 9%, as against 20% and 4% for
urban natives, respectively. Migrants also stand out as earning significantly less.
The simple t test reported in Table A3 shows that migrants’ monthly income is 17%
lower than urban natives’; the difference increases to about 40% when one takes into
account the fact that migrants are attracted to prefectures where they can expect
higher wages.44 As expected, notable differences from urban natives in the 2005
mini-census data can be spotted. This should be kept in mind when extrapolating
results based on the UHS to the rest of China.
B.2 NBS data
We discuss here some issues with NBS data and how we tackle them, and provide
some descriptive statistics.
B.2.1 Issues with the firm panel There are a number of issues with using the
NBS data to study the effect of migration on firm growth. We now discuss these
issues and explain how we take them into account while constructing our variables
of interest.
First, firms may have an incentive to under-report the number of workers as it
serves as the basis for taxation by the local labour department. This should be a par-
ticular concern with migrants, who represent a large share of the workforce and may
be easier to under-report. Along the same lines, workers hired through a “labour
dispatching” (laodong paiqian) company are not included in the employment vari-
able.45 This implies that migrant workers are likely to be severely under-counted in
the firm data. We will estimate the impact of migration inflows on firm performance
without being able to observe the firm-specific increase in employment.46
44Results available upon request.45In manufacturing SOEs, there was also a practice of reclassifying and gradually excluding laid-
off workers—euphemistically, on “furlough” (xiagang)—from their accounts. Although much ofthis process had been completed by the start of our study period, it may still induce some declinein employment in the first couple of waves.
46Wage bill may also be slightly under-estimated as some components of worker compensation
47
Second, some variables are not documented the same way as in standard firm-
level datasets. In particular, fixed assets are reported in each data wave by summing
nominal values for different years. We use the procedure developed in Brandt et al.
(2014) using (i) the change in nominal capital stock as a proxy for nominal fixed
investment, (ii) a fixed depreciation rate at 9% and (iii) the investment deflator
developed by Loren Brandt and Thomas Rawski. Following Brandt et al. (2014),
if the firm’s past investments and depreciation are not available in the data, we
use information on the age of the firm and estimates of the average growth rate of
nominal capital stock at the 2–digit industry level between 1993 and the firm’s year
of entry in the database.
Descriptive statistics from the firm panel Table A4 displays key descrip-
tive statistics across public, domestic private and foreign private firm ownership
over the period 1998-2007.47 Public enterprises, a broad category that encompasses
state-owned and collective enterprises, have a larger capital stock, spend more on
their wage bills and have more employees than domestic private firms. Conversely,
the latter report significantly higher sales revenues and perform better in terms of
value added. Table A4 yields a very different image of state-owned and collective
enterprises when compared to the foreign private sector: Real capital stock, sales
revenues, value added and the total wage bill are all higher in foreign-owned firms;
only the total number of employees is higher in the public sector.
Figure A8 shows the evolution of the share of private firms in the NBS sample
along the same characteristics. Private firms still accounted for a relatively small
share of total real capital stock, value added, sales revenues, wage bill and employ-
ment in 1998 but represented over 80% of the total under all five indicators by 2007.
The evolution in terms of employment is particularly striking: Whereas only 32% of
total employment could be attributed to private firms in the NBS sample in 1998,
they accounted for 89% of it in 2007.
B.2.2 Issues with non-stationary variables In order to estimate the effect of
migration on firm growth, we use a strategy which accounts for the non-stationarity
of firm size and thus most variables characterizing firm output or factor use. In
are not recorded in all years, e.g. pension contributions and housing subsidies, which are reportedonly since 2003 and 2004, respectively but accounted for only 3.5% of total worker compensationin 2007.
47Ownership type is defined based on official registration (qiye dengji zhuce leixing). Out of 23exhaustive categories, Table A4 uses three categories: (i) state-owned, hybrid or collective, (ii)domestic private, and (iii) foreign private firms, including those from Hong Kong, Macau, andTaiwan.
48
order to illustrate our approach, consider a certain firm j located in city d and using
a bundle of input Hj,dt in order to produce a numeraire good. Let W d
t denote the
unit cost of input, and Aj,dt the firm-specific productivity.
The firm maximization problem is:
maxHj,d
t
{Aj,dt (Hj,d
t )α −W dt H
j,dt
},
which generates the following input demand schedule (in which lower case letters
are the logarithm of variables):
hj,dt =ln(α)
1− α+
aj,dt1− α︸ ︷︷ ︸
firm-specific growth process
− wdt1− α︸ ︷︷ ︸
factor shock
.
As a consequence, the stationarity of firm demand (and the subsequent firm out-
comes) depends on the stationarity of the firm-specific technological process (Evans,
1987). For instance, under Gibrat’s law, firm i would grow at a certain given growth
rate νi and aj,dt+1 = aj,dt + νi + εj,dt+1 where εj,dt+1 is the innovation. In such case, it
is important to take the difference in the previous equation in order to have the
firm-specific growth component as a “fixed effect”:
∆t,t−1hj,dt =
νi1− α
+∆t,t−1w
dt
1− α+ εj,dt .
We base our empirical strategy on this assumption of constant firm-specific growth
rate, and consider first-differences so as to keep stationary variables on both sides.
We then estimate the impact of migration on firm growth for firm j in destination
d at time t by regressing each firm outcome, which we denote yj,d,t, on predicted
migration, and time and firm fixed effects.{∆t,t−1Md,t = b0 + bm∆t,t−1Md,t + bzZi + ed + nt + ed,t
