Prefabrication policies and the performance of construction industry in China

Journal Pre-proof Prefabrication Policies and the Performance of Construction Industry in China

Yue Gao, Xian-Liang Tian PII:

S0959-6526(20)30089-5

DOI:

https://doi.org/10.1016/j.jclepro.2020.120042

Reference:

JCLP 120042

To appear in:

Journal of Cleaner Production

Received Date:

02 July 2019

Accepted Date:

07 January 2020

Please cite this article as: Yue Gao, Xian-Liang Tian, Prefabrication Policies and the Performance of Construction Industry in China, Journal of Cleaner Production (2020), https://doi.org/10.1016/j. jclepro.2020.120042

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Journal Pre-proof

Prefabrication Policies and the Performance of Construction Industry in China Yue Gao Center for Industrial and Business Organization, Dongbei University of Finance and Economics, China Email: [email protected] Xian-Liang Tian1 Wenlan School of Business, Zhongnan University of Economics and Law, China Email: [email protected]

Abstract Environmental concern is one of the driving forces behind residential industrialization policies in developing countries. Using manually collected data on prefabrication policies in Chinese provinces and the province-level data from China Construction Industry Yearbooks, this paper investigates the effects of China’s prefabrication policies on the performance of the construction industry in Chinese provinces. The difference-in-differences (DID) and the synthetic control methods are used in the analysis. The effects of two kinds of prefabrication policies (directive vs. supportive) in China are investigated. And the results show that supportive prefabrication policies significantly increase the labor productivity of construction firms and decrease the usage of construction materials whereas directive policies without substantive supporting measures do not have the above effects. The results imply that only the government policies with substantial incentives/supporting measures can encourage the widespread use of the modern prefabricated methods of construction that not only increase the labor productivity but also save construction materials.

Keywords: Prefabrication; labor productivity; construction industry; material; China JEL codes: L52; H79; R38; Q58; L74

1

Corresponding author: Xian-Liang Tian, Wenlan School of Business, Zhongnan University of Economics and Law, No. 182, Nanhu Avenue, East-lake Hi-tech Zone, Wuhan, Hubei Province, P. R. China 430073. Email: [email protected] 1

Journal Pre-proof

Prefabrication Policies and the Performance of Construction Industry in China

Abstract Environmental concern is one of the driving forces behind residential industrialization policies in developing countries. Using manually collected data on prefabrication policies in Chinese provinces and the province-level data from China Construction Industry Yearbooks, this paper investigates the effects of China’s prefabrication policies on the performance of the construction industry in Chinese provinces. The difference-in-differences (DID) and the synthetic control methods are used in the analysis. The effects of two kinds of prefabrication policies (directive vs. supportive) in China are investigated. And the results show that supportive prefabrication policies significantly increase the labor productivity of construction firms and decrease the usage of construction materials whereas directive policies without substantive supporting measures do not have the above effects. The results imply that only the government policies with substantial incentives/supporting measures can encourage the widespread use of the modern prefabricated methods of construction that not only increase the labor productivity but also save construction materials.

Keywords: Prefabrication; labor productivity; construction industry; material; China JEL codes: L52; H79; R38; Q58; L74

1

Journal Pre-proof 1. Introduction Traditional methods of construction have recently been challenged by environmental concerns (Zhang et al., 2016), increased production costs (Barlow et al., 2003) as well as decreased supply of construction labor (Li et al., 2014). An alternative modern way of construction is prefabricated construction.1 Despite the potential benefits of the prefabricated methods of construction (Hashemi, 2015), the adoption of the prefabricated construction is rather slow around the world, except in a few countries such as Japan and Sweden (Barlow et al., 2003; Pan and Goodier, 2012; Johnson, 2007). The main reasons for the lagged adoption of prefabrication in the construction industry include insufficient R&D expenditures and the lack of necessary government regulatory efforts to promote prefabricated construction (Stainhardt et al., 2013).2 During the last four decades, China has achieved astonishing economic growth through market-oriented economic reforms. At the same time, it also suffers from worsening environmental problems, a large part of which is caused by construction activities (Wang et al., 2010). To address the construction-induced pollution, China’s governments have recently emphasized the modern prefabricated construction in the long-run national development plans as well as short-run industrial policies in the construction sector. On the one hand, China’s central government has put forward national policies to encourage prefabricated construction since as early as the year 1999. However, these polices turned out almost ineffective before they were

1

In the literature, such terms as pre-assembly, prefabricated construction, factory-based construction, manufactured construction, and off-site construction are often used interchangeably (Arif and Egbu, 2010). 2 Government regulation has been shown effective in accelerating the development of the construction industry across countries (Bjorvatn and Coniglio, 2012). 2

Journal Pre-proof complemented by follow-up industrial policies initiated by provincial governments. On the other hand, more recently, Chinese local governments have begun to put forward industrial policies about prefabrication construction since 2009. Some policies are supportive and with detailed enforcement measures while the others are directive and general guidelines that only literally encourage the use of prefabricated construction without specific incentive schemes and enforcement measures. For example, Beijing’s supportive prefabrication policies specify that the construction firms that use prefabricated method of construction are allowed a maximum floor-area ratio 3% higher than the ratio for the firms using conventional construction methods. The profit margin of the extra 3% floor-area ratio offers great incentives for Beijing’s construction firms to adopt prefabricated construction (Beijing Housing and Urban-rural Construction Commission, 2010). Most of other provinces, however, only come up with directive documents. For example, Ningxia government in China first introduced the concept of prefabrication in its official documents in 2009 where prefabrication was considered as a strategy of industrializing its housing sector (Sui, 2009). Although Ningxia government literally encouraged the use of prefabricated method of construction, there are no specific measures in the documents to promote prefabrication. Then natural questions arise: Are China’s local prefabrication policies effective in promoting prefabricated construction? If so, is there any difference between the effects of supportive vs. directive prefabricated policies? Answering these questions not only provides valuable guidance for Chinese governments to better design 3

Journal Pre-proof effective prefabrication policies, but also contributes to the literature on the evaluation of industrial policies. Although many countries in the world have adopted various industrial policies for increased productivity or other purposes, rigorous empirical investigation on the causal effects of the policies is still inadequate (Criscuolo et al., 2019), which is especially the case for empirical study of industrial policies in the construction and housing sector. This paper addresses these questions by empirically testing the effects of China’s prefabrication policies at the province level on the labor productivity and construction material usage in the construction industry within Chinese provinces. The authors manually collect policy documents on prefabricated construction from official websites of Chinese provinces to construct dummy (0-1) variables of prefabrication policies. Province-level data drawn from China Construction Industry Yearbooks (China National Bureau of Statistics, 2001-2018) and China Statistical Yearbooks (China National Bureau of Statistics, 2001-2018) are also used in the analysis. Then, the authors examine the effects of the (supportive and directive) prefabrication policies in China using a difference-in-differences (DID) framework. As a robustness check, they also investigate the effects of the supportive prefabrication policies in Beijing and Shanghai on the labor productivity of the construction industry in the two cities, using the synthetic control method. The rest of this paper is organized as follows: Section 2 is the literature review. Section 3 presents the background of China’s prefabrication policies in its construction industry and proposes empirical hypotheses based on the existing 4

Journal Pre-proof literature. Section 4 is about empirical specification and the data used in the analysis. Section 5 presents main results of the empirical analysis while Section 6 offers a robustness check using the synthetic control method. Then, Section 7 is discussion and Section 8 concludes.

2. Literature review 2.1 Prefabricated method of construction This study directly relates to the research on prefabricated method of construction. Arif and Egbu (2010) suggest that prefabricated construction is a desirable and cost-efficient way of construction that can meet the increasing demand for quality housing in China’s residential market. Chiang et al. (2006) investigate whether or not prefabrication has increased entry barriers in the residential market and find that the requirement of prefabrication is relatively low. Thus, prefabricated construction will not constitute a big barrier of entry into the housing sector. In addition, Lovell and Smith (2010) address the case of being locked in traditional methods of construction in the UK’s residential market. However, their results cannot be readily extended to other countries due to the lack of external validity. More research on prefabricated construction can be found in Jin et al. (2018) that provides an in-depth review of the literature on the off-site/prefabricated construction published during 2008-2018.

2.2 The performance of construction industry This study is also related to the literature that evaluates the performance of 5

Journal Pre-proof construction industry as well as the change of the performance upon policy shocks. For example, Fleming (1965) summarizes the methods of constructing cost and price indexes in the construction sector, which can be used to assess the performance of the sector. Horta et al. (2013) evaluate the efficiency levels of construction industries across countries and find that the efficiency level of North American construction companies is higher than that of their European and Asian counterparts. Siegel (1964) examines the stability of coefficients of production function in residential building; and he finds that the stability is time-dependent. Zhang et al. (2016) analyze the market demand in China’s housing market and find that Chinese urban households demand more “green” apartments when they are provided more reliable environmental information of the apartments. In addition, Zhang and Rasiah (2014) investigate the impact of institutional change in China on the performance of state-owned enterprises (SOEs) in the housing market. Their research shows that the SOEs respond to the institutional change by improving their construction and design technology while maintaining their role of providing affordable houses to the public. Chancellor and Abbott (2014) find a role of apprenticeship training in improving the productivity of Australian construction industry. Rosen (1982) examines the impact of state-level property taxes on house prices in Northern California and finds that the property tax reduction is related to the increase in house prices. Using a natural policy experiment of property taxation in two Chinese cities – Chongqing and Shanghai, Bai et al. (2014) investigate the effects of property taxes on housing prices in the two cities. They find that the property-tax policy in Shanghai significantly lowered 6

Journal Pre-proof Shanghai’s average house prices while the policy in Chongqing resulted in increased average house prices.

2.3 Prefabricated construction in China Finally, this research relates to the literature that investigates prefabricated construction in China. A closely related paper is Li et al. (2014) who use a dynamic system approach to assess the impact of prefabricated method of construction on waste reduction in the construction sector of China. However, Li et al. (2014) mainly measure the environmental benefits of the modern prefabricated method of construction while the authors of this paper look at the benefits of increased labor productivity and reduced material usage of prefabricated construction. In addition, some recent studies have qualitatively assessed the application of prefabricated construction in different Chinese provinces/regions. For example, Ren (2019) summarizes the problems with prefabricated construction in Anhui province and discusses possible solutions to the problems. Huang (2019) discusses how to promote prefabricated construction in rural Henan province. Cheng (2019) shows the lagged adoption of prefabricated cement works in Chinese western regions. Also, Zhao (2019) explains the policies on prefabricated construction in Shenzhen city. Shao et al. (2019) construct an evaluation system and evaluate the development of prefabrication in Tianjin. They also analyze the main obstacles of promoting prefabrication in Tianjin and put forward policy suggestions to address the obstacles. Compared to these qualitative studies, the authors of this paper quantitatively analyze the effects of 7

Journal Pre-proof prefabrication policies in China, especially in Beijing and Shanghai.

3. Background and hypotheses 3.1 The background of prefabrication policies in China During the last four decades, China has achieved an impressive economic growth at the expense of rapid environmental deterioration. To a large extent, China’s environmental problems are due to its fast-expanding construction activities, which is partly an outcome of increased ratio of urbanization in China. Studies show that each year, China produces nearly 30% of total municipal solid wastes in the world and more than 40% of total solid wastes in Chinese cities are generated by construction activities (Dong et al., 2001; Wang et al., 2008; Wang et al., 2014). Without proper and timely disposal, the construction-induced wastes lead to worsening environmental degradation in the cities. Prefabrication has widely been regarded as a modern method of construction that greatly reduces the generation of construction wastes (Lu and Yuan, 2013; Zhang et al., 2011; Aye et al., 2012). Further, compared to traditional on-site construction, prefabricated construction has shown advantageous in decreasing noise/dust, operation time/cost, labor/resource demanded (Jaillon and Poon, 2009; Lu et al., 2011; Pan et al., 2007), and increasing productivity, quality as well as health and safety standards in construction industry (Pan et al., 2012). Thus, prefabricated method of construction can improve the performance of construction industry in many aspects, as proved by the prefabrication practice in the housing sectors of the U.K., the U.S., 8

Journal Pre-proof Singapore and Japan (Xu and Zhao, 2010). Also, China’s central government gradually regards prefabricated construction as a key strategy to reduce construction wastes and modernize its construction sector. Although China has started to use prefabricated housing technologies in as early as the 1970s (Wang, 2006)3 and has developed the concept of “industrialized housing” at the end of the 1980s (Chu, 2009), it is not until 1999 that China issued its first national policy regarding prefabricated construction. In 1998, China set up the Ministry of Housing Industrialization Promotion Center and next year (in 1999), the State Council of China published the document, To Improve Residential Housing’s Quality via Industrialization and Modernization. More recently, China has passed a number of national industrial policies that emphasize the role of the prefabricated construction in sustainable development. For example, in the Plan for Green Building (China National Development and Reform Commission and Ministry of Housing, 2013) and the National Plan for New Urbanization (The State Council of China, 2014), industrialized/prefabricated housing is considered among the several key elements that contribute to environment-friendly urbanization in China. In 2015, the State Council of China passed the National Standards for the Assessment of Industrialized Building (The Ministry of Housing and Urban-rural Construction of China, 2015) where prefabricated construction is again regarded as a key strategy to improve China’s urban planning and construction. Despite that the central government of China issued national policy in as early as

3

The prefabricated construction technologies China used in the 1970s are mostly learned from the former Soviet Union (Wang, 2006). 9

Journal Pre-proof 1999 to encourage prefabrication, the application of prefabricated technologies remained nascent in China’s construction sector until the mid-2000s when increasing labor and energy costs as well as environmental concerns call for labor/energy saving and environment-friendly way of construction. Firms’ technical breakthrough and market endeavor since the middle 2000s also make the spread of prefabricated construction possible. For instance, in 2005, the Vanke Corporation in China successfully developed its prefabricated shear wall structure system (Yang et al., 2012). Since 2009, the corporation has undertaken experimental prefabricated construction in some pilot cities in order to accumulate necessary technologies, personnels and experience before large-scale application of prefabrication in China (Qin, 2011). At the same time, the recent development of prefabrication in China comes along with serried local policies/regulations intended to promote prefabricated method of construction. Since the late 2000s, Chinese governments at the province level have consecutively issued official documents encouraging prefabrication. And how these local policies affect the performance of construction industry in China will be investigated in the rest of this paper. To find the documents, the authors manually browse the official website of the bureau/commission of housing and urban-rural construction in each province/autonomous region/centrally-administered municipality of China.4 Specifically, they first pick up the documents that contain one or more keywords 4

as

follows

–

industrialization,

modernization,

prefabrication,

China has 22 provinces, 5 autonomous regions and 4 centrally-administered municipalities. They are at the same administration level. To simplify the sentences, they will be called provinces in the rest of the paper when no confusion will be caused. 10

Journal Pre-proof energy-efficient, environment-friendly, etc. Then they carefully examine the chosen documents and decide whether the documents are mostly about promoting prefabrication or not. It is found that there are two types of prefabrication policies – directive vs. supportive. Directive prefabrication policies are generally titled as “directive

suggestion

in

order

to

promote

prefabrication/industrialization/modernization in the housing sector…” and the contents are general and without specific enforcement measures. In contrast, the supportive policies are titled as “measures for the implementation/enforcement of …”, which contain detailed quantitative incentive schemes (such as land, fiscal and financial incentives) to promote prefabrication. For example, in 2010, Beijing’s supportive prefabrication policy specifies that the construction firms that use prefabricated method of construction are allowed a maximum floor-area ratio 3% higher than the ratio for the firms using conventional construction methods. If any, the supportive prefabrication policy of a province is generally put forward after the directive policy. Table 1 lists the year when the first document on each type of prefabrication policy was issued in each province during the period 2000-2017. The table shows that by 2017, 24 (15) Chinese provinces have issued directive (supportive) prefabrication policies to promote prefabricated construction.

(Table 1 about here)

3.2 Hypotheses 11

Journal Pre-proof Despite of various potential benefits of prefabricated construction, transferring from conventional on-site construction to the modern off-site/prefabricated construction requires huge initial investment in prefabricating factories and other fixed expenditure (Steinhardt et al., 2013). Also, the transfer needs intensive R&D activities and personnel training, which is costly and risky (Qin, 2011). It also takes time. So without government support, most Chinese construction enterprises (especially those with small/medium sizes) cannot (are not willing to) bear the huge costs/risk of transferring to prefabricated construction. However, directive prefabrication policies in China without substantive incentive schemes will not effectively facilitate prefabrication as the firms responding to government regulation will compare the benefits and costs of adopting the policies (Xiao and Gao, 2017). Greenstone and Hanna (2004) also find that the water quality regulation in India (namely, the National River Conservation Plan in India) has not resulted in significant reduction of water pollution because of inadequate government funding and poor implementation of the regulation. On the other hand, supportive prefabrication policies with detailed quantitative incentive measures may effectively encourage the adoption of prefabricated construction in China. In the paper studying India’s environmental regulation, Greenstone and Hanna (2014) find that mandated environmental policies and action plans with detailed implementation measures are effective in reducing pollutions. Further, the role of government intervention in economic development has long been debated. For example, Bjorvatn and Coniglio (2012) argue that some kinds of 12

Journal Pre-proof government intervention can be effective in promoting economic growth especially when economic development is at low levels. If this is true, then it is expected that supportive industrial policies by Chinese local governments intended to promote prefabricated construction would be necessary and effective. The spread of prefabrication will improve the overall performance of construction industry, including increased labor productivity and energy/material saving, as indicated by the literature on the benefits of prefabricated construction. Hong et al. (2016) find that prefabricated construction can achieve energy saving via recycling, waste reduction, and quality control. They conclude that prefabrication is an effective way to improve the productivity of the whole construction industry. Blismas et al. (2006) summarize the benefits of prefabrication, such as duration minimization and decreased personnel demanded, etc., all of which have positive effects on the labor productivity of construction enterprises. Based on the experience on prefabricated construction of a major construction enterprise in China, Yang (2009) reports some evidence of labor and construction material saving via prefabrication. Thus, the authors of this paper propose the following hypothesis:

Hypothesis 1: On the one hand, directive prefabrication policies without substantive enforcement/incentive schemes will not accelerate the adoption of prefabrication in China’s construction industry. On the other hand, supportive prefabrication policies in China with detailed quantitative incentive schemes will help the spread of prefabricated construction and thus increase (decrease) the labor productivity 13

Journal Pre-proof (construction material usage) in the construction sector.

Then, a related question arises: Are the effects of prefabrication policies heterogeneous across construction firms? The authors hypothesize that the policies will have larger effects on public-listed firms for the following reasons. Anecdotal evidence shows that large public-listed construction corporations such as Vanke Corporation are pioneers of prefabricated construction in China (Yang et al., 2012; Qin, 2011). Further, transferring from traditional on-site construction to the modern off-site/prefabricated construction requires huge initial investment in prefabricating factories and other fixed expenditure (Steinhardt et al., 2013). It also needs intensive R&D activities and personnel training, which is costly and risky (Qin, 2011). So large public-listed construction corporations in China are more likely to take advantage of the prefabrication policies as they are more able to raise initial investment, undertake related R&D and bear the risk. Accordingly, another hypothesis emerges as follows:

Hypothesis 2: China’s supportive prefabrication policies have larger effects on public-listed construction firms in terms of labor productivity improvement as well as material saving.

The research flow chart in Figure 1 shows the steps of this study to test the above hypotheses.

14

Journal Pre-proof (Figure 1 about here)

4. Econometric model and data 4.1 The difference-in-differences (DID) method Suppose that the construction industry within a Chinese province has the following Cobb-Douglas production function, Y = Akαlβmγ, where Y, A, k, l, m represent total output, total factor productivity (TFP), capital stock, labor and material, respectively.5 Dividing both sides of the function by l and taking log-linear form, it follows that log

( ) = logA + αlogk + (β ― 1)logl + γlogm. Based on the latter form, the authors Y l

specify the following econometric model to test the effects of prefabrication policies in China:

log ypt = α0 + α1logkpt + α2loglpt + 𝛂𝟑𝐥𝐨𝐠𝐦𝐩𝐭 + 𝛽1Policy_dp,t ― 1 + 𝛽2Policy_sp,t ― 1 + ∑tDt + ∑pDp + τpt + τ2pt + εpt,

(1)

where ypt stands for the labor productivity of the construction sector in province p and year t. Later on, it also represents the material usage per construction area in the province.6 kpt, lpt are the capital stock and labor used in the construction industry of

5

The Cobb-Douglas production function is widely used in economics to model the technical relationship between the output and the inputs used in the production. Based on statistical evidence, Charles Cobb and Paul Douglas developed the specific form of the Cobb-Douglas production function between 1927 and 1947. The Cobb-Douglas production function is remarkably famous for being used as an economy-wide or industry-level production function in macroeconomics (Brown, 2017). Since we are modeling the production within an industry – the construction industry in a Chinese province, the use of the Cobb-Douglas production function is quite appropriate in this study. 6

Using the production function Y = Akαlβmγ, it can be shown that log

() m Y

= ―logA ― αlogk ― βlogl + (1 ― γ)logm. The latter equation implies that Eq. (1) is also an appropriate econometric specification when the outcome variable ypt stands for material usage per output. 15

Journal Pre-proof the province, respectively. 𝐥𝐨𝐠𝐦𝐩𝐭 is a vector consisting of the logarithm of the cement, timber and steel used in the construction industry of province p in year t. In addition, ∑tDt and ∑pDp include the full set of year and province fixed effects, absorbing yearly shocks and time-invariant determinants of labor productivity (material usage) at the province level. τpt and τ2pt are province-specific linear and quadratic time trends, respectively, controlling for time-varying determinants of ypt that have approximately linear/quadratic trends (Cesur et al., 2017). Policy_dpt (Policy_spt) is a dummy (0-1) variable that equals 1 if province p has put forward directive (supportive) prefabrication policies at or before year t and 0 otherwise. These two variables are constructed based on the years of the first documents on the two types of prefabrication policies listed in Table 1. Considering that the effects of industrial policies generally lag behind the implementation of the policies (Hibbs, 1986; Easterly, 2001), the authors include one-year lagged Policy_dpt/Policy_spt in (1). Lastly, εpt is the error term. Eq. (1) is a generalized difference-in-differences (DID) model7 where the coefficients of interest, βs, reflect the change of labor productivity (material usage) before and after the implementation of prefabrication policies relative to the change within the provinces without such policies (Cantoni et al., 2017; Cesur et al., 2017). Taking into account possible serial correlation within the same province, the standard errors clustered at the province level are used in the estimation. Considering that the

7

The DID analysis is widely used in social sciences to estimate the effects of some treatment or policy intervention. Using panel data, the DID analysis estimates the effects by calculating the differences of the over-time changes in the outcome variable between the treatment vs. control groups. Compared to the pooled cross-sectional analysis, the DID analysis is advantageous in that it can mitigate the influence of the selection bias and other extraneous factors (Angrist and Pischke, 2008). 16

Journal Pre-proof small number of provinces in China might bias the estimated standard errors, the authors also try the wild cluster bootstrap-t procedure and report the resulted p-values of estimated coefficients (Cameron et al., 2008).

4.2 The data The outcome variable ypt in Eq. (1) represents labor productivity or material usage intensity in the construction sector of province p in year t. The authors use two measures of labor productivity – the per-worker value added of the construction sector (Xu and Zhao, 2010) and the construction area per worker.8 In this paper, the usage of major materials (such as steel, cement, and timber) in construction industry is analyzed (Hong et al., 2016); and the material usage intensity is measured by the amount of each material used per construction area. On the right-hand side of Eq. (1), kpt is the real capital stock in the construction sector, which is constructed using the perpetual inventory method (Brandt et al., 2012). lpt is the average number of personnel each year in the construction sector.9 𝐥𝐨𝐠𝐦𝐩𝐭 consists of the logarithm of the amount of steel, cement and timber used in the construction. All the annual data are drawn from (or constructed using variables from) China Construction Industry Yearbooks published during 2001-2018.10 Table 2 presents the descriptive statistics

8

The value added per worker of the construction sector is calculated by dividing the total value added in the sector by the total employment while the construction area per worker is calculated by dividing the total construction area by the total employment in the construction sector. We argue that the construction area per worker is the more accurate measurement of the physical productivity of labor in the construction sector since the value added per worker not only depends on the physical productivity of labor in the sector, but also reflects price levels. Thus, in the analysis below, this paper will use the construction area per worker as a major measurement of the labor productivity in the construction sector. 9 Although the number of worker-hours is a better measure of labor input, the authors do not have the data on the average working hours per working day in the construction sector. 10 The data for the major material usage are missing for the years 2002 and 2003. Also note that China Construction Industry Yearbook (China Statistical Yearbook) in each year contains the data of last year. So China 17

Journal Pre-proof of main variables used in the analysis.

(Table 2 about here)

5. Results 5.1 Results using the DID method Table 3 presents the results on the effects of China’s prefabrication policies on labor productivity in its construction sector. In this table, the authors use the construction area per worker in the construction industry of each province to measure the labor productivity. And the estimation controls for province and year fixed effects. In column (1), the coefficient of Policy_dt ― 1 is marginally significant while it turns insignificant in column (3) when both Policy_dt ― 1 and Policy_st ― 1 are added in the regression. In contrast, the coefficient of Policy_st ― 1 remains significant in columns (2) and (3). So the results lead to the conclusion that supportive prefabrication policies help increase labor productivity in China’s construction industry while directive policies without detailed incentive schemes have no such effects. In column (4), the major material usage is added in the specification; and the estimation further controls for province-specific linear and quadratic time trends of construction sector in column (5) (see, Cesur et al., 2017). However, there is no substantive change in the significance and magnitude of the effects of supportive policies. The estimated coefficient on Policy_st ― 1 implies that after the policies, the

Construction Industry Yearbooks (China Statistical Yearbooks) published during 2001-2018 contain the data of 2000 through 2017. 18

Journal Pre-proof average construction area per worker in construction sector has been increased by roughly 20%, which is quite significant in economic perspective. This result is compatible with the finding of Pan et al. (2012), i.e., prefabrication can increase the labor productivity in construction industry.

(Table 3 about here)

Besides, the coefficients on material usage and the labor input are all insignificant. The estimated coefficient on capital stock is significantly positive, which is compatible with economic theory. Note that the standard errors clustered at the province level are used in the estimation to take into account serial correlation of labor productivity within a province. Then the small number of Chinese provinces (31) may lead to the few-cluster problem, i.e., the estimated standard error may be biased downward when clusters are few. To address this problem, Cameron et al. (2008) develop the wild cluster bootstrap-t procedure. The authors apply this procedure and report the resulted p-value of coefficients in brackets. They find that the statistical significance has been reduced but still remains significant at 5% significance level.

(Table 4 about here)

Table 4 analyzes the effects of prefabrication policies on labor productivity in construction industry using the value added per worker to measure the labor 19

Journal Pre-proof productivity. Again, it is found that supportive prefabrication policies in China significantly improve labor productivity in the construction sector while the effects of directive policies are not significant. The coefficient of Policy_st ― 1 is stable across different specifications, which is also significant in all cases regardless of the way the statistical significance is inferred. The magnitude of the coefficient indicates that supportive prefabrication policies increase average value added per worker in China’s construction industry by around 45%. Compared to Table 3, the much greater effects of the policies on the value added per worker can be interpreted in that prefabricated construction not only increases working efficiency of building workers but also improves the quality of completed buildings, which also increases firms’ value added (Pan et al., 2012; Li et al., 2011).

(Table 5 about here)

Table 5 further looks at material saving of prefabricated construction. The authors focus on three major materials used in construction, steel, timber and cement, and examine the effects of prefabrication policies on each material used per construction area. Again, they use Eq. (1) in the estimation. As shown in Table 5, supportive prefabrication policies significantly reduce material usage intensity in construction, which is compatible with Hypothesis 1 as well as the literature on material saving of prefabricated construction. The effects, however, are not found for directive prefabrication policies. The coefficient of Policy_st ― 1 indicates that since 20

Journal Pre-proof the implementation of supportive policies, the material used per construction area has been decreased by 16.7%, 30.7%, and 23.3% for steel, timber and cement, respectively. This finding is compatible with the vast literature showing that prefabricated method of construction is energy and resource saving and can lead to reduced construction waste generation (Lu and Yuan, 2013; Zhang et al., 2011; Aye et al., 2012; Jaillon and Poon, 2009; Lu et al., 2011; Pan et al., 2007; Hong et al., 2016; Yang, 2009). Interesting, it is found that capital stock in construction sector is negatively correlated with material usage intensity, suggesting that bigger construction companies are more successful in material saving. This can be interpreted by the fact that bigger construction companies in China have more advanced construction process, equipments and management, which are material and cost saving.

(Table 6 about here)

Hypothesis 2 will be tested below using the following specification,

log ypvt = α0 + α1logkpvt + α2loglpvt + 𝛂𝟑𝐥𝐨𝐠𝐦𝐩𝐯𝐭 + β1Publicpvt + β2Policy_sp,t ― 1 + β3Policy_sp,t ― 1 × Publicpvt + ∑tDt + ∑pDp + τpt + τ2pt + εpt,

(2)

China Construction Industry Yearbooks provide aggregate information not only for the whole construction industry, but also for all the public-listed construction 21

Journal Pre-proof firms in a Chinese province. Then, for each variable (y, k, l, or each component of m in (2)), the authors construct two aggregate observations for each province in each year – one for all the public-listed construction firms and the other for all the rest construction

firms.

So

v∈

{public listed construction firms;all the other construction firms} Publicpvt

is

a

dummy

(0-1)

variable

that

equals

in

(2).

1

when

v = {public listed construction firms}, and 0 otherwise. The definitions of other variables in (2) are the same as in (1). Policy_dp,

t―1

is not included in (2) since its

estimated coefficient is mostly insignificant in the analysis above. Then, the coefficient of the interaction term in (2), β3, reflects the heterogeneous effects of the prefabrication policies on the public-listed construction firms in China. The results in Table 6 indicate that the public-listed construction firms in China are more productive and material conserving. The estimated coefficient on the interaction term in (2) means that China’s supportive prefabrication policies have larger effects on the public-listed construction firms in terms of labor productivity improvement as well as material saving. This lends support to Hypothesis 2: since transferring from traditional on-site construction to modern prefabricated construction requires substantial initial investment, intensive R&D expenditure and personnel training, large public-listed construction firms in China will be more likely to take advantage of the prefabrication policies (Steinhardt et al., 2013; Qin, 2011).

5.2 Is parallel trend assumption valid? 22

Journal Pre-proof A potential concern regarding the estimated effects of China’s supportive prefabrication policies is that the policies are not randomly assigned across Chinese provinces. If the policy implementation is conditional on some province characteristics that are also determinants of labor productivity or material intensity in the construction industry, then the estimated coefficients above will be biased. In terms of the language of DID method, when the provinces (treatment group) putting forward supportive prefabrication policies have different pre-policy trends than other provinces (control group) in labor productivity/material intensity in their construction sector, the estimated effects above will capture the pre-policy trends instead of true effects of the policies.

(Figure 2 about here)

To address this concern, first, the authors divide all Chinese provinces into two groups – the provinces that have implemented the supportive prefabrication policies during 2000-2017 (the treatment group), and all the other Chinese provinces (the control group). Figure 2 depicts the evolving of the average labor productivity in the construction industry of the treatment vs. control groups. The divergent trends after 2010 signify the effects of the supportive prefabrication polices while the trends of the two groups are quite similar before 2010, implying the validity of the parallel-trend assumption. Second, the authors argue that our econometric specification has reduced the 23

Journal Pre-proof concern to a large extent. Suppose that the implementation of the policies indeed depends on some province variables, these variables will be absorbed by the province-specific linear and quadratic trends in Eq. (1) when they approximately have linear or quadratic time trends (Cesur et al., 2017). Then the estimated coefficients will be unbiased since the determinants of the policies have been controlled for.

(Table 7 about here)

Third, a placebo test is conducted to check if the estimated effects reflect pre-policy trends. Specifically, for each province that has implemented supportive prefabrication policy, the authors set up a pseudo policy that is a fixed number of years ahead of the true policy. They also drop observations at the year of true policy and after in case these observations contaminated the effects of pseudo policies that will be estimated below. Then the effects of the pseudo policies are estimated using Eq. (1). If no significant effects of the pseudo policies are found, then it implies that the estimated effects above for true policies do not just reflect pre-policy trends. Table 7 presents the effects of different pseudo prefabrication policies and in most cases, the coefficients are not statistically significant. Thus, it cannot be precluded that the parallel trend assumption is valid in this study.

6. Robustness check using synthetic control method This section conducts a robustness check using the synthetic control method (Abadie 24

Journal Pre-proof and Gardeazabal, 2003; Abadie et al., 2010; Anukriti, 2018). The basic idea of the synthetic control method is to use a weighted average of other provinces (the synthetic province) to mimic the treated province. If predictors of the outcome variable in the synthetic province resemble the predictors in the treated province in the years before the policy implementation, then it is expected that after the year of the policy, the outcome variable in the synthetic province will resemble the (counterfactual) outcome variable in the treated province. Thus, when a synthetic province has been constructed, the authors can use the difference between the outcome variable in the treated province and that in the synthetic province after the year of the policy to evaluate the effects of the policy. More formally, following Abadie and Gardeazabal (2003), let J to denote the number of other (control) provinces. Let W = (w1,…,wJ)' be a (J × 1) weight vector with nonnegative components summing to 1. The scalar wj (j = 1,…,J) denotes the weight of province j in the synthetic province. X1 represents a (K × 1) vector of pre-policy values of K predictors of the outcome variable in the treated province, and X0 a (K × J) matrix of the values of the predictors in the J control provinces. A diagonal matrix of nonnegative components, V, is used to reflect relative importance of the predictors. Then the distance between the treated and synthetic provinces can be characterized by (X1 ― X0W)'V(X1 ― X0W), subject to wj ≥ 0 and w1 +… + wJ = 1. Suppose that the vector of weights W ∗ minimizes the above distance, then W ∗ can be used to construct the synthetic province that resembles the treated province to the highest extent before the policy implementation. 25

Journal Pre-proof Since the synthetic control method requires quite long pre-policy periods, the authors use quarterly data of China’s construction industry at the province level in the analysis (Abadie et al., 2010). However, they have to compromise on the following things because of the use of quarterly data. First, the data are only available during Q2, 2003-Q3, 2016. Second, there is no information on material usage in the quarterly data; for labor productivity, only quarterly construction area per worker can be calculated. So in the analysis using the synthetic control method below, the outcome variable is quarterly construction area per worker in Chinese provinces. The province-level prediction variables include the number of construction workers, construction area, total output value of the construction industry, GDP, and the construction area per worker at Q1, 2006-2008. Following Abadie et al. (2010), all predictors except the construction area per worker are averaged during Q2, 2003 and Q1 of the year of the policy. Table 1 shows that Beijing and Shanghai are among the first provinces (municipalities) that have implemented supportive prefabrication policies. Anecdotal evidence also shows that the adoption of prefabricated construction is quite fast in the two cities. So the authors would demonstrate the effects of the policies on labor productivity of construction sector in the two cities using the synthetic control method. For that purpose, they first construct a synthetic Beijing and a synthetic Shanghai to best mimic the true Beijing and Shanghai before the year of the policy. Mathematically, a weight vector W ∗ is solved, which is described in Table 8.

26

Journal Pre-proof (Table 8, 9 about here)

The authors construct synthetic Beijing and Shanghai based on the weights assigned to each control province in Table 8. Table 9 demonstrates the similarity between the treated (real) and synthetic (counterfactual) municipalities before the implementation of the policy by comparing the predicting variables. The construction area per worker is measured at Q1, 2006-2008 while the other variables are averaged between Q2, 2003 and the year of policy implementation. It is obvious from Table 9 that the synthetic Beijing (Shanghai) resembles the treated one before policy implementation.

(Figure 3 about here)

Since they resemble each other, it is feasible to use the outcome variable in the synthetic Beijing (Shanghai) after the year of policy intervention to mimic the (counterfactual) outcome variable in the real Beijing (Shanghai) without policy intervention. Then the post-policy difference between the outcome variables in treated and synthetic municipalities can be used to evaluate the effects of the policy. Figure 3 depicts the (quarterly) evolving of construction area per worker in the treated and synthetic Beijing (Shanghai) and the dashed vertical line indicates the year of supportive prefabrication policy in each city.11 Before policy intervention, the curves

11

Since the authors do not know in which quarter the supportive prefabrication policy occurs in each city, they use the second quarter (Q2) as the quarter of policy implementation in Figure 3. The results do not change much when 27

Journal Pre-proof move closely, confirming the resemblance between the treated and synthetic cities. After the intervention, however, the two curves diverge substantially, demonstrating the positive effects of supportive prefabrication policy in Beijing (Shanghai) on the labor productivity in its construction sector.

(Table 10, 11 about here)

Further, the authors do additional two robustness checks using the synthetic control method. First, they use the above procedures to investigate the effects of directive prefabrication policy in Ningxia where the directive policy happened in 2009 and no supportive policy occurred. Table 10 lists the weights for synthetic Ningxia and Table 11 compares predicting variables in the treated and synthetic Ningxia. Again, the synthetic Ningxia resembles the treated one before the directive policy. The left panel in Figure 4 demonstrates the effects of the directive prefabrication policy in Ningxia. The two curves do not diverge after policy intervention, implying that the directive policy has no significant effects on the labor productivity in construction sector.

(Figure 4 about here)

Second, the authors pick up Inner Mongolia where no prefabrication policies

they use other quarters. 28

Journal Pre-proof occurred and arbitrarily assign a (pseudo) prefabrication policy (at Q3, 2009) to that province. Then they examine the effects of the pseudo policy. The weights for the synthetic Inner Mongolia are listed in Table 10 and the similarity between the treated and synthetic Inner Mongolia is shown in Table 11. The right panel of Figure 4 illustrates the effects of the pseudo prefabrication policy and no effects are observed. This placebo test implies that the effects of supportive prefabrication policies in Beijing and Shanghai (shown in Figure 3) are unlikely to be accidental discoveries.

7. Discussion Prefabricated construction is advantageous over traditional construction in many aspects, such as being more environmental friendly (Zhang et al., 2011; Aye et al., 2012; Lu and Yuan, 2013), more labor/resource saving (Jaillon and Poon, 2009; Pan et al., 2007; Lu et al., 2011), and increased productivity, quality as well as health/safety standards in the construction industry (Pan et al., 2012) among others. To address the environmental problems in the construction industry, different Chinese provinces have sequentially put forward (directive or supportive) prefabrication policies since the late 2000s to encourage the spread of prefabricated construction. The effects of these policies, however, have never been investigated in the literature. The significance of this research is at least four-fold: First, to the best of our knowledge, this is the first study in the literature that empirically examines the effects of China’s prefabrication policies on the labor productivity and material conserving in the construction industry of China. More notably, this study separately looks into the 29

Journal Pre-proof different effects of directive vs. supportive prefabrication policies. Second, the authors carefully design the econometric specification and robustness checks to ensure the rigorousness of the analysis. They mainly use a generalized DID framework in the analysis controlling for the province and year fixed effects. Further, to control for the unobserved time-varying determinants of the outcome variable, a set of province-specific linear and quadratic time trends are added in the specification in light of Cesur et al. (2017). The authors also apply the wild cluster bootstrap-t procedure (Cameron et al., 2008) in order to infer robust p-values of the estimated coefficients. This procedure helps correct the bias caused by the few-cluster problem. Lastly, this study uses the synthetic control method (Abadie et al., 2010) to double-check the estimated effects of prefabrication policies in China. All the results imply that supportive prefabrication policies lead to increased labor productivity and more material saving in the construction industry of China while only directive prefabrication policies have no such effects. Third, this study provides new evidence of the benefits of prefabricated construction. Prefabrication has widely been regarded as a modern way of construction that reduces the generated construction wastes (Lu and Yuan, 2013; Zhang et al., 2011; Aye et al., 2012), decreases noise, dust, and operation time/cost (Jaillon and Poon, 2009; Lu et al., 2011; Pan et al., 2007), and increases construction quality, and health/safety standards (Pan et al., 2012). By empirically investigating the effects of provincial prefabrication policies in China, this study also finds that prefabrication is also material saving and can improve the labor productivity in 30

Journal Pre-proof construction industry. Fourth, the results of this research have important policy implications for the design of prefabrication policies in the developing countries like China. Since transferring from conventional to prefabricated construction needs huge initial investment (Steinhardt et al., 2013) and intensive/costly R&D expenditures (Qin, 2011), governments need to offer a helpful hand via prefabrication policies to reduce firms’ costs/risk related to prefabricated construction. However, only properly designed supportive prefabrication policies will be effective in promoting the prefabricated method of construction. And the policies need to have detailed quantitative incentive schemes and enforcement measures, instead of only directive principles and guidelines. Of course, this study is also subject to some limitations as follows. First, due to data availability, the analysis in this paper uses the data at the province level. And firm-level analysis is necessary when the micro-data of Chinese construction firms are available in the future. Second, some provinces implemented the supportive policies only recently. So the data covering more years are needed to investigate the effects of the prefabricated policies in those provinces. Third, more detailed mechanism analysis is also needed in the future. For example, it is necessary to explore how the construction firms respond to the prefabrication policies. It is also worthwhile to investigate the effects of China’s prefabrication policies on other outcome variables, such as building quality, noise/dust emission, health and safety standards, etc.

31

Journal Pre-proof 8. Conclusions This paper empirically examines the effects of China’s prefabrication policies on the labor productivity and material usage intensity in its construction industry, using the panel data drawn from the China Construction Industry Yearbooks. The analysis is conducted in a difference-in-differences framework controlling for a set of province and year fixed effects as well as province-specific linear and quadratic time trends. The results show that supportive prefabrication policies in China have increased (decreased) the labor productivity (material usage intensity) in the construction industry of China. Significant effects of directive prefabrication policies, however, are not found. The robustness checks using the synthetic control method confirm the above findings. All the results imply that only supportive prefabrication policies with detailed quantitative incentive schemes and enforcement measures can encourage the use of the prefabricated method of construction.

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Table 1: The year of the first documents on two types of prefabrication policies The year of the first document

Provinces 2009 Ningxia 2010 Beijing 2011 Shanghai, Chongqing, Shandong, Liaoning 2012 Zhejiang, Heilongjiang 2013 Hebei, Jilin Directive policy 2014 Anhui, Jiangsu, Tianjin, Sichuan, Shanxi, Guangdong, Hunan 2015 Hubei, Fujian 2016 Jiangxi, Hainan 2017 Guangxi, Henan, Guizhou 2010 Beijing 2011 Liaoning 2013 Shanghai 2014 Zhejiang, Hunan, Hebei, Shandong, Jilin Supportive policy 2015 Jiangsu, Fujian, Guangdong 2016 Hubei 2017 Shaanxi, Sichuan, Jiangxi Notes: The first documents on prefabrication are manually collected from the official websites of the bureau/commission of housing and urban-rural construction in each province.

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Table 2: Descriptive statistics of main variables Variables

Mean

Std.

Min

Max

Log(value added per worker)

0.995

0.506

0.851

2.374

Log(construction area per worker)

4.799

0.515

3.487

6.39

Log(steel per construction area)

5.212

3.392

2.556

8.591

Log(timber per construction area)

4.32

3.36

2.124

7.633

Log(cement per construction area)

6.419

3.232

3.551

11.378

Log(number of workers)

9.146

1.455

4.465

12.281

Log(real capital stock)

14.417

3.955

8.158

16.543

Log(steel)

14.452

4.035

3.221

18.848

Log(timber)

13.56

3.993

1.955

18.296

Log(cement)

15.659

3.863

2.658

20.862

Policy_d

0.193

0.395

0

1

Policy_s

0.077

0.266

0

1

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Table 3: Effects of prefabrication policies on labor productivity in the construction sector of China – Part 1 Model #

(1)

Dependent var. Log(real capital stock)

Log(number of workers)

(2)

(3)

(5)

Labor productivity: log(construction area per worker) 0.257

0.268

0.268

0.251

0.262

(0.07)***

(0.075)***

(0.075)***

(0.075)***

(0.074)***

0.42

0.39

0.394

0.421

0.41

(0.52)

(0.34)

(0.36)

(0.37)

(0.36)

-0.04

0.011

(0.029)

(0.02)

0.024

0.035

(0.031)

(0.022)

-0.0082

-0.012

(0.048)

(0.022)

Log(steel)

Log(timber)

Log(cement)

Policy_dt ― 1

(4)

0.141

-0.00065

0.0034

0.0037

(0.077)*

(0.056)

(0.052)

(0.06)

[0.112]

[0.851]

[0.775]

[0.822]

0.214

0.196

0.225

0.204

(0.019)***

(0.017)***

(0.021)***

(0.025)***

[0.024]**

[0.013]**

[0.036]**

[0.029]**

Policy_st ― 1

Prov. fixed effects

Yes

Yes

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Yes

Yes

Prov.-specific linear trend

No

No

No

No

Yes

Prov.-specific quadratic trend

No

No

No

No

Yes

Obs.

527

527

527

465

465

0.746

0.754

0.769

0.847

0.891

R-square

Notes: Standard errors clustered at the province level are in parentheses. P-values resulted from the wild cluster bootstrap-t procedure are included in brackets. ***, **, and * represent 1%, 5% and 10% significance level, respectively.

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Table 4: Effects of prefabrication policies on labor productivity in the construction sector of China – Part 2 Model #

(1)

Dependent var. Log(real capital stock)

Log(number of workers)

(2)

(3)

(5)

Labor productivity: log(value added per worker) 0.131

0.139

0.133

0.134

0.133

(0.063)**

(0.062)**

(0.064)**

(0.067)*

(0.063)**

0.00612

0.00066

-0.00052

-0.0068

0.00061

(0.071)

(0.072)

(0.07)

(0.075)

(0.075)

-0.026

-0.031

(0.031)

(0.045)

0.018

0.021

(0.022)

(0.032)

0.035

0.036

(0.023)

(0.025)

Log(steel)

Log(timber)

Log(cement)

Policy_dt ― 1

(4)

-0.0308

-0.104

-0.099

0.054

(0.081)

(0.068)

(0.063)

(0.071)

[0.611]

[0.415]

[0.466]

[0.732]

0.335

0.425

0.489

0.45

(0.032)***

(0.067)***

(0.067)***

(0.071)***

[0.015]**

[0.008]***

[0.013]**

[0.017]**

Policy_st ― 1

Prov. fixed effects

Yes

Yes

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Yes

Yes

Prov.-specific linear trend

No

No

No

No

Yes

Prov.-specific quadratic trend

No

No

No

No

Yes

Obs.

527

527

527

465

465

0.753

0.761

0.796

0.811

0.875

R-square

Notes: Standard errors clustered at the province level are in parentheses. P-values resulted from the wild cluster bootstrap-t procedure are included in brackets. ***, **, and * represent 1%, 5% and 10% significance level, respectively.

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Table 5: Effects of prefabrication policies on material usage intensity in China’s construction sector Model # Dependent var.

Log(real capital stock)

Log(number of workers)

Policy_dt ― 1

Policy_st ― 1

Obs. R-square

(1)

(2)

(3)

Log(steel per

Log(timber per

Log(cement per

construction area)

construction area)

construction area)

-0.355

-0.117

-0.171

(0.105)***

(0.031)***

(0.091)*

-0.281

-0.069

-0.113

(0.301)

(0.166)

(0.162)

-0.0059

-0.16

-0.07

(0.075)

(0.134)

(0.069)

[0.655]

[0.421]

[0.417]

-0.167

-0.307

-0.233

(0.021***)

(0.122)**

(0.094)**

[0.009]***

[0.023]**

[0.031]**

465

465

465

0.803

0.814

0.821

Notes: The material usage, province and year fixed effects, as well as province-specific linear and quadratic trends have been controlled for. Standard errors clustered at the province level are in parentheses. P-values resulted from the wild cluster bootstrap-t procedure are included in brackets. ***, **, and * represent 1%, 5% and 10% significance level, respectively.

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Table 6: Prefabrication policies have larger effects on public-listed firms? Model # Dependent var.

Public

Policy_st ― 1

Policy_st ― 1 × Public

Obs. R-square

(1)

(2)

(3)

(4)

(5)

Log(construct

Log(value

Log(steel per

Log(timber per

Log(cement per

ion area per

added per

construction

construction

construction

worker)

worker)

area)

area)

area)

0.196

0.215

-0.105

-0.164

-0.224

(0.077)**

(0.071)***

(0.038)**

(0.041)***

(0.064)***

[0.081]*

[0.036]**

[0.055]*

[0.006]***

[0.031]**

0.105

0.121

-0.054

-0.155

-0.139

(0.038)**

(0.021)***

(0.021)**

(0.059)**

(0.052)**

[0.033]**

[0.027]**

[0.033]**

[0.061]*

[0.05]**

0.264

0.411

-0.358

-0.25

-0.261

(0.105)**

(0.137)***

(0.119)***

(0.096)**

(0.058)***

[0.045]**

[0.053]*

[0.012]**

[0.102]

[0.019]**

930

930

930

930

930

0.812

0.815

0.853

0.861

0.877

Notes: The material usage, firm capital stock and labor, province and year fixed effects, as well as province-specific linear and quadratic trends have been controlled for. Standard errors clustered at the province level are in parentheses. P-values resulted from the wild cluster bootstrap-t procedure are included in brackets. ***, **, and * represent 1%, 5% and 10% significance level, respectively.

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Table 7: Is parallel trend assumption valid? A placebo test Model # Dependent var.

(1)

(2)

(3)

(4)

(5)

Log(construct

Log(value

Log(steel per

Log(timber per

Log(cement per

ion area per

added per

construction

construction

construction

worker)

worker)

area)

area)

area)

Panel A: Pseudo policies are set 4 years before true supportive prefabrication policies. Policy_st ― 1

0.108

0.121

-0.217

-0.064

0.122

(0.176)

(0.271)

(0.38)

(0.051)

(0.246)

Panel B: Pseudo policies are set 5 years before true supportive prefabrication policies. Policy_st ― 1

0.195

0.311

-0.155

-0.215

-0.175

(0.138)

(0.22)

(0.121)

(0.143)

(0.152)

Panel C: Pseudo policies are set 6 years before true supportive prefabrication policies. Policy_st ― 1

0.257

0.265

0.185

-0.204

-0.216

(0.175)

(0.157)

(0.126)

(0.107)*

(0.127)

Notes: The material usage, firm capital stock and labor, province and year fixed effects, as well as province-specific linear and quadratic trends have been controlled for. Years at and after true supportive prefabrication policies are dropped. Standard errors clustered at the province level are in parentheses. ***, **, and * represent 1%, 5% and 10% significance level, respectively.

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Table 8: Weights for synthetic Beijing and synthetic Shanghai Weights Province

Synthetic Beijing

Synthetic Shanghai

Tianjin

0

0.265

Hebei

0

0

Shanxi

0

0

Inner Mongolia

0

0

Jilin

0

0

Heilongjiang

0

0

Jiangsu

0.057

0

Zhejiang

0

0

Anhui

0

0

Fujian

0.486

0.503

Jiangxi

0

0

Shandong

0

0

Henan

0

0

Hubei

0

0

Hunan

0

0

0.143

0.145

Guangxi

0

0

Hainan

0

0

Chongqing

0

0

Sichuan

0.029

0

Guizhou

0

0.087

Yunnan

0

0

Shaanxi

0.285

0

Gansu

0

0

Qinghai

0

0

Ningxia

0

0

Xinjiang

0

0

Tibet

0

0

Liaoning

0

0

Guangdong

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Table 9: Comparison of treated and synthetic municipalities Beijing

Shanghai

Treated

Synthetic

Treated

Synthetic

Number of workers

108.473

103.714

89.595

87.521

Construction area

4984.799

5102.658

4250.655

4392.336

1096.788

985.853

1308.459

1231.109

GDP

5468.175

6015.416

8527.775

8217.075

Construction area per worker (Q1, 2006)

115.921

115.126

114.813

113.755

Construction area per worker (Q1, 2007)

133.300

131.176

130.800

129.214

Construction area per worker (Q1, 2008)

154.618

155.203

153.025

153.817

Total output value of the construction sector

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Table 10: Weights for synthetic Ningxia and synthetic Inner Mongolia Weights Province

Ningxia

Inner Mongolia

Tianjin

0

0

Hebei

0

0

Shanxi

0

0

Inner Mongolia

0

-

Jilin

0

0

Heilongjiang

0

0.577

Jiangsu

0

0

Zhejiang

0

0

Anhui

0

0

Fujian

0

0

Jiangxi

0

0

Shandong

0

0.065

Henan

0

0

Hubei

0

0

Hunan

0

0

Guangdong

0

0.021

Guangxi

0

0

Hainan

0.827

0.312

Chongqing

0.041

0

Sichuan

0

0

Guizhou

0

0

Yunnan

0

0

Shaanxi

0

0

Gansu

0.069

0

Qinghai

0.063

0

Ningxia

-

0

Xinjiang

0

0.025

Tibet

0

0

Liaoning

0

0

Beijing

0

0

Shanghai

0

0

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Table 11: Comparison of treated and synthetic provinces Ningxia

Inner Mongolia

Treated

Synthetic

Treated

Synthetic

11.250

11.168

36.550

36.472

456.2344

462.034

1537.139

1517.315

Total output value of the construction sector

73.143

72.551

282.831

295.153

GDP

723.241

778.150

3627.682

3476.375

Construction area per worker (Q1, 2006)

52.64

51.96

28.575

28.142

Construction area per worker (Q1, 2007)

58.490

58.451

31.060

31.106

Construction area per worker (Q1, 2008)

63.168

63.371

33.476

33.841

Number of workers Construction area

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Figure 1: Research flow chart showing the steps of this study

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Value added per worker

0

50

100

100000

150

200000

200

300000

250

300

400000

Construction area per worker

2000

2005

2010 year

2015

2020

2000

2005

2010 year

2015

Treatment group

Treatment group

Control group

Control group

2020

Notes: The treatment group consists of the provinces that have implemented the supportive prefabrication policies during 2000-2017 (see Table 1). And the control group consists of all the other Chinese provinces. The figure shows the evolving of the average construction area per worker and the average value added per worker in the construction industry of the treatment as well as control groups.

Figure 2: The trends of labor productivity in the construction industry of the treatment vs. control groups

45

Shanghai 300

Beijing

100

100

200

Construction area per worker 300 400

Construction area per worker 150 200 250

500

600

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Figure 4: Labor productivity in the construction sector of treated and synthetic provinces, a placebo test

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Credit Author Statement Yue Gao: Conceptualization, Software, Data preparation, Writing- Original draft preparation, Software, Validation. Xian-Liang Tian: Methodology, Visualization, Investigation, Supervision, Software, Validation, Writing- Reviewing and Editing

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Declaration of interests × The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Supportive prefabrication policies increase labor productivity of construction. Supportive prefabrication policies decrease material usage in construction. Effects of directive prefabrication policies are not statistically significant. Supportive prefabrication policies have larger effects on public-listed firms. Robustness check using synthetic control method confirms results of DID analysis.