Public infrastructure and regional growth: Lessons from meta-analysis

Journal of Transport Geography 58 (2017) 1–8

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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo

Public infrastructure and regional growth: Lessons from meta-analysis Zeynep Elburz a,⁎, Peter Nijkamp b,c, Eric Pels b a b c

Dokuz Eylul University, Department of City and Regional Planning, Tinaztepe Campus, No: 209, 35160 Buca, Izmir, Turkey VU University, Department of Spatial Economics, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands A. Mickiewicz University, Wieniawskiego 1, 61-712 Poznan, Poland

a r t i c l e

i n f o

Article history: Received 4 May 2015 Received in revised form 27 July 2016 Accepted 31 October 2016 Available online xxxx Keywords: Public investment Transportation Infrastructure Growth Meta-analysis

a b s t r a c t The aim of this study is to synthesize the current literature on infrastructure and growth by determining sources of variation in empirical results by means of a meta-analysis. We use an ordered probit model for investigating changes in the probability of finding negative, positive, and insignificant impacts. The total data base consists of 912 observations from 42 studies conducted between 1995 and 2014. The meta-analytical results show that study characteristics do matter for the magnitude and sign of the variables concerned. We find that studies which employ data from the US are more likely to register a negative impact of public infrastructure on regional growth. We also find that type of infrastructure, research methodology, time span, type of infrastructure measure, and geographical scale affect the outcomes of the primary studies. Studies that take into account interregional, interstate and interprovincial relations have a higher chance of finding negative effects, which gives an idea about the spillover effects of these investments. In contrast, some characteristics like output measure and selection of a particular sector appear to have no effect on obtaining positive, negative or insignificant outcomes. The findings of this study offer new insights to policy makers on the variation in empirical results regarding the relationship between public investment infrastructure and regional growth. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction The effect of public infrastructure investment on economic growth has captured more attention in the economic literature over the past two decades. Public infrastructure – more precisely, transport infrastructure – has been widely used by policy-makers as a policy instrument to reduce regional disparities and promote regional growth in both developed and developing countries (Bröcker and Rietveld, 2009). After the seminal work of Aschauer (1989), many researchers have investigated the link between public infrastructure and economic growth. However, the results of empirical studies show often a high degree of variation. Even though there is a generally accepted belief in a positive link between infrastructure and economic growth, some studies find insignificant and even negative outcomes. Such results would make transport infrastructure investments non-effective as a policy instrument. Since public infrastructure investments are often motivated by regional policy goals which intend to benefit lagging regions (Bröcker and Rietveld, 2009), it is crucial to identify the reasons that underlie the sharp differences in results of empirical studies on the impacts of public infrastructure investment. The aim of the present study is therefore; to synthesize the current applied literature on this phenomenon ⁎ Corresponding author. E-mail address: [email protected] (Z. Elburz).

http://dx.doi.org/10.1016/j.jtrangeo.2016.10.013 0966-6923/© 2016 Elsevier Ltd. All rights reserved.

by using a meta-analysis to determine the sources of variation in the empirical results. Meta-analysis is a systematic framework that synthesizes and compares past studies, and extends and re-examines the results of the available data to produce more general results than earlier attempts have been able to do (Florax et al., 2002). There are only a few studies which use meta-analysis as a tool to investigate the sources of sharp differences in outcomes in the infrastructure and economic growth literature. This study means an extension previous meta-analysis studies on infrastructure and economic growth in three ways. First, we employ studies with different methodology, such as a production function, total factor productivity, a growth equation, and spatial regression. Researches often choose to focus on only one estimation method, or on models which can be transformed to produce other model estimation outcomes in order to overcome the comparability problem of studies with different model specifications. Most recently, Melo et al. (2013) conduct a meta-analysis to find the effects of transport infrastructure on private output with only studies that employ a production function approach. However, investigating only one approach, mostly a production function approach, with a meta-analysis can lead inconclusive results of the effects of public infrastructure. To our knowledge, this study is the first meta-analysis research to compare different model specifications in transport infrastructure and economic growth relationship. Second, we include studies that only examine the relationship between infrastructure and growth at a sub national level (region, state, and province) to obtain a better understanding of sub

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Z. Elburz et al. / Journal of Transport Geography 58 (2017) 1–8

national spillover effects. And finally, we collect studies that were published after 1995 in the meta-analysis to avoid methodological inconsistencies in the earlier studies discussed in the literature review. In the present study, we focus only on the direction and the statistical significance of the estimates to compare the empirical results of different model specifications. That is why we employ an ordered probit analysis, which is a standard practice in meta-analysis (Koetse et al., 2006), for presenting the changes in the probability of finding negative, positive, and insignificant results. The remainder of the paper is organized as follows. Section 2 presents an overview of the primary literature, and demonstrates the variations in findings from these studies. Section 3 provides information about over meta-probit analysis, while Section 4 explains the explanatory and dependent variables used in the model. The results of the meta-probit models are next presented in Section 5. Finally, Section 6 concludes the research with further suggestions for policy makers. 2. Literature review The economic impacts of public infrastructure have been a major concern for both economists and policy makers. From a policy perspective, public infrastructure is seen as the key to economic growth and a source of convergence. One of the most explicit examples of this notion is offered by regional development policies in the EU, which are heavily

based on transport infrastructure investments, in order to reduce regional disparities and to promote economic development (Crescenzi and Rodríguez-Pose, 2012). In spite of this optimistic view on the impact of public infrastructure, empirical evidence on the effect of transport infrastructure investment is still inconclusive in the literature. Plenty of studies have examined the relationship between public infrastructure, in general - and transportation infrastructure, in particular and regional growth since the early 1970s (see Mera, 1973; Blum, 1982). Yet, following the seminal works of Aschauer (1989, 1990) in the late 1980s, there have been a huge increase in the number of empirical studies on the impact of public infrastructure. Following neoclassical theory, Aschauer (1989) uses a Cobb-Douglas production function approach, and finds, for the USA, that public capital is an important input with a high elasticity of 0.39. He also suggests that a high stock of public infrastructure contributes to economic growth. This view has been supported by many researchers, such as Munnell (1990),Duffy-Deno (1991),Duffy-Deno and Eberts (1991), and García– Milá and McGuire (1992), whereas subsequent studies, e.g. HoltzEakin (1994) and Gramlich (1994), criticized Aschauer's (1989) and Munnell's (1990) findings, and underlined various econometric flaws in their methods. A common criticism is the direction of causality between public infrastructure and economic growth, which is not clear in the production function methodology. Eisner (1991) was the first researcher questioning the cause-effect relationship of public capital and

Table 1 List of included studiesa.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 a

Authors

Journal

Country

Observation

De la Fuente and Vives (1995) Holtz-Eakin and Schwartz (1995) Garcia-Mila et al. (1996) Mas et al. (1996) Martin (1998) Ghosh and De (1998) Boarnet (1998) Lall (1999) Haughwout (1999) Acconcia and Del Monte (2000) Petrakos and Saratsis (2000) Demurger (2001) Kumar (2002) Canaleta et al. (2002) Cappelen et al. (2003) Gonzales-Paramo and Martinez (2003) Rodriguez-Pose and Fratesi (2004) Ghosh and De (2005) Cantos et al. (2005) Costa-i-Font and Rodríguez-Oreggia (2005) Berechman et al. (2006) Martinez-Lopez (2006) Lall (2007) Moreno and López-Bazo (2007) Zou et al. (2008) Ozbay et al. (2007) Crescenzi and Rodríguez-Pose (2008) Sloboda and Yao (2008) Del Bo et al. (2010) Önder et al. (2010) Kuştepeli and Akgüngör (2010) Cohen (2010) Hong et al. (2011) Jiwattanakulpaisarn et al. (2012) Rodriguez-Pose et al. (2012) Crescenzi and Rodríguez-Pose (2012) Del Bo and Florio (2012) Gómez-Antonio and Garijo (2012) Jiwattanakulpaisarn et al. (2012) Ding (2013) Yu et al. (2013) Tong et al. (2013) Tsekeris and Vogiatzoglou (2014)

Economic Policy International Tax and Public Finance The Review of Economics and Statistics Regional Studies The World Economy Economic and Political Weekly Journal of Regional Science Economic and Political Weekly Growth and Change StudiEconomici Papers in Regional Science Journal of Comparative Economics Regional Studies European Urban and Regional Studies Journal of Common Market Studies The Review of Regional Studies Regional Studies Journal of Asian Economics Transport Review Economic Geography Transportation International Review of Applied Economics The Annals of Regional Science International Regional Science Review The Annals of Economics and Finance Transport Policy European Investment Bank Working Paper The Annals Regional Science Transition Studies Review European Planning Studies DEU Isletme Fakultesi Dergisi Transportation Research Part E Transportation Transportmetrica Papers in Regional Science Papers in Regional Science European Planning Studies International Regional Science Review Journal of Transport Economics and Policy Urban Studies Journal of Transport Geography Journal of Transport Geography Regional Science Policy and Practice

EU Member USA USA EU Member EU Member Other USA Other USA EU Member EU Member Other Other EU Member EU Member EU Member Other EU Member EU Member Other USA EU Member Other EU Member Other USA EU Member USA EU Member Other Other USA Other USA EU Member EU Member EU Member EU Member USA Other Other USA EU Member

2 20 12 13 12 8 12 12 24 4 1 16 9 17 6 18 4 16 100 8 9 19 21 26 8 11 28 12 14 3 8 2 30 24 48 63 49 24 6 44 68 48 66

Additional information is available upon request.

Z. Elburz et al. / Journal of Transport Geography 58 (2017) 1–8

state output in Munnell's (1990) study. Other major criticisms concern spurious correlations due to the non-stationarity of the data and misspecification due to missing variables. Taking these problems into account, Holtz-Eakin (1994), Evans and Karras (1994), Tatom (1991), and Hulten and Schwab (1991) find no significant evidence of the positive effect of public capital on productivity. A subsequent study of Aschauer (1990) focuses on the impact of core infrastructure on economic growth at the state level for the US. He finds lower elasticities than at the aggregate level, ranging from 0.055 to 0.11, while Munnell (1992) states that the impacts of highway capital becomes smaller as the geographic scale narrows. This conclusion has prompted new studies to investigate the spillover effects of public infrastructure at the regional level. Holtz-Eakin and Schwartz (1995) examine this effect for 48 states over the years 1969 to 1986, and find no evidence of important spillover effects of the highway system. Álvarez et al. (2006) reach a similar conclusion for 47 Spanish provinces, whereas Pereira and Roca-Sagalés (2003) find significant positive spillovers for 17 Spanish regions. Besides positive or negligible results for spillover effects, some studies find also negative spillover effects. Boarnet (1998), using highway and street data of California counties' from 1969 to 1988, suggests that neighbouring counties output negatively affects the counties' output. Over the past years, the heterogeneity across the empirical findings of many studies, which range from insignificant, positive or to even negative outcomes, has attracted surprisingly little attention. Some studies, such as Lakshmanan (2011), Deng (2013) and Straub (2008),pointed out the sharp differences in empirical results and reviewed the related literature to identify the essential sources of these. As Button (1998) states, the empirical evidence is far from conclusive; so, it is important to understand the factors which underlie these differences. From this perspective a meta-analysis may be a useful tool. 3. Methodology Meta-analysis is a systematic quantitative review or synthesis of all relevant scientific knowledge on a specific subject (Stanley and Doucouliagos, 2012). Glass (1976), the first researcher who used the term meta-analysis, explains meta-analysis as follows: ‘Meta-analysis is a statistical analysis of a large collection of results from individual studies for the purpose of integrating the findings. It connotes a rigorous alternative to the casual, narrative discussions of research studies which typify our attempt to make sense of the rapidly expanding research literature’ (Glass, 1976). As stated by Stanley (2001) and Stanley and Jarrell (1989), metaanalysis is a body of statistical methods which provide a more formal and objective process of reviewing, evaluating, and synthesizing the empirical literature. According to Florax et al. (2002), meta-analysis provides the researcher with a tool to compare and/or combine the outcomes of different experiments with similar set-ups. Subsequent studies, such as Nijkamp et al. (2011) and Poot (2014), emphasize that meta-analysis can also be used as an informed systematic tool to design the next empirical study, and provides a mechanism to address the impact of differences between studies in terms of the design of the empirical analysis. Meta-analysis has been widely used in the field of spatial economics since the first meta-analysis study by Stanley and Jarrell (1989). More recently, meta-analysis has been used inter alia by Kremers et al. (2002) in studies on price elasticities, with 25 studies; Abreu et al. (2005) in convergence studies, with 48 studies and 619 observations; Debrezion et al. (2007) in studies on property values, with 73 observations, and Celbis et al. (2015) in studies on infrastructure and trade, with 36 studies and 542 observations. In the infrastructure and economic growth literature, studies that use meta-analysis are rather limited. Button's (1998) study was the first attempt to employ a meta-analysis for empirical studies of that relationship. Button (1998) uses output elasticities from 28 studies to

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estimate a background model with OLS, and finds evidence that studies that use US data tend to produce lower elasticities than other studies. Nijkamp and Poot (2004) investigate the link between long-run growth and infrastructure, on the basis of 39 studies by means of rough set analysis. Their results indicate that model specification, the type of data used, and the econometric methodology, all matter for measuring the impact of fiscal policy on growth. Melo et al. (2013) examine the productivity and transport infrastructure relationship, on the basis of 563 observations from 33 studies. They find evidence of higher productivity effects for roads, compared with other transport modes, such as airports, railways, and ports. They also find that estimates of the output elasticity of transport tend to be higher for the US economy, compared with those of European countries. And, finally, Bom and Ligthart (2014) develop a meta-regression model to quantify the contribution of public capital to private sector production, using578 estimates from 68 primary studies. They conclude that the average output elasticity of core public capital is 0,193 in the long run. Meta-analysis typically involves a regression-type analysis of a sample of original studies, where the dependent variable is the estimated effect reported in a study, and the explanatory variables are the characteristics of the original studies (Waldorf and Byun, 2005). The estimated effects of the studies need to be comparable; in order compare the estimated effects, it is necessary for the estimates to be measured in a common metric. It is possible to transform regression coefficients to Table 2 Descriptive analysis of variables. Freq.

%

Std. deviation

Min

Max

Dependent variable Negative Insignificant Positive

103 412 430

10,90 43,60 45,50

– – –

– – –

– – –

Explanatory variables Mid-year of the observations Time span of the observations Publication year Methodology - growth regression Methodology - production function Methodology - total factor P. Methodology - spatial regression Estimation - FE Estimation - RE Estimation - OLS Estimation - GMM Input measure - ratio Input measure - capital Input measure - length Output measure - GVA Output measure - GDP Output measure - employment Country - EU Country - USA Country – other economies Infra. type - public infrastructure Infra. type - land transport Infra. type - air transport Infra. type - water transport Infra. type – telecommunication Infra. type - roads Infra. type - railways Infra. type - airports Infra. type - ports Infra. type - other infrastructure Scope - interregional Scope - interstate Scope - interprovincial Spatial weight - distance Spatial weight - contiguity Spatial weight - economic Manufacturing sector Data type - cross section Data type - panel

– – – 209 443 50 49 343 21 211 71 289 388 82 200 612 24 538 168 239 204 352 46 46 45 297 67 36 36 37 293 73 78 45 165 66 61 161 725

– – – 22,1 46,8 5,3 5,19 36,3 2,2 22,3 7,51 30,6 41,1 8,7 21,2 64,8 2,5 56,9 17,8 25,3 21,6 37,2 4,9 4,9 4,8 31,4 7,1 3,8 3,8 3,9 31,0 7,7 8,2 4,8 17,5 7,0 6,5 17,0 76,7

9,20 9,94 5,40 0.41 0.50 0,22 0,22 0.48 0.15 0.42 0.26 0,46 0,49 0.28 0.41 0,48 0,16 0.49 0.38 0.43 0.41 0,48 0,21 0,21 0.21 0.46 0.26 0,19 0,19 0,19 0.46 0.27 0.27 0.21 0,38 0.25 0.24 0.38 0.42

1969 1 1995 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2006 33 2014 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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Z. Elburz et al. / Journal of Transport Geography 58 (2017) 1–8

elasticities if the studies concerned report all related information. Researchers mostly prefer to exclude those studies that do not report related information for transforming the estimated effects. This can lead however, to a serious loss of information. To be able to compare the studies, regardless of the model specification, and to transform the estimated effects, one can focus on the direction and the significance, instead of on the magnitude of the study estimates, with an alternative method. In our applied econometric work, this alternative model is the ordered probit model. This has become common practice in a

situation where the construction of a common metric to characterize the variation in the underlying primary studies is problematic (de Groot et al., 2009). The ordered probit model has been conducted in meta-analysis by various researchers, for instance, Babetskii and Campos (2007) on reform and growth; Jeppesen et al. (2002)on environmental regulations; Waldorf and Byun (2005)on age structure and fertility; Koetse et al. (2006)on investments and uncertainty; Case et al. (2008) on income and child health; and Horváthová (2010) on environmental and financial performance relationships.

Table 3 Weighted ordered probit analysis. Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Methodology Production function Total factor productivity Growth regression Spatial regression

0,362 (0,264) 0,771 (0,402) −0,416* (0,205) 0,040 (0,374)

0,088 (0,238) 0,675 (0,394) −0,357 (0,204) −0,226 (0,385)

0,206 (0,222) 0,716 (0,383) −0,478* (0,206) −0,378 (0,370)

0,249 (0,222) 0,766* (0,376) −0,250 (0,210) −0,026 (0,383)

0,307 (0,258) 0,814* (0,387) −0,139 (0,218) 0,399 (0,399)

0,138 (0,232) 0,736 (0,382) −0,152 (0,207) −0,098 (0,394)

Estimation Fixed effects Random effects Ordinary least squares Generalized method of moments

0,074 (0,265) 0,461 (0,308) −0,174 (0,232) −0,327 (0,370)

−0,103 (0,253) 0,065 (0,289) −0,495 (0,221) −0,579 (0,330)

−0,063 (0,238) 0,268 (0,287) −0,378 (0,211) −0,428 (0,318)

−0,012 (0,229) 0,409 (0,309) −0,425* (0,209) −0,306 (0,290)

−0,092 (0,255) 0,427* (0,310) −0,349* (0,230) −0,245 (0,344)

−0,065 (0,242) 0,213 (0,302) −0,548*(0,214) −0,460 (0,297)

Input measure Index Capital Length

0,535* (0,234) 0,162 (0,220) 0,737*** (0,221)

0,447 (0,241) 0,015 (0,222) 0,659** (0,217)

0,590* (0,245) 0,163 (0,228) 0,819*** (0,229)

0,428 (0,237) 0,204 (0,237) 0,605** (0,221)

0,286 (0,234) 0,116 (0,233) 0,511* (0,206)

0,361 (0,236) 0,088 (0,231) 0,466* (0,205)

Output measure GVA GDP Employment

−0,170 (0,308) 0,013 (0,199) 0,831 (0,487)

0,133 (0,335) 0,027 (0,209) 0,230 (0,426)

0,108 (0,310) 0,180 (0,208) 0,635 (0,431)

0,166 (0,303) 0,274 (0,210) 0,636 (0,457)

0,019 (0,315) 0,126 (0,203) 0,554 (0,503)

0,219 (0,327) 0,165 (0,207) 0,303 (0,451)

0,516** (0,175)

0,481** (0,182)

Country EU USA Other

−0,959** (0,275)

−0,498 (0,265) −0,098 (0,181)

−0,179 (0,184)

Infrastructure type Public infrastructure Land transport Air transport Water transport Telecommunication Roads Railways Airports Ports Other infrastructures

1,243*** (0,229) 0,875*** (0,157) −0,407 (0,278) −0,134 (0,221) −0,471* (0,208) 0,044 (0,264)

1,026*** (0,229) 0,441** (0,146) −0,601* (0,277) −0,196 (0,225) −0,529* (0,209) −0,135 (0,253)

1,100*** (0,244) 0,478** (0,150) −0,526* (0,263) −0,163 (0,227) −0,498* (0,211) −0,128 (0,255)

Scope Interstate Interprovincial Interregional

−0,561 (0,291) −1,079*** (0,325) −0,676*** (0,196)

0,915*** (0,277) −0,915** (0,325) −0,443* (0,173)

Spatial weight Distance Contiguity Economic

−0,303 (0,362) 0,327 (0,242) −0,393 (0,271)

Sectors Manufacturing sector

0,107 (0,187) 0,405* (0,163) −0,422 (0,223) −0,306 (0,258)

0,085 (0,207) 0,524** (0,163) −0,480* (0,219) −0,362 (0,252)

0,134 (0,184) 0,373* (0,158) −0,487* (0,224) −0,368 (0,258)

0,818** (0,264) 1,025** (0,325) 0,513** (0,185)

0,926 ***(0,263) −0,964** (0,311) −0,581** (0,191)

−0,823 **(0,291) −0,947** (0,312) −0,652*** (0,196)

−1,040*** (0,273) −0,867 ** (0,310) −0,501** (0,178)

−0,447* (0,356) 0,337 (0,250) 0,532** (0,277)

−0,462 (0,357) 0,421 (0,248) −0,620* (0,275)

−0,486 (0,374) 0,440 (0,246) −0,663* (0,276)

−0,429 (0,384) 0,353 (0,240) −0,528 (0,277)

−0,494 (0,369) 0,376 (0,247) −0,604* (0,279)

0,421 (0,353)

0,365 (0,368)

0,349 (0,350)

0,386 (0,351)

0,425 (0,359)

0,398 (0,365)

Research period Mid-year Time span

0,031* (0,015) 0,029* (0,012)

0,032* (0,015) 0,021** (0,011)

0,033* (0,014) 0,016 (0,010)

0,036* (0,015) 0,011 (0,011)

0,034* (0,014) 0,019 (0,011)

0,036* (0,015) 0,014 (0,011)

Data type Cross-sectional Panel

0,686* (0,318) 0,530* (0,250)

0,362* (0,307) 0,164 (0,226)

0,394 (0,300) 0,283 (0,223)

0,518 (0,304) 0,307 (0,220)

0,691* (0,322) 0,362 (0,240)

0,448 (0,311) 0,193 (0,221)

Publication data Publication year Limit point 1 Limit point 2 Pseudo-R2 Log pseudo likelihood

−0,035 (0,020) −9,492 −8,133 0.1018 −38,269

−0,040 (0,020) −15,777 −14,434 0,089 −38,795

−0,033 (0,019) −1,232 0,125 0.0978 −38,440

−0,037 (0,019) −2,264 0,932 0,0816 −39,132

−0,040* (0,019) −13,359 −12,038 0,0782 −39,277

−0,041* (0,020) −11,140 −9,819 0,0751 −39,408

Standard errors in parentheses. *, **, *** significant at, respectively, a 10%, 5% and 1% level.

Z. Elburz et al. / Journal of Transport Geography 58 (2017) 1–8

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Table 4 Marginal effects of weighted ordered probit analysis. Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Methodology Production function Total factor productivity Growth regression Spatial regression

0,106 (0,102) 0,288 (0,127) −0,161* (0,077) 0,016 (0,149)

0,035 (0,095) 0,257 (0,132) −0,139 (0,076) −0,088 (0,146)

0,081 (0,088) 0,270 (0,126) −0,184* (0,075) −0,145 (0,133)

0,098 (0,087) 0,287* (0,120) −0,098 (0,087) −0,010 (0,152)

0,121 (0,101) 0,301* (0,119) −0,055 (0,086) 0,048 (0,085)

0,054 (0,092) 0,277 (0,124) −0,060 (0,081) −0,039(0,157)

Estimation Fixed effects Random effects Ordinary least squares Generalized method of moments

0,029 (0,105) 0,180 (0,115) −0,069 (0,091) −0,126 (0,137)

−0,040 (0,100) 0,025 (0,115) −0,193* (0,083) −0,216 (0,109)

−0,025 (0,094) 0,106* (0,113) −0,149 (0,081) −0,163 (0,133)

−0,004 (0,091) 0,161 (0,118) −0,166* (0,080) −0,118 (0,108)

−0,036 (0,101) 0,168 (0,117) −0,137 (0,089) −0,096 (0,130)

−0,025 (0,095) 0,085 (0,119) −0,214* (0,080) −0,174 (0,104)

Input measure Index Capital Length

0,210* (0,088) 0,064 (0,087) 0,280*** (0,075)

0,176 (0,093) 0,006 (0,088) 0,253** (0,077)

0,231* (0,092) 0,065 (0,090) 0,308*** (0,075)

0,169 (0,091) 0,081 (0,093) 0,234** (0,080)

0,113 (0,092) 0,066 (0,092) 0,200* (0,077)

0,143 (0,092) 0,035 (0,091) 0,183* (0,078)

Output measure GVA GDP Employment

−0,066 (0,119) −0,005 (0,790) 0,307 (0,150)

0,053 (0,133) 0,010 (0,082) −0,091 (0,168)

0,043 (0,123) 0,071 (0,081) 0,243 (0,150)

0,066 (0,121) 0,108 (0,081) 0,244 (0,160)

0,007 (0,125) 0,049 (0,080) 0,215 (0,182)

0,087 (0,129) 0,065 (0,081) 0,120 (0,176)

0,203** (0,067)

0,189** (0,070)

Country EU USA Other

−0,349*** (0,085)

−0,192 (0,097) −0,039 (0,071)

−0,070 (0,072)

Infrastructure type Public infrastructure Land transport Air transport Water transport Telecommunication Roads Railways Airports Ports Other Infrastructures

0,419*** (0,053) 0,337*** (0,056) −0,155 (0,099) −0,053 (0,086) −0,177* (0,071) 0,017 (0,105)

0,366*** (0,063) 0,174** (0,056) −0,221* (0,089) −0,076 (0,086) −0,196* (0,069) −0,053 (0,098)

0,386*** (0,064) 0,188** (0,058) −0,196* (0,088) −0,064 (0,088) −0,186* (0,071) −0,050 (0,099)

Scope Interstate Interprovincial Interregional

−0,210 (0,098) −0,353*** (0,072) −0,255*** (0,067)

−0,316*** (0,074) −0,313** (0,083) −0,171* (0,064)

Spatial weight Distance Contiguity Economic

−0,117 (0,135) 0,129 (0,949) −0,150* (0,097)

Sectors Manufacturing sector Research period Mid-year Time span

0,042 (0,074) 0,160* (0,079) −0,160 (0,079) −0,118 (0,095)

0,034 (0,082) 0,206** (0,063) −0,180* (0,075) −0,139 (0,091)

0,053 (0,076) 0,148* (0,062) −0,182* (0,076) −0,140 (0,093)

−0,289** (0,076) −0,340** (0,075) −0,197** (0,066)

−0,319*** (0,070) −0,324** (0,076) −0,221** (0,067)

−0,291** (0,083) −0,321** (0,078) −0,246*** (0,067)

−0,347*** (0,066) −0,192** (0,064) −0,192** (0,064)

−0,169 (0,124) 0,133 (0,097) −0,198 (0,092)

−0,176 (0,123) 0,166 (0,095) −0,227* (0,087)

−0,182 (0,128) 0,173 (0,094) −0,240* (0,085)

−0,163 (0,135) 0,140 (0,093) −0,197 (0,093)

−0,182 (0,125) 0,149 (0,096) −0,221* (0,089)

0,165 (0,134)

0,144 (0,142)

0,138 (0,135)

0,152 (0,135)

0,167 (0,136)

0,157 (0,140)

0,012* (0,005) 0,011* (0,004)

0,012* (0,005) 0,008 (0,004)

0,013* (0,005) 0,006 (0,004)

0,014* (0,005) 0,004 (0,004)

0,013* (0,005) 0,007 (0,004)

0,014* (0,005) 0,005 (0,004)

Data type Cross-sectional Panel

0,268* (0,118) 0,207* (0,094)

0,143 (0,120) 0,065 (0,083)

0,156 (0,117) 0,112 (0,087)

0,204 (0,117) 0,121 (0,085)

0,269* (0,120) 0,142 (0,093)

0,177 (0,121) 0,076 (0,087)

Publication data Publication year

−0,014 (0,007)

−0,015 (0,008)

−0,013 (0,007)

−0,014 (0,007)

−0,016* (0,007)

−0,016* (0,008)

Standard errors in parentheses. *, **, *** significant at, respectively, a 10%, 5% and 1% level.

Since our study tries to combine empirical studies that use either regression coefficients or elasticities, we use an ordered probit model to explore the study characteristics that lead to certain unambiguous results (Stanley and Doucouliagos, 2012). We collect also the t-statistics, standard errors or p-values from 945 observations of 43 empirical studies in order to capture the statistical significance. The ordered probit model is built around a latent regression, the structure of the model is as follows (Greene, 2002). y
¼ Xβ þ ε;

ð1Þ

where β is the vector of parameters, and ε represents the normally

distributed error terms. The classification of the estimated effects of y* is described as1: Negative and Significant Estimates y = 0if y* b 0; Insignificant Estimates y = 1if 0 b y* b μ; Positive and Significant Estimates y = 2if μ b y*. De Groot et al. (2009) note that the interpretation of the estimated coefficients of an ordered probit analysis is not straightforward, and Greene (2002) emphasizes that it is quite unclear how the coefficients 1

10% significance is usually baseline.

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in the ordered probit model should be interpreted without a fair amount of extra calculation. That is why we compute the marginal effect, which is the effect on the dependent variable that results from changing an independent variable by a small amount (Wooldridge, 2002), to simplify the comparison of the estimated effects. 4. Data The empirical literature on the public infrastructure and regional growth relationship is vast and is still continuing to increase. As a first step in the search process for studies in our meta-analysis, we defined keywords such as ‘public infrastructure’, ‘transport infrastructure’, ‘economic growth’, ‘regional disparity’, and ‘regional growth’ in various combinations to search for relevant studies, by using the ISIWeb of Knowledge, Research Papers in Economics (RePEc), and Google Scholar. After gathering the initial studies, we checked the references of those studies with the snowball technique to identify and add more observations. We collected the studies that use public infrastructure at least as one of the explanatory variables and regional economic outcome as a dependent variable. Ultimately, our data set consisted of 945 observations from 43 studies conducted between 1995 and 2014. As mentioned in Section 3, the dependent variable of our model is categorical in nature ordered according to a negative, insignificant, and positive effect. Tables 1 and 2 show the descriptive statistics and further basic information about the studies that we have employed in our meta-analysis. There is no consensus about the number of observations per study in the meta-analysis. Since selecting only one observation for each study leads to loss of information, we preferred to add all estimates reported in the studies. However, in our case, the number of observations per study ranges from 1 to 100, which creates a sampling weight problem. According to Nelson and Kennedy (2009), using multiple observations from one study is likely to cause econometric problems if the researcher uses unweighted observations. As a sampling weight, we use a weighting method which is equal to 1/number of observations per each study. We were able to identified 12 study characteristics based on Section 2 that can explain the sources of variation in the findings of the empirical studies in the literature. These explanatory variables are as follows: research methodology (production function, total factor productivity, growth model or spatial regression); estimation (fixed effects, random effects, ordinary least squares, or generalized method of moments); measurement of infrastructure (ratio, monetary or length); measurement of output (GVA, GDP or employment); country coverage (USA, EU Member State or other countries); type of infrastructure (public infrastructure, land transport, air transport, water transport, telecommunication, roads, railways, airports, ports or other infrastructure); geographical scale (interregional, interstate or interprovincial); type of spatial weight (based on distance, contiguity or economic outcome); type of sector (manufacturing sector); research period (mid-year of the observations and time span); type of data (panel, cross-section); and publication year. We coded these explanatory variables as 0 or 1, except for continuous variables, mid-year, publication year, and time span. A solid research methodology is a substantial study prerequisite for our meta-analysis. The production function has been the most common and popular methods since the seminal work of Aschauer (1989) while more recently, the Barro-type growth model, and especially the spatial regression model, have attracted much attention from researchers. In our study, there are 209 observations that use the growth model, 443 observations that employ the production function method, 50 observations for total factor productivity and 49 observations for spatial regression models. As Button (1998) emphasized already, countries with a high GDP per capita do have lower elasticities; so, it is not a surprise that the impact of infrastructure on regional growth and productivity can differ among developed or developing countries. To test this view with more recent data, we include studies that employ data from the

US (168 observations), the EU Member States (538 observations), and other countries, for instance, China, India and Turkey (239 observations). We expect the studies that use data from the USA or the EU member states to have a lower probability for obtaining positive results than other countries. Other important study characteristics that might affect the results of the studies are the operational measurement of infrastructure and output. Bröcker and Rietveld (2009) pointed out that physical measures of infrastructure make more sense than monetary units. However, many studies prefer to use public capital to measure infrastructure and GDP per capita to measure the dependent variable. In our dataset, 289 observations used an index to measure infrastructure, 82 observations used length (km) and 388 observations used monetary terms. Besides measurement, the type of infrastructure could also have an effect on economic benefits owing to the differences of each infrastructure's market size and production cost asymmetries (Deng, 2013). We hypothesize that land - locked transport -especially road infrastructures - and telecommunication infrastructure have a positive effect on finding significant and positive results. Since our study focuses on studies that employ sub-national data, it is important to distinguish the differences in the effects of the interrelationship at sub national levels such as: regions (293 observations), states (73 observations), and provinces (78 observations). Nijkamp (1986) states that the time period of the research that examines the impact of infrastructure should be long enough to cover all direct and indirect effects; our data set shows that the average time span2 of all studies in the meta-analysis is 15 years and the maximum value is 33 years. 5. Results The results of our weighted ordered probit analysis on the basis of six different models are shown in Table 3. The first three models include an aggregate infrastructure type, such as public infrastructure, land, air or water transportation variables, while Models 4, 5 and 6 include detailed public infrastructure variables with different country coverage. We have also computed the marginal effects for the six ordered probit models to facilitate the comparison of the outcomes3 (Table 4). According to the applied econometric results, the growth regression is a substantial factor for obtaining significant results. Using a growth model has significant negative effects, whereas total factor productivity has significant positive effects on economic benefits. On the other hand, employing the production function method and the spatial regression do not seem to have any impact on regional growth. Estimation methods also matter for obtaining significant results. Estimating the models with OLS estimation decreases the probability of finding positive effects by 21% in Model 6 (Table 4). Unlike the study of Melo et al. (2013), we find that studies that employ data from the US are more likely to find a negative impact of public infrastructure on regional growth by 35,1% (Model 1). Interestingly, studies that use the EU member states data tend to find strongly significant positive effects by 20% (Model 3), while studies that employ data from China, India or Turkey–coded other countries - tend to offer no significant effects on regional growth. The findings suggest that the EU member states still benefit from public transport infrastructure due to the lack of a well-connected transport infrastructure network. These results are in line with studies such as Banister and Berechman (2001), Canning and Bennathan (2000), Bröcker and Rietveld (2009), and Fernald (1999) which highlight the different effects of transport infrastructure investments on specific countries with different development levels. One of the most important results of the ordered probit analysis is the impact of measurement type of infrastructure on regional growth. 2 Most recent year of observation minus earliest year plus one (Nijkamp and Poot, 2004). 3 Results of the sensitivity analysis based on Fisher Z effect sizes with WLS regression is available upon request.

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The findings indicate that measuring infrastructure by an index or length increases the chance of finding positive significant effects by 23% and 30% (Model 3), while measuring infrastructure by monetary terms has no effect on finding significant effects, on contrary to Melo et al. (2013). The results also show that the probability of reaching strongly significant positive effects is greater when studies focus on land transport, telecommunication or roads, a result that is in line with studies by Demurger (2001), Del Bo and Florio (2012), Ding et al. (2008), and Hong et al. (2011). On the other hand, studies that focus on air transport, railways and ports tend to find significantly negative effects on regional outcomes. These results underscore the work of Yamaguchi (2007) who pointed out that peripheral areas suffered negative growth from the infrastructure development in air transport in Japan. Another substantial result is about the spatial spillover effects. In our analysis, we test this effect by interrelations of regions, provinces and states. Our findings show that studies that investigate the interregional, interprovincial or interstate effects of public infrastructure on growth have a lower chance of finding a positive effect. As Rietveld (1989) states, loss of markets due to increasing competition may cause these negative spillover effects between sub national levels. The studies that employ spatial weights based on economic outcomes such as GDP or GVA tend to find significant negative effects, by 24% (Model 4) which corroborates the hypothesis that including spatial effects in models causes negative outcomes of public infrastructure. We find that reaching positive results are more likely, as the time span and middle year of the observations from the study increase by 1. The results of the data type variables also show the importance of obtaining long-run effects of investments that one dominated by productivity effects. As a supporting result for the time span effect, studies that use cross-sectional data which tend to yield long-run responses (Baltagi and Griffin, 1984) are more likely to find positive effects of infrastructure investments. Finally, the publication year of a study has an effect on obtaining significant results. According to our results, recent studies have a higher probability to find negative effects of infrastructure on regional growth. Contrary to our expectations, some study characteristics, such as output measurement and selection of a particular sector, appear to have no effect on obtaining positive, negative, or insignificant outcomes, which is contrasting to the results of Fernald (1999). 6. Concluding remarks Heterogeneity in outcomes of the relationship between public infrastructure and regional economic growth has attracted relatively little attention so far. In our study, we have tried to understand the effects of study characteristics on obtaining positive, negative, or insignificant results on this relationship and to fill a gap in the empirical literature. We use meta-analysis to review, synthesize, and compare the related literature and focus on the direction and significance of the estimated coefficients, instead of on the magnitude of the effect, by employing an ordered probit model. Our findings do not able to answer the order of magnitude of the effects of public infrastructure on regional economic growth, but instead, they do give a clear picture of the model building process to investigate this relation and of the sign direction of impacts. The results of the ordered probit model and the marginal effects indicate that research methodology, estimation method, measurement of input, country coverage, type of infrastructure, geographical scope, spatial weight, research period, data type and publication year all play a prominent role in obtaining positive or negative results. On the other hand, the selection of a particular sector or output measure seem to have no effect on the results from our primary studies. These variables are crucial for policy makers who utilize public infrastructure, especially transport infrastructure, to promote regional growth and reduce regional disparities. Considering marginal effects of these variables, we can conclude that telecommunication infrastructure investments seem to

7

be the most effective type of infrastructure in all countries. Another important conclusion for policy makers is that in the long run, it is more likely to reach positive effects of infrastructure investment on regional growth due to the long gestation period. And finally, policy makers should always take into account the emergence of negative spillover effects of public infrastructures in a given area on neighbouring regions. Acknowledge This research was funded by 2214-A programme of The Scientific And Technological Research Council of Turkey (TUBITAK). We are grateful to Professor Jacques Poot for helpful comments and suggestions. References Abreu, M., de Groot, H.L.F., Florax, R.J.G.M., 2005. A meta-analysis of b-convergence: the legendary 2%. J. Econ. Surv. 19, 389–420. Acconcia, A., Del Monte, A., 2000. Regional development and public spending: the case of Italy. StudiEconomici 72. Álvarez, A., Arias, C., Orea, L., 2006. 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