Connective Financing Chinese Infrastructure Projects and ...pubdocs.worldbank.org/en/776921560791835421/Strange-ABCDE.pdf · Connective Financing Chinese Infrastructure Projects and

Connective FinancingChinese Infrastructure Projects and the Diffusion of Economic

Activity in Developing Countries

Austin Strange (Harvard University)With Richard Bluhm, Axel Dreher, Andreas Fuchs,

Bradley Parks, and Michael Tierney

World Bank ABCDE

June 2019

Motivation

Source: mtega.com

Austin Strange Connective Financing June 2019 2 / 27

Congestion, transport costs and dispersion

Many developing countries have high spatial concentration of activity:

Dar es Salaam home to 8% of the Tanzanian population and 53% ofmanufacturing value-added in 2002–Internal transport costs are veryhigh and isolate secondary cities (Storeygard 2016)

Many African cities congested, crowded and unproductive (WorldBank 2018)

Evidence suggests transport and other connective infrastructuredisperses economic activity in developing countries: e.g. see Straub(2015) on Brazil, Baum-Snow et al. (2017) on China, or Bayes(2017) on Bangladesh

However, infrastructure projects have been underfinanced in recentdecades . . .


Official development assistance in the transport sector


What’s new?

We investigate effect of Chinese development finance on spatialdistribution of economic activity

Georeferenced data on China’s development projects in the entiredeveloping world

Inequality in light intensity as proxy for distribution of economicactivity

Exploit domestic overcapacity in China as a global project inputsupply shock

Main finding: Chinese government-financed projects help diffuseeconomic activity in developing countries at all levels ofaggregation


Should “connective financing” lower spatial inequality?

Infrastructure investments

reduce trade costs and make migration easier (Krugman 1996)

lower inter-region price differences/volatility (Cirera and Arndt 2008)

lower cost of firm entry (Shiferaw et al. 2015, Ghani et al. 2016)

lower cost of inputs/consumption (Bayes 2007, Parada 2016)

raise land values (Donaldson and Hornbeck 2016)

Transport investments in one region can spill over into others and haveunintended consequences (e.g., more labor moving from poor region)


Spatial inequality and development finance

Western aid mostly unsuccessful in targeting inequality (Briggs 2017)

Mixed signals from Chinese-financed projects...

I Subject to regional and ethnic favoritism in Africa (Dreher et al. 2019)I Worsen perceptions of local corruption (Isaksson and Kotsadam 2018)I Promote short-to-medium run economic development (Dreher et al.

2017, 2019)


Chinese development finance polarizes observers

China as a pernicious partner

“rogue aid” (Naim 2009)

“debt trap diplomacy” (Chellaney 2017)

“white elephants” (Australian Dev. Minister 2018)

China as a promising alternative

“no strings attached” (State Council 2011)

“record time” (Senegalese President 2008)

“connectivity is the foundation of development” (Xi Jinping 2017)

China focuses on transport and economic infrastructure, replacingand surpassing investments of Western donors in this sector


Chinese financing in the 1970s: TAZARA railway


Chinese financing today: Addis Ababa light rail

AidData record.


AidData’s China data

AidData first released data on Chinese Official Finance in 2013 for Africaand in 2017 for the developing world

Tracking Underreported Financial Flows (TUFF) method based onpublicly available information (3.05 sources/project)

I documents from Chinese ministries and embassiesI aid and debt info management systems in recipient countriesI case study and field research by scholars and NGOsI English, Chinese and local-language news reports

Systematic, transparent and replicable

Categorization scheme for Chinese peculiarities

Projects tracked from pledge to completion

Accessible in China and for users worldwide

Flows Status Sectors


AidData’s China data


Addis Ababa light rail on china.aiddata.org


china.aiddata.org

Project locations and values (2000–2014)

Geocoded all projects that reached the implementation stage

3,485 projects worth 2014 US$273.6b at 6,184 sites in 138 countries


Measuring spatial inequalities

Dispersion in nighttime light intensity (Henderson et al. 2018)

Divide the entire world into a grid of 6 arc minute cells and intersectthe grid with the global administrative boundaries

Compute the sum of light (si ), the land area in km2 (ai ), and thelight intensity in the cell (xi = si/ai )

Calculate the spatial Gini coefficient in region r as

Gr =

∑i w

ni

∑nj wj |xi − xj |

2∑n

i wi∑n

i wixi

where wi = ai/∑n

i ai is an area weight and n is the number of lit cells

The index captures the overall dispersion of economic activity, i.e. theproduct of the distributions of population and light per capita


Changes in spatial inequalities (2000–2013)

First-order regions (ADM1) Observations Mean St. dev. Min Max

Gini of light per km2, within 29881 0.51 0.18 0.00 0.85Gini of light per km2, between 23744 0.45 0.20 0.00 0.98


How to identify the effects of Chinese projects?

China needs lots of material to build all its homes, trains andtunnels. Even so, it produces more than it can use.(Economist, 09/09/17)

[Mr Xi] hopes to create new markets for Chinese companies, suchas high-speed rail firms, and to export some of his country’svast excess capacity in cement, steel and other metals.(Economist, 05/17/17)

Nearby, along a newly laid road, another Chinese factory wasproviding cement for tunnel construction. Nearly everything forthe Laos project is made in China. Almost all the labor force isChinese. (NY Times, 05/13/17)


Identification strategy I

Bartik-style supply-shock instrument (similar to Autor et al. 2013)

Variation across time: Proxy for potential development project inputsI China’s production of steel, cement, iron, timber, glass, and aluminum

Variation across regions: Exposure to Chinese development projectsI “Probability of receiving a development project”, i.e.

pir =∑2014

2000 I(ChnProjectirt > 0)/15

Similar to a differences-in-differences setting

Exploit differential effect of Chinese project inputs on aid toregions with high compared to low exposure to Chinesedevelopment projects

Works if differences in spatial inequality in high (low) exposureregions is not affected differently by China’s input production


Variation in overcapacity


Identification strategy II

Equation(s) of interest:Girt = βChnProjecti ,t−2 + x′irtγ + µir + λit + εirt (1)

ChnProjecti ,t−2 = δ(Ft−3 × ρir ) + x′irtπ + ωir + φi ,t−2 + υir ,t−2 (2)

ChnProjecti ,t−2 is a dummy for a Chinese project (one for 3 yrs afterassumed “completion”)

Ft−3 is the principal factor of the time series of six project inputs

ρir is the probability of receiving Chinese projects

xirt is a vector of controls (e.g., population density)

ωir and µir are region FEs (removing ρir )

φi ,t−2 and λit are country-year FEs (partialling out all commontrends)


OLS and reduced form (ADM1 and ADM2)

First-order admin Second-order adminAll Trans. All Trans.(1) (2) (3) (4)

Panel a) OLS estimates

ChnProjecti,t−2 -0.0004 -0.0080** -0.0031* -0.0030(0.0019) (0.0035) (0.0018) (0.0034)

Panel b) Reduced-form estimates

Ft−3 × ρir -0.0215*** -0.0646*** -0.0290*** -0.0584**(0.0066) (0.0176) (0.0062) (0.0249)

Observations 29,810 29,810 356,046 356,046Regions 2,668 2,668 32,603 32,603Countries 158 158 129 129


2SLS and first stages (ADM1 and ADM2)


Panel a) IV estimates

ChnProjecti,t−2 -0.0541*** -0.0491*** -0.0436*** -0.0397**(0.0186) (0.0123) (0.0096) (0.0177)

Panel b) First-stage estimates

Ft−3 × ρir 0.3967*** 1.3143*** 0.6643*** 1.4710***(0.0801) (0.1519) (0.0762) (0.2438)

Kleibergen-Paap F-stat 24.51 74.87 75.92 36.40Observations 29,810 29,810 356,046 356,046Regions 2,668 2,668 32,603 32,603Countries 158 158 129 129


Unpacking concentration (2SLS at ADM1 and AMD2)

Non-zero Quintile sharesshare 0-20% 20-40% 40-60% 60-80% 80-100%(1) (2) (3) (4) (5) (6)

Panel a) First-order admin

ChnProji,t−2 0.0390** 0.0086** 0.0093* 0.0156** 0.0134 -0.0469**(0.0185) (0.0042) (0.0053) (0.0068) (0.0117) (0.0201)

Kleibergen-Paap F-stat 30.05 20.68 20.68 20.68 20.68 20.68Regions 2760 2424 2424 2424 2424 2424Countries 159 146 146 146 146 146

Panel b) Second-order admin

ChnProji,t−2 0.0258** 0.0048 0.0074** 0.0057 0.0195** -0.0374***(0.0109) (0.0030) (0.0036) (0.0056) (0.0078) (0.0132)

Kleibergen-Paap F-stat 99.64 49.61 49.61 49.61 49.61 49.61Regions 35544 23720 23720 23720 23720 23720Countries 129 123 123 123 123 123


Extensions

Diffusion of activity or increasing inequality?

Small and mostly negative effects on (interpolated) population,opposite but unstable effect on light per capita Regressions

Similar effects for between regional inequalities Regressions

Does not unambiguously imply that inequality in wages andproductivity is rising, could be an artifact of the data

Comparison with geocoded World Bank development finance

Use IBRD’s equity-to-loans ratio as an IV for geocoded WB projectlocations

Find no comparable effects of WB projects (overall or transportsector) but instrument is weak and LATE differs Regressions


Robustness

Identification

Effects similar to main results with different IVs: detrended factors,just steel, detrended steel, iron, cement etc. Regressions

Parallel trends assumption is satisfied: instrument does not affectsome subgroups more than others Plots

Timing and world regions

Meaningful effects when using different dummies (1 to 5 yrs)Regressions

Regional decomposition: effects driven by Africa, Asia, and EuropeRegressions

Financial amounts

Qualitatively similar results where amounts are known Regressions


Conclusion

We find that Chinese state-financed projects abroad reducespatial inequalities in economic activity within and betweenregions

Effects are sizable: “connective infrastructure” produces significantpositive spillovers

Our LATE is driven by big infrastructure projects requiring largeamounts of physical inputs

Broader impact of Chinese projects on wages and social structure is amore complex question


Thanks for your attention!

[email protected]


Projects and values

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Project status

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Project sectors

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Alternate concentration measures (ADM1 and ADM2)

Unweighted WeightedGini Bias-corr. Gini Theil CV(1) (2) (3) (4)

Panel a) First-order admin

ChnProji,t−2 -0.0497*** -0.0478** -0.0753** -0.1164*(0.0166) (0.0204) (0.0370) (0.0598)

Kleibergen-Paap F-stat 24.51 17.19 24.51 24.51Regions 2668 2132 2668 2668Countries 158 130 158 158

Panel b) Second-order admin

ChnProji,t−2 -0.0513*** -0.0387*** -0.0335** -0.0719***(0.0108) (0.0124) (0.0145) (0.0218)

Kleibergen-Paap F-stat 75.92 57.86 75.92 75.93Regions 32603 12712 32603 32603Countries 129 105 129 129


Population and per capita Ginis (ADM1 and ADM2)


Panel a) Population Gini

ChnProjecti,t−2 -0.0114* 0.0012 -0.0001 0.0054(0.0061) (0.0048) (0.0040) (0.0067)

Panel b) Per-capita Gini

ChnProjecti,t−2 0.0545** 0.0346 0.0343*** 0.0096(0.0248) (0.0217) (0.0101) (0.0139)

Kleibergen-Paap F-stat 23.82 73.90 73.81 35.37Observations 29,436 29,436 346,029 346,029Regions 2,636 2,636 31,720 31,720Countries 157 157 129 129

Go back.


Different instruments

Factors Det. Factors Steel Det. Steel(1) (2) (3) (4)

Panel a) IV estimates – first-order regions

ChnProjecti,t−2 -0.0541*** -0.1375* -0.0457*** -0.0295*(0.0186) (0.0769) (0.0165) (0.0175)

Kleibergen-Paap F-stat 24.51 4.35 27.86 23.17Observations 29,810 29,810 29,810 29,810

Panel b) IV estimates – second-order regions

ChnProjecti,t−2 -0.0436*** -0.0608*** -0.0372*** -0.0156(0.0096) (0.0183) (0.0091) (0.0111)


Go back.


Between regional inequalities (ADM1 and ADM2)

Between second-order Between first-orderAll Trans. All Trans.(1) (2) (3) (4)

Panel a) IV estimates – light Gini

ChnProjecti,t−2 -0.0589** -0.0472 -0.1264 -0.0517(0.0262) (0.0299) (0.1710) (0.0353)

Panel b) IV estimates – per capita Gini

ChnProjecti,t−2 0.0618** 0.0489** -0.5140 -0.0555(0.0273) (0.0229) (0.4426) (0.0464)

Kleibergen-Paap F-stat 17.18 63.57 1.54 21.75Observations 23,510 23,510 1,772 1,784Regions 2,129 2,129 – –Countries 126 126 157 158

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World Bank projects


Panel a) IV estimates

WBProjecti,t−2 -0.0756 -0.0293 -0.0430 -0.0641(0.0566) (0.0192) (0.0313) (0.0658)

Panel b) First-stage estimates

IBRDt−3 × ρir 0.0278*** 0.0672*** 0.0166 0.0478(0.0087) (0.0190) (0.0154) (0.0428)


Go back.


Parallel trends

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Short and medium-term effects

Project dummy one for X years . . .One Two Three Four Five(1) (2) (3) (4) (5)


ChnProjecti,t−2 -0.0802*** -0.0610*** -0.0541*** -0.0515*** -0.0503***(0.0263) (0.0202) (0.0186) (0.0181) (0.0182)

Observations 29,810 29,810 29,810 29,810 29,810Kleibergen-Paap F-stat 41.17 27.16 24.51 22.00 20.48


ChnProjecti,t−2 -0.0892*** -0.0550*** -0.0436*** -0.0374*** -0.0337***(0.0207) (0.0123) (0.0096) (0.0082) (0.0075)

Kleibergen-Paap F-stat 45.39 65.55 75.92 82.76 83.75Observations 356,046 356,046 356,046 356,046 356,046

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Regional decomposition

ContinentAfrica Americas Asia Europe

(1) (2) (3) (4)


ChnProjecti,t−2 -0.0924** -0.0069 -0.0442 -0.0607**(0.0348) (0.0083) (0.0439) (0.0260)



ChnProjecti,t−2 -0.0479*** -0.0114 -0.0553** -0.0810***(0.0130) (0.0140) (0.0227) (0.0205)


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Amounts and completed projects

Log US$ amounts Completed dummyAll Trans. All Trans.(1) (2) (3) (4)


ChnProjecti,t−2 -0.0085*** -0.0068*** -0.0542*** -0.0816***(0.0029) (0.0018) (0.0182) (0.0241)



ChnProjecti,t−2 -0.0088*** -0.0056** -0.0548*** -0.0671**(0.0023) (0.0026) (0.0121) (0.0314)


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Connective Financing Chinese Infrastructure Projects and ...pubdocs.worldbank.org/en/776921560791835421/Strange-ABCDE.pdf · Connective Financing Chinese Infrastructure Projects and