Aid curse with Chinese characteristics? Chinese development flows and economic reforms

Abstract

The emergence of China as a major development partner requires a reassessment of traditional donor–recipient dynamics. In addition to adopting new rhetoric like “South–South cooperation” or “Win–Win,” China has eschewed classifications and practices of the traditional donors of the Organisation for Economic Co-operation and Development’s Development Assistance Committee. Yet the “new approach” and willful ignorance may not spare China from encountering traditional development challenges. In this paper, we consider whether Chinese development efforts have disincentivized difficult economic reforms by providing recipient governments with alternative resources for building support. Using an instrumental variable approach with panel data covering 106 countries during the 2000–2014 period, we find that when comparing Chinese development flows to several Western donors, the former’s flows inhibit broader economic reform. The findings are robust to alternative specifications, data, instruments, and approaches.

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Notes

  1. 1.

    http://pacificpolicy.org/2013/05/fsm-tax-reform/, accessed 03-01-2018. The same domestic constituency was also broadly opposed to trade liberalization efforts (Brazys 2014).

  2. 2.

    After making a commitment of US $ 4 million in 2008 (http://china.aiddata.org/projects/40039, accessed 03-01-2018), China disbursed US $1.5 million in 2011 (http://www.fsmpio.fm/RELEASES/2011/february/02_14_11.html, accessed 03-01-2018) before committing a further US $10 million, or roughly 4% of FSM’s GDP in 2015 (http://www.guampdn.com/story/news/2015/12/03/fsm-seeks-end-compact-agreement-us/76755600/, accessed 03-01-2018).

  3. 3.

    Ibid. While working for the FSM’s chief executive, on numerous occasions this manuscript’s author overheard senior policymakers, including the president, invoke China as an alternative to US support. The unconditional nature of Chinese budgetary grants was touted in contrast to the US funding, which is approved by an annual meeting of a Joint Economic Management Committee (JEMCO), established by the Compact of Free Association treaty between the US and FSM (Brazys 2014), comprising three US and two FSM members, making budgetary decisions by simple majority vote.

  4. 4.

    Members of the FSM Congress, state legislatures, governors, and the president are allocated “representation funds” (see http://www.fsmcongress.fm/pdf%20documents/19th%20Congress/BILLS/CB%2019-32.pdf). While working for the FSM Executive, the present manuscript’s author overheard several second-hand accounts of politicians’ “representation funds” being utilized to buy rice, other consumables, or both for constituents. Those impressions are substantiated by various public auditor accounts that have found irregularities with respect to the funds (see http://www.kpress.info/index.php?option=com_content&view=article&id=531:pohnpei-files-criminal-charges-against-former-governor-john-ehsa&catid=8&Itemid=103 or http://www.fm/news/kp/2008/june08_3.htm). Representation funds are allocated from general funding, which would include tax revenues and unconditional budget support, such as the Chinese grants, but not conditional budget support like the US funding.

  5. 5.

    Invisible to consumers in that it is embedded in retail prices (Musgrave 1972).

  6. 6.

    A substantial body of literature exists on the possibility of an (Western) institutional aid curse. In particular, numerous scholars have investigated the extent to which aid might undermine domestic tax and revenue efforts (see, e.g., Moss et al. 2006; Besley and Persson 2014). Furthermore, as helpfully observed by a referee, heterogeneity amongst (and even within) DAC donors is likely. That said, as DAC donors all adhere, at least in principle, to DAC rules, we believe it to be reasonable to consider them as a whole.

  7. 7.

    Further details on the dataset can be found in de Soysa and Vadlamannati (2017, pp. 275–276). Also see https://www.fraserinstitute.org/economic-freedom/dataset?geozone=world&page=dataset.

  8. 8.

    See http://aiddata.org/data/chinese-global-official-finance-dataset.

  9. 9.

    It is important to note, however, that the methodology and the resulting dataset have been subject to scholarly critique: some projects have been found to be in error. See http://www.chinaafricarealstory.com/2013/04/rubbery-numbers-on-chinese-aid.html, https://www.aiddata.org/blog/a-rejoinder-to-rubbery-numbers-on-chinese-aid, https://www.economist.com/china/2017/10/12/despite-its-reputation-chinese-aid-is-quite-effective (Accessed 28-06-2018).

  10. 10.

    Note that DAC aid is measured in current US dollar prices, but the inclusion of year fixed effects should capture inflation.

  11. 11.

    The countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Luxembourg, the Netherlands, New Zealand, Norway, Portugal, Spain, South Korea, Sweden, Switzerland, the United Kingdom, and the United States. We enter gross disbursements for DAC aid because it is a more accurate measure of actual aid activity (commitments sometimes are cancelled or altered). We rely on gross commitments for China’s aid because, unfortunately, those are the only amounts reported by AidData. Updating the results with China aid disbursements when that information becomes available would be a useful exercise.

  12. 12.

    The empirical evidence, however, suggests a strong negative correlation between Chinese aid allocation and per capita income in recipient countries (Dreher et al. 2018).

  13. 13.

    We lag the IV by 3 years in order to allow sufficient time (at least 2 years) for steel production’s effects to materialize. Recall that our key explanatory variable is lagged by 1 year.

  14. 14.

    For the US government, the fractionalization index score is always zero. Hence, we replace it with legislature fractionalization.

  15. 15.

    The finding is corroborated by Faini (2006) and Beenstock (1980), who report that the size of the donor country’s aid budget is a function of its fiscal condition.

  16. 16.

    One obvious problem we encounter is that in a short panel of 15 years that includes fixed effects and a lagged EPR level might cause inconsistent estimations resulting in a downward bias of the coefficient, known as the “Nickell bias” (Nickell 1981). We therefore rely on a system-generalized method of moments (SGMM) estimator to counter that problem.

  17. 17.

    Our controls include the GDP growth rate, the Polity IV regime type, Laeven and Valencia's (2008) economic crisis dummy measure, which captures systemic banking, currency, and debt crises, IMF program participation as in Boockmann and Dreher (2003), a dummy assigning the value of 1 for a left-wing government in power and 0 otherwise sourced from Beck et al. (2001), and a measure of natural resource rents as a share of GDP. Full justification for the controls can be found in the Online Appendix.

  18. 18.

    The results are reported in the Online Appendix.

  19. 19.

    This “null” effect (rather than a positive effect) may well be the result of the heterogeneity of DAC donors as noted in footnote 6 above.

  20. 20.

    See the AidData glossary for a more elaborate definition (https://www.aiddata.org/pages/tuff-glossary, accessed 26-05-2019).

  21. 21.

    https://www.nytimes.com/2017/12/12/world/asia/sri-lanka-china-port.html, accessed 08-02-2018.

  22. 22.

    http://www.scmp.com/news/china/diplomacy-defence/article/2127626/china-funding-white-elephant-infrastructure-projects, accessed 08-02-2018.

  23. 23.

    http://www.pireport.org/articles/2013/12/20/chinese-loan-puts-tonga-difficult-position-%E2%80%98akilisi-pohiva, accessed 08-02-2018.

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Acknowledgements

We thank the editor and two anonymous referees for helpful comments. We also thank Jonathon Moses, Kai Ghering, Aidan Regan, Thomas Halvorsen, Indra de Soysa, Jo Jakobsen, Artur Tamazian, and participants at the IPE UCD Dublin Workshop, 2018, for helpful comments and suggestions.

Funding

This work was supported by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 693609 (GLOBUS).

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Correspondence to Krishna Chaitanya Vadlamannati.

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Appendices

Appendix 1: List of countries

Afghanistan Czech Republic Latvia Sao Tome and Principe
Albania Denmark Lebanon Saudi Arabia
Algeria Djibouti Lesotho Senegal
Angola Dominican Republic Liberia Serbia
Antigua and Barbuda Ecuador Libya Seychelles
Argentina Egypt Lithuania Sierra Leone
Armenia El Salvador Macedonia Singapore
Australia Equatorial Guinea Madagascar Slovakia
Austria Eritrea Malawi Slovenia
Azerbaijan Estonia Malaysia Solomon Islands
Bahrain Ethiopia Maldives South Africa
Bangladesh Fiji Mali South Korea
Barbados Finland Mauritania Spain
Belarus France Mauritius Sri Lanka
Belgium Gabon Mexico Sudan
Belize Gambia Moldova Suriname
Benin Georgia Mongolia Swaziland
Bhutan Germany Morocco Sweden
Bolivia Ghana Mozambique Switzerland
Bosnia-Herzegovina Greece Myanmar Syria
Botswana Grenada Namibia Taiwan
Brazil Guatemala Nepal Tajikistan
Brunei Guinea Netherlands Tanzania
Bulgaria Guinea-Bissau New Zealand Thailand
Burkina Faso Guyana Nicaragua Togo
Burundi Haiti Niger Tonga
Cambodia Honduras Nigeria Trinidad and Tobago
Cameroon Hungary North Korea Tunisia
Canada India Norway Turkey
Cape Verde Indonesia Oman Turkmenistan
Central African Republic Iran Pakistan Uganda
Chad Iraq Palestinian Adm. Areas Ukraine
Chile Ireland Panama United Arab Emirates
China Israel Papua New Guinea United Kingdom
Colombia Italy Paraguay United States of America
Comoros Jamaica Peru Uruguay
Congo, Democratic Republic Japan Philippines Uzbekistan
Congo, Republic Jordan Poland Vanuatu
Costa Rica Kazakhstan Portugal Venezuela
Cote d’Ivoire Kenya Qatar Vietnam
Croatia Kuwait Romania Yemen
Cuba Kyrgyz Republic Russia Zambia
Cyprus Laos Rwanda Zimbabwe

Appendix 2: Descriptive statistics

Variables Mean SD Minimum Maximum Observations
Change in Economic freedom index 0.03 0.19 −1.09 1.34 1982
Economic freedom index t − 1 6.71 0.92 2.93 8.86 1864
Chinese aid per capita 45.34 389.15 0.00 14,361 1793
Chinese aid per capita (log) −1.73 4.50 −6.91 9.57 1793
Chinese ODA per capita (log) −2.83 4.17 −6.91 8.50 1560
Chinese OOF per capita (log) −4.31 4.29 −6.91 8.69 1113
Share of discretionary projects 42.28 35.12 0.00 100.00 1263
GDP growth rate 8.00 1.58 4.78 11.12 2580
Polity democracy index 4.22 5.83 −82.48 104.48 2580
Economic crises 3.61 6.43 −10.00 10.00 2375
Natural resource rents/GDP 11.50 16.48 −1.19 100.37 2580
IMF program 0.03 0.17 0.00 1.00 2576
Left governments 0.09 0.28 0.00 1.00 2579

Appendix 3: Data sources and definitions

Variables Data definition and sources
EFR EFR is made up of five sub-indices capturing: expenditure and tax reforms; property rights and legal reforms; trade reforms; reforms related to access to sound money; labor, business and credit reforms. These five sub-indices are made up of 35 components of objective indicators. The final index is ranked on a scale of 0 (not free) to 10 (totally free) and is sourced from the Fraser Institute (available at: https://www.fraserinstitute.org/economic-freedom/dataset?geozone=world&page=dataset)
Change in EFR Year-to-year change in EFR sourced from the Fraser Institute
Chinese aid per capita Aid flows including ODA and OOF-type flows measured in US$ constant prices (logged) and sourced from the AidData’s Global Chinese Official Finance Dataset, version 1.0 (AidData 2017) developed by Dreher et al. (2018)
Chinese ODA per capita ODA flows measured in US$ constant prices (logged), sourced from the AidData’s Global Chinese Official Finance Dataset, version 1.0 (AidData 2017) developed by Dreher et al. (2018)
Chinese grants per capita Grants flows measured in US$ constant prices (logged) and sourced from the AidData’s Global Chinese Official Finance Dataset, version 1.0 (AidData 2017) developed by Dreher et al. (2018)
Chinese aid projects Count of all aid (ODA and OOF) projects in country i and year t (logged) based on the information sourced from AidData’s Global Chinese Official Finance Dataset, version 1.0 (AidData 2017) developed by Dreher et al. (2018)
Per capita GDP (log) GDP per head in 2000 US$ constant prices, sourced from the World Development Indicators (WDI) 2017, World Bank
Polity democracy Polity IV, polity 2 index coded on a scale of −10 to +10, where the highest value implies full democracy lagged by a year, sourced from Gurr and Jaggers (1995)
Economic crises Dummy takes the value 1 if a country is exposed to either currency crisis, banking crisis, debt crisis (or all together) lagged by a year, sourced from Laeven and Valencia (2008)
GDP growth rate Rate of growth of GDP, sourced from the WDI, World Bank 2017
Natural resource rents/GDP Total rents from natural resources as a share of GDP, sourced from the World Bank dataset on resource rents, 2017
IMF program Dummy takes the value 1 if a country is in an IMF program for more than 5 months during the year, and 0 otherwise, obtained from Dreher (2006)

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Brazys, S., Vadlamannati, K.C. Aid curse with Chinese characteristics? Chinese development flows and economic reforms. Public Choice (2020). https://doi.org/10.1007/s11127-020-00836-z

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Keywords

  • Development aid
  • Economic reforms
  • Endogeneity
  • China

JEL Classification

  • P1
  • F35
  • C33
  • C36
  • O5