Instrumental Variable Approach

Our instrumental variable strategy exploits the heterogenous impact of a plausibly exogenous time-series interacted with a (pre-determined or fixed) cross-sectional differ-ence.¹⁶ The identifying assumption is that in absence of a change in the time series

15In line with personal correspondence with staff from aid agencies, China would disburse commit-ments quickly with a lag of one to three years. We assume a two-year lag structure as reasonable.

16This builds on Nunn and Qian (2014), who exploit temporal variation in US wheat production,

there would be common trends in aid allocation in low and high aid probability recipi-ent regions. As in any Difference-in-Difference (DiD) setup, the first and second stage control for the main constituting terms forming the interaction and only the interaction term is used as the conditionally exogenous instrument.

For both the WB and China, we use a cumulative (initial or pre-determined) prob-ability as opposed to a constant probprob-ability over the whole sample period. This is computed by dividing the number of years a region 𝑖 has received aid in the past by the number of years passed until period𝑡.¹⁷ Beyond the donor-specific probability, the World Bank and China differ only in the time-varying factor𝑇𝑡used to induce variation in project allocations over time.

Instrumenting World Bank Aid

For the World Bank, we use exogenous yearly variation in the availability of free IDA resources. This funding position is defined as “the extent to which IDA can commit to new financing of loans, grants and guarantees given its financial position at any point in time” (World Bank, 2015a).¹⁸ Starting in 2008, we use the measure publicly disclosed in the annual financial reports. From 1995 to 2008 we rely on the reconstructed time series by Dreher et al. (2017). Thus, the first stage equation has the following form:

𝐴𝑖𝑑𝑖,𝑐,𝑡−1 =𝛼₁𝑝𝑟𝑜𝑏𝑖,𝑐,𝑡−2+𝛼₂𝐼𝐷𝐴𝑡−1+𝛼₃𝑝𝑖,𝑐,𝑡−2𝐼𝐷𝐴𝑡−1+𝑋𝑖,𝑐,𝑡−2+𝜖𝑖,𝑐,𝑡−1, (3.2) where 𝑋𝑖,𝑐,𝑡−2 is again a vector of lagged control variables. Figure 3.5 shows the fluctuations in the indicator. The variation can be caused by internal adjustments, the timing of payments by the shareholders, as well as repayments by large borrowers like India or Nigeria. Conflict in any individual African region cannot plausibly affect the measure to a significant degree. Overall, there is a downward trend, partly caused by some major shareholders failing to deliver on payments promised before. However, despite the general decline, the indicator also fluctuates strongly between the years.

For instance, it initially increases between 1996 and 1997, before it falls sharply in the following years.

We then interact this time-varying variable with𝑝𝑟𝑜𝑏_{𝑖,𝑐,𝑡}, the probability of a region receiving aid. Based on anecdotal evidence, for instance from personal correspondence

17If our sample begins in 1995, and a region received aid in three out of five years, the value of the probability in 1999 would be 0.6. If aid receipts stop in 1999, the probability would decline to 0.5 in 2000 as the country would have received aid in three out of six years. The constant probability used in Nunn and Qian (2014) or Bluhm et al. (2018) relies on all observed treatment values per unit, i.e., the term for region𝑖in year𝑡also depends on the values in𝑡+1, 𝑡+2, .... These future values can themselves be a function of conflict. Nizalova and Murtazashvili (2016) show that under certain assumptions the

Due to data limitations, there is no exact equivalent to the IDA’s funding position. In-stead,𝑇_{𝑖,𝑐,𝑡} is a time series on production in the country’s over-sized steel sector (World Steel Association, 2009, 2014). The production level was shown to affect the overall amount of Chinese aid as China would commit to more aid projects to clear markets and protect domestic companies from potential losses (Dreher et al., 2016). These projects are often large-scale infrastructure projects (Bräutigam, 2011), but Bluhm et al. (2018) show that steel production also induces variation in other sectors (social, education or health) beyond roads and railways.²⁰ China is also generally known as engaging in “mega-deals” (Strange et al., 2017), which are generally larger than WB projects.

Thus, the local average treatment effect we want to estimate with the IV is not atyp-ical for its activities. The time series is again plausibly exogenous to any individual region in Africa, and we then interact it with the cross-sectional specific cumulative probability to receive Chinese aid. Theoretically, one would expect that overcapacities in steel benefit regions with a low probability of previous aid receipts more as China expands its activities to new regions. However, the existing literature indicates that an increase in steel overproduction benefits regions with an initially high probability the most (Dreher et al., 2016; Bluhm et al., 2018). The first stage equation for Chinese aid has the following form:

𝐴𝑖𝑑𝑖,𝑐,𝑡−2 =𝛼₁𝑝𝑟𝑜𝑏𝑖,𝑐,𝑡−3+𝛼₂𝑆𝑡𝑒𝑒𝑙𝑡−3+𝛼₃𝑝𝑖,𝑐,𝑡−3𝑆𝑡𝑒𝑒𝑙𝑡−3+𝑋𝑖,𝑐,𝑡−2 +𝜖𝑖,𝑐,𝑡−2 (3.3) One potential issue is a long-term upward trend in Chinese steel (over-)production and the fact that there is less year-on-year variation than in the WB funding position.

This linear trend increases the risk of picking up trends in other variables that differ between high and low probability regions and overlap with the conflict trends, one of the concerns raised by Christian and Barrett (2017). For that reason, we de-trend steel production for our main specification, so that we exploit only deviations from the long-term production trends.²¹

19Because the World Bank’s fiscal year ends in June, the reported position in the fiscal years t and t-1 can both affect disbursements in t-1. Using only the position in t-1 is a viable alternative and also works well in first stage estimations, which is demonstrated in Appendix Table C.10. Using both fiscal

Examining the first stages

In order to provide readers with a transparent depiction of trends in the outcome and instrumental variable as suggested by Christian and Barrett (2017), Figure 3.4 shows the de-trended time series that we use, along with the variation in conflict in low and high probability regions. On the left panel, we show the raw variation in conflict, on the right panel we show the residual variation net of fixed effects and time trends that we exploit in our estimations. There is no clear overlap between trends in the time series variable and outcomes in either low or high probability regions, in particular when considering the residual variation used in our subsequent analysis. The same holds true for the WB (Figure 3.5).²²

Figure 3.4 Deviations from Chinese Steel Production Trend & Battle-Related Deaths

a)

Mean Battle-Related Deaths (raw)

b)

Mean Battle-Related Deaths (residual)

Source: Authors’ calculation.

Note: Figure 3.4a displays the log of the detrended Chinese Steel Production (thick line), the mean Battle-Related Deaths per low probability recipient regions (thin line) and the mean Battle-Related Deaths per high probability recipient regions (dashed line). Figure 3.4b displays the log of Chinese Steel Production (thick line), the mean residual of the Battle-Related Deaths per low probability recipient regions (thin line) and the mean residual of the Battle-Related Deaths per high probability recipient regions (dashed line). The residuals refer to the underlying variation used in our preferred specification from column (4) in Table 3.3 and are net of FE and time trends.

Goldsmith-Pinkham et al. (2018) describe the potential risks and caveats of simi-lar IV strategies and highlight the importance of considering differences in the cross-sectional units and emphasize the need to consider whether the first stage is driven by only a few observations or outliers. Christian and Barrett (2017) emphasize potential

Figure 3.5 World Bank IDA funding Position & Battle-Related Deaths

a)

Mean Battle-Related Deaths (raw)

b)

Mean Battle-Related Deaths (residual)

Source: Authors’ calculation.

Note: Figure 3.5a displays the IDA Funding Position (thick line), the mean Battle-Related Deaths per low probability recipient regions (thin line) and the mean Battle-Related Deaths per high probability recipient regions (dashed line). Figure 3.5b displays the IDA Funding Position (thick line), the mean residual of the Battle-Related Deaths per low probability recipient regions (thin line) and the mean residual of the Battle-Related Deaths per high probability recipient regions (dashed line). The residuals refer to the underlying variation used in our preferred specification from column (4) in Table 3.3 and are net of FE and time trends.

3.5 Results

Im Dokument Promises and Perils of Globalization (Seite 115-119)