Evaluation Results

Our main spatial econometric results are presented in Table 4.3. The first two columns report the results from the OLS estimations using our preferred W matrix: one that measures competition based on ISIC 2-digit export similarity as the interstate weights, and the other using the geographic contiguity matrix. Looking at our preferred specification, Column (1), we see that there is a statistically and substantively significant positive impact of export share to China and the child labour rate in a given state, sector, and year in Brazil. We find that a 1% increase in the share of exports to China out of the total exports leads to an average increase in the child labour rate of nearly 0.1%. While this may seem negligible, when applied to the estimated numbers of child labourers in Brazil this can be an absolute increase of thousands of child workers.³⁵ This finding is in line with the expectation set out in Hypothesis 1 . If we look to Column (2), where we report the findings from the SLX models using the geographic contiguity matrix we can see that the results for the direct effect of increasing export share to China hold up and are nearly identical.

The models also find that a 1% increase in the share of exports going to competing states actually leads to a similar and larger (up to 0.5%) decrease in the share of child labour. What this appears to show is that, on average, as exports to China in a given state increase, there is a subsequent increase in the child labour rate; yet as exports to China in competitor (or neighbouring) states increase, the incidence of child labour will actually decline. This finding is in line with Hypothesis 2.2 (Divergent competition) suggesting that rather than triggering a race to the bottom, the competitors’ ability to target the Chinese market creates incentives to reduce the number of children in the workforce targeting high-end markets. In other words, competing or neighbouring exporters are less likely to engage in child labour as they aim at targeting the other main export destinations (i.e. the US, EU, Argentina and Japan) which are less amenable to the use of child labour.

35Do note that in the period analysed the export share to China increased by almost 17% and that the number of children working in 2015 was over 600.000. Hence, this effect may have had an impact on the life of tens of thousands of children.

4.5. RESULTS AND DISCUSSION CHAPTER 4

In the third and fourth columns of Table4.3, we report the estimates from the SLX-IV models, along with the IV diagnostics. Here we find our initial findings not only supported but, in fact, inflated, suggesting that endogeneity in the OLS estimates was downwardly biasing our coefficients.

Here we see that an increase of exports shares to China by 1%, in fact, is associated with a roughly 0.5% increase in the child labour rate and that the indirect effect of competitors’ exports to China is in fact much larger than that of neighbouring states.

Table 4.3: SLX Results

SLX w/Comp W SLX w/Geo W SLX-IV w/Comp W SLX-IV w/Geo W

Export Share to China 0.093^∗∗ 0.090^∗∗ 0.517^∗∗∗ 0.469^∗∗

(0.043) (0.045) (0.139) (0.205)

W.Export Share to China −0.099^∗∗∗ −0.449^∗∗∗ −0.829^∗∗∗ −0.114^∗∗∗

(0.024) (0.149) (0.008) (0.187)

Education Rate −0.004^∗∗ −0.004^∗∗ −0.003^∗∗∗ −0.003^∗∗∗

(0.002) (0.002) (0.001) (0.001)

Race/Ethnicity (non-white) 0.000 0.001 −0.000 −0.000

(0.001) (0.001) (0.001) (0.001)

Female Population −0.003 −0.003 −0.001 −0.000

(0.003) (0.003) (0.002) (0.002)

PT Government −0.008 −0.007 −0.011 −0.011

(0.010) (0.009) (0.010) (0.010)

Informality −0.002 −0.001 −0.001 −0.001

(0.001) (0.001) (0.001) (0.001)

Ln. UF Population −0.078 0.017 −0.108^∗ −0.183

(0.097) (0.068) (0.099) (0.101)

Ln. UF GDPpc −0.177^∗∗ −0.184^∗∗ −0.271^∗∗∗ −0.256^∗∗∗

(0.080) (0.085) (0.025) (0.025)

DeltaUF GDP −0.000 −0.000 0.000 0.000

(0.001) (0.001) (0.000) (0.000)

Weak Instrument 248.493^∗∗∗ 414.197^∗∗∗

Wu-Hausman 4.499^∗∗ 8.007^∗∗∗

R² 0.557 0.544 0.542 0.555

Adj. R² 0.554 0.541 0.539 0.552

Num. obs. 12285 12285 12285 12285

∗∗∗p < 0.01;^∗∗p < 0.05;^∗p < 0.1

By and large, the controls either function as expected or simply show no discernible or significant correlation with the child labour outcome. Education is understandably negatively and

4.5. RESULTS AND DISCUSSION CHAPTER 4

significantly correlated with the child labour rate, since it represents the inverse, for many children, of the choice to work (excluding idle children) and likely captures the effect of public programs such as the Bolsa Familia in Brazil. The proportion of female residents in the state population is negative though not significantly correlated with the child labour rate, and the logarithm of GDP per capita is negatively and highly significantly correlated with the child labour rate, as one might expect with such a proxy for local development.

As Cook, An, and Favero (2019) point out, when using spatial econometrics, researchers are rarely interested solely in the reported coefficients, which “contain a wealth of information on relationships among the observations” (LeSage and Pace 2009 at p. 33). Within the reported coefficients in a spatial models, which represent pre-spatial effects within a given unit, are contained the direct and indirect (spillover) effects which we capture in the Export Share and W.Export Share variables in Table 4.3 which provide insight into the complex interrelationships between international economic activity, the changing global economy, and important social sustainability outcomes such as child labour.

If, as expected, exports to China and the “primaritization” of the Brazilian economy are having the perverse effect we hypothesise and the results in Table 4.3 support, we should expect the strongest impact to occur in export sectors such as mining, agriculture, and low skill manufacturing.

In order to test this, we re-estimate the above equations and interact the export share to China variables with a categorical sector identifier at the ISIC rev.3 sector level (i.e. 1-digit sector codes).

The results are displayed in Table 4.4. The significant effects are in the agricultural, hunting, forestry, and manufacturing sectors with the strongest effect being in manufacturing. By and large, this fits well within theoretical expectations concerning the “Shanghai Effect”. Chinese import may change the incentives structure of developing country triggering lower levels of regulatory enforcement. However, Table 4.4 also reveals important sectoral differences. We do not find a significant effect on the mining sector. This is in line with qualitative evidence that shows that in Latin America, Chinese mining companies have been sensitive to international criticism, and adopted a series of best practices to ensure that decent labour standard was ensured to employees (Kotschwar, Moran, and Muir2012).

Table 4.4: Within Sector Effect of Export Share to China

Effect of Export Share to China Agriculture, hunting and forestry 0.003^∗

(0.002)

Manufacturing 0.016^∗

(0.010)

Mining and Quarrying 0.002

(0.001)

AIC (Spatial model) −18906.663

LR test: statistic 113.380

LR test: p-value 0.000

∗∗∗p < 0.01;^∗∗p < 0.05;^∗p < 0.1

We also conduct a number of alternative model specifications to test the robustness of these results. The tables can be found in the Appendix, although we provide an overview here. First, as reflected in the Moran’s I statistic tests conducted earlier, there is a significant degree of spatial dependence in child labour rates between states. Motivated by this and the concern that this omitted source of spatial dependence is driving our SLX results, we estimate Spatial Durbin Models using MLE, which include the spatially lagged dependent variable (i.e. the global spillover effect mentioned before). Second, we added state-year and sector-year fixed effects (by interacting state and year dummies) to our original SLX specification along with the standalone state and year fixed effects to control for unmeasured or immeasurable time-varying omitted variables along with those that are constant within states or years. These results are reported in Table 4.8. Third, given that there may be an autoregressive pattern in the year to year evolution of child labour rates, we further added time-lagged child labour rate and these results are reported in Table 4.9. Our original results are robust to each of these supplementary analyses.

4.5. RESULTS AND DISCUSSION CHAPTER 4