Comparing settler mortality and early human capital formation

Settler mortality and early human capital formation should be negatively correlated, following from the discussion above. Which of the two variables can serve as better potential instruments of later institutional quality and education? We focus on those countries in which settler mortality and early human capital formation deviated (Figure 2.10). In general, both indicators correspond, with low settler mortality in the high-education cases of the US, Canada, New Zealand, Hong Kong, and South Africa.

Interestingly, Malaysia, Guyana, and Mauritius also fit this pattern. On the other hand, countries such as Nigeria, Mali, and the Gambia had low numeracy and high settler mortality and, therefore, serve as classical West African cases. There were five interesting deviations, with only modest settler mortality but high population density and very low early human capital values in 1900: Bangladesh, India, Pakistan, Egypt, and Morocco.

Settler mortality in those countries is estimated to be lower than in most Latin American countries, including Argentina and Uruguay. To a lesser extent, Indonesia also belongs to this group, although settler mortality was somewhat higher. All of those are still poor countries today, and their institutional and human capital level during the second half of the 20^th century was modest. This finding would suggest that the early human capital formation had a stronger influence than the settler-mortality-related factors, i.e., the institution building and the direct human capital impact of migrants.

We tested the various instruments in an instrumental regression framework, following AJR and Glaeser et al. A replication of their results is reported in Table 2.1.

Glaeser et al. used regression analysis to show that the ultimate causality chain ran from settler mortality and early population density via modern human capital to modern income. In contrast, we argue that the human capital of the indigenous people and the spill-over effects from migrants should also be acknowledged in the model. Hence, we will test whether those instruments can be replaced by early human capital formation or, more precisely, basic numeracy. This proves to be the case (Table 2.2). Early human capital might be interpreted as the outcome of idiosyncratic developments and contact learning effects in countries with substantial immigrant minorities. From the comprehensive data of the late 19^th century, we find a causal chain from early human capital via recent (1960-1990) human capital to income differences today. For the early 19^th century, our data set is too small for a reliable interpretation but the causal chain remains significant.

We were also curious about whether the instrumental variables that had an effect via human capital level of the recent past (1960-1990) would also have a significant effect on GDP per capita today in a cross-section of countries. This does not mean that we have a different theoretical model in mind; we still think that those variables create an impact

how much difference a unit of learning investment around 1900 made, and how much a percent of settler mortality matters. Moreover, we can examine the interactions with initial GDP levels and climatic effects. We find that even after controlling for climate and French legal origin, both settler mortality and early numeracy had an impact on income differences in the present (Table 2.4, columns 1-3, 5-6). In a direct comparison between the two variables in two regressions, settler mortality demonstrates a higher explanatory share of R² (not shown). The coefficient of both variables increases when the share of the population living in temperate zones is not controlled for (column 2). The climatic factor is associated with a more or less difficult disease environment for the whole population (and not only for settlers) and clearly interacts with the two variables studied here but has no independent effect once initial GDP is included (column 3).

However, one could argue that human capital measures the early deviation between incomes, hence we need to control for initial GDP as well. Unfortunately, GDP estimates around 1900 are only available for 23 colonised countries (Maddison, 2010).

Controlling for GDP, the effect of early human capital investments and the settler mortality effect remains significant (columns 3 and 4).

Finally, we would like to disentangle the direct and indirect impacts of European migrants on the human capital levels in the colonies. Examining the relationship of settler ratios and the ABCC values around 1900 in the countries analysed (Figure 2.11), we observe the following pattern: countries with a substantial population ratio of Europeans (greater than or equal to 5 percent) all have ABCC values over 65. Furthermore, all of the Neo-European countries (countries with a share of Europeans over 50 percent) reveal numeracy levels of almost 100 ABCC points. This finding meets the expectation that a high share of Europeans increases the overall ABCC level of the population in the settlement countries. However, countries without a noteworthy European community

above 95 ABCC points in Guyana, Cambodia, Mauritius, and Hong Kong. Among this group of countries, we find our second expectation confirmed, namely that the colonies had a great variety of different human capital endowments around 1900. This large variation supports the argument that, in contrast to the view of Glaeser et al., it was not only the ‘imported’ human capital of the settlers that mattered for economic performance but also the idiosyncratic human capital endowment of the local population.

To test whether the presence of Europeans had a systematic influence, we included additional interaction terms for the ABCC with the settler ratios, one for the Neo-European countries and another for the countries without a substantial Neo-European population ratio (under 5 percent), leaving the group of countries with settler ratios between 5 and 50 percent in the reference category (see Table 2.3 for country classification). Those are the cases of ‘substantial European minorities’, where we would expect contact learning to be most important. Column 5 in table 2.4 displays the baseline model with additional interaction terms. The main results are a positive coefficient of the interaction term between the ABCC and the Neo-European countries and a strongly negative coefficient of the interaction term between the ABCC and ‘No-Settler countries’

(all compared to countries with a substantial settler minority). However, the coefficients are significant only for the latter case. If we compare these results with the more parsimonious model in column 6, we observe that the coefficient for the Neo-European interaction term does not change much when the climate variable is included as compared to the interaction term with the No-Settler countries. We can interpret this interaction effect such that countries without a substantial settler minority faced greater difficulties transforming their idiosyncratic human capital into welfare in the long run. The mechanism behind this phenomenon might be the contact-learning effect.

What did we learn from this exercise? To summarize, because our ABCC

idiosyncratic human capital endowment of the whole population matters strongly and impacts the long-term economic performance via human capital and institutions.¹³ Countries without contact learning effects faced greater difficulties to transform their idiosyncratic human capital into welfare growth.