rafmacalaba/datause-encoder-data huggingface dataset:
Beach (1903) are reported for the other regressions.<sup>14</sup> These two atlases are standard sources for georeferenced mission stations in Africa in the economic literature. However, Jedwab, Meier zu Selhausen, and Moradi (2018) have documented that both sources are subject to measurement error in the exact locations of missions due to geocoding mistakes. They also showed that any issue of _classical_ measurement error can be substantially mitigated by using large enough cells. In particular, classical measurement error is almost completely eliminated when the cell size is increased to 0 _._ 3<sup>_◦_</sup> _×_ 0 _._ 3<sup>_◦_</sup> . Reassuringly, the present study uses even larger cells, with a resolution of 0 _._ 5<sup>_◦_</sup> _×_ 0 _._ 5<sup>_◦_</sup> . Jedwab, Meier zu Selhausen, and Moradi (2018) also collected more complete records of missions in Ghana from multiple sources. They were able to show that for Ghana, the correlation between their geocoded locations and those reported in Beach (1903) and Roome (1924) is very high only for those missions that were established early, but is lower for missions that were established later.<sup>15</sup> As early missions were located in better and more accessible areas, this might induce nonclassical measurement error, which tends to magnify the omitted variable bias induced by self-selection of missionaries. The next section explains how the analysis deals with the selection issue.

# **2.3 Selection of Missions and Historical Controls**

Missionary activity in Africa was not randomly assigned across the continent, as illustrated by the case studies in Johnson (1967) and confirmed empirically in Jedwab, Meier zu Selhausen, and Moradi (2018). If the factors that determine the selection of mission station locations correlate with present-day aid allocation, the coefficient _β_ in equation (1) will be biased.

The first factor to consider is accessibility. Missionaries came by sea, and inland penetration was difficult, so they followed the tracks of early European explorers, which partly correspond to the courses of the main rivers. There is evidence that areas along the coast and along large rivers have an advantage in development and are more densely populated (Gallup, Sachs, & Mellinger, 1999), so the regressions control for the (log) distance (in kilometers) from the closest point on the coast and from the closest main river.<sup>16</sup>

> 14Results obtained using the Roome (1924) data are available from the authors upon request.

> 15The correlation increases as the cell size increases.

> 16Logarithms of distances are used because the marginal effect of a unit of proximity

11
– Table 4: Kolmogorov-Smirnov (KS) and Cram ́er-von Mises (CVM) Tests US

|Null Hypothesis|KS|Cri|tical Val|ues|CVM|Crit|ical Va|lues|
|---|---|---|---|---|---|---|---|---|
|||1%|5%|10%||1%|5%|10%|
|No Efect: _GIC_(_τ_) = 0|0.378|0.067|0.0601|0.057|15.677|1.661|1.379|1.184|
|Mean Efect: _GIC_(_τ_)= _γAGR_|0.306|0.058|0.055|0.051|7.689|1.333|1.252|1.093|
|No Efect: _GIC_<sup>_∗_</sup>(_τ_) = 0|0.205|0.080|0.069|0.059|6.590|2.235|1.849|1.694|
|Mean Efect: _GIC_<sup>_∗_</sup>(_τ_)= _γAGR∗_|0.204|0.075|0.063|0.054|5.648|1.472|1.269|1.186|

educational make up of the full-time labor force - account only for the residual change from 0 _._ 250 to 0 _._ 254. Formal tests, also presented in Table 4, confirm that the _GIC_<sup>_∗_</sup> ( _τ_ ) was neither constant nor distribution neutral over the period. We first test whether _GIC_<sup>_∗_</sup> ( _τ_ ) = 0. The results indicate rejection of the null at 1% level for both the KS and CVM tests. And when we test distribution neutrality of growth conditional on the joint distribution of covariates, _GIC_<sup>_∗_</sup> ( _τ_ ) = _γAGR∗_ the null is again rejected at all reported levels of significance.

# **4.2.2 Brazil**

The results for the Brazilian _GIC_ and _GIC_<sup>_∗_</sup> ( _τ_ ) for 1995-2007 are displayed in Figure 2. As before, the blue line displays the actual _GIC_ , and the dashed black line denotes the growth rate of mean wages, _γAGR_ . The green line displays the counterfactual growth incidence curve, _GIC_<sup>_∗_</sup> _,_ and the dashed red line shows its corresponding mean effect, _γAGR∗_ Remarkably, there was even less growth in average wages for full-time workers in Brazil than in the US over this period. Cumulative growth in real wages was a paltry 1 _._ 7% - a tenth of the US rate.<sup>11</sup> However, the distribution of that growth was completely different from the US case. Brazil’s _GIC_ rises sharply up until the first quintile, at which point in the distribution wages grew by 40% or more over the period. The _GIC_ is then downward sloping from _τ_ = 0 _._ 2 to _τ_ = 1 _._ 0. It crosses the x-axis near the 7th decile, and is negative thereafter. This growth pattern is consistent with a substantial decline in wage inequality among full-time workers, as shown in Table 3. Whereas all five inequality indices reported rose for the US, all five declined for Brazil. The Gini coefficient fell by almost four points,

> 11It is quite likely that this dismal performance is due, at least in part, to a composition effect. Ferreira, Firpo, and Messina (2016) report that formal employment in Brazil rose by a fifth, from 48 to 58% of the labor force, between 1995 and 2012. While not strictly the same, formal employment is highly correlated with full-time status. The same authors also report that the formalization of labor contracts was more common among lower earners. Such a process is likely to lower average earnings in that sample through a composition effect.

29
**Table 1: Number of Firms by Type of Firm and Sector**

|**Type **|**Sector**|**No. of firms**|**%**|
|---|---|---|---|
|Foreign investor|Agribusiness|30|34.5%|
|Foreign investor|Apparel|43|49.4%|
|Foreign investor|Mining|14|16.1%|
|_Foreign investor_|_All sectors_|_87_|_100.0%_|
|Domestic producer|Agribusiness|46|71.9%|
|Domestic producer|Apparel|13|20.3%|
|Domesticproducer|Mining|5|7.8%|
|_Domestic producer_|_All sectors_|_64_|_100.0%_|

# **3.2 Differences between Foreign Investors and Domestic Producers**

In this section, we assess if foreign investors are different from domestic producers in terms of their potential to generate positive spillover effects for domestic suppliers. In the following, we look at three types of indicators that all influence the spillover potential, namely the firms’ overall performance, their linkages with the local economy, and supplier assistance.

## **_Performance Indicators_**

Table 2 (column 1) shows the mean differences, controlling for country-sector fixed effects. Column (2) additionally controls for employment, since firm size may also explain some of the differences between multinationals and domestic producers. All variables refer to FY 2012. The summary statistics for both foreign investors and domestic producers can be found in Appendix 1.

The results indicate that multinationals sell significantly more than domestic suppliers (ln _sales_ ), although the effect becomes smaller when controlling for firm size. Foreign firms are also more productive (ln _labprod_ ), and this effect is slightly larger when we additionally control for firm size. They also have a smaller technology gap ( _tech_ ) to the leading domestic competitor (i.e. domestic producers generally lag further behind the domestic leader in the sector) which could be the result of being more productive.

The positive coefficient sign on the share of workers with tertiary education ( _emp_ter_ ) and the negative coefficient sign on the share of workers with secondary education ( _emp_sec_ ) seem to indicate that foreign firms have a labor force that is more skilled, although the effects are not significant. Foreign firms are more likely to export ( _exporting_ ). The share of direct exports is clearly 11
Table2: Comparing Census and POF pseudo-sample variables

||Cens|us|'POF|'|Δ|
|---|---|---|---|---|---|
||μ|σ|μ|σ||
|Piped water connection|0.82|0.00|0.84|0.01|0.99|
|Well in the property|0.15|0.00|0.14|0.01|1.06|
|Washing Machine|0.27|0.00|0.27|0.01|1.02|
|White head|0.52|0.00|0.53|0.01|0.98|
|Male head|0.79|0.00|0.78|0.01|1.01|
|TV set|0.90|0.00|0.90|0.01|0.99|
|Head with no schooling|0.14|0.00|0.14|0.01|1.04|
|Head with 1-3 y.s.|0.21|0.00|0.23|0.01|0.94|
|Head with 4-7 y.s.|0.36|0.00|0.35|0.01|1.03|
|Head with 8-10 y.s.|0.11|0.00|0.11|0.01|0.96|
|Head with 11-14 y.s.|0.12|0.00|0.12|0.01|1.01|
|Family type 1|0.03|0.00|0.03|0.00|0.89|
|Family type 2|0.07|0.00|0.07|0.00|1.00|
|Share of Inactive people at the district level|0.43|0.00|0.43|0.00|0.99|
|Share of Informal workers at the district level|0.32|0.00|0.32|0.00|0.99|
|Share of Formal workers at the district level|0.29|0.00|0.28|0.00|1.03|
|Age of the Head|46.44|0.03|47.03|0.32|0.99|
|Number of Chidren in the household|0.64|0.00|0.65|0.03|0.98|
|Ratio of rooms serving as dorms over total number of rooms|0.68|0.00|0.68|0.01|0.99|
|Average Household Per Capita Income at the district level<br> Source: Census; Pseudo-survey and Authors’ calculation. A standard error of 0.00|1014.50 <br>indicates a level be|2.29 <br>low 0.0|1009.19<br>05.|8.59|1.01|

# **V. Validation Exercise**

# **1. Spatial differences in returns**

As noted above, the ELL method is predicated on the assumption that a model estimated for household survey data at an aggregated region level, _R_ , can generate predictors and error term distributions that can be used to estimate welfare in a small area _A_ . Ideally, one would like to estimate a separate model for each area, _A_ , or at least allow for different slopes for different areas within a single model. However, no sample survey is representative at the small area level, or even covers all small areas. Thus the ELL method cannot be based on separate models for each small area and it is impossible to control for small area effects via a fixed-effects specification. Instead, the ELL approach inserts into the survey a number of variables aggregated at the small-area level, 14
<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Source: Universal Postal Union database.<br>Note: Data are from 2015–17. No data were available for Bangladesh, Kyrgyzstan, and Senegal.<br>from a postal establishment<br>Percent of population having to collect mail<br><!-- End of picture text --> In particular, the emergence of cross-border e-commerce calls for reexamining the efficiency of customs processes. Cross-border e-commerce now accounts for 10–15 percent of the volume of global e-commerce, and it is expected to grow at twice the rate of domestic e-commerce until 2020.<sup>84</sup> A simplified customs procedure such as prearrival risk assessment and digitizing the process through electronic data exchange and payment could all contribute to a more cost-efficient customs process.

Given the growing flow of low value cross-border shipments, customs authorities face more severe challenges on the long-standing trade-off between foregone revenue and the cost of collection and longer clearance times. Many customs authorities around the world have adopted a de minimis threshold—that is, a “valuation ceiling for goods, including documents and trade samples, below which no duty or tax is charged and clearance procedures, including data requirements, are minimal.”<sup>85</sup> How to set a reasonable de minimis threshold has become of special importance. The calculation and collection of customs duties and taxes on low-value shipments disproportionally burden small e-commerce exporters because they are unable to streamline these tasks. Research has shown that a reasonable de minimis threshold can be vital for small business firms that depend on cross-border sales.<sup>86</sup> Among the 21 countries covered under Digital Business Indicators project, Bangladesh, Burkina Faso, Pakistan, Senegal, and Tunisia do not have a de minimis threshold in place. Thresholds are often set based on the value of the consignment, ranging from zero up to €1,000 (figure 10). Kenya and Tanzania set a low consignment value threshold of less than US$5. In member countries of the Eurasian Customs Union (ECU), the threshold is also based on the weight of the consignment.<sup>87</sup>

**Figure 10. Countries set different levels of a de minimis threshold for customs procedures**

22
The experiment tested impact on students’ test scores in math and English at the beginning of each term of the school year and at the end of the year, along with several implementation indicators. The tests included questions developed by the Ministry of Education and the Ghanaian National Education Assessment Unit (for grades 4 and 6); they also included questions from the Early Grade Reading Assessment (EGRA) and Early Grade Mathematics Assessment (EGMA).

Table 3 presents the results.<sup>19</sup> Teacher-only training increased combined English/math test scores by 0.108 standard deviations and English test scores by 0.065 standard deviations. The teacher-plus-administrator training increased combined English/math test scores by 0.107 standard deviations and English test scores by 0.076 standard deviations. All results are statistically significant at the one percent level.<sup>20</sup> The benefit-cost ratios for the teacher-only training are higher than our estimates for the quantity of schooling, and about twice as high when using our own estimates of the value of completing fifth grade versus the Evans and Yuan (2019) calculation based on GNI per capita. Including head teachers and circuit supervisors in the training has much lower benefit-cost ratios, the difference between the two experimental arms being entirely one of greater costs for the latter. The impact on test scores is very similar across the experimental arms.

> 19 Table C.3 presents the monetary costs and benefits per student in terms of 2017 cedis. 20 Beg et al. do not break out their results by gender.

27
expensive and most strictly enforced class of informal debt.<sup>21</sup> Could the observed shift towards informal sources of financing be driven by changes in the supply of credit? Evidence from new loan applications after the program suggests that this is unlikely to be the case. To verify that there was in fact no differential discrimination against beneficiary households applying for new loans, Table D.6, Panel A, presents summary statistics on new loan applications after the program. Despite the fact that all households in the 100% relief category qualified for new bank credit, only 376 of the 1,181 households in the treatment group applied for new loan after the program. However, at 2.5% versus 1.92%, households in the treatment group were not significantly more likely to be denied credit than households in the control group. The regression results in Table D.6, Panel B, confirm this finding: beneficiary households were no more likely to be turned down for a loan and, conditional on a loan being approved, interest rates did not differ between treatment and control. While the statistical power of the test is limited by the relatively small number of new loan applications after the program, the results provide strong suggestive evidence that the documented shift in the composition of borrowing is not driven by changes in the supply of credit or differential discrimination against debt relief recipients.

# **6.3 Debt Relief, Investment and Productivity**

An important economic argument in favor of large-scale debt relief programs is their potential to increase productivity by reducing disincentives for investment arising from high levels of pre-existing debt. The testable implications of this debt overhang argument are two-fold: _First_ , debt relief should lead to an increase in investment expenditures because households, once freed of their debts, are now the residual claimants of their investment returns. _Second_ , debt relief should reduce investments in risky production technologies, since the risk of project failure is no longer borne by debt holders. As I show in this subsection, I find no evidence in favor of these debt overhang effects. Instead, households that benefited from debt relief, on average, did not access new crop and investment loans which, in

> 21While reliable data on the interest rate structure of such internal financing is limited, stylized evidence suggests that internal financing is both more expensive than bank credit and also poorly suited to the purpose of financing recurrent investment expenditures. One reason for this is that informal loans are generally term loans, while crop loans from institutional lenders tend to be structured as a revolving line of credit, which allows for the partial settlement of outstanding loans. In addition, the increased reliance on internal funding observed in the sample is unlikely to compensate for shortfalls in bank credit, which still accounts for nearly half of the total financing requirements of households in the treatment group.

20
the effectiveness of physical punishment as a method of disciplining children. Then, we use this variable as a secondary outcome in our regressions.<sup>38</sup> Results using specification (1) are presented in Table 6. Columns (1)–(4) compare consistency between midline and baseline surveys, and columns (5)–(8) compare consistency in self-reports between midline and endline surveys by the mode of the survey. For each outcome, we present models that include or exclude interviewer fixed effects. Overall, we find no differences in the self-reports of stress or the perception of the effectiveness of physical punishment across survey waves by survey mode.

# **6 Robustness Checks**

In addition to assessing whether our main results were affected by the inclusion of control variables, we conducted several additional robustness checks. Results are summarized in Table A4 in the Appendix section. Column (1) presents the estimated coefficient of the main model, which includes age and sex as control variables, and municipality fixed effects. We first test that the absence of baseline information for some of the participants does not affect the results by including in column (1) indicators for completing baseline survey or type of recruitment that are equal to 1 if recruitment was done through Tigo Clients and 0 if through other channels. These results are in columns (2) and (3), respectively. We show that the estimated coefficient does not change after the addition of these control variables.

Second, since conventional p-values typically rely on approximations that assume that the pool of individuals is large enough that the test statistics follow a specific sampling distribution (Ding et al., 2016), we conduct an adjustment of the estimated standard errors using randomization inference. Results in brackets in column (4) show that the statistical significance did not change and the precision of the estimation improved. Lastly, we show that the inclusion of an educational-level variable as control does not change the main results, especially when we impute 0 for the missing values for caregivers who did not complete the

> 38An important caveat from this analysis is that we are measuring consistency in self-reports comparing responses that were reported five to six weeks between midline and baseline, and six to seven weeks between midline and endline, which can be relatively long periods of time. Yet, we are finding no differences between the two reports.

21
_Table 3: Distribution of annual household expenditures between GLSS6 (GAMA) and Poverty-DRM surveys in Ghana by quartile_

|Quartile|GLSS6 (GAMA) in cedis|Poverty-DRM in cedis|
|---|---|---|
|Q1|2,115|2,248|
|Q2|3,810|3,907|
|Q3|5,781|5,904|
|Q4|10,715|11,988|
|Source: Authors'|estimation using GLSS6 and Po|verty-DRM surveys|

Household heads in our sample are more likely to be women and have a lower education level than those of the GLSS6 sample for the full GAMA area. Further, households in our sample live in relatively smaller dwellings despite having the same number of household members, indicating that households are more densely crowded in the surveyed areas than in the rest of the city.

Households surveyed appear to have a lower access to services than the rest of city. For instance, the use of public toilets is significantly higher for slum-dwellers than for Accra residents overall. While 32% of households in the GLSS6 survey reported using public toilets as their main toilet facility, the number for the DRM survey is 67%. The primary source of drinking water in the slum areas is sachet water, which is even more prevalent (at 70%) than in the rest of the city (54%). Most households (74%) reported having their waste collected, a high level for an informal settlement.

Almost half of the households (44%) reported being directly affected by the 2015 flood. As expected, more households in the low elevation areas were affected by the flood. However, many households in the high elevation areas (29%) were also affected. These results confirm that elevation is not a good proxy for flood exposure in Accra. This is because flood risk is associated with characteristics other than elevation, such as insufficient drainage infrastructure, or poor waste management.<sup>5</sup>

# **5. Exposure: No visible difference between poorer and richer households**

The first question analyzed in this study is whether the affected population was poorer than the average. While it is often assumed to be the case, the review provided in Hallegatte et al. (2017) shows that this is far from universally true.

For the 2015 flood in Accra, there is no significant difference in expenditure levels between the affected and non-affected households. The SWIFT-based estimates of household expenditures allow us to define four quartiles in terms of expenditure in the sample, and to explore how different quartiles experienced the flood. Applying these categories, we see an extremely small and statistically non-significant difference between the likelihood of poorer and wealthier households being affected (see Table 17 in Appendix

> 5 Theoretically, it could also be linked to impacts on the workplace or school that may be located in a different (more flood-prone) area. But the analysis of losses below shows that this is not a significant explanation.

12