rafmacalaba/datause-encoder-data huggingface dataset:
(year 2015) provide built-up estimates at a resolution of 12 meters per pixel.<sup>12</sup> Global Human Settlement Layer’s (GHSL) built-up estimates are at approximately 38 meters per pixel. We also use Facebook’s High Resolution Settlement Layer (HRSL) built-up area measure, available at a resolution of 30 meters per pixel.

Additionally, we use contextual and object features from Engstrom et al. (2017) which used proprietary imagery for 1,360 villages within 55 sub-districts obtained from DigitalGlobe. These images cover 3,500 _km_<sup>2</sup> of area in Sri Lanka, and are mostly obtained for the years 2011 and 2012, although some images were also captured for the year 2010. The list of 55 sub-districts is available in Appendix B. Object features extracted include the number of cars, building count and size, roof type, shadow pixels, road length and type, type of farm land (paddy or plantations).<sup>13</sup> Further, seven contextual features are calculated: Fourier transform, Gabor filter, Histogram of Oriented Gradients (HoG), Line Support Regions (LSR), Pantex, Normalized Difference Vegetation Index (NDVI), and Speed-Up Robustness Features (SURF).<sup>14</sup> More details on these objects and features can be found in Engstrom et al. (2017).

Table 1 concisely presents the key variable types, their source, time frame, resolution type, and geographical coverage. Tables 2 and 3 present the summary statistics for the population, geographic, and satellite imagery based variables for the national sample and the 55 subdistricts, respectively.

# **3 Modeling**

## **3.1 Predicting population density**

We use Poisson regressions to model population density at the village level. This takes the following form: where _Pv_ is the population density (persons/km<sup>2</sup> ) of village _v_ . _Xv_ consists of the set of satellite imagery-based indicators defined for village _v_ . We include the natural logarithm of village area ( _Ln areav_ ), and an indicator for urban villages ( _ρS_ ).<sup>15</sup> _ηd_ represents binary indicator

> 12 While GUF was made with TerraSAR-X/TanDEM-X, which are radar data sets, GUF+ adds Sentinel and Landsat which are optical remotely sensed data which substantially improves the estimation.

> 13 Supplementary Figure S1 shows examples of developed area building classification, with the raw image (left) and CNN based building classification (right) in Panel A, and a sample car classification in Panel B. In a test sample, the average precision of the building classification was 71 percent, indicating a moderate share of false positives.

> 14 The contextual and object features are identified using both deep learning-based Convolutional Neural Networks (CNN) and classification of spectral and textural characteristics (Engstrom et al., 2017).

> 15 While the official sectoral classifications in Sri Lanka include three categories: the urban, rural, and estate sectors, we combine the second and the third to represent two categories: (1) Urban (2) Rural and estate.

7
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**Figure 5: VAT Flow for a Typical Wind Farm Project**

32. Given the high initial investment and low operation costs of wind power projects, the revised fiscal policy caused significant changes in government fiscal revenues. Under this new consumption-oriented taxation system, the deduction of the VAT associated with equipment purchased by wind farm developers largely decreased the VAT payments from developers to the local governments during the early years of operation, _but not necessarily over the lifetime of the wind farm_ . Local governments appear to have overconcentrated on lost VAT revenues without taking into account the revenue impacts of large-scale wind power development.

33. Since 2009, wind farm investments averaged US$1.4 million/MW, tax included. Out of the total investment, the equipment cost has been US$1.175 million/MW, tax included. The following estimation of the impact of the VAT reform is based on the following assumptions:

- Investment cost for equipment: US$1.005 million/MW, before VAT (US$1.175 million/1.17).

- VAT per megawatt (eligible for deduction after the reform): US$171,000/MW (US$1.005 million * 17 percent).

- Electricity generation per megawatt: 2,200 MWh (1 MW * 2,200h<sup>19</sup> )

- Tariff: RMB 0.51/kWh.

- Annual revenue per megawatt installed: US$171,600 (2,200 MWh * US$78/MWh).

- Total Annual VAT revenue per megawatt installed: US$14,586 (171,600 * 8.5 percent); 50 percent rebate for VAT of wind power (half of 17 percent);

- Local government share of VAT per megawatt per year: US$3,646 (14,586 * 25 percent); 25 percent of VAT goes to local government according to the regulation.

> 19 Average In type I and II areas with about 10 percent generation losses.

14
More educated pradhans also seem better able to protect slum dwellers from the police. The pradhan’s influence is even larger than that of the householder’s own wealth, even though the effect of having an additional Rs. 10,000 in assets increases police interaction significantly from 6 to 22 percent in the IV specification.

Finally, Table 5 examines access to elected politicians. Here again we see a strong elite effect with wealthier individuals (coefficients increasing from 9 to 20 percent in the IV specification for a 10,000 rupee increase in wealth), and those who had close relatives in the city prior to migrating, having a much higher chance of knowing and interacting with an elected politician.

Once again, the importance of the pradhan is largely lost unless we consider the different role informal leaders play in different types of slums. Two effects are worthy of note. First, more educated pradhans reduce the probability of direct interaction between slum dwellers and politicians in the newest slums by around 1% for every additional year of education. This effect however is much less if the slum dweller is embedded among fellow villagers. As in the case of Madanpuri, this suggests that in more heterogeneous slums, pradhans gain part of their ability to speak for the slum through education.

In contrast, the interaction between slum dwellers in ethnic enclaves and politically affiliated pradhans increases the probability of slum dweller interaction with politicians by around 37 percent. This effect reverses that seen for the police. Once again, the qualitative evidence, particularly from Lakshmi colony, provide an interpretation: politically affiliated pradhans in ethnic enclaves are better able to deliver blocks of manpower to politicians’ rallies, this very political access protecting them from police intervention.

24
Table N12: Effect of SNAP-benefit changes on consumption of households, weekly

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Specifcation|OLS, log-log|IV, log-log|OLS, level-level|IV, level-level|
|VARIABLES||Con|sumption||
|Both ineligible|0.105**|0.287**|0.0281*|0.0321|
||(0.0501)|(0.125)|(0.0166)|(0.0404)|
|Household eligible only|0.0508|0.239*|0.00297|0.00526|
||(0.0778)|(0.139)|(0.0210)|(0.0403)|
|Product eligible only|0.0174|0.223*|0.0221*|0.0273|
||(0.0393)|(0.113)|(0.0121)|(0.0375)|
|Both eligible|0.158***|0.348***|0.0521**|0.0563|
||(0.0491)|(0.118)|(0.0229)|(0.0434)|
|Both ineligible x fraction issued|0.00846|0.0101|0.00717|0.00410|
||(0.0248)|(0.0294)|(0.0117)|(0.0136)|
|Household eligible only x fraction issued|0.0380|0.0396|0.0436***|0.0404**|
||(0.0252)|(0.0300)|(0.0131)|(0.0153)|
|Product eligible only x fraction issued|0.00561|0.00724|0.00801|0.00485|
||(0.0247)|(0.0293)|(0.0116)|(0.0135)|
|Both eligible x fraction issued|0.0439*|0.0455|0.0796***|0.0765***|
||(0.0242)|(0.0291)|(0.0125)|(0.0152)|
|Observations|6797627|6797627|6798234|6798234|
|R-squared|0.271|0.271|0.270|0.270|
|Prob _>_ F|0.000|0.000|0.000|0.000|
|Number of units|23789|23789|23789|23789|
|Number of clusters|49|49|49|49|
|First stage F-stat||6.840||8.643|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Each coefficient comes from a regression of (log) weekly consumption on (log) monthly SNAP benefits per recipient, interacted with four group indicators and the fraction issued. Both ineligible refers to expenditures by SNAP-ineligible households on SNAP-ineligible products, household eligible only refers to expenditures by SNAP-eligible households on SNAP-ineligible products, product eligible only refers to expenditures by SNAP-ineligible households on SNAP-eligible products, and both eligible refers to expenditures by SNAP-eligible households on SNAP-eligible products. Fraction issued refers to the fraction of days out of a month in which SNAP benefits are issued during that week. Only the first four weeks of each month are kept in the sample. Control variables as well as household-month and year-month-week fixed effects are also included. Observations are weighted by sampling weights.

80
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financial sustainability and viability, job creation potential, and a capstone score to allow for their overall assessment of the chance of business success (Appendix A.1). Importantly these judges were asked to assess which firms were most likely to succeed, not which firms would have the largest treatment effects from receiving a grant from the competition. A typical plan took 30 to 45 minutes to mark. A random sample of 204 of the business plans were then re-scored by an independent team from Plymouth Business School and any significant disparities reviewed. The mean score in this sample was within 0.4 points out of 100 of the mean from the original judges, with this difference not statistically significant.

Figure 2 plots the distribution of business plan scores according to their competition outcome. We see that that distributions of scores are wide for both the winners and the non-winners. The winning scores range from 30 to 91 out of 100, with a mean of 56 and standard deviation of 12. The scores of the non-winners in our sample range from 2 to 73 out of 100, with a mean of 51 and a standard deviation of 11. This shows that the judges did view firms as substantially different from one another in terms of their potential for business success. Moreover, we have seen that the firms also had wide dispersion in subsequent outcomes. This then raises the question of whether the judges were able to identify in advance which firms were more likely to succeed.

# **3.2 Do Judges’ Scores Predict Business Success?**

Since the winning firms received substantial business grants, which were shown in McKenzie (2017) to have a causal impact on firm outcomes, we separate all our analysis by whether or not the firm was a competition winner. We are then interested in seeing whether the winners scored more highly by the judges did better than winners with lower scores, and likewise whether the non-winners with higher business plan scores did better than those with lower business plan scores.

Figure 3 then plots the relationship between our four key outcomes (business operation and survival, sales, profits, employment) and the judges’ scores for the non-winners. We see that the likelihood of operating a firm three years later is actually slightly lower for those in the upper quintiles of the business plan score. We then fit both unconditional lowess (which codes employment, profits, and sales, as zero for firms not operating) and conditional lowess (which only considers firms in operation) and plot these lines along with the scatterplots for sales, profits and employment. It is apparent that the business plan scores explain very little of the variation in these outcomes, with the fitted lines showing, if anything, poorer results for those with higher scores.

The first column of Table 1 (survival), Table 2 (employment), Table 3 (profits), and Table A1 (sales) then tests whether the relationship between business plan scores and firm outcomes is statistically significant for these non-winners by estimating a logit (survival) or least squares regression (other outcomes) of the 8
The methodology followed by this paper is a Net Present Value (NPV) analysis. This paragraph will show the passages that led to estimate the mineral wealth of the gold and the iron ore clusters in Mauritania. Such methodology is based on the estimation of resource rents over the life of the mine. The resource rents (RR), in year t are calculated according to the following formula: Where: VCu is the unit variable cost associated with the extraction of one ton Pu is the unit price of a ton Qt is the annual mineral production FCt corresponds to the annual fixed costs A represents the amortized capital The amortized capital is the opportunity cost of capital or “allowed profits”: Where _n_ = the life of the mine and _pr_ = the private sector discount rate.

The NPV of the rents are calculated by discounting the sum of annual rents using a social discount rate of 4 percent. Year t0 is 2013 and the final year, year tn corresponds to the end of the mining operations.

In order to obtain wealth per capita estimates, the figure W is then divided by population figures<sup>60</sup> for Mauritania.

The assumptions used to calculate wealth estimates for Mauritania’s iron reserves are based on qualitative and quantitative fact-finding mission and empirical research conducted in Mauritania, during the months of

> 60 Data source: WDI, World Bank, population figure for 2010.

27
materialize, then a doubling of future per capita income relative to the status quo corresponds to roughly 1.4 percentage points of additional growth per year.

The calibrated relationship between the HCI and future income levels described here is simple because it focuses only on steady‐state comparisons. In related work, Collin and Weil (2018) elaborate on this by developing a calibrated growth model that traces out the dynamics of adjustment to the steady state. They use this model to trace out trajectories for per capita GDP and for poverty measures for individual countries and global aggregates, under alternative assumptions for the future path of human capital. They also calculate “equivalent” increases in investment rates in physical capital that would be required to deliver the same increases in output associated with improvements in human capital.

# **References**

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‐ <u>https://www.riseprogramme.org/sites/www.riseprogramme.org/files/inline files/Evans%20Returns%20to%20Learning%20‐%20v2017‐06‐09%20CLEAN.pdf.</u> 55
4 investments and interventions and the more narrow, traditional focus of many of these interventions on on-farm storage techniques, where losses are substantially lower (in the 5-15 percent range) (World Bank, 2011b; De Groote, 2013). Success stories in promoting improved on-farm storage technologies have indeed been rare in SSA (World Bank, 2011b). Past interventions too often proved to be financially unsustainable, ill-aligned with farmer’s economic incentives to store and better protect food, or too short to pay off.

To be sure, these findings and observations do not negate possible benefits from improved storage and post-harvest handling practices in advancing food security in SSA, even when only _onfarm_ grain losses are considered. More importantly, they underscore the need for more and better quantification of (on-farm) grain loss (which can then be compared with the costs of improved postharvest practices)<sup>6</sup> as well as a better understanding of farmers’ behavior in adopting improved postharvest technologies.

The nationally representative household surveys in six African countries south of the Sahara collected under the Living Standards Measurement Study-Integrated Surveys on Agriculture initiative (LSMS-ISA)<sup>7</sup> provide an opportunity to contribute to this agenda. In particular, under the LSMS-ISA surveys information is collected on households’ demographic characteristics, their agro-ecological and market environment, their consumption patterns, assets and income sources (including detailed information on their agricultural production) and most importantly, also self-reported estimates of their post-harvest loss by crop.

> 6 Benefits from improved storage technologies are seldomly systematically compared with their costs. Exceptions are Kimenju and De Groote (2010) and De Groote (2013) whose initial findings highlight that either prices and seasonal price differences or post-harvest losses need to be sufficiently high for modern storage technologies to pay off, and that technologies such as metal silos, which also pay off when prices/losses are low, require higher upfront investments, consistent with Lipton’s (1982) earlier proposition. This poses usually a challenge for credit constrained smallholder farmers in SSA.

> 7 The initiative, which started in 2009, is funded by the Bill and Melinda Gates Foundation and other donor agencies and implemented by the Research Department of the World Bank and the national statistical agencies. The countries include Malawi, Uganda, Tanzania, Ethiopia, Nigeria and Niger.  The data is publicly available on www.worldbank.org/lsms-isa and fully documented with questionnaires, details on survey design and implementation, and enumerators’ manuals.