rafmacalaba/datause-encoder-data huggingface dataset:
2018/19 rainy season, and 3,265 IHS5 plots associated with the 2017/18 season. The total number of agricultural households that are associated with these observations is 1,470 in the IHPS and 5,432 in the IHS5.

Table 1: IHPS 2019 and IHS5 2019/20 rainy season plots by <u>georeferenced information availability</u>

|**Plot category**|**IHPS**|**2019**|**IHS5 2**|**019/20**|
|---|---|---|---|---|
||Obs|%|Obs|%|
|Plots with no geolocation information|334|6.2|1,105|6.4|
|Plots with a corner point, but no polygon boundary|1,365|25.4|4,871|28.4|
|Plots with a corner point and a polygon boundary, but dropped from analysis|874|16.3|2,139|12.5|
|Plots with a corner point and a polygon boundary, used for analysis|2,792|52.0|9,059|52.7|
|**Total # of Plots**|**5,365**|**100.0**|**17,174**|**100.0**|
|**Total # of Associated Households**|**2,3**|**35**|**8,7**|**70**|

Table 2: IHPS 2019 and IHS5 2019/20 rainy season plots by maize cultivation status, conditional on being used for analysis

||**IHPS**|**2019**||**IHS5**|**2019/20**||
|---|---|---|---|---|---|---|
|**Season**|2018|/19|201|7/18|201|8/19|
|**Crop type**|Obs|%|Obs|%|Obs|%|
|Maize|2,033|72.8|2,330|71.4|4,222|72.9|
|Non-maize|759|27.2|935|28.6|1,572|27.1|
|**Total # of Plots**|**2,792**|**100.0**|**3,265**|**100.0**|**5,794**|**100.0**|
|**Total # of Associated Households**|**1,4**|**70**|**1,9**|**26**|**3,5**|**06**|

To begin investigating how the approach to georeferencing plot locations would affect the accuracy of remote sensing models that combine survey and satellite data for high-resolution crop type mapping, we used the full plot boundaries to first derive several additional sets of coordinates that could have been generated with alternative plot geolocation methods and that include:

1. The coordinates of one plot corner that is recorded by the enumerator, i.e. “corner point.”

2. The coordinates of the plot centroid that is derived from the full boundary, i.e. “centroid.” 3. The coordinates of 4 to 8 plot corner points that are derived from the boundary, based on the complexity of the plot shape (geometric simplification) and that are in turn used to:

- a. Derive the geospatial predictors for each pixel corresponding to a given corner point and use these pixels and the associated predictors as the training data, i.e. “boundary points.”

7
<!-- Start of picture text --> 20%<br>16%<br>12%<br>8%<br>4%<br>. ee<br>2017 2018 2019<br>4%<br>— Using variables common between PLFS-NSS and CPHS-NSS<br>— Using variables common between all three surveys PLFS-CPHS-NSS<br><!-- End of picture text -->
characteristics include literacy of the father and the mother.<sup>16</sup> Due to the depth of the LSMS survey, there are numerous household variables that capture the income status of the household which include floor type, roof type, and access to improved toilet facilities.<sup>17</sup> Because child undernutrition is highly influenced by sanitation, we control for improved sanitation facilities.<sup>18</sup> For community-level characteristics, we use distance to main road and distance to market as control variables. Since there is a non-linear relationship between the community variables, we control for distance to main road, distance to main road squared, distance to market and distance to market squared.

We apply a panel data fixed effects approach to equation (1). We measure changes for children who were included both in wave 2 and wave 3 of the LSMS survey data set – those aged 6-41 months old in wave 2 and 24-59 months old in wave 3. A panel data fixed effects model controls for unobservable time-invariant factors, thereby focusing on what drives changes between periods and eliminating the effects of time invariant factors such as gender. We note that other characteristics, for example, the literacy rates of mothers and fathers, are very unlikely to change in a relatively short panel.<sup>19</sup>

# **5. Results and Analysis**

# **5.1. Descriptive Statistics: Child Undernutrition in Ethiopia**

Table 3 summarizes the information on nutritional outcomes for children between 6 and 59 months from the LSMS data set. Stunting increased from 40 percent in wave 2 (2013/14) to 41.7 percent in wave 3 (2015/16). The LSMS data also show similar small increases in the proportion of children who were wasted. It is noteworthy that in Ethiopia there is little difference in the prevalence of stunting between boys and girls. For wasting the prevalence is

> 16 While some studies use mother’s education, we use mother’s literacy since the majority of our sample comes from rural areas and most of the women surveyed have no education. Cintron, Seff, and Baird (Cintron et al., 2016) use the same approach.

> 17 Since there is a possibility of correlation between household variables, we use the Pearson Correlation Coefficient to select variables that highly influence child undernutrition. The Pearson Correlation Coefficient is a number between -1 and 1 that indicates the extent to which two variables are linearly related. For this analysis, we choose 0.6 as the rule of thumb. If two variables have a correlation coefficient of greater than 0.6, we select one of the variables and drop the other. The variable selected is the one that highly influences child undernutrition. In this instance, we choose improved sanitation.

> 18 For a detailed analysis on how each household variable is associated with child nutritional outcomes, see Cintron, Seff, and Baird (2016).

> 19 Contrary to Cintron et al (2016) who apply a fixed-effects model including only independent variables for which 10% or more of the sample experienced a change between periods 1 and 2, we do not exclude any of our control variables from the analysis.

9
<!-- Start of picture text --> Me<br>q , Conflicts Number (from 2016 to 2023)<br>! [J[J 100156 -- 156212<br>bane (7) 212 - 267<br>1D 267 - 323<br>1 323 - 379<br>GS 379 - 435<br>M- Gl 435 - 491<br>/ HH 491 - 546<br>Hl 546 - 602<br>HB 602 - 658<br><!-- End of picture text --> <!-- Start of picture text --> 800 -<br>600<br>a)<br>ee<br>E<br>400<br>8 ,<br>5<br>2<br>200 -<br>0 fl<br>2016 2018 2020 2022 2024<br>year<br><!-- End of picture text -->
In view of the wide-ranging spending strategies suggested by the recent literature, we will focus mainly on three options in our simulations, which cover possible conservative to aggressive expenditure policies for Niger: 1) bird-in-hand (BIH) strategy; 2) permanent income hypothesis (PIH); and 3) borrow now to raise consumption and investment significantly in the short-term. Figure 3 illustrates the incremental spending for each option given the expected time path of mineral revenue. Although expenditure pattern and timing are different in the short to medium term, all three strategies will maintain sustainability of mineral wealth in the long term when mineral reserve is depleted in period 20 (by current estimate).<sup>21</sup> That is, the income from mineral sales that is not spent and put aside over the years is expected to last beyond 20 years in all scenarios, albeit the level will differ for each strategy. By year 20 and beyond, the standing wealth in each case is expected to earn a constant interest income that lasts continuously. The world interest rate assumed is 2.4 percent, the same rate used in the debt sustainability analysis for Niger (IDA and IMF 2013). As seen in Figure 3 and the discussion below, the BIH strategy, being the most conservative will have the most mineral revenue accumulated when the mineral reserves are depleted at the end of the 22 year period. We explain each option further below.

**Figure 3** : **Possible incremental spending in Niger under alternative strategies** (in FCFA billions)

<!-- Start of picture text --> 450<br>"feast"<br>350<br>250<br>Bird in Hand (BIH)<br>Permanent Income Hypothesis<br>150 (PIH)<br>"slow recovery"<br>Borrow Now<br>50 Boom-bust cycle<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22<br>-50<br>"famine"<br>-150<br>Period<br><!-- End of picture text --> <!-- Start of picture text --> Source: Authors’ calculations.<br><!-- End of picture text --> 21 We do not consider the case where government spends every year all mineral revenue it gets. This is because the revenue reaches close to 20 percent of GDP at its peak, a very significant amount, and spending it all would result in excessive appreciation of the real exchange rate and a severe reduction of tradable sectors. Also, there is no sustainability in the long-term when revenue drops to zero at the terminal period, requiring substantial economic adjustment.

22
and employed in Russian WTO accession analysis by Rutherford and Tarr (2008 and 2010). The tariff rates for the analysis in this section were based on trade flow data at a slightly aggregated level and were therefore not precise. Shepotylo and Tarr have shown, however, that the tariff calculations used in this paper are not subject to significant errors.

Based on a mapping (of the Russian Statistical Office) from the tariff line data of the Customs Committee to the sectors in our input output table, we calculated a weighted average tariff rate for the sectors of our model. We calculated these rates two ways: based on all imports (where the collected tariff rate as a percent of all imports is 8.1%) and on non-CIS imports (where the collected tariffs as a percent of non-CIS imports is 11.1%). The rates we employ in the model are the rates based on all imports. The rates based on all imports are lower since the base on the calculation includes CIS imports on which no tariffs are imposed. We believe collected tariff rates more closely approximate the protection a sector receives and the incentives it faces. Similar procedures are applied for export taxes. The results at the sector level are in table 3.2.

Applying these tariff rates across all sectors implies that tariff revenue in our model is about 1.6 percent of GDP in the initial equilibrium. Collected tariffs in Russia are closer to 1.1 percent of GDP.<sup>29</sup> There are several reasons that the collected tariffs in Russia are less than the legal rates on most favored nation (MFN) imports. Most notably, exemptions to the Russian tariff are available for regional agreements (most notably the CIS), personal imports and shuttle trade. We adjust for the CIS trade, so we are applying the MFN rates on all imports from the non-CIS. This slightly, but not significantly biases upward the rates we employ relative to collected rates.

**Export tax data.** Analogous to the import trade data, the Russian State Customs Committee publishes data on export volumes and values. Similar to the tariff data, the export

> 29 International Monetary Fund, ―Russian Federation: Selected Issues and Statistical Appendix,‖ 2002.

47
Table 14. Regression results for July floods in Nigeria and large conflict buffers testing common trends using log(nightlights), 1 to 6 months before the floods

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||t-2 to t-1|T-3 to t-2|T-4 to t-3|T-5 to t-4|T-6 to t-5|
|VARIABLES|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|
|Treated|-0.0597<sup>***</sup>|-0.0575<sup>***</sup>|-0.0579<sup>***</sup>|-0.0677<sup>***</sup>|-0.0733<sup>***</sup>|
||(0.0108)|(0.0120)|(0.0120)|(0.0132)|(0.0137)|
|PostPeriod|-0.0493<sup>***</sup>|-0.0225<sup>***</sup>|0.0524<sup>***</sup>|-0.000375|0.0389<sup>***</sup>|
||(0.00454)|(0.00633)|(0.00625)|(0.00726)|(0.00377)|
|Treated*PostPeriod|0.00693|-0.000900|-0.00428|0.00614|0.00676|
||(0.00468)|(0.00651)|(0.00669)|(0.00729)|(0.00488)|
|PopDens|0.000507<sup>***</sup>|0.000554<sup>***</sup>|0.000568<sup>***</sup>|0.000606<sup>***</sup>|0.000697<sup>***</sup>|
||(8.21e-05)|(8.77e-05)|(9.17e-05)|(9.92e-05)|(0.000112)|
|Constant|0.488<sup>***</sup>|0.508<sup>***</sup>|0.457<sup>***</sup>|0.457<sup>***</sup>|0.413<sup>***</sup>|
||(0.00609)|(0.00685)|(0.00660)|(0.00787)|(0.00771)|
|Observations|13,330|13,330|13,330|13,330|13,330|
|R-squared|0.679|0.682|0.682|0.679|0.681|
|Treatment Obs|5342|5342|5342|5342|5342|
|Control Obs|7988|7988|7988|7988|7988|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 0.14 to 16.64.

# Limitation and Discussion

Assessing the quantitative impact of disasters on populations and economies is particularly challenging in poor and conflict-affected regions, where reliable socio-economic data may be lacking. Our analysis of descriptive statistics has indicated a tendency towards lower nightlight density in rural areas, which could potentially lead to an underestimation of economic activity due to the limitations in spatial resolution. Nightlight radiance in rural settings is typically less intense than in urban centers, and a higher incidence of such readings could result in a skewed sample. Furthermore, sectors like agriculture, which are less dependent on electricity, might not be fully represented by nightlight data. This is particularly true for poorer households in rural areas that may not have access to electricity, rendering them invisible in satellite-based observations. Consequently, vital aspects of a developing economy might be missed when relying exclusively on nightlight data for analysis.

Nonetheless, and despite these limitations, access to advanced earth observation technologies, including regularly updated satellite imagery, presents new avenues for analysis using quasiexperimental approaches. Nightlight satellite data can provide valuable insights into economic activity, primarily enabling the assessment of immediate to short-term impacts. Our case studies highlight the exacerbated effects of floods on nightlight in conflict-affected regions. For instance, we observed a decrease in nightlight radiance following the July 2022 floods in Nigeria, with conflict28
The household maximizes utility from consumption _C_ in both the planting and the harvesting periods. The utility function is additive over both periods and future utility is discounted by the factor _δ_ . The utility function satisfies the usual properties: it is twice differentiable and increases in _C_ but at decreasing rates, _∂U/∂C >_ 0 and _∂_<sup>2</sup> _U/∂C_<sup>2</sup> _<_ 0. This also implies that the household is risk averse. Tis model abstracts from leisure because it does not change the choice under uncertainty.<sup>9</sup> The household generates income from wage employment on local labor markets and from agricultural production. Building on the full-income approach, the household maximization problem can be described as follows: Total time endowment is represented by _T_ 1 and _T_ 2. In both periods total time can be allocated between working in the labor market and working in own fields. In the first period, the household obtains income from wage work at level _w_ 1 and from borrowing _B_ . Inputs for agricultural production can be purchased at price _g_ . In the second period, the household obtains income from the sale of its own agricultural production _p_ ( _Q_<sup>_d_</sup> + _Q_<sup>_s_</sup> ) and wage work at level _w_ 2. Note here that the household has to allocate labor to harvesting in order to generate income from agricultural production. It seems plausible that the household always prioritizes its own harvest over wage employment; therefore the cost of harvesting is valued at reservation wages rather than market wages. The wage cost of harvesting _w_ 2 _l_ 2 in the budget constraint can then be replaced with _αw_ 2<sup>_r_(</sup><sup>_Qd_+</sup><sup>_Qs_),where</sup>

> 9By dropping leisure, I ignore possible income effects of increases in wage levels on a household’s time allocation between labor and leisure. But since my main interest lies in crop choice rather than in production levels, ignoring leisure is not of major concern. Similar approaches can be found in Rosenzweig and Binswanger (1993), Fafchamps and Pender (1997) and Dercon and Christiansen (2011).

6
coverage is assumed to be linear in treatment costs and this may have some bearing on our qualitative results. One alternative is to use a non-linear function with decreasing marginal costs of coverage initially when coverage is small, and increasing marginal costs of coverage when all the easily accessibly patients have been reached and additional patients are more expensive to treat. Simulations are run for a ten-year planning horizon. For cost-effectiveness calculations, the streams of costs and health benefits are discounted at three percent.

Finally, the quality of ACT treatment, including the sensitivity and specificity of diagnosis, and treatment compliance is an important consideration but one that is outside the purview of this report. Compliance with different drug regimens may vary due to difference in the patient (i.e., education, household income, etc.), in the quality of service delivery because of differences in duration of treatment, side-effects, rapidity with which a cure is experienced and other factors; however, there is little quantitative evidence on how compliance is influenced; in general, the role of treatment compliance in the evolution of resistance remains poorly understood<sup>6</sup> .

# _3.1 Malaria Model_

An entomological-epidemiological model was developed to model transmission of malaria, and emergence and spread of single and multi-drug resistance to antimalarials. Details of the model are in Appendix A.

# _3.2 Treatment Demand_

The demand for ACTs for malaria treatment depends on the efficacy of ACTs and its cost to patients. Other factors that may influence the decision to seek treatment include age, sex, geographical location, degree of malaria endemicity, proximity to a trained health care provider, and other socio-cultural factors and are assumed to be the same for

> 6 While there is a general notion that improved compliance lowers the likelihood of emergence of resistance, it is possible that this is true only for _de novo_ resistance that emerges in infected individuals receiving antimalarials, and not for epidemic resistance, which is transmitted between individuals.

4
banks in Africa actually deteriorated after privatization. It is important to note, however, that these privatizations were share-issue privatizations of only a portion of the governments’ ownership stakes—as noted, both of those features are associated with reduced gains in developing countries (Clarke et al., 2005a; Megginson, 2005a).

A second, and potentially greater concern, is the importance of UCB in the Ugandan banking sector. The privatized bank, Stanbic, holds about 30 percent of loans and deposit accounts.<sup>6</sup> Moreover, none of the 68 branches operated by UCB were closed after privatization, and thus Stanbic maintains the most geographically extensive branch network of any bank. As of 2005 Stanbic had 74 branches, 54 outside of Kampala. Only one other commercial bank, the agriculturally oriented Centenary Rural Development Bank, has more than four branches outside Kampala (see Appendix, Figure 9). Stanbic, therefore, faces little or no direct competition in most areas in which it operates.

This could affect the success of the privatization in at least two ways. First, it might affect the way the government treats privatization.<sup>7</sup> The privatization of a large bank with a dominant rural network is likely to be fraught with political difficulty, and, as described below, this was true for the privatization of UCB. Political accommodations that make a deal palatable to the government, but which handcuff the new owners, might have negative consequences for postprivatization performance. Moreover, although Stanbic is now the largest bank in Uganda, it is still a small bank by the standards of industrialized countries, and therefore not able to reap the economies of scale that one might associate with size in other contexts.

Second, because of its extensive network outside of Kampala, UCB faced little competition in many areas of the country. Although few studies of bank privatization have looked at the effect of competition on privatization outcomes, studies in other sectors have often suggested that privatizations are more successful when the privatized firms face competition. For example, although privatization appears to be associated with performance improvements in the telecommunications sector, many studies have

> 6 World Bank (2005).

> 7 Clarke and Cull (2005) look at the political factors that affected provincial bank privatization in Argentina.  They find that privatization contracts were less favorable to the government with respect to the concessions that they made to the new owners in provinces where the public bank was more dominant (that is, where it faced less competition).  They attributed this to the fact that these provinces were less attractive to bidders.

3
characteristics that influence project investment decisions. Our results suggest these factors are consequential and explain why the current pool of project investments differs from ex ante predictions.<sup>6</sup> While previous studies provide useful estimates of abatement costs of various pollutants, a majority of those are based on secondary data or approximated coefficients in the abatement functions. In this paper we take advantage of available data on CDM projects to answer several questions that are important for the future of CDM policy design but have not been addressed earlier. The project-level data distinguishes among various types of projects, methodologies for calculating emissions reductions, the countries hosting the projects, and sequence of new project investments for the period 2003-2010. Thus, our dataset allows us to draw distinction among projects across types (technologies), methodologies, locations, and time.

These features of the data allow us to examine the relative role of mitigation and project costs in explaining the pool of observed investments. It also allows us to test two hypotheses important for policy: (1) whether CDM projects exhibit economies of scale in emission abatement, and (2) whether the marginal cost (as well as the average cost) of abatement of CDM projects has decreased over time, presumably due to accumulated experience.

The remainder of the paper is organized in the following way. After selectively reviewing the relevant literature, the following section describes the conceptual model and empirical framework for estimating mitigation cost of CDM projects. Section three describes the CDM project-specific data. Section four delineates the estimation procedures and results. Finally, the last section concludes and discusses the policy implications.

# **2 Estimating Emissions Abatement Cost of the CDM**

One of the early studies on pollution abatement cost was undertaken by Rossi, Young, and Epp (1979). They estimated a cost function in which abatement cost is a function of the volume and quality of both effluent and influent streams and factor prices (i.e., prices of land, labor, capital, and materials). Fraas and

> 6 See Rahman et al. (2011) for a review of ex ante predictions for the CDM.

4