rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9609

# **Abstract**

With the surge in publicly available high-resolution satellite imagery, satellite-based monitoring of smallholder agricultural outcomes is gaining momentum. This paper provides recommendations on how large-scale household surveys should be conducted to generate the data needed to train models for satellite-based crop type mapping in smallholder farming systems. The analysis focuses on maize cultivation in Malawi and Ethiopia, and leverages rich, georeferenced plot-level data from national household surveys that were conducted in 2018–20 and that are integrated with Sentinel-2 satellite imagery and complementary geospatial data. To identify the approach to survey data collection that yields optimal data for training remote sensing models, 26,250 in silico experiments are simulated within a machine learning framework. The best model is then applied to map seasonal maize cultivation from 2016 to 2019 at 10-meter resolution in both countries. The analysis reveals that smallholder plots with maize cultivation can be identified with up to 75 percent accuracy. However, the predictive accuracy varies with the approach to georeferencing plot locations and the number of observations in the training data. Collecting full plot boundaries or complete plot corner points provides the best quality of information for model training. Classification performance peaks with slightly less than 60 percent of the training data. Seemingly small erosion in accuracy under less preferable approaches to georeferencing plots results in total area under maize cultivation being overestimated by 0.16 to 0.47 million hectares (8 to 24 percent) in Malawi.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings
in Almarj with respect to surrounding provinces might indicate that this area acts as a buffer zone separating provinces with a high concentration of migrants from areas less involved in migrant passages. By contrast, that there are more migrants in Wadi Ashshati than in all other provinces on the Libyan western border suggests that this area was a preferred point of access for migrants arriving from the western side of the country in 2017 and 2018. Likewise, the emergence of hotspots in 2018 indicates a more concentrated presence of migrants in some provinces (Ejadabia and Murzuq) and a decrease in adjacent provinces (Ghat and Tobruk). The fact that these provinces are located at the Libyan border suggests that they are used as new entering points in Libya, which is consistent with the hypothesis of the emergence of new migrant routes.

Our analysis also reveals a cluster of provinces around Tripoli hosting a large number of migrants. The level of spatial autocorrelation in the distribution of migrants in this area increased from 2017 to 2018 and became significant at the 5% level in 2018.

# _5.2.2 Migrant movements within Libya_

FMP data can be used to map the internal movements of migrants (between Libyan provinces). For each FMP, DTM data indicate which Libyan province is the most common origin for the migrants at the FMP. Using this information for all FMPs in our data set, we determine the internal movements of migrants across all Libyan provinces.

The results of the analysis for 2017 and 2018 are shown in figure 8. Capitals of provinces _i_ and _j_ are connected if there is movement from an FMP located in _i_ to an FMP located in _j_ . The size of the flow is proportional to the average number of migrants registered in the FMP.

For each year, the graphs show a dense net of connections; each province is connected to at least two other provinces, suggesting that, in general, for each province, there is not a unique migration route from or to any other province. In determining the migration route chosen, it
the type of event (conflictive or cooperative) and its intensity using the BAR scale, which ranges from –7, the most conflictive (war), through 0 (neutral events), and up to +7, the most cooperative (voluntary merging of countries).

To extract the events examined in this study, we categorized the events data as they relate to country-basin dyads with no treaties and those country-basin dyads with treaties. For those country-basin dyads with treaties, we also categorized the events data paying close attention to the period before and after a treaty governed the basin in order to compare the two time-periods. The data processing produced a database listing the number and intensity (BAR values) of all the events relevant to each country-basin dyad. This is in line with previous studies using BAR events data (e.g. Yoffe and Larson, 2001; Brochmann, 2012). ). There were no examples of extreme scores (- 7 or 7). As one of the primary goals of this study was to investigate the impact of treaties, in cases with a BAR score of positive 6 (the signing of a treaty), we subtracted one observation from the total number so as not to attribute the signing of the treaty as an outcome of the treaty. The distribution of events is shown in Figure A1 in the Appendix. In conducting our regression analysis, we used ( **_AvgBAR_ )** as a dependent variable, which was the mean of the anti-logged values of conflictive and cooperative relations between the countries. Anti-logs were used instead of actual BAR scores in order to amplify the increasing differences in scale for more extreme events, as is common in previous work using BAR values (e.g., Yoffe et al., 2003).

# _<u>Water Variability</u>_

While water variability is an inherent characteristic across river basins, climate change is predicted to intensify such variability. All else being equal, higher water variability has been shown to
schedules, accounting for both productivity and preference heterogeneity, under different values of the relevant labor supply elasticities and compare these schedules to the optimal tax schedules in which all income inequality is due to productivity heterogeneity as in Mirrlees (1971) or Saez (2001).

Under our baseline elasticity estimates from Chetty (2012), we find that optimal tax rates are actually slightly higher than the Mirrleesian reference case in which all income inequality is due to productivity heterogeneity. Essentially, the elasticity estimates from Chetty (2012) imply that high income individuals actually have lower preferences for consumption, on average, than middle income individuals. Therefore, high taxes are even more desirable than in the Mirrleesian benchmark. However, this finding is sensitive to the elasticity estimates used to recover individual productivities and preferences. We find that a larger difference between the income and hours worked elasticities and larger income effects both imply lower tax rates relative to the Mirrleesian optimal tax schedule.

> 5Because the literature has not reached a consensus on the magnitudes of the different labor supply elasticities, and because current data provides imperfect measurements of hours worked, our empirical application aims to investigate how changing labor supply elasticities affects the relative importance of productivities vs. preferences in driving income inequality.

4
an enterprise skills survey which could be replicated in other countries as part of a regional approach to collect and analyze data within a common framework that would allow comparisons across countries.<sup>6</sup> Conducted in 2015, the Tanzania Enterprise Skills Survey elicited detailed information from firms about the educational and occupational composition of their workforce; workers’ technical and vocational training qualifications and non-cognitive competencies (such as teamwork, communication, language and IT skills) that many employers want; employer perceptions about skill gaps and constraints to operations, assessments of skills supply from education and training institutions, and their hiring and in-service training practices. The TESS survey instrument incorporates questions on firm characteristics and performance in line with the World Bank Enterprise Surveys, key questions in common with STEP employer surveys,<sup>7</sup> as well as some unique questions on employer strategies to address skills constraints and on their supply chain of skills and training providers. Drawing on these data, the study uses an analytic framework in which heterogeneous firms make optimizing decisions about the skill mix of their workforce given available skills supply and uncertain demand. Changing workforce composition takes time, and skill deficits arise in the short-term when the actual skills mix is inadequate, while the effect on productivity depends upon how employers respond.

Using this approach, the study addresses five broad areas of policy concern regarding enterprise skills in Tanzania’s formal sector:

- _Skill Profile_ : What is the educational and training composition of the workforce, and how do skills profiles vary across firms? Which factors shape employers’ skills demand most: firms’ productivity attributes or broader-based demand from international trade and technological change?

- _Skill Deficits_ : What do employers’ assessments of workforce education and training say about the adequacy and quality of skills supply, including the soft skills that many employers say they want?  Do these skill deficits pose significant obstacles to the operation of firms? If so, how are these deficits distributed across firms, and do they vary systematically across firms?

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interpret the magnitude of the estimated regression coefficients.

The second important variable is household consumption expenditure. The average adult equivalent consumption in 2011 per day is 14.5 Birr (about 2.75 in current PPP $). The median adult equivalent consumption per day is about 11 ETB (about 2 in current PPP $). The national poverty line for Ethiopia during the same year is 10.35 Birr (World Bank Group, 2015). The average (median) adult equivalent consumption decreases slightly to 1.94 in current PPP $ (1.68 in current PPP $) in 2015. Figure 4 presents the distribution of log total adult equivalent consumption spending.<sup>9</sup> The left panel in this figure shows the distribution of nominal

> 7WAZ, WHZ and HAZ values are computed using the stata program, which is based on 2006 WHO child growth standards.

> 8Ethiopia performs worse than Sub-Saharan African (SSA) countries’ average in the proportion of under-5 children stunted, underweight, and wasted. Ethiopia has the highest percent (76%) of children who have not received any of the eight EPI immunizations in SSA countries. See Kanamori and Pullum (2013) for a detailed comparison of child health outcomes across 30 SSA countries using DHS data.

> 9For details about construction of the consumption variable from survey data, see `https://microdata.`

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Moreover, we also use the Hsieh and Klenow (2009, 2014) way of estimating TFP which is relatively new and follows a different structure. More precisely, in their methodology the authors follow a structural approach using a Cobb‐Douglas estimate with commonly used labor shares so as to explicitly take into account policy distortions that would otherwise affect traditional TFP measures in their revenue as opposed to physical TFP.

We estimate all four approaches and use them in our regression output discussion. In order to do so one needs to estimate sector‐specific production functions. These production functions are sector‐specific because industries differ in production technology. The main idea of estimating these production function is that through this way, unobserved firm productivity shocks can be approximated by a non‐ parametric function of observable firm characteristics. Since we are dealing with multiple countries in our data set, we estimate these production functions for each 2‐digit services and manufacturing sectors by country, although in some cases we regroup some countries due to their insufficient numbers. They are Germany with Austria, the Benelux, Sweden with Denmark, Estonia with Latvia, and finally UK with Ireland. The reason for choosing these groupings is that we think that each pair are fairly like‐minded in their economic structures. Regarding sector division, in total we have 57 different sectors, which is considerably more than in previous studies, because the high number of firm observations in each sector allows us to go ahead with this selection although some industries are also regrouped together. The industry groupings together with an overview of firm observations can be seen in Table A4 in the Annex.<sup>9</sup> Table A5 reports a similar overview by country.

To start estimating production functions we need firm‐level data on value‐added. Normally value‐added at the firm is defined as sales minus the value of inputs, which includes materials, services and energy. In the Amadeus data set, unfortunately only operating net revenue and material costs are reported, which we
# **B Data Appendix**

All statistical codes to replicate the paper are available at https://github.com/ pierrebachas/Informality_Taxes_Redistribution. This includes the cleaning files for the micro data of each country’s survey, as well as all files generating the tables and figures of the paper.

## **B.1 Core Sample**

## **Inclusion Criteria**

Our dataset consists of 31 nationally representative household expenditure surveys. We use surveys which satisfy the following four criteria:

1. The household expenditure survey is nationally representative and dates from the 21st century.

2. The expenditure module(s) in the survey is structured as an open consumption diary, rather than a pre-fill diaries for a limited set of products.

3. The expenditure survey includes a variable for the place of purchase (data on where each item was purchased). The place of purchases are detailed enough for us to B.2.

apply our taxonomy of store types, as further outlined in section 4. The place of purchase variable rarely contains missing values, particularly for food, clothing and household goods product categories (see Figure A3).

## **Data Sources and Coverage**

We obtained the data principally from two sources: (i) the World Bank Microdata Library and (ii) National Statistical Agencies. Our first step for accessing data was to search the restricted-access World Bank Microdata Library for household Income and Expenditure, Living Standards, and Budget Surveys to see in which countries criteria (1)-(4) above appeared to be satisfied. The datasets which satisfied these criteria varied in their ease of access: for some countries, the micro data were accessible for download on the World Bank platform, others were licensed and required applications through the World Bank, which would in turn sometimes contact the country’s national statistical agency for approval. If a survey was listed without its micro through the World Bank platform, we reached directly the country’s’
|**National sample**<br>**Table 5. Food sec**|Mean<br>**urity measure**|25th<br>**s across the**|50th<br>**distribution**|75th|
|---|---|---|---|---|
|Real value of per capita monthly<br>food consumption|929|612|810|1101|
|Daily per capita caloric intake|2601|2030|2441|3006|
|Food consumption score|61|46|61|74|
|**Agricultural households**|Mean|25th|50th|75th|
|Real value of per capita monthly<br>food consumption|911.29|606.95|801.34|1088.28|
|Daily per capita caloric intake|2586.81|2026.04|2419.25|2964.53|
|Food consumption score|61.59|48.00|62.00|74.00|
|**Nonagricultural households**|Mean|25th|50th|75th|
|Real value of per capita monthly<br>food consumption|951.86|619.16|821.26|1121.48|
|Daily per capita caloric intake|2619.47|2033.54|2470.99|3044.94|
|Food consumption score|60.07|44.00|59.50|75.00|
|See Table 4 notes.|||||

**Table 6. Effects of wheat flour price increases on food security**

||OLS|_25th quantile _<br>Uncondi|_50th quantile _<br>tionalQuantile R|_75th quantile_<br>egression|
|---|---|---|---|---|
|Log Real Value of
# **1 Introduction**

Developing countries are the home to millions of microenterprises, which provide an important source of income for the poor. For example, the Mexican Economic Census found 4.1 million firms with zero to ten workers (INEGI, 2016). Many governments and NGOs offer business training programs to try to help owners of these firms improve their business practices and increase their incomes. A recent meta-analysis of business training experiments found statistically significant, but modest, average impacts of a 4.7 percent improvement in sales and 10.1 percent improvement in profits (McKenzie, 2021). The most typical training programs take place in-person in classroom settings, requiring groups of 20-30 business owners to travel to a common location for several days, with an average cost of $177 per participant (Van Lieshout and Mehtha, 2017). This raises questions and skepticism about the cost-effectiveness and scalability of such programs (e.g. Fox and Thomas (2016)), and how they can be expanded to a scale where they can reach thousands or millions of firms.

Digital technology offers the potential to both lower the costs of delivering training, and to enable it to be scaled across a wider geographic area. However, experience in developed countries with asynchronous voluntary learning on massive open online courses (MOOC) platforms such as Coursera shows incredibly large drop-out rates (Rivard, 2013). Moreover, poor entrepreneurs in a developing country setting may face further technological obstacles in accessing online training. Reich and Ruipérez-Valiente (2019) note that almost all of the pre-pandemic growth in MOOC registration and certification came from high-income countries. An alternative to self-paced asynchronous courses without any human interaction is to hold video-based synchronous courses that mimic the format of in-person training, while allowing for remote delivery. The rise of Zoom and other video meeting platforms opens up this possibility, but raises three key questions: how feasible is this with microenterprise owners in a developing country setting? How much of a cost-saving is achieved by offering live classes
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Climate change may add to the complexity of the problems in the energy sector. In Albania, energy efficiency is becoming more important from both energy security and climate change perspectives. The country is dependent more than 95 percent on hydropower for domestic electricity generation and therefore considered among the most vulnerable countries to external energy shocks and climatic conditions. In the past, severe droughts posed significant challenges on Albania (ESMAP, 2009). Meanwhile, the demand for electricity is steadily increasing with economic growth. As the result, if drought happens as in 2002 and 2007, the country will have to import a large amount of energy from abroad at significantly high costs. Otherwise, massive electricity supply cuts will be unavoidable (Figure 1). Therefore, longterm energy planning and demand management continue to be critical for Albania to achieve energy security and economic growth. To this end, metering is expected to have an important role to play in incentivizing consumers to reduce non-essential use of electricity. The efficient energy use will mitigate the need for investment in new electricity generation capacity and thus contribute to emissions reduction.

**Figure 1. Electricity Transmission and distribution losses (percent of total)**

<!-- Start of picture text --> 9,000<br>Load shedding<br>8,000 Imports<br>Domestic production<br>7,000<br>6,000<br>5,000<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>2001 2002 2003 2004 2005 2006 2007<br>Source: KESH data.<br>GWh<br><!-- End of picture text --> Pricing is a key policy instrument to manage the demand for energy in general. It is not only about the level of prices but also about the structure of tariff. If prices are low and the demand is price-elastic, raising the prices
provides a simple and robust model with basin-relevant results for policy, which measures gauge station runoff with wetness values derived from satellite data (BWI) over the period 1988-2010, as proof of the concept on how sub-basin stream flow information can be expanded to the river basin where natural flow gauge information is limited and can provide the possibility of timely updates. Then, we demonstrate the use of the approach for monitoring various flow levels that provide estimates of monthly runoff in near real time in two international river basins, Zambezi and Mekong. We show how information from the model can strengthen the institutional capacity within the basin to understand the probability of various levels of extreme flow, has potential to realize the economic consequences of extreme flow events, and prepares or adjusts proper institutions to address it.

The paper is organized in five sections. Below we begin by couching the discussion of the BWI in a socio-political context by highlighting the links between climate change and water variability, conflict and cooperation, and the role of international institutions such as international water treaties. We also stress the importance of data availability given the uncertainties inherent in climate change and the consequences of water variability. Second, we describe a number of methodologies that have been used to derive flow data and likewise discuss the BWI and demonstrate its use in the Zambezi and the Mekong. Third, we discuss the accuracy and predictability of the BWI‘s estimated flow probability models. Fourth, we develop a basin economic model that incorporates the flow probability and the existing institutional frameworks that exist in these basins to gauge economic and political impact. We compare these institutions based on their relevance, structure, and design. Fifth, and in an effort to demonstrate the applicability of the BWI to a growing research agenda on conflict and cooperation over shared rivers, we review historical political events related to floods. We conclude that the satellite data we derive may reduce the uncertainty inherent in negotiating an international water agreement and help policy makers devise more efficient institutional apparatus.

# **THE SOCIO-POLITICAL CONTEXT: