rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 14 Fixed-line telecommunication services: Change in STRI by level of income**

<!-- Start of picture text --> Sector: Fixed-line telecom<br>BGD EGY CRI URY OMN<br>LKALKA<br>CHNCHN<br>CAN<br>IND PHL KAZ<br>MEX<br>KOR CAN<br>PAKIND CHL<br>BGD PAK VNM UKR BRA KAZ POL<br>IDNIDN PER COL ARG CHL RUSHUN NZL<br>KENKEN NGANGA ECU TUN ZAF THATHA MEX PAN TUR MYSMYSRUSHUNLTULTUPRTPRT GRCCZE KORESPESP ITAJPNFRA GBRGBRFINFIN BEL OMN DEUAUSAUT SWESWEDNKDNK NLD USA IRLIRL<br>ECU<br>DOM TUR<br>8 9 10 11<br>Avg income per capita (2008-16)<br>Note: STRI 2008-11 in blue, STRI 2016 in orange.<br>100<br>80<br>60<br>STRI 2008-11, 2016<br>40<br>20<br><!-- End of picture text -->

# <u>Mobile telecommunication services</u>

As indicated above, there have been fewer changes in the mobile telecommunications services sector, where all economies in the sample were open to foreign establishment in the mobile segment even by the end of the previous decade. Only a small number of countries had foreign equity limits. Some of those were relaxed (Canada, Korea as described above) or removed (in Malaysia and in India, subject to government approval beyond 49%) between 2008 and 2016. Hungary now allows greenfield investment. Colombia and India have relaxed the limits on the number of suppliers.<sup>19</sup>

**Figure 15 Mobile telecommunication services: Change in STRI by income group**

<!-- Start of picture text --> Sector: Mobile telecom<br>L LM UM H<br>STRI 2008-11 STRI 2016<br>50<br>40<br>30<br>20<br>STRI 2008-11, 2016<br>10<br>0<br><!-- End of picture text -->

> 19 In Colombia the number of licenses was fixed in the 1993 bidding process. In 2016, the quantitative restrictions were removed. For India, the maximum number of firms or suppliers in the sector was four in 2008. This restriction has been lifted and no quantitative limit is applied anymore.

23
# **1.  Introduction**

In the past few years, the term “pro-poor growth” has become pervasive in discussions of development policy. Despite the widespread use of the term, there appears to be much less consensus as to what exactly pro-poor growth means, let alone what its determinants are. According to one view, growth is pro-poor if the accompanying change in income distribution by itself reduces poverty (Kakwani and Pernia 2000). However, this definition is rather restrictive, as it implies that, for example, China’s very rapid growth and dramatic poverty reduction during the 1980s and 1990s was not pro-poor because the poor gained relatively less than the non-poor. A broader and more intuitive definition is that growth is pro-poor if the poverty measure of interest falls. Ravallion and Chen (2003) propose this definition and apply it to a particular poverty measure, the Watts index.

In this paper, I adopt the broader definition, and then apply standard poverty decomposition techniques to identify three potential sources of pro-poor growth: (a) a high rate of growth of average incomes; (b) a high sensitivity of poverty to growth in average incomes; and (c) a poverty-reducing pattern of growth in relative incomes. I implement this decomposition for several poverty measures, using household survey data for a large sample of countries in the 1980s and the 1990s. I then use variance decompositions to summarize the relative importance of these sources of pro-poor growth. Finally, I investigate the correlates of the sources of pro-poor growth in a large panel of observations on changes in poverty.

The main results of this paper are the following. First, regarding the relative importance of the three potential sources of pro-poor growth, I find that roughly half of the variation in short-run changes in poverty can be explained by growth in average incomes. In the medium to long run, between 66 and 90 percent of the variation in changes in poverty can be accounted for by growth in average incomes. Virtually all of the remainder is due to changes in relative incomes. In contrast, cross-country differences in the sensitivity of poverty to growth in average incomes account for very little of the variation in changes in poverty.

1
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that individuals had at a point in time before the migration decision, or on the poverty of the members of the pre-migration household, or look at overall total combined impacts that incorporate both impacts on the composition of the household, and on how those members are affected.

# **5.5 Which parts of migration research are most and least affected by these issues?**

While our focus is on the counterfactual families that can arise with migration, many of the same issues will be present when examining impacts of other development interventions such as conditional cash transfers or girls’ schooling programs. These other programs can also affect when and who individuals marry, fertility, and hence family formation. However, the differences are that these programs still involve less of a change in marriage market and life options than international migration, and that it is more common for studies of those programs to focus on impacts on an existing pre-defined family, than on outcomes for the future spouses and children of beneficiaries.

What types of migration research need to be most concerned about the issues raised in this paper, and which analysis can ignore it? First, in most cases the issues raised here will not affect studies in which the family is not being considered as either a determinant of migration, nor the unit on which to examine impacts. For example, studies of the impacts of migration on the well-being of the migrants themselves, or of how migration affects trade flows or wages of other community members will typically not need to content with the issues raised here. Second, the identity of some family members such as parents and siblings will not change with migration, so studies which look at the impacts of migration on these migrants’ relatives can work with pre-determined family relationships (whereas impacts on parentsin-law or siblings-in-law will be affected). Third, the issues will be less severe in contexts where migration is short-term, temporary, and offers less time and fewer possibilities for family changes to occur. Seasonal migration programs and contract labor may fall into this category. However, it can be common for individuals to engage repeatedly in such programs and over longer periods family changes will become more prevalent.

The issue of counterfactual families is likely to be most severe when migration is anticipated or planned in advance, occurs for younger people who are still in the process of forming families, and is for longer-term duration migration movements. But this describes many mi29
modeling pipeline in steps of 2 percentage points. Subsampling of training data was also done in a stratified manner (by district and class label). Each subsample was passed through the maize classification pipeline and evaluation results were recorded. In total, we tested 26,250 scenarios, comprising:

1. 7 geolocation methods - boundary points, centroid, convex hull, corner, hull mean, plot points, and plot mean

2. 50 data subsets - 2% to 100% subsets of training data, at an increment of 2% points 3. 5 area thresholds - 0, 0.05, 0.1, 0.15, and 0.2 ha 4. 3 feature types - optical only, radar only, both optical and radar 5. 5 replications to capture variability due to random sampling To compare performance across countries, we applied a similar workflow to the Ethiopia survey data set. However, due to the limitations of that data set, we only tested the corner point geolocation method, with no area threshold, and with optical data only.

# **4. Results and discussion**

We conducted sensitivity analyses to understand how survey data properties, especially their number and geolocation method, affect the performance of maize classification predictions. We constructed a maize classification model for each unique combination of the data collection scenarios and compared performance across these scenarios. We would like to note here, that we calculated performance on plot centroids in the test subset regardless of the data collection scenario being evaluated, to ensure that our performance metrics were comparable across geolocation methods. In this section, we present the drivers of maize classification performance by first focusing on the effects of survey sample size and geolocation method, then plot area thresholds, and finally the source of satellite data used.

# **4.1. Effect of the approach to georeferencing plot location**

Maize classification accuracy scores across geolocation methods typically fell within 2.5 percentage points of each other, with gaps increasing with the number of plots used for model training (Figure 7a). The variation in maize classification performance across geolocation methods was more pronounced in the MCC curves (Figure 7c), with the “hull mean” and “plot mean” methods producing the most significant performance advantage.

The difference in the accuracy and MCC training curves arises because MCC takes into account all the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), thus providing a more balanced measure of performance. This becomes especially important when we consider geolocation strategies where a number of training points are collected for a single plot, such as “boundary points”, “convex hull”, and “plot points” - they further increase 19
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39
PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA

**Figure 10 Value of  own generation as share of GNI and gross fixed capital formation, 2005**

<!-- Start of picture text --> 6.0% 40.0%<br>35.0%<br>5.0%<br>30.0%<br>4.0%<br>25.0%<br>(% of GNI)<br>3.0% 20.0%<br>(% of GFCF)<br>15.0%<br>2.0%<br>10.0%<br>1.0%<br>5.0%<br>0.0% 0.0%<br>Mauritius Congo Rep. Mauritania Swaziland Zimbabwe Eq. Guinea Nigeria Ghana Malawi Togo Tanzania DRC Uganda Zambia Gabon Guinea Kenya Mozambique Sudan South Africa Niger Mali<br><!-- End of picture text --> Source: UDI World Electric Power Plants Data Base (WEPP); World Bank 2005; World Bank, World Development Indicators database.

Note: The start-up value of installed capacity was estimated at the individual plant level by the product of the plant’s capacity (in kW) and capital cost per kW of installed capacity, and then aggregated at the country level. The data for capital cost per kW of installed capacity (for different technology types) came from the World Bank’s Energy and Water Department (2005). The value of own generation as a percentage of GNI is either too small or missing for the countries not reported in the figure.

Annual gross additions to own-generated capacity are shown in figure 11 as a share of total installed own-generation capacity. Consistent with the literature on investment in infrastructure (see, for example, Turvey 2002), the figure illustrates that capacity additions are indivisible, entailing lumpy investment. The largest additions to own-generation capacity occurred in 1994, 2000, 2002, and 2005. These spikes may reflect growing demand, coupled with adverse shocks in the supply of public power, such as economic recessions, armed conflicts, droughts, or increases in oil prices.

The differences in size and scope of own generation in Sub-Saharan Africa can be explained by various factors, including spatial characteristics, economic development, conflict status, and industrial structure (see table 1).

9
human capital) were **not** improved in the combined treatment (IPT-G+) arm when compared with the control group, either.<sup>23</sup>

# **3.4 Why are there negative impacts of cash?**

As motivated in the introduction, we hypothesized that the marginal impacts of the cash transfer might be positive on both mental health and human capital outcomes. But, instead, we find large and statistically significant negative impacts of cash on minimal depression that persist until endline. We also see that cash unravels the impacts of the therapy on school enrollment, desired fertility, and expectations of paid work. We hypothesize that this adverse finding is linked to the timing of the transfer in relation to an unexpected negative shock, as the cash was distributed immediately prior to the onset of the COVID-19 pandemic. We provide some exploratory analysis to support this explanation.

Descriptive statistics from our data show that 83% of the sample in the IPT-G+ arm at the 12-month follow-up survey reported that cash helped them during the pandemic, largely to support their families and buy food. However, 50% also said that COVID-19 impacted how they spent the money, and 81% felt they had to give at least some of the cash to their family and friends. Many had hoped to start a business or support schooling but had to divert the money to essential needs for self and family. One adolescent noted: “I had planned to start a business, however I was inclined to spend part of the money to feed my family during social containment.” Another one said: “I wanted to start up a business of selling fruit but when the Corona pandemic arrived and the lockdown, I used it for buying food for my guardians who were not working anymore and the situation had gotten difficult.” Hence, there is a sense in which cash transfers may have increased aspirations, but ended up becoming a source of frustration to the adolescent beneficiaries when their families became reliant on them for basic necessities during (at least the early stages of) the pandemic. This is similar to the finding in Baird et al. (2013), where being the target beneficiary of a cash transfer program that is providing a non-negligible sum of money for the household can become a burden for adolescents and increase their psychological distress. This finding is also consistent with a recent evaluation of a cash transfer program trialed in Oakland, California, which finds overwhelmingly zero or negative impacts on outcomes and suggests that “...the windfall made participants’ (unmet) needs more salient, which caused distress” (Jaroszewicz et al., 2022).

Quantitative survey data also support this interpretation when we examine the adolescent’s own response to COVID-19, as well as their perception of their household’s response to it. Table 11 shows the impacts on a set of outcomes regarding how the respondent and their

> 23Impact estimates with covariate adjustments for Table 8, Table 9, and Table 10 can be seen in Appendix Tables A7, A8, and A9. Impact findings remain qualitatively the same.

17
**Figure 1:** _Share of married women aged 20-24, who married before the age of 15 and 18, respectively, by state_

<!-- Start of picture text --> Panel A: Marriage before the age of 15 Panel B: Marriage before the age of 18<br>% women married before age 15 % women married before age 18<br>(32% - 40%] (77% - 86.8%]<br>(16% - 32%] (62.19% - 77%]<br>(6% - 16%] (50% - 62.19%]<br>[1% - 6%] (37.56% - 50%]<br>No data (28.44% - 37.56%]<br>[17.16% - 28.44%]<br>No data<br><!-- End of picture text --> Notes _: This figure shows the proportion of married, women (or in cohabitation wit a man or have been married once) aged 20-24 who married before 15 (Panel A) and 18 (Panel B). The data source is the 2018 Nigerian DHS._ Figure 1 reveals the existence of an internal child marriage belt along the Sahel part of the country stretching from North-West to North-East Nigeria. In particular, marriage before the age of 15 (Panel A), although still widespread country-wise, is more prevalent in Northern states, with rates ranging from 32% to 40%. Marriage before the age of 18 is much more widespread in Nigeria, as shown in Panel B. However, in this case as well, northern states are clearly more affected. In some areas, the prevalence rate is well above 80%—which is huge even by Sub-Saharan African standards. For example, despite not being directly affected by the security crisis involving _Boko Haram_ , North-West Nigeria is among the heavily affected areas, both for less than 15 and 18 marriages.

10
# International Reserves and Central Bank Independence<sup>*</sup>

Agustin Samano<sup>†</sup>

**JEL Codes:** E58, F32, F34, F41

**Keywords:** International Reserves, Central Bank Independence, Sovereign Debt

> *The ideas presented here are based on the first chapter of my dissertation at the University of Minnesota. I am particularly grateful to Manuel Amador, Javier Bianchi, and Tim Kehoe for all of their advice and guidance. For helpful comments, I thank Roman Acosta, Cristian Aguilera, Marco Bassetto, Carlos Esquivel, Eugenia Gonzalez-Aguado, Daniel Samano, Juan Sanchez, Ana Maria Santacreu, and the participants of the Minnesota Trade Workshop, the Minnesota-Wisconsin International/Macro Workshop, the XXIV Workshop on Dynamic Macroeconomics at Vigo, the Federal Reserve Bank of St. Louis PhD Students Workshop 2020, the 15th Economics Graduate Student Conference at Washington University in St. Louis, and the SED 2021 Annual Meeting. I am grateful to Lars Peter Hansen and the Macro Finance Research Program for supporting this research project. The generous financial support from donor, Edward R. Allen, is greatly acknowledged. I am grateful to the Center for Latin American Monetary Studies (CEMLA), the Federal Reserve Bank of Boston, and the Federal Reserve Bank of Minneapolis for their hospitality. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are my own.

> †The World Bank Research Department; Email: asamanopenaloza@worldbank.org