rafmacalaba/data-use-mentions-extended huggingface dataset:
appendix S1).

More important, it is clear from figs. 2 and 3 that what may be effective in promoting exports (focusing in a few sectors and destinations or a large share of the budget coming from fees for services, for example) may be less effective in increasing GDP per capita. One important message that comes out of this is that trying to evaluate the performance of EPAs by looking at increases in exports may create the wrong incentives when the ultimate goal of EPAs is social and economic welfare proxied here with GDP per capita.

A potential concern with the sample is that the third wave of EPA surveys for the period 2010–2014 only concerns 14 European countries. The study therefore tested the robustness of the results to the exclusion of this third wave from the sample and found that the same variables that are statistically significant in the full sample are also statistically significant for the years 2005–2010 with the exception of the strategic focus on a few sectors and destinations in the export equation. Also, the share of budget spent on established exporters becomes statistically significant in the smaller sample in both the export and GDP per capita equations. Overall the results, which are available in the supplementary appendix, are qualitatively similar in the 2005–2010 sample.

Finally, this study is interested in computing the returns to export promotion in each country as a function of EPA’s characteristics. The study proceeds as follows. First, because the control function parameter is never statistically significant the control function is dropped from the estimation of (3). This makes it possible to avoid estimating standard errors over the two stages, which in this case would be inefficient. Then, for each country _c_ and year _t_ the returns to export promotion are estimated by adding the functions corresponding to each characteristic _i_ in that country and year. Finally, these yearly returns are averaged for each country. The result is provided in uu table 4. ii bb To estimate the standard errors the study uses a wild bootstrap version introduced by
. local time, across countries around the world. We cleaned<sup>8</sup> and used this data to approximate measures of local economic development in Africa, consistent with Mveyange, (2015), Alesina et al., (2015), Gennaioli et al. (2014), and Henderson et al. (2012), among others.

Our second source is the United States Geological Survey (USGS),<sup>9</sup> which provides data on spatial location of mineral deposits and mining activities in Africa (see Table 8 in Appendix II for the list countries). This data enabled us to identify the spatial references of the places where mineral deposits are present and in which mining is active relative to places with no such deposits. As explained below, these differential variations in mineral deposits and mining activities inform the baseline empirical strategy. Further, with the USGS data we were able to identify three interesting sources of exogenous variations. We identified mining sites with different attributes such as those involved in minerals extraction or transformation; those with high- or low-value minerals extracted; and those where the scale of mining operations is large or small. As described later, we

> 7The data are available at: <u>https://www.ngdc.noaa.gov/eog/dmsp/downloadV4composites.html</u>

> 8 Lights data are derived in three different formats: cloud-free, average visible, and stable light composites. Consistent with other studies, we employed the stable light composites, which are free from background noises, summer lights, ephemeral events, and gas flares.

> 9 The data are available at: <u>http://minerals.usgs.gov/minerals/pubs/country/africa.html.</u>

8
|Amenity_an_(**x**)|Data source|
|---|---|
|Distance to the ocean|Shoreline shapefle from City of Cape Town<br>Open Data portal|
|Distance to an Urban Heritage Site|Sites shapefle from City of Cape Town OD<br>portal|
|Distance to a district park|District parks shapefle from City of Cape<br>Town OD portal|
|Distance to a protected natural area|Shapefle of protected area layer (SAPAD Q4)<br>from the Environment Department of South<br>Africa.|
|Distance to a train station|Train stations from OpenStreetMap open data|
|Average slope|USGS Digital Elevation Model|
|Presence in the Airport Noise Cone|Shapefle from City of Cape Town OD portal|

Table B.4: List of amenities used in the model 31
.5 million IDPs including some significant IDP hosting countries (Turkey, India, Ethiopia, Bangladesh and Kenya); and (b) IDPs in countries where UNHCR is active who are not protected or assisted by the agency. In 2015, IDMC’s aggregate figure for conflict-induced internal displacement was 3.3 million higher than UNHCR’s aggregate figure for IDPs protected or assisted by the agency.

> 29 IDMC’s 2016 report presents both data sets alongside each other. In certain contexts, there can be significant overlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement and natural disasters (e.g. in Afghanistan) leading to possible gaps or double counting if these categories are combined.

30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility. It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been active in over 40 countries since its inception in 2004. See http://www.globaldtm.info/.

31 This is typically defined as nomads not having access to their traditional routes, but routes can vary.

32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements are reflected in IDMC’s end-2015 data.

33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance rather than to determine status.
column (4) of Table 3. Predictions from diaries are also subject to a large variance, which is the takeaway message from Panel A of Figure 5. It follows that when aggregate statistics such as poverty rates or inequality are of interest, recall data yield more reliable figures than diary interviews.

On the bright side, diary errors present important similarities with classical errors in measurement. This conclusion can be reached by noticing that the error densities in Panel B of Figure 5 are reasonably stable across households with different levels of consumption. Column (5) of Table 3 also shows that MSE varies little across consumption percentiles. By taking logs, we conclude that log _Y_<sup>_d_</sup> is centered below log _Y_<sup>_∗_</sup> and has variance approximately independent of log _Y_<sup>_∗_</sup> , which is a finding with important implications for applied research.<sup>15</sup>

# **5.2 Consumption Measurements and the Frequency of Purchases**

Large deviations from the mean in diary error distributions in Figure 5 can hardly be explained by the misreporting of diary entries, as respondents are assisted by frequent visits from enumerators. The most likely explanation is differences between acquisition in the diary week and the household’s usual consumption _Y_<sup>_∗_</sup> . As mentioned in the Introduction, one possible reason for these differences is acquisition patterns arising from stock inflows or consumption out of existing stocks. The practical question then arises of whether diary errors would be substantially lower if one could adjust for stock inflows and outflows, as in Beegle et al. (2012) or Sharp et al. (2019). Although theory implies a negative answer unless household consumption is smoothed exactly across weeks (that is, the case _τ_ = 1 in the consumer demand model), this ultimately remains an empirical matter.

As we cannot observe stock inflows and outflows during the diary week,
: Results shown in percentage points. Quintiles based on the distribution of consumption per capita. Results for the bottom quintile are not shown as it is considered that there is too little information (i.e sample size) to draw reliable estimates.

# Conclusion

In this paper, we bring some insights as to the hypothetical gains to the population that an opening of the telecom sector could bring about in Djibouti. The ICT sector is currently believed to be below its potential, thus limiting the access to such services and, somewhat indirectly, preventing more spaces for the private sector in Djibouti to become bigger players in the economy. From a relatively narrow angle of welfare gains to households, the simulations presented here hint at important gains in welfare due to the expected price decreases following increased competition in the sector.

More work is needed to fully understand the implications of changes to the telecom sector in Djibouti. For instance, a yet to be published 2018 survey on telecoms usage conducted by DISED can provide additional insights on the sector. The huge potential the sector holds for contributing to the development of the country should be enough incentive to pursue this important agenda.

14
Table A2 Comparison of empirical data and simulated data

||W|ages and em|ployment t|ransition p|robabilitie|s||||
|---|---|---|---|---|---|---|---|---|---|
||Wages|Food &<br>beverages|Textiles|Minerals|Metals|Other<br>manuf.|Non<br>traded|Unemp.||
|A) Simulated data||||||||||
|Food & Beverages|0.958|0.866|0.023|0.030|0.023|0.029|0.029|0.001||
|Textiles|0.955|0.023|0.864|0.031|0.023|0.029|0.029|0.001||
|Minerals|1.039|0.018|0.018|0.902|0.018|0.022|0.023|0.001||
|Metals|0.959|0.023|0.023|0.030|0.867|0.028|0.029|0.001||
|Other Manuf.|1.012|0.020|0.020|0.026|0.020|0.891|0.024|0.001||
|Non-Traded|1.016|0.019|0.019|0.026|0.020|0.023|0.893|0.001||
|Unemployment||0.086|0.086|0.115|0.087|0.106|0.108|0.413||
|B) Empirical data|||||
economic volatility.

Dynamic considerations are extensively analyzed in a series of recent papers on temporary and return migration (see Dustmann and G ̈orlach (2016) for a recent survey). As several papers illustrate, return migration levels have always been quite high. Bandiera, Rasul and Viarengo (2013) show out-migration rates from the United States were over 60% during the age of mass migration at the turn of the 20th Century. Bijwaard, Schluterare and Wahba (2014) use administrative data from the Netherlands and Bratsberg, Raaum and Sorlie (2007) use register data from Norway and Sweden to explore more recent patterns. Several papers on return and circular migration have used more formal and explicit dynamic models. Among the first examples, Kirdar (2012) develops a dynamic stochastic model to jointly explore return migration and savings decisions of migrants and estimates it using panel data from Germany. Among more recent and prominent examples are Thom (2015) and Lessem (2015) who use data from the Mexican Migration Project on detailed migration histories of individuals. Among their findings is the importance of border enforcement (similar to our simulations of blocked corridors) to circular and return migration decisions. Using multiple data sources from Mexico, G ̈orlach (2016) develops a comprehensive dynamic life cycle model to analyze the role of financial constraints on emigration, return migration and re-emigration decisions. In an another lifecycle model, Adda, Dustmann and G ̈orlach (2015) explore human capital accumulation and migration duration decisions using data on Turkish migrants in Germany. All of these papers rely on detailed panel data from a single origin or destination country (or a single corridor) as the data requirements of such dynamic models are quite demanding. One of our main contributions is to analyze a model of global migration patterns and find empirical solutions to address some of the data constraints.

The next section presents the dynamic structural model. Then, we discuss the data used in the paper, followed by the estimation algorithm. We next present the estimation results and
Data Note: The figure is a representation of unweighted averages across ECA countries. The names of the sectors had been shortened to fit the axis. The full names are Mining and Quarrying; Electricity, Gas and Water Supply; Transport, Storage and Communications; Manufacturing; Financial Intermediation; Hotels and Restaurants; Real Estate; Health and Social Work; Public Administration and Defense, Construction; Education; Wholesale and Retail Trade; and Agriculture

> 12 In terms of data, the first component we use while calculating vulnerability is the potential energy price hikes as mentioned in the previous section. This data was derived from IEA (2011) and ERRA tariff database. We rely on United Nations Statistics Division (UN) data for both employment (at the ISIC Rev 3 level) and value added statistics (at the SNA93 2.1 level).

8
to August. Second, the Mini-Susenas is nationally<sup>representative and perhaps the</sup> 100 villages population is, by virtue of being smaller,<sup>poorer, villages also less</sup> variable. Third, maybe the Mini-Susenas<sup>has worse inter-temporal measurement</sup> error - but we know of no way of checking that.

These estimates of vulnerability are probably<sup>too high as they do not account</sup> for measurement error. We try to address this<sup>by the admittedly crude expedient of</sup> reducing the estimated standard deviation<sup>of changes in log expenditure by the</sup> estimated measurement error from the appendix,<sup>i.e. 30 percent for Mini-Susenas and</sup> 50 percent for 100 Village Survey data. In<sup>this case the level of vulnerability of the</sup> household at mean income and with mean variability<sup>of Mini-Susenas is 0.39,</sup> roughly a 40 percent chance of an episode of<sup>poverty in three years. It is still the</sup> case that even though the poverty line is chosen<sup>so that only 20 percent are "poor,"</sup> nearly a half of the population (47 percent)<sup>are vulnerable to poverty, in the sense of</sup> facing even odds (or worse) of one episode<sup>of poverty in three years. The lower</sup> variability in 100 Village Survey, meanwhile,<sup>resulting in 30 percent headcount</sup> vulnerable rate.

These high levels of vulnerability confirm the<sup>voluminous literature based on</sup> qualitative assessments of the importance of vulnerability<sup>in the analysis of poverty.</sup> The poor at any point in time are only a fraction<sup>of those who must
consequences of child poverty, there is a need to implement policies that focus on increasing parental work and income, along with increasing the efficiency of social protection spending on families and children, while maintaining the level of spending. The regression results for the whole EU also show that non-means tested benefits could have a more significant impact on child poverty than means-tested benefits<sup>6</sup> . Additionally, public investments in education, childcare, health, and housing are essential to mitigate the lasting effects of child poverty. There is no comprehensive evidence to date for Bulgaria on the combined incidence and impacts of taxes and public spending focusing on households with children and child poverty.

**In this context, Bulgaria needs to implement sound and equitable fiscal policies and ensure their effectiveness in promoting growth, expanding opportunities, reducing inequality, and accelerating poverty reduction, including among children** . This requires a comprehensive approach to fiscal reforms that not only focus on economic growth but also address poverty and inequality at both the overall and specific group levels, particularly households with children.

**To gain a deeper understanding of the distributional effects of individual programs and policy measures, as well as the overall impact of Bulgaria's fiscal policies, the World Bank conducted the first comprehensive fiscal incidence analysis (CEQ) in 2018; this paper provides an update of this analysis to provide a dynamic picture of the impact of fiscal policies on poverty and inequality, considering the effects of the COVID-19 pandemic and introduces some methodological improvements on the social transfer side.** The analysis uses the Commitment to Equity (CEQ) framework to examine how the fiscal system, as a whole and in its components, affects poverty and inequality in Bulgaria. The CEQ approach allows for assessing the net effects of various policies and programs in the country's fiscal framework. Building on the existing  CEQ 2018 (Vaughan & Cabrera, 2022), the World Bank updates the analysis using the latest available data on the COVID-19 pandemic, including the 2021 Survey
<!-- Start of picture text --> 60<br>3 @ 0@ ° °<br>x=2 oO fo] ee @ @<br>2 @ eo °e @<br>g @e eoe@ oe ee @ @C00 ®<br>3 40 oO eo 8 @ @ [s)<br>£ @ @ @ @e @ @<br>oO ® @@e@ @ @ @<br>a @ e@ ®@ @@ Ce qmeee<br>77) (} ee ° e® @&@<br>3 ao @8@ @ ee feo) G@® @<br>3 @ ct) oe e @ @<br>5 e@ © ee @ @<br>oO ee ° @0 & @ cs)<br>‘5 °° @ e@ oe @ @<br>20 ee<br>= e@ ° @<br>a] @ @ @<br>= fo) fo) @<br>g e@ @ fe)<br>*<br>e@ @<br>y = 17.76 + 0.24 x, R2=0.18<br>(2.73) (0.04)<br>0<br>0 25 50 75 100<br>SPI Overall Score<br>regF n @@ EastEuropeAsia & Central& Pacific Asia @@ LatinMiddle AmericaEast & North& Caribbean Africa @@ SouthNorthAmerica Asia @ Sub-Saharan Africa<br>Source: SPI Overall Score from the World Development Indicators (1Q.SPI.OVRL). The availability of<br>Gender SDG Indicators based on authors calculations based on the UN Global SDG Database.<br><!-- End of picture text -->
Since 1998, the importance of the FAIS in municipal public finances has increased. In 2002, nearly 92 percent of the municipalities reported that they had received FAIS transfers (2,248 of 2,443 municipalities). Between 2004 and 2014, the resources allocated to the FAIS exhibited an average annual growth of 4.7 percent in real terms, reaching Mex$43.4 billion in 2014. On average, 11.5 percent of the per capita fiscal resources in a municipality in 2014 were provided through the FAIS, although the share is larger in municipalities characterized by greater marginalization. According to the 2015 marginalization index by the National Population Council of Mexico (CONAPO), those municipalities with high levels of marginalization had an average 47.1 percent of income per capita attributable to the FAIS, while municipalities with very low levels of marginalization had a share of only 11.9 percent.

# **4. Data**

The analysis exploits longitudinal data on municipalities from comparable poverty maps and administrative data on public expenditure. The panel includes information on 2,120 municipalities (of 2,443 municipalities across Mexico) and covers 2000, 2005, 2010, and 2014. The data are taken mainly from three sources: (a) income, poverty, and inequality variables from poverty maps; (b) public expenditure variables from administrative records; and (c) economic and other nonmonetary variables from economic and population censuses.

# i. Longitudinal data on municipalities from the panel of poverty maps

In Mexico, the agency responsible for producing official poverty rates, the National Council for the Evaluation of Social Development Policy (CONEVAL), has the mandate to measure poverty in the municipalities every five years. Given the lack of data at this geographical level, CONEVAL jointly with the World Bank, produced a panel of municipal poverty maps for 1990, 2000, 2005, and 2010.<sup>13</sup> More recently, the World Bank has produced a comparable municipality-level poverty map for 2014 combining
Figure A14: Theil indexes at the national level and across urban versus rural areas, sorted by GDP per capita <!-- Start of picture text --> (A) DOU (B) DB<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>Urban Rural National<br>NERMWITCDETHGNBBFAUGAGINTZALSOSENGHA CIVMRTAGOGAB NERMWITCDETHGNBBFAUGAGINTZALSOSENGHA CIVMRTAGOGAB<br>Theil index Theil index<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Countries are sorted in ascending order of log of GDP per capita, measured in PPP (constant 2017 international $). WorldPop 250m is used for the DOU and DB methods. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. See Figure A15 for the same chart with countries reordered by the highest to the lowest Theil indexes.

Figure A15: Theil indexes at the national and across urban versus rural areas, sorted by Theil index <!-- Start of picture text --> (A) DOU (B) DB<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>National Urban Rural<br>MWIAGOLSOUGAGHA BFA TZANERGABSENETH CIVTCDGNBMRT GIN MWIAGOLSOUGAGHA BFA TZANERGABSENETH CIVTCDGNBMRT GIN<br>Theil index Theil index<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, WorldPop 250m is used. For the DOU and DB methods, WorldPop 250m is used. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method, and the categories “Core” and “Suburb” for the DB method.

37
countries in LAC—with the exception of Guatemala, Panama, Peru, and El Salvador— experienced a depreciation of their currencies as the 2011 PPP values are higher than the 2005 PPP values.

To understand the effect of each component mentioned above, we estimated the Shapley value of the marginal effect of each component in the change of poverty headcount. That is, by leaving two of the components constant, we change one of the components from its corresponding value in 2005 PPP values to its value in 2011 PPP values. Then, we repeat this process five more times until we have calculated all the possible combinations.<sup>29</sup> Figure 7 shows the results of the Shapley decomposition for the changes in both extreme and moderate poverty using the welfare aggregate of the most recent household survey for each country (i.e., circa 2013). In the case of moderate poverty, the decomposition shows a substantial poverty-increasing effect of 9.7 percent from changing the nominal value of the poverty line and leaving unchanged the other two components. However, the currently-used $4 per person per day poverty line at 2005 PPP values and the $5.3 per person per day poverty line at 2011 PPP values are expressed in different spatial and temporal units. With respect to the PPP effect (i.e., the spatial adjustment effect), poverty headcount would increase by 3.0 percent, everything else constant, and the base period effect (i.e., the temporal adjustment effect), would reduce poverty by 10.6 percent. The net change is approximately 2.1 percent in 2013.

Unlike the majority of countries in Africa and South Asia, where the changes in poverty measurement using the 2011 PPP values reinforced poverty reduction (Dykstra, Kenny, and Sandefur 2014), the poverty measurement with the 2011 PPP values lead to an increase in poverty rates in LAC.

# **5 Conclusions**

Most of the countries in the world monitor their own citizens