rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> ~<br>©<br>o<br>2<br>&s<br>Sao<br>°<br>Feb'20 Apr Jun Aug Oct Dec Feb'21 Apr Jun<br>—®— Mean for refugees<br>——®— Mean of urban nationals<br><!-- End of picture text --> <!-- Start of picture text --> rey<br>23Sasg<br>£<br>2s<br>- oe _<br>°<br>Jun'20. Aug Oct Dec Feb'21 Apr Jun<br>—®— Mean for refugees<br>——®— Mean of urban nationals<br><!-- End of picture text --> <!-- Start of picture text --> n<br>©<br>o<br>abe<br>£<br>aoSs<br>”<br>“<br>Jun'20. Aug Oct Dec Feb'21 Apr Jun<br>—®— Mean for refugees<br>——®— Mean of urban nationals<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>o<br>2®go<br>£<br>Qo3°<br>" cy©<br>Ny<br>Feb'20 Apr Jun Aug Oct Dec Feb'21 Apr Jun<br>—®— Mean for refugees<br>——®— Mean of urban nationals<br><!-- End of picture text -->
# Figure 3. Monetary policy

# A. Inflation in EMDEs

<!-- Start of picture text --> Percent<br>80 Median Inter-quartile range<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->

# C. Inflation expectations in EMDEs

<!-- Start of picture text --> Percent<br>9 Median Interquartile range<br>6<br>3<br>0<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2018<br><!-- End of picture text -->

# E. Inflation, by central bank independence and transparency

<!-- Start of picture text --> Percent High central bank independence<br>10 Low central bank independence<br>8<br>6<br>4<br>2<br>0<br>All AEs EMDEs All AEs EMDEs<br>Inflation level Inflation volatility<br><!-- End of picture text -->

# B. EMDEs with inflation targeting

<!-- Start of picture text --> Number of countries Index<br>30 Adopted inflation targeting 7<br>25 Central Bank transparency (RHS) 6<br>5<br>20<br>4<br>15<br>3<br>10<br>2<br>5 1<br>0 0<br>2000 2003 2006 2009 2012 2015 2018<br><!-- End of picture text --> D. Countries with increasing central bank independence and transparency <!-- Start of picture text --> Percent of countries<br>100<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>Advanced EMDEs LICs<br>economies<br><!-- End of picture text --> F. Central bank independence and pass-through from exchange rate changes to inflation <!-- Start of picture text --> Number of branches and subsidaries<br>600 Within region<br>Other EMDEs<br>400<br>200<br>0<br>Before After Before After Before After<br>GFC GFC GFC GFC GFC GFC<br>SSA ECA EAP<br><!-- End of picture text --> Source: Dincer and Eichengreen (2014); Ha, Kose, and Ohnsorge (2019); International Monetary Fund; World Bank. A. Based on data for 155 EMDEs. B. Dincer/Eigengreen Transparency Index. The index ranges from 0 (least independent and transparent) to 15 (most independent and transparent). C. Inflation expectations are long-term (five-year-ahead) expectations of annual inflation, measured at a biannual frequency. Based on a sample of 23 EMDEs during 1995H1-2018H1. D. Based on data from 1998 to 2014. Figures shows percent of countries with a higher index (by at least 0.1) in 2014 than 1987.
**Figure 7**

# **(a) Gross inflows: variance decomposition over time**

<!-- Start of picture text --> (i) Advanced countries<br>1.0 1.0<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Global share Group share Country share<br><!-- End of picture text -->

**(ii) Emerging countries**

<!-- Start of picture text -->
1.0 1.0<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Global share Group share Country share<br><!-- End of picture text -->

52
# **Blending Top-Down Federalism with Bottom-Up Engagement to Reduce Inequality in Ethiopia**<sup>*****</sup>

Qaiser Khan<sup>†</sup> , Jean-Paul Faguet<sup>‡</sup> and Alemayehu Ambel<sup>§</sup>

**Keywords:** Inequality, Federalism, Local government, Accountability, Citizen Engagement, Public services, Aid, Ethiopia.

**JEL Classification:** F35, H51, H52, I31

* We are grateful to Chris Gaukler for research assistance. We thank Ruth Hill and colleagues at the 2014 Annual Bank Conference on Africa (ABCA), Paris, France, for thoughtful suggestions on an earlier draft of the paper. All remaining errors are our own. We thankfully acknowledge the World Bank for supporting this research.

† Africa Region, World Bank, 1818 H St., N.W., Washington, D.C. 20433, USA ‡ Departments of International Development and Government, London School of Economics, Houghton Street, London WC2A 2AE, UK, +44-20-7955-6435 (o), 7955-6844 (f), j.p.faguet@lse.ac.uk (contact author) § DEC Surveys and Methods, Development Research Group, World Bank, 1818 H St., N.W., Washington, D.C. 20433, USA, aambel@worldbank.org (contact author).
a country’s inflation and other public policies that may impair an efficient allocation of factors of production.

_i_ In Section 2 we describe how we quantify changes in _ψk_ through an export growth decomposition using bilateral trade data disaggregated at the HS 6 digit sector level. In so doing we are also able to quantify the effect of export composition and changes in importing country’s demand (pull effects). Export composition refers to a country’s mix of trade partners (market effects) and exported goods (sectoral effects). Trade costs, which do not vary too much over time, do not appear in the export growth decomposition. Their varying component is accounted endogenously in the push and pull effects.

**Annex 2: Export Competitiveness Database, variable list**

|variable|label|
|---|---|
|iso|country code 3 digits|
|iso2|country code 2 characters|
|country|name of the country|
|quarter|quarter|
|XG_v|export delta log in values|
|MSG_v|export market share delta log in values|
|Push_v|Performance effect in values in values|
|Geo_v|Geographical effect|
|Sect_v|Sectoral effect in values|
|sumwi|Weigth of the countryin terms of values|
|XG_uv|Export delta log in unit values|
|MSG_uv|Export market share delta log in unit values|
|Push_uv|Performance effect in unit values|
|Geo_uv|Geographical effect in unit values|
|Sect_uv|Sectoral effect in unit values|
|sumwi_uv|Weigth of the countryin terms of unit values|
|XG_q|Export delta log in quantities|
|MSG_q|Export market share delta log in quantities|
|Push_q|Performance effect in quantities|
|Geo_q|Geographical effect in quantities|
|Sect_q|Sectoral effect in quantities|
|sumwi_q|Weigth of the countryin terms ofquantities|

40
# **Table 18 Welfare implications of Tunisian reform of agricultural and manufacturing protection (US $ million)**

|_Reform scenario_||_Welfar_|_e in_||
|---|---|---|---|---|
||_Tunisia_|_EU_|_GAFTA_|_Rest of world_|
|Tunisia removes border protection on agriculture|114|63|13|-52|
|from the EU|||||
|EU reciprocates|295|-70|-1|3|
|10 percent productivity improvement in Tunisia’s<br>agricultural exportables|258|58|12|5|
|**_Subtotal_**<sup>**_a_**</sup>|**_733_**|**_183_**|**_23_**|**_-61_**|
|Tunisia eliminates remaining tariffs on<br>manufactures from EU|-184|417|-3|-133|
|Tunisia lowers tariffs on manufactures from other<br>sources by 25 percent|74|-38|3|65|
|**_Subtotal_**<sup>**_a_**</sup>|**_-104_**|**_388_**|**_-1_**|**_-93_**|
|**Total**<sup>**a**</sup>|**641**|**570**|**22**|**-153**|
|Full unilateral liberalization in manufacturing|65|203|6|210|

> **a** Individual entries may not add to total because of interaction effects.

_Source_ : FTAP model projections.

48
When comparing the results of Colombia with other countries in the region, it turns out that Colombia is the second country, after Bolivia with the most regressive indirect taxes in the region and that additionally, indirect taxes in Colombia are the ones that most contribute to an increase in inequality throughout the region (Figure V-5- Panel B).

<!-- Start of picture text --> FIGURE V-6: PROGRESSIVITY AND REDISTRIBUTIVE EFFECT OF INDIRECT TAXES<br>(FROM MARKET INCOME + PENSIONS TO CONSUMABLE INCOME)<br>0.3000 0.0140<br>0.0120<br>0.2000<br>0.0100<br>0.1000 0.0080<br>0.0060<br>0.0000 0.0040<br>-0.1000 0.0020<br>0.0000<br>-0.2000 -0.0020<br>-0.0040<br>-0.3000<br>-0.0060<br>-0.4000 -0.0080<br>Kakwani Coefficient<br>Marginal Contribution<br>Source: World Bank Group staff estimates based on ENPH 2017<br>Kakwani Coefficient Redistributive effect<br>Regressive <---------> Progressive VAT Unequalizing <---------> Equalizing<br>All direct taxes and C.S.S. Consumption tax Tobacco Tax Tax on alcoholic beverages All taxes and C.S.S.<br><!-- End of picture text -->

# **_b) Direct transfers and subsidies_**

Total direct transfers, like the subsidies that households receive, are progressive and help reduce poverty. Direct transfers are progressive in relative and absolute terms since these not only represent a higher percentage of the income of the poorest, but a higher percentage of these is given to people from the lowest deciles. Thus, while decile 1 receives 16 percent of the total of these transfers, households in decile 10 receive 4 percent of these transfers (see Figure V-2). The expenditure on social programs made by the government represents 2.5 percent of GDP, and 56 percent of the benefits are delivered to the poorest 40 percent of the population. Most of the benefits of government social programs are targeted to urban areas; only 3.2 percent of total national transfers are delivered to households residing in rural zones.

The subsidies referred to in this document are those received by households in the provision of utilities such as electricity, gas, water, sewage, and waste collection. These are progressive in relative terms. Thus, while subsidies represent 18 percent of the market income plus pensions of the poorest 10 percent of the population, this percentage only amounts to 0.25 percent for households in the richest decile. However, in absolute terms, only 8 percent of the total subsidies are given to the households of the lowest decile, and 7 percent of the total of these are delivered to the households of decile 10 (see Figure V-2 ).

Jointly, all direct transfers and subsidies in Colombia are progressive. However, their effect on reducing poverty and inequality is neutral.<sup>47</sup> The marginal contributions for the reduction of inequality and poverty vary depending on the size of the program and its progressivity, as well as the geographic location of the

> 47 Although upon including transfers and subsidies, poverty and inequality tend to decrease, such reductions are not statistically significant.

37
Finally, the interactions of known and “new” shocks have raised the scope of potential welfare losses. One of those new shocks is open communal conflict. To be clear, the Sahel and especially these six countries, like most of Sub-Saharan Africa, have had their periods of political instability, characterized by coups and low scale violence. But as Figure 3 shows, there has been a major spike in conflict in some of the countries in recent years. Furthermore, these conflicts seemed to be more common during the dry season – perhaps because mobility of combatants is easier – when the populations are already under stress.

Figure 3: Conflict days and weather in Sahel West Africa <!-- Start of picture text --> Total conflict days Total conflict days<br>Dry season Dry season<br>Mali Nigeria Senegal Burkina Faso Ghana Niger<br>1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014<br>Year Year<br>Rainy season Rainy season<br>Mali Nigeria Senegal Burkina Faso Ghana Niger<br>1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014<br>Year<br>Year<br>Note: Different scale<br>Note: Different scale<br>400 60<br>300<br>200 40<br>100 20<br>0 0<br>200 40<br>150 30<br>100 20<br>50 10<br>0 0<br><!-- End of picture text --> Source: Authors’ own calculation using ACLED data.

A look at self-reported shocks from recent surveys confirms the multiplicity of shocks that matter to households. More than half the households consider price and weather shocks as the most important shocks in their lives. Furthermore, the share of households who report experiencing two or more shocks ranges from a low of 6 percent in Senegal to 50 percent in 8
# 6. References

Apella, I., 2007. “Determinantes de la cobertura del sistema de pensiones argentino. Un enfoque desde los microdatos”, _Documentos de Trabajo CEDES 36, Buenos Aires, Argentina_ Celis Calderón, K.G., 2015. “El envejecimiento y el sistema general de pensiones en Ecuador”. _Posgrado Economía y Negocios, Universidad de Chile_ <mark>Galvis Muñoz, A.P., 2015. “Sistema de pensión público, Colombia y Ecuador”,</mark> _<mark>Universidad de Manizales, Especialización en Seguridad Social.</mark>_ González Jaramillo, V. H., D. Sabando-Vera, C. Amaya Peso y J. Noboa Panchana, 2018. “Propuesta de sustentabilidad para el fondo del Instituto Ecuatoriano de Seguridad Social (IESS)”, _Revista Espacios, 39(07)._ <mark>Ibarra – Jarrín, R., 2015. “The inclusion of homemakers and household labour in the Ecuadorian social security system”.</mark> _<mark>Certified actuary of the ‘Institut des Actuaires’ France</mark>_ Mantilla C. y E. Abad, 1984. “El Instituto Nacional de Previsión (1935-1970)”. en Núñez, J. (ed.). _Historia del seguro social ecuatoriano. La evolución institucional_ . Quito: Editorial Voluntad.

OIT, 2008. “Reunión regional tripartita sobre el futuro de la protección social en América Latina. Santiago, Chile, 12 al 14 de diciembre de 2007”. _Documentos de Política de Seguridad Social. Documento 4. Departamento de Seguridad Social_ Rofman, R., 2018. “Thepensionsystem” en Giugale, M., V. Fretes-Cibils y E. Somensatto, (ed.). _Revisiting Ecuador's Economic and Social Agenda in an Evolving Landscape._ Banco Mundial.

Sasso, J., 2011. “La seguridad social en el Ecuador, historia y cifras”, _Programa de Políticas Públicas, FLACSO,_ Ecuador Valverde, F.D., 2008. “Diagnóstico del sistema de seguridad social del Ecuador” _Oficina internacional del trabajo oficina subregional de OIT para los países andinos Bolivia-Colombia-Ecuador-Perú-Venezuela_ .

_Velásquez Pinto, M.D., 2003. “The Bono Solidario in Ecuador: An exercise in targeting”. Extension of Social Security Paper No 17. Social Security Policy and Development Branch International Labour Office_ 37
_ **33** <!-- Start of picture text --> ,~ ~~0 -<br>It~ ~ ~ .i<br>I= \<br>00<br>I L~~~~~I-<br>_ __ ~ ~ ~ ~~~~<br>O" O O C0<br><!-- End of picture text --> Y:Lgure s: Actual and Counterfactual export volumse; BMARK: Benchmark case; BRER: Benchmark came plus actual real exchange rate changes; BRERXS: Benchmark came plus actual real exchange rate changes and actual export subsidies; RXWSU: Benchmark case plus actual real exchange rat* changes. actual export subsidies and actual real import growth Lnto oil-exporting countries.
<!-- Start of picture text --> 20 Kisumu 5 20 Kisumu 60<br>18 Discordant 9, -—a 2 General Population<br>couples ry Paina | 45 = 18 o 70<br>—_ 16 4 § 16 _<br>32 Goneral Population . - 60 a<br>ane 14 7 35 J o£ 14 50 s<br>&e 12 3 8 aB 12 8<br>7 10 Mutipte sites | 2S <p 10 40 8<br>> PRoP trial o> = o<br>& 3 oa 2c 8 8 onde - 30<br>6 > , 15 5 6 en in Prep 20 be<br>2 as: z2 3 condantKisumucouples |eo MultipleF Ra trial sites 10<br>o<br>| 2Se meet || | aeScee ae | |<br>7990 1995 2000 2005 2010 2000 2002 2004 20106 2008 2010 2012<br>“Horizontal error bars denote time period in which prevalence ar incidence was measured. Value is at midpoint of time period.<br>ees Modelled incidence (%), adults 15-49, from Spectrum software<br>Prevalence (%), females 15-24 (source: national DHS/AIS reports)<br>ames Prevalence (%), females 15-24 (source: national ANC reports)<br>ees Prevalence (%), young females (source: published studies, see Annex)<br>=== Incidence rate (per 100 person years), adults (source: published studies, see Annex)<br>ames ART coverage (%)}(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013<br><!-- End of picture text -->
According to IEG (2010: 49), interviews with regional reviewers suggested that some of these regional chief economist’s offices play a much more active role than others.<sup>17</sup> As merely one small example, these offices could provide their own guidance on useful region-specific information sources. More importantly, in those regions where sector specialists have a relatively minimal role in the process, the regional chief economists’ offices should take steps to enhance their role.

This study does not intend to represent the last word on validity and reliability of the CPIA ratings. Assessments of the quality of write-ups could also be broadened and deepened. Another important limitation of this study is that it does not analyze changes in write-up length, criteria coverage or ratings over time. Adding a time dimension to the analysis would also make it possible to test other independent variables, such as turnover of key staff involved in the CPIA process. Results of such a study would be useful in assessing the validity of CPIA time series data. Although the CPIA is intended primarily for comparing countries to each other at a point in time, analysts are often tempted to use it for assessing trends (e.g. World Bank, 2006). Use of the data in this way is premised on the untested assumption that year to year changes primarily reflect real progress (or deterioration). Alternatively, a large proportion of changes over time may merely reflect belated corrections to incomplete information, or inferences from changes in economic performance, or even changes in the identity of staff responsible for the ratings.

Finally, further research could also add to the debates on the appropriate content of the CPIA and on the appropriate question or cluster weighting in the IDA allocation formula. Analyses of the validity of the other three CPIA clusters could usefully complement this one. Results for cluster D questions – which are more subjective, and count more for IDA allocations – will not necessarily generalize to clusters A (macro policies), B (structural policies) and C (social sector and environmental policies).

> 17 It should be noted however that regional differences likely go well beyond the identity of staff in the RCE offices. E.g., the ECA region has been easily the most assertive region in proposing increases for a period encompassing the tenure of at least two RCEs.

24
Reporting actual mortality numbers would portray an unrealistic impression of precision. To avoid literal interpretation of the disaster index as the number of persons expected to be killed in a 20-year period and in recognition of the many limitations of the underlying data, we convert the resulting measures into index values from one to ten, classifying the global risk distribution into deciles and providing relative presentations of disaster risk.

# **4.5 Interpretation**

The calculated risks in the index assign a value to the city as a whole and are based on the three factors of hazard severity in the city, city population, and the vulnerability of the particular World Bank cluster. The mortality risk in a city is the potential extent of total fatality numbers that a city could incur rather than the extent of risk that a single person experiences in that city. Similarly, economic risk mirrors total potential damage extent.

Result interpretation needs to consider that a number of constraints. In an index, interesting and idiosyncratic detail is hidden, and indexing cannot replace detailed research at local level. Constraints in globally available data limit the sophistication of the methods that were employed to investigate urban risk on a global level. Although we use the best available data, gaps in the data limit our analysis. For example, deductive modeling has weaknesses in determining risk in contexts where disasters occur infrequently and where historical data are scarce. Moreover, disaster loss data in EM-DAT is recorded on country-level and does not allow for a differentiation between urban and rural loss rates and vulnerabilities. The relatively small number of disaster events leads us to calculate vulnerability coefficients on regional levels using groups of countries. Aggregating across more than 8,000 entries in EM-DAT helps compensate for missing data and inaccuracies and reflect broad patterns of vulnerability. It cannot, however, reveal protection mechanisms (land use planning, regulations) that individual cities might have implemented. Another limiting factor is the relatively crude delineation of some hazards. For example, earthquakes with pathological damage patterns are represented incompletely. The cities investigated in this study stem from the Henderson city database (see Table 2). This data set contains 17
To examine more carefully the relationship between private and public employment, we follow Behar and Mol (2013) and estimate the following two regressions: i 1 i + γ i ii ii EEi = ββ + ββ1GGi + γ + δδ + εεi ii ii UU where = ββ i+ ββ corresponds to private employment over labor force, GG + δδ + εε i public employment over labor force, i unemployment rate, and Z a vector of controls that includes log of GDP per capita, urbanization rate, EE GG trade openness, and union density rate. The data come from sources already described and we use 5- UU year non-overlapped averages.

We do not find evidence of a crowding-out of private employment or an effect on the unemployment rate (see Tables 10 and 11). This result holds using the three measures of public employment. In addition, we find that the union density rate decreases the size of private employment (see column 2 in Table 11) and the unemployment rate (see column 2 and 4 in Table 10 and 11). This is consistent with our finding that union density increases the size of the public sector, while the effect on the unemployment rate suggests a stronger positive effect in public employment than the negative impact on private employment, however further research is needed to make that statement categorically.

Table 10: Dependent Variable: Unemployment rate

||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|---|---|
|_General gov. (% LF)_|-0.158|-0.172|||||
||(0.112)|(0.150)|||||
|_Central gov. (% LF)_|||0.021|0.281|||
||||(0.175)|(0.252)|||
|_Public sector (% LF)_|||||-0.019|-0.050|
||||||(0.056)|(0.105)|
|_Urbanization rate_|11.022|-5.910|11.579|-7.137|8.058|-6.857|
||(8.240)|(11.021)|(10.738)|(13.361)|(8.623)|(13.844)|
|_Openness_|-0.001|-0.001|-0.002|0.006|0.002|-0.006|
||(0.009)|(0.015)|(0.014)|(0.018)|(0.009)|(0.015)|
|_log of GDP per capita_|-10.248***|-14.034***|-13.275***|-17.466***|-10.991***|-15.544***|
||(1.234)|(2.180)|(1.963)|(2.641)|(1.311)|(2.607)|
|_Union density rate_||-0.073**||-0.100*||-0.077|
|||(0.031)||(0.051)||(0.050)|
|_Constant_|103.412***|158.969***|131.728***|191.178***|109.165***|171.264***|
||(14.398)|(24.664)|(21.897)|(29.856)|(15.151)|(29.993)|
|_Observations_|270|174|183|141|287|152|

24