rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 5 Poland: Contribution of Income Sources to Total Inequality, 1987-2002</u>**

||**1987**|**1994**|**1998**|**2002**|**1994-1987**|**2002-1994**|
|---|---|---|---|---|---|---|
|||**Income str**<br>|**ucture: percen**<br>|**t**|||
|Work income|60|54|56|55|-6|+1|
|Of which wages|55|46|48|47|-9|+1|
|“entrepreneurial”|5|8|8|8|+3|+0|
|Income from farm|13|10|7|4|-3|-6|
|Old age pension|17|24|26|24|+7|+0|
|Social transfers|5|5|6|9|+0|+4|
|Other income|5|6|4|8|+1|+2|
|**TOTAL**|100%|100%|100%|100%|||
||**Ine**<br>|**quality: Conce**<br>|**ntration Coef**<br>|**ficients**<br>|||
|Work income|0.260|0.330|0.388|0.431|+27%|+31%|
|Of which wages|0.251|0.302|0.350|0.394|+20%|+30%|
|Entrepreneurial|0.360|0.488|0.613|0.650|+35%|+33%|
|Income from farm|0.415|0.390|0.471|0.575|-6%|+47%|
|Old age pension|0.171|0.175|0.204|0.263|+3%|+50%|
|Social transfers|-0.100|0.080|-0.017|-0.011|+180%|-86%|
|Other income|0.340|0.283|0.450|0.263|-17%|-7%|
||**Deco**|**mposition: Gi**|**ni index, contr**|**ibutions**|||
|**Gini, per capita inc.**|**0.250**|**0.280**|**0.320**|**0.343**|**+0.030**|**+0.063**|
|Work income|0.156|0.178|0.217|0.237|+0.022|+0.059|
|Of which wages|0.138|0.139|0.168|0.185|+0.001|+0.046|
|Entrepreneurial|0.018|0.039|0.049|0.052|+0.021|+0.013|
|Income from farm|0.054|0.039|0.033|0.023|-0.015|-0.016|
|Old age pension|0.029|0.042|0.053|0.063|+0.013|+0.021|
|Social transfers|-0.005|0.004|-0.001|-0.001|+0.009|-0.005|
|Other income|0.017|0.017|0.018|0.021|+0.000|+0.004|

_Source: Milanovic for 1987, ECAPOV I for 1994-1998 and Poverty assessment_ Staff calculations based on CSO’s data for 2002.

The results are very different for the simulations focusing on the impact of changing concentration coefficients. Application of end-period concentration coefficients to Russia’s original income structure would have resulted in inequality exceeding its actually observed level by at least 5 percentage points. For Poland the result is striking: application of Polish end-period concentration coefficients to the original income structure would have resulted in a Gini coefficient of about 0.45, a level observed in Russia during this period and 10 points higher than the actual outcome in Poland (Figure 4).

This exercise shows therefore that changes in structure and in concentration coefficients offset each other, more so in Poland, but that the factors which increase inequality within income sources clearly dominate. Among these sources of change three need to be mentioned.

20
|**Table 22: Sensitivity Ana**<br>|**lysis of Ken**<br><br>P|**ya-EU FTA**<br> <br><br>arameter Value|**% Wel**|<br>**fare Chang**|**e(EV)**|
|---|---|---|---|---|---|
|Parameter|Lower|Central<br>Upper|Lower|Central|Upper|
|σ(qi, qj)– services sectors|1.5|3<br>4.5|9.99|0.67|0.50|
|σ(qi, qj)–goods sectors||see below|1.06|0.67|0.59|
|σ(va,bs)|0.625|1.25<br>1.875|0.55|0.67|0.82|
|σ(D,M)|2|4<br>6|0.65|0.67|0.69|
|σ(L,K)|0.5|1<br>1.5|0.64|0.67|0.70|
|σ(A1,...An)|0|0<br>0.25|0.67|0.67|0.67|
|σ(D,E)|2|4<br>6|0.65|0.67|0.69|
|εKEN|Central v|alues of all 4 sets of eta|0.61|0.67|0.72|
|εEU|paramete|rs are listed in table 6B|0.25|0.67|0.96|
|εAFR|Lower values|are 0.5 central values and|0.68|0.67|0.67|
|εROW|upper values|are 1.5 times central values|0.90|0.67|0.55|
|share of rents captured|0|0<br>1|0.67|0.67|0.49|
|CRTS--share of rents captured|NA|0<br>1|NA|0.09|-0.06|
|θm|0.025|0.05<br>0.075|0.67|0.67|0.67|
|σ(qi, qj)–goods sectors||||||
|sugar and bakery|2.12|2.93<br>3.74||||
|beverages and tabacco|1.52|2.33<br>3.14||||
|chemicals|2.01|2.82<br>3.63||||
|metals and machines|8.345|16.69<br>25.035||||
|gain milling|2.43|3.24<br>4.05||||
|nonmetallicproducts|2.805|5.61<br>8.415||||
|petroleum|2.75|3.56<br>4.37||||
|Source: Authors' estimates||||||

66
Wilde 2018, Armand et al. 2017), research has not yet examined behavioral factors that may contribute to the persistence and spread of malaria in the long-term.

This paper uses novel data to causally estimate a constraining factor for elimination: the reintroduction of malaria into elimination zones by population movement. While this phenomenon has been documented in at least 61 countries by the epidemiology literature, this has not been done in a causal framework (Justin Cohen et al. 2012, Lu et al. 2014). In this research, I quantify the negative externality of mobility empirically using a similar setting in Senegal. This paper leverages policy simulations based on these estimates to show how aggregated big data on individuals' geolocation can inform more cost-eective targeting strategies to reduce transmission generated by population mobility, which would be a complementary component of a campaign to successfully eliminate malaria.

The negative externality from travel is generated when people are unaware of their risks to others because they do not know that they are disease vectors.<sup>3</sup> Yet given the benets of travel, an information campaign is unlikely to cause people to internalize the externality and choose not to travel to prevent infecting others. Measuring the size of the externality and identifying those people that contribute the most can allow for targeted policies that can help address this market failure.

The main challenge in estimating the size of the externality from mobility is that while disease transmission may respond quickly to changes in migration patterns, existing survey data that record these patterns are often infrequent or do not have coverage across a country.<sup>4</sup> Therefore, the only strategy available to policymakers to address this externality is using incidence in the previous year to identify where and who to target. This paper is able to signicantly improve on this strategy by utilizing a new source of data to track population 3As will be described in more detail in the next section, the long incubation period for malaria allows people to travel without knowing they are infected. Additionally, those in high malaria settings typically develop immunity and do not experience malaria symptoms yet can infect mosquitoes when they travel to low malaria settings.

> 4In Senegal, the main ocial source of data on population movement is census data that only includes long-term migration statistics every 10 years. There are other surveys that ask about commuting, such as the Household Mobility Survey of Dakar (EMTASUD), but it is only for one point in time, it is focused only on Dakar, and it was done in 2000 and in 2015).

2
**Assessing the Affordability of Nutrient-Adequate Diets**

**_Kate Schneider, Luc Christiaensen, Patrick Webb, and William A. Masters_**<sup>**_1_**</sup>

**JEL codes** : Q18, D63, I30, J18, C61, D16

**Keywords** : food prices, seasonality, diet costs, nutrient adequacy, affordability, meal sharing

1 Kate Schneider (kschne29@jhu.edu), Johns Hopkins University; Luc Christiaensen (lchristiaensen@worldbank.org, corresponding author), World Bank; Patrick Webb (patrick.webb@tufts.edu), Tufts University; and William A. Masters (william.masters@tufts.edu), Tufts University. Work on this paper was funded by the Bill & Melinda Gates Foundation and UKAid as part of a project on Food Prices for Nutrition (INV-016158) and its predecessor project on Changing Access to Nutritious Diets in Africa and South Asia (OPP1182628), with additional support from USAID through the Feed the Future Innovation Lab for Nutrition (AID-OAA-L-10-00006), as well as the World Bank Social Protection and Jobs Global Practice.
Undervaluation through Foreign Reserve Accumulation: Static Losses, Dynamic Gains<sup>_∗_</sup> Anton Korinek Luis Serv ́en University of Maryland World Bank

**JEL Codes:** F31, F41, F43 **Keywords:** foreign reserve accumulation, real exchange rate undervaluation, neo-mercantilism

> _∗_ The authors would like to thank Ibrahim A. Elbadawi, Zheng Michael Song, Joseph E. Stiglitz and Shang-Jin Wei as well as participants at a World Bank seminar and at an HKIMR conference for helpful comments and discussions. Korinek is grateful for the World Bank’s hospitality and financial support for this research project. The views expressed in this paper are ours and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.

1
thus allow us to provide a representative analysis of the incidence and distribution of household debt by country.<sup>11</sup> Table 4.3 displays the use of credit card and mortgage debt for households as well as the currency structure of mortgage debt by country. The table suggests that only a minor share (6%) of all households in the region which own their dwelling have mortgage debt. Moreover, only one-third of these mortgages are denominated in foreign currency, implying that only 2% of the owner-occupier households in the region have a foreign currency mortgage. Consumer credit via credit cards is used by a much larger share of the population (26%). Household use of bank credit differs strongly across the region: In the EU member countries 9% of households have a mortgage while this is the case for only 4% of households in candidate countries and 2% of households in EU neighborhood countries. Credit card use is also much less prominent in neighborhood countries (10%) than in either EU members (30%) or candidate countries (30%). Even within the EU member countries there are large differences in household use of credit. In Hungary 16% of all owner-occupier households have a mortgage, and 55 % of all households have a credit card. By contrast in Lithuania only 6% of households have a mortgage and 12% a credit card.

**Table 4.3.  Household Use of Credit by Country**

||Credit Card|Mortgage (%)|FX mortgage (%)|
|---|---|---|---|
|_EU members_|_31_|_9_|_4_|
|Bulgaria|15|4|1|
|Czech Rep|41|11|0|
|Estonia|31|17|9|
|Hungary|55|16|9|
|Latvia|34|9|7|
|Lithuania|12|6|2|
|Poland|19|4|2|
|Romania|13|5|3|
|Slovak Rep|41|13|0|
|Slovenia|47|4|4|
|_EU (potential ) candidates_|_30_|_4_|_2_|
|Albania|18|2|1|
|Bosnia|15|4|1|
|Croatia|37|7|6|
|Macedonia|33|2|0|
|Serbia|21|4|3|
|Turkey|58|3|0|
|_EU neighborhood_|_10_|_2_|_1_|
|Armenia|9|4|1|
|Georgia|7|2|1|
|Moldova|2|1|0|
|Ukraine|21|1|1|
|**Mean**|**26**|**6**|**2**|

Source: LITS survey and authors‟ calculation. Note: This table reports means for each variable by country. Observations are weighted to account for the varying size of the sampling units within countries. Credit Card is 1 if any member of the household has a credit card and 0 otherwise. Mortgage is 1 if the household owns its dwelling and currently has a mortgage and 0 if it owns its dwelling but has no mortgage. FX Mortgage is 1 if the household owns its dwelling and currently has a foreign currency mortgage and 0 if it owns its dwelling but has no FX mortgage.

> 11 See Beck and Brown (2010) for a detailed discussion of the LITS survey. Information on the 2006 survey is available from: http://www.ebrd.com/pages/research/economics/data/lits.shtml

30
_Figure 1: Poverty versus vulnerability to poverty in El Salvador, 2019_ <!-- Start of picture text --> Poverty versus Vulnerability Rate (2019)<br>Poverty Rate Vulnerability Rate<br>41.2%<br>32.6%<br>23.7%<br>21.7%<br>15.0% 13.5%<br>All Rural Urban<br><!-- End of picture text --> _Note: The poverty rate reported here is the estimated poverty rate from the model and not the observed poverty rate. Source: World Bank estimates based on EHPM (2019)._ _Figure 2: Vulnerability Decomposition_ <!-- Start of picture text --> Poverty versus risk-induced vulnerability (2019)<br>Poverty-induced Risk-induced<br>0.24<br>0.15<br>0.18 0.10<br>0.09<br>0.04<br>All Rural Urban<br><!-- End of picture text --> _Source: World Bank estimates based on EHPM (2019)_

**Idiosyncratic shocks are more prevalent in El Salvador than covariate shocks.** Risk-induced vulnerability can arise from idiosyncratic or covariate shocks. The ratio of idiosyncratic to covariate vulnerability is 2.5 in El Salvador (see Figure 3). The ratio of idiosyncratic to covariate vulnerability is 2.2 in rural areas and 3.4 in urban areas. These results suggest that shocks at the household level are more marked for urban areas than municipality-level shocks. In addition, households in urban areas are relatively more vulnerable to crime, illnesses, or price increases than rural households. Still, rural households are also more affected by these individual shocks than those happening at the municipality level.

_Figure 3: Sources of vulnerability_ <!-- Start of picture text --> Idiosyncratic versus covariate vulnerability (2019)<br>Idiosyncratic Covariate<br>0.41<br>0.24<br>0.19<br>0.13<br>0.10<br>0.04<br>All Rural Urban<br><!-- End of picture text --> 12
Figure 3: Depiction of the major features of the general equilibrium model including sectoral and spatial disaggregation and embedded farm model <!-- Start of picture text --> Non-agriculture<br>Six sub sectors Agriculture: 66 crop production<br>activities, 5 livestock and 11 food<br>Irrig. distr. one processing<br>Up to 4<br>separate Agriculture out –<br>irrigation side of irrigation<br>Embedded perimeters districts, both<br>rain fed and<br>micro-farm<br>model: irrigated, with<br>includes Irrig. distr. seven input and output<br>links to the<br>monthly<br>water Up to 33 prod. act. including 22 respective irrigation<br>allocation by irrigated 11 rain districts<br>crop fed<br>Markets: Input-output links to non-agriculture, irrigation districts, agriculture<br>outside the districts, and households. Water supplies and demand are irrigation<br>district specific; Some restrictions on labor flows between rest of economy and ag.<br>Imports<br>Non-agricultural and exports to the rest of the<br>world including capital<br>flows<br>Agricultural foreign trade<br>world<br>Imports and exports to rest of<br>Imports and exports to the European Union<br><!-- End of picture text -->

# Source: Roe et al., 2005b

Note: A similar structure, except for the sectoral specifications was used also for Turkey, South Africa and Mexico.

## Note for Morocco:

Households: six groups, four in agriculture, one non agriculture and public group **.** Structure of Production: 88 activities; 49 commodities; up to eight primary factor inputs plus intermediate factors of production for each activity. Policy Instruments: taxes, subsidies, tariffs, and water charges, quotas by region.

# Figure 4: Illustration of the major features of farm level model

<!-- Start of picture text --> Production of ten irrigated crops; Purchased & inter. inputs Constraints<br>Annual crops<br>Resource balance constraints<br>Production input/output relationships<br>Multiple-cropping<br>Family supplied inputs<br>Monthly water assignments by<br>Production input/output<br>relationships & resource crop<br>allocation constraints<br><!-- End of picture text --> Source: Roe et al. 2005a Note: Similar structure was used also for the case of Turkey, South Africa and Mexico.

20
low malaria districts (type 2) to targeting all travelers that enter only specic health post catchment areas in those ve districts (type 1), using the cell phone data for both. Targeting specic travelers rather than only areas is 257% more eective with a budget of $400,000.

The previous scenarios consider either targeting particular travelers or targeting particular areas, but it is possible to combine the two types of targeting to target particular travelers going to particular areas in certain months. This would not be possible without the cell phone data, which provides extremely granular information on spatial movement of individuals across time so that we know how many travelers enter a specic area in a given month and this can be used to calculate the cost of targeting each of these travelers and the benet of quickly identifying and treating any cases they may have brought through travel. Figure 8 shows the cost curve in this scenario where travelers from a particular district to a particular health facility area are targeted in a specic month.<sup>28</sup> This curve is compared to the previous curves of just targeting travelers, just targeting areas, and also the best strategy without using cell phone data of targeting travelers based on incidence in 2012. Targeting both travelers and areas leads to 52% better performance on average compared to the non-cell phone data strategy when focusing on a budget of under $400,000. Using cell phone data for targeting both travelers and areas compared to just targeting travelers is 19% more eective on average with a budget under $400,000.

There are two important limitations in conducting this type of targeting. The rst is related to potential risk of targeting areas based on movement information from a previous year, given that population movement patterns may change drastically from one year to the next. Other research that has used cell phone data for several years in Namibia nds very consistent short term movement patterns across three years (Milusheva et al. 2017, Wesolowski, Erbach-Schoenberg, et al. 2017 ). Additionally, if short term movement matrices from cell phone data were made available to policymakers on an ongoing basis, it would be possible to adjust targeting in real time as information becomes available.

> 28The xed cost of an area is added when the rst set of travelers in a month is treated in that area.

33
and to disclose the amount of these subsidies on their books. This requires the development of a system of fair-value accounting for intangible safety-net subsidies. If the probability of bailouts can be reduced, regulators can use CDS prices to estimate individual-institution and aggregate values of safety-net subsidies (Kane, 2008). For example, the spread at which credit default swaps trade can be used to strip out the value of the safety net subsidies imbedded in that firm’s market capitalization.

In government enterprises, decision-making horizons could be lengthened if employment contracts included a fund of deferred compensation that heads of supervisory agencies would have to forfeit if a crisis occurred within three or four years of leaving their office (Kane, 2002). Calomiris and Kahn (1996) show that such a system worked well in the 19<sup>th</sup> century Suffolk banking system, where claims to deferred bonuses were paid only after losses had been deducted. The public embarrassment of having to forfeit compensation would incentivize top supervisors to use market signals more efficiently and help them to resist political pressure to bail out zombie firms.

None of these accountability enhancements --better crisis preparedness, greater use of market information to track risks and subsidies, publicizing estimates of safety-net subsidies or offering deferred compensation—would be costly to implement.

_6. Basel II:_ Because credit ratings and sophisticated risk-management models have been discredited, it is clear that Basel II must be re-worked significantly. Some have suggested that the Basel Committee acknowledge that risk-management standards have changed so much that it is necessary to move directly to a new agreement: Basel III. Ideally, pillar two of the new system would include a simple leverage requirement and PCA rules for structured early intervention into the operations of loss-making firms. This would further enhance supervisory and regulatory accountability. Framers of the new accord should find ways to use market signals from CDS and subordinated-debt markets to estimate and disclose how regulatory decisions in different countries affect safety-net costs in other countries.

_What policy lessons can developing countries take from the recent crisis?_ In most crises, the pain that financial-center countries suffer is felt far more intensely at the 44
# **Sub-Saharan Africa’s Recent Growth Spurt:**

# **An Analysis of the Sources of Growth**<sup>**†**</sup>

Yoonyoung Cho<sup>†</sup> Bienvenue N. Tien<sup>‡</sup> World Bank and IZA World Bank

**Keywords** : growth accounting; Sub-Saharan Africa; Demographic Transition; Foreign Direct Investment; Total Factor Productivity

**JEL codes** : E22, O40, J11

> † The authors thank Sangeeta Raja and David Canning for their support and guidance. We appreciate Barry Bosworth and Susan Collins for sharing their data with us. We also thank Luc Christiaensen, Pierella Paci, Alfredo Cuevas, Seok-Gil Park, Rodrigo Garcia-Verdu, Isabell Adenauer, Kareem Ismael, and Deon Filmer for valuable comments.

> † Corresponding author. Address: 1818 H St NW, Washington, DC 20433. USA, phone: +1-202-458-7493, email: ycho1@worldbank.org

> ‡ The World Bank, email: btien@worldbank.org
Figure 20 <!-- Start of picture text --> New entry and re-entry to registered<br>unemployment since January 1993.<br>Thousands<br>70<br>60<br>50<br>40- 1<br>30<br>2 0 - - wM=<br>10 - ~ *.<br>Jan Mar May Jul Sep Nov Jan<br>1 1993 1104<br>MI New entry Reentry<br>Source: NatIonal Labour Centre<br><!-- End of picture text --> <!-- Start of picture text --> 36<br><!-- End of picture text -->
**Table A.2. E-commerce development and various categories of household expenditure per capita by region (Extended Model 1)**

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All|Urban|Rural|East|Central|West|
|Total household expenditure|0.257***|0.194***|0.695***|0.248***|0.247*|0.538***|
||(0.067)|(0.058)|(0.103)|(0.080)|(0.126)|(0.157)|
|Observations|10,872|5,072|5,800|4,359|3,428|3,163|
|Adjusted R square|0.343|0.320|0.379|0.346|0.320|0.338|
|Cosmetics and beauty|0.404***|0.339***|0.945***|0.462***|0.289*|0.996***|
||(0.115)|(0.112)|(0.235)|(0.151)|(0.154)|(0.334)|
|Observations|10,764|5,023|5,741|4,312|3,399|3,053|
|Adjusted R square|0.344|0.367|0.326|0.330|0.361|0.361|
|Food|0.311***|0.238***|0.816***|0.308***|0.283**|0.523**|
||(0.065)|(0.056)|(0.121)|(0.079)|(0.116)|(0.229)|
|Observations|10,720|4,993|5,727|4,279|3,396|3,045|
|Adjusted R square|0.345|0.343|0.360|0.359|0.309|0.386|
|Of which: Food at home|0.345***|0.288***|0.773***|0.322***|0.331***|0.458**|
||(0.071)|(0.060)|(0.160)|(0.092)|(0.116)|(0.224)|
|Observations|10,717|4,991|5,726|4,277|3,395|3,045|
|Adjusted R square|0.389|0.400|0.388|0.333|0.379|0.475|
|Of which: Dining out|0.424***|0.286**|1.275***|0.302*|0.561**|1.168**|
||(0.142)|(0.136)|(0.272)|(0.168)|(0.240)|(0.453)|
|Observations|10,878|5,075|5,803|4,363|3,430|3,085|
|Adjusted R square|0.332|0.323|0.364|0.322|0.328|0.357|
|Clothes|0.217**|0.155*|0.671***|0.196|0.243*|0.489*|
||(0.095)|(0.089)|(0.171)|(0.129)|(0.128)|(0.262)|
|Observations|10,606|4,922|5,684|4,225|3,342|3,039|
|Adjusted R square|0.325|0.342|0.319|0.327|0.350|0.292|
|Utilities|0.156***|0.153***|0.251*|0.188***|0.094*|0.417|
||(0.035)|(0.035)|(0.148)|(0.045)|(0.049)|(0.309)|
|Observations|10,658|4,945|5,713|4,282|3,363|3,013|
|Adjusted R square|0.305|0.294|0.317|0.312|0.297|0.325|
|Communications|0.189***|0.150***|0.523***|0.149***|0.223***|0.402**|
||(0.044)|(0.038)|(0.078)|(0.052)|(0.069)|(0.190)|
|Observations|10,546|4,902|5,644|4,212|3,344|2,990|
|Adjusted R square|0.337|0.353|0.335|0.308|0.387|0.313|
|Local transport|0.353***|0.295***|0.659***|0.277**|0.402***|0.307|
||(0.083)|(0.078)|(0.153)|(0.114)|(0.088)|(0.291)|
|Observations|10,490|4,865|5,625|4,188|3,323|2,979|
|Adjusted R square|0.306|0.318|0.299|0.290|0.321|0.318|
|Travel|0.713***|0.549***|1.495***|0.668**|0.643***|1.768***|
||(0.192)|(0.177)|(0.260)|(0.284)|(0.162)|(0.508)|
|||||||··31|