rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

It  is  a  well-­‐established  empirical  regularity  in  the  international  economics  literature  that even  within  narrowly-­‐defined  industries,  globally-­‐engaged  firms  and  domestic  firms  differ in  terms  of  their  productivity,  size,  employment  composition,  and  wages  (Bernard  and Jensen  (1995,  1997)  and  Bernard,  Jensen,  and  Schott  (2009)).<sup>1</sup> In  the  presence  of  such heterogeneity,  trade  liberalization  will  induce  inter-­‐firm  reallocations  within  an  industry  as the  more  productive,  exporting,  firms  expand  and  the  least  productive  firms  shrink  or  exit the  industry  (Melitz,  2003).  In  an  environment  with  labor  market  frictions,  this  within-­‐ industry  reallocation  can  have  distributional  consequences  for  workers  employed  in  firms with  differing  levels  of  global  engagement.

The  international  trade  literature  has  discussed  the  putative  impact  of  trade  liberalization on  wages  at  different  levels  of  aggregation,  examining  this  question  alternately  at  the  level of  the  firm  and  at  the  level  of  the  individual  worker.   There  are  various  channels  through which  a  decline  in  trade  protection  could  result  in  differential  changes  in  average  wages  at exporting  firms  relative  to  firms  selling  only  to  the  domestic  market.  For  instance,  if liberalization  is  associated  with  a  change  in  relative  returns  to  worker  characteristics,<sup>2</sup> differences  in  work  force  composition  will  imply  that  average  firm-­‐level  wages  in  exporting firms  will  change  differentially  relative  to  domestic  firms  following  liberalization.  If  trade liberalization  does  not  affect  the  returns  to  worker  characteristics,  a  differential  wage  effect may,  nevertheless,  be  observed  if  changes  in  trade  policy  induce  compositional  changes  in the  workforce  of  exporting  firms  that  are  different  from  those  in  non-­‐exporting  firms.<sup>3</sup> 1  Recent  work  extends  the  findings  of  Bernard  and  Jensen  (1995)  using  matched  employer-­‐employee data  for  Germany  (Schank,  Schnabel,  and  Wagner  (2007)  and  Klein,  Moser,  and  Urban  (2010
headed by men as compared to households headed by women but this difference is not very large and the number of households headed by women is relatively small. Depending on the size of households, double poverty increases with household size. It displays its maximum when the household is composed of 10 or more people with a percentage of 7.3% against 0.9% for those living in households with less than 4 people. Interestingly, households that are poor according to non-monetary criteria but non-poor according to monetary criteria seem to experience higher poverty the smaller is the household size. This might be explained by the fact that smaller families have fewer assets than larger families, income being equal. By grade level of the household head, 23.8% of people living in households headed by someone with no education are at risk of at least one form of poverty. Double poverty affects 5.5% of those households headed by people who never attended school. Also, the higher the educational level of the household head the lower is the risk of poverty as one would expect. Overall and considering urban and rural areas together, in 2007 3.8% of people were below the poverty line according to both CWI and expenditure criteria. This can be considered as the core of poverty in Morocco while 82.8% of the population is above all forms of poverty.

# [Table 10]

## **6.3 Multidimensional poverty over time**

As already discussed and in order to conduct the MCA analysis over time, we used the weights from the NSLS 2006/07 for both time periods considered. We also followed exactly the same methodology we used for the 2006/07 MCA for the 2000/01 data. The multidimensional poverty line for 2000/01 is equal to 60% of the CWI median value and amounts to 1.1 in rural areas and 0.6 in urban areas.

The analysis of CWI and expenditure poverty over time shows that the two forms of poverty declined significantly
it is obligatory to examine if there is any selective attrition with respect to the program. The panel structure of the IFLS can be used to test for this possibility. First, consider the entire sample of births for IFLS between 1984-1994 from IFLS 1 and IFLS 2 and calculate the amount of exposure to midwives in their community of birth. Finally, match this sample of children with the set of individuals who could not be tracked in IFLS 5 and regress a dummy, if an individual could be tracked in IFLS 5 on early life midwife exposure. Appendix Table S7.11 shows the results. This result suggests that the main results do not suffer from the

> 41The authors thank an anonymous referee for suggesting this exercise.

20
# **3. Data**

The main analyses in this paper use data on the younger cohort collected in 2013 (at age 5) and in 2016 (at age 8). The key outcomes of interest are (i) enrollment in different types of early childhood education services; (ii) child development outcomes using the Early Development Instrument (EDI); and (iii) tests scores in early grades of primary school.

We measure enrollment in three types of early childhood education services. The first are project playgroups, those established under the project’s block grant. The second are nonproject playgroups, which refer to all other playgroup services that exist in the communities. The third are kindergartens, which are early childhood education programs catering to children before they enroll in primary school. We collected information about enrollment in each type of service by collecting a retrospective enrollment history for each academic year from 2008 to the survey year from the primary caregiver of the child.

The EDI measures children’s school readiness across five domains: physical health and well-being, social competence, emotional maturity, language and cognitive development, and communication skills and general knowledge (Janus and Offord, 2007). The EDI has been validated and tested for reliability. Overall, the construct validity, predictive validity, and interrater reliability of the EDI in Indonesia are comparable to that found in other countries, making the EDI a suitable instrument for measuring school readiness in Indonesia (Brinkman et al. 2016). In this paper, we use the short-form of the caregiver-rated EDI.<sup>6</sup> We standardized the variables for each EDI domain to have a mean of 0 and standard deviation of 1, using the mean and standard deviation of the comparison group in the younger cohort.

A school-based test was developed for this evaluation based on learning standards in Indonesian schools. Children were assessed in a classroom under the guidance of a member of the data collection team and no classroom teachers were present. The tests were divided
locations i and j. But it does not include the temporal and user components of accessibility (e.g. mode preference or subset of opportunity preferences).

We utilize an urban accessibility tool, which utilizes the road network (in this case OpenStreetMap), and transit attributes (specifically, the cities’ General Transit Feed Specification (GTFS) files and/or a transit network GIS layer), to calculate the travel times between origin and destination pair in the city. The tool then combines the estimated travel times and location data for employment opportunities (or other opportunity inputs) to calculate the accessibility value for every point in the city.

This flexible open source tool provides, with ease and detailed granularity, the basis for the accessibility analysis of the city. It is developed by Conveyal,<sup>6</sup> and uses a simple web-based interface to generate detailed raster accessibility maps. Raster maps offer a representation of the area divided into a regular, standardized grid of cells and are useful for storing high resolution data that vary continuously. This output can be processed and analyzed in greater depth and collated with other sources of data with other GIS software.

When averaged over the whole urban area, localized accessibilities are weighted by population n�, to account for population distribution within the city. This produces an average accessibility for the urban area, A̅.

While the time threshold can be set to any value, this analysis takes 60 minutes as the threshold, which has been recognized as meaningful to capture labor market access in urban areas (Bertaud 2014).

A second set of metrics and visuals complement the average accessibility indicator. They provide information on the distribution of accessibility – spatially through maps and in aggregate through Lorenz curves and the calculation of Gini coefficients. The distribution of accessibility is an important component of the overall picture, in tandem with the average accessibility, which brings to light the equity distribution of access within a city.

Similar to the way income inequality is often captured, we
GCI 2.01-2.09,<br>WEF TEI 4.01-7.05, WDI);<br>– technological readiness (WEF GCI 9.01-9.06)<br>•<br>Africa infrastructure country diagnostic (AICD)|
|•|Development of the ICT sector and the<br>Internet (infrastructure, regulation,<br>competition, access)|•<br>Liner shipping connectivity index (UNCTAD, WTI 4.3)<br>•<br>Baltic Exchange Dry Index (WTI 4.3)<br>•<br>Lead time to export/import (WDI)|
|•|Reforms of the transport, logistics and<br>ancillary services, including<br>infrastructure, regulation, competition<br>for land (road and rail),<br>|<br>•<br>Port container traffic (WDI, WTI 4.3)<br>•<br>Total / air freight and costs (WTI, 4.3)<br>•<br>Number of seats available, airlines, international routes, airport passenger statistics<br>(IATA, WDI)|
|•|maritime/water and air<br>Regional infrastructure for trade<br>corridors, and other forms of<br>regulatory harmonization and<br>cooperation|•<br>World telecommunication/ICT indicators database and ICT Development Index (ITU)<br>•<br>Foreign participation / ownership in telecoms (ITU, WTI 1.14)<br>•<br>Competition index in telecoms (ITU, WTI 1.14)<br>•<br>Number of international gateways, landing stations, licenses for fixed and mobile phone,<br>Internet providers (national data, WB and OECD STRI)<br>•<br>Mobile and fixed-line telephone subscribers / population covered by mobile cellular<br>
country: _α_ would have to be at least as high as that ratio for an observer to judge that the mortality cost exceeds the poverty cost.

Drawing on a rich combination of data sources on Covid-19 mortality, on demographic structures and age distributions, and on income distributions for 150 countries in the world, we estimate that the pandemic has generated 4.3 million lost years and as many as 68.2 million additional poverty years globally (using the extremely frugal international poverty line of 1 _._ 90 per day) by early June 2020. If median poverty lines are used for LICs, LMICs, UMICs and HICs, we estimate that 235 million poverty-years are being generated this year.

Across countries, the poverty-years to lost-years ratio ranges from just above 1 to more than 10,000. It is 3.6 in Belgium and 195 in Pakistan, for example. We document a very strong association between this ratio and GDP per capita, with poverty costs being systematically larger in poorer than in richer countries, both in absolute terms and relative to mortality costs. These results are for our baseline scenario, which conservatively assumes a distribution-neutral allocation of the income losses associated with the declines in GDP. Poverty costs are even greater in an alternative scenario where we allow for some increase in inequality, based on the pattern estimated for Argentina by Bonavida Foschiatti and Gasparini (2020). For most developing countries, it is dicult then to avoid the conclusion that one would have to place a very low weight on the welfare cost of falling into poverty to conclude that mortality costs exceed poverty costs.

It is important to note that this does not mean that public interventions aimed at containing the spread of the virus have been a cure worse than the disease. Indeed, we show that, for most countries, the absence of any non-pharmaceutical interventions would have led to mortality costs that far exceed the total welfare losses accrued as of early June. In a counterfactual scenario in which infection rates are allowed to rise until
Chart 7 **Costa Rica: Total and Domestic Debt** Ratios to GDP <!-- Start of picture text --> TD 1.1<br>0.35 DD<br>domestic debt<br>1.0 "0.30<br>0.9 - 0.25<br>0.8 - -- 0.25<br>0.8, \/ total debt 0.20<br>0.7 ,' 0.15<br>0.6= __ 0.10<br>85 86 87 88 89 90 91 92 93 94 95<br>CRTTVGDP- CRDDP<br><!-- End of picture text --> Source: IMF, International Financial Statistics If we only focus on the total debt to GDP ratio, we are tempted to be optimistic. True, the total debt to GDP decreased from 100 percent of GDP in 1985 to about 61 percent in 1995, which approximates the "sustainable" ratio calculated by equation (9). Nevertheless, this picture is deceiving. First, a large share of the fall in the foreign debt ratio to GDP is the product of the exchange rate appreciation, which increases dollar GDP without changing the actual dollar debt figure. Second, the fiscal situation has deteriorated significantly and interest rates have increased substantially. The primary 1996 surplus of 3.2 percent of GDP could not compensate a large interest rate bill of 6.4 percent of GDP, which produced an operational deficit of 3.2 percent of GDP.

So far, Costa Rica has had a relatively low income velocity of money (about 6 per year over M2). If this were to change, as expected if the fiscal situation does not improve, this nation would have a proportionally higher level of inflation if it wanted to keep its debt ratio at "reasonable" levels.

26
Integrating mortality into poverty measurement through the Poverty Adjusted Life Expectancy index.<sup>∗</sup> Jean-Marie Baland,<sup>†</sup> Guilhem Cassan,<sup>‡</sup> Benoit Decerf<sup>§</sup> Originally published in the Policy Research Working Paper Series on _July 2022._ This version is updated on _December 2023._ To obtain the originally published version, please email <u>prwp@worldbank.org.</u>

# **JEL** : D63, I32, O15.

**Keyworks** : Multidimensional poverty, Poverty, Mortality, Mortality paradox.

> ∗Acknowledgments : We express all our gratitude to Kristof Bosmans, James Foster, Dilip Mookherjee and Jacques Silber for helpful discussions and suggestions. This work was supported by the Fonds de la Recherche Scientifique - FNRS under Grant n<sup>_◦_</sup> 33665820 and Excellence of Science (EOS) Research project of FNRS n<sup>_◦_</sup> O020918F. We are grateful to the audience at the World Bank seminar for providing insightful comments. All errors remain our own. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequence of their use.

- †CRED, DEFIPP, University of Namur.

> ‡CEPR, CRED, DEFIPP, University of Namur.

- §World Bank, bdecerf@worldbank.org.
alternative I-sets in order to gauge the robustness of the results to measures of political organization.

It is instructive to examine some features of PAC spending data, and assess whether they are in line with the Grossman-Helpman idea of money buying policy (as different from money buying mere access to policymakers).<sup>36</sup> Farm PACs contributed between $5.5 million and $7 million during each of the five election cycles. Among farm products, the most politically active were sugar PACs, dairy PACs and ranch PACs. Together these three PACS accounted for about 75% of total farm PAC contributions.<sup>37</sup> Over 200 PACs were politically active during this period. Cotton, dairy and wheat had the highest degree of PAC concentration, and represented by the equivalent of three or four equal sized PACs. Ranch, sugar, and fruits and vegetables were represented by the equivalent of eight to ten equal-sized PACs.<sup>38</sup> PAC money is clearly influential in agriculture. A study of the 1985 and 1990 voting during sugar legislations by Brooks, Cameron and Carter finds that in the House and Senate both votes were responsive to sugar PAC contributions.<sup>39</sup> Further, the value of sugar production in their constituency also determined how congresspersons voted. Sugar lobbies also targeted candidates that were likely to advance pro-sugar policies.

Two-thirds of agricultural PAC money went to House candidates and one-third to Senate candidates. Figure 1 indicates the top 20 recipients in the House of agriculture PAC contributions during the 1993-94 election cycle.<sup>40</sup> During this cycle, 13 of the top 20 House recipients of 36 Gawande (2006) details the structure of lobbying in agriculture over the 1990-2000 period in the United States.

37 Food manufacturing PACs, forestry and nursery PACs, agriculture service PACs and agriculture distribution PACs contributed more than did farm PACs.

38 Based on Herfindahl indices computed by the authors.

39 Brooks, Cameron and Carter
evaluation; Figure 2b shows the precise location of the treatment sites based on Global Positioning System data.<sup>16</sup> The most common types of subprojects are feeder road rehabilitation projects (67 percent), inland valley swamp rice projects (9 percent), and other agricultural projects (13 percent).<sup>17</sup> The average labor intensity of the subprojects—the share of subproject costs allocated toward wages—was 60 percent, but there was some variance; road subprojects were typically less labor intensive. Given that the program was put in place following the food and fuel price crisis, seasonal considerations were not detailed in the project documents (World Bank 2010); however, where possible, the program aimed to align the timing of subproject implementation with seasonality concerns. In practice, the treatment subprojects were implemented between April and July or August, which coincides with the end of the agricultural harvest period and the beginning of the lean season.

## **B. Beneficiary Characteristics**

The data from the beneficiary tracker survey indicate that the CfW program reached primarily youth with low levels of educational attainment who were working in the agricultural sector. The program also met the gender targets and did not induce negative impacts on schooling among the young participants. While in this sense the program succeeded in reaching the target population, targeting performance was weaker

> 16 Global Positioning System data were collected only from treatment sites.

> 17 Inland valley swamp rice projects refer to the establishment of a specific type of rice production system.

9
The baseline scenario assumes the continuation of past trends with no decline in trade costs, the details are presented in Annex A along with the population and GDP data in Tables 1-3. In the counterfactual scenarios we study the impacts of the BRI-induced trade cost reductions. We apply the lower bound estimates of trade cost reductions due to BRI-related infrastructure improvements from de Soyres et. al. (2018) to generate the low case scenario (BRILBD). The upper bound estimates allow for switching across transport modes leading to deeper reduction of trade costs and form our high case scenario (BRIUBD). Further gains could be realized if in addition to improving the transport infrastructure, the trade facilitation reforms would lead to halving of border delays for BRI countries (BRIUBBD). Finally, we augment the last scenario with two variants of complementary policies where BRI countries implement a 50% reduction of tariffs in trade with each other (BRIUBBTD) or reduce their tariffs in trade with all trading partners (BRIUBBTPD). The scenarios are summarized below:

|**Code**|**Description**|
|---|---|
|`BRILBD`|BRI lowerbound estimates|
|`BRIUBD`|BRIupperbound estimates|
|`BRIUBBD`|BRIUBBD andreduced borderdelays|
|`BRIUBBTD`|BRIUBBDandtariff reductionapplied ona preferentialbasis amongBRIcountries|
|`BRIUBBTPD`|BRIUBBD andtariff reduction applied on a MFNbasis byBRIcountries|

Table 4 summarizes the shocks for the regions in the BRI area. The shock occurs in two time periods. One-half of the shock is assumed by 2025, and the remainder by 2030. The fourth and last simulation involves tariff reductions applied on MFN basis. Figure 1 displays the trade weighted aggregated across sectors bilateral trade costs reductions on exports originating from countries on the left-hand side of figure in our central scenario (BRIUBD). Most of BRI-induced bilateral trade costs reductions are below 10%, but several exporters experience higher trade costs reductions to most destinations. Those biggest cost reductions on trade costs of exporting are expected in
1

# CONTENTS

# **Introduction**

|**1. Section 1.**|Housing Privatization in the Forner Soviet Union:||
|---|---|---|
||Factors and Social Effects|6|
|**2. Section 2.**|The Development of the Housing Market in the Context of||
||<br>Public Priorities|18|
|**Conclusions a**|**nd Policy Issues**|37|
|**Annex**|Social Survey Background|38|
|**References**||**40**|

# **List of Tables in Text**

|1.1. Lithuania, housing stock by form of ownwership (% of total)|6|
|---|---|
|1.2. Estonia, housing stock by form of ownwership (% of total)|6|
|1.3. Ukraine, housing stock by form of ownership (% of total)|7|
|1.4. Russia, housing stock by form of ownership (% of total)|7|

- 1.4. Russia, housing stock by form of ownership (% of total)

- **_1.5._** Privatization of housing in Russia and separately in Moscow, 1990-96 (cumulative)

|7|
|---|

- 1.6. Average price of housing unit quality and location, Moscow, April, 1995 (thousand dollars) 11

- 1.7. Correlation between the category and location of housing and its privatization rate in Moscow **(%/o** of households that privatized before 1995) 11

- 1.8. Housing privatization by demographic categories (% of Moscow households that privatized before 1995) 12

- 1.9. Moscow households who privatized their dwellings by type of category of housing (% of total