rafmacalaba/datause-encoder-data huggingface dataset:
region only in the presence of non-humanitarian, non-neutral interventions (Abu-Bader and Ianchovichina, 2018).

While in the MENA region there are many unrefuted studies on the limited linkages between relative welfare distribution measures and perception (Verme et al., 2014; Ianchovichina et al., 2015), the link between perception and absolute measures has been less explored. As opposed to relative measures, perception and absolute measures unambiguously indicate an increase in many MENA countries; this is reflected in the close to 20 percent increase in the absolute Gini and absolute Foster and Wolfson indices.<sup>2</sup> When focusing on absolute polarization and applying the relative distribution method,<sup>3</sup> we observe two contemporaneous effects: (i) a general hollowing out of the center of the distribution (see, _inter alia,_ Alderson and Doran 2011, 2013); and (ii) a significant concentration in the lower tail, representing the so-called _downgrading effect_ . The question that arises in this context is whether this growing absolute polarization, besides showing a similar trend, is also related to the generalized sense of dissatisfaction observed in many Arab countries.

Our hypothesis is that absolute polarization is indeed highly correlated with the perception of households of their socioeconomic status. The worse perception of poor, vulnerable, lower-middle class households, as compared to middle-and-upper-class households mirrors the ongoing process of the hollowing out of the welfare distribution and its concentration in the tails, in other words it mirrors polarization.

Using consumption and socioeconomic perception data from the three comparable rounds of household surveys, we test whether polarization is perceived by households and seek to establish if it contributes to the general malaise and sense of instability reported by Moroccan households. Moreover, as polarization is a synonym of the hollowing out of the central deciles and growing gap between the poorest and the richest, we expect that the lower and central deciles will, as the years go by, feel relatively more threatened by economic downturns and by the fear of losing ground _vis-à-vis_ richer households. Another interesting aspect of our analysis is that it covers a

> 2 These variations are calculated on the seven countries in the MENA region for which we have at least two comparable surveys, notably Morocco, Tunisia, Egypt, West Bank and Gaza, Jordan and Iraq.

> 3 See in the methodological section a detailed explanation of the method that despite the name (relative) is in fact an absolute measure. In this paper we report results for Morocco only, yet the other six countries follow a very similar pattern; these results are available upon request.

3
# **_Table A2. Model-based estimates of achievement change_**

## **_<u>Change in 2021 scores</u>_**

||No controls|Control for<br>general linear<br>time trend in<br>achievement|Control for general<br>linear time trend in<br>achievement, age<br>& grade effects|Control for<br>country-specific<br>linear time trend<br>in achievement|Control for<br>country-specific<br>linear time trend<br>in achievement,<br>age & grade<br>effects|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Azerbaijan|-27.55***|-35.56***|-30.28***|-41.98***|-44.13***|
|Austria|-6.26**|-17.47***|-16.26***|-8.56*|-12.19**|
|Bulgaria|-5.78|-20.71***|-10.41**|-0.89|-2.08|
|Croatia|3.51|-7.57*|-37.78***|-16.43|-49.63***|
|Cyprus|16.89***|-5.27|-9.55|-23.00|-43.72|
|Czechia|-1.72|-15.45***|-13.75***|-7.91*|-16.36***|
|Denmark|-10.27***|-21.58***|-20.40***|-11.74**|-17.21***|
|Finland|-17.65***|-25.99***|-23.21***|-14.84**|-12.76**|
|France|-5.61*|-19.25***|-12.92***|5.12|-6.92|
|Georgia|12.14***|0.94|-45.43***|-5.71|-63.96***|
|Germany|-17.34***|-31.85***|-25.78***|-14.94***|-21.53***|
|Hungary|-7.35*|-21.89***|-53.35***|-12.82**|-42.78***|
|Ireland|18.13***|9.86***|-25.56***|-4.26|-26.22***|
|Italy|-8.07***|-22.11***|-17.80***|-11.26**|-15.37***|
|Kazakhstan|-32.46***|-38.00***|-71.48***|-42.43***|-74.69***|
|Latvia|-19.16***|-35.68***|-57.26***|-32.31***|-58.26***|
|Lithuania|12.64***|-2.58|-30.89***|13.18***|-16.91*|
|Malta|50.17***|41.86***|46.53***|87.55***|78.27***|
|Netherlands|-20.87***|-34.81***|-29.01***|-13.45***|-20.46***|
|Norway|23.52***|9.56***|-37.64***|-23.82***|-31.99***|
|Poland|13.90***|2.45|-34.64***|-31.76***|-28.94***|
|Portugal|-15.25***|-23.86***|-32.24***|5.01|-22.33**|
|Russian<br>Federation|9.94*|-4.71|-12.68**|-35.09***|-22.42***|
|Slovak<br>Republic|0.66|-14.20***|-13.67***|-14.90***|-14.91***|
|Slovenia|-3.71|-17.75***|-24.13***|-37.31***|-37.30***|
|Spain|3.06|-7.74**|-6.86**|-12.14**|-14.91***|
|Sweden|-8.95***|-23.14***|-17.90***|-2.34|-2.67|
|England|6.79**|-7.15*|-0.23|-0.39|-0.48|
|N Ireland|4.21|-3.93|-29.01***|-4.86|-29.57***|
|Belgium<br>(Flemish)|-25.04***|-35.97***|-33.39***|-3.34|0.61|
|Belgium<br>(French)|-6.77*|-17.65***|-13.94***|-4.38|-1.43|
|<br>N|582,112|582,112|581,887|582,112|581,887|

Note: * p<0.05; ** p<0.01; *** p<0.001 18
parameter (γ1). Model (2) provides more immediate feedback: The difference between the average CH4 anomaly in the most recent year and the average for all preceding years can be inferred from the sign, size and significance of parameter (δ1).<sup>18</sup> Although the Sentinel-5P platform provides the best available CH4 data, its coverage for our 5- km grid cells is not necessarily complete for all sample months. From an econometric perspective, this implies an unbalanced panel for estimation. Within the Madrid GGU, typical CH4 concentration anomalies can differ substantially across cells (see Figure 7c). To cite one possible consequence, a naive trend analysis could generate spuriously-positive results in cases where early-period observations are more numerous in lower-anomaly cells and later observations are more concentrated in higher-anomaly cells. We control for this possibility with a panel estimator that incorporates cell-specific fixed effects.

Table 7 reports estimates for the Madrid GGU from OLS, Random Effects and Fixed Effects models. In this case the two trend models align with very high significance in all cases. The monthly trend (γ1) is negative and highly significant, yielding an estimated decline of about 0.19 ppb/month (or about 9 ppb over four years). The final-year deviation is also negative and highly significant, yielding an estimated difference of about 3.5 parts per billion between 2022 and the previous three years.

**Table 7:  Trend model results, Madrid GGU**

|Dependent varia|ble: Monthly<br>|Mean CH4 An<br>|omaly<br>||||
|---|---|---|---|---|---|---|
||(1)|(2)<br>Panel|(3)<br>Panel|(4)|(5)<br>Panel|(6)<br>Panel|
||OLS|RE|FE|OLS|RE|FE|
|Month|-0.186***|-0.191***|-0.195***||||
||(-9.88)|(-10.32)|(-10.46)||||
|[Year = 2022]||||-3.343***|-3.503***|-3.635***|
|||||(-7.07)|(-7.56)|(-7.81)|
|Const.|5.929***|6.101***|6.194***|1.641***|1.710***|1.743***|
||(9.96)|(9.53)|(10.53)|(5.89)|(4.64)|(6.39)|
|N|3,300|3,300|3,300|3,300|3,300|3,300|

18 Results for models (1) and (2) can diverge in cases where a large discontinuity emerges in the most recent year. For example, a modest uptrend from 2019 to 2021 followed by a large decrease in 2022 can produce a small, positive, statistically-insignificant estimate for γ1 and a negative, statistically-significant estimate for δ1.

20
While implementers will be interested in the population counts and characteristics available from the enumeration exercise, they will also want to use the much more extensive information collected through in-depth interviews with the randomly selected subset of informal businesses, which captures more detailed information on informal businesses’ operations. In the case of the ISBS, second-stage selection for an interview happens in real-time, during the enumeration of BAs. This is made possible using readily available CAPI software.<sup>29</sup> As explained above, the first-stage enumeration of BAs ( ) is unbiased even when unequal probabilities of selection are considered. This requires the calculation of first-stage weights ii implied by the inverse of E . 1, that is w 1 i (that is, the first-stage weight). A sub-script for stratification is omitted, but readers should note that BA selection probabilities ( i) are often = 1/ππ within strata, which can be multiple urban areas and/or categories within these urban areas.<sup>30</sup> ππ Specifically, re-write E . 1 by substituting , giving: ii ii GG= NN−mm −aa 1 −<sup>GG! (NN−nn)!</sup> (E . 3) E . 3 can be expressed in exponentiated natural logs (as shown in NN! (GG−nn)! E . 4) which can be computed using programs such as Stata and R.<sup>31</sup> Implementers can also calculate a second-stage weight, as the inverse probability of selection for an interview within an enumerated BA. Analysis of the data of interviewed businesses, at the level of SSUs ( ), can then be done using weighting given by w ∗ w . 1(ii) 2(jj) jj

> 29 The ISBS currently uses the World Bank’s Survey Solutions. For more details, see https://mysurvey.solutions. 30 Note that while stratification can help reduce fieldwork effort due to the grouping of similar BAs, it can present an issue when there is the possibility of networks crossing strata due to expansions (as initial PSU selection probabilities are a function of strata). The ISBS deals with this issue by not allowing expansions that cross strata. Also, note that where stratification is defined as separate urban areas (and not further stratification within an urban area), the issue is fully ignorable.

> 31 For example, the Stata function ln factorial(n) can be applied to E . 4. Code for this calculation is available from the authors upon request. Implementers should also note that it is important to set variable storage to ‘double’ to maximize storage precision. Note that storage precision may be machine and processor dependent.

23
**Table 3: Republic of Congo: Child undernutrition and maternal schooling**

|||**(1)**<br>**Ad-hoc model**|||**(2)**<br>**DML approach**||
|---|---|---|---|---|---|---|
||**Odds**<br>**Ratio**<br>**(OR)**|**95% CI**|**P-value**|**OR**|**95% CI**|**P-value**|
|**Sample: Children 0-59 months**|||||||
|Child HAZ score|0.048<sup>a</sup>|[0.036,0.060]|0.000|0.041<sup>a</sup>|[0.024,0.058]|0.000|
|Whether child is stunted|0.936|[0.920,0.952]|0.000|0.952|[0.928,0.977]|0.000|
|Whether child underweight|0.945|[0.926,0.966]|0.000|0.958|[0.926,0.991]|0.014|
|Whether child is wasted|0.945|[0.925,0.965]|0.000|0.953|[0.920,0.987]|0.008|
|**Sample: Children < 36 months**|||||||
|Child HAZ score|0.048<sup>a</sup>|[0.032,0.064]|0.000|0.048<sup>a</sup>|[0.027,0.070]|0.000|
|Whether child is stunted|0.940|[0.920,0.961]|0.000|0.945|[0.912,0.979]|0.002|
|Whether child underweight|0.950|[0.921,0.971]|0.000|0.957|[0.913,1.003]|0.066|
|Whether child is wasted|0.945|[0.920,0.971]|0.000|0.955|[0.911,1.002]|0.059|
|**Sample: Children 36-59 months**|||||||
|Child HAZ score|0.048<sup>a</sup>|[0.030,0.065]|0.000|0.027<sup>a</sup>|[0.003,0.050]|0.026|
|Whether child is stunted|0.928|[0.901,0.955]|0.000|0.964|[0.923,1.007]|0.101|
|Whether child underweight|0.943|[0.911,0.977]|0.001|0.954|[0.922,0.987]|0.007|
|Whether child is wasted|0.943|[0.911,0.977]|0.001|0.959|[0.928,0.991]|0.014|
|Shaded cells denote variables with si<br>a: This is not an OR but simply the re<br>Confidence intervals reported are ba<br>The control variables used in the ad-<br>squared, household size, the young a<br>or older than 64 and the total numbe<br>wealthquintile,a binaryvariable for|gnificant coeff<br>gression coeffi<br>sed on robust<br>hoc model incl<br>nd old depend<br>r of working a<br>rural areas an|icient estimates (P<br>cient of mother’s y<br>standard errors<br>ude: the child gen<br>ency ratios (ratio<br>ge members 15-64<br>d a set of binaryva|-value < 0.0<br>ears of edu<br>der, child ag<br>of househol<br>years old),<br>riables for r|5)<br>cation<br>e in mont<br>d members<br>binary vari<br>egion of re|hs and age in mont<br>younger than 15 y<br>ables for the house<br>sidence.|hs<br>ears old<br>hold’s|

A different picture emerges from the same analysis with data from São Tomé and Príncipe. The estimates based on the ad-hoc choice of controls (column 1 Table 4) suggest that HAZ, stunting, and maternal education are mostly significantly associated with early childhood development. Only stunting status is not correlated with the measure of a child being on track in ECD.

17
**World Deveobpntent Report**

Since 1970, the intemational credit market has grown in size and sophistication. It offers developing countries many opportunities to meet their growing need for extemal resources, to manage optimally their foreign exchange assets and liabilities, to accelerate their pace of development, and .o, expand export possibilities in financial services (particularly relevant for countries like India).

It is essential for developing countries to participate in this market, but to do so they must develop institutional and policy frameworks that help them establish their creditworthiness, integrate their domestic financial markets and organically link them to the international capital market, and develop appropriate institutional expertise and technology.

India is one of the few developing countries capable of developing the expertise and technology needed to participate in the international capital markeL It is urgent that India do so quickly, to reduce the foreign exchange cost of operating in this market and to enlarge its export effoit To take advantage of export possibilities for financial and other services and products, developing countries should also participate actively in the Uruguay Round of multilateral trade negotiations on services.

The idea is to open up opportunities to export skill-intensive services - such as **finian-** cial services and computer software, in which countries like India may have a comparative advantage. Increased competition would also tend to improve the domestic financial system, which would stimulate productivity and investment in the productive sectors.

Confrontation and rhetoric about special and differential treatment are self-defeating in these negotations and give more discretionary power to administrative authorities in the developed countries. Without a negotiated system of multilateral rules, it is easier for the economically strong countries to dominate the weaker ones through political and economic power.

This is a background paper for the 1989 World Development Report. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact the World Development Report office, room S13-060, extension 31393 (36 pages with tables).

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. Thc findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Ccnter
from ICAO, concerning a possible common agreement for how to handle emissions from this sector.<sup>31</sup>

# **References**

Bénabou, Roland and Tirole, Jean (2010), Individual and Corporate Social Responsibility. Nota di Lavoro 23.2010, Milan and Venice: Fondazione Eni Enrico Mattei.

Bhatia, Pankaj et al (2004), _The Greenhouse Gas Protocol: A Corporate Accounting and Reporting Standard._ World Business Council for Sustainable Development/World Resources Institute.

Brekke, Kjell Arne and Nyborg, Karine (2008), Moral Hazard and Moral Motivation: Corporate Social Responsibility as Labor Market Screening. _Resource and Energy Economics_ , 30, 509-526.

Broderick, John (2009), Voluntary Carbon Offsetting for Air Travel. In Gössling, Stefan and Upham, Paul (eds.) (2009), _Climate Change and Aviation_ . London: Earthscan, pp. 329-346.

Doganis, Rigas (2002), _Flying Off Course: The Economics of International Airlines_ . 3<sup>rd</sup> Edition. London and New York: Routledge.

Heal, Geoffrey (2005), Corporate Social Responsibility – Economic and Financial Perspectives. _Geneva Papers_ , 30, 387-409.

ICAO (2011), _ICAO Carbon Emissions Calculator_ . Montreal: International Civil Aviation Organization.

IPCC (1999), _Aviation and the Global Atmosphere_ , Special Report, Inter-Governmental Panel on Climate Change.

Jardine, Christian N. (2005), Calculating the Environmental Impact of Aviation Emissions. Research Report, Environmental Change Institute, Oxford University Centre for the Environment.

Keen, Michael and Strand, Jon (2007), Indirect Taxation on International Aviation. _Fiscal Studies_ , 28, 1-41.

Keen, Michael, Parry, Ian and Strand, Jon (2012), Market-based Instruments for International Aviation and Shipping as a Source of Climate Finance. World Bank Policy Research Working Paper WPS 5950. Washington DC: The World Bank, January 2012.

> 31 The EU’s enforcement of its inclusion of non-EU airlines in the EU-ETS has currently been put on hold, until the end of 2013, awaiting ICAO’s decision on a possible global scheme for handling airlines’ carbon emissions.

23
Policy Research Working Paper 5909

# **Abstract**

This working paper assesses cross-border economic integration in the Lower Congo region. It focuses on the Kinshasa–Brazzaville conurbation, which is projected to become Africa’s largest urban area by 2025, and is already serving as the gateway to large hinterlands. Despite their size and proximity, formal economic exchanges between the two cities are extremely limited. The volume of recorded passenger travel between Kinshasa and Brazzaville corresponds to about one-fifth of the volume of traffic between East and West Berlin during the time of the Berlin Wall, and formal trade volumes are derisorily small. As a consequence, the authors find evidence of statistically significant differences in retail prices, indicating unexploited scope for cross-river arbitrage. Through a survey of firms, they find that local traders perceive substantial scope for increasing crossborder economic activity if cross-river trade costs were reduced. Trade in locally produced goods and by small firms would especially benefit from such reductions. Existing high trade costs mainly result from a lack of competition in cross-river transport services, which are dominated by a duopoly of state-controlled operators. High administrative border costs, exacerbated by the presence of multiple government agencies at the border, act as a further obstacle. Liberalization of cross-river transport and customs reform could yield large economic benefits for local producers and consumers.

This paper is a product of the Poverty Reduction and Economic Management Unit, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mhoppe@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**<u>(b)  Just before and just after onset of particular event</u>**

|||Befo|re|Just after|onset|
|---|---|---|---|---|---|
|ARG|Argentina, 1995|45.4|_1992_|48.6|_1996_|
|BRA|Brazil, 1994|59.8|_1993_|61.5|_1995_|
|ARG|Argentina, 1989|44.5|_1986_|45.4|_1992_|
|MEX|Mexico, 1994|50.3|_1992_|51.9|_1996_|
|PHL|Philippines, 1998|46.2|_1997_|46.1|_2000_|
|KEN|Kenya, 1993|57.5|_1992_|44.5|_1994_|
|THA|Thailand, 1997|43.4|_1997_|41.4|_1998_|
|ZMB|Zambia, 1995|52.6|_1993_|49.8|_1996_|
|CRI|Costa Rica, 1994|46.3|_1993_|47.1|_1996_|
|UGA|Uganda, 1994|43.2|_1992_|37.4|_1996_|
|JAM|Jamaica, 1994|35.7|_1993_|36.4|_1996_|
|BGD|Bangladesh,1980s|26.9|_1985_|28.8|_1988_|
||Mean|46.0||45.1||
||Standard deviation|8.9||8.5||

**<u>(c)  Just before and just after onset of particular event - Transition economies</u>**

|||Bef|ore|Just afte|ronset|
|---|---|---|---|---|---|
|BGR|Bulgaria, 1996|31.1|_1995_|26.4|_1997_|
|CZE|Czech Republic, 1989|19.4|_1988_|26.5|_1993_|
|EST|Estonia, 1992|23.0|_1988_|39.5|_1993_|
|HUN|Hungary, 1991|25.0|_1989_|27.9|_1993_|
|LVA|Latvia, 1995|27.0|_1993_|31.6|_1996_|
|LTU|Lithuania, 1995|37.2|_1994_|32.4|_1996_|
|POL|Poland, 1992|26.9|_1989_|32.7|_1996_|
|ROM|Romania, 1990|23.3|_1989_|25.5|_1992_|
|RUS|Russia, 1995|46.2|_1996_|45.6|_2000_|
|RUS|Russia, 1998|48.3|_1993_|46.2|_1996_|
|SVK|Slovakia, 1991|19.5|_1988_|19.5|_1992_|
|UKR|Ukraine,1997|33.2|_1996_|29.0|_1999 _|
||Mean|30.0||31.9||
||Standard deviation|9.6||8.1||

Note: Crisis dates are drawn from Caprio et al. 2004. For panel (a), in respect of each country in the Povcal database, other than Transition economies, Gini coefficients were extracted where available for dates (i) _before any_ crisis hit (within 10 years); (ii) _soon after the end_ of all crises (within 5 years); (iii) _long after the end_ of all crises (at least 10 years). Ginis falling into this category were averaged and entered into the table together with the date to which they refer (where the Ginis are averaged over several years, the mean date is shown). For panel (b), in respect of each crisis, again excluding Transition economies, Gini coefficients were extracted where available for dates both just _before_ and _just after the onset_ of the crisis (within 3 years in each case). Panel (c) repeats the exercise of panel (b) for Transition economies. Gini coefficients are expressed throughout as percentages.

20