rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4: Stochastic Dominance and official poverty lines**

<!-- Start of picture text --> Stochastic dominance and Official Poverty lines<br>Niassa Rural vs Maputo Urban Nampula Rural vs Maputo Rural<br>15.8 30.9 60 80 90 15.8 24.8 60 80<br>consumption per capita per day in MT consumption per capita per day in MT<br>Niassa Maputo Nampula Maputo<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br><!-- End of picture text --> High PL differentials significantly impact the calculation of poverty figures, and two specific examples corroborate this conclusion. The left-hand graph in Figure 34 above, compares the per capita consumption distribution of urban Maputo Province and rural Niassa and Cabo Delgado<sup>18</sup> (N-CD) Provinces, while the right-hand graph in Figure 3 compares rural Maputo to rural Nampula.<sup>19</sup> Per capita consumption in both urban Maputo and rural Maputo is stochastically dominant throughout the distribution, even though in these areas underestimation affects about 50 percent of the observations. Yet the official poverty figures imply the opposite: while urban Maputo registers a poverty rate of 63.7 percent, rural N-CD only registers a rate of 32.7 percent; rural Maputo, meanwhile, has an official poverty rate of 76.3 percent, while rural Nampula’s rate is 56.7 percent (see Table 2, column 2, below).

In both cases the cause of the difference is the differing PLs (the vertical lines in the two graphs). The PL for urban Maputo is 93 percent higher than that of rural N-CD, while the PL for rural Maputo exceeds the PL for Nampula by 73 percent (see Table 4, column 6, in Appendix). As shown in Figure 3 the magnitude of the PL differentials completely offsets the dominance of per capita consumption in Maputo, both for urban and rural areas. The key question, therefore, is whether these sizeable

> 18 The location-specific PLs used in the official poverty statistics group homogeneous urban and rural areas into 13 regions, sometimes combining areas from different provinces, as is the case with Niassa and Cabo Delgado Provinces (see Table 1). 19 In both cases, we truncated the upper side of the distribution in order to close up on the lower percentiles: this way we simply disregard the upper 1<sup>st</sup> percentile. What concerns us is the rest of the distribution, in particular the part where the poor are identified.

13
are available through Our World in Data (https://ourworldindata.org/). The results are presented in Annex Table A. With the consumption-based emissions, China’s payments decline significantly – from USD 35 billion to USD 20 billion. This adjustment does not materially impact other countries.

# _Modification 2: Stock of Carbon Emitted_

The proposal has ignored the stock of emissions thus far. Some countries would argue that they should not be held responsible for emissions when there was little knowledge that emissions were damaging. Others would argue that regardless of foreknowledge, countries that have used more of the collective carbon budget have benefited from the associated development and should pay for it. A middle ground may be sensible here, backing out the stock of carbon emitted thus far, with 1990 as the start year. This is well into a timeframe where high- and low-income countries alike were aware of climate change; the Rio Earth Summit was held in 1992.

One could follow the same principle as above and argue that every individual today has the right to have emitted the average stock of carbon between 1990 and today (again, the fund can consider either carbon production emissions or carbon consumption emissions). So excess emission countries will be those whose per capita average is more than the global per capita average stock emitted between 1990 and today.<sup>5</sup> The last question is what rate the excess stock of emissions should attract. Given that this one-off payment will not change any incentive to act (since the emissions have already occurred), its value can be debated. Arguably, the cost of additional emissions should grow as the carbon budget shrinks, so emissions in the past ought to be costed at a lower rate than current emissions. What payments are required with a “moderate” rate of $0.3 /ton and how does this compare to a GCI of $10/ton? Once again, this would be a one-off payment rather than a recurring payment, though it is also possible to consider amortizing the payment over say 20 years and adding it to the annual GCRI payments. Table 2 presents the payments and receipts for the top 10 countries; Annex Table B provides the full list.

> 5 An alternative would be to calculate country payments each year if the GCRI scheme were in operation since 1990 and cumulate the payments. This more detailed calculation takes into account the variation in population over time, as well as allows for possible variation in the GCI.

9
# **Deep Integration and UK‐EU Trade Relations**<sup>**1**</sup>

**Alen Mulabdic, Alberto Osnago, Michele Ruta**<sup>**2**</sup>

World Bank Keywords: Deep Trade Agreements, Trade Effects, European Union, United Kingdom JEL codes: F13, F15 1 Paper prepared for the edited volume “The Economics of the UK‐EU Relationship: From the Treaty of Rome to the Vote for Brexit”. We would like to thank Nauro Campos and Fabrizio Coricelli (the editors) for detailed comments on an earlier draft of the paper and colleagues at the World Bank for many useful conversations. Errors are our responsibility only.

> 2 Contact author: Michele Ruta, Trade & Competitiveness Global Practice, World Bank. Email: <u>mruta@worldbank.org</u>
# WPS3626

**Global Monetary Conditions versus Country-Specific Factors in the Determination of Emerging Market Debt  Spreads**

**Mansoor Dailami**<sup>**1**</sup> **Paul R. Masson**<sup>**2**</sup> **Jean Jose Padou**<sup>**3**</sup>

## Abstract

We offer evidence in this paper that US interest rate policy has an important influence in the determination of credit spreads on emerging market bonds over US benchmark treasuries, and therefore on their cost of capital. Our analysis improves upon the existing literature and understanding by addressing the dynamics of market expectations in shaping views on interest rate and monetary policy changes and by recognizing nonlinearities in the link between US interest rates and emerging market bond spreads, as the level of interest rates affects the market's perceived probability of default and the solvency of emerging market borrowers. For a country with a moderate level of debt, repayment prospects would remain good in the face of an increase in US interest rates, so there would be little increase in spreads. A country close to the borderline of solvency would face a steeper increase in spreads. Simulations of a 200 basis points (bps) increase in US short-term interest rates (ignoring any change in the US 10 year Treasury rate) show an increase in emerging market spreads ranging from 6 bps to 65 bps, depending on debt/GDP ratios.

## World Bank Policy Research Working Paper 3626, June 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1  World Bank, Development and Prospects Group. 2 Rotman School of Management, University of Toronto 3 University of Toronto
# **I. Introduction**

Market-oriented reforms and a favorable external environment, especially between 2000 and 2007, have propelled Jordan’s real economic growth to an average of 6.1 percent over the last decade. Consistently outperforming MENA’s growth, estimated at an average of 4.5 percent, this improvement in economic activity has translated into a significant growth in consumption and household welfare, with poverty incidence numbers showing a concrete decline in poverty<sup>1</sup> indicators across the whole Kingdom. Tellingly, this reduction in poverty levels came amidst evidence of a decline in inequality, one that is supported empirically by the improvement in the Gini index calculated across the distribution of household consumption distribution between 2002 and 2010<sup>2</sup> .

Yet, despite these impressive economic indicators, the overall sentiment in recent years seems to point to a worsening of inequality levels. This comes amidst persistently high unemployment<sup>3</sup> , weaker economic growth since the end of 2008<sup>4</sup> , and an inequitable welfare growth throughout 2008-2010 where wealthier Jordanians reaped much of the benefits<sup>5</sup> . This recent deterioration in the economic situation threatens to overturn all the progress made throughout the decade. Moreover, and combined with the general regional sentiment, it is perhaps less surprising to note that the wave of demonstrations have reflected the population’s discontent with not only what they perceive as worsened economic welfare, but also with the current state of the country’s institutions. In particular, the effectiveness of both the government and the Parliament has been called into question, and the population is, more vocally than ever, demanding greater accountability and transparency in the management of public affairs. Clearly then, these demands are a sign that macroeconomic indicators, as good as they are, have not resonated with the country’s popular sentiment and aspirations. It can also be argued that political exclusion may have trumped purely economic advancements.

With perceptions and economic indicators apparently at odds, the interest of this paper is focused on examining household welfare in Jordan, defined by per capita expenditure, across the

> 1 As calculated in this paper and in other works such as World Bank (2009).

> 2 Also computed in later sections of the paper.

> 3 Unemployment declined from 15.3 percent in 2002 to 12.5 percent in 2010.

> 4 GDP growth between 2009 and 2011 has grown at an average of 3.5 percent compared to 6.6 percent between 2000 and 2008.

> 5 This is explained later in the paper but can be observed visually through the growth-incidence curve of Figure 1.

2
-7-

tractable characterization of the conditions under which regional disparities can be said to contribute to aggregate poverty.

Consider the situation in which the reduction in regional disparities takes the form of lump-sum transfers, which are uniformly levied on, or received by, all persons in a given region. This is termed "additive absorption." Thus, the transfer paid or received by an individual is solely determined by his/her region of residence. An attraction of this case is that it does not assume that the government knows anything more than each person's region of residence and is, thus, informationally feasible from the point of view of the central government. However, it may be argued that multiplicative transfers are more feasible politically, at local level, insofar as they do not involve any change in intra-regional inequalities. It may also be argued that the same local political-economy factors (endowment distributions, local tax powers, the social preferences of governments, etc.), which determined initial intraregional distributions will operate persistently to preserve those distributions. Thus there are good arguments for considering both additive and multiplicative absorption.

The outcome will also depend on how poverty is measured. We shall consider various members of the Foster-Greer-Thorbecke (FGT) class of measures. The level of poverty in the ith region is denoted Pai for the parameter a 2 0. The well-kncwn headcount index of poverty (proportion of people who are poor) is the FGT measure for a=O. For a=1 we ootain the FGT version of the poverty gap measure (mean income shortfall as a proportion of the poverty line), and a recently popular distributionally sensitive measure of poverty is obtained by setting a=2. The l,tter is our preferred measure, as it satisfies Sen's (1976) Transfer Axiom (in that transfers
A further external validity concern is that, while partial equilibrium assumptions may be fine for a pilot, general equilibrium effects (sometimes called “feedback” or “macro” effects) can be important when the pilot is scaled up nationally. For example, an estimate of the impact on schooling of a tuition subsidy based on a randomized trial (such as for Mexico’s _PROGRESA_ program) may be deceptive when scaled up, given that the structure of returns to schooling will alter. Heckman et al., (1998) demonstrate that partial equilibrium analysis can greatly overestimate the impact of a tuition subsidy once relative wages adjust, although Lee (2005) finds a much smaller difference between the general and partial equilibrium effects of a tuition subsidy in a slightly different model.

A special case of the general problem of external validity is scaling up. There are many things that can change when a pilot program is scaled up: the inputs to the intervention can change, the outcomes can change, and the intervention can change; Moffitt (2006) gives examples in the context of education programs. The realized impacts on scaling up can differ from the trial results (whether randomized or not) because the socio-economic composition of program participation varies with scale. Ravallion (2004) discusses how this can happen in theory, and presents results from a series of country case studies, all of which suggest that the incidence of program benefits becomes more pro-poor with scaling up. Trial results could over or under estimate impacts on scaling up. Larger projects may be more susceptible to rent seeking or corruption (as Deaton, 2006, suggests); alternatively, the political economy may entail that the initial benefits tend to be captured more by the non-poor (Lanjouw and Ravallion, 1999).

Evaluative research should regularly test the assumptions made in operational work. Even field-hardened practitioners do what they do on the basis of some implicit model of how the world works, which rationalizes what they do, and how their development project is expected to have impact. Existing methods of rapid _ex ante_ impact assessment evidently also rely heavily on the models held by practitioners. Researchers can perform a valuable role in helping to make those models explicit and (where possible) helping to assess their veracity.

A case in point is the assumption routinely made by both project staff and evaluators that the donor’s money is actually financing what recipients claim it is financing. Research has pointed to a degree of fungibility in development aid. Donors probably do not fund exactly what aid recipients claim they have funded, although there is some evidence that external aid sticks to its sector; see van de Walle and Mu (2007). The existence of fungibility and flypaper effects 18
# Table of Contents

||Page|
|---|---|
|I.*|Introduction...<br>.......................................<br>1|
|II.|Saxonhouse (1982, 1983, 1985, 1986)<br>.........................<br>9|
|III.|Bergsten and Cline (1985)..................................13|
|IV.|Balassa (1986b,<br>18).................1|
|V.|Sato (1986)<br>.............................-..<br>17|
|VI.|Barbone (1988)<br>............<br>s<br>19|
|VII.|Carliner (1985) ..........<br>oe*ooeoeooeoooo*oo20|
|VIII.|Noland (1987)...............<br>o..o....o.....oo....o....oo25|
|IX.|Lawrence (1987)..o<br>.o.o..ooo..o.o....<br>.o.oooo......e..o.<br>27|
|X.|Conclusionsoo.o*oo*eoo.oo*e*ooooooo30|

References Cited....

Acknowledgments: The paper was stimulated by a literature survey on the market potential of Japan for manufactured exports from developing countries, which is under preparation.

The author is grateful to Messrs. Bela Balissa, Riccardo Faini, Junichi Goto, Refik Erzan, Sam Laird, Edward Lincoln, Paul Meo and Marcus Roland, for their helpful coments on earlier drafts.
Appendix Table 7 continued: RDD estimates of gaps in teacher behaviors and teacher absence associated with teachers hired pre- and <u>post-FPE (bandwidths of 5, 7, and 10 years)</u>

||Bandwidth||Good<br>teacher<br>demeanor|Good<br>pedagogical<br>practices|Classroom<br>environment|Good<br>teaching<br>practices|Share of<br>time<br>teaching|Absent from<br>school|Absent from<br>classroom|
|---|---|---|---|---|---|---|---|---|---|
|Mozambique|5|Coef|0.106|0.107|0.014|0.076|0.002|-0.329|-0.726|
|||SE|(0.097)|(0.078)|(0.027)|(0.044)|(0.018)|(0.274)|(0.250)|
|||Pval|0.276|0.176|0.611|0.090|0.905|0.235|0.005|
||7|Coef|0.053|0.063|0.008|0.042|0.010|-0.184|-0.592|
|||SE|(0.086)|(0.066)|(0.027)|(0.039)|(0.018)|(0.234)|(0.221)|
|||Pval|0.538|0.340|0.765|0.292|0.568|0.432|0.009|
||10|Coef|-0.019|0.037|-0.011|0.002|0.002|-0.013|-0.262|
|||SE|(0.073)|(0.053)|(0.023)|(0.033)|(0.017)|(0.190)|(0.188)|
|||Pval|0.797|0.485|0.651|0.941|0.886|0.947|0.164|
|Tanzania|5|Coef|0.013|0.026|0.020|0.020|-0.129|-0.110|-0.116|
|||SE|(0.042)|(0.053)|(0.059)|(0.031)|(0.073)|(0.190)|(0.238)|
|||Pval|0.754|0.629|0.737|0.529|0.078|0.564|0.629|
||7|Coef|-0.022|0.080|0.044|0.034|-0.102|-0.187|-0.255|
|||SE|(0.038)|(0.046)|(0.051)|(0.024)|(0.064)|(0.152)|(0.200)|
|||Pval|0.573|0.080|0.385|0.156|0.116|0.221|0.206|
||10|Coef|0.018|0.055|0.064|0.045|-0.067|-0.059|-0.108|
|||SE|(0.033)|(0.040)|(0.044)|(0.021)|(0.059)|(0.118)|(0.170)|
|||Pval|0.600|0.170|0.152|0.034|0.257|0.615|0.527|
|Togo|5|Coef|-0.053|-0.061|0.056|-0.020|0.087|-0.357|-0.333|
|||SE|(0.080)|(0.073)|(0.068)|(0.052)|(0.093)|(0.159)|(0.188)|
|||Pval|0.511|0.402|0.415|0.709|0.348|0.028|0.081|
||7|Coef|-0.055|-0.061|0.033|-0.028|0.069|-0.461|-0.364|
|||SE|(0.077)|(0.068)|(0.061)|(0.048)|(0.085)|(0.154)|(0.181)|
|||Pval|0.476|0.369|0.589|0.567|0.416|0.003|0.047|
||10|Coef|-0.065|-0.053|0.068|-0.017|0.026|-0.405|-0.362|
|||SE|(0.062)|(0.059)|(0.052)|(0.040)|(0.069)|(0.146)|(0.159)|
|||Pval|0.297|0.371|0.200|0.680|0.710|0.006|0.025|

Notes: Table reports coefficient estimates (Coef), Standard Errors (SE), and p-values derived from estimating Equation (1). Each estimate and associated standard error and p-value are the result of a separate regression model. Pooled model includes country fixed effects.

A-15
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**Table 4.2: Average Fractions of Passenger Floor Space taken up by Different Passenger Class Seats, Wide-Body and Single-Aisle Aircraft**

|**Class**|**Widebody**|**Single**|
|---|---|---|
|**Type**||**Aisle**|
|Economy|0.705|0.756|
|Business|0.185|0.165|
|First|0.110|0.078|

Source: Authors’ calculations These figures also have implications for the relative floor space taken up by each economy, business and first class passenger seat, as fractions of the average floor space per seat taken up by all passenger seats, for the two main types of aircraft. These numbers are given in Table 4.3 below.

**Table 4.3: Average Floor Space Taken up by Each Passenger Seat in Different Classes, Relative to the Average for all Seats, Wide-Body and Single-Aisle Aircraft**

|**Class**|**Widebody**|**Single**|
|---|---|---|
|**Type**||**Aisle**|
|Economy|0.81|0.87|
|Business|1.85|1.64|
|First|3.67|2.54|

Source: Authors’ calculations

# **4.2 Step 2: Load Factors by Travel Class**

In step 2 of our carbon footprint calculations we expand our calculations by differentiating load factors by route groups, and to reflect passenger aircraft capacity reserved for cargo. Table 4.4 shows average load factors for 2008 for the 17 route groups defined by the ICAO (see Appendix 2 below). We see that load factors vary widely, but they vary less and are generally higher for more traveled routes. The globally average load factor for flights involving WBG travel was about 71 percent in 2008, the year of the ICAO study.<sup>19</sup> Reliable numbers seem today not to be publicly available for load factors differentiated by travel class, neither industry-wide nor by airline. It is however widely recognized and assumed that 19 Note that the (unconditional) average load factor for all flights globally in 2008 was slightly higher, near 75 percent.

12
(Tumen, 2018, 2019, 2021). A study by Altindag and Kaushal (2021) analyzes the effect of the refugee inflow on voting but finds no significant effects. With respect to social connections, there is only limited evidence so far. A qualitative study by Seyidov (2021) and a survey by WFP (2020) suggest that the command of the Turkish language is key to building social ties between refugees and natives. Overall, the effects of the refugee inflow on the Turkish economy appear larger than the effects on the German economy, although more research is needed to evaluate thde long-run consequences of

## **3.4 Main Hypotheses and Contribution**

Guided by the theoretical literature on immigration, diversity and social cohesion, we test whether the inflow of refugees and asylum seekers in Germany negatively affected social cohesion. Moreover, we test whether the effect is stronger in areas with lower base levels of social cohesion as well as lower economic performance.

We make two contributions to the existing empirical literature. First, we document the effect of a large inflow of refugees and asylum seekers on social cohesion. Although there is plenty of literature on diversity and social cohesion, the evidence for the inflow of refugees or other forced migrants is limited. Providing such evidence is important because refugees are a major immigrant group whose motivations, characteristics and needs differ greatly from those of other migrants, and whose effect on a host country is likely different from the effect of economic migrants (Becker and Ferrara, 2019).

Second, the sudden and unexpected inflow of over a million people allows us to quantify the _causal_ effect of the inflow of forced migrants on social cohesion. In many studies, it is difficult to separate correlation from causation because diversity and social cohesion may be determined by the same social processes. In our case, the inflow occurred within a few months and led to an unprecedented increase in the number of asylum seekers. By exploiting pre-determined rules in the allocation of asylum seekers, we are able to estimate the causal effect of the local presence of asylum seekers on several dimensions of social cohesion.

# **4 Data and Research Design**

## **4.1 Data Sources, Key Variables and Descriptive Statistics**

To quantify the effect of the inflow of refugees and asylum seekers on various aspects of social cohesion, we combine regional data on asylum seeker inflows with survey data as well as data on anti-immigrant violence scraped from newspaper articles.

**Data on Asylum Seeker Inflows** The data on the presence of refugees and asylum seekers in Germany comes from the German Federal Statistical Office (Destatis). We observe the number of asylum seeker residing in a county in each year between 2007 and 2019, as well as the number of asylum seekers by gender, citizenship and category of protection status. In our analysis, we include all counties for which we have data on asylum seeker inflows for the entire period 2007-2019. This leaves us with 373 out of 401 counties, representing 91% of the German population in 2014.

13