rafmacalaba/data-use-mentions-extended huggingface dataset:
data do not include paramilitary personnel, i.e., Gendarmerie (8,000), and the Ba'th Party Workers Militia.

GDP, and data on Consolidated Central Government wages and salaries are taken from IMF Senior economist from Syria Leigh Alexander and refer to 1994.

# Tunisia

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1989.

Data relating to Central Government employment is taken from Republique Tunisienne: Enieux de la reforme de la fonction publigue written by Maria Pigato (MN1CO) in June 1995 and relates to the year 1994. Education and Health data are taken from the IMF Algerian Background paper. Health does not include persons working in Hospitals and other PSE. Local Government data are also taken from Miria Pigato and are only restricted to communes. Military employment data are taken from International Institute for Strategic Studies: The Militar Balance Survey of 1995-96. Data include conscripts (26,400), but do not include paramilitary, i.e., Police (13,000), and National Guard (10,000).

Data on average wages in the Central Government is taken from Republique Tunisienne: Enjeux de ia reforme de la fonction publique of June 1995 (non official working document) which states that " average central government salaries are three times per capita GDP".

# West **Bank/Gaza**

Data on population and labor force are from the Civil Service Review mission of January, 1996 (EMTPM, DP). The unemployment figure is found by taking the weighted average of unemployment rates in West Bank and Gaza. All statistical data exclude the population of East Jerusalem.

All numbers relating to Central Government employment are drawn from the Civil Service Study of June 1996. Of this number, 934 are daily workers and relate to 1995 levels. Data on civil service employees include 25,000 police force. Data on Health employment include 673 daily workers and are from 1996 Civil Service Review Mission, Aide Memoire. Data
reserves. However, the supporters do not provide any logical explanations on the inclusion, and the change in international reserves is the concept of balance of payments crises rather than currency crises. Several currency crisis studies mix up balance of payments crises with currency crises. It seems more appropriate to use only successful currency attacks. Therefore only exchange rates should be used as an indicator.<sup>(6)</sup> In relation to the second issue, following the conceptual definition, nominal exchange rates are a suitable indicator regardless of the reasons behind the depreciation such as hyperinflation, political situation and macroeconomic conditions.<sup>(7)</sup> At the same time, real exchange rates are informative as the comparison between nominal and real exchange rates can be a clue for the cause of depreciation.

# **2.3.4 Debt Crises**

Finding an indicator fully compatible with the conceptual definition of external debt crises, which is _inability to service debts_ , is difficult. Debt related indicators show the severity of debt burden rather than inability to service debts. Paris Club debt rescheduling and/or debt reduction are implemented when a country faces external debt repayments difficulties. However, Paris Club deals with only bilateral public debt rather than whole external debt, and sometimes terms of rescheduling and/or debt reduction are politically determined. Therefore the incidences of Paris Club are not necessarily suitable to identify debt crises.

Consequently, the debt burden indicators are used as a proxy for a debt crisis indicator. Among numerous debt indicators, the IMF (2000, p.3 and p.26) states that the external debt to exports ratio is an especially useful indicator of trends in debt and repayment capacity, since the debt to exports ratio is less volatile than the debt to GDP ratio. While both denominator and numerator are measured in the US dollar for the debt to exports ratio, GDP data are basically on a local currency basis and then converted to the US dollar for the debt to GDP
<!-- Start of picture text --> Transport r<br>Networks Street Operational Socio-<br>(GTFS Feeds — RESTS Costs} Economic Data<br>Incl. Fare Info) (OSM) Fare Structure<br>Pareto<br>Frontiers<br>Baseline Accessibility Fuel Price Sensitivity Income Distribution Inequality Analysis Exclusion Analysis<br>Analysis Analysis and Spatial Analysis (Palma Ratio) ¥<br><!-- End of picture text -->
|3.1|17.3|
|Upper Rural|18.0|12.9|9.8|19.2|31.0|19.3|28.1|14.9|
|Frontier Urban|1.3|0.3|0.3|0.8|2.3|17.8|9.4|1.3|
|Frontier Rural|0.2|0.2|0.7|0.0|0.0|0.5|15.6|0.3|
|Total|100|100|100|100|100|100|100|100|

Source: Authors’ calculations using Labor Force Survey Migrants now residing in Cairo came mainly from urban areas in either Lower (30.8 percent) or Upper (31 percent) Egypt. Aside from those now in Cairo, the majority of migrants stayed within their region, for example 32.7 percent of those living in Urban Lower Egypt came from different governorates in Urban Lower Egypt. Almost half (48.4 percent) of all migrants from all regions reside in Lower Urban Egypt. (See tables 3 & 4.) Urban governorates - except Cairo - absorb the highest inflows of migrants, followed by Frontier governorates (South Sinai and Red Sea) where tourism offers employment possibilities (Table 5).

> 4 The high migration rate from Cairo to urban Upper Egypt is due to the inclusion of urban Giza, which is part of Greater Cairo, within the urban Upper Egypt category.

6
map areas cultivated with maize across Malawi and Ethiopia at 10-meter spatial resolution.

There are six headline findings that emerge from our analysis. First, a simple machine learning workflow can classify pixels with maize cultivation with up to 75 percent accuracy - though the predictive accuracy varies with the survey data collection method and the number of observations available for model training. Second, georeferencing plot locations by collecting either full plot boundaries or complete plot corner points provide the best quality of information for model training. Third, classification performance almost always peaks before or around 4,000 plots - corresponding to slightly less than 60 percent of the training data. Fourth, the seemingly-small erosion in predictive accuracy under less preferable approaches to georeferencing plot locations results in total area under maize cultivation being overestimated by 0.16 to 0.47 million hectares (8 to 24 percent) in Malawi vis-a-vis the results from the best performing model. Fifth, to avoid overfitting models, it is preferable not to exclude from the training data set any plot observations based on a minimum area threshold. Finally, in the case of maize area mapping in Malawi, optical features alone can provide sufficient signal to maximize prediction quality and the potential benefits offered by synthetic aperture radar, i.e. satellite data unaffected by cloud cover, can be offset by additional noise introduced with its use.

4
in order to adequately capture frequency, extreme severities, and full geographical location. This process is computationally intensive, and great care must be taken to ensure that resultant event sets are constrained by observed physical processes; for example, definition of hazard parameter distributions of climatological characteristics must maintain spatial and temporal correlations. Model calibration and validation against historical information are a valuable check, but inevitably assumptions are required on data processing and sampling steps, ideally supported by academic reference (ABI 2011). Though the event-by-event loss calculations of catastrophe modeling are computationally intensive, especially for hazards that demand high geographic resolution (which can be less than 10 m for flood), these calculations also allow differing aspects of model uncertainty to be quantified and communicated.<sup>7</sup> Only such a large time series of synthetic but nevertheless realistic events can generate enough samples of losses to robustly define the remote tail of EP curves that are of interest to the insurance industry, such as 1-in-100-year and 1-in-250-year return periods. Taking such a probabilistic loss perspective also ensures the EP is smoothed out by having many thousands of data points. For the purposes of this paper, a 10,000-year stochastic catalog of synthetic simulations was generated across Malawi at a spatial resolution of 0.05° and dekadal temporal resolution for several hazard variables (including soil moisture).<sup>8</sup> These 10,000 years of hazard events are all potential versions of what could happen in the next 12 months and can be combined with vulnerability and exposure information to estimate longer return period tail behaviors) to get a fuller risk perspective. This step requires making assumptions about the relationship between hazard and impact, using historical data as a starting point. In a data-scarce environment, it can be very hard to validate the performance of the vulnerability function in the tails. This paper is a starting point, and more investigation is required.

As noted above, these 10,000 years of hazard events reflect events that are likely to occur under
- Jaglom, Wendy S. et al. (2014). “Assessment of projected temperature impacts from climate change on the U.S. electric power sector using the Integrated Planning Model”. _Energy Policy_ 73, 524–539.

- Javorcik, Beata (2019). _Eight Things Development Professionals Should Know about Foreign Direct Investment - FDI inflows may help improve environmental performance_ . Report. PEDL Synthesis Paper.

- Jiang, Yan and Le Luo (2018). “Market reactions to environmental policies: Evidence from China”. _Corporate Social Responsibility and Environmental Management_ 25. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/csr.1505, 889–903.

- Jiménez, Orlando (2007). “Voluntary agreements in environmental policy: an empirical evaluation for the Chilean case”. _Journal of Cleaner Production_ 15, 620–637.

- Jin, Zuben et al. (2021). “Do Firms Adapt to Rising Temperatures? Evidence from EstablishmentLevel Data”.

- Jones, Benjamin F and Benjamin A Olken (2010). “Climate Shocks and Exports”. _American Economic Review: Papers & Proceedings_ 100, 454–459.

- Keller, Wolfgang and Arik Levinson (2002). “Pollution Abatement Costs and Foreign Direct Investment Inflows to U.S. States”. _The Review of Economics and Statistics_ 84, 691–703.

- Khanna, Madhu and Lisa A Damon (1999). “EPA’s Voluntary 33/50 Program: Impact on Toxic Releases and Economic Performance of Firms”. _Journal of Environmental Economics and Management_ 37, 1–25.

- Kling, Gerhard et al. (2018). “Climate Vulnerability and the Cost of Debt”.

- Kraehnert, Kati et al. (2021). “Insurance Against Extreme Weather Events: An Overview:” _Review of Economics_ 72. Publisher: De Gruyter Oldenbourg, 71–95.

- Kumar, Surender and Shivananda Shetty (2018).
# **I. INTRODUCTION**

Some progress has been made towards developing an understanding of the causes and consequences of child malnutrition in the development process of poorer countries, but substantial gaps remain in our knowledge about the size and distribution of health and nutrition problems, the determninants of health and nutrient status, the impact of health and nutrition on socioeconomic development, and appropriate nutritional policy design. This paper focuses on the first two issues, namely the extent and the determinants of poor child health and nutritional status as reflected by attained height, in the context of a socially, economically, ethnically, and geographically diverse developing country such as Guatemala.

Special attention is paid to the impact of community factors, which, in recent studies, have been found to affect a number of demographic behaviors and outcomes such as contraceptive use (Entwisle, Casterline and Sayed 1989; Entwisle _et al._ 1996), poverty (Tienda 1991), the use of modem health services (Pebley, Goldman and Rodriguez 1996), and child survival (Sastry 1996). We also investigate empirical questions that have been ignored in previous anthropometric research, such as the distribution of child stunting across communities and the magnitude of intra-family correlation of height-for-age outcomes, before and after controlling for observed covariates.

Guatemala is among the poorest countries in Latin America. According to the National SocioDemographic Survey, 65.6% of Guatemalan people lived below the poverty line in 1989. Among these, 38.1% were below the extreme poverty line. These two figures are higher for the indigenous subpopulation: 86.6% and 61.0% respectively (Steele 1993). The poverty of Guatemalans is revealed indirectly by the anthropometric outcomes of their children. The WHO Global Database on Child Growth, based on nationally representative cross-sectional data gathered between 1980 and 1992, covers 87% of the total population under age five in developing countries (de Onis _et al._ 1993). It reveals that the prevalence of stunting (low height-for-age) among Guatemalan
20

**Figure 9**

<!-- Start of picture text --> Simulated changes in poverty<br>using growth rates by region, but substituting urban rate for Dar<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year<br>Mainland Tanzania Dar es Salaam<br>Other urban areas Rural areas<br>.45<br>.4<br>.35<br>.3<br>.25<br>Fraction below poverty line<br>.2<br><!-- End of picture text -->

# **5.6 Simulation with Regional Growth Rates and Inequality Adjustment**

The simulations presented thus far assume that growth was distribution neutral.<sup>9</sup> According to the survey data, inequality jumped up in Dar es Salaam, while remaining essentially unchanged in other urban areas, rural areas, and the country as a whole. The survey data shows that the Gini coefficient for Dar increased from 0.30 to 0.34 between 1992 and 2001.

In this simulation, the inequality adjustment proposed by Datt and Walker (2002) and in Datt et al. (2003) and described in the methodology section is applied by sector (Dar es Salaam, other urban, and rural) in a simulation, using region-specific growth rates. The overall urban growth rate is again substituted for the Dar-specific growth rate. A constant inequality adjustment is applied such that the Gini coefficient in the simulation increases from 0.30 to 0.34 for Dar es Salaam.

Results from this simulation are presented in Appendix Table A10 and Figure 10 below. While the general path of the evolution of poverty is similar, the net drop in poverty in Dar is smaller than that observed in the survey data.

> 9 More strictly, the simulation with separate urban-rural growth rates entails the assumption of distribution-neutral growth within each sector, while the simulation with separate regional growth
. Similar to the formal sector, the informal sector became largely male dominated or ‘de-feminized’ (Assaad 2002). Accordingly, the informal sector has further entrenched the gender employment gap in the country. Whilst policies induced these substantial shifts in the gender gap, the role of social norms and gendered socioeconomic roles is as important.

Apart from these long term trends, the gender gap could also suddenly change. An economic crisis can potentially be a driver of change and so can either decrease or exacerbate the gap. Through imposed lockdown measures to limit the spread of the virus, the breakout of COVID-19 has simultaneously induced a demand and a supply side shock with potential job losses following a demand-pushed recession and a supply-side contraction.

This paper is positioned within the recent literature investigating the effect of the pandemic on the gender gap, but is also more broadly situated within the historical evolution of

> * We are thankful for the generous financial support provided by the project ‘Stability and Development in the Middle East and North Africa’, funded by the German Federal Ministry for Economic Cooperation and Development (BMZ) towards the administration of the 2020/21 Egyptian Industrial Firm Behavior Survey (EIFBS) instrument. The contents of this document are the sole responsibility of the authors and do not reflect the position of the BMZ. We are grateful to Dr. Zakaria Othman for undertaking the sampling. Thanks are also due to Marian Adel for research assistance. The dataset can be downloaded here: Egypt, Arab Rep. - Egyptian Industrial <u>Firm Behavior Survey, EIFBS 2020/21 (erfdataportal.com)</u>

> * Senior Economist, German Institute of Development and Sustainability (IDOS) formerly German Development Institute (DIE), Tulpenfeld 6 D-53113 Bonn, +49(0)228 94927-253, Amirah.El-Haddad@idos-research.de Professor of Economics, Faculty of Economics and Political Sciences, Cairo University Amirah.elhaddad@feps.edu.eg; Fellow ERF Web:  https://www.die-gdi.
some differences across regions, this was mostly driven by a very low nonresponse rate of 3.8% in one of the smaller administrative regions (Barisal).

One strong relationship we find in the data is how the types of firms that failed to answer direction questions on corruption, and firm answers on the RRT questions on paying bribes. We find that a large number of firms that failed to answer the direct corruption question, when answering the RRT, indicated no incidence of bribery. Our data shows that firms that indicated bribes were used to obtain permits in the randomized response question had a 6% lower nonresponse rate (13%) than firms that indicated bribery wasn’t used to obtain permits (19%) in the direct corruption questions.<sup>8</sup> Or, put another way, the mangers that answered the direct question on corruption had a much higher rate of answer “yes” on the using bribes to obtain permits (61.7%) relative to firms that firms that failed to responded to the corruption question (50.1%).

This result could fly in the face of the idea that the firms failing to respond to corruption questions are more likely to engage in corruption. This descriptive data suggests that firms that fail to answer direct questions on corruption are _less_ likely to have engaged in bribes. What explains this pattern? One possibility is that the firms that fail to answer direct questions on corruption are also likely to underreport corruption in the RRT. In short, firms providing nonresponses to some sets of politically sensitive questions could be the same firms providing false responses to other questions.

How do we evaluate potential false responses? One strategy commonly employed in surveys is to instruct enumerators to evaluate the perceived truthfulness of the respondent. In our survey of firms, enumerators were instructed to classify respondents as “very truthful”, “somewhat truthful” or “somewhat untruthful” or “very untruthful” at

> 8 The exact differences is .0588 with a
Policy Research Working Paper 9913

# **Abstract**

In 2015, Germany welcomed close to one million asylum seekers and refugees from Syria, Afghanistan, the Western Balkans and elsewhere. Although the country was often praised for its welcome culture, the inflow has spurred a debate about identity, social cohesion and the limits of multiculturalism. This paper analyzes the effect of this inflow on various dimensions of social cohesion. To separate causation from correlation, it exploits the fact that asylum seekers in Germany are allocated to local areas based on an area’s tax revenues and population several years prior. Therefore, the allocation is unrelated to current economic, political or social conditions. Based on survey data as well as data scraped from newspapers, the paper documents two sets of results. First, it finds no effect on self-reported indicators of trust and perceived fairness, and a small negative effect on and attitudes towards immigrants. In contrast, it finds that the refugee inflow led to an increased incidence of anti-immigrant violence that lasted for about two years. This increase is larger in areas with higher unemployment and greater support for right-wing parties.

This paper is a product of the Social Sustainability and Inclusion Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at benjamin.elsner@ucd.ie.

_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
accompanied these apparatuses that became clearly coercive. Moreover, MENA authoritarian governments have always found external support, in particular, within the superpower rivalry and the oil supply security concern (Khalidi, 2004). In fact, it has been argued that these regional factors have played a much more significant role than any others, including religion and culture, in hindering democracy in MENA (Ross, 2001).

> 10 Desai et al. (2009) provide an econometric confirmation of this authoritarian bargain hypothesis using panel data on 80 non-democratic states during the 1975-1999 period including countries of the MENA region. In the context of our study that uses data only on MENA countries, an implication of this hypothesis is the existence of a negative relationship between Participation and Provision on which our analysis provides robust evidence.

> 11 Boosted by the large oil revenue intakes of the 1960s and 1970s, the MENA region's economies reached one of the highest growth rates in the world at the 5% to 6% levels. This extraordinary growth has however not been without raising concerns, in particular, because it was often the result of a highly positive rate of capital accumulation and a negative rate of total factor productivity (Page, 1998). A good example is Algeria where the implementation of the "industrializing industries" development model (Destanne de Bernis, 1966) led to the quadrupling of the rate of physical capital accumulation whereas the total factor productivity collapsed from 1.9% in the 1960s to -0.7% in the 1970s (Yousef, 2004).

> 12 MENA countries ranked very low in two important indices that measure this gap, namely, public accountability and institutional quality with values of respectively -0.78 and -0.32 while those of the OECD countries were 1.89 and 1.38 (Yousef, 2004). This gap turned out to be even wider for MENA oil-exporters (World Bank, 2003).

7