rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 6. Decomposition of Accumulated Domestic Food Price Increases since December 2006 (% change)**

<!-- Start of picture text --> WBG<br>Tunisia<br>Morocco<br>Lebanon<br>Jordan<br>Egypt<br>Djibouti<br>Yemen<br>Syria<br>Iraq<br>Iran<br>Algeria<br>UAE<br>Saudi Arabia<br>Qatar<br>Oman<br>Kuwait<br>Bahrain<br>-20 0 20 40 60 80 100<br>World food price (increase) World food price (decrease)<br>Exchange rate (depreciation) Exchange rate (apreciation)<br>Other factors Unexplained (residual)<br>Oil importers<br>exporters<br>Developing oil<br>GCC countries<br><!-- End of picture text --> _Note:_ The figure shows accumulated percentage increase in domestic food prices from December 2006 to mid-2011. The increase in domestic food prices is then decomposed into the effects of world food prices, the domestic exchange rate, and other factors using variance decomposition.

Domestic factors, notably inflexible procurement and poor logistics, have also played a major role in explaining domestic food inflation in nearly all MENA countries. A recent World Bank (2012b) study finds that countries could generate significant cost savings by improving the efficiency of the wheat import supply chain. Evidence suggests that it takes on average 4 times longer and costs 3.5 times more to import wheat in an Arab country than in the Netherlands. However, there are significant differences in supply chain performance across the MENA region, with some countries having bottlenecks at the port, while others having inefficient inland transportation systems.

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# **1 Introduction**

Migration has become an increasingly politicized issue, in particular given the rise in unauthorized migration, igniting public and policy interest in its impacts and sparking new debates on the relationship between migration and economic development. Not surprisingly, the focus of the academic literature on unauthorized migration has been on the impact of irregularity on the host country. However, little is known about the consequences of irregular migration on sending countries. Studying the impact of illegality on the origin country is particularly important since unauthorized migration is often temporary, and many irregular migrants return to their home country either because they are deported or because they planned on temporary migration all along.

Return migration is an important phenomenon in the broad picture of international mobility. Although statistics on return migration are not reliable, recent estimates are that roughly one-quarter of all migration events are returns (Azose & Raftery, 2019). According to the OECD (2018), the five countries that experienced the largest outflows of foreign populations in 2016 were Germany (1.09 million), the Republic of Korea (325,000), Spain (243,000), Japan (234,000), and the United Kingdom (195,000). At the same time, irregular migration has been on the rise. According to the European Commission, every year, between 400,000 and 500,000 foreign nationals are ordered to leave the European Union because they have either entered or stayed irregularly in the destination country.

Irregular migration is not only an issue for OECD countries, but is also a challenge for the Gulf states and middle income countries like Malaysia and Thailand. For example, the United Nations Office for Drugs and Crime (UNODC) estimates that every year more than 660,000 people migrate irregularly to Thailand (Schloenhardt, 2014). Another example is Mexico, known for being a major migrant sending country to the United States, for its unauthorized emigration, and for its substantial return migration flows. According to the Mexican
. D. (2013). _Refugee livelihoods in Kampala, Nakivale and Kyangwali refugee settlements: Patterns of engagement with the private sector_ . Refugee Studies Centre, Oxford Department of International Development, University of Oxford.

- Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010). Introducing ACLED: an armed conflict location and event dataset: special data feature. Journal of peace research, 47(5), 651-660.

- REACH Initiative (2018). Uganda Joint Multi-Sector Needs Assessment. Identifying humanitarian needs among refugee and host community populations in Uganda. August 2018.

- Ruiz, I., & Vargas-Silva, C. (2016). The labour market consequences of hosting refugees. _Journal of Economic Geography_ , 16(3), 667-694.

- Ruiz, I., & Vargas-Silva, C. (2017). The consequences of forced migration for host communities in Africa. _Revue D'economie Du Developpement, 25_ (3), 135-154.

- Tatah, L., Delbiso, T. D., Rodriguez-Llanes, J. M., Cuesta, J. G., & Guha-Sapir, D. (2016). Impact of refugees on local health systems: a difference-in-differences analysis in Cameroon. _PLOS one, 11_ (12), e0168820.

- Thomas, M. (2017). Turning the Comprehensive Refugee Response Framework into reality. _Forced Migration Review_ , (56).

- United Nations Development Program (2017). Uganda’s contribution to refugee protection and management. Retrieved from https://data2.unhcr.org/en/documents/download/64687.

- United Nations High Commissioner for Refugees. (2005). “Handbook for Self-Reliance” Reintegration and Local Settlement Section Division of Operational Support UNHCR, Geneva. Retrieved from: https://www.unhcr.org/44bf7b012.pdf

38
of LARCs, along with high cost of removals, contrast with the per-month cost of protection from unintended pregnancies. The IUD can be used for up to 10 years while the implant lasts three-to-five years. In contrast, the injectable (Depo-Provera) provides protection for only three months, while the pill needs to be taken everyday.

> 2In principle, there could be negative consequences of the counseling intervention for women’s autonomy and decision-making. Using follow-up surveys with a latter cohort of clients in the adaptive experiment, We found no differences between the two counseling arms in terms of client satisfaction or their willingness to return to the hospital for FP services in the future (see Section 4.2).

3
Policy Research Working Paper 9157

# **Abstract**

This paper studies employment patterns and trends in South Asia to shed light on determinants of extremely low female employment rates in the region. After a comprehensive literature review, the authors use employment data from about one hundred censuses and surveys from Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka to compare employment trends across countries over time. They work through data inconsistencies to standardize definitions of variables to compare demographic and labor market determinants: age, sector, contract type, location, and education. The paper finds that (i) overall since 2001, women’s employment participation across South Asian countries has been low and broadly unchanged; (ii) the gender employment gap emerges more clearly in middle age brackets; (iii) rural female employment is higher than urban; (iv) agriculture is the economic sector accounting for the greatest share of female employment, although this is slowly changing in some countries, and; (v) women with mid-level education tend to have lower employment rates than those with both lower and higher education.

This paper is a product of the Poverty and Equity 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 gacevedo@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/
Figure 2: Pre-Primary Enrollment Rate by State in Brazil, 2008

**Crèche Access by State, 2008**

<!-- Start of picture text --> 5‐10%<br>10‐15%<br>15‐20%<br>> 20%<br><!-- End of picture text -->

**Preschool Access by State, 2008**

<!-- Start of picture text --> 55‐65%<br>65‐75%<br>75‐85%<br>85‐95%<br><!-- End of picture text --> _Notes:_ Cr`eche (Preschool) access is the rate of enrollment in school (public or private) for children aged 0-3 (aged 4-6), based on parental self-reports. _Sources:_ Pesquisa Nacional por Amostra de Domicilios (PNAD) (2008), with calculations performed and maps drawn by author.

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# **1 Introduction**

A balloon is elastic and can expand when filled with air. Good latex balloons can contain even more air. One thing is certain however, inject more air than the capacity of that balloon and it will burst. The noise of the pop is loud and the consequences sudden.

A country’s debt is very much like a balloon. There exists a threshold beyond which a country cannot accumulate more debt lest it defaults. The problem for the economist is that each country is a different balloon, and it is up to country officials and multilateral institutions to understand a country’s capacity to accumulate debt in a sustainable manner. Many countries already have inflated balloons or sizable debts. Even though they do not necessarily need to default, it is important to understand their ability to repay debts in the face of unknown events.

Some of these debt sensitivities and vulnerabilities depend on economic fundamentals, political changes, and transparent debt accounting practices. Assuming that debt is transparently communicated and the political environment known, then policymakers can use, as best as possible, available fiscal and economic data to evaluate debt sustainability.

Using the analogy of the balloon, debt vulnerability analysis is further complicated by the environment, which is state and time dependent. First, one needs to understand the structure of debt (i.e. the balloon itself), which includes expected repayments, ownership, debt size, sensitivity to domestic economic environment, and maturity structure. Next, one needs to consider the operator of the balloon (e.g. institutions, policy makers, and politicians). Political statements and the credibility of institutions may change the fiscal policy landscape and hence influence debt sustainability. Finally, one has to consider the external environment in which the balloon floats (e.g. investor perceptions and exchange rates). Unforeseen exogenous shocks do occur and, depending on the debt structure, may have a material impact on debt sustainability. Careful debt vulnerability analysis considers all three aspects.

Reflecting on
that generates pseudo samples from the interval-censored income variable to estimate poverty and inequality indicators. While the interval regression approach we propose fits with the models described in Chen (2017), Jenkins et al (2011), and Walter and Weimer (2018), these papers focus on recovering the unconditional distribution of income, without considering the relationship with explanatory variables.

Zhou et al. (2017) and Chih-Yuan et al. (2021) propose methodologies for the estimation of conditional quantile regressions using interval censored data, under different distributional
in services was never significant, so we omitted it because it introduced collinearity problems. As expected, we find that growth in agricultural value added is associated with slower urbanization rates, while increases in industrial value added are positively correlated with urbanization. The explanatory power of structural change variables is small, however, as they only increase the R2 by about 1 percentage point. However, their explanatory power increases substantially when we exclude Africa from the sample (then they add about 4 percentage points to the R2.) Neither ZYAGS nor ZYINDS is significant in the African sample.

We also test whether differences in rural and urban wages affect the speed of the urbanization process. Since we want to establish the direction of causality, we used lagged values of RUW. The negative coefficient on LAGRUW confirms what most micro studies have found, namely that low rural (relative to urban) wages contribute to rural-urban migration. In addition, the AFRICA*PRE80 dummy is generally not significant once LAGRUW is included in the regression. In other words, the speed of Africa's urbanization prior to 1980 is no longer extraordinary if we take into account the very large disparities between urban and rural wages. In Africa in 1970, the average product of labor in agriculture was only 15% of that in industry and services. In South Asia and East Asia it was about 26%.

> 17 These are calculated as annual average growth in value added in agriculture and in industry, weighted by their shares of GDP. They are not deflated by growth in population.

23
a firm as woman-led if at least one of these conditions is met - i.e. the business is managed by a woman and/or has a female owner.<sup>4</sup> The BPS have been implemented in collaboration with private sector associations, statistical agencies, and other government agencies (mainly Ministries of Finance and Economy), with implementation guidelines offered by the World Bank. The questionnaire did not ask about the sex of the owner or top manager, but the guidelines strongly suggest interviewing preferably the owner or top manager of the business, and we use the information on the respondent’s sex and position within the firm (e.g. owner, top manager or other) to proxy for whether the business is led by a man or a woman.<sup>5</sup> Notice that in cases where the survey respondent is neither the owner nor the manager, the gender indicator will be missing. To avoid any bias resulting from missing values for our main variable of interest, we drop country-level data sets where the gender and position of the respondent are not generally available or where typically someone other than the firm’s owner or manager provided answers to the questionnaire. More precisely, we drop country-level data sets where the fraction of missing values for the gender indicator is 30% or more, which excludes eight countries from the data set harmonized by Apedo-Amah et al. (2020)–Colombia, Ghana, Indonesia, India, Liberia, Madagascar, Mali and the Philippines. We also drop countries where one or more regressors are not available (Afghanistan, Armenia, and the Comoros). In the remaining 49 countries, the share of observations without information on whether the firm is led by a woman or man is relatively small (around 9% on average; see Table B1). Moreover, when we drop observations that contain no information on whether the firm is led by a woman or man, the distribution of observations in our final sample across
Africa, Zambia, and Zimbabwe|
|SITC_5|277|Diamond|Yes|27|17|Angola, Botswana, Cameroon, Liberia,<br>Namibia, Sierra Leone, South Africa,<br>Tanzania,and Zimbabwe|
|SITC_6|278|Barite, bentonite, clay,<br>dolomite, fluorspar,<br>graphite, salt,<br>vermiculite, fluorine, and<br>graphite|Yes|42|35|Algeria, Botswana, Chad, Djibouti,<br>Ethiopia, Kenya, Madagascar, Morocco,<br>Mozambique, Namibia, South Africa,<br>Tanzania, Tunisia, Uganda, and<br>Zimbabwe|
|SITC_7|281|Iron ore|Yes|12|10|Sierra Leone, South Africa, Tunisia,<br>Zambia,and Zimbabwe|
|SITC_8|283|Copper|Yes|27|18|Algeria, Democratic Republic of Congo,<br>Mauritania, Morocco, Namibia, Nigeria,<br>Republic of Congo, South Africa,<br>Zambia, and Zimbabwe|
|SITC_9|287|Cobalt, chromium,<br>niobium (columbium),<br>tantalum, titanium,<br>zirconium, and tungsten|Yes|18|17|Botswana, Burundi, Ethiopia, Gambia,<br>Madagascar, Mozambique, Nigeria,<br>Rwanda, Sierra Leone, South Africa,<br>Uganda, and Zimbabwe|
|SITC_10|522|Arsenic trioxide, lithium,<br>manganese, pyrophyllite,<br>soda ash, sodium silicate,<br>and wollastonite|Yes|13|11|Chad, Cote d’Ivoire, Kenya, Morocco,<br>Namibia, South Africa, Zambia, and<br>Zimbabwe|
|SITC_11|523|Phosphate rock, and<br>phosphoric acid|Yes|8|6|Malawi, South Africa, Tanzania, Togo,<
# **_Improving migration measurement in household surveys_**

The first and most straightforward issues when using household surveys to study migration are the limited amount of adequate migration information that is generally collected as well as the lack of consistency in survey-based measures of migration. As already discussed, this is in part due to the absence of standardized, internationally recognized definitions of different migrant typologies, including the new category of “climate migrants”. ‘Rare event’ issues aside, this paucity of information translates into a limited ability of household surveys to capture (present and past) migrants in the original household sample even when they actually exist. To tackle these challenges, we recommend the following improvements:

- Starting with the few available official definitions and looking at best practices in the design and collection of migration data, efforts should be devoted to developing **state-of-the-art migration questions and modules** that can be embedded within multi-topic household surveys with sufficient consistency across space and time.

- As migration is not a unimodal concept, it is important that the information collected through the survey instruments allows for **differentiating categories of migrants** (permanent, temporary, seasonal, circular, return, etc.) **and types of migration** (rural, urban, internal, international, economic/voluntary, forced, etc.) with the highest possible precision and, at the same time, in an efficient way (i.e., without overburdening the questionnaire).<sup>28</sup> This implies testing and validating alternative approaches for collecting key migration information such as the length and frequency of the migration episodes, the geography of the migration moves, and the motives of migration.<sup>29</sup>

- Special attention must be devoted to identifying innovative methods for reducing well-known issues related to the **use of proxy respondents and recall bias** . The adoption of mixed-mode survey designs leveraging the use of new technologies, such as mobile phones and widely used free
|_DVAR*Health expenditure_||||-0.0019<br>(0.0031)|
|_Constant_|4.822***|4.990***|4.617***|5.012***|
||(0.173)|(0.161)|(0.166)|(0.185)|
|Observations|7,045|6,926|6,700|6,925|
|R-squared|0.875|0.874|0.876|0.875|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. FVAX and DVAR lagged. All level variables in natural logarithms.

# **_Environmental Standards_**

The final policy area we look at considers environmental standards. Here we find a slightly more differentiated picture. Regarding country-level policies, table 11 indicates a mediating effect for both GVC integration as a seller and a buyer. High air pollution and extensive water usage have negative and significant coefficients for backward links (columns 5 and 6); and heavy pesticide use has a negative and significant coefficient for forward links (column 4). While our firm-level indicator for the stock of ISO certifications related to environmental protection has the positive and significant sign, only for GVC integration as a seller. This is in line with what we found for the previous standards and fits the signaling channel as dominant transmission mechanism between standards and GVC-induced upgrading.

30
seem to not only affect the poor or near-poor. Some weather shocks induce income declines (or increases) on average, also in traditionally more stable sectors, like the tertiary, and to some extent the secondary sector. This may be driven by the fact that richer households have a larger asset base and may as such incur in larger financial losses, for instance business or farm owners involved in soy production (Wesz Junior, 2022). Secondly, the poorest may find themselves at welfare levels that are so low, that (weather) shocks create limited financial havoc on a living standard that is already at subsistence level. However, on top of that, there also seems to be a vulnerable middle class that remains exposed to the negative effects of precipitation shocks in urban areas.

# _Cumulative and overlapping effects of weather shocks_

We conclude that the effects of different temperature and precipitation shocks on income and poverty in Paraguay are not to be interpreted in isolation. Within a year, or across years, there may be multiple and overlapping weather shocks with which households must cope. More importantly, the interaction between such shocks and the rate at which they re-occur may have cumulative effects on poverty. A preliminary analysis using the data from this paper suggests that in urban areas, the combined effect of heat and flood shocks on poverty is significant and would increase poverty levels, though a deeper exploration of these joint effects remains for future research. Moreover, as the EPH data does not cover the departments Boquerón and Alto Paraguay, which are in the north of the country and may exhibit the most extreme weather conditions and anomalies (Figure 1), the effects of weather shocks on poverty could be even higher than our estimates suggest. In light of this and the increasing frequency and intensity of weather shocks in the future, we believe that our results are a lower-bound estimation of the impacts of weather shocks on poverty, and more severe effects are to be expected without long-term adaptation and climate risk mitigation strategies.

The long-term effects of weather
P a g e <u>|</u> **33**

productivity. This was the case in large cities of Asia like Hong Kong SAR, China, Bangkok and Seoul. All of these cities were able to accommodate a large influx of poor migrants for decades while the percentage of slums was decreasing. It is only when the supply of housing is quite flexible, as in the case of Chile, that targeted demand side subsidies can reduce the residual of very poor households who cannot afford a formal home (Bertaud: 2010).

- **D. Market Analysis and Its Consequences for Developing an Affordable Housing Policy in Ahmedabad and Mumbai**

Figure 6 in section B. sets out a framework for a strategy to make housing more affordable and improve living conditions on a large scale in urban India. In this section, we sketch out how this strategy could be applied using two city examples: Ahmedabad and Mumbai. Aside from illustrating how specific data on real estate policies and market outcomes can be used to formulate a strategy, these examples demonstrate how important it is to design policy based on each city’s unique real estate market conditions. National standards and templates offer blunt edged and potentially expensive or redundant tools for resolving the slum problem.

# **Understanding the level and distribution of household income**

The first task in defining a housing policy in a metropolitan area is to understand the parameters of the housing affordability problem in the city. Essentially, this involves measuring purchasing power in comparison to prevailing prices. This analysis must start by obtaining the distribution of income among slum dwellers and the population living in formal dwellings. The task is not easy because many slum dwellers work in the informal sector so their income is difficult to measure with accuracy and can be quite variable. However, in India there are many surveys on income and consumption of slum dwellers that have been conducted by universities and NGOs, so it should be possible for every major city to have a fair idea of