rafmacalaba/datause-encoder-data huggingface dataset:
and having a second phase to develop full proposals that included specialized technical assistance. For this evaluation, we focus on the initial proposal submission of phase one. The call for proposals was announced in August 2018 and the deadline to submit phase one applications was at the end of March 2019.

Even though the call for proposals already addressed several structural barriers in the design, additional behavioral barriers were identified in a qualitative study with rural women in Mexico (World Bank, 2018) conducted previous to the current evaluation. The qualitative study (World Bank, 2018) identified: (i) scarcity of time and choices for women, given household chores and the limited activities they can take part in as a result of them; (ii) gender norms and stereotypes that default women to traditional gender roles, limiting their motivation, self-efficacy, self-advocacy, and aspiration to participate in new activities; (iii) process complexity in learning about and applying to the call for proposals, which, if simplified, could encourage unexperienced populations, particularly rural women, to sign up for productivity programs; and (iv) the importance of trustworthy agents of change, such as communal leaders or role models, in building engagement among women and men.

The WB project did not have the resources to apply additional strategies to address these barriers in all localities, which worked as an opportunity to conduct a RCT aimed at testing whether these would increase women's participation in NRM programs.

# _2.2 Randomized controlled trial_

We utilized an RCT with three experimental groups to compare outreach strategies at the locality level that aimed to increase the number of application submissions coming from each locality, with an emphasis on increasing women’s participation in the applications. Even though there were many complementary outreach strategies used to address the barriers previously mentioned, we did not have enough resources to test all possible combinations. Hence, we had to combine multiple strategies across two treatment groups (plus an active “business-as-usual” control group), as explained below. Table 1 summarizes the strategies used in each experimental group and the hypotheses underlying each addition, even though we were not able to test each hypothesis independently. Each treatment group mostly _added_ strategies rather than replacing them (i.e. localities in T1 had access to all activities from T0, and localities in T2 had access to all activities from T1 and T0), allowing us to test the _additional_ effect of the included strategies.<sup>2</sup>

> 2 This RCT adheres to the principles in the Bellmont Report of respect for persons, beneficence, and justice. Inhabitants of all participating localities had full autonomy to decide whether or not to apply to the NRM program or participate in activities. The official outreach strategy covered all localities and no treatment left participants in a worse status.

4
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3 _9_
# <u>REFERENCES</u>

Backiny-Yetna, Prospère, Quentin Wodon, Rose Mungai, and Clarence Tsimpo. no date. Poverty in Liberia: Level, Profile and Determinants. World Bank.

Bourguignon, François (2003). “The growth elasticity of poverty reduction; explaining heterogeneity across countries and time periods,” pp. 3-26 in Eicher, Theo S. and Stephen J. Turnovsky (eds.), <u>Inequality and Growth: Theory and Policy Implications. Cambridge: The MIT</u> Press.

Dessus, Sébastien, Jariya Hoffman, and Hans Lofgren. 2012. Liberia: Strategic Policy Options for Medium Term Growth and Development. World Bank Policy Research Working Paper WPS 6081.

Easterly, William (2009). “How the Millennium Development Goals are unfair to Africa”. World <u>Development, Vol. 37, No. 1, pp. 26-35.</u> Estache, Antonio, and Rafael Muñoz. 2007. <u>Building Sector Concerns into Macroeconomic Financial Programming: Lessons from Senegal and Uganda. Africa Region Working Paper Series</u> No. 108. World Bank.

Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2010. Africa’s Infrastructure: A Time for <u>Transformation. Agence Française de Développement and World Bank.</u> IMF. 2007. Financial Programming and Policies. IMF Institute.

IMF. 2010. Liberia: 2010 Article IV Consultation -- Background Papers. August 31.

IMF. 2011. Liberia: 2011 Sixth Review Under the Three-Year Arrangement Under the Extended <u>Credit Facility, Request for Extension of the Arrangement, and Augmentation of Access—Staff Report; Staff Supplement; Press Release on the Executive Board Discussion; and Statement by the Executive Director for Liberia. Country Report No. 11/174. July.</u> IMF. 2012. Liberia: 2012 Article IV Consultation. Country Report No. 12/340. November.

Jack, William and Maureen Lewis. 2009. “Health Investments and Economic Growth: Macroeconomic Evidence and Microeconomic Foundations,” pp. 1-39 in eds. Spence, Michael and Maureen Lewis. Health and Growth. Commission on Growth and Development.

LISGIS (Liberia Institute for Statistics and Geo-Information Services). 2011. Liberia Supply and <u>Use Tables for the Year 2008: Handbook. March. Monrovia.</u> 14
# **References**

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- Belloni, A., Chen, D., Chernozhukov, V., and Hansen, C. (2012). Sparse models and methods for optimal instruments with an application to eminent domain. _Econometrica_ , 80(6):2369–2429.

- Belloni, A., Chernozhukov, V., and Hansen, C. (2014). Inference on treatment effects after selection among high-dimensional controls. _Review of Economic Studies_ , 81(2):608–650.

- Berg, C. N., Deichmann, U., Liu, Y., and Selod, H. (2017). Transport policies and development. _Journal of Development Studies_ , 53(4):465–480.

- Bertschek, I., Briglauer, W., HÃŒschelrath, K., Kauf, B., and Niebel, T. (2015). The economic impacts of broadband internet: A survey. _Review of Network Economics_ , 14(4):201– 227.

- Bom, P. R. and Ligthart, J. E. (2014). What have we learned from three decades of research on the productivity of public capital? _Journal of economic surveys_ , 28(5):889–916.

- Brodeur, A., Carrell, S., Figlio, D., and Lusher, L. (2023). Unpacking p-hacking and publication bias. _American economic review_ , 113(11):2974–3002.

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- Canning, D. and Bennathan, E. (2000). The social rate of return on infrastructure investments. Policy research working paper series, 2390, The World Bank.

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Table 3: Estimated Effect of Fuel Subsidies on Fishing Hours and Distance Traveled

||log(fshi|Dependent<br>ng hours)|variable<br>log(km|:<br> traveled)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Conventional|1.125|-0.084|1.934|-0.197|
||(0.233)|(0.061)|(0.209)|(0.047)|
|Bias-Corrected|1.565|-0.040|2.208|-0.170|
||(0.233)|(0.061)|(0.209)|(0.047)|
|Robust|1.565|-0.040|2.208|-0.170|
||(0.276)|(0.067)|(0.345)|(0.060)|
|Bandwidth|0.199|0.310|0.199|0.310|
|N|1,050|6,799|1,234|7,665|
|Binding vessels|X||X||
|Non-binding vessels||X||X|

Notes: In each regression, the unit of observation is a vessel-month-year, we cluster standard errors at the vessel-level, we use default options from the rdrobust package, and we control for gear fixed effects, gross tonnage, engine horsepower, length, and year built. In Columns 1 and 2, the dependent variable is log(fishing hours) and in Columns 3 and 4 the dependent variable is log(distance traveled in km). The data used to estimate the regressions in Columns 1 and 3 are Chinese distant water fishing vessels built before 2016 for which the engine power ceiling is binding. In Columns 2 and 4, the data are Chinese distant water fishing vessels built before 2016 for which the engine power ceiling is not binding. In Columns 1 and 2, the bandwidth selection procedure is mean-squared error optimal. In Columns 3 and 4, the bandwidth is from Columns 1 and 2.

For binding vessels, the estimated effects of the subsidy program on distance traveled are larger in magnitude and more precise than the effects of the policy on fishing hours. In Column 3, we find that the fuel subsidy policy increases distance traveled by a vessel in a month by 1.934 log points (standard error = 0.209). As expected, there is no effect of the policy on distance traveled for non-binding vessels (Column 4); the coefficient is much smaller in magnitude and its statistical significance does not survive randomization inference below. The large and precise effect of the policy on distance traveled by binding vessels further demonstrates that fuel subsidies have a sizable impact on vessel behavior. In addition to increasing fishing activities, fuel subsidies could be increasing the amount of time vessels 19
the length of time required to resolve them -- hence when we refer to "defaults" we will be referring to the first year of a given default episode.

The Tomz-Wright (2010) data on expropriation primarily refer to expropriations of US-owned direct investments abroad. We work with a dummy variable equal to one indicating countryyears in which such an expropriation event occurred. Their dataset is based on primary research into reports by the US Department of Commerce and Bureau of Economic Analysis which provide regular reporting to Congress on expropriations affecting US investors. This is the largest systematic dataset on expropriation events currently available.

A drawback of this data is that it does not cover episodes of default affecting only non-US foreign direct investors. As a partial remedy for this problem, we augment their dataset using data on expropriation and breach of contract claims paid by insurers who are members of the Berne Union, an association of public sector and private sector political risk insurers. This source provides data on the total amount of the two types of claims paid, by country in which the insured event occurred, and by year in which the claim itself was paid. A shortcoming of this data is that it is not possible to identify when precisely the expropriation event occurred, since we only have information on when claims are paid. Absent better information, we assume that expropriations occurred in the year before the Berne Union reports claims were paid, and include any expropriations identified in this way in our dataset if they were not already included in the Tomz-Wright dataset.

Since most of our explanatory variables, which we describe below, are only available starting in 1970 or later, we restrict attention to the period 1970-2004. During this period, the dataset includes 118 episodes of sovereign default, and 282 episodes of expropriations. Of these, 65 episodes are identified using the Berne Union data on claims paid.

# **4.  A First Look at the Data**

**Figure 1** provides a first look at the distribution of default and expropriation events across countries.  It reports the average number of defaults and expropriations per year, over the

7
Surprisingly, only two studies focus on estimating the effect of information and communication technologies (ICT) on the consumption of paper. Zhang and Buongiorno (1997) developed a model to estimate the effects of computers, televisions, and radios on the demand for printed materials in the USA. While this study did not find that ICT affected the demand for paper, it focused on a period of time when the Internet was in its infant stages of development. Hujala (2011) focused on how Internet adoption affected paper consumption using data at the country level and therefore her paper is more closely related to our study. She used a regression analysis model and a large global scale panel data from 1990–2007 to analyses the effect of ICT on paper demand for three paper grades: newsprint, office paper and magazine paper. Her results show that the Internet is a substitute for newsprint paper and possibly for magazine paper, and she also finds that both mobile telephones and a growing number of personal computers increase office paper consumption.

Our study uses a panel of data at the country level and complements the previous literature in several important dimensions. We created a data set combining several sources and therefore use different data than in previous studies. Our data on paper consumption come from the Food and Agriculture Organization of the United Nations and include 66 countries. Our data include information regarding a paper category that is unlikely to be affected by the rise in Internet use (Household and Sanitary paper), allowing us to conduct a falsification test. Our estimates are somewhat different than in Hujala (2011), which is the closest reference. While we find that the consumption of the paper categories that are more likely to be displaced by the Internet (newsprint) decreases substantially when Internet penetration increases, Hujala (2011) finds that the displacement for this paper category is negative but very small (nearly zero). These differences may arise from our use of different data or may also be due to our differing identification approach, which includes year effects to account for the possibility of correlated time trends in Internet adoption and paper consumption.

8
# **1. Introduction**

Globally, about 55 percent of the world’s population resides in urban settlements. It is expected that this proportion will increase significantly by 2030, adding an estimated 1.6 billion people to urban areas. About 90 percent of this increase is projected to take place in Asia and Sub-Saharan Africa, particularly in India, China, and Nigeria (UN 2020). Unprecedented population growth and urbanization put pressure on the housing market as increased demand for housing often outpaces the construction of new homes. The demand for housing, particularly in urban areas, will very likely further rise due to socio-demographic transition processes, changes in intra-household (HH) dynamics, or increased migration flows due to climate change. To understand the forward-looking housing demand that economies will face in the coming decade (the flow deficit), we need a thorough understanding of the inadequacy of existing housing (the stock deficit).

Therefore, in this paper, we introduce a new _Adequate Housing Index (AHI)_ , which is based on a uniform methodology to quantify housing deficits across emerging markets. The _AHI_ draws on countries’ latest HH expenditure and consumption surveys (HCES) to estimate the adequacy of housing conditions at the HH level. The uniform measure of the _AHI_ allows for comparability across countries, provides an assessment of the general state of housing within a given economy, and allows to raise awareness regarding the overall housing situation within a given country. We implement the _AHI_ for 64 emerging economies representing about 88 percent of emerging markets’ population, excluding China. We select these 64 emerging economies based on data availability, income group, population size, and region.

Access to an adequate place to live is fundamental for economic well-being, human dignity, physical and mental health, and overall quality of life. Low-quality housing is often observed in slums or informal settlements, in which most urban poor HHs live and work (Bah et al. 2018). Yet, finding an adequate house is also often a challenge for urban dwellers in formal settlements, for low- and higher-income HHs alike. Particularly, the current COVID-19 pandemic has highlighted the urgent need for adequate housing to cope with the health crisis for all segments of the population. Families who live in sub-standard or inadequate housing units often suffer from overcrowding, poor construction to protect them from heat, cold or rain, inadequate access to water, sanitation, and other basic services, and insecure property titles. These poor housing conditions often have detrimental consequences for families’ health and/or economic well-being. Housing is, therefore, included as a key principle in the United Nations’ (UN) 2030 Agenda for Sustainable Development and reflected in Sustainable Development Goal (SDG) 11, which targets access for all to adequate, safe, and affordable housing and basic services and upgrading slums (target 11.1).
- 14 -

The OPRC roads were not selected randomly but chosen based on existing agricultural potential. For each of the OPRC roads, a comparable road was selected in the same and adjoining districts, based on socioeconomic characteristics, proximity to social facilities, such as schools and health clinics, as well as access to the main trunk road ( **Figure 7** ). For each road (both treatment and control), at least 20 households were surveyed in each of the standard enumeration areas (SEAs) that the road passes. In total, the baseline survey covers about 650 households in Chipata ( **Table 2** ) and about 780 households in Katete ( **Table 3** ).

**<u>Table 2. Sample size for Chipata</u>**

|||Treatment|Obs.|Control|Obs.||
|---|---|---|---|---|---|---|
|Road p|air||_t=_0|_t=_1|_t=_0|_t=_1|
|A1||R128 up to RD596|120|119 RD403|120|120|
|A2||R128 first part|39|<br>39 D131|40|<br>40|
|A3||R348-RD596||U33|||
|A4||R348-RD596|40|<br>40 U33|20|<br>20|
|A5||RD116/B28/R267|40|<br>40 RD709, RD710|40|<br>40|
|A6||RD116/B28/R267|20|<br>20 D12-R273, RD121<br>R269, RD268, RD270-|20|<br>24|
|A7||R266-RD166|60|<br>60<br>RD118|60|<br>58|
|B1||RD596|20|20D130inChadiza|20|20|
|||Total|339|338|320|322|

# **<u>Table 3. Sample size for Katete</u>**

||Treatment|Obs.|Control|Obs.||
|---|---|---|---|---|---|
|Road pair||_t=_0|_t=_1|_t=_0|_t=_1|
|A1|U29|20|20 RD585|40|<br>41|
|A2|R291|40|39 D583-RD409|40|<br>0|
|A3|RD410|20|19 D60-R296|20|<br>20|
|A4|R306|40|40 RD703-R308|40|<br>39|
|A5|U20-RD412|40|60 RD704, RD705, RD135|<br>40|<br>40|
|B1|RD411-R303, RD687|140|119 RD582 in Petauke|12|<br>11|
|B2|U20-RD412|80|100 RD582 in Petauke|60|<br>62|
|B4|RD412|40|39 RD413 in Petauke|41|<br>42|
|B6|U29|41|43R286inChadiza|39|35|
||Total|461|479|332|290|
WPS4050

# **The Demographic Benefit of International Migration: Hypothesis and Application to Middle Eastern and North African Contexts**

**Philippe Fargues**<sup>*****</sup>

## **European University Institute**

## World Bank Policy Research Working Paper 4050, November 2006

_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._

* Philippe Fargues is Director of the Euro-Mediterranean Consortium for Applied Research on International Migration (CARIM) at the European University Institute in Florence. An early version entitled “How international migration may have served global demographic security” was presented at the World Bank ABCDE Conference in Amsterdam, May 23-24, 2005. The paper has benefited from constructive comments by Maurice Schiff and Refik Erzan.
_Note:_ Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors.

— = Not available.

*traced to needs.

# What else can be done?

Broadly speaking there are three alternative ways to close the infrastructure funding gap. The first is to raise additional finance. The second is to find most cost-effective ways of meeting the infrastructure targets. The third is to lengthen the time period needed to reach the targets, beyond the ten years assumed in this analysis.

Uganda’s prospects for raising the volumes of additional infrastructure funding needed look quite hopeful for a number of reasons. Once efficiency gains are fully captured there would likely be surplus spending in the power sector that could potentially be reallocated toward sectors with funding gaps. Looking ahead, Uganda’s new-found oil wealth will in due course boost fiscal revenues part of which could be allocated to close the infrastructure funding gap. This gives Uganda an important new source of funding, which – however – should not become a pretext for overlooking efficiency issues. Furthermore, Uganda has been one of the most successful African countries at attracting private finance to the infrastructure sectors, and not only into telecommunications. It is unclear whether this track record suggests that even more could be done, or whether it rather signals that all of the most relevant opportunities have already been tapped (figure 33).

**Figure 33. Numerous African countries capture more private investment than Uganda**

<!-- Start of picture text --> 3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power ICT Transport Water<br>% GDP<br><!-- End of picture text --> _Source:_ Private Participation in Infrastructure Database, 2010 .

_Note:_ GDP = gross domestic product; ICT = information and communications technology.

_Continuing the existing strategy of relying on lower-cost technologies could somewhat reduce the funding gap for water and sanitation, but only to a modest extent._ Meeting the MDGs for water supply and sanitation with lower-cost technologies than those that have been used to date (entailing greater reliance on stand posts, boreholes, and improved latrines) could reduce the associated price tag from $348 million to $329 million each year. But the savings, at just under $20 million are relatively small, reflecting the fact that Uganda has already gone a long way toward adopting lower-cost technologies, and the impact on the sectoral and global infrastructure funding gap would be modest.

50