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23
small fraction of the households report opening such account<sup>11</sup> , the results are positive and significant for all the countries (except for Uganda in some of the estimations), indicating that international remittances increases the probability that the household opens a bank account after the migrant left.

# **IV. Conclusion**

Remittances inflows to SSA have been increasing over recent years, growing on average at about 4 percent between 2010 and 2013. The region is estimated to have received about $32 billion US$ in 2013. In addition to serving as a lifeline for many households, remittances inflows can impact the aggregate level of financial development and households’ use of formal financial services. Although there are studies examining the impact of remittances on aggregate financial sector development in SSA, to our knowledge, there are no studies on the impact of remittances on households’ financial inclusion.

Using survey data for approximately 10,000 households in five countries in SSA, we show that international remittances play an important role in enhancing households’ financial inclusion. The findings of the paper add to the nascent literature on remittances and financial inclusion at the household level.

> 11 Note that the variable “bank account after international migrant left” is constructed only for households that have international migrants. Therefore, the regression results reported in Appendix tables 5 and 6 are based on sub-set of sample used in the rest of the tables.

15
intensive manfuactures. In contrast, developing countries experienced declining shares in the aggregate for non-labor intensive products, while their shares fue capital intensive manufactures increased but remained low. Table 2 examines variations in developed countries imports of labor intensive products in more detail. The top third of the table shows the 1986 value of these imports from developing countries, in total and for major product groups. 10/ The middle third shows the share of developing countries, while the lower third reports imports from developing countries on a per capita basis. Similar information is shown for total developed countries' imports.

Table 7 further confirms the superiority of the United States as a market for developing countries' labor intensive products. With $70.2 billion in 1986 imports, the U.S. absorbed approximately 55 per cent of total developing country shipments of these items to developed countries. In 1986, United States per capita imports were more than three times those of the European Communities and about three and one-half times that of Japan. Aside from the overall level of imports, Table 2 shows that the share of U.S. imports of these products from developing countries is almost double that for developed countries as a group.

The statistics in Table 2 can be used to determine whether the overall differences between the United States imports of labor intensive

> 10/ Appendix Table 1 provides the SITC numbers and a description of the products comprising each of these aggregate groups. It also gives detailed statistics on trade changes and trends in labor intensity ratios for these items over the period 1965-1982.
Table 9: Appendix: Results Using Weights

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Treatment|0.766***|0.672**|0.663**|0.688**|0.738***|0.738***|0.771***|0.751***|0.749***|0.749***|0.665**|
||(0.277)|(0.278)|(0.274)|(0.275)|(0.275)|(0.274)|(0.275)|(0.271)|(0.270)|(0.263)|(0.261)|
|Urban_×_Dept|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|HH indigenous||✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Age|||✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Gender||||✓|✓|✓|✓|✓|✓|✓|✓|
|# Children in HH|||||✓|✓|✓|✓|✓|✓|✓|
|# Children under 5||||||✓|✓|✓|✓|✓|✓|
|# Adults|||||||✓|✓|✓|✓|✓|
|Parent present||||||||✓|✓|✓|✓|
|Father/Mother_>_65|||||||||✓|✓|✓|
|Parent educ||||||||||✓|✓|
|Log Househol Inc|||||||||||✓|
|N|3645|3645|3645|3645|3645|3645|3645|3645|3645|3645|3645|
|_R_<sup>2</sup>|0.242|0.254|0.283|0.286|0.291|0.293|0.296|0.301|0.302|0.356|0.363|

Standard errors in parentheses, * _p <_ 0 _._ 05, ** _p <_ 0 _._ 01, *** _p <_ 0 _._ 001
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44
# **2.1.2. Empirical measurements of HIV incidence**

## **2.1.2.1. Cohort studies and ALPHA Network**

While ideal, directly and accurately measuring HIV incidence on a population level is challenging. Doing so requires prospectively following a cohort of HIV-negative individuals and diagnosing new HIV infections. This type of study is logistically complex and expensive and thus often considered to be unrealistic for measuring national incidence in resource poor settings. Importantly, enrollment into a cohort can influence behavior changes and bias HIV incidence estimates. Additionally, individuals or populations participating in biomedical trials may have been chosen for their high HIV risk.

Despite the challenges of maintaining them, several long-term cohorts have been established in various parts of Sub-Saharan Africa, generating invaluable epidemiological data. To facilitate meta-analysis of multiple cohort data, collaborations between groups with established HIV cohorts and comparable data sets have been facilitated through the ALPHA Network (Analysing Longitudinal Population-based HIV/AIDS data on Africa). Set up in 2005 with funding from The Wellcome Trust and coordinated by the Department of Population Health at the London School of Hygiene & Tropical Medicine, the ALPHA Network “aims to maximize the usefulness of data generated in communitybased longitudinal HIV studies in Sub-Saharan Africa for national and international agencies involved in designing or monitoring interventions and epidemiological forecasting” (http://www.lshtm.ac.uk/eph/dph/research/alpha/index.html). The Network links 10 previously existing sites spread across Kenya (African Health Population Research Centre; Kisumu), Malawi (Karonga), South Africa (uMkhanyakude; Agincourt), Tanzania (Kisesa; Ifakara), Uganda (Masaka; Rakai), and Zimbabwe (Manicaland).

Prior to forming the Network, each site had been individually conducting its own studies on HIV spread and risk, prevention and treatment, and developing field and analytical methods. Now together, these sites have been able to replicate technical methods and analyses across cohorts and share the results of their studies in regular teaching and analytical workshops. A key finding from a joint HIV incidence analysis of ALPHA Network sites revealed a common age-sex distribution pattern of HIV incidence among young ages at all sites, but more inter-site variation at older ages (Todd et al. 2008). Further analysis revealed a secondary HIV incidence peak occurring among older participants in some of the sites, prompting investigation of HIV risk factors at all ages, not just among youth (Barnighausen et al. 2007; Barnighausen et al. 2008; Lopman et al. 2008). Additionally, more recent data from ALPHA Network sites have been used to inform the Spectrum model’s assumptions on the age-pattern of HIV incidence and survival (UNAIDS Reference Group on Estimates 2012). However, no comprehensive 14
# **1. Survey of the Literature**

In this section we discuss the main findings of the literature on both advanced economies and developing countries, though the latter is more limited. These studies have focused on the evolution of the gender age gap over the life-cycle, by asking such questions as: When does the gender wage gap emerge? Does one exist at labor market entry or does it emerge over time?

Manning and Swaffield (2008) are among the first to ask such questions using UK data. They find no gender gap at labor market entry but a gap of 25 log points emerges after 10 years. They attribute the gap to a number of factors, such as human capital differences, maternity leave, psychological factors and, to a lesser extent, the lower degree of job mobility, which prevents women from finding higher-paying jobs. Although lower, a gap also emerges ten years from the beginning of the career of women without any children.

Del Bono and Vuri (2011) also find that the gender wage gap appears not immediately but a few years after workers first join the labor market. Compared to Manning and Swaffield (2008), however, they find a greater impact of returns to job mobility on the gender wage gap. They use administrative social security data containing detailed information on job moves of men and women during the first ten years of labor market entry.<sup>1</sup> Bertrand et al. (2010) look at a particular sample, namely a sample of graduates from an MBA of a major American university. As such, the sample presents little heterogeneity and allows the comparison of men and women with very similar skill levels and motivation. The results of the analysis of this sample also suggest that a conditional gender wage gap emerges only after some time, after maternity and the ensuing childcare activities which apparently involve women more than men.

Such findings are echoed in a recent literature on developing countries. Pastore (2010a) finds that the gender wage gap, which is not statistically significant among young teenagers in Mongolia, becomes statistically relevant and sizeable among the adults and the prime-aged group. The paper uses a data set similar to the one used in this paper and is hence particularly interesting for comparative purposes, although cultural, economic and political differences between Mongolia and Azerbaijan are quite important, despite both having been in the Soviet (and more recently, Russian) sphere of influence.

Pastore, Sattar and Tiongson (2013) use the same data for Kosovo, a country with documented experiences of gender discrimination and gender-based violence. Nonetheless, the authors find that

> 1 Kunze (2003; and 2005) is the only author who finds a gender wage gap from labor market entry on, but this is probably due to the specific sample of manual workers with education in vocational training. These workers are essentially employed in male-dominated professions, where men have a competitive advantage which apparently pays off in terms of earnings.

3
A small private sector in uncontestable markets may be more vulnerable climate shocks. The opportunities for creative destruction are limited, and the lack of competitive forces may restrict innovation or adaptation. Thus, analysis of the private sector is valuable given the unique context. In general, the private sector is an important engine of growth, creating jobs, providing essential products and services, and spurring innovation. Using data for around 9,500 firms across seven countries in MENA, this study finds that negative precipitation shocks are detrimental for private sector firms in terms of lower sales, lower labor productivity, and a lower probability of investing in fixed assets. The seven economies in the sample include Egypt, Jordan, Lebanon, Morocco, Tunisia, the West Bank and Gaza, and the Republic of Yemen. Five countries in the sample have two waves of data (2013 and 2019/2020), except for Egypt and Yemen. Egypt that has three waves of data (2013, 2016 and 2019/2020). The two waves of data for Yemen are 2010 and 2013.

The study also attempts to unmask various channels identified in the literature through which precipitation shocks might affect firm performance in MENA. The literature largely focuses on global or country-specific studies that are outside the MENA region to delineate possible channels. A study in India found that hot days could lead to more absenteeism and lower the labor productivity of workers (Somanathan et al., 2021). Global studies have uncovered infrastructure as another potential channel. Using a global sample of manufacturing firms, Islam and Hyland (2019) find that negative precipitation shocks (droughts) increase the intensity of water outages. Droughts have also been found to increase the frequency of power outages in Latin American cities (Desbureaux and Rodella, 2019; Damania et al., 2017).

Access to finance is another channel by which precipitation shocks could affect the private sector. Using a global sample of firms, Kling et al. (2021) find that frequent climate shocks might impair the ability of banks to predict outcomes, and thus may increase the cost of capital due to additional risk. A study on Mexican firms found that weather shocks can also generate liquidity shortages, increasing the chances of loan default, and reducing access to future credit by lowering credit scores (Aguilar-Gomez et al., 2023).

3
A natural departure point to analyzing FHHs is to adopt the self‐reported identification of the head by the survey respondent (our first type, self-reported FHHs), which falls under the _de jure_ FHHs group. Marital status is a key characteristic in this respect. A large share of FHHs are formed as the result of a major marital shock such as divorce or widowhood. If, prior to the shock, the husband was the primary income-earner, the newly-formed FHH may be more likely to fall into poverty (Brown and van de Walle, 2021). FHHs formed through widowhood, especially at a young age with children present, can exhibit both more poverty and higher persistence of poverty (Appleton, 1996; Dreze and Srinivasan, 1997; Horrell and Krishnan, 2007; van de Walle, 2013; Munoz Boudet _et al._ , 2018; Brown and van de Walle, 2021) than FHHs formed largely “by choice”, through divorce or migration of the male spouse (Quisumbing, _et al._ , 2001; Klasen _et al._ , 2015; Beegle _et al._ , 2016; Bradshaw _et al._ , 2017).

Females who never marry or who seek divorce might have chosen this status because they have strong prospects for supporting their newly-formed households on their own, such as higher personal incomes or enabling family-support systems. Ignoring such considerations could mask differences between self-reported FHHs that are financially secure and those that are economically vulnerable (Kabeer, 1997; van de Walle, 2013; Milazzo and van de Walle, 2017). Consequently, it may also be useful to consider alternative types of households based on their marital status— never married, married, divorced or separated, and widowed.

In our samples, most self-reported MHHs are married, and this group is the largest in the sample. By contrast, from 69 to 77 percent of self-reported FHHs in all six countries are widowed, except for Mauritania and the West Bank and Gaza, where 35 and 53 percent, respectively, of selfreported FHHs are widowed households when considering all years together (Appendix B, Tables B.1-B.7). The second-largest group of self-reported FHHs have married heads, but this share 8
Policy Research Working Paper 9948

# **Abstract**

How do refugee camps impact the natural environment? This paper examines the case study of Cox’s Bazar, Bangladesh, a district that hosts nearly 1 million Rohingya refugees in refugee camps. Using spatially explicit data on land-use / land cover and proximity to a camp boundary, the paper quantifies land-use changes across the district over time. To evaluate the extent to which the camps triggered additional forest loss, the analysis calculates total forest loss in the district and uses a difference-in-difference model that compares areas 0–5 kilometers from a camp boundary (treatment) to areas 10–15 kilometers away (control). The findings show that the rate of forest loss intensified near camps relative to the control area. The analysis reveals that areas experiencing camp-stimulated reductions in forest cover are also experiencing faster settlement expansion relative to the control area. Settlement expansion is largely concentrated in areas outside protected areas. This enhanced settlement expansion still occurs when pixels 0–1 kilometer from the camps are omitted, which is evidence that the results are not due to camp settlements expanding beyond the official camp borders. The results suggest that camps stimulate in-migration as Bangladeshis seek new economic opportunities and improved access to resources.

This paper is a product of the Environment, Natural Resources & the Blue Economy 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 ndampha@worldbank.org, damph002@umn.edu. salem043@umn.edu, and polasky@umn.edu.

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