rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

A humanitarian crisis is a singular event, or series of events, that threatens the health, safety, or well-being of a community or large group of people.<sup>1</sup> According to the 2021 Global Humanitarian Overview Report, an estimated 235 million people would need humanitarian assistance and protection in 2021, an increase of more than 65 million since 2020 (UNOCHA 2020). In addition, over 91.9 million individuals were estimated to have been forcibly displaced worldwide in 2020 due to prosecution, conflict, generalized violence, or other human rights — violations , representing an increase for the eighth consecutive year (UNHCR 2020). The need for humanitarian assistance and social protection has been exacerbated by the COVID-19 pandemic, which presents an unprecedented challenge to the humanitarian system in both scale and complexity. It is, therefore, more important than ever to have reliable and rigorous evidence on both the impact of humanitarian response programs, that is, “what works,” in addressing the needs of crisis-affected populations, as well as how cost-effective the implementation of such programs is, so that donors, aid agencies, and policy makers can make informed decisions and target those in greatest need.

Over the last decade, social protection programs in humanitarian contexts have increasingly emerged as a policy response tool to address poverty and hunger (Ulrichs and Sabates-Wheeler 2018).<sup>2</sup> Moreover, as part of the commitments under Sustainable Development Goal 1, the global community has agreed to expand the coverage of social protection measures for all and to achieve substantial coverage of the poor and the vulnerable by 2030 (United Nations 2015).<sup>3</sup> This expansion also includes the scale-up of social protection programs in humanitarian contexts (fragile and conflict-affected populations), which we define in this paper as humanitarian assistance interventions.

> 1 For more information on humanitarian crises, see Humanitarian Coalition (last accessed December 2021). 2 In more stable developing contexts, a vast literature in social sciences shows that social protection programs help reduce poverty and inequality, enhance livelihoods, and have long-term positive impacts on human capital development (Baird et al. 2014; Bastagli et al. 2016; Davis et al. 2016; Handa et al. 2018; Hidrobo et al. 2018). 3 According to the 2020 Global Humanitarian Assistance Report, there is a clear upward trend in the use of social protection programs in the form of cash and vouchers. For example, the use of cash-based transfers in humanitarian crises has doubled since 2015, amounting to a total volume of US$5.6 billion or 17.9 percent of the global humanitarian assistance in 2019. However, cash and voucher transfers still account for a minor share of humanitarian assistance, with the majority of the humanitarian portfolio being provided in-kind (CaLP 2020; Overseas Development Institute 2015).

2
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Klaassen, G.,and Nentjes, A., (1997). “Sulfur Trading Under the 1990 CAAA in the US: an Assessment of the First Experiences”.Journal of Institutional and Theoretical Economics 153 (2), 384-410. [In Skytte, K., Meibom P., Uyterlinde M.A, Lescot, D., Hoffmann, T., del Rio P., (2003). “Challenges for Investment in Renewable Electricity in the European Union”. Background report in the ADMIRE REBUS project supported by the European Commission. November 2003.] Klein, B., Crawford R., and Alchian A. (1978). ‘‘Vertical Integration, Appropriable Rents, and the Competitive Contracting Process”. Journal of Law and Economics. Volume 21. Pages 297–326.

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Kuperan, K., A., N.M.R., Pomeroy, R.S., Genio, E.L. and Salamanca, A.M. (1998). “Measuring Transaction Costs of Fisheries Co-management”. Presented at 7th Common Property Conference of the International Association for the Study of Common Property, Vancouver, Canada.

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# **Food Prices, Road Infrastructure, and Market Integration in Central and Eastern Africa**

# **Paul Brenton**<sup>**†**</sup> **Alberto Portugal-Perez**<sup>**§**</sup> **Julie Régolo**<sup>♣</sup>

Keywords: border effects, Africa, road infrastructure, market integration JEL classification:F1, O1 †World Bank, email: pbrenton@worldbank.org; § World Bank, email: aportugalperez@worldbank.org; ♣ University of Geneva, email: Julie.Regolo@unige.ch; The authors are grateful to Jaime de Melo, Olivier Cadot, Céline Carrère, Marcelo Olarreaga, Frederic RobertNicoud, and participants at the CSAE Conference held at the University of Oxford for helpful comments.
The rest of the paper is structured as follows. Section 2 describes the recent evolution of the wholesale funds market, the main events of the financial crisis, and the evolution of the cost of liquidity during these episodes. Section 3 describes the empirical methodology and data sources. Sections 4 and 5 present the main results and robustness analysis, respectively. Section 6 explores country characteristics and vulnerability to a liquidity crunch. Section 7 looks at the real consequences of the use of wholesale funds on banks’ lending activity. Finally, section 8 concludes with some final remarks.

# **2 Wholesale Funding Market, the Financial Crisis, and the Cost of Liquidity**

## **2.1 The wholesale funds market**

Banks have traditionally financed their operations with demand deposits, suggesting that the incentives that the threat of a run imposes on bank managers make them a superior form of financing despite their fragility (Diamond and Rajan, 2001). However, banks also rely on other types of markets for their funding needs: commercial paper, repo market, and federal funds (interbank) loans. These markets, which typically offer short-term financing, compose the _wholesale funding markets_ .

The reliance of various types of intermediaries, particularly commercial banks, on these sources of funding has increased importantly in recent years in the US and around the world (Huang and Ratnovski, 2008; Feldman and Schmidt, 2001). For instance, the gross amount of deposits from foreign banks has increased from 6,240 to 13,856 billion US dollars between 2001 and 2006 among the 41 countries surveyed by the Bank of International Settlements (BIS, 2008). This increased reliance on wholesale funds can also be seen in bank-level data from Bankscope that show an increase in the use of wholesale sources of funds in the 5 years preceding the beginning of the 2007-2008 financial crisis. Across all banks located in the 44 countries covered by Bankscope that have an FTSE financial sector index (which will constitute this paper’s sample, as explained below), the average fraction of liabilities that do not correspond to deposits increased in around 60 percent between the end of 2001 and 2006. The increases are larger among commercial banks, reaching about 80 percent. This is shown in Figure 1, which plots the average fraction of liabilities not represented by deposits across all banks (Panel A) and all commercial banks (Panel B). The figure shows the average ratio as a fraction of the 2001 value, so that the ratio equals one in 2001 by construction, and the values in other years directly show the percentage increase with respect to the initial year. Beyond these global tendencies, the 7
21 Table 1s Expected Signs of the Parameters

||y|va|r|(r*+Z)|<br>QDp/P<br>_OLP/P_|
|---|---|---|---|---|---|
|Ah|21 >0<br>e80|eg<br>£>0<br>**_68>°_**<br>**_a2*Ao_**|R<0<br>**eaS,C**|e4<°0<br>**a,94'10-**|0%>0<br>g20"<br> (KW%<br>,,2°<br>>0|

Table 2 s Estimates of the Model for Argentina (1961 -82) (t-statistics _are_ In parenthesis) <!-- Start of picture text --> (4a) Om -0.652 + 0.06 9 y + 1.0121 + 3.67r + 0.6730D,/P +<br>(-0.36) (1.79) (1.96) (1.94) (3.47)<br>0.953bI/P - 0.012wp-1 - 1.238VE 12 0 .954<br>(5.33) (-1.00) (-4.61) DV - 1.95<br>(4b),dh 5.063 + 0.063y + 1.447F - 1.23r - 0.118DP/IP +<br>(4.07) (1.75) (4.67) (2.02) (-0.94)<br>0.678AL0 /P - 0.023wp-1 0.418VE 12 0.935<br>(5.98) (-3.11) (-2.63) DV o 1.65<br>(13) ip C -4.959 - 0.OlSy-l + 9.229INDW + 0.258ALo/P P<br>(-1.92) (-0.54) (3.58) (4.85)<br>0.201oD0 /P + 0.09 1kp, + 0.448io R2 - 0.920<br>(-2.53) (0.79) (3.89) DV - 2.08<br>(6) (ALD/P) - m- (OR/P) - (OLO/P) + Onw<br>(15) OL0IP - def0 - Ob - O /P<br>(16) Ob O Oh - i,<br><!-- End of picture text -->
## CHAPTER 2: MODIFYING EXTENSION TO REACH **VOMEN** FARMERS: INFORMATION NEEDS

2.01 Accurate information is the fundamental factor underpinning the improvement of extension services to women farmers. Accurate assessments of the financial and social benefits of the goods and services produced by women and of the loss of potential benefits if women farmers continue to be neglected, are needed to convince policymakers and planners of the importance of initiatives to help women farmers. Knowledge and understanding of women farmers -- what they do, how they do it, why they do it, and why they don't do something else -- is essential if the research and extension services are to produce and deliver technical advice that is needed by these farmers and is appropriate to their circumstances. This chapter examines the range of information needed to develop, monitor, and evaluate an extension program for women farmers, and describes ways of collecting such information.

# 1. Understanding Women Farmers

2.02 Characteristics of African Households. Households in Sub-Saharan Africa should not be viewed as a single profit-maximizing unit with one set of objectives, but rather as a group of interdependent people with separate responsibilities and income streams and with resources allocated according to different preferences and needs.

2.03 This has important implications for identifying the extension messages that farmers want. Such messages can be identified, developed, and indeed delivered only if there is an understanding of who is doing what. with what. on the farm. Extension staff need to compile information on the farming population in their area and use this information to diagnose who is carrying out different farm activities aud under what constraints.

2.04 Household Obligations by Gender. In many African societies, women use income from their own crops to meet a variety of household and personal expenses. For their personal fields and household crops under their charge, women must often pay for production inputs, hired labor or food for reciprocal labor groups. Women must also provide a variety of goods and services for the household -- particularly food, clothes, and medicine for themselves and their children. Men tend to be responsible for housing, taxes, production inputs and labor hired for their own crops -- and at least part of school fees and ceremonial and religious obligations.

2.05 Women's agricultural activities have tended to be undervalued in agricultural censuses and project baseline studies, resulting in incomplete, inadequate, or inaccurate information about women farmers. Such data, when used to design extension activities, has deprived women farmers of needed information and inputs. Jiggins (1986) notes that "what is measured is determined by researchers' fields of observation and perception of what it is important to record. For example, the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT) decided not to record backyard production in its village studies, even though one of its mandate crops, pigeonpea, is an important backyard crop."
# Health Insurance in Sub-Saharan Africa: A Survey and Analysis

by Ronald J. Vogel

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|What Constitutes Health Insurance?|2|
|III.|The Prevalence of Health Insurance|5|
|IV.|The Contributi-.of Insurance to Health Finance|7|
|V.|The Characteristics of the Health Insurance|10|
|VI.|Analysis of the Major Issues in Health Insurance and<br>Lessons to be Learned|14|
|VII.|Summary and Conclusions|21|
|Refer|ences|25|
|Table|s|27|

This study was conducted as part of the Africa Regional Studies Program on Health Financing in Sub-Saharan Africa, managed by the Africa Technical Department, Population, Health, and Nutrition Division, with financial support from NORAD and SIDA.
A key module of the survey contains information about the names of the firms’ trading partners and their approximate geographical locations. The respondents were asked to name up to three clients and three suppliers.<sup>13</sup> Using the information from this module, we construct simple measures of network proximity between the firms in the two samples. Summary statistics for these measures are reported in table 2.

We begin by constructing a dyadic dataset of unique firm pairs. For instance, because there are 304 firms in the Ethiopian sample, there exist 304 × 303 / 2 = 46, 056 unique enterprise pairs ( _i, j_ ) in that sample. For each pair ( _i, j_ ), we construct dummy variables that capture the different concepts of network proximity. When two firms are close in that network, we consider them to be linked. The most direct network proximity measure that we use is whether _i_ and _j_ buy or sell from each other. We are only able to identify a small number of such links in our data: 60 in Ethiopia and 5 in Sudan. The fact that there are so few upstream and downstream links among the sample firms is partly driven by the focus of the surveys on light manufacturing because clients are seldom manufacturers. We also construct dummy variables that indicate whether _i_ and _j_ have a common supplier or a common client. These types of links are more common: there are 481 supplier-based links and 273 client-based links in the Ethiopian data and 171 supplier-based links and 678 client-based links in the Sudanese data. These network proximity variables constitute the core of our vector **g** _ij_ . The last proximity dummy is the distance _d ij_ , which is defined as the log of the distance between _i_ and _j_ plus one.

# **<<A>>IV. Empirical Analysis**

Our objective is to test whether the outcomes and practices that are related to technology, human capital, contracting, and reputation are more similar among firms that are close to each other, either in a network sense or geographically. To that end, we estimate the parameters of 18
Table 7: Differences between Bank/Fund Fsthmates and IDS Data GNP

# (Billions of Rubles)

# Bank/Fund

<!-- Start of picture text --> IDS<br><!-- End of picture text -->

|||Estimates<br>1987||
|---|---|---|---|
|FSU|817.7||797.2|
|Armenia|8.3||8.1|
|Azerbaijan|14.7||14.1|
|<br>Belarus|32.5||31.2|
|Estonia|5.4||5.5|
|Georgia|14.2||13.1|
|<br>Kazakhstan|_35.0_||36.7|
|Kyrgyzstan|6.3||_6.5_|
|<br>Latvia|_9.5_||9.0|
|Lithuania|11.0||11.0|
|Moldova|9.4||9.4|
|Russia|495.7||480.6|
|Tajikistan|6.0||6.1|
|<br>Turkmenistan|6.1||6.3|
|Ukraine|136.3||131.1|
|Uzbeldstan|27.3||28.6|

32
On the other hand, P4P did not increase the initiation of prenatal care, the number of women who completed four visits. The reason that P4P did not increase prenatal care and contraceptive usage is that these services had a high marginal cost and low payoff for providers. The payment for prenatal care is $0.09 for women who initiate care; an additional $0.37 for woman completes 4 visits. At baseline 95 percent of women had at least one prenatal visit. It would be very costly and difficult for providers to go out into the community and find the remaining 5% of women and convince them to get care. Reaching out to women in early stages of pregnancy is a costly activity for providers, not only because of the travel and time that are involved, but also because the targeting of any information campaigns would necessarily be poor. In addition, getting women in for early prenatal care involves changing women’s behavior, which is difficult in any case. It is unlikely that providers put much effort into reaching out to these women given the $0.09 return.

Similarly, getting women to complete 4 visits is also difficult. For women to complete four prenatal care visits, it would probably be necessary that they start prenatal care before the fourth month of pregnancy, a time at which pregnancy is usually hard to observe for an outsider. Culturally, Rwandan social norms dictate that women do not acknowledge pregnancies before they have to. Our local collaborators reported that women are concerned that the evil eye could be used to cause the women to spontaneously abort a fetus and this is the reason that women hid their pregnancies as long as possible. Hence, finding and getting women to initiate care before they are showing is almost impossible. Once they initiate care late, then it is hard to get them to return multiple times in a short period of time. From the providers perspective all this effort only yields them an additional $0.37. Moreover, providers can still get the payments for providing tetanus vaccine and malaria prophylaxis as long as women get any care.

Finally, we find no evidence that P4P led to increases in contraceptive prevalence rate, despite the relatively high payment rate for new acceptors. This may be due to the fact that we measured actual use at the time of the household survey, which is influenced more by resupply rates than by initial use rates, while P4P payments were made for initial acceptors of contraceptives only.

22
Heterosexual transmission is responsible for the vast majority of new HIV infections, particularly in Sub-Saharan Africa. Thus, the probability of not being infected by HIV conditional on _ki_ risky sexual encounters is (1 − _π_ )<sup>_ki_</sup> , where _π_ is the HIV positive-tosusceptible-partner per-risky act transmission probability. The unconditional probability of not being infected by HIV is therefore Letting _λ_ = (1 − _π_ ) _μ_ , we can rewrite equation (4) as (5) where the second equality follows since ∑<sup>∞</sup> _k_ =0<sup>_e_−</sup><sup>_λλk_/</sup><sup>_k_!=1.UsingtheHIVincidence</sup> data and an estimate of _π_ , we can use maximum likelihood to estimate the unknown parameter vector _μ_ in equation (5).<sup>13</sup> Individuals may not observe, or at least not perfectly, whether their sexual partner is HIV positive or not. One of the strengths with the estimation procedure laid out above is that we can estimate the unobserved average number of risky sexual acts. With an additional assumption on the link between the number of unprotected sexual acts and the risk of becoming infected with HIV, we can also use the model to estimate the reduction in the number of unprotected sexual acts. Specifically, under the assumptions that individuals randomly choose their partners for unprotected sex from the village in which they reside and that HIV status is unobservable, we can use baseline HIV prevalence by village as a proxy measure for the unconditional probability that the partner is HIV positive. That is, the HIV positive-to-susceptible partner per-unprotected sex act transmission probability is _θπ_ , where _θ_ is the unconditional probability that a randomly chosen partner is HIV positive and _π_ is as before the HIV positive-to-susceptible partner per-risky act transmission probability.

The estimate of the number of sexual acts should be viewed with cau- unprotected tion. First, the baseline HIV prevalence rate provides a lower bound of _θ_ , even if the individuals choose their partners randomly in the village, since the HIV prevalence rate is increasing over time in our study population. Not taking this into account will lead to

> 13The log-likelihood function is

13
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**Table 14**

|**Turkey:Ratioof**|**Bank Assets toGNP**|
|---|---|
|Year|ratio|
|1980|28.5%|
|1981|34.4%|
|1982|38.2%|
|1983|41.5%|
|1984|41.3%|
|1985|40.9%|
|1986|48.6%|
|1987|54.0%|
|1988|51.0%|
|1989|46.3%|
|1990|43.2%|
|1991|46.4%|
|1992|49.9%|
|1993|52.2%|
|1994|51.3%|
|1995|51.8%|
|1996|59.8%|
|1997|65.3%|

Source: The Banks Association of Turkey.

As our analysis in the preceding sections suggest, Turkey's banks operate in one of the world's most volatile macroeconomic environrnents. During the 1990s annual inflation in Turkey has ranged from 70-125%, while double digit foreign exchange and interest rates movements have not been unusual. Despite such volatility, Turkey's private sector banks seem to have remained profitable by tailoring their strategy to this environment, although it is hard to measure profitability in Turkey due to lack of inflation accounting and ongoing problems with accounting standards.

High inflation and volatile asset prices have distorted banking activity and performance. One consequence is the prevalence of short-term lending - over 85% of bank loans have a maturity under one year. At the same time, high real interest rates on liradenominated instruments crowded out lending to the real sector, and at the end of 1997, loans (excluding government-directed preferential credits) accounted for only 35% of total assets of the banking sector. With lending activity restricted, banks placed 10% of their assets in government securities and 14% on the inter-bank market at the end of 1997.

However, banking sector exposure to the government securities market is much higher than 10% of their assets. Banks have large investment positions in government securities funded by repurchase agreements (repos) operations, both held off balance sheet. There has been phenomenal growth in the repo volume in recent years, largely a reflection of policy distortions such as differential tax treatments and reserve requirements. By the end of 1997, commercial banks had a net repo position with private sector customers of US$ 10