rafmacalaba/data-use-mentions-extended huggingface dataset:
sufficiently small to rule out mortality selection as an important source of bias. With less than 1 percent of the population forcibly displaced, the sample selection bias potentially caused by forced migration is not as high as in many other conflict situations, but should not be ignored. More importantly, Nepal has seen growing numbers of voluntary migration over the past decade, and these migration patterns may well be correlated with conflict intensity. Since conflict intensity is measured at the district level in this paper, migration within district does not pose any estimation issues. In their case study of Chitwan, Bohra-Mishra and Massey (forthcoming) find that conflict-related violence _does not_ increase the probability of migration outside the district, but the experience in Chitwan is unlikely to be representative of the whole country. I tackle the migration issue in several ways, depending on data availability. In the marriage analysis, migration bias is likely to be particularly severe since women move to the place of residence of their groom upon marrying, which could be in a different district. Even if these movements were unrelated to conflict intensity, classical measurement error in exposure to conflict would lead to attenuation bias. Therefore, I provide estimates for the whole DHS sample as well as for the sample of women who, at the start of the conflict in 1996, already lived where they are interviewed in 2006. I also provide a robustness check in which I use the NLSS, and so can construct the conflict exposure variable on the basis of conflict-related casualties in the district of residence year by year. In the education analysis, there may also be a migration bias when education outcomes are observed at an age at which some individuals may have migrated away from the district. One of the specifications addresses this concern by comparing the change in educational outcomes for individuals aged 14 or younger, between the 2001 DHS and the 2006 DHS. At such a young age, voluntary migration is extremely unlikely to have occurred.

# **Education**

# a. <u>Specification 1: Plain
# **_Welfare effects_**

So far we have seen that flood prone Upazilas, and local rainfall variability pushes households to diversify their occupational choices. We have also seen that policy action variables like access to credit, safety net, and market may reduce or eliminate the need to diversify that stems from either living in flood prone Upazilas, or living in Upazilas with high rainfall variability.

To test the hypotheses that household members accept less productive occupations to cope with local rainfall variability, or flood, we focus on per capita consumption. We expect households living in high rainfall variability Upazilas to have lower per capita consumption welfare.

A similar argument can be made about flood risks and low productivity leading to low consumption. However, others have shown that “normal” flood may increase productivity and wage rates (Islam et al 2004, Quasem 1992, Banerjee 2007). Thus, productivity loss from diversification may be partly or fully compensated by the productivity gains from “normal” flood.

The key results of equations (3) are presented in Table 4. Columns 1, 2, and 3 of Table 4 show the estimation coefficients for the household consumption equation (3) for the pooled sample and for historically non-flood prone, and flood prone Upazilas respectively. In the pooled sample (column 1) the coefficient of the flood prone indicator variable has the right sign but is not statistically significant. That is, households living in historically flood prone Upazilas do not appear to have lower (or higher) consumption as compared with those in non-flood prone Upazilas after controlling for other factors.

The insignificant coefficient of the flood prone indicator may be explained by two reasons. First, the beneficial effects of low level floods may compensate the risks of high flood risks to some extent. Second, the ability to diversify occupational choices within household in the historically flood prone Upazilas as evidenced in Table 2 may allow households to better protect their consumption welfare.

**Table 4: Effects of flood and local rainfall variability on consumption welfare**
While more than 70% of leases are for terms of less than 14 years, which provides the most flexibility given the current legal environment of a minimum lease term of 7 years, about 17% and 11% are concluded for terms between 14 and 20 or above 20-years, respectively. The average leased parcel is 2.1, 7.0, 12.1, and 27 km from the next primary, secondary, tertiary road or motorway, respectively and 9.9 and 12.9 km from the next railway station and grain elevator (appendix table 3). Remotely sensed data suggest that about 77% of the area of leased parcel is cultivated, 20% and 2% are covered with grass or trees, respectively, and there are small shares of built up and other land uses (appendix table 3).

Linking cadastral records to field boundaries from remotely sensed imagery as discussed above points to an average field size of 63 ha (68 ha for commercial and 49 ha for personal land) with about 6% of field area not mapped in the cadaster. Tenant information, which is available for 60% (534,127/884,936) of lease transactions with reported prices, points to a mean size of 345 and 261 hectares of registered land for tenants of commercial and personal land, respectively with these tenants having registered rights to between 20% and 24% of cultivated land in the villages where transactions were recorded.<sup>17</sup> Tenants who had rights to less than one third, between one third and half, and more than half of village land were involved in 62%, 14%, and 18% as well as 63%, 13%, and 13% of new lease contracts for commercial and personal land,

> 17 Note that 6.6% of transactions for commercial and 7.7% for personal land were concluded by tenants who did not previously have registered use rights to land in the village.

11
car owners and substantially increases the number of households without a car (Matas et al., 2009). The findings in New York by Salon (2009) look consistent: New Yorkers are sensitive to travel time and costs to decide how many vehicles they own.

6. From the gender point of view, labor economics discusses gender differences in access to jobs in the labor market. Gobillon et al. (2015) analyze micro employment data in France and find that females have significantly lower access to jobs, especially, high-paid jobs, such as executive job positions. There are two different arguments: First, as Gobillon et al.
In the last ten years, all countries in the Sahel region have launched some kind of pilot social safety-net projects, and some have already reached the first scale-up phase (Beegle et al., 2018). Cash transfer programs constitute one of the main components of the social safety-net systems implemented in the Sahelian countries. These cash transfer programs have been shown to generate various improvements in household consumption levels, food security, human capital, productive activities and resilience (Akresh et al., 2016; Stoeffler et al., 2019, Kandpal et al., 2021; Premand & Stoeffler, 2020).

However, there is a heated policy debate on the best targeting method to use in a Sahel environment, typically opposing PMT and CBT (Schnitzer, 2019). Besides, there is also a policy disagreement on the well-being metric that should be used for targeting households, namely food security or per-capita consumption. This calls for a clarification of the concepts and objectives employed in the targeting policy discussion and related analyses.

# _3.2 Targeting methods and existing evidence_

Proxy means testing (PMT) and community-based targeting (CBT) are the two most common methods employed for selecting beneficiaries of social safety-net programs in the Sahel and in other developing countries (Monchuk, 2013).<sup>16</sup> In the Sahel as well as in low-income settings where the coverage of safety net interventions is small relative to needs, PMT and CBT methods are usually applied only after geographical targeting is conducted (focusing on the poorest areas of the country in principle).

The PMT method relies on predicting household income, consumption or poverty status from a limited set of observable household characteristics (Grosh & Baker, 1995; Stoeffler et al., 2015; Brown et al., 2018). In contexts in which the means-testing of benefits is not an administratively feasible option, PMT provides the advantage of relying on information that can be measured relatively quickly and transparently. While the implementation of PMT does not vary as much (
component of the program. We use household-level information from the post-program survey round of October 1998 on the uptake of three screening tests that form part of the health requirements of the _Progresa_ program: hypertension (blood pressure test), diabetes (blood sugar test), and cervical cancer (via the PAP smear test).<sup>20</sup> 20. The household respondents were asked whether or not any household member had been screened for these conditions in the previous six months. In order to maintain full comparability with the estimates of program spillover reported in the paper, we estimate at the household-level the linear model reported in equation (1) in the paper on the same sample that we consider in the main empirical analysis. Results (available upon request) are very similar in both magnitude and precision if we instead consider the larger sample of all program-eligible households in October 1998.

23
and every Indonesian village, quelling any latent unrest. As Malley (2001) notes, the New Order used “coercive bureaucratic and military measures to prevent social tensions from developing into open, violent conflicts and to subdue them when they did.” High-profile conflict in East Timor, Central and West Kalimantan, Central Sulawesi, Papua, the Malukus, and Aceh have attracted international attention (Wilson 2001). However, there has been an increasing awareness that many regions of Indonesia are prone to destructive conflict (Barron and Madden 2003; Smith 2003; Welsh 2003). A recent government census of more than 69,000 villages and neighborhoods provides a unique opportunity to get a better understanding of the distribution of (violent) conflict across Indonesia, to investigate the empirical considerations related to tracking local conflict, and to test hypotheses relating to factors that may potentially be associated with local conflict. More than 7 percent of villages and neighborhoods in the census reported local conflict during 2002.

A growing international literature has sought to investigate conditions that affect the probability of countries experiencing large-scale violence and, in particular, the likelihood of civil war (cf Collier 2002; Humphreys 2003; World Bank 2003a).<sup>1</sup> A range of arguments have been put forward as to the potential factors associated with such conflict. Economic arguments have focused on poverty, inequality, property rights, and the process of economic development and structure change. Social arguments have focused on the role of ethnicity and religion.

> 1 The recent international literature has investigated those characteristics that make countries more prone to violence.  The Correlates of War study at the University of Michigan and Pennsylvania State University (Singer 1990), and the Conflict Data Project at the University of Uppsala (Wallensteen and Sollenburgh 1998) have both compiled large cross-national datasets on wars and armed conflicts.  Ted Gurr and Montey Marshall’s work seeks to look at which state forms make civil war more or less likely (Gurr _, et al._ 2001).
maps for the years 1993, 1998, 2003, 2008, and 2012.

calculate these changes, we first calculated changes in each road category in each municand summed them In so we in road ipality up. doing dropped negative changes length that in some cases might reflect either an upgrade from low to high quality roads or the deterioration of existing networks. As this is what is most relevant to do, we thus restrict our analysis to positive changes in road construction. Our data on the physical extent of roads suggest that 14,204 km of multi-lanes, 2,828 km of two-lanes, and 3,574 of divided roads were built over the 1993-2012 period. As for other types of pavement, we are able to track 9,347 km of new roads over the same period. These important investments in

> 9 To determine the municipality boundaries, we use the shapefiles from INEGI.

7
**Evidence from the Delhi Primary Survey** The results on female time use so far are based on the CPHS data which does not separate travel times into purpose of travel (i.e., commuting to work vs non-work related trips). The Delhi Survey, on the other hand, reports the purpose of travel and the average travel distance for each respondent. While this data does not report employment status, the survey answer ‘ _travel for work_ ’ is meant to capture travel _to work_ in the case of employed women and travel _to search_ for work in the case of unemployed women.

We use the Delhi data to compare the purpose of travel and the average travel distance between new and continuous users of bus transit in the post-implementation periods. We acknowledge that the new users are induced into using the buses because they are free and hence are selected relative to incumbent users. The comparison group of continuous users is matched on observable characteristics to the new users as discussed in Section E.2. While this does not address the selection issue completely, this comparison is still revealing and useful. With this disclaimer in mind, we estimate the following regression: where _Yi_ is a dummy variable for travel purposes which is equal to one if it is for work or a dummy variable for average travel distance via buses which is equal to one if the average distance exceeds 10 kilometers. _I_ new users is a dummy variable set to one for new users and zero for continuous users. _X_ is a vector of control variables including an individual’s marital status and education level. The results are reported in Table A8.

In column 1, we find that new bus users are 6.8% more likely to travel for work than continuous users. The estimated coefficient decreases slightly to 6.6% after conditioning on marital status and education. We then restrict the matched sample to users whose travel purpose is work, which includes travel to work or job search depending on unreported employment status. In columns 3 and
statistical significance. For instance, the 5% confidence interval for the estimate of α of 0.0891 for 1998‐2003 is [0.040, 0.139] which includes the point estimate of 0.0494 for the period 2004‐ 2015. On the basis of this comparison alone we could not conclude that there is a _statistically significant_ decline. However, the changes in all four of the yellow‐shaded periods are in the same direction, and the changes using breakpoints from 2007 onwards suggest – not prove – that there is a real effect in the earlier period but none in the later. Putting all of this together it appears that at some point between 2003 and 2006 there may have been change in the relationship between policy actions and the quality of government policy as measured by Clusters A & B of the CPIA. However, we will have to await more or better data sets before we can be sure.

To explore further the possibility of regime change, we deploy our second dependent variable, the Economic Freedom Index of the Heritage Foundation. As described above in the section about the data set, the content of the Economic Freedom Index largely overlaps with the content of the CPIA. We split the samples in the same way as we did for Table 15. The results are presented in Table 16.

**Table 16.  Estimates of α and γ in model (8) (with Economic Freedom Index as dependent variable), for different sub‐periods**

||**Coeffic**|**ients**|||**Coeffici**|**ents**|||
|---|---|---|---|---|---|---|---|---|
|**Sub‐**<br>**SMPL**|**α**|**γ**|**N*
providing the first systematic crosscountry study on the effect of climate and environmental related physical risks on banking sector stability. It provides generic estimates of the effect of such natural disasters on an entire set of financial sector soundness indicators based on the most comprehensive cross-country historical sample. We also discuss the relevance of our results for policy work in this area.

# **3 Data**

This study uses a large panel dataset at annual frequency which combines data on natural disasters, financial sector soundness and other variables from a variety of sources for the period 1980-2019 and for 184 economies. Our sample covers a large number of countries in all income groups and from all World Bank regions and for the longest time period for which major financial sector indicators of interest are available. Data on the instances of climate change, natural disasters and environmental events are from EM-DAT2 and data on financial sector soundness (with a focus on the banking sector) are obtained by combining information from the World Bank’s Finstat database and the IMF’s Financial Soundness Indicators. Since EM-DAT has a number of data quality issues, such as incomplete information for disasters happening before 2000 and bias towards disasters reported by countries themselves or by UN agencies, we have also looked at other sources of disaster data, such as DISINVENTAR, but find that it would significantly reduce the number of

> 2EM-DAT: The Emergency Events Database - Université catholique de Louvain (UCL) - CRED, D. Guha-Sapir - www.emdat.be, Brussels, Belgium.

5
|---|
||2017<br>/18|2015|Δ<br>2017/18<br>-2015|2017<br>/18|2015|Δ<br>2017/18<br>-2015|2017<br>/18|2015|Δ<br>2017/18<br>-2015|Δ 2017/18-<br>2015|
|National|31%|25%|6%|34%|25%|10%|25%|25%|1%|10%|
|Metropolitan|25%|11%|14%|25%|13%|12%|29%|28%|2%|14%|
|<br>Lower Urban|13%|10%|4%|19%|10%|9%|25%|24%|2%|7%|
|Poverty<br>rate<br>Lower Rural|26%|17%|8%|31%|17%|14%|23%|24%|-1%|15%|
|<br>Upper Urban|28%|24%|4%|32%|23%|9%|26%|25%|2%|8%|
|Upper Rural|52%|55%|-3%|53%|52%|0%|24%|24%|0%|0%|
|Borders|42%|15%|27%|60%|18%|42%|30%|22%|8%|37%|

**Source** : Authors’ calculations based on HIECS 2015 and 2017/18. **Note** : The official methodology is run based on total expenditures.

# **Section 5: Concluding remarks**

Several countries rely on non-standard poverty
**Figure 3 - Estimated dwell time distributions in the port of Durban and impact of dwell time reduction**

<!-- Start of picture text --> capacity<br>increase:<br>53%<br><!-- End of picture text --> <!-- Start of picture text --> capacity<br>increase:<br>118%<br><!-- End of picture text --> <!-- Start of picture text --> capacity capacity<br>increase: increase:<br>53% 118%<br>50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>Day 1 Day 2 Day 3 Day 4-8 Day 8-28 Over 28<br><!-- End of picture text --> Source: Local interviews (TPT, MSC and Maersk) and own estimates.

By using a simulation model of operational movements in container yards we show in addition that using information about dwell time for the optimal allocation of containers in the container yard would lead to additional capacity release of up to 40% in a congested container yard. Containers that are expected to be released quickly are indeed best positioned on the top of container piles to avoid double-handling. On the contrary the long stay containers should be stored in the lower levels of the piles or separately to avoid multiple repositioning before clearance.

# **5. Lessons for Ports in SSA**

As articulated in the State of Logistics (SoL) surveys conducted annually by CSIR and others, “the performance of the supply chain depends on the quality of the service provided by the private sector, in collaboration with the proficiency and diligence of public agencies”(
# **Introduction**

The fiscal and debt landscape has changed significantly for many Sub-Saharan African (SSA) countries since the onset of the global financial crisis in 2007-2008. Record low interest rates worldwide coupled with the lowest SSA debt levels in decades after successful HIPC and MDRI debt relief has led to increased access to new sources of finance, especially non-concessional. For some countries there has been a sharp rise in indebtedness within a short time period, which if unchecked can lead to debt overhang problems similar to the ones seen in past decades among LICs and MICs. Further, volatile and changing global economic and financial conditions warrant a close monitoring of country debt situations in SSA. This paper moves beyond the aggregate picture to look at more detailed debt profiles and dynamics of SSA countries, and aims to unravel more country-specific risks. The paper is structured as follows. Section 1 notes important data and methodology considerations. Section 2 presents an update on debt patterns in SSA countries, covering public debt and external debt separately. Section 3 reports post-global financial crisis debt dynamics, analyzing the underlying driving forces behind recent changes in debt burdens and comparing these factors with earlier periods. Section 4 discusses key vulnerabilities to debt sustainability in SSA countries, and Section 5 provides concluding remarks.

# **Section 1. Data and Methodology**

The paper draws on a number of data sources to conduct analyses. The analysis of debt stocks in Section 2 draws on World Development Indicators (WDI) data, which are available for a broad sample of 45 SSA countries during the period 1980-2013. Insights on debt dynamics in Section 3 and the latest picture on debt sustainability in Section 4 draw on a group of 33 SSA countries with recent joint World Bank / IMF debt sustainability analyses (DSAs) conducted in either 2013 or 2014 (see Annex Table 1 for this country list).

For the identification of debt sustainability risks, the methodology includes sensitivity analyses conducted through simulations to evaluate the impact of different macro-fiscal assumptions on a country’s projected debt burden
27 used successfully in East Africa to measure basic reading competencies among young school-age children (UWEZO). The test was applied to ten children in the second grade in each village, 2100 children in all. The average age of the tested children was 8.75 years.<sup>10</sup> Measured literacy rates vary widely among the 210 village public schools. Nineteen percent of all tested children, across all villages, could read simple sentences and paragraphs in French. In the median village, only 10 percent of tested children could read at this level. In 67 villages, or 32 percent of the sample, none of the tested children was able to read at this level while in 12 percent of the sample, 26 villages, more than half the children could.

The central hypothesis in this paper is that a key public service in villages in northern Benin – the quality of education in the village public school – is influenced by village access to community radio. To test the hypothesis, we estimate (1) where _j_ indexes the 210 villages and _k_ the 32 communes, is a commune fixed effect, and **X** is a vector of village controls. The key coefficient is which, because of exogenous variation within communes in the number of community radios to which villages have access, is identified through the inclusion of commune fixed effects. The dependent variable is the proportion of children tested in the village public school who could read sentences and paragraphs.

All of the variables in Table 2 are controls in **X** . In addition, **X** includes the availability of other public goods (literacy centers, health centers, the availability of a secondary school) and whether the village is one of the few with a functioning private school. The effects of access to community radios depend on the village‘s access to other types of radio, as well. A highly popular 10 We did not administer the literacy test in our household survey because we did