rafmacalaba/data-use-mentions-extended huggingface dataset:
|**Table 2: Characteristics of the 100 Discovery Schools**|
|---|

|_School var_|_iable_|_Number_|
|---|---|---|
|**Gender**|Male<br>Female<br>|42<br>44|
||Co-ed school (up to 4<sup>th</sup>grade only)|14|
|**Level**|Primary: grades 1–9|24|
||Secondary: grades 10–12|76|
|**Size**|1–499|21|
|(number|500–999|43|
|of|1,000–1,499|34|
|students)|1,500–2,000|2|

Source: Authors’ calculations.

The program has succeeded in introducing ICT equipment and software into 100 schools, providing, on average, 6 to 8 laptop computers to each school and creating at least one (in some cases, up to six) computer lab per school. The initiative has also trained 3,000 teachers and promoted the development of six packages of educational software and corresponding teacher training modules (Table 3). In fact, initial investments made during the first phase of operations resulted in products ready for export. JEI also introduced the concept of ―in-classroom‖ technology and advocated the teaching of IT to students based on practical, hands-on use of technology as a tool for learning.

**Table** **<u>3: Core Data on Discovery School Beneficiaries</u>**

|_Beneficiary _|_Number_|_Comments_|
|---|---|---|
|Schools|100|Discovery Schools (DS)|
|Administrators|200|Principals and assistant principals|
|Teachers|3,000|Users of e-content in DS|
|Students|80,000|Users of e-content in
- 19 -

# **V. CONCLUSION**

It has been increasingly recognized that the climate is changing, which is creating various challenges to public infrastructure. Depending on the changing climate, technical standards and project specifications may have to be adjusted. However, it is less recognized that the procurement process of public infrastructure also needs to be adjusted. This paper examined a particular case of rural road procurement, which remains a significant challenge in many developing countries. Severe weather conditions, such as heavy rains and storms, are likely to interrupt civil works and wash away unpaved or gravel roads.

In general, ex post contract adjustments, such as cost overruns and project delays, have long been problems in public infrastructure procurement. Auction efficiency would be undermined, and the budgetary credibility would be ruined.

Using the procurement data from rural road projects in Nepal, the paper estimates the determinants of cost overruns and project delays, focusing on anticipated and unanticipated precipitation. It finds that precipitation causes delays, but not cost overruns. It also shows that even simple precipitation forecasts can have significant explanatory power for project delays, and cost overruns to a much lesser extent. If future precipitation were anticipated based on the backward-looking expectation, many massive delays could be avoided. If the autoregressive model were used, most of the observed delays could be avoided. The paper demonstrates a way of adapting public infrastructure procurement to the changing climate. Better climate forecasts can contribute to avoid unnecessary contract amendments and thus improve the predictability and efficiency in the budget formulation and execution.
level welfare indicators, something that household surveys typically do not. Here, I use the 2006 DHS for Mali.

The 2006 survey identifies current marital status for all women aged 15 to 49, and uniquely, also whether a currently married woman was previously widowed or divorced. Having this detail on marital history information is quite unusual and allows an examination of whether remarriage provides the insurance one would expect.<sup>17</sup> However, there is no way to separately identify widows who are in a leviratic union in the DHS. Furthermore, it is not immediately clear whether such widows are categorized as currently married or as currently widowed in the survey.

> 17  Unfortunately, the 1996 DHS identifies only current marital status so that a comparison over time is not feasible.

16
female workers), firm-level average wage (total and female workers), the share of exports in firmlevel total sales, and the share of exports to specific countries in firm-level total exports.

We use the National Statistics Office of Georgia (GEOSTAT) Statistics Survey of Enterprises for 2006-2017, which is a panel with the same firm identified across years. The data are then merged with customs-level transactions data using unique firm IDs. The GEOSTAT survey covers more than 61,000 firms and 132,000 firm-year observations during this period, with data on employment (total and female), wages (total and female), and sales. Customs data record covered more than 81,000 entries, with information on export value and destination for each transaction. Data on effective exchange rates are collected from the International Monetary Fund’s International Financial Statistics.

**Export share:** Share of establishment’s sales that are exported. Firm-level export shares for Georgia are collected from the GEOSTAT Statistics Survey of Enterprises for 2006-2017, calculated as total sales by each type of activity without VAT and excise divided by total exports (converted from USD to GEL using the corresponding year’s official exchange rate reported by the World Bank World Development Indicators). Note that because a panel dimension is necessary to conduct the analysis of changes in export shares and changes in labor market outcomes, the selection of firms was limited to those observed in the data across multiple years. A firm was included in the sample if it was observed in any two sample years, had exported in one of those years, and had non-zero exports in the first year or a previous year.<sup>7</sup>

**Market share:** Share of establishment’s exports that are exported to a particular country market. Firm-level market shares for Georgia are collected from the GEOSTAT Statistics Survey of Enterprises for 2006-2017, calculated as exports to a particular market (e.g., the European Union, the Russian Federation, non-EU non-Russia
goals. Governance in this context and as we use it here includes the effects of the goals of different ownership types, as well as the ability of the owners to minimize agency costs with management.

There is a wealth of research on bank governance and performance. Many studies assess the static effects of different types of bank ownership – the long-run performance effects associated with constant domestic, foreign, or state ownership – but typically do not consider more than one static difference at a time. Others address the dynamic effects of changes in bank ownership – the performance effects associated with domestic M&As, foreign acquisitions, or privatization – but typically do not consider more than one of these types of dynamic changes together. In some cases, these studies also examine the selection effects of which banks undergo these dynamic governance changes, but typically do not examine more than one of these types of selection effects together. The static, selection, and dynamic effects of any of the types of governance are typically not all examined in the same model.

We argue that it is important to account for the static, selection, and dynamic effects of all the major different types of governance that are important for a nation in the same model of bank performance. To the extent that any of the relevant effects are excluded, the model may be misspecified, and might give biased and misleading results. Consider the example of a nation with significant numbers of all three types of dynamic events – domestic M&As, foreign acquisitions, and privatization – in which all three types of events significantly improve bank performance. If only privatization is included in the model, the performance effect of privatization might mistakenly be measured as unfavorable simply because it raises performance less than the other two types of dynamic events. That is, the privatized banks might be measured as having negative effects on performance simply because they were not involved in domestic M&As or foreign acquisitions.

We test the effects of governance on bank performance using data from Argentina in the 1990s. We
based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age.

Figure 20 displays a further breakdown of the state level estimates by gender and sector. While evidence from Bangladesh and India (see Khatun et al. 2004; Pande, 2003) as well as different development programs suggests that girls are disadvantaged compared to boys in terms of nutrition, the estimates based on the 2016/2017 MICS show that only in certain states, such as urban Borno and Kebbi and rural Lagos such hypothesis is found to be in line with Bangladesh and India. But in most cases, evidence for the opposite is found. The finding that boys are in many states more likely to be food deprived than girls is though consistent with results of girls and boys overall based on the National Nutrition and Health Survey 2018 data discussed in NBS (2018).This finding is also consistent with findings from a meta-analysis using DHS data from 10 Sub-Saharan African countries by Wamani et al. (2007).Also, there are sizeable differences between urban and rural findings within states in the north, which may call for further research. These estimates should however be treated with caution since a detailed breakdown has severely reduced sample sizes.

32
_Note_: Each column report|0.130<br>s results from a singl|0.114<br>e regression of the|

_<mark>Note</mark>_ <mark>: Each column reports results from a single regression of the</mark> prevalence of either moderate or severe food insecurity or just severe food insecurity. Food insecurity is measured using the FIES. The FIES variable is regressed on indicators for each country, with Malawi as the base case, and round, along with an indicator equal to 1 if the household head is female, as opposed to male. Data come from all rounds of phone survey data in which FIES questions exist. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

43
Policy Research Working Paper 10787

# **Abstract**

Small Island Developing States (SIDS) are a group of 39 United Nations (UN) member states and 18 dependent territories mostly located in the Caribbean and Oceania. They are among the most vulnerable countries to losses and damages from climate change globally, and they are especially exposed to tropical cyclones and sea level rise. This paper evaluates the climate change-related vulnerabilities of the SIDS. It uses data from the International Disaster Database (EM-DAT), to examine the magnitude of damages incurred over the last three decades and conducts an event study analysis to examine the fiscal impacts of large tropical cyclone disasters in the last decade. SIDS need to invest substantial resources over the long term for climate change adaption. The paper reviews the limited studies that have been conducted so far, on the potential long-term costs of effective adaptation investment programs. Using the most recent Debt Sustainability Analyses of SIDS, the paper discusses the challenges countries with high public debt levels face and whether a general program of debt relief might be a feasible way to fund the adaptation investment requirements of SIDS. Finally, the paper discusses the type of assistance that SIDS need to strengthen their resilience to climate change in a cost-effective manner, and the role development partners can play in supporting climate-resilient development.

This paper is a product of the Climate Change Group. 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 scanagarajah@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
The international handbook of gender and poverty: concepts, research, policy</mark>_ <mark>. Edward Elgar Publishing.</mark> Verwimp, P. (2012). Returning Home after Civil War: The Consequences of Forced Displacement for Food Security, Nutrition and Poverty among Burundese Households. _IDEAS Working Paper Series from RePEc_ , ECARES Working Paper 2012-033.

<mark>World Bank. (2011).</mark> _<mark>World development report 2012: Gender equality and development</mark>_ <mark>. The World Bank.</mark> World Bank (2020a) World Development Indicators: Population dynamics. Last updated: 16 December 2020. Retrieved from http://wdi.worldbank.org/table/2.1# World Bank (2020b) World Development Indicators: Poverty rates at international poverty lines. Last updated: 16 December 2020. Retrieved from http://wdi.worldbank.org/table/1.2# Zenn, J., and Pearson, E. (2014). Women, Gender, and the evolving tactics of Boko Haram. _Journal of Terrorism Resarch_ , 5(1): https://doi.org/10.15664/jtr.828.

Zetter, R., and Ruaudel, H. (2014). Development and protection challenges of the Syrian Refugee Crisis. _Forced Migration Review,_ 47:6-10 33
indicator examines laws that constrain a woman’s freedom of movement. The _Workplace_ indicator evaluates laws that may constrain a woman’s ability to work. The _Pay_ indicator assesses legislation that may affect a woman’s pay. The _Marriage_ indicator looks at how married men and woman are treated under the law. The _Parenthood_ indicator assesses the legislation that may impact a woman’s ability to partake in the workforce after having a child. The _Entrepreneurship_ indicator examines how legislation may impact a woman’s ability to start and run a business. The _Assets_ indicator considers how the law may constrain a woman’s ability to own and manage assets. Finally, the _Pension_ indicator examines how the law may affect the size of a woman’s pension upon her retirement. Each indicator is scaled from 0 to 100, where 100 is a perfect score indicating no legal gender discrimination. The aggregate WBL index is an unweighted average of the underlying eight indicators. The WBL data cover the period 1970–2020; according to the most recent data, the global average score is 76.1, indicating that women have, on average, just over three-quarters the rights of men in the areas covered by the index.<sup>8</sup>

# <u>Other macro-level variables</u>

In our regressions examining the relationship between legal gender equality and the probability that a firm began informally, we control for income level, rule of law and religion. Income level is measured as real per capita gross domestic product (GDP), from the World Bank’s World Development Indicators database.<sup>9</sup> Our measure of the rule of law is from the _Worldwide Governance Indicators_ (WGI).<sup>10</sup> This variable captures the perceptions of citizens’ confidence in and adherence to rules within their society; it captures the quality of contract enforcement, property rights, policing, the courts, and the likelihood of crime and violence (Kaufmann, Kraay
_3_ ( _4_ ) iirr iirr VV = αα+ ββ ZZ + εε Where _Vi,r_ is again a dummy for whether respondent i selected by method r is vaccinated. The subscript _r_ denotes whether _V_ was obtained by interviewing a purposively selected respondent vs. a randomly selected respondent (equation 2); whether _V_ was obtained from the purposively selected respondent vs. as part of eliciting the (proxy-reported) vaccination status of all household members aged 15 and above (equation 3)<sup>9</sup> ; or whether _V_ was obtained through random selection of a respondent vs. from the proxy reported vaccination status of all individuals in the household aged 15+ (equation 4). Our estimates of interest are _β1_ , _β2_ , and _β3_ , the coefficients on _Di,r_ , _Ti,r,_ and _Zi,r_ , respectively, which are dummies for whether observation _i,r_ is part of the randomly selected sample of respondents as opposed to the purposively selected sample ( _Di,r_ in equation 2); part of the sample for which a proxy reported vaccination status was collected as opposed to the sample of purposively selected respondents ( _Ti,r_ in equation 3); or part of the randomly selected sample as opposed to the proxy-reported sample ( _Zi,r_ in equation 4); . If our predictions are confirmed, _β1_ and _β2_ should be significant and negative. Additionally, we would expect _β3_ to be insignificant as the randomly selected respondent is sampled from the list of all household members aged 15 or older. To facilitate comparisons with the administrative data (without the need for assumptions regarding non-coverage of children), we additionally use the proxy reported vaccination status of all household members (i.e. not just those aged 15+) to estimate general population vaccine coverage.

# 3.2 Measurement errors

## <u>Hypothesis 3: Survey mode effects</u>

Our third hypothesis relates to differences in survey modes. Concretely, it is conceivable that interviews conducted
bursements are usually spread out over many quarters at different stages of the project. Actual disbursements may deviate substantially from the originally planned disbursement schedule; for instance, disbursements may be accelerated in response to natural disasters or delayed in the face of civil conflict. However, the amount of aid disbursed in a given quarter is largely the result of project approvals made in previous quarters, which creates variation in disbursements that is arguably exogenous to contemporaneous shocks.

Following Kraay (2012, 2014), we build an instrument by predicting quarterly disbursements for each project based on the initial commitment and the average disbursement schedule across all other projects implemented in the same sector and the same geographical region. Summing over predicted disbursement at the project-level, we predict aggregate disbursements for each country and quarter.<sup>10</sup> We never use predicted disbursements for the commitment quarter as an instrument since it suffers from the same potential endogeneity as the actual disbursements. In the most rigorous tests, we only use predicted disbursements related to projects approved at least 3 quarters before as an instrument to strengthen the case for exogeneity.

## **2.3 Other variables**

We collect information about events that may be associated with simultaneous changes in aid disbursements and cross-border capital flows: Wars from the PRIO Armed Conflict Dataset; Coups from Powell and Thyne (2011); Natural disasters from the International Disaster Database; Financial crisis from Laeven and Valencia (2012); Petroleum rents and financial sector development from World Development Indicators (WDI). We also collect information on country characteristics that may mediate the effect of aid disbursements on haven deposits: Corruption from Worldwide Governance Indicators (WGI); Disclosure requirements for members of parliament from Djankov et al. (2010); Capital account openness from Chinn and Ito (2006); Political regime characteristics from the Polity IV Project. We document these variables in more detail and provide summary statistics in Table A2 in the Online Appendix.

# **3 Empirical strategy**

To assess whether disbursements of
the dynamics of labour market outcomes: Evidence from refugees resettled in the US. _Review of Economic Studies_ , _79_ (1), 128-161.

- Bogic, M., Njoku, A., & Priebe, S. (2015). Long-term mental health of war-refugees: a systematic literature review. _BMC International Health and Human Rights_ , _15_ (1), 1-41.

- Brell, C., Dustmann, C., & Preston, I. (2020). The labor market integration of refugee migrants in high-income countries. _Journal of Economic Perspectives_ , _34_ (1), 94-121.

- Cameron, A. C., & Miller, D. L. (2015). A practitioner’s guide to cluster-robust inference. _Journal of Human Resources_ , _50_ (2), 317-372.

- Chatterji, P., Alegria, M., & Takeuchi, D. (2011). Psychiatric disorders and labor market outcomes: Evidence from the National Comorbidity Survey-Replication. _Journal of Health Economics_ , _30_ (5), 858-868.

- Chen, W., Ling, L., & Renzaho, A. M. (2017). Building a new life in Australia: an analysis of the first wave of the longitudinal study of humanitarian migrants in Australia to assess the association between social integration and self-rated health. _BMJ open_ , _7_ (3), e014313.

- Clarke, D., & Matta, B. (2018). Practical considerations for questionable IVs. _Stata Journal_ , _18_ (3), 663-691.

22
on men and women in Ethiopia. It analyzes the incidence, progressivity, and pro-poorness of various taxes and transfers and their effect on income mobility, poverty and inequality. The study uses data from the 2018/19 Ethiopia Socioeconomic Survey, which collected individual level information on such variables as labor market outcomes, land ownership, transfers, and formal and informal taxes. In addition, the study generated individual-level income using a combination of intrahousehold allocation approaches. The gendered distributional impacts of fiscal policy are then examined at different income concepts.

The net fiscal system is progressive, equalizing, and poverty-reducing. It moved about one in five individuals from one income group to another, and more women than men transitioned to a higher income group, making them relatively better-off. However, different elements of taxes and transfers are found to have different effects on men and women. Direct taxes are progressive and equalizing, but their progressivity and inequality-reducing effects are stronger for men. Informal taxes and agricultural income taxes appear to be regressive, unequalizing, and poverty-inducing. However, informal taxes do have more poverty effects on women. Agricultural income taxes and land user fees, levied mostly on rural households, are regressive and exacerbate poverty. In part, this reflects the fact that agricultural households tend to be poorer than non-agricultural 23
# **Appendix S6: Simulating the impact of measurement error on intra-household inequality.**

The exercise is the following. Assume that the true distribution of consumption is such that there is no intra-household inequality. Nevertheless, because of (classical) measurement errors at the cell level, the observed per capita cell consumption differs from the per capita household consumption. We simulate the observed distribution of per capita cell consumption varying the magnitude of the error term, by drawing it from a normal distribution with a variance chosen as a percentage of the variance of the original distribution of log-consumption. This percentage varies from 10 to 80%. From this simulated cell consumption data, we compute again both the per capita household consumption and the per capita cell consumption. We then calculate the Gini and Theil indices of the 2 distributions and assess the variance of the white noise that would be enough to explain a level of intrahousehold inequality equal to 14.6% of total inequality. We replicate the simulation 100 times to compute the standard errors of the indices. We will not focus on the total level of inequality, as the addition of such white noise will mechanically increase it.

Table S6.1. below gives the result of these simulations. It appears that it requires an error term with a variance fixed at 70% of the variance of the original distribution of logconsumption for the decomposition of the Theil index to indicate a within-household share of total inequality of 14%. At 40%, the Gini index for the distribution of per capita cell consumption is 2.75% higher than the one for the per capita household consumption, as we actually observe in our data (2.56%). In both cases, such levels of measurement error are unrealistically large compared to the 20% benchmark mentioned above, so that we are confident measurement error is not the only force driving our results.

25