rafmacalaba/data-use-mentions-extended huggingface dataset:
sup>2</sup> in Kigali, where more than 70 % of sales are concentrated, to US$ 4.75/m<sup>2</sup> in the East which accounts for slightly more than 10% of registered sales. Agricultural land has a mean sales price of US$ 4.45/m<sup>2</sup> (US$ 6.65/m<sup>2</sup> in Kigali to US$ 1.47/m<sup>2</sup> in the East). In principle, the registry can provide near-real time information on land prices at high levels of temporal disaggregation to inform private sector decision-making. To check if subsequent transactions maintain the levels of gender equality attained by LTR, panel C of table 3 disaggregates registered land transaction by gender. Comparing to table 1 points towards a decline of the number of parcels for which at least one claimant is female from 86% for first (see table 1) to 77% for subsequent registrations. Attention to this issue may thus be warranted.

Having the land registry seamlessly integrated with other parts of the public sector opens opportunities to add value in a number of related areas. For example, RNRA embarked on an effort to ensure that, for any civil dispute that may affect rights to a parcel of land, a caveat is placed on this parcel automatically and in real time, thus eliminating a source of potential fraud and insecurity. Access to LAIS data can allow the Rwanda Revenue Authority to simplify billing and payment of lease fees, thus not only saving costs but also ensuring greater transparency and equity and allowing greater revenue collection: in 2015 only a third of non-exempt residential properties in urban Kigali paid their lease fees, resulting in lost revenue of some US$ 4.8 million. If, e.g. due to more efficient automatic billing, LAIS were to allow full collection of this amount without additional cost, the resulting benefits would be
>-20 -20<br>-30 -30<br>20 20<br>10 10<br>0 0<br>-10 -10<br>Change in food insecurity Change in food insecurity<br>-20 -20<br>-30 -30<br><!-- End of picture text --> _Sources_ : The Y-axis variable is from the GWP (Gallup 2011) . Economic growth data are from the IMF (2010), and food inflation data are from the ILO (ILO, 2011).

Turning to some results, we begin with descriptive statistics and correlations for our dependent and independent variables (tables 4 and 5). Over the entire period, the mean change in the first difference of the food insecurity measure was close to zero (0.2), although the standard deviation and range of this variable is quite large. The statistics for the percentage change in food insecurity show a similar pattern and indicate the presence of some of the previously mentioned problems with the use of percentages of a prevalence variable. There is a tendency to inflate 19
**Figure 21: Financial indicators for MERALCO, 2005-2015**

<!-- Start of picture text --> Debt Service Coverage Ratio Debt-to-Equity Ratio Net Profit Margin<br>16 8%<br>14 7%<br>6%<br>12<br>5%<br>10<br>4%<br>8<br>3%<br>6<br>2%<br>4 No data, 1990-2004 1%<br>2 0%<br>0 -1%<br>Source: Rethinking Power Sector Reform Project<br>Figure 22: Financial indicators for BENECO, 2005-2015<br>Debt Service Coverage Ratio Debt-to-Equity Ratio Net Profit Margin<br>6 2%<br>4 1%<br>0%<br>2<br>No data, 1990-2004<br>-1%<br>0<br>-2%<br>-2<br>-3%<br>-4<br>-4%<br>-6 -5%<br>-8 -6%<br>Financial ratios<br>Net profit margin<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>Financial ratios<br>Net profit margin<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Source: Rethinking Power Sector Reform Project Table 9 summarizes the financial performance of MERALCO and BENECO and compares this to the average of firms included in the Rethinking Study. MERALCO stands out as having a positive 44
the single largest item accounting for 38 percent of messages received. Analysis of the expenditure priorities identified by SMC members during the participatory capacity building exercise carried out as part of ELECTIONS-CAPACITY **(Figure 5B)** reveals a similar priority placed on physical infrastructure, with building, classrooms and toilets accounting for a total of 34 percent.

# **5.4 Teacher attendance and learning outcomes**

Despite improvements in the availability of teachers, none of the interventions significantly improved the number of teachers actually present at schools at endline. Comparison of means **(Table 5)** demonstrates that the share of teachers absent at endline was actually greater in MEET and SMS schools, suggesting that gains in teacher availability were balanced out by greater absenteeism. This is in line with findings from India and Kenya, which suggest that increased staffing at schools is often counteracted by a reduction in effort and presence by existing teachers (e.g. Muralidharan and Sundararaman, 2013; Duflo et al, 2015).

The result suggests that the interventions were not successful in catalyzing improved community monitoring of resource use in schools. Data from household surveys at endline reveal a high level of community engagement with schools: more than one-third (34.5 percent) of households reported having met the head teacher of their child’s school on at least a monthly basis in the 2015/16 school year, two years after the intervention. However, we find no systematic evidence of greater levels of school visits in either SMS or MEET schools than in control schools.

Furthermore, analysis of households’ reported concerns about their children’s schools reveals that teacher absence was not considered a significant problem by households, suggesting that community engagement may be of limited impact if misconceptions persist about the severity of 49
consumer price index (CPI) calculation purposes.

There are two main estimation-based approaches in the literature to estimate trade costs, the gravity approach and the price differential approach (Co ̧sar, 2022). The gravity approach looks at the impact of varying travel distance or travel time on trade flows, which are inversely proportional to each other.<sup>2</sup> Using gravity estimation to estimate the impact of travel distance on trade between Colombian cities, Duranton (2015) estimates that increasing the travel distance between them by 10% decreases trade flows by 7% and weight by 6%.

The price differential method combines data on prices from different markets or cities with variables that are thought to affect the cost of shipping goods between locations. Using these data, one can compute price differences across space (spatial price gaps) and assess how different cost shifters affect these differences. Distance between where the

> 1See, for example, Atkin and Donaldson (2015) on the estimates for Ethiopia, Nigeria, and the U.S., and Bougna, Ewane, Jones and Kondylis (2020) on Rwanda.

> 2Head and Mayer (2014) summarize the literature on gravity equations and provide an extensive overview of best-practice methods and a step-by-step cookbook.

2
who are absent (as they are currently overseas) but are expected to return. When family migration is not possible, and the rest of the family remains behind at origin, the surveys ask questions about the migrant to the family members.<sup>6</sup> Such modules are well suited to assess the impacts of temporary migration on family members left behind, especially spouses and children. These survey typically include detailed questions on various labor market, health, education, and consumption outcomes of household members.

When entire households migrate temporarily, however, absentee questions underestimate the incidence of temporary migration. In addition, since the questions asked in those modules are not asked directly to the migrants themselves, the data can be quite parsimonious or unreliable. Thus, it is typically restricted to the migrants’ demographic characteristics along with basic migration parameters such as year of departure, destination country and occupation.

The second way nationally-representative household surveys in origin countries collect information on temporary migration is through questions on past migration experiences of current household members. As these modules are part of nationally-representative surveys, they are especially well suited to carry out detailed comparisons between the labor market outcomes of return migrants and non-migrants. To estimate the causal impacts of past migration on outcomes back in the home country, the best data sets are those that collect information on both return migrants and current migrants, as they allow to account for the double selectivity of return migration, which can bias estimates of temporary migration impacts (Wahba 2015).<sup>7</sup> These data sets, however, differ in the level of detail of the information collected on current and return migrants. The Egypt Labor Market Surveys (ELMS) and Albania Living Standard and Measurement Surveys (LSMS) are the most comprehensive in that regard. One drawback of most returnee modules, however, is that they typically collect parsimonious information on past migration experience overseas. This prevents an in-depth analysis of the linkages between past migrant experience and current labor outcomes. In addition
It is not difficult to picture an ideal study of productivity spillovers in host countries. To examiine how the development of technology and productivity in individual local firms is related to the presence of foreign MNC in the local market, the study would require detailed micro data, both quantitative and qualitative. The study would have to cover several years, to take into account the fact that spillovers are not instantaneous. It should also include a large number of firms and industries, so that inter-industry spillovers could be observed, and so that it would be possible to draw statistically significant conclusions. With this kind of detailed information, it would also be possible to study productivity spillovers in the home countries of MNCs, and examiine the presence of market access spillovers. However, to the best of our knowledge, no comprehensive analyses of this character have ever been made - one reason, of course, is the externe data requirements. Additional empirical evidence on spillovers must therefore be drawn from two other sources.

Firstly, in addition to the few case studies focusing directly on spillovers, there is a large number of detailed case studies discussing other aspects of FDI in different countries and industries, and these studies often contain valuable "circumstantial evidence" of spillovers. For instance, many analyses of the linkages between MNCs and their local suppliers and subcontractors have documented learning and technology transfers that may make up a basis for productivity spillovers or market access spillovers. These studies seldom reveal whether the MNCs are able to extract all the benefits that the new technologies or information generate amonlg their supplier firms, so there is no clear proof of spillovers, but it is reasonable to assume that spillovers are positively related to the extent of linkages. Similarly, there is much written on the relation between MNC entry and presence and market structure in host countries, and this is closely related to the possible effects of FDI on competition in the local markets. There are also studies of demonstration effects, technology diffusion, and labor training in foreign MNCs that are relevant for our purposes
# **<u>Table A.5. Poverty Lines, the West Bank and Gaza, in LCU, Per Capita Annual Consumption in Survey Year Prices</u>**

||**Food**|**poverty line**||**Po**|**verty line**||
|---|---|---|---|---|---|---|
||2009|2011|2017|2009|2011|2017|
|**Gaza**|567|570|567|712|714|710|
|**West Bank**|609|632|710|765|792|889|
|**Total**|603|620|668|757|776|836|

Source: Compiled from various PCBS poverty reports. Spatial deflator provided by PCBS was used to calculate regional poverty lines for Gaza and the West Bank relative to the national poverty line available from PCBS publications.

48
research is often concerned with the impact of network disruption and in particular transport disruption (commuting in particular) using transport model based on criticality assessment.<sup>4</sup> They found that the consequences of infrastructure‐related indirect impacts are often larger than the direct impact on firms. West and Lenze (1994) summarize the impact of hurricane Andrew on Florida, including job market con‐ sequences. The Bureau of Labor Statistics (2006) of the US also provides a detailed analysis of the large labor market consequences of hurricane Katrina. Using household survey in three counties and 16 cities after the 2004 hurricane landfalls in Florida, Smith and McCarty (2006) show that households are more often forced to move outside the affected area by infrastructure problems than by structural damages to their home.

The ECLAC methodology (UN ECLAC, 2003) assesses direct indirect and secondary effects of single events by a collection of data and information of various types (physical, monetary, and expert judgment) in each sector. Indirect costs are thus estimated through collection of information from economic agents, governments and experts, taking in consideration particular aspects such as transport disturbances cost, loss of opportunities etc. Of course, the aggregation of data from very different sources should be done with care to avoid gaps and double counting.

# _Econometric approaches_

Econometric approaches are based on statistics and do not investigate a single event. They focus on se‐ ries of events and investigate the “mean” indirect cost of these events, like the average impact on long term economic growth.

Econometrics analyses at national scale have reached different conclusions on the impact of disasters on growth. Alabala‐Bertrand (1993) and Skidmore and Toya (2002) suggest that natural disasters have a pos‐ itive influence on long‐term economic growth, probably thanks to both the stimulus effect of reconstruc‐ tion and the productivity effect (also labeled “Schumpeterian creative destruction effect” or productivity effect, described earlier in Section 4.b.). Others, like
protection programs might also be larger in pandemic times.<sup>3</sup> However, Banerjee et al. (2020) report finding similar estimates when they compare the effects of cash on mental health before the pandemic to effects during the pandemic. Alloush et al. (2022), which studies a nation-wide government program like we do (as opposed to a researcher-driven RCT as in Banerjee et al. (2020)), concludes that effects on psychological distress are at least as large, and possibly larger, in pandemic times.

In this paper, we ask whether social protection matters more for mental health during a pandemic – a question for which theoretical predictions are ambiguous and empirical evidence is scant. We use data from the Philippines, where the government imposed strict quarantine measures and rolled out a large emergency cash transfer program in response to the COVID-19 pandemic. We are interested in the importance of the pre-existing Pantawid Pamilyang Pilipino Program (commonly known as the “4Ps”), a national conditional cash transfer (CCT) program that distributes money to households who satisfy various education and health requirements.

Using a panel survey of low-income households who were just below and just above the poverty score cutoff used to determine 4Ps eligibility, we compare depression rates for 4Ps beneficiaries and for non-4Ps beneficiaries both before and during the pandemic. We find that rates of severe depression were similar for both groups prior to the pandemic (around 1%), but significantly lower for 4Ps beneficiaries as of July 2020. While depression increased for both groups, it increased substantially more for non-4Ps households (33 percentage points) than 4Ps households (23 percentage points). This is in spite of the more generous emergency cash transfers that were distributed to both groups.

That a large difference between beneficiaries and non-beneficiaries emerged only during the pandemic could be an indication that the existing 4Ps program mattered more for mental health in crisis than non-crisis times. However, there could be other explanations for the larger increase in depression rates for non-4Ps compared to
.

As the survey was launched in the second half of October, we set Oct. 15, 2022, as the cut-off date to construct the sample frame by dropping farms without any registered land or a valid phone number. Table 1 provides comparison of the resulting sample frame with the 2020 all registered farms survey (Form 29) of SSSU by farm size category (<50, 50-120, 120-500 and > 500 ha). Panels A and B of table 1 presents a simple comparison of number of establishments and total area cultivated of the two frames. Panels C and D draw out implications for informality in areas controlled by Ukraine (ACUs) and areas affected by conflict (AACs) by comparing, for each of these, the area cultivated by farms that are included in our and SSSU’s frame or in either of these frames separately to identify the area cultivated informally (i.e., by farms that are neither included in Form 29 nor the SAR).

> 10 For example, the SAR automatically gathers information on any outstanding debts to the state (which would legally disqualify them from receiving state support) and on farmers’ registered livestock from the animal registry and the government plans to add information from other registries, including the mortgage registry and the registry of court cases, in the near future.

> 11 The PSG that targeted to farmers who cultivated less than 120 ha provided a cash grant equivalent to US$100 for each hectare of land cultivated during the 2022 agricultural season in non-conflict affected areas. Information from the SAR was first used to establish whether the farmer was below the 120 ha threshold. After subtracting land parcels registered in the farmers’ name located in conflict affected areas, maps with registered parcel boundaries were used to cross-check cultivation status against a crop map elaborated based on remotely sensed imagery to compute the total grant amount. The € 50 mn. available under the program had been fully disbursed by Nov. 2022 and an evaluation that aims to compare differences over time in parcels by
employment (and value added); the treatment of households is explained further down in this section and in Appendix 1.<sup>6</sup> Given this information and related endogenous data on household incomes and spending, it is possible to compute per-capita household consumption in rural and urban areas, both of which enter some of the MDG functions (cf. Table 2.1).

The above discussion refers to how the model works in any given year. Over time, production growth is determined by growth in factor employment and TFP. Growth in capital and labor stocks is endogenous. For capital stocks, growth depends on new investment and depreciation. Growth in the aggregate labor stock depends on growth in the population in labor force age, the labor force participation rate, and school attendance. At a disaggregated level -- the labor force is disaggregated by educational attainment into segments with less than completed secondary, completed secondary but not completed tertiary, or completed tertiary – growth depends on the evolution of the educational system. For other factors (in this application agricultural land), stock growth is exogenous. TFP growth is made up of two components, one that is exogenous and one that responds to endogenous developments, in this application, growth in government infrastructure capital stocks, urbanization, and MDG 4 (as a proxy for the health status of the population). The inclusion of a link between urbanization and TFP (with a stronger link for non-agricultural sectors) is based on research indicating urban agglomeration tends to generate more rapid productivity growth.

A typical MAMS application requires an extensive data set for the application base-year – a social accounting matrix (SAM);<sup>7</sup> stocks for production factors (including different types of labor and capital), population, labor unemployment (broadly defined to include underemployment) and school enrollment; indicators for MDGs and the educational system – as well as a set of elasticities (for production, consumption, trade, and MDG, and non-MDG education relationships
Policy Research Working Paper 10135

# **Abstract**

Child marriage has lasting negative health, human capital, and welfare consequences. Conflict settings are characterized by a number of complex changes that can potentially increase the risk of child marriage, but there has been limited population-based research directly estimating the relationship between conflict and child marriage. Using Demographic and Health Survey data from 19 conflict-affected countries, this paper estimates the relationship between conflict and child marriage. It identifies the relationship based on variation over space and time in conflict intensity. The findings are mixed; in some countries conflict is associated with an increase in child marriage, in others it is associated with a decrease in child marriage, and in some cases there is not a statistically significant relationship. This overall pattern is robust to a variety of approaches to measuring conflict. These findings underscore how efforts to reduce child marriage need to consider conflict as a potential risk factor, but also one that is likely to interact with local economic, social, and demographic environments.

This paper is a product of the Gender Global Theme. 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 darango@worldbank.org; cgkrafft@stkate.edu; jkelly@hsph.harvard.edu; and amalia.h.rubin@gmail.com.

_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
3.2|-9.5*|
|(17.7)|(17.6)|(6.4)|(2.3)|(2.7)|(4.9)|
|Panel A of Table 8: Capital market openness and develop|ment|||||
|De-jure fnancial openness index<br>-226.4|0.8|0.3|0.2|-1.4|-3.5*|
|(193.6)|(3.9)|(1.8)|(1.7)|(1.6)|(1.9)|
|Private sector credit (% of GDP)<br>9.5|-2.2|-3.0|-4.3**|-6.2**|-2.1|
|(38.4)|(8.7)|(3.9)|(1.7)|(2.7)|(3.5)|
|Panel A of Table 10: Changes in macroeconomic variables|before f|rst cash|fow|||
|Real GDP<br>-35.2|0.5|0.5*|0.2|0.1|0.2|
|(43.4)|(0.4)|(0.3)|(0.2)|(0.2)|(0.4)|
|Local currency GDP defator<br>77.5|-0.1|-0.0|-0.0|-0.1|0.1|
|(55.2)|(0.1)|(0.1)|(0.0)|(0.1)|(0.2)