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18 lasts from 1st April 2010 to 31st March 2011. Eight months of the fiscal year thus fall in the year 2010. Accordingly, the fiscal data reported for fiscal year 2010/2011 are matched with calendar year data from 2010.

With respect to election timing, ideally the election would be recorded as occurring in the fiscal year in which most pre-election expenditures occurred. For example, if the election takes place two months into the fiscal year, and it is in those two months prior to the election that election expenditures are concentrated, then the election year should be the same as the fiscal year. In fact, Mironov and Zhuravskaya (2011) find that shadow transfers from firms to politicians in Russia are concentrated in the four weeks prior to the election. If the transfers support campaign expenditures, including votebuying, this suggests that even elections occurring in the first month of the fiscal year are appropriately coded as occurring in that same fiscal year. However, we do not know whether the particular timing of campaign expenditures found in Russia is pervasive across countries.

The prior literature simply matches the election year to the fiscal year in which it occurs. In our main specifications, we follow this methodology, replicating Brender and Drazen’s (2005) election dummy (with a small number of adjustments reported in Table A2). However, it is possible that election expenditures are spread out over several months prior to the election. In this case, it would be more appropriate to code an election occurring early in the fiscal year as having occurred in the prior fiscal year. We therefore also demonstrate the robustness of our results to the use of an alternative election dummy, _election (M1)_ . This reports an election as occurring in the previous fiscal year if it fell in the first month of the fiscal year. The purpose of this re-coding is to ensure that we capture a fiscal expansion _before_ the election. If an election is held in the first month of the fiscal year, most of the electorally-motivated pre-electoral expenditures may have taken place in the prior fiscal year.

# **Estimation**

The empirical model for the analysis is
We now determine the optimal probability threshold that maximizes coverage and minimize leakage, by computing the Youden and the Distance indices (Figure 4). In general, the two indicators may suggest different optimal thresholds (indicated in Figure 4 by the two vertical lines). In our case, we find the two values rather close: 0.703 for the Youden index (corresponding coverage, 0.766; corresponding leakage, 0.184), and 0.685 for the Distance index (corresponding coverage, 0.782; corresponding leakage, 0.201). Also noteworthy and from a graphical perspective, one could consider as an optimal choice, the maximum vertical distance between the Youden and Distance indices curves. This falls indeed within the two optimal thresholds indicated respectively by the two indices and could solve the issue of choosing between two optimal values.

**Figure 4 - Youden and Distance indices for different cut point**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Cutpoint<br>youden dist<br>1<br>.8<br>.6<br>.4<br>Youden and Distance indices .2<br>0<br><!-- End of picture text --> Source: Authors’ estimations based on Jordan UNHCR data.

As an alternative, it is possible to select the optimal probability threshold using the available budget for the food voucher program as baseline. The WFP would normally know the budget available for targeting and this is what frequently drives targeting decisions. The WFP or the UNHCR do not have large regular annual budgets and they rely on donors’ contributions pledged in correspondence of humanitarian crises. Based on the effective contributions received following the pledges, these organizations take targeting 14
5 these problems are taken into account. There are essentially two major shortcomings in all these earlier applications. First, they ignore a rather important institutional feature of most developing countries, namely the extended family, usually containing adult earners besides the household head and spouse who are likely to influence decision-making on internal resource allocation. The average non-nuclear household size is likely to be significantly larger because of a larger number of adults, and usually greater adult employment. This would violate the assumption underlying the Rathbarth model, which presupposes that expenditure patterns reflect the preferences of the parents as the sole decision-makers<sup>4</sup> , see Deaton and Muellbauer (1986), i.e. it is a model of internal allocation in a _nuclear_ family. The extended family is a major household type in most developing countries, and in many likely to constitute the principal unit of consumption<sup>5</sup> . An indiscriminate application of the model to all types of household, as in all previous attempts, is likely to prevent the evidence of child gender bias in consumption pattern to reveal itself. Second, the earlier applications assume gender bias is related to the age of children, and are therefore based on indices of child age/gender; yet no hypothesis, or systematic evidence, is available to support the presumption that child gender bias related to the age of children. By contrast, there is quite a plausible hypothesis that such a bias relates to the _number_ of children: parents with strong preferences for boys, who have girls early in their fertility cycle, are likely, in the absence of prenatal screening technology, to continue having more children until they meet their targeted sex composition of children, see for example Muhuri and Preston (1991), suggesting unwanted girls are more likely to live in larger households, and be among the older children. Evidence about the presence of such a mechanism has so far come from demographers, usually in the form of correlations between children’s proportions of boys

> <sup>4</sup> Most of the economic functions of the extended family in developing countries relate to support for adult relatives. Examples of such functions are: transfers to the elderly, protection against unexpected economic shocks such as sickness or unemployment, provision of a viable unit of consumption when individual incomes are low. Although little is known about how these functions relate to intrahousehold allocation, see, however, Attanasio and Rios-Rull (2000), it would be surprising if they did not affect the assumptions of the Rothbarth internal allocation rule.

> <sup>5</sup> For example, Lanjouw and Ravallion (1993, table 1) for rural Pakistan contains demographic groups by the number of adults. If we assume there are no extended families in groups with up to three adults, and households with four or more are exclusively of non-nuclear types, then the extended families account for 56% of their sample.
In this section, we follow Schafer and Kang (2008) and Austin (2011) to explain the inverse-propensity-score approach in constructing weights. In the context of propensity score matching, we can make the average outcome of the control group comparable to that of the treatment group by multiplying the initial sampling weight of the control group by the odds of a sample being selected for the treatment group.

weight of the control group by the odds of a sample being selected for the treatment group. Suppose the original sampling weight of the control group is , the propensity score for the sample to be cc selected into the treatment group , and the control group 1- . Then, a new weight to make the sample ii ww ii in the control group comparable to the treatment group counterpart is: ii ii pp pp ii ∗ cc ii pp ii ww Note that the second term is the odd for selecting this sample from the control group into the treatment group. 1 −ppii The average of outcomes weighted by this new sampling weight in the control group is now comparable to that of the treatment group (see more details in Schafer and Kang 2008 or Austin 2011). By the same token, this approach can be used to reweight the phone survey. Now, suppose the original sampling weight of the phone survey is and the propensity score of sample to be selected for the phone survey is pp , and the reference survey . Then, a new weight to make sample in the phone survey comparable ii ww ii to the counterpart in the reference survey is: ii ii pp 1 − pp ii ∗<sup>1 −pp</sup> pp ii ii ww The second term is the odd for sample in the phone survey to be selected for the reference survey. The ppii average outcomes weighted by this new sampling weight in the phone survey are now comparable to that of the reference survey (see formal proofs in Schafer and Kang 2008 and Austin 2011). ii <u>Selection of covariates for the estimation of propensity scores</u> Propensity score weighting is feasible when the probability predictors are available from both the reference and phone surveys.<sup>2</sup>

> 2 For the units where some of the covariates are missing, propensity scores cannot be computed, which hinders the adjustment (Lee 2006).

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general government gross debt among non-SSA developing countries. It dropped from 55 percent of GDP in 2002 to about 30 percent of GDP in 2008 (Anderson, Silva, and Velandia-Rubiano 2010). Public debt also experienced a sharp decline in Sub-Saharan African countries, from nearly 100 percent of GDP in 2001 to about 35 percent of GDP in 2008. This reduction was primarily driven by debt forgiveness granted to African countries through the HIPC initiative and Multilateral Debt Relief Initiative (MDRI).

<!-- Start of picture text --> Figure 1. Primary fiscal balance Figure 2. Overall fiscal balance<br>4 2<br>3 1<br>2<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>1<br>‐1<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 ‐2<br>‐1<br>‐3<br>‐2<br>‐4<br>‐3<br>‐4 ‐5<br>‐5 ‐6<br>Industrial Countries Non‐SSA Developing Countries Sub‐Saharan Africa Industrial Countries Non‐SSA Developing Countries Sub‐Saharan Africa<br><!-- End of picture text --> _Note: All figures reported are group medians and are expressed as percentage of GDP. Calculations based on data from Kose et al. (2017)._ <!-- Start of picture text --> Figure 3. General government gross debt Figure 4. Fiscal Space<br>100 8<br>90<br>7<br>80<br>6<br>70<br>60 5<br>50<br>4<br>40<br>3<br>30<br>20 2<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Industrial Countries Non‐SSA Developing Countries Sub‐Saharan Africa Industrial Countries Non‐SSA Developing Countries Sub‐Saharan Africa<br><!-- End of picture text --> _Notes: All figures reported are group medians and are expressed as percentage of GDP. In Figure 4, fiscal space is calculated by the ratio of general government gross debt to average tax revenues (Aizenman and Jinjarak 2010). Calculations based on data from Kose et al. (2017)_ 7
Appendix: Definitions of Variables in Table 1-6 AVG EX70: Average ratio of exports to GDP for the years 1960-85; from World Bank BESD.

AVG17085: Average ratio of public plus private investment over GDP for the years 1970-85; from Barro [1991].

GOV7085: average ratio of real government "consumption" expenditure to real GDP for the years 1960-85; from Barro [1991] GRNDI_Y: government funded expenditures on R&D as a percentage of GDP; average for the years 1970-85; calculated from UNESCO data set.

GR7085: growth rates of GDP per capita, in 198W) $US, for the years 1970-85; from World Bank BESD.

GrP70: GDP per capita in 1980 $US, for the year 1970; from World Bank BESD.

GPOP7085: growth rates of population for the years 1970-85; from World Bank BESD.

GRSCP7085: growth rates of the number of R&D scientists and engineers per million population for the years 1970-85; calculated from UNESCO data set.

GRSC17085: growth rates of the number of R&D scientists and engineers for the years 1970-85; calculated from UNESCO data set.

LNDIFF70: log difference between GDP70 of each country relative to U.S. GDP70.

PRND1_Y: privately funded expenditures on R&D as a percentage of GDP; average for the years 1970-85; 'calculated from UNESCO data set.

PRIM: primary school enrollment rate; from Barro [1991] RND_Y: Expenditure on R&D as a percent of GDP; average during the period 197075; calculated from UNESCO data set.

RND1 Y: same as above for the years 1970-85.

SCI_P: average number of R&D scientists and engineers per million population for the years 1970-75; calculated from UNESCO data set.

SEC: secondary school enrollment rate; from Barro [1991] TGDP7085: growth rates of GDP, in 1980 $US, for the years 1970-85; from World Bank BESD.

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statically not significant when we measure the effects of the 10-session or 20-session CBT workshops separately (odd columns) or if we combine the two treatment types (even columns). We find positive but statistically insignificant effects when we look at the probability of re-entering the COMIPEMS entry examination (columns 5 and 6). When we combine both variables into a single indicator that proxies for “Student Engagement”, we find a non-statistically significant effect (columns 7 and 8).

**Table 3: ITT on Math Score, Student enrollment in 11**<sup>**th**</sup> **grade, re-taking COMIPEMS 2017 and School Engagement**

||(1)<br>Math|(2)<br>Score|(3)<br>Student en|(4)<br>rollment|(5)<br>Re-taking|(6)<br>COMIPEMS|(7)<br>Student En|(8)<br>gagement|
|---|---|---|---|---|---|---|---|---|
|10 Sessions (T1)|0.013||-0.023||0.001||-0.021||
||(0.128)||(0.016)||(0.010)||(0.016)||
|20 Sessions (T2)|-0.019<br>(0.140)||-0.012<br>(0.018)||-0.000<br>(0.010)||-0.013<br>(0.018)||
|T1 and T2 combined||-0.043||-0.023||0.004||-0.021|
|||(0.114)||(0.015)||(0.009)||(0.015)|
|Observations|1443|1400|5273|5026|5274|5027|5274|5027|
|Mean Dep. Control<br>Group|5.681|5.711|0.515|0.531|0.094|0.091|0.594|0.607|
|SD Dep. Control<br>Group|2.167|2.174|0.500|0.499|0.292|0.287|0.491|0.489|

Notes: columns 1 to 3 show the intention to treat (ITT) effects of PODER on the probability of enrolling in 11<sup>th</sup> grade. Columns 4 to 6 show the same effect but on the probability of re-taking COMIPEMS entry examination. All specifications include school fixed effects.

# **5.2 Average Treatment Effects**

Under the assumption that among students in the treatment groups the decision to attend a certain number of sessions depends exclusively on observable characteristics, we use matching to isolate the average treatment effects (ATE) of being exposed to different “doses” of CBT-based sessions on the socioemotional and education outcomes, vis-à-vis being assigned to the control group. We follow Cattaneo (2010) and specify four values for the treatment: 0 denoting the value assigned to students in the control group, 1 for students in the treatment group with 0 sessions, 2 for students in the treatment group who attend between 1 and 4 sessions, and 3 the value assigned to students who took 5 or more CBT-based sessions. The first stage is modeled using a multinomial logit with a flexible specification. The predicted probabilities are then used to estimate eq. (2) via a regression-adjusted inverse probability weighting (RAIPW). The graph that plots the density distributions of the propensity scores for the different exposures to the program, presented in <u>Annex E, support the hypothesis that individuals from the different groups are relatively similar in</u> 18
of displacement (IRC 2015). In some circumstances, women and girls who are sexually assaulted are forced to marry their perpetrators to avoid social stigma (Elia 2007). In South Sudan, sexual assault and abduction have been used as a means to initiate marriage while circumventing high bride prices (Amnesty International 2017). While the questionnaire does not capture indicators of gender‐based violence due to ethical concerns, the conflict increased gender‐based violence (Scott et al 2013).

Conflict affected girls were less likely to be pregnant than girls not affected by conflict. In the context of South Sudan, high rates of male mortality or morbidity due to conflict, the general absence of men from home areas due to abnormal migration or engagement in combat are contributing factors. Population statistics indicate the absence of men in the respective age groups (Figure 13). Additionally, fertility rates may be impacted by additional factors, including poor nutritional status and maternal stress, which serve to lower fecundity and increase spontaneous abortions (Blanc 2004).

**Figure 13: Population distribution, 2016.**

<!-- Start of picture text --> Above 80 Years<br>75 - 79 Years<br>70 - 74 Years<br>65 - 69 Years<br>60 - 64 Years<br>55 - 59 Years<br>50 - 54 Years<br>45 - 49 Years<br>40 - 44 Years Men<br>35 - 39 Years<br>30 - 34 Years Women<br>25 - 29 Years<br>20 - 24 Years<br>15 - 19 Years<br>10 - 14 Years<br>5 - 9 Years<br>Under 5 Years<br>15% 10% 5% 0% 5% 10% 15%<br><!-- End of picture text --> Source: High Frequency Survey South Sudan Wave 4, 2016.

Conflict affected girls had higher empowerment scores.<sup>10</sup> With the absence of men, girls might have recently assumed responsibility as head of household and responsibility for household decision making. Similarly, men may be spending most of their time outside the house fighting or looking for sources of income, which may have resulted in women taking more control of the household. This

> 10 The empowerment score considers 7 questions relating to gender roles within the household, such as ‘Who should earn money for the household? – Men, Women, Both’.

21
Tsunami, are separated by affected country and screened; thus, some countries affected by the events are not included. Therefore, some years in figure 4-1 have much smaller total damages than in figure 2-2.

The relationships among damages, losses, and total impacts appear different each year. For example, in 1990<sup>21</sup> , the aggregated total impacts are the largest, followed by the aggregated losses and the aggregated damages. In 1999<sup>22</sup> , for instance, the aggregated total impacts are the largest, followed by the aggregated damages and the aggregated losses. On the other hand, in many years, aggregated damages are the largest, followed by aggregated total impacts and aggregated losses. Since the estimation of losses (and of higher-order effects based on losses, and the construction of SAMs) relies a great deal on capital stock data, which are rarely available and thus are estimated based mostly on the available data in recent years, and thus the estimated results are sensitive to capital stock estimation, the above observations of relationship among these economic impacts cannot be easily generalized. In addition, the relationship between losses and total impacts needs further attention, because the damages and/or losses to specific industries can cause different higher-order effects: for example, damages and/or losses of manufacturing industry may result in a production bottleneck via forward linkage (supply chain) and backward linkage (demand chain) and can cause effects in a broader range of industries, depending on how the domestic (or international) interindustry relationships are intertwined. This kind of disaggregated analysis of higher-order effects for the recent disasters can be found in a companion paper, ‘Impact Estimation of Higher-Order Effects: Case Studies’.

It is a common practice to normalize data in current value with time varying factors. The analysis above is based on the constant value (in 2007 US$), controlling inflation over time. In disaster impact analysis, some other factors may need to be controlled. For instance, Pielke _et al_ . (2008) used the changes in inflation and wealth at the national level and the changes in population and housing units at coastal county level for analyzing the trends of hurricane damage in the United States between 1900

> 21 In 1990, five events are included: floods in Honduras; earthquake in Iran; drought in Mozambique; drought in Namibia; and earthquake in Philippines.

> 22 Nine events are included in 1999: earthquakes in Columbia, Greece, and Turkey; storms in Denmark and St. Kitts and Nevis; droughts in Iran, Mauritius, and Morocco; and, flood in Venezuela.

17
# **8. References**

- Ayyagari, M., Beck, T., and Demirgüç-Kunt, A., 2007. Small and Medium Enterprises across the Globe. _Small Business Economics_ 29, 415-434.

- Beck, T., Demirgüç-Kunt, A., Honohan, P., 2008. _Finance for All? Policies and Pitfalls in Expanding Access._ World Bank. Washington, DC.

- Beck, T., Demirgüç-Kunt, A., Laeven, L., and Maksimovic, V., 2006. The Determinants of Financing Obstacles. _Journal of International Money and Finance_ 25, 932-952.

- Beck, T., Demirgüç-Kunt, A., and Maksimovic, V., 2005. Financial and Legal Constraints to Firm Growth: Does Firm Size Matter? _Journal of Finance_ 60, 137-177.

- Beck, T., Demirgüç-Kunt, A., and Maksimovic, V., 2008. Financing Patterns around the World: Are Small Firms Different? _Journal of Financial Economics_ , forthcoming.

- Beck, T., Klapper, L., Mendoza, J.C., 2008. The Typology of Partial Credit Guarantee Funds around the World. Mimeo. World Bank.

- de la Torre, A., Gozzi, J. C., and Schmukler, S., 2007. Innovative Experiences in Access to Finance: Market Friendly Roles for the Visible Hand? World Bank Policy Research Working Paper 4326.

- de la Torre, A., Martínez Pería, M. S., and Schmukler, S., 2008. Bank Involvement with SMEs: Beyond Relationship Lending, World Bank Policy Research Working Paper 4649.

- de la Torre, A., Martínez Pería, M. S., Politi, M., Schmukler, S. and Vanasco, V., 2008. How Do Banks Serve SMEs? Business and Risk Management Models. Mimeo. World Bank.

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