rafmacalaba/datause-encoder-data huggingface dataset:
# **2.2 Humanitarian Shelter Program**

In Jordan, there is limited secure and affordable housing available for Syrian refugees. The UNHCR Quarterly Analysis of Refugees in Jordan found that in 2022, 25% of Syrian households perceived a current threat of eviction.<sup>4</sup> Additionally, one in four refugees applying for rental assistance from a major Jordanian humanitarian organization had moved at least three times in the previous year (NRC 2015), demonstrating the widespread precariousness of their housing situation. Refugees often rely on humanitarian assistance to manage rising rental costs, but these funds are insufficient to cover the full expense, and hence 70% of Syrian households living in local communities reported having rental debt (Hagen-Zanker et al. 2018; Lehmann and Masterson 2014). Frequent relocation due to rental affordability issues and a lack of stable rental contracts may also make it challenging for refugees to maintain necessary documentation and access public services (NRC 2015).

Given this situation, the implementing partner designed the Shelter Program to secure more stable housing for Syrian refugees living in host communities. The intent was also to use the program to facilitate social cohesion between refugees and hosts by including direct benefits for the landlords renting to refugees. It focuses on Syrian refugees in the country’s northern governorates of Irbid and Mafraq, which are adjacent to the Syrian border and contain a large share of the country’s displaced persons.

The overall Shelter Program is composed of several different assistance modalities, including projects focused on water and sanitation, eviction protection, and “inclusion kits” for individuals with disabilities. This study evaluates the impacts of a housing shelter modality, which we will refer to in what follows as the Housing Subsidy Program (HSP).<sup>5</sup> Under HSP, the implementing partner directly compensated Jordanian landlords for rent on behalf of their refugee tenants, with the refugee household receiving rent-free housing for 9 to

> 4Determined using the Vulnerability Assessment Framework, a bi-annual survey of registered refugees in Jordan.

> 5The pre-analysis plan proposed the evaluation of the far less generous energy subsidy program as well. That will be the subject of future research.

8
We analyzed determinants of caregiver under- and over-estimation using two samples (within-community and whole) and present results by each sample.

# 4.1. <u>Within-community Rankings</u>

Using ASQ-I ranked percentiles within communities, 8.2% (N=275) of caregivers underestimated and 49.3% (N=1,655) over-estimated their children’s abilities. In contrast, 42.6% (N=1,431) caregivers had matched scores.

Child age was associated with a small, decreased odds in under-estimation of child abilities (Table 2, HAZ model: OR=0.97, 95% CI: 0.94-0.99; WAZ model: OR=0.96, 95% CI: 0.94-0.99). Caregivers who believed they have a lot of influence on their child’s intelligence had lower odds of under-estimation compared to those who did not believe they have any influence, controlling for all other covariates (HAZ and WAZ models: OR=0.68, 95% CI: 0.49-0.94).

For every standard deviation increase in HAZ, the odds of caregiver over-estimation decreased 29% (OR=0.71, 95% CI: 0.66-0.76), controlling for child, caregiver, and household covariates. Similarly, for every standard deviation increase in WAZ, the odds of caregiver overestimation decreased 20%, after controlling for all covariates (OR=0.80, 95% CI: 0.74-0.85). In model (1) with HAZ, caregivers with secondary or higher education had 0.70 times the odds of over-estimation compared to those who did not attend school (95% CI: 0.54-0.91). Similar results were found in model (2) with WAZ (OR=0.72, 95% CI: 0.56-0.93).

14
<!-- Start of picture text --> Higher than Secondary School<br>No Difficulty Any difficulty<br><!-- End of picture text --> <!-- Start of picture text --> Afghanistan<br>Bangladesh<br>Dominican Rep.<br>Ethiopia<br>Gambia<br>Indonesia<br>Kiribati<br>Liberia<br>Malawi<br>Mexico<br>Namibia<br>Nigeria<br>Panama<br>Papua New Guinea<br>Philippines<br>Rwanda (Census)<br>Rwanda (LFS)<br>South Africa (Census)<br>South Africa (GHS)<br>Tanzania (Census)<br>Tanzania (LSMS)<br>Uganda<br>Vanuatu<br>Vietnam<br> - 0.20 0.40 0.60 0.80 1.00<br><!-- End of picture text --> Table 4.1 presents several educational indicators at the household level for the three disability categorizations. It starts with the share who ever attended school for the household head with and without adults with functional difficulties. Households with functional difficulties have heads with a significantly lower share who ever attended school in all but two countries (Afghanistan and The Gambia). As found earlier for individuals in Figure 4.1, panel 3 of Table 4.1 shows that there is a gradient in the educational attainment of household heads between No difficulty, Some difficulty and At least a lot of difficulty. Results in Table 4.1 also indicate that there is no consistent and significant difference in terms of children out of school rates depending on the household’s functional difficulty status nor in terms of the share of household consumption expenditures dedicated to education.

22
pant schools to three ASP interventions: one based only on recreational activities such as sports, art, and dancing ( _Clubs_ ) and two psychological interventions based, respectively, on a curriculum that aims to strengthen character and virtues ( _Virtue_ ) and a mindfulnessand-relaxation program ( _Mindful_ ). Every _Virtue_ and _Mindful_ meeting was immediately followed by _Clubs_ activities. By comparing the average outcomes of students enrolled in schools assigned to either of the two psychology-based curricula with students enrolled in schools assigned to _Clubs_ , we measure the net socio-emotional-learning channel. We provide evidence that the psychology-based interventions improve students’ behavior at school and decrease valence, a proxy for emotion regulation.

We also find policy-relevant heterogeneous effects. First, the psychology-based curriculum has larger effects on students more vulnerable to violence: boys, students older than 14 years, those with bad or average school behavior at baseline, those in urban schools within high-homicide-rate municipalities, and those not living with both of their parents. Second, the type of psychology-based curriculum matters. On average, the _Virtue_ curriculum affects student behavior, risk-taking, and emotion regulation (that is, valence). Meanwhile, the _Mindful_ curriculum improves behavior at school, decreases school dropout relative to _Virtue_ , and increases fluid intelligence after negative stimuli. Therefore, we conclude that _Mindfulness_ , which is a standard intervention, is more effective in improving students’ outcomes.

# **References**

- Alan, S. and Ertac, S. (2019). Mitigating the gender gap in the willingness to compete: Evidence from a randomized field experiment. _Journal of the European Economic Association_ , 17(4):1147–1185.

- Alan, S. and Kubilay, E. (2024). Empowering adolescents to transform schools: Lessons from a behavioral targeting. Working Paper 18735, Centre for Economic and Policy Research.

36
**Figure 4 – Coverage of commonly used perceptions and attitudes-based data surveys**

|**Survey**|**Coverage **|**Periodicity**|**Since**|**Latest round**|
|---|---|---|---|---|
|World Values<br>Survey <sup>5</sup>|80 countries|Every 3-5 years|1981|2017-2021|
|Afrobarometer<sup>6</sup>|40 African<br>countries|Every 2 years|1999 (9 waves)|ongoing, to be<br>completed in<br>October 2022|
|Arab Barometer<br>7|12 countries|Ever 2-5 years|2006 (6 waves)|2020-2021|
|Asian<br>Barometer<sup>8</sup>|18 Asian<br>countries|Every 2-3 years|2001|2018-2021|
|Latin<br>Barometer<sup>9</sup>|18|Every 1-2 years|1995|2020|

# _C ONSTRAINTS_

While these indices contain a wealth of data, it is important not to rely solely on a composite index to avoid missing key nuances or changes in components of social cohesion. For example, a country or region may show an improvement in social cohesion overall, but this difference may be driven by an improvement in people’s perceptions of state services and miss out on growing polarization among dominant and minority groups.

In addition, it is important to remember that these surveys are not specifically geared to capture social cohesion. Instead, indices capture a wide variety of dimensions such as political freedoms, democracy, and perceptions of economic opportunities. Moreover, indices use a standard set of questions for quantifying levels of trust, which may or may not correspond to the social cohesion issues most salient within a given context. For instance, for a context where out-groups are not a dominant concern, the question on trusting other people may be interpreted differently by respondents. In surveys specific to social cohesion, this could be addressed by asking a set of questions that attempt to capture the same element, allowing for validation at the analysis stage. When using existing indices, one option may be to use these as starting points for additional analysis to allow for contextualization at a project level.

Given the tradeoffs of breadth versus depth, perceptions data have limitations when it comes to their use for monitoring, predictive and evaluative purposes. It is difficult to meaningfully interpret changes in perceptions through statistical methods alone given the intangible nature of such concepts – for instance, more ground level context through qualitative studies may be needed to understand how an increase in trust towards out-groups, as captured in surveys, manifests contextually.

Finally, given their large-scale nature and regular data collection cycles, these indices may be poorly suited to tracking impacts of specific events. While social cohesion overall is a sticky concept that generally changes slowly over time, it is sensitive to shocks that can effect significant changes. To understand the impacts of

> 5 https://www.worldvaluessurvey.org/wvs.jsp

> 6 https://afrobarometer.org/

> 7 https://www.arabbarometer.org/

> 8 http://www.asianbarometer.org/

> 9 https://www.latinobarometro.org/lat.jsp?Idioma=0

16 | P a g e
**-** **<u>Table 4: Effects of gender differentiated land alienation rights on agricultural investment (IHPS)</u>**

||<br>**Organic**<br>**app**|<br>**manure**<br>**lied**|**Cash cro**|**p planted**|
|---|---|---|---|---|
|Women have the right to make decisions on crop cultivation (_β_11)|0.034|0.037|-0.004|-0.001|
||[0.271]|[0.206]|[0.920]|[0.991]|
|Women have the right to invest (_β_12)|-0.014|-0.012|-0.018|-0.015|
||[0.854]|[0.855]|[0.797]|[0.836]|
|Women have the right to bequeath (_β_13)|0.158***|0.164***|0.046|0.049|
||[0.001]|[0.000]|[0.393]|[0.394]|
|Women have the right to sell (_β_14)|0.052|0.051|-0.047|-0.047|
||[0.328]|[0.352]|[0.653]|[0.616]|
|Men have the right to make decisions on crop cultivation (_β_21)|<br>0.060|<br>0.062|<br>0.050|<br>0.046|
||[0.552]|[0.567]|[0.502]|[0.524]|
|Men have the right to invest (_β_22)|-0.096|-0.084|0.052|0.064|
||[0.430]|[0.501]|[0.498]|[0.399]|
|Men have the right to bequeath (_β_23)|<br>0.021|<br>0.012|<br>0.179**|<br>0.177**|
||[0.858]|[0.916]|[0.015]|[0.014]|
|Men have the right to sell (_β_24)|<br>0.053|<br>0.033|<br>0.026|<br>0.019|
||[0.564]|[0.704]|[0.690]|[0.739]|
|Parcel characteristics|No|Yes|No|Yes|
|Observations|2,011|2,011|2,011|2,011|
|R-squared|0.777|0.780|0.585|0.588|
|Mean of dependent variable|0.233|0.233|0.070|0.070|
|Sd. of dependent variable|(0.416)|(0.416)|(0.238)|(0.238)|
|Mean of women’s right to bequeath|0.454|0.454|0.454|0.454|
|<br>Sd. of women’s right to bequeath|(0.498)|(0.498)|(0.498)|(0.498)|
|<br>Mean of women’s right to sell|<br>0.402|<br>0.402|<br>0.402|<br>0.402|
|<br>Sd. of women’s right to sell|(0.490)|(0.490)|(0.490)|(0.490)|
|<br>Mean of men’s right to bequeath|<br>0.334|<br>0.334|<br>0.334|<br>0.334|
|<br>Sd. of men’s right to bequeath|(0.472)|(0.472)|(0.472)|(0.472)|
|<br>Mean of men’s right to sell|<br>0.299|<br>0.299|<br>0.299|<br>0.299|
|Sd. of men’s right to sell|(0.458)|(0.458)|(0.458)|(0.458)|

_Notes_ : IHPS data are used and the sample includes owned parcels only. Indicator variables for women’ ownership or men’s ownership are controlled for. Household fixed effects and parcel size are controlled for throughout. Parcel characteristics include soil type (sandy, between sandy and clay, or clay), land slope (flat, slight slope, moderate slope, or hilly), and whether land is in swamp/wetland. Robust standard errors are clustered by district. Figures in square brackets are p-values from wild cluster bootstrap (Roodman, 2018) with Rademacher weights and 1000 replications. *** significant at 1%; significant at 5%; * significant at 10%.

19
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include both instrument-level (investment grate, amount issued, tenor) and issuer-level (total firm assets, firm leverage, dummy for public listing status) characteristics. The regression is run at the penalty-level rather than the bond-level as both the target date and penalty type can vary by penalty within the same SLB. The regression sample includes both types of financial incentives (step-ups and step-downs) for which the payoff may be influenced by the target date. The chosen controls ensure that variation attributable to certain types of issuers and instruments is accounted for. The former is relevant as some firms may have greater monitoring or disclosure than others (such as larger firms, publicly listed firms and those with creditors), making them less likely to exploit such features for their benefit. The latter ensure the variation due to various bond characteristics other than those under study is controlled for.

Table 3 presents these results. Model 1 only includes our variable of interest, the dummy for a step-up penalty, for which the coefficient is positive and significant. The coefficient indicates that a step-up penalty is associated with a target date that is set later by a period of approximately 21 percent of the bond’s tenor. The average target date for step-downs is set at about one-third (36 percent) of the bond’s tenor. Comparatively, the average target date for step-ups is set significantly later at approximately 57.2 percent of the bond’s tenor.

**-** **<u>Table 3: Penalty Level Regression Analysis for Target Period</u>**

||(1)<br>Model 1|(2)<br>Model 2|(3)<br>Model 3|(4)<br>Model 4|
|---|---|---|---|---|
|Coupon step-up penalty dummy|0.212***|0.235***|0.170***|0.134**|
||(0.025)|(0.034)|(0.038)|(0.059)|
|Investment grade dummy||0.078***||0.099**|
|||(0.028)||(0.042)|
|Amount issued (US$ billions)||-0.027||0.123*|
|||(0.041)||(0.069)|
|Tenor (years)||-0.009**||-0.007|
|||(0.004)||(0.007)|
|Total assets (US$ billions)|||0.0003|0.0006|
||||(0.001)|(0.001)|
|Leverage (%)|||-0.318***|-0.124|
||||(0.088)|(0.139)|
|Public organization dummy|||-0.146***|-0.075|
||||(0.048)|(0.058)|
|Constant|0.361***|0.386***|0.645***|0.506***|
||(0.022)|(0.027)|(0.068)|(0.095)|
|Observations|248|158|138|80|
|Adjusted R-squared|0.093|0.053|0.162|0.112|

_Robust standard errors are in parentheses *** p<.01, ** p<.05, * p<.1_ Model 2 includes instrument-level controls in the above specification. Results show that the coefficient associated with the dummy variable for step-up penalty continues to remain significant, with little change in the magnitude. Additionally, the paper finds that a bond that is investment grade is likely to have a later target date but an earlier one for bonds with longer 12
-35-

||(B|illions<br>of U.S.|Dollars)|||
|---|---|---|---|---|---|
||Sok|Disbursement|s<br>nsBa|||
|1970:|0.4|0.1|0.07|0.02|--|
|1980:|13.0|4.0|1.2|1.2|--|
|1983:|18.5|3.0|2.2|1.5|-|
|1984:|17.7|2.8|2.9|1.7|(0.07)|
|1985:|18.5|2.2|3.1|1.4|1.7|
|1986:|18.0|2.6|1.5|1.2|(0.9)|
|1987:|23.0|2.0|1.6|0.8|4.6|
|1988:|22.1|1.9|1.5|1.1|(1.3)|
|||....|---|....|--<br>-|
|1983-88<br>TOTAL||14.5|12.8|7.7|4.03|

Source: World Bank Debt Tables, 1989-1990.
# **1. Introduction**

Matching grants consist of partial subsidies (typically 50 percent) provided by government programs to try to spur firms to undertake innovative activities such as creating new products, technological upgrading, exporting, using business development services, and more broadly, firm expansion. They are one of the most common policy tools used in private sector development projects in developing countries, and have been included in more than 60 World Bank projects totaling over US$1.2 billion, funding over 100,000 micro, small and medium enterprises.<sup>1</sup> Yet credible evidence on whether these grants induce firms to undertake innovative activities that they would not otherwise do ( _additionality_ ) is limited: there are several case studies and nonexperimental evaluations<sup>2</sup> that are subject to the concern that firms that self-select into such programs may differ in unobservable dimensions from those not selected for funding, while a number of recent attempts to conduct randomized experiments to evaluate these programs in SubSaharan Africa have failed for reasons such as lack of applications or governments changing their minds on random selection (Campos et al, 2014). The one exception is work by Bruhn et al. (2012) on a matching grant program in Puebla, Mexico, that had a higher than usual match rate (70 to 90% subsidy) for consulting services, and found firms receiving the subsidy did not significantly innovate more, but did do more marketing, improved their accounting, and increased sales and profits.

A potential rationale for these subsidies is that owners of small firms may under-invest in learning and innovation, either because they under-estimate the gains from using business development services, because these gains spill over to other firms, or because of credit market imperfections. In particular, two characteristics of innovation make financing more difficult: (i) innovation produces an intangible asset, and (ii) the returns to innovation investment are uncertain (e.g. Hall and Lerner (2009) and Kerr and Nanda (2014)). Reluctance to make such investments may be particularly high in fragile and conflict-afflicted states, in which market failures may be more pervasive and the underlying uncertainty increases the option value of waiting to make investments

> 1 Data from a World Bank Latin American and the Caribbean overview available at <u>http://go.worldbank.org/OVDGTHSWY0.</u> 2 See Biggs, 1999; Phillips, 2001, 2002; Castillo et al., 2011; Crespi et al., 2011; Gourdon et al., 2011, and LopezAcevedo and Tan, 2011.

2
- 48 -

Table 4.3at PER CAPITA REVENUES OF SUBRATIONAL GOVERNMENTS a/ -In constant 1980 US dollars-

||1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|---|---|---|
|LACSAMPLEP/|||||||||||
||94|95|99|101|104|109|103|89|69|74|
|MARKET ECONOMIESh/|2064|2190|2265|2383|2417|2327|2097|L009|2030|2046|

-------------------------------------------------------------------

__--------__-----------___------------a/ Revenues lnclude own revenues and grants. _hi_ Weighted Averages. Comparability over tine affected by the availability of data.

Table 4.3b: PER CAPITA REVENUES OF SUBNATIONAL GOVERNMENTS -Variation Index, 1975=100- a/

||1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|---|---|---|
|LAC SAMPLE|100|100|105|107|110|115|109|94|73|78|
|MARKET ECONOMIES|100|106|110|116|117|113|102|97|98|99|

-----------------------------------------------------------------------

__----__---------------------------a/ The variation index for the Lndividual countries is based on constant national currencies time series while the variation index for the sample average is based on constant US dollars.

Source: IMP, Government Flnance Statistics Yearbook, various years.

a period of steady erosion of personal incomes and wage control. Eighth, a large informal sector 5 S growing in the metropolitan areas of LAC countries, which remains outside the reach of fiscal authorities. Finally, the less urbanized areas are negatively affected by the loss of active population and enterprises to other regions.

4.04 Main Revenue Sources. For simplicity, the revenue sources of LAC regional and local govern-ments will be reviewed in four conventional categories: (a) Tax Revenues; (b) Non-Tax Revenues; (c) Intergovernment Transfers; and (d) Borrowing. The first two categories, which group the only truly independent sources of subnational revenues, are significant in those regional and local jurisdictions which are more urbanized, but have little weight in local rural comnuniticis. Transfers become the most important category of revenues in predominantly rural areas. Borrowing has never been a significant source of revenues for local authorities as we shall see below. Table 4.4a shows the main structure of revenues of regional and local governments in Argentina, Brazil, Colombia and Mexico. The variation over time of the yield from different sources in constant national currencies is shown in Tables 4.4 b, c, d, & e in Annex). It is interesting to note the instability of the grant elements over the years