rafmacalaba/datause-encoder-data huggingface dataset:
where **X** _i_ is a vector of long-term attributes of the household (i.e. size of the house or education level of the survey respondent) and **Z** _i_ is a vector of short-term behavioral measures (i.e. how long they typically leave the windows open in a day). We run several versions of equation 3 that separately estimate the impact of long-term and short-term characteristics on infiltration and we also jointly estimate their effect. As in our previous analysis, we cluster errors at the census block level. We detail the specific long-term and short-term characteristics that we examine when we discuss the results of these regressions. In all cases we use the level of these characteristics that households provided in response to the first round of our survey, prior to our intervention.

# **4 Results**

## **4.1 Effectiveness of informational treatments**

Existing work indicates that providing information about the negative health effects of air pollution substantially increases willingness-to-pay for reductions in pollution exposure (Greenstone et al., 2023). The _Leaflet_ treatment tests this hypothesis by only providing information about the consequences of air pollution exposure and how to avoid it. Table 1A reports the impact of the leaflet treatment on our measure of knowledge of air pollution, avoidance behaviors, and health symptoms. All estimates are insignificant, indicating that we find no evidence that information provided through a leaflet changes knowledge of air pollution or increases avoidance behavior (Columns 1 and 2).

Our _Outdoor_ and _Indoor and Outdoor_ treatments provided households with information about indoor and outdoor pollution levels. Households appear to have consumed this information. In the survey we asked households about the frequency with which they check information about air pollution levels. Table A5 shows that no household checked indoor or outdoor pollution prior to our intervention. However, after our intervention, 56% of households in the _Outdoor_ treatment and 70% in the _Indoor and Outdoor_ treatment reported checking outdoor air pollution levels. Similarly, 85% of households in the _Indoor and Outdoor_ treatment reported checking indoor pollution levels. In contrast, households in the _Leaflet_ treatment did not change their behavior with respect to checking pollution levels in between the survey rounds.

In contrast to the _Leaflet_ treatment, the _Outdoor_ treatment may have increased households’ 11
_Social Capital, Household Welfare and Poverty in Indonesia_ 37

**Table 8: Poverty and Social Capital: Quanfile Regression Results**

|**_I_________________________________________**|i<br>**percentile**|**25th**<br>**percentile**|**Median**|**75t**<br>**percentile**|**gotn**<br>**percentile**|
|---|---|---|---|---|---|
|Social Capital Index|0.0096*|0.0090*|0.0078*|0.0048*|0.0049*|
|Number of Memberships|0.01 66*|0.0213*|0.0208*|0.0078|0.0106|
|Heterogeneity Index|0.0018|0.0044*|0.0043*|0.0022|0.0034*|
|Meeting Attendance|0.0013|_-0.0005_|-0.0025|-0.0027|-0.0032|
|Index of Participation in Decision Making|0.0047*|0.0023*|0.0023*|0.0018*|0.0018*|
|Cash Contribution Score|0.0145|0.0110|0.0117|0.0150|0.0201*|
|Work Contribution Score|0.0057|0.0025|0.0004|-0.0040|-0.0053|
|| Community Orientation|0.0004|0.0001|0.0002|-0.0003|0.0001|
|<br>Years of Education|0.02'85*|0.0312*|0.0290*|0.0396*|0.0519*|

Note: Asterisk (*) indicates that the coefficient is significant at the 90% confidence level.

The contrast between the pattern of returns to social capital with that of human capital is remarkable: the returns to education get larger as one moves up the distribution and are almost twice as high at the 90th percentile than at the 1Oth percentile. In terms of relative returns, one can indeed say that social capital is the capital of the poor.

The third and final method we use to investigate differential returns to social capital between the poor and the non-poor is the split-sample approach. However, we cannot simply split the sample at the poverty line, or use a conventional interaction variable between the regressors and the poverty status variable (which is equivalent econometrically), because the latter is endogenous. Indeed, the poverty line is defined in terms of household expenditure per capita--the dependent variable of the model. Hence we need to split the sample on the basis of exogenous assets. In the context of a poor rural area, land holdings is an obvious choice. We split the sample into households below and above the median land holding<sup>23</sup> , and estimated equation (1) on each half sample (Table 9). The returns to the aggregate social capital index are slightly higher for households with below-median land holdings. The disaggregated model makes it clear
**Figure 13: HH consumption in the conflict scenario relative to the baseline in 2015, 2020 and 2025.**

<!-- Start of picture text --> 2.0<br>0.0<br>-2.0<br>-4.0<br>-6.0<br>-8.0<br>-10.0<br>-12.0<br>2015 2020 2025<br><!-- End of picture text --> Source: Authors’ simulations and calculations.

# **Impact on poverty**

To examine poverty and welfare implications of these scenarios, we compare the distributional incidence of the scenarios, including the baseline case, i.e. by looking at growth incidence curves, or the change in income across households at different points in the income distribution. More specifically, for all the scenarios we calculate the post-shock percent change in per capita income across the regional income distribution for all Africa and contrast it to the pre-shock income distribution in 2010. We take the year 2015 for its post-shock income distribution mainly because most of the shocks are introduced in the first few years after 2012. The resulting growth incidence curves are shown in Figure 14, which depict the impact across the 2010 income distribution for the six scenarios.

The first observation is that the curve associated with the baseline case shows a negative slope, indicating overall progressive effects or the inclusiveness of growth, since in general, the poorest households will gain more or do better than the richest by 2015. Despite the generally negative shocks in the alternative scenarios, the inclusiveness of the curve is not completely reversed (see figure 16), but the curve associated with each scenario will shift up and down relative to the reference case, depending on the severity of the shocks. Comparing across our six scenarios, households in the 6<sup>th</sup> decile or below would generally not be worse off with a prolonged recession combined with financial flow restrictions to SSA scenarios. This seems to confirm that Africa’s growth and poverty performance going forward, as in the recent post-recession period, is generally more robust and relatively “less reliant” on the state of high income countries or the world economy. That said, any drop in financial capital flows will however hit the richer household severely. The opposite is true for droughts, during which the poor are disproportionally hard hit. The dependence on primary exports and commodity prices is still reflected in 19
40

# **Table 7**

# _Delivery—Diagnostic Table_

|Delivery Variables|Top<br>Performers|Obs|At-scale<br>programs|Obs|
|---|---|---|---|---|
|_Robust characteristics_|||||
|Initial period of face-to-face training for several days in a<br>row|100.00%|15|85.42%|48|
|Training held at central location including hotel<br>conference room etc.|37.50%|16|72.97%|139|
|Includes distance learning|9.09%|11|NA|NA|
|_Characteristics with large coefficients_|||||
|Proportion of face-to-face training spent practicing with<br>teachers|39.81%|9|15.57%|34|
|Trainers are education university students|6.25%|16|0%|139|
|Follow-up visits to review material|12.50%|16|10.42%|48|
|Includes follow-up visits|84.62%|13|49.64%|139|
|Median Number of follow upvisits|3.5|13|0|130|

Obs refers to the number of PD programs in each sample (top performing, evaluated programs, and at-scale programs) that report whether or not they have a given characteristic.
# **REFERNCES**

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- Brechet, Thierry and Benoit Lussis (2006).  “The Contribution of the Clean Development Mechanism to National Climate Policies.” _Journal of Policy Modeling_ 28:981-994, 2006.

- Buys, Piet, Uwe Deichmann, Craig Meisner, ThaoTon That, and David Wheeler (2007). “Country Stakes in Climate Change Negotiations: Two Dimensions of Vulnerability.” World Bank Policy Research Working Paper No. 4300, Washington, D.C.

- Chakrabarti, Avik (2001). “The Determinants of Foreign Direct Investment: Sensitivity Analyses of Cross-Country Regressions.” _Kyklos_ 54(1): 89-113.

- Congleton, Roger, 1992. ‘Political Institutions and Pollution Control’, Review of Economics and Statistics 74: 412-421.Congleton 1992

- Cosbey, Aaron, Warren Bell, Deborah Murphy, Jo-Ellen Parry, John Drexhage, Anne Hammill, and John Van Ham (2005).  “Which Way Forward? Issues in developing an effective climate regime after 2012. Winnipeg: International Institute for Sustainable Development.

- Da Cunha, Kamyla Borges, Arnaldo Walter and Fernando Rei (2007). “CDM Implementation in Brazil’s Rural and Isolated Regions: The Amazon Case.” _Climatic Change_ 84:111-129.

- Dinar, S. A. Dinar and P. Kurukulasuriya, SCARPERAION: An Empirical Inquiry into the Role of Scarcity in Fostering Cooperation between International River Riparians.  World Bank Policy Research Working Paper No. 4294, Washington, D.C., 2007.Dinar et al. (2007)

- Dinar, Shlomi, (2009). “Scarcity and Cooperation along International Rivers”, _Global Environmental Politics_ 9(1), forthcoming.Dinar 2009

- Dunning, John H (1993). “Multinational Enterprise and the Global Economy.” Wokinghan: Addison-Wesley.

22
# **Table 6: Cross-country covariates associated with deposit account penetration**

The results of OLS regressions of the natural logarithm of number of deposit accounts per 1,000 adults in commercial banks on different country characteristics.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||0.685**|0.666**|0.506**|0.467**|0.675**|0.708**|0.561**|0.476**||0.589**|0.669**|0.716**|
|GDP per capita (log)|*|*|*|*|*|*|*|*|0.209*|*|*|*|
||(0.055)|(0.053)<br>0.204**|(0.078)<br>0.170**|(0.083)|(0.062)<br>0.205**|(0.057)<br>0.219**|(0.053)<br>0.213**|(0.162)<br>0.180**|(0.109)<br>0.217**|(0.068)<br>0.169**|(0.055)<br>0.209**|(0.067)<br>0.205**|
|Population density (log)||*|*|-0.084|*|*|*|*|*|*|*|*|
|||(0.064)|(0.056)<br>*|(0.130)|(0.065)|(0.070)|(0.062)|(0.062)|(0.066)|(0.061)|(0.063)|(0.064)|
|Branches per adult (log)|||0.244<br>(0.125)||||||||||
|Branches per km<sup>2</sup>(log)||||0.243**<br>(0.114)|||||||||
|Expected real economic|||||0.272||||||||
|growth|||||(0.438)||||||||
|Inflation||||||0.003<br>(0.008)|0.641**||||||
|Deposit insurance|||||||*<br>(0.160)||||||
|Electricity consumption||||||||0.110<br>(0.175)|0.877**||||
|Land line and cell phone users|||||||||*<br>(0.244)||||
|Absence of violence||||||||||0.023<br>(0.095)|||
|Offshore financial center|||||||||||-0.109<br>(0.300)||
|Deposit rate||||||||||||0.047**<br>(0.019)|
||0.716|-0.046|3.738**|4.211**|-0.356|-0.514|0.421|0.955|-0.074|0.839|-0.082|<br>-0.695|
|Constant|(0.484)|(0.579)|(1.748)|(1.694)|(0.899)|(0.663)|(0.514)|(0.612)|(0.638)|(0.694)|(0.590)|(0.679)|
|N|100|99|93|85|97|94|98|76|73|88|99|70|
|Adjusted R<sup>2</sup>|0.630|0.673|0.690|0.648|0.674|0.681|0.711|0.581|0.706|0.636|0.669|0.678|
|Robust standard errors in parenth<br>***p<0.01,**p<0.05,*p<0.1|eses||||||||||||

26
<!-- Start of picture text --> Bamako<br>—— Minibus Taxis Sotrama<br>Percentage Access:<br>| High : 70<br>= Low :0 ,2 at [ae ee i va<br>/ ial<br>|=-|<br>a<br>‘ay #4. \<br>! | is al 7 ~ |<br><!-- End of picture text -->

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handling practices. Under-pricing by small companies is often combined with under-reserving, leading to prolonged disputes and settlement delays. This affects the reputation of insurers and contributes to pervasive public mistrust. Competition for market shares in a small market can ultimately result in pyramidal structures, leading to larger losses and even insolvency. Table 4 shows MTPL loss ratios in countries for which the data is available on a net incurred basis.

**Table 4: MTPL Loss Ratios** **<u>(%) in Selected Countries</u>**<sup>**8**</sup>

|**Country**|**2004**|**2008**|
|---|---|---|
|Egypt|292|316|
|Jordan|87|101|
|UAE|76|89|
|Tunisia|114|87|
|Morocco|84|75|

Sources: AXCO, Supervisors Reports

**Property and construction is the second most important line of business, is clearly larger in oil producing countries and clearly associated with coverage of risks in oil and gas extractive industries.** Interestingly, revenues are larger in the oil producing countries outside the GCC.  It is also noticeable that agriculture does not yet generate a substantial income stream in the relevant countries (Egypt, Morocco, Syria).

**Transportation (MAT) premiums account for a relatively low share of the non-life premium in all sub-groups.** Not surprisingly, it is larger in the countries that have a major international airline, such as Egypt, the KSA and the UAE.  The strategic location of these countries between the Western and Eastern hemispheres is being exploited with a number of successful international airlines being established. The resulting hull and liability insurance business contributes to premium income, although most of this risk is reinsured internationally.

**Seven countries have shares in excess of 10% under the ‘Other’ heading.** In the case of the oil and gas producing countries this reflects partly liability and engineering business arising from oil and gas extraction and a requirement (particularly in GCC countries) that employers cover for occupational injuries. Workman‟s compensation is compulsory for expatriates in some states.  In the case of Morocco, compulsory workman‟s compensation is handled through the private sector.

**All in all, the insensitivity of non-life and life premiums to income levels suggests that insurance penetration in MENA is still primarily driven by large enterprises, compulsory insurance, and effective enforcement, rather than voluntary purchases by the private sector** . This is true for both the non-life and life sectors.  The countries with partially developed life insurance sectors have historical European connections (Jordan, Lebanon, Tunisia and Morocco).  Most of the remainder has virtually non-existent life markets despite, in some cases, very high income levels.

**Finally, health insurance is becoming an important line of business in some MENA countries, although growing from a low base.** Health insurance is becoming a sizable industry

> 8 The Tunisian ratios reflect an effective reform program while the Egyptian ratios reflect a significant upgrading of claims provisions in the state owned insurers.

12
3 theoretical studies on fiscal policy and the real exchange rate (see Penati 1983 -or a survey) have focused on the real exchange rate effect of changes in the level of total government expenditure.

Only recently, a few papers have examined the theoretical link between the changes in the composition of government exDenditure and real exchange rate (Monteil 1986, Khan and Lizondo 1987). Khan and Lizondo (1987) show that for a small open economy, the reduction in government expenditure that falls wholly on tradables has no effect on the real exchange rate because prices of non tradables are not altered by this reduction. However, when the same reduction in expenditure falls entirely on non-tradables, it depreciates the real exchange -ate. Similarly, increases in government's propensity to spend on non-tradables with no change in expenditure levels, appreciates the real exchange rate (Monteil 1986).

Most empirical work on real exchange rate models for developing countries (Edwards 1989, Cottani et al 1991, Easterly 1991, Rodriguez, 1991) have also ignored the effect of changes in the composition of government expenditure on RER. Barring one exception (Ramangkura and Nidhiprabha 1991), the comoosition of government expenditure do not feature in their econometric models.

This paper develops a dependent economy model of 'equilibrium' real exchange rate that incorporates not only the level but also the composition of government expenditure. The distinctive feature of this model is that it allows for differences between current and capital expenditure in terms of their propensities to spend on non-tradables. This means changes in the share of
Bloom, H.S., and C. Weiland. 2015. _Quantifying variation in Head Start effects on young children's cognitive and socio-emotional skills using data from the National Head Start Impact Study._ Available at SSRN 2594430.

- Bouguen, A., D. Filmer, K. Macours, and S. Naudeau. 2018. "Preschool and parental response in a second best world evidence from a school construction experiment." _Journal of Human Resources_ (The World Bank Working Paper Series) 53 (2): 474-512.

- Bowne, J.B., H. Yoshikawa, and C.E. Snow. 2016. "Experimental impacts of a teacher professional development program in early childhood on explicit vocabulary instruction across the curriculum." _Early Childhood Research Quarterly_ 34: 27-39.

- Brinkman, S.A., A. Hasan, H. Jung, A. Kinnell, and M. Pradhan. 2017. "The impact of expanding access to early childhood education services in rural Indonesia." _Journal of Labor Economics_ 35 (S1): S305-S335.

- Brotman, L.M., S. Dawson-McClure, D. Kamboukos, K.Y. Huang, E.J. Calzada, K. Goldfeld, and E. Petkova. 2016. "Effects of ParentCorps in prekindergarten on child mental health and academic performance: Follow-up of a randomized clinical trial through 8 years of age." _JAMA Pediatrics_ 170 (12): 1149-1155.

- Carneiro, P., and R. Ginja. 2014. "Long-term impacts of compensatory preschool on health and behavior: Evidence from Head Start." _American Economic Journal: Economic Policy_ 6 (4): 13573.

- Clements, D.H., J. Sarama, M.E. Spitler, A.A. Lange, and C.B. Wolfe. 2011. "Mathematics learned by young children in an intervention based on learning trajectories: A large-scale cluster randomized trial." _Journal for Research in Mathematics Education_ 42 (2): 127-166.

- Clements, D.H., J., Sarama, C.B. Wolfe, and M.E. Spitler. 2013. "Longitudinal evaluation of a scaleup model for teaching mathematics with trajectories and technologies: Persistence of effects in the third year." _American Educational Research Journal_ 50 (4): 812-850.

- Diamond, A., W.S. Barnett, J. Thomas, and S. Munro. 2007. "Preschool program improves cognitive control." _Science_ 318 (5855): 1387.

- Dillon, M.R., H. Kannan, J.T. Dean, E.S. Spelke, and E. Duflo. 2017. "Cognitive science in the field: A preschool intervention durably enhances intuitive but not formal mathematics." _Science_ 357 (6346): 47-55.

- Drange, N., T. Havnes, and A.M. Sandsør. 2016. "Kindergarten for all: Long run effects of a universal intervention." _Economics of Education Review_ 53: 164-181.

- Felfe, C., N. Nollenberger, and N. Rodríguez-Planas. 2015. "Can’t buy mommy’s love? Universal childcare and children’s long-term cognitive development." _Journal of Population Economics_ 28 (2): 393-422.

- Frisvold, D.E., and J.C. Lumeng. 2011. "Expanding exposure can increasing the daily duration of head start reduce childhood obesity?" _Journal of Human Resources_ 46 (2): 373-402.
**Table 16. List of selected product lines facing tariffs above 30 percent in Russia**

|**Code**<br>**6 digits**|**Product Description**|**MFN Tariff**<br>**Rate**|
|---|---|---|
|**2003**<br>|||
|020120|Meat of bovine animals: other cuts, fresh or chilled, with bone in|30|
|020210|Meat of bovine animals: carcasses and half-carcasses, frozen|30|
|220860|Vodka|30|
|020220|Meat of bovine animals: other cuts, frozen, with bone in|31|
|650692|Other :-- Of fur skin|31|
|020230|Meat of bovine animals: boneless, frozen|32|
|020726|Meat and edible cuts and offal of turkeys, fresh or chilled|32|
|611710|Shawls, scarves, mufflers, mantillas, veils and the like|32|
|020130|Meat of bovine animals: boneless, fresh or chilled|33|
|160250|Other prepared or preserved meat, meal offal or blood of bovine animals|33|
|640320|Footwear with outer soles of leather, & uppers consisting of leather straps|34|
|630190|Other blankets and traveling rugs|37|
|640420|Footwear with outer soles of leather or composition leather|37|
|160100|Sausages and similar products of meat, meat offal or blood|37|
|020110|Meat of bovine animals: carcasses and half-carcasses, fresh or chilled|38|
|420310|Articles of apparel, of leather or of composition of leather|38|
|640510|With uppers of leather or composition leather|41|
|620510|Ensembles: Of wool or fine animal hair|50|
|020621|Of bovine animals, frozen – tongues|50|
|420321|Gloves, mittens and mitts, especially designed for use in sports|82|
|640590|Footwear with outer soles of rubber, plastics, leather or composition<br>leather|82|
|621131|Other men's or boys' garments of wool or fine animal hair|105|
|**2005**|||
|640320|Footwear with outer soles of leather, and uppers consisting of leather straps|30|
|160220|Other prepared or preserved meat, meat offal or blood: of liver of any<br>animal|30|
|611110|Babies’ garments and clothing accessories, knitted or crocheted – of wool<br>or fine animal hair|33|
|020230|Boneless cuts|36|
|160100|Sausages and similar products of meat, meat offal or blood|37|
|160249|Other prepared or preserved meat, meat offal or blood|38|
|020220|Other cuts with bone in|38|
|420329|Gloves, mittens and mitts - Other|56|
|020321|Frozen – Carcasses and half-carcasses|58|
|020621|Of bovine animals,frozen - Tongues|70|

Source: Tarr, Shepotylo, and Koudoyarov (2005). See Tarr et al. (2005) for a complete list of products facing tariffs above 30 percent.

inputs but to all sorts of product lines.<sup>57,58</sup> They also disadvantage domestic SMEs as small companies lack the expertise, incentives and political connections to go through the

> 57 While 31.5 percent of imports were duty exempt, only 10 percent of imports were duty-exempt imports used for the production of exports in 2004 (World Bank 2006b).

58 Studies have shown that duty exemptions are more likely to have a positive welfare effect if the economy is small with high input tariffs, low administrative costs, and uncorrupt tax authorities (Ianchovichina, 2007).

30