rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1:** Baseline balance of double grant experiment

||Con<br>Grant|trol:<br> & Info|Tre<br>Double|atment:<br>Grant & Info|Di|ference:|
|---|---|---|---|---|---|---|
||Mean|(S.D.)|Mean|(S.D.)|T-C|(S.E.)|
|_Panel A: School characteristics_|||||||
|Type of school|||||||
|General school (1=Yes)|0.600|(0.492)|0.496|(0.502)|-0.104|*<br>(0.063)|
|Indigenous school (1=Yes)|0.400|(0.492)|0.504|(0.502)|0.104|*<br>(0.063)|
|Parent association president|||||||
|Highest edu. is primary (1=Yes)|0.776|(0.419)|0.816|(0.389)|0.040|(0.051)|
|Years as president|1.376|(1.336)|1.328|(1.148)|-0.048|(0.158)|
|Indigenous (1=Yes)|0.400|(0.492)|0.504|(0.502)|0.104|*<br>(0.063)|
|Teachers|||||||
|Prop. with teaching college degree|0.165|(0.315)|0.210|(0.361)|0.045|(0.043)|
|Prop. with university degree|0.606|(0.446)|0.544|(0.462)|-0.063|(0.057)|
|Failure rate|0.099|(0.066)|0.097|(0.093)|-0.003|(0.010)|
|Repetition rate|0.070|(0.060)|0.068|(0.061)|-0.003|(0.008)|
|Dropout rate|0.022|(0.039)|0.024|(0.072)|0.002|(0.007)|
|Number of schools|1|25||125|||
|_p-value of joint F-test_||||||_0.768_|
|_Panel B: Student characteristics_|||||||
|Indigenous (1=Yes)|0.391|(0.488)|0.426|(0.495)|0.036|(0.064)|
|Female (1=Yes)|0.487|(0.500)|0.487|(0.500)|0.000|(0.012)|
|Household wealth index (S.D.)|0.000|(1.000)|-0.027|(1.012)|-0.027|(0.088)|
|Grade 3 (1=Yes)|0.333|(0.471)|0.338|(0.473)|0.004|(0.008)|
|Grade 4 (1=Yes)|0.337|(0.473)|0.341|(0.474)|0.004|(0.008)|
|Grade 5 (1=Yes)|0.329|(0.470)|0.321|(0.467)|-0.008|(0.008)|
|Language score|440.623|(87.430)|437.804|(89.606)|-2.819|(9.750)|
|Math score|450.740|(97.461)|447.853|(102.326)|-2.886|(11.477)|
|Number of students|47|96||4570|||
|_p-value of joint F-test_||||||_0.943_|

Notes: Robust standard errors clustered at school level in Panel B. Reported p-value is from a joint F-test of the null hypothesis that there are no mean differences between treatment and control across all variables in the panel. All variables measured in 2007. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01
Panel C: Guinea

# Panel D: Mali

<!-- Start of picture text --> Guinea Mali<br>Error term Random Effect Error term Random Effect<br>2 4 .4<br>.2<br>1 .1 .2<br>2<br>0 0 0<br>0<br>-1 -.1 -.2<br>-.2<br>-2 -2 -.4<br>-2 -1 0 1 2 -.2 -.1 0 .1 .2 -2 -1 0 1 2 -.3 -.2 -.1 0 .1 .2<br>Inverse normal Inverse normal Inverse normal Inverse normal<br>Quantiles of Pearson residuals Quantiles of Random Effects Quantiles of Pearson residuals Quantiles of Random Effects<br><!-- End of picture text --> Panel E: Niger <!-- Start of picture text --> Niger<br>Error term Random Effect<br>3<br>.2<br>2 .1<br>1 0<br>0 -.1<br>-1 -.2<br>-2 -.3<br>-2 -1 0 1 2 -.2 -.1 0 .1 .2<br>Inverse normal Inverse normal<br>Quantiles of Pearson residuals Quantiles of Random Effects<br><!-- End of picture text -->

# **4. Evaluation exercise: Comparison of geospatial and census-based estimates of headcount poverty in Burkina Faso**

Before presenting estimates of headcount poverty rates for the four focus countries that lack recent census data, we conduct a sensitivity analysis with data from Burkina Faso. The availability of a recent census in Burkina Faso creates an opportunity to assess the estimates produced with geospatial covariates and the unit context model against officially adopted EBP census-based estimates as described below.

Burkina Faso’s National Institute of Statistics and Demography carried out a census in 2018 which was utilized by the Burkina Faso poverty team of the World Bank to generate small area estimates of poverty for Communes using the EBP census methodology (Molina and Rao, 2010) under a two-fold nested error regression model ( <mark>Marhuenda</mark> et al., 2017). Because the census and the survey data are from a similar period, the small area estimates using census auxiliary information are considered the gold standard. Comparing the census-based estimates with estimates produced using geospatial covariates offers an appropriate testing ground for assessing the extent of discrepancies between census-based and geospatial-based estimates. This framework can also be used to compare the estimates produced by different models.

13
promiscuous sexual behavior, in which case women may be encouraged to marry early. In their study from northern Uganda, Oleke _et al._ (2006) also report that respondents identified early marriage as a means of preventing infection.

In an _ex post_ framework, as HIV/AIDS affects households through deaths of prime-age household members and increased orphan rates, it may affect marriage patterns. It may, for example, prompt earlier marriage among surviving young adults in instances where these deaths result in income shocks. Among young women, this could occur if they are not earning enough income to support their consumption while at the same time their marriage would contribute resources to the household.<sup>4</sup> This is a potential impact hypothesized by Foster and Williamson (2000) in their literature review, although they cite no studies which examine this issue. This is also the trend observed by Oleke _et al_ . (2006) in their study of determinants of orphans’ wellbeing. However, they find that early marriage among female orphans is explained by the desire of the women themselves, rather than financial or other pressure from their households. They find that female orphans view marriage as an opportunity to improve their quality of life, which tends to be characterized by heavy domestic workload and dropping out of school. In contrast to this line of argument, deaths that lead to labor shortages in the household may result in delayed entry into marriage. Thus, taking a more broad view of family formation and economic crisis, there are theoretical reasons for why economic crisis may delay _and_ hasten marriages, as discussed by Nobles and Buttenheim (2006) in their study of the effect of economic crisis on marriage timing in Indonesia.

While there is little evidence on the impact of adult mortality on children’s family formation, there is some indirect evidence that changes in household composition are one of the responses among households to being affected by HIV/AIDS. Hosegood _et al._ (2004) use demographic surveillance data from rural South Africa and conclude that households experiencing an adult death are vulnerable to dissolution. Yamano and Jayne (2004) find that in rural households in Kenya the death of a male head of household significantly reduces the number of adult women in the household. This trend is proposed

> 4 The custom is the region of study in this paper, Kagera, is for a bride price to be negotiated between the man wanting to marry and the woman’s parents (or other relatives) (De Weerdt, 2001). Every first marriage is expected to have a bride price associated with it. The bride price includes the _makula_ which is the main gift/transfer. In addition, the _makula_ is accompanied by a long list of other gifts (to other relatives/caregivers).

2
female teachers to teach in rural areas and sensitize students, parents, and community members on girls’ education (U.S. State Department).<sup>10</sup>

# **5. Estimation methodology**

Our data set consists of a four-year panel of 405 clusters. Our dependent variable is the average share of girls aged 5-15 who attend school in the community, measured as a fraction between zero and one. Standard linear regression models are not well suited for such estimation since they may produce predicted values greater than one. We instead follow a method proposed by Papke and Wooldridge (2008), to deal with fractional response dependent variables in a panel data context. This approach will allow us to control for cluster/community unobserved heterogeneity possibly correlated with the explanatory variables, in particular water infrastructure (in our case, time to the water source). We impose bounds on the proportion of girls attending school by using a Probit functional form for the mean proportion. This gives us the following model: where i = 1,...,C represent clusters, t = 1 to 4 the survey year, and Φ ( ) . the standard normal cumulative distribution function. The dependent variable, _sit_ , represents the proportion of girls aged 5-15 attending school, where 0 ≤<sup>_s_</sup> _it_ ≤ 1. The **x** it vector is the set of explanatory variables, _ci_ stands for cluster-specific unobserved heterogeneity, possibly correlated with some explanatory variables, and _vit_ is the time-varying error term. Following Papke and Wooldridge (2008) we make the following assumption along the lines of Chamberlain (1980): 1 4 where **~~x~~** _i_ ≡ ∑ **x** _it_ is the vector of cluster means (calculated over the four time periods) and 4 _t_ = 1 2 _ai_ **x** _i_  Normal ( 0, σ _a_ ) .

Inserting (2) into model (1) yields:

> 10 See http://www.state.gov/g/drl/rls/hrrpt/2008/af/119004.htm

10
# Table 2

## ADF Tests of Stationarity

||Included|Test|1 %<br>critical|5 %<br>critical|||
|---|---|---|---|---|---|---|
|Variables|in regression|statistic|Value|value|Conclusion||
|log (levels)|||||||
|GDP / L<br>GDP / L|Constant<br>Constant,<br>Trend|-1.92<br>-2.02|-3.6<br>-4.2|-2.93<br>-3.5|Unit Root<br>Unit Root|I(1)<br>I(1)|
|K / L|Constant<br>Constant,|-0.54|-3.6|-2.93|Unit Root|I(1)|
|K / L|Trend|-2.01|-4.18|-3.52|Unit Root|I(1)|
|log (1st difference)|||||||
|Δ (GDP / L)<br>Δ (GDP / L)|Constant<br>Constant,<br>Trend|-6.4<br>-6.56|-3.6<br>-4.2|-2.93<br>-3.5|Stationarity<br>Stationarity|I(0)<br>I(0)|
|Δ (K / L)<br>Δ (K / L)|Constant<br>Constant,<br>Trend|-6.4<br>-6.33|-3.6<br>-4.19|-2.93<br>-3.52|Stationarity<br>Stationarity|I(0)<br>I(0)|

Source: Authors’ calculations. Note: For the regression equations without a linear trend, critical values are from MacKinnon (1996).

Table 3 <u>Cointegration Rank Test (Trace)</u>

|Hypothesized||Trace|0.05||
|---|---|---|---|---|
|No. of CV's|Eigenvalue|Statistic|Critical Value|p– value|
|None*|0.92|105.8|25.9|0.00|
|At most 1|0.2|8.70|12.5|0.19|

Note: Trace test indicates 1 cointegrating vector at the 0.05 level * = rejection of the hypothesis at the 0.05 level.

12
By virtue of the rich LSMS-ISA data and in combination with geospatial rainfall data, we can control for many of these factors and identify the amount of variation in self-reported shock (loss) reports that is due differences in _observable_ factors (such as rainfall intensity or SLM practices). Conversely, we assume that the remaining variation should then be attributable to _unobservable_ factors such as idiosyncratic perceptions of farmers. This allows us to (i) identify the amount of variation in farmers’ reports that should be retained as it reflects differences in shock exposure and contrast this with (ii) undesirable variation that should ideally be eliminated.

# <u>What do we see in the data?</u>

To obtain an objective drought measure, we exploit geospatial, yearly total rainfall data for the year in which the agricultural season in question started provided by the National Oceanic and Atmospheric Administration Climate Prediction Center’s (NOAA CPC) Africa Rainfall Climatology 2 (ARC2) dataset at 0.1° (~10km) resolution. As drought is a slow onset disaster and effects on crop losses should be greatest during the planting and growing season, this provides a basic, local measure of drought intensity.

_Table 14_ : Drought incidence for self reported exposure and a GIS rainfall data-based drought indicator.

|Percent|ofplots|with drou|ght exposur|<br>e based on..|
|---|---|---|---|---|
|**Country**|**Crop**|**Round**|**Self-reports**|**GIS rainfall**|
|||ESS13|12.5%|0%|
||Maize|ESS15|39.3%|39.1%|
|Ethioia||ESS18|15.4%|33.3%|
|p||ESS13|18.7%|0%|
||Sorghum|ESS15|64.1%|64.7%|
|||ESS18|29.0%|37.0%|
|||IHPS10|28.3%|53.8%|
||Maize|IHPS13|5.2%|69.4%|
|||IHPS16|21.7%|19.8%|
|Malawi||IHPS19|3.7%|0.6%|
|||IHPS10|33.8%|49.9%|
||An|IHPS13|8.1%|68.9%|
||y|IHPS16|32.3%|24.9%|
|||IHPS19|7.1%|0.5%|

Note: Percent of plots with drought exposure based on farmers' self reports (area or productivity losses due to drought) and based on total annual rainfall below the long-term average (since 2001). ESS = Ethiopia Socioeconomic Survey, IHPS = Integrated Household Panel Survey Comparing self-reported drought exposure, taken from reports of a planted area or plant productivity loss due to drought, to a drought indicator taken from 12-month total rainfall intensity being below the long-term average (since 2001), differences in incidence do not seem systematic (Table 14). Across years, farmers report either a notably higher or a notably lower incidence of drought exposure than the geospatial rainfall drought dummy. One notable exception is the 2015/16 drought in Ethiopia where incidences between both measures are strikingly aligned 40
- 2.11 -

# **IV.** BRIEF HISTORY OF **COCOA PRODUCTION PRICING** AND **TAXATION** IN **GHANA**

Figure 1 gives a graph of cocoa production since the 1947/48 crop year, and the five ysar moving average smoothed trend. It shows reported<sup>2</sup> production roughly doubling between 1957 and 1962, then holding roughly constant until about 1972, and then doclinng fairly steadily to Its current levil somewhat below the start of the period. _Flgwo_ 2 gives the real prIce of cocoa (deflated by the rural cost of living Index)<sup>3</sup> since 1940/41. It showS the dramatlc rise In prkcos after World War 11, their steady fall up to the mid 1980s, and their second main fall In the mld 1970s. Prices In the recent past have been less than one quarter of their value in the early 19509. The latest figures, based on an assumed producer prico of 150,000 cedis per MT, and using the August 1987 prico **inex as** deflator (pwrhaps optnisticaliy) shows that real prices appear to have returned to their 1970 level.

Figure 3 suporlmposes the prico serles and the hIpiled total producer Income (real prico thues production), showing that Income remained roughly constant until the early 1960s (as productlon Increases offset price falls), but since then the docilne In producer Income has be(en even more dramatic than that of producer prIces.<sup>4</sup> Naturally one asks whether the fall In output can be attributed to the fall In prices, and Flgure 4 graphs the movement In output and the real pries of eleven years earlier. Cocoa takes about 4-6 years to come Into frult from planting, and ylelds Increase steadily thereafter. The 2 The _fgwuros_ refer to deliveries to buying agents, and thus Ignore productlon which Is Illegally smuggled IntO nelghboring countries, such as the Coto d'lvoire. Konings (1986), citing World Bank esthuates, suggests that a minhum of 40,000 tons were smuggled out of the country In 1977, whilst a Brong-Ahafo Regional Admuinistrator has asserted that as much as one-third or the region's output Is sold llelly In the Coto d'lvoirs. (West Africa, June 12, 1978.) 3 See the discussion In the Appendix on defiating cocoa prices, and the relative movements In the different price **indlces.** 4 **See** also footnote 2. To the extent that cocoa was smuggled, farmers Incomes will be understated by this calculation.
|**Table OA5: Descriptive statistics of Arusha vs. other re**|**gions**|||P-value of|
|---|---|---|---|---|
|**Characteristics**|Arusha|Kilimanjaro|Morogoro|<br>F-test|
||(1)|(2)|(3)|(4)|
|_Panel A: Village characteristics_|||||
|Number of agro-dealers|1.02|0.06|0.27|_0.024_|
|Number of Observations|46|16|15||
|_Panel B: Household characteristics_|||||
|Household head age|48.68|51.39|41.88|_0.000_|
|Household head is male (0/1)|0.613|0.509|0.502|_0.001_|
|Household head is married (0/1)|0.813|0.494|0.262|_0.000_|
|Household head can read and write (0/1)|0.754|0.885|0.825|_0.000_|
|House roof made of corrugated iron sheet (0/1)|0.930|0.865|0.390|_0.000_|
|House has concrete floor (0/1)|0.459|0.400|0.147|_0.000_|
|Total area cultivable land owned (acres)|2.08|2.29|5.57|_0.000_|
|Is related to government officials or influentials (0/1)|0.157|0.268|0.180|_0.000_|
|Number of Observations<br>Note: This table reports means of characteristics of baseline househo<br>(0/1) indicates dummy variables, equal to one if the response is yes. Se|460<br>ld survey respon<br>e Appendix Table|340<br>dents in Arusha, Kil<br>1 for definition of v|400<br>imanjaro and Mo<br>ariables.|rogoro regions.|
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 3 Table 1. A summary of previous estimates of global protection costs against sea-level rise.

|Study|Cost Estimate<br>(2014 US dollars)|Comments|
|---|---|---|
|Dronkers et<br>al., 1990|$815 billion|For 1-m rise, capital costs mainly reflecting flood<br>protection, but other aspects (e.g. port upgrade) also<br>considered.|
|Hoozemans<br>et al., 1993|$1,630 billion|For 1-m rise, as Dronkers et al. (1990) with a more<br>realistic consideration of storm surge hazard and<br>resulting protection needs.|
|Fankhauser,<br>1995|$284 billion<br>(OECD only)|For 1-m rise, using Dronkers et al. (1990) data and<br>capital costs of optimal protection.Notglobal.|
|Bijlsma et<br>al., 1996|At least $590<br>billion (NOT<br>global)|For 1-m rise. Aggregation of 17 national studies in<br>their Table 9.3, sonotglobally comprehensive. All<br>types of adaptation considered, but floods dominates<br>protection costs. Capital costs only.|
|Tol, 2002|$1,524 billion|For 1-m rise applying cost-benefit analysis to the<br>protection decision using the FUND model. Capital<br>costs only. Protects 348,000 km of the world’s<br>coastline.|
|Hallegatte et<br>al. 2013|$50 billion per year<br>(NOT Global; to<br>2050)|Considers the 136 largest coastal cities to 2050, and<br>scales up from a few recent city examples, rather<br>than using unit cost estimates as most other studies<br>considered here.|
|Hinkel et al.<br>2014|$32-$84<br>billion/year (for<br>RCP8.5) (costs for<br>the year 2100)<br>Accumulated<br>costs: $1.9-$4.2<br>trillion for 21st<br>century|Based on a demand for safety analysis for protection<br>need (Yohe and Tol, 2002) using the DIVA model.<br>Reports capital costs and maintenance costs of<br>dikes built since 2000. (For RCP2.6 corresponding<br>costs are $14-$37 billion/year in the year 2100 and<br>accumulated costs US$1.3 trillion - US$ 2.8 trillion).<br>Protects about 500,000 km of the world’s estimated<br>1,000,000 km coastline length. Capital and<br>maintenance costs.|

The available protection cost estimates are summarized in Table 1. They nearly all depend on estimates of the length of coast which requires protection and unit costs for that protection. The studies are not independent, and most studies build on earlier studies in terms of adding incremental improvement on issues such as the length of protection, except Bijlsma et al (1996) and Hallegatte et al (2003) which consider different approaches. The unit costs of defense types have often been shared between studies with the original Dronkers et al (1990) costs being influential (see Appendix 1).
primary school enrollment of 14.8 million students in 2000 (SEP 2000), these estimates suggest that if PEC had operated in all schools in the year 2000 and had the estimated mean impacts, 35,500 Mexican primary school students would not have repeated a grade or dropped out of school, and 46,000 students would not have failed a grade. Students who drop out, fail, or repeat generally represent Mexico’s poorer and more disadvantaged students, so for these outcomes PEC enhances equity across students. These represent a fairly large group in absolute magnitude, though a small group relative to all Mexicans.

Our comparisons highlight the well known fact in the evaluation literature that the choice of the comparison group is one of the most important components of a credible impact evaluation. The fact that the counterfactual constructed by the propensity score matching method weights observations differently than the regression approach and uses non-PEC school that are very as similar to PEC schools as possible, makes a large difference on the estimated impact of the program.

The results suggest that PEC’s combination of increased school resources and local school management can produce small but statistically significant improvements in learning. The smaller sample for indigenous schools, which represent some of Mexico’s poorest communities with the worst education outcomes, may contribute to the larger standard errors in estimates of PEC impact these communities. But the smaller impact estimates for indigenous schools may reflect that PEC has less impact in them. This finding would cohere with others (Galiani et al., 2004) showing that decentralization of public management can improve outcomes in wealthy areas but has smaller or even negative impact in the most disadvantaged areas.

Dropout, repetition, and failure rates represent good but not complete outcomes - measuring impact on test scores, completion rates, parental involvement in schools, and school autonomy would give a more complete picture of PEC’s impact. Collecting data on 27
<mark>Including state trends, an interaction between state indicator variables and a continuous year variable resulted in even stronger magnitude and significance in the homicide outcome (a</mark> ppendix A, <mark>table A7)</mark> .

A placebo test using homicides and hospitalizations for traffic accidents resulted in no significant associations, suggesting that the results are not derived from economic circumstances associated with changes in the ability to treat victims (appendix A, table A8). The magnitudes are also smaller.

# **Heterogeneity Analyses**

The analysis divided the municipalities into more or less poor as determined by the median human development index for the sample in 2011 (table 4, panel a). The human development index is chosen because better health, education, and living standards are often associated with less stress and may therefore result in lower levels of conflict (Renzetti 2009; Pattavina, Socia, and Zuber 2015). The main result—a statistically significant association between the gender wage ratio and homicides—is found concentrated in municipalities with a lower human development index. Changes in wages between men and women in municipalities with higher human development indexes may not impact homicides because the stress of wages or gender norms is less binding if additional resources are available, such as better health and wealthier friends and relatives.

Because the legal and civil context may influence women’s situation at home, the analysis contrasted municipalities with and municipalities without women’s police stations in 2011 (table 4, panel b). Research has found that the establishment of women’s police stations is associated with a reduction in female homicides (Perova and Reynolds 2017). The possibility of police intervention specifically to support women may reduce male backlash as a mechanism. The hypothesis here is that the presence of women’s police stations makes an outside option, enabled by a lower wage gap, more credible, while, in municipalities without enforcement mechanisms for the legal and civil protection of women from violence, greater empowerment may trigger a backlash. Indeed, in municipalities with women’s police stations, the analysis found that an increase in equality in the gender wage ratio was associated with significant reductions (0.35 percent) in reports of the least severe types of violence. In municipalities without women’s police stations, the finding was the opposite: an increase in the wage ratio in favor of women was associated with a 0.2 percent increase in reports.

The analysis also contrasted the impacts of gender wage inequality on younger women (ages 15–29) and impacts on older women (ages 30–49) given that research 17