rafmacalaba/datause-encoder-data huggingface dataset:
<mark>with specific information on best practices for different crops suited to varying agro-climatic conditions, database of input retailers and prices. Similarly, e-extension can be in the form of participatory training videos disseminated via farmer groups and cooperatives for sending real time updates and pictures of damaged crops, for instance, to identify the cause and advice on treatment. In rural areas, the added value of e-extension is often, that with the help of communication tools, such as simple mobile phones, the extension officers can reach out to many more farmers than solely through field visits, especially in situations where the extension officer-to-farmer ratio is approximately 1:1000, a case common in many Sub-Saharan African countries, including Zambia and Uganda. Aker (2011) estimated that instead of traveling to visit a farmer, extension agents may use mobile phones for a comparative cost of one-fourth of the price of a visit (Figure 4).</mark>

**Figure 4: Marginal (per search) cost of obtaining agriculture information in 2005 USD$**

<!-- Start of picture text -->
1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>Landline Radio Mobile Phone Personal Visit Newspaper<br><!-- End of picture text -->

Source: Aker (2011) <mark>In addition to making extension services more efficient, the improved outreach leveraged by such technologies contributes to inclusion and equity among the farmers. Although e-extension cannot entirely replace field advisory visits, it can still have a positive impact on farming and growth. Cole and Fernando (2012) show that in rural India, information provided via mobile phones to farmers increased their knowledge of available options for inputs such as seeds and fertilizers as well as choices of different crops leading to changes in their investment decisions</mark> 15
bia across the full analysis period, scaled by municipal population in 1990 to express displacement intensity as a share of baseline population. This generates variation in displacement intensity that is plausibly exogenous to local economic trends. The key identifying assumption is that municipalities with higher and lower 1990 foreign settlement shares would have followed similar development trajectories absent the displacement crisis. Although municipalities with a larger foreign presence in 1990 were on average somewhat richer, we show that these municipalities followed parallel pre-crisis trends in our main outcomes prior to 2013, which supports this assumption. The exclusion restriction further requires that the 1990 foreign settlement share affected outcomes only through its influence on displacement outflows. We provide several pieces of evidence to support this requirement. We show that municipalities with a larger foreign settlement share do not have systematically different economic structures that would cause them to respond differentially to aggregate shocks. In fact, all estimates are robust to the inclusion of interactions between a rich set of municipal controls and year trends, including oil-production concentration. We further show that national economic policies during the analysis period, namely discriminatory repression, electricity shortages, expropriations, and access to social programs, did not differentially target municipalities with a higher foreign settlement share.

Our analysis employs data on night-light density, household living conditions, political elections, and organized crime for the period 1992 to 2021. Night-light data serve as a proxy for economic growth and inequality.<sup>2</sup> We measure inequality using a spatial measure that estimates the dispersion of night-light density at the _parroquia_ (county) level within each municipality. We find that this measure positively correlates with traditional income-based inequality metrics, validated using census data from Venezuela and Colombia. To examine how forced displacement facilitated the perpetuation of the autocratic regime, we use unique web-scraped data on the results from the last five presidential and mayoral elections, as well

> 2Using ENCOVI, we validate these measures for correlation with household-level income measures. Moreover, we control for the presence of oil fields in our estimates to rule out concerns that luminosity driven by gas flares could affect our main results.

5
outcomes. We define dummy variables equal to one if the age of the household head is above the median, if the head is literate, if the wealth index is above the median, if the household size is above the median, and if the predicted outcome is above the median.<sup>38</sup> For each outcome and each covariate, we report the BHM posteriors of the average effect of index insurance offers for the group with the covariate equal to zero as well as the additional effect for the group with the covariate equal to one (Figure 3). In addition, using the four studies designed as price experiments, we report the effect of receiving insurance offers at a price below vs. above average. The results show limited heterogeneity along the covariates we study. We find some evidence that treatment effects are larger for households with low levels of predicted outcomes. The effect on seeds is +0.12 SD higher for households who would have used less seeds in the absence of index insurance offers. The effect on the risk index is +0.18 SD higher for households who would have cultivated less risky crops in the absence of index insurance offers.<sup>39</sup> Treatment effects also seem larger among worse-off households (according to the wealth index), among households with a younger head, and among households offered a lower price for the index insurance product – but posterior distributions include zero comfortably for these covariates. Results for household head literacy and household size display little heterogeneity. Overall, we find limited evidence that the heterogeneity in treatment effects can be explained using basic household and product characteristics. This suggests that most of the heterogeneity in treatment effects stems from more complex household/product characteristics (e.g. preferences or trust levels) as well as from contextual variables. Exploring the role of these covariates is a fruitful – and challenging – area for future research.

# **5.4 The predicted effect of index insurance in a new study**

We use estimates from the Bayesian hierarchical model on _στ_<sup>2to predict the effect of index insur-</sup> ance offers in a new context ( _τk_ +1). Figure 4 reports the posterior means and intervals of _τk_ +1 as well as the estimates from the pooling model for comparisons. Overall, we find very uncertain predicted effects. The posterior intervals of _τk_ +1 are considerably wider than the confidence intervals from the pooling model, reflecting the sizable heterogeneity in treatment effects documented in Section 5.2, especially for outcomes with the largest heterogeneity (seeds, fertilizers

> 38See Section 3.2 for more details.

> 39These patterns are visible across all the studies, although the 95% posterior distributions sometimes include zero (Figure A6). While interesting, these patterns must be interpreted with caution due to the high number of parameters estimated and to the potential bias resulting from overfitting (see footnote 26). In addition, for these analysis, we could not cluster standard errors at a higher level than the household in _baggr_ .

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Table 7. TEGRA attrition rates and balance of the non‐attrition sample by sub‐sample

||all|treatment|control|difference|
|---|---|---|---|---|
|Attrition rates<br>|||||
|_1 year exposure sub‐sample_|||||
|sample size at baseline|2540|1268|1272|n.a.|
|sample size at end‐line|2199|1226|1073|n.a.|
|attrition rate (pop. est)|0.13<br>(0.006)|0.111<br>(0.008)|0.155<br>(0.008)|‐0.044***<br>(0.012)|
|_2 year exposure sub‐sample_|||||
|sample size at baseline|1239|620|619|n.a.|
|sample size at end‐line|818|383|184|n.a.|
|attrition rate (pop. est)|0.331|0.299|0.381|‐0.081***|
||(0.013)|(0.017)|(0.018)|(0.025)|
|Non‐attrition sample baseline balance|||||
|_1 year exposure sub‐sample_|||||
|average TEGRA score (SD)|‐0.026|‐0.059|0.018|‐0.078***|
||(0.014)|(0.019)|(0.019)|(0.027)|
|female|0.44|0.414|0.475|‐0.061***|
||(0.004)|(0.004)|(0.007)|(0.008)|
|home support index|0.526|0.52|0.538|‐0.018|
||(0.006)|(0.009)|(0.008)|(0.012)|
|_2 year exposure sub‐sample_|||||
|average TEGRA score (SD)|‐0.034|‐0.064|0.021|‐0.085**|
||(0.017)|(0.018)|(0.032)|(0.038)|
|female|0.423|0.392|0.478|‐0.086***|
||(0.006)|(0.008)|(0.01)|(0.012)|
|home support index|0.537|0.536|0.539|‐0.003|
||(0.007)|(0.009)|(0.009)|(0.013)|

Standard errors included in parentheses. For differences, statistical significance at the 1, 5 and 10 percent levels are denoted by ***, **, and *, respectively.

# _Effect of the CLRW intervention_

The effect of the CLRW intervention on the percent of teachers fully implementing the teaching practices promoted by the intervention is presented in Table 8. The effects for each category of teaching practice are presented in Annex Table 6. Statistically significant, positive effects were 27
# **Revisiting the Poverty Trend in Rwanda: 2010/11 to 2013/14**

Freeha Fatima and Nobuo Yoshida<sup>1</sup> JEL classification: E31 I32 O55 Key Words: Welfare monitoring; Poverty; Prices; Africa; Rwanda

> 1 <u>ffatima@worldbank.org, nyoshida@worldbank.org. The authors are grateful to the National Institute of Statistics of</u> Rwanda for data sharing and support for this study.
# 3 Results

**Tables 4 to 13** present ITT findings using ANCOVA. All student-level findings use Equation 1, while all school-level findings use Equation 2.

## 3.1 Test Scores

Student level results on test scores, are reported in **Table 4** . We include controls for students’ baseline scores. We observe significant impacts from the treatment. Students in (or tracked from) treated schools achieved scores an average 13 points higher in mathematics scores, equivalent to 0.1 S.D. English scores also appear to have been increased by the treatment, but the result is not statistically significant. A statistically significant impact of 10.4 points in total scores is observed, but we cannot reject the hypothesis that this is driven by the impact on mathematics scores. We present findings for the same sample of students, without controlling for baseline scores, in Appendix B; we observe similar impacts with a slightly stronger treatment effect.

## 3.2 Student Progression

**Table 5** presents the school-level results on dropout and repetition rates. We find evidence that the intervention significantly reduced repetition rates in lower primary by 2.2 points compared to control, approximately a nine percent reduction. Upper primary repetition rates also appear to have been reduced by the intervention but the result is not statistically significant. We do not observe significant impacts on dropout rates.

## 3.3 Heterogeneity

Malawi’s schools operate in a diverse range of conditions, with wide variation in remoteness, staffing, and learning outcomes, while Malawi’s students also possess a wide range of different characteristics with significant impacts on learning (Asim and Casley Gera, 2024). In order to investigate the differential impact of the intervention on schools in different contexts, we conduct heterogeneity analysis. Tables 6 to 9 show student-level estimates on test scores interacted with dummies for the relevant characteristics. As an additional check, we conduct sub-group analysis presenting ITT findings for particular sub-groups of the school and/or student sample (see Appendix B).

**_PTR._ Table 6** shows estimates on test scores interacted with a school-level dummy for whether a schools’ PTR in lower primary was below the median at baseline. We observe significantly different impacts from the treatment for students in these schools, with English scores in

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# **1. INTRODUCTION**

Improving health outcomes is a high priority in the vast majority of low-income countries. However, despite dramatic increases in public spending on health in the last decade, most low-income countries have made little progress and will not reach the 2015 Millennium Development Goal health targets (United Nations 2010 and 2011).<sup>1</sup> One reason is the poor performance of medical care providers. Absenteeism among providers is widespread (Chaudhury et al. 2006; Reinikka and Svensson 2009). Provider knowledge of proper clinic procedures for basic care is low (Das and Hammer 2004). And providers give a standard of care that is well below the state their clinical knowledge (Das and Gertler 2007; Das, Hammer and Leonard 2008; Leonard and Masatu 2010a and 2010b). In this context, finding ways to increase health provider productivity in terms of supplying higher quality care to more patients is critical.

One promising intervention designed to improve health provider productivity is to pay them for performance. So-called Pay-for-Performance (P4P) schemes give bonuses to providers that give higher quality of care to more patients. P4P affects health care provision in two ways; first, through incentives for providers to expend more effort in specific activities; and second, through an increase the amount of financial resources. Proponents highlight P4P incentives and argue that they strengthen the link between productivity and rewards (Levine and Eichler 2009).

In this paper we provide evidence on the effect of P4P incentives on health outcomes and the associated pathways through which the incentives work. The evaluation uses data from Rwanda, one of the poorest countries in the world, where we nested a prospective evaluation into the national rollout of P4P. In order to identify the P4P incentive effect separately from the increase in resources, the program agreed to hold constant the level of resources constant across treatment and comparison facilities. It did so by increasing the traditional input-based budgets of the comparison group by the average amount of P4P payments to treatment facilities. As a result, while treatment and comparison facilities had the same average level of resources, a portion of the treatment facilities’ resources was determined based on their performance whereas the comparison facilities’ resources were not.

> 1 Among the 67 countries with highest child mortality rates, only 10 are on track to reduce mortality by two thirds. And the rate of decline in maternal mortality for all is well short of the 5.5 % needed to achieve 2015 MDG target.

2
**Table 2: Summary Statistics and Correlation** <u>Panel A: Summary Statistics</u>

|Variable|Observations|Mean|Median|Std. Dev.|Minimum|Maximum|
|---|---|---|---|---|---|---|
|Narrow Spread|1862|0.189|0.164|0.172|-0.150|1.092|
|Wide Spread|1864|0.105|0.095|0.069|-0.058|0.398|
|Overhead|1904|0.057|0.049|0.038|0.000|0.743|
|Liquidity|1888|0.864|0.781|0.435|0.022|3.862|
|Loan Loss Provisions|1888|0.156|0.050|0.326|-2.573|3.120|
|Market Share Deposits|1907|0.013|0.006|0.021|0.000|0.204|
|Equity|1907|0.132|0.111|0.094|0.000|0.906|
|Interest Income Share|1880|1.203|1.058|0.739|0.000|8.733|
|Intermediation|1907|0.355|0.350|0.145|-0.642|0.915|
|Herfindahl Deposits|24|0.047|0.044|0.011|0.036|0.076|
|Herfindahl Loans|24|0.035|0.032|0.007|0.028|0.052|
|Inflation|24|0.144|0.138|0.067|-0.019|0.285|
|Real Treasury Bill Rate|23|0.007|0.016|0.085|-0.184|0.189|
|Industrial Production Growth|23|0.009|0.003|0.031|-0.042|0.074|
|Exchange Rate Depreciation|24|0.012|0.004|0.025|-0.025|0.077|

Source: Own calculations based on CBN data Note: The summary statistics are calculated for the sample period 2000 I-2005 IV; The 1st and 99th percentiles of the distribution both the narrow and wide spread are excluded; the 99th percentile of the distribution liquidity of loan loss provisions and interest income share is excluded; negative observations for the equity variable are excluded.

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# **Table 3: Contemporaneous and lagged effects of internet access on municipality-level worker outcomes by sector**

Municipality-year variation

|**Outcome/sector**|**InternetCoe**|**f. lag InternetCoef.**|**_N_**|**Adj. ****_R _**<sup>**_2_**</sup>|
|---|---|---|---|---|
|Employment - all sectors|-0.008|-0.005|74170|0.955|
|Employment - Wholesale & Retail|-0.038***|-0.079***|68288|0.972|
|Employment - Electric, gas, & water|-0.025**|-0.020*|68288|0.871|
|Employment - Manufacturing|0.024|0.025*|68288|0.944|
|Employment - Public Administration|-0.044***|-0.017|68288|0.766|
|Employment - Real Estate|0.018|0.046***|68288|0.914|
|Employment - Construction|0.008|0.004|68288|0.819|
|Employment - Agriculture|0.020*|-0.009|68288|0.942|
|Employment - Transport & Storage|0.066***|0.060***|68288|0.938|
|Employment - Other Soc. Svcs.|-0.005|0.007|68288|0.933|
|Employment - Health and Social Svcs.|0.030**|-0.015|68288|0.941|
|Employment - Hospitality|0.031***|0.017|68288|0.951|
|Employment - Education|0.007|0.019*|68288|0.937|
|Employment - Finance and Insurance|0.088***|0.043***|68288|0.953|
|Employment - Mining|0.019|0.019|68288|0.851|
|Employment - Fishing|-0.048***|0.054***|68288|0.732|

Note: Estimates based on RAIS employee-employer records aggregated by municipality and year merged to internet access data from the School Census. Outcome measures listed in rows are in natural log. Interpretation of coefficients relate an increase in internet access from 0% to 100% in the municipality to the percent change in the outcome measure. Rows indicate outcome used in a common specification including unreported vectors of municipality and year fixed effects and a constant term. Standard errors clustered by municipality in parentheses. * p<.1, ** p<.05, *** p<.01.

Wage effects by industry group, presented in Table 4, are less stark. We find positive and statistically significant coefficients in agriculture, hospitality, real estate, and education sectors, with negative wage effects in utilities, transport & storage, and public administration.

11
Facto Crawling Peg). In this case, 59% of the observations in the sample are classified as fixed ER, and 52 countries have at least one year in this regime.

**Control variables.** Our specification in equation (5) allows the MPS to vary with other covariates. In addition to a generic control for the level of development, measured as the log of Gross Domestic Product Per Capita (GDPPC; constant dollars, from World Development Indicators (WDI)), we include two other controls from the literature.

The first control variable is motivated by Calderón and Schmidt-Hebbel (2008), Frankel et al. (2013), Cespedes and Velasco (2014), and Carneiro and Garrido (2016) who argue that the ability of countries to conduct countercyclical fiscal policy depends on the quality of their institutions. Following this literature, we construct a country-specific institutional quality (IQ) index as the average of four indicators: (i) government effectiveness, (ii) rule of law, (iii) regulatory quality, and (iv) control of corruption. The indicators are provided by the International Country Risk Guide for 146 countries from 1984–2016. The IQ index ranges from zero (lowest institutional quality) to one (highest institutional quality).

Second, Gavin and Perotti (1997), Riascos and Vegh (2003), and Caballero and Krishnamurthy (2004) argue that imperfect credit markets prevent developing countries from borrowing during economic downturns and hence affect their ability to conduct countercyclical fiscal policy. To assess the development of domestic financial markets, we use Loayza and Ranciere’s (2006) measure of financial depth as liquid liabilities as a share of GDP (data from the World Bank WDI).

**Outliers.** As commodity prices and fiscal policy in developing countries are volatile, we need to be careful to make sure that our results are not being driven by a small number of influential observations. We check for the presence of outliers and influential observations by calculating the Cook’s distance from simple OLS regressions of the first stage  (similar to equation (6)) and second stage (similar to equation (4)). We run these regressions by splitting the sample between fixed and floating ERs and classify the five observations with the highest Cook’s distance in

37
Rather than sweep out variation in export supply with product-origin-year fixed effects, we include in the regression explicit measures of export supply. We do this because of our interest in _HOMEj_ , which would be co-linear with the usual full set of fixed effects. Instead, we fully parameterize export supply - using the total volume of exports of each product from each origin in each year - since these data are readily available in the trade data we have.<sup>30</sup> This approach allows us to estimate a coefficient on _HOMEj_ itself, not only on the associated interaction terms.

We do include year fixed effects in the regressions. These are useful as controls for aggregate shocks, but they also assist in moving between the general equilibrium theory and the constant elasticity of substitution import demand model we estimate. Fally (2015) shows that fixed effects in a PPML specification have a useful adding up property (in this context<sup>�</sup> _k M_<sup>�</sup> _j,t_<sup>_k|_</sup> _t_ = _T_<sup> ̄=�</sup> _k_<sup>_M k_</sup> _j,t_<sup>_|_</sup> _t_ = _T_<sup> ̄,where</sup><sup>_T_ ̄isaspecific</sup> year). The implication in this case is that the other parameters of the regression model determine the share of each product-country combination in PR’s annual import flows; they do not have independent effects on the level of trade.

# **5.3.1 Model specifications**

Our reduced-form is a Poisson Pseudo Maximum Likelihood (PPML) model: _Mj,t_<sup>_k_=</sup><sup>_e_[</sup><sup>_δ_(</sup><sup>_h−_1(</sup><sup>_X_</sup> _j,t_<sup>_k_))+</sup><sup>_f_(</sup><sup>_distj,_</sup> _→_<sup>_−_</sup> _Z_<sup>_k_</sup> _t_<sup>_,ρ_)+</sup><sup>_βHOMEj_+</sup> _→_<sup>_−_</sup> _ω −→Zt_<sup>_k_+</sup> _→_<sup>_−_</sup> _β →_<sup>_−_</sup> _Z_<sup>_k_</sup> _t_<sup>_HOMEj_+</sup><sup>_φt_]</sup> + _ε_<sup>_k_</sup> _jt_ (13) where _h_<sup>_−_1 �</sup> _Xj,t_<sup>_k_</sup> � is the inverse hyperbolic sine of the value of total exports of commodity _k_ in year _t_ from each region _j_ , _δ_ the associated regression coefficient, _f_ ( _distj,_<sup>_−→_</sup> _Z_<sup>_k_</sup> _t_<sup>_, ρ_)isaflexiblefunctionofdistance,productcharacteristicsandpa-</sup> rameters that controls for region _j_ ’s distance to PR.<sup>_−→_</sup> _ω_ is a vector of estimated coefficients on the product characteristics themselves. _β_ ,<sup>_−→_</sup> _β_ and<sup>_−→_</sup> _Z_<sup>_k_</sup> _j_<sup>areasde-</sup> 30In some instances, the PR data report trade flows arriving from an origin, even though our corresponding data shows no exports of that product from that origin in that year. In these cases we add the PR trade flow to total exports, and include a dummy variable indicating that we made this transformation.

22