rafmacalaba/data-use-mentions-extended huggingface dataset:
415|0.000|0.000|0.000|0.000|0.000|
|Sample Size|1,965|2,019|2,049|2,000|2,000|2,000|2,000|2,048|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. The cognitive test score is based on Raven’s Colored Progressive Matrices. Math and English reading comprehension tests were developed based on the Malawian school curricula. Five questions (four from the Fourth Grade test and one from the Eighth Grade test) from Trends in Mathematics and Science Study (TIMMS) 2007, which is a cycle of internationally comparative assessments in mathematics and science carried out at the fourth and eighth grades every four years, were added to the math test. Competencies represent a set of skills that were anticipated to be sensitive to education and relevant for non-formal employment. The skills tested included reading and following instructions to apply fertilizer; making correct change during hypothetical market transactions; sending text messages and using the calculator on a mobile phone, and calculating profits under hypothetical business scenarios. All test scores and the competency index were standardized to have a mean of zero and a standard deviation of one in the control group. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, an indicator for never had sex, and whether the respondent participated in the pilot phase of the development of the testing instruments. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Note that in Rounds 2 and 3, highest grade _completed_ is actually highest grade _attended._ Parameter estimates statistically different than zero at 99% (***), 95% (**)
in equation 1, where we model heterogeneity by GDP and an index of regulation. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of the regulatory index and columns labeled “high” evaluate the estimated model at the 90th percentile of the regulatory index within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

42
(or status as a post-Soviet state) from various sources.

**Income and pollution in the United Stats–** To assess the correlation between income and ambient pollution in the United States we utilize census tract level median income from the American Community Survey 5-year estimate 2016-2020. Ambient pollution comes from the same PM2 _._ 5 data used in the global analysis but averaged to the census tract level.

# **4.2 Methods**

To measure correlation between wealth and ambient concentrations of pollution we estimate fixed effects regressions of the form: where _PMg_ is the measure of pollution at RWI grid-cell _g_ aggregated over our time period in one of the three ways described in the Data section. _RWIg_ is the RWI value at that grid-cell. _ψc_ is a country fixed effect that we include in the pooled regressions (these are omitted in the country specific regressions). We employ various weighting schemes, weighting by: grid-cell population, the inverse of the reported RWI estimation error, the grid-cell population multiplied by the inverse of the error, the population of the country containing the grid-cell, and the inverse of the error times the population of the country containing the error.

In a separate analysis we compare the share of pollution in urban and rural areas coming from dust and sea salt to the share coming from other sources. To do this we first measure the average levels of exposure in rural and urban areas using the total PM2 _._ 5 pollution data. We then repeat this calculation using the dust and sea salt removed measures of PM2 _._ 5. We attribute the difference in these averages to dust and sea salt.

To assess heterogeneity we use several different approaches. To examine heterogeneity based on primary emissions sources, level of per capita income, or colonization history we simply stratify the sample by the relevant measure of heterogeneity and re-estimate equation 1 (omitting the fixed effects) within each country (or city) within each strata.

To assess the role that elevation plays in driving heterogeneity across cities
-2 -

results when different institutions, like non-life insurance companies are considered. The literature is not clear on its assumption regarding causality between contractual savings and capital market development. A one-way or a two-way relationship is assumed, usually interchangeably. In this paper, we address the question of which relationship leads empirically. The evidence, including descriptive statistics as well as Granger causality tests is presented for OECD countries and some other countries such as Chile, Malaysia, Singapore, South Africa, and Thailand. The paper does not present a theoretical framework but explains with clear statements and intuitive examples the way in which we think the growth of the contractual savings sector promotes financial development.

**Table 1: Contractual savings ratio to GDP (percent)**

|**Counts**|**1980**|**1985**|**1990**|**1996**|
|---|---|---|---|---|
|Netherlands|66.90|93.65|108.11|148.19|
|United Kingdom<br>|38.81|74.77|86.90|141.72|
|Switzerland|70.00||88.5|131.38|
|United States|43.01|59.33|69.20|94.80|
|Canada<br>|30.29|38.08|47.80|64.59|
|Australia<br>|||33.49|57.52|
|Sweden||23.92|28.63|47.96|
|**Norway**|13.15|17.29|25.80|30.02|
|Belgium<br>||16.42|20.55|27.20|
|Korea, Rep.<br>|4.06|10.48
) The main impacts of transport policies are expected to be within the transport networks (such as reduced travel time); and iii) The analysis is focused on changes in trade flow patterns assuming fixed total demand values. As such, I/O models are not suited to model indicators that are sensitive to price changes, such as GDP.

With regard to the impacts of mitigation measures, I/O models can be used to assess transport dependency, connectivity to main markets, socio-economic progress and development, cargo type and value, and cost-effectiveness.

# **5.1.3 (Spatial) Computable General Equilibrium models**

Despite of the ability of CGE models to simulate the wider economic impacts of mitigation measures, these models also have their shortcomings. These include extensive estimation processes for numerous elasticity parameters, sensitivity of the model results to base-year calibration data, extensive data needs, and relatively more effort to explain complex causal relationships produced by the model.

Since CGE models are able to take into account the interaction of different markets and the behavior of economic agents at the micro level while taking into account macroeconomic feedbacks, they excel at estimating the longer-term evolution of economic variables in a technically consistent manner. Furthermore, CGE models are typically able to produce multiple economic indicators of interest, such as GDPs,<sup>7</sup> trade flows, and welfare in a single run.

SCGE models are best suited to estimate the long-term redistribution of trade flows and economic indicators of States at the global level based on strong/disruptive mitigation measures that portend significant economic consequence. Within this context, it is also important for CGE models to use the proper specification of transport costs between States—that is, costs of transport that include the factors mentioned in section 2.2.1 to ensure the accuracy and the validity of the results.

> 7 SCGE models do not produce GDP indicators as their direct output but they are able to produce such indicators with additional assumptions, please also see footnote 2
major state; (c) 5 percent on the basis of infrastructure; (d) 5 percent on the basis of the area of states subject to certain normative limits; and (e) 10 percent on the basis of tax effort defined as the ratio of per capita own tax revenue to the square of per capita income. This formula differs from the previous ones by reducing (for income taxes) the weight of population; increasing the weight given to "distance" of per capita income; introducing a weight for infrastructure; and removing the "gap filling weight." (World Bank, 1995, p.57) It is expected that this fornula will strengthen the redistribution function of the Finance Commission transfers.

The detailed procedure for applying the above formula is as follows:12 Step 1. Divide the whole pool for transfers into five parts, 20%, 60%, 5%/6, 5%, 10%.

Step 2. Allocate 20 percent of the pool on the basis of population. That is, the ith state gets P,/P of the 20 percent, where Pi is the ith state's population, and P is the country's total population. The population figures used are those in the 1971 Census.

Step 3. Allocate 60 percent of the pool on the basis on income distance. The respective "distances" are multiplied by the population of the states and the share of each state is obtained by dividing the product for that state by the sum of the products for all states. That is, the ith state gets PiDI£jPjDj of the 60 percent, where Pi is the ith state's population, and Di is the per capita income distance of the ith state from the state with the highest per capita income (Goa in the case of the Tenth Commission). Goa is taken to be the same as for the state with the second highest per capita income (Punjab) from that of the next one (Maharashtra) since
|**Lodging to payment**|**Payment to gate**|
|---|---|---|---|---|
|**Min**|1|1|1|1|
|**1**<sup>**st**</sup>**quartile**|8|3|1|1|
|**Median**|14|7|3|4|
|**Mean**<br>|**24,0**|**13,0**|**4,7**|**11,4**|
|**3**<sup>**rd**</sup>**quartile**|26|15|5|8|
|**Max**|566|446|340|387|
|**Inter quartile range**|18|12|4|7|

**Table 2 – Quartile distrib ution of cargo dwell time sequential components (container imports in Douala, 2009)** Source: CAMEROONIAN CUSTOMS, 2009 – Values in days

> 11 Data set of all containerized imports through the port of Douala in 2009 destined to local consumption (regime IM4). 12 In addition to the different long dwell times categories observed in graph 1 there are a few hundred containers  that do not appear in statistics which were already in the terminal as of January 1st, 2009 but have not been cleared before December 31st, 2009 (Source: Interview with DIT, October 2010.)

9
governing individual interaction, and are also influenced by values and beliefs; for example, Harrison and Huntington (2000) discuss the link between formal institutions and culture. In the long run, culture influences the evolution of institutions (North 1990). However, political actions also can influence culture through institutions, making culture and institutions difficult to disentangle. Formal institutions are readily observable, and provide some insight into culture, while informal rules are more difficult to observe. Indeed, social norms and institutions can be in harmony, or in direct conflict with each other.

Fernandez (2008) outlines three different empirical approaches to the study of the role of culture in economic outcomes. The first of these is survey-based, and uses country-level economic indicators to examine their correlation with beliefs and values (from, e.g., the World Values Survey). The difficulty with this approach has to do with reverse causality, which is mitigated by using an instrumental variables technique (see also Guiso et al. 2003; 2005; 2006; Tabellini 2005). The second approach is what she terms the “epidemiological” approach, where outcomes of immigrants are compared to natives in a host country. The major strength of such an approach is that it holds institutional factors constant and only allows norms and values of the country of origin to vary, which then allows causal relationships between culture and behavior to be isolated and identified. The major drawback of such an approach is that the intrinsic factors are not perfectly transmitted. That is, (1) immigrants may not be a representative sample of their population, and (2) intrinsic factors may change as a result of the migration. The third approach utilizes historical case studies as „natural experiments‟ (e.g., Greif 1994; Nunn and Wantchekon 2011). The drawback of these studies is their limited generalizability.

Experiments help address the limitations of the methods used above. To reiterate the discussion above, the primary benefits of experiments are isolation of variables, identification of
Policy Research Working Paper 10099

# **Abstract**

Situations of forced displacement create unique challenges for social cohesion because of the major disruption of social dynamics among both displaced persons and host communities. This paper uses a sequential mixed method approach to analyze the relationship between hosting displaced persons and perceptions of social cohesion in eastern Democratic Republic of Congo. First, participatory research methods in focus groups empowered participants to pro-duce a locally driven definition of social cohesion. The results from these exercises inform the quantitative assessment by dictating measurement strategies when analyzing original surveys.

Combining almost 50,000 responses to 11 cross-sectional surveys between 2017 and 2021, displacement is negatively associated with perceptions of social cohesion in aggregate. But at the individual level, those who report hosting displaced populations in their communities often have higher perceptions of social cohesion. These results are strongest among respondents who self-report hosting IDPs as opposed to refugees, but important heterogeneity across indicators, local context, and gender should guide policy meant to promote social cohesion in forced displacement.

This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ppham@hsph.harvard.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or
120<br>itation utilities database(www.infrastructureafrica.org/aicd/to|3–60<br>ols/data<br>).|

_Source:_ Demographic and Health Surveys and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Access figures from the Demographic and Health Surveys for 1998 and 2006. [1] Cost recovery is estimated based on the assumption of a capital cost of 40 cents/m3. — = Not available.

In 1999 the government introduced an ambitious urban water sector reform aimed at improving the operational and financial performance of the sector by reducing operating costs, introducing private sector commercial management, achieving financial autonomy, and expanding access. The new institutional framework involved four main actors: the government, which was responsible for defining sector policy, managing water resources, and developing tariff policy; a multisector regulatory agency (Autorité de Regulation Multi-Sectorielle, ARM), responsible for developing a regulatory framework and monitoring urban water services and contractual arrangements among stakeholders; a public asset-holding company (Société de Patrimoine des Eaux du Niger, SPEN), responsible for investments; and a private operator selected by competitive bidding (Société d’Exploitation des Eaux du Niger, SEEN), in charge of delivering services. In March 2001 SPEN and the government granted SEEN a 10-year performance concession to operate the water supply systems of 51 urban centers—representing 72 percent of the population.

23
situations depict a typical village and varied either social empirical expectations (most people in the village defecated in the open or used a latrine) or social normative expectations (most people found it acceptable to defecate in the open or found this to be wrong). The vignette then asked where a typical resident (matched to the gender of the respondent), who had access to a latrine, would defecate. Vignettes, in this case, were used to test a counterfactual—norms of latrine use vs. norm of open defecation. This survey experiment is used to measure how social empirical and social normative expectations affect behavior. In this case the behavior is another villager, rather than the respondent him or herself, in order to mitigate social desirability bias in the responses. The survey experiment with vignettes provides further evidence on the existence of a social norm beyond the correlations in the regression table above.</mark> <mark>We conduct simple factorial analysis without interaction effects, representing each of the above factors with a dummy variable. Simple regression results are shown in Table 7. The results suggest that the likelihood of latrine use on the part of a villager would increase by 18.9% if he or she moved to a village where most people used latrines, and by 21.7% if most people in the village found it wrong to defecate in the open. We also test for robustness using a multilevel model with a random intercept, since each individual is asked to respond to 4 vignettes. The results were similar. Overall, the analysis of the vignettes confirms the importance of both social normative and social empirical expectations for individual behavior.</mark> 13
# **1. Introduction**

The number of randomized experiments being conducted in economics has exploded over the past decade, especially in development economics. The vast majority of these studies use only a single baseline and single follow-up survey, or a single follow-up with no baseline. This has been the case in the early randomized experiments looking at education (e.g. Glewwe et al, 2004) and health outcomes (e.g. Miguel and Kremer, 2004), and has remained true as experiments have expanded to consider other interventions and outcomes, such as recent high-profile experiments in microfinance (Karlan and Zinman, 2010; Banerjee et al, 2010). In the rare cases where multiple post-treatment survey waves have been conducted, they have been typically taken relatively far apart in time, with the goal of examining whether the treatment effect differs in the short-term and medium-term. For example, Banerjee et al. (2007) examine impacts of educational interventions at one year and two year horizons. Indeed, so much is the paradigm of baseline plus follow-up accepted that the excellent toolkit for randomization of Duflo et al. (2008) does not discuss at all the possibility of doing more than one pre-treatment or posttreatment round of surveying, let alone the choice of how many such rounds.

In contrast, the clinical trials literature has noted the potential advantages of taking repeated measures of outcomes of interest not just to study the time course of treatment effects, but to obtain more precise estimates of effects around particular endpoints (Frison and Pocock, 1992). Vickers (2003) argues that the number of repeat measures should be a key design choice in conducting experiments, and concludes that the benefit of such additional measures is of greatest value when the autocorrelation of measures is low, such as with episodic conditions like headaches. Such a description would certainly seem to fit key economic outcomes like business profits, and incomes and consumption of the poor. As the recent work by Collins et al. (2009) makes abundantly clear
Policy Research Working Paper 7921

# **Abstract**

The WHO has recently debated whether to reaffirm its long-standing recommendation of mass drug administration (MDA) in areas with more than 20 percent prevalence of soil-transmitted helminths (hookworm, whipworm, and roundworm). There is consensus that the relevant deworming drugs are safe and effective, so the key question facing policymakers is whether the expected benefits of MDA exceed the roughly $0.30 per treatment cost. The literature on long run educational and economic impacts of deworming suggests that this is the case. However, a recent meta-analysis by Taylor-Robinson et al. (2015), (hereafter TMSDG), disputes these findings. The authors conclude that while treatment of children known to be infected increases weight by 0.75 kg (95 percent CI: 0.24, 1.26; p=0.0038), there is substantial evidence that MDA has no impact on weight or other child outcomes. This paper updates the TMSDG analysis by including studies omitted from that analysis and extracting additional data from included studies, and finds that the TMSDG analysis is underpowered: Power is inadequate to rule out weight gain effects that would make MDA cost effective relative to comparable interventions in similar populations, and underpowered to reject the hypothesis that the effect of MDA is different from the effect that might expected, given deworming’s effects on those known to be infected. The hypothesis of a common zero effect of multiple-dose MDA deworming on child weight at longest follow-up is rejected at the 10 percent level using the TMSDG dataset, and with a p value < 0.001 using the updated sample. In the full sample, including studies in settings where prevalence is low enough that the WHO does not recommend deworming, the average effect on child weight is 0.134 kg (95 percent CI: 0.031, 0.236, random effects). In environments with greater than 20 percent prevalence, where the WHO recommends mass treatment, the average effect on child weight is 0
# **S2 Detailed Country Results**

## **S2.1 Using Harmonized Household Data**

Table S2.1 Trends in Overall Inequality

||||||**Level**|||||
|---|---|---|---|---|---|---|---|---|---|
|||Gini||V|arianc|e|log|(p90/p|10)|
|Country|1995|2002|2015|1995|2002|2015|1995|2002|2015|
|Argentina|34.76|39.16|33.32|0.40|0.52|0.41|1.61|1.84|1.66|
|Bolivia|49.43|52.28|40.74|1.02|1.35|0.74|2.59|2.92|2.14|
|Brazil|49.37|48.14|43.91|0.78|0.72|0.54|2.26|2.15|1.70|
|Chile|44.86|40.98|38.01|0.61|0.52|0.44|1.98|1.80|1.57|
|Colombia|n.a.|47.86|40.19|n.a.|0.78|0.57|n.a.|2.34|1.89|
|Costa Rica|36.59|38.72|42.12|0.43|0.49|0.61|1.62|1.75|2.01|
|Ecuador|n.a.|43.81|33.99|