rafmacalaba/data-use-mentions-extended huggingface dataset:
- Likewise, to ensure these students are not enrolled in overcrowded rooms – an additional 22,358 rooms will be needed over the next 12 years.

- To provide an augmented service delivery package (enhanced infrastructure, improving quality of service delivery, providing school meals, etc.), the current budget for ECE in Balochistan must be increased from PKR 4.5 billion (US$ 0.02 billion) to PKR 28.5 billion (US$ 0.13 billion) by 2035. This corresponds to an annual increase of 14 percent on average.<sup>15</sup>

- If alternate delivery mechanisms (community construction and vouchers) are used, the budget requirement by 2035 will be substantially reduced to PKR 19.4 billion (US$ 0.09 billion), with an annual increase of 11 percent on average.

The simulation modeling relied heavily on data availability, which varied from province to province. Due to data limitations, several assumptions are made throughout data consolidation and analysis. Population, public enrollment, and unit rates are largely available for the exercise. However, data on human resources and infrastructure was unavailable at the pre-primary level. The figures were, however, available at the primary level. We assume that if a primary school has six classrooms and six teachers, one of them should be for the pre-primary level. So, the total number of teachers at primary level was divided by six to calculate the pre-primary level figures. Similar assumptions were used for baseline education financing data.

The baseline enrollment in private schools was estimated based on private to public enrollment at the pre-primary level ratio using the latest available Pakistan Education Statistics Report 2020-21 (Pakistan Institute of Education, 2022). The data on teacher training was also not available. It was assumed that public school ECE teachers are currently untrained in ECE teaching and must undergo extensive training. Table 1 provides an overview of the baseline data used for simulation modeling.

> 15 These calculations assume that the baseline year is 2022
(Figure 4, left panel) or using 5-year averages to decrease the potential role of measurement error in employment levels (Figure 4, right panel). Both results suggest that, across countries, changes in employment explain a large share of the variance of the wage bill. This result is only suggestive and must be taken with a grain of salt, given the quality of the information and the assumptions to construct the average wage.

_Figure 4: Variance decomposition (annual frequency on the left and 5-year averages on the right)_ <!-- Start of picture text --> 1 1<br>Var(Wage) Var(Employment) 2*Covariance Var(Wage) Var(Employment) 2*Covariance<br>4<br>4<br>3<br>3<br>2 2<br>1 1<br>0 0<br><!-- End of picture text --> Source: calculations based on ILO Stats and IMF

# **iv. Public employment across countries and over time**

The different measures of public employment are highly correlated (Table 1), although when using a cross section, like year 2005 in the table, the correlation may be stronger. The data set is an unbalanced panel which increases its coverage in the mid-1990s reaching almost 60 countries in some years in the mid2000s. However, as Figure 5 shows, the coverage varies over time and across the three different measures.

Table 1: Correlation of different measures of public employment as a share of the labor force

|**a) Panel data**||||
|---|---|---|---|
||Central Government|General Government|Public Sector|
|Central Government|1.00|||

7
# V – Discussion

This study sheds new light on the effectiveness of messaging campaigns in promoting risk reducing behaviors during a crisis, such as the COVID-19 pandemic. The results show that both key measures of interest (either (i) relying on SMS as a source of information on COVID-19, or (ii) receiving an official message in the week preceding the interview) significantly increase reported risk reduction behaviors in a nationally representative sample of adults in Tajikistan. This suggests that SMS-based campaigns can be an effective mode of mass communication to encourage compliance with public health guidance during such crisis situations.

In addition, a strong feature of the mass messaging undertaken in this case was its relatively affordability. It was a cost-effective means to quickly reach a large and geographically dispersed audience. The cost per message for the Mobile Engage initiative varied depending upon the telecom provider, but most messages were sent at a unit cost of .006 somoni (about .0005 USD). This is with respect to a unique audience and compares favorably to the cost of many modes available in mass media campaigns. In addition, though not the case in Tajikistan, many countries legally require mobile phone providers to send emergency messages on behalf of the authorities, further reducing the direct cost of such campaigns in times of need.

The findings described in this study are likely relevant for similar campaigns going forward in Tajikistan, and potentially other contexts as well. For instance, as COVID-19 vaccination campaigns are in the early stages in many countries, additional improvement in outcomes may be effectively encouraged using similar messaging approaches. Both Mobile Engage and the Listening to Tajikistan survey have been endorsed to evaluate the effectiveness of this application in the context of the vaccination campaign underway in 2021 in Tajikistan.

22
# **1. Introduction**

That modern infrastructure is a prerequisite for economic development seems almost a truism and one that is widely accepted by politicians around the world. However, rigorous measurement of the causal relationship between infrastructure and development has proved challenging for economists, due to methodological difficulties in unravelling the direction of causation exacerbated by significant data limitations. Attempts to answer this question began in the 1980s and relied primarily on high-level cross-country panel data econometrics incorporating rather coarse proxies for infrastructure quantity (and sometimes) quality (Straub, 2008). Since 2010, however, there have been significant methodological innovations as well as new sources of data, spawning an unprecedented volume of empirical microeconomic research often exploiting economic geography approaches and spatially granular data. This second wave of literature provides a much more parsimonious analysis of the specific channels through which specific types of infrastructures contribute to specific dimensions of the development process. It has also resulted in a notable expansion in the volume of studies available that look specifically at the developing world.

The purpose of this paper is to provide a systematic qualitative overview of this more novel microeconomic literature covering over 300 research papers focusing primarily on specific infrastructure sectors, especially in developing countries. While the dataset covers studies conducted between 1983 and 2022, more attention is given to the period since 2010, during which an increasing volume of studies has looked at how different types of infrastructure drive microeconomic development outcomes. As such, it is the most substantive effort of its kind to date. The paper is targeted primarily at development practitioners, and hence the emphasis is on surfacing the insights these papers provide regarding the underlying processes through which infrastructure drives development outcomes. These include household welfare, employment, and human capital, as well as firm-level productivity, output, job creation, and trade; but, also, the impact on spatial patterns of economic activity in the urban and rural spheres. Such insights are relevant to inform the selection and design of investments and policy interventions. A forthcoming companion paper (Foster et al,
level weighted conflict index from the Databanks International Cross-National Time-Series Database.<sup>23</sup> The results reported in column 3 of Table 3 show that controlling for the incidence of conflicts does not alter our baseline results. Quite intuitively, conflicts are negatively (positively) associated with SAW (SAR), although the associated coefficients are very small in magnitude.

Our baseline results are robust to the inclusion of additional regressors. Income continues to negatively influence radicalization and the negative effect strengthens. The inverted U-shaped relationship between age and radicalization also persists and the coefficients on the dummies for employment status, education levels, radicalization and sacrificing life for believes also remain unchanged in sign and significance.

# **6 Concluding remarks**

This paper studies the common characteristics of extremists in the developing world. Using information from the Gallup World Poll on attitudes towards extreme violence and other characteristics of 30,787 individuals from 27 developing countries around the world and employing a variety of econometric techniques, we identify a set of factors that are associated significantly with extremism. The results suggest that the typical extremist who supports attacks on civilians tends to be relatively young (below 33), unemployed and struggling to make ends meet, poorly educated, and not as religious as others but more willing to sacrifice own life for his or her beliefs. These results are robust to various estimation techniques, approaches for dealing with non-responses to the radicalization question, and additional controls. They also hold for a global sample that includes both non-OECD and a few OECD countries and across geographic regions although the effects may vary in magnitude and significance across countries and regions.

> 23 These data are available at <u>http://www.databanksinternational.com.</u>

35
Edwards, Sebastian (1995); "Why are Savings rates so different across countries?";<sup>NBER</sup> Working Paper No. 5097.

European Bank for Reconstruction and Development (EBRD) Transition Report, 1994 and 1995.

Ickes, Barry W. "Saving in Eastern Europe and the Former Soviet Union." in World Savings: An International Survey (1993), 209-237.

RMF, World Economic Outlook, May 1995.

Keren, Michael and Gur Ofer. Trials of Transition: Economic Reform in the<sup>Former Communist</sup> Bloc. Westview Press, Boulder, 1992.

Kornai, Janos. The Socialist System. Princeton: Princeton University Press, 1992.

Loayza, Norman, Klaus Schmidt-Hebbel and Luis Serven (1998); "What Drives Saving<sup>Across the</sup> World"?; Mimeo, The World Bank.

Masson, Paul, T. Bayoumi, and H. Samiei. "Saving Behavior in Industrial and<sup>Developing</sup> Countries." in Staff Studies for the World Economic Outlook, Washington,<sup>D.C., 1995.</sup> Ostry, Jonathan and Carmen Reinhart (1992); "Private saving and terms of Trade<sup>Shocks"; [IMF</sup> Staff papers, Vol. 39: September: 495-517.

Pickersgill, Joyce. "Recent Evidence on Soviet Household Saving Behavior."<sup>Review of</sup> Economics and Statistics; Vol LXII, Nov/80, No. 4.

Quant, Richard, E. "Disequilibrium econometrics for centrally planned economies" in<sup>Davis and</sup> Charemza (eds.), Models of Disequilibrium and Shortage in Centrally Planned Economies, London and New York: Chapman and Hall, 1989.

Portes, Richard. "The Theory and Measurement of Macroeconomic Disequilibrium in Centrally Planned Economies." Center for Economic Policy Research Discussion Paper No.
3.271|-15.972***|12.291***|-7.061|
||(2.355)|(4.860)|(3.402)|(5.951)|
|land|3.641***|1.291***|4.160***|4.341***|
||(0.129)|(0.296)|(0.260)|(0.536)|
|land2|-0.041***|-0.011**|-0.046***|-0.054***|
||(0.002)|(0.004)|(0.003)|(0.007)|
|1.pr<br>~~n~~co2|||-76.017|-86.825|
||||(92.276)|(61.018)|
|Constant|927.127***|673.685***|499.366***|525.518***|
||(22.777)|(26.364)|(92.027)|(66.540)|
|Observations|202,189|113,497|106,724|65,853|
|R-squared|0.135|0.169|0.351|0.274|

_Source_ : Authors’ calculations using National Family Health Survey rounds 2, 3, and 4. _Note_ : Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All regressions use the provided sample weights. Reported National Classification of Occupation <u>(NCO) codes use 1968 classification, with
– capita the remaining covariates are sourced from the 2009 employment survey conducted by the NSS and we interact each covariate with a 2015 indicator to avoid collinearity with the district Our primary enterprise outcomes of interest are value-addition, revenues, employment and wage per worker. In additional specifications, we also identify the impact of rainfall shocks on binary measures of credit, registration status, and qualitative measures of firm – performance. Our coefficient of interest is _β_ the impact of a positive rainfall shock on enterprise outcomes. We exclude _NegShock_ as no district in our sample faced a negative shock in the years 2010 and 2015. _β_ therefore estimates the change in firm outcomes if the district receives a positive rainfall shock, relative to the “zero shock” case – when the district receives neither a positive, nor a negative rainfall shock.

## **3.3 Rainfall Shocks and Household Consumption**

The empirical specification used to identify the impact of weather-induced shocks on household consumption is: The unit of observation in (4) is the household with _α_ and _δ_ now denoting household and year fixed effects. _PosShock_ is defined as before while **X** denotes household-specific time-varying covariates such as average years of education, average age, household size, and employment characteristics.

The outcome of interest is logged per capita monthly expenditures, where monthly expenditures are computed after taking the average across all months in the year. Additional outcomes of interest are monthly food, essential food and luxury expenditures per capita. As the outcomes are logged, the _β_ coefficient estimates the percentage change in per capita monthly household expenditures due to a positive rainfall shock, relative to years in which there is neither a positive nor negative shock. As no district faced a negative shock in the years 2014, 2015 and 2016, we do not have negative rainfall shocks in this specification.

# **4 Results**

## **4.1 Precipitation Shocks and Farm Productivity**

We begin by identifying the impact of precipitation shocks on farm productivity, estimated
>-1</sup> near Miami, Florida and 72mL year<sup>-1</sup> in the Coachella Valley, California. And even higher spatial variability between the driest and wettest areas is found in India (10,083mL year<sup>-1</sup> ), Colombia (7,138mL year<sup>-1</sup> ), Peru (6,518mL year<sup>-1</sup> ) or Papua New-Guinea (6,476mL year<sup>-1</sup> ). Temperature also varies within countries (e.g., a 40 oC difference between the coldest parts of Alaska and Miami, Florida) but globally, the within-country coefficient of variation is twice as large for precipitation than it is for temperature. Therefore, more heterogeneity is lost for precipitation than for temperature in studies at the country level, causing important statistical distortions that have direct impacts on the results.

We use recent global sub-national aggregated output data (Kummu, Taka and Guillaume 2018a) derived from Gennaioli et al. (2013) and show a robust impact of rainfall on local GDP growth between 1990 and 2014. Following the literature, we use fixed effects panel regressions to isolate the impact of weather variables from time invariant factors or common time-varying factors that could be correlated with both weather and aggregate economic outputs.

At the smallest spatial resolution available (0.5 degree), we find a concave relationship between precipitation and GDP growth. Rainfall increases total economic productivity at a decreasing rate, until it reaches a peak 3
simple model of parental investment with local spillovers to formalize the idea that parents have an active role in promoting educational opportunities, and that educational investment at the community level are a socially determined outcome (List et al., 2020). We show that the extent to which an educational program preserves or loses impact at scale depends upon its ability to promote parental coordination and engagement in the local community. We empirically corroborate these predictions by leveraging the changes in community-level parental engagement induced by the experiment. Using this variation, we document that parents prevent schools from closing, and as a consequence promote the effectiveness of the mentoring intervention during the government implementation. We find that an increase of half standard deviations in parental engagement decreases the probability of school closures by 11 percentage points over the subsequent two years. Our qualitative data from in-depth surveys of mentors and local instructors further corroborate the role of parents in guaranteeing the continuation of educational activities in the communities, in a context with poor school infrastructure and where schooling activities are often disrupted.

Finally, we study the educational impacts of the policy reform across the overall population of schools in the state of Chiapas. The assignment of the mentoring program under the _Plus_ modality at scale was done through a rotating scheme with a priority-based mechanism. We exploit the quasi-experimental variation in the program rollout once we condition on the set of eligibility criteria officially used by the government. After providing evidence that this variation appears conditionally “as-good-as random” via various placebo tests, we show that the program was successful in the schools that were previously part of the evaluation sample as well as in the rest of the schools in Chiapas. Within the evaluation schools, the marginal effect of the _Plus_ modality after one year of government implementation on the probability of enrolling in seventh grade is +5.4 percentage points. The cumulative effect of continuous exposure to the program for three years (two years under the experiment and one year under the government) implies that the enrollment rates in these disadvantaged and rural areas achieve the secondary
However, the indirect channels refer to societal changes that could take place when the state fails, composition of the community is affected by displacement, local market operations are altered, and the way individuals relate to each other changes. All of these effects are likely to change social norms that govern the role of women (Justino 2018), potentially impacting their empowerment. These more substantial and long-term effects on female empowerment are harder to measure and remain to be observed in the case of Yemen.

# IV. Data and methodology

- a. Survey data: HBS 2014 and YHDS 2021

The analysis in this paper draws on two household survey datasets: The Household Budget Survey (HBS) of 2014 and the Yemen Human Development Survey (YHDS) of 2021.

The HBS 2014 is the main welfare survey for Yemen used to construct household consumption and official poverty statistics. It is a multiuse survey, with different modules on welfare, women’s role in the household, health, and income. The YHDS 2021 was collected from the areas under the control of the IRG in Yemen. In the first stage of sampling, 105 accessible enumeration areas (EAs) were drawn from the 1,200 EAs of the HBS 2014, providing a panel of EAs over time. The YHDS 2021 then visited a sample of 1,681 households, representative of four regions under IRG control, urban and rural locations, and displacement status. The YHDS provides key indicators on welfare, living conditions, human development outcomes, and women’s role in household decision-making.

We construct a combined dataset of 2014 and 2021, limiting the data to the common EAs. This limits the representativity of the analysis, since the YHDS 2021 was not collected across the entire country for security reasons, but it does allow for the analysis of relationships over time. The resulting dataset is 2,469 observations. The pattern of conflict and violence observed in the sample is likely to be different for Yemenis living in the Northern areas and under the control of the
research design for both Phase 1 and 2. In addition, we also re-estimate equations (3) and (4) using quantile regressions which allows us to explore both cross-school and cross-student heterogeneity.

In a third part of our empirical analysis we estimate whether the impacts are sustained beyond the intervention period. In order to do this, we re-estimate equations (2) and (4) replacing the dependent variable with the math test scores that were collected from Phase 2 students in late 2015—almost 9 months after they would have been covered by the intervention.

In the last part of our empirical analysis we explore various pathways through which impacts might have been generated. In order to do so, we re-estimate models (1)-(4) replacing the dependent variables with measures of teacher attendance, teacher pedagogical behaviors, and student attendance.

# **3. Results**

## **3.1 Balance at baseline**

Before turning to the analysis of impacts, we first establish that our experimental design is valid by examining the first moments of selected characteristics of schools, teachers, and students in control and treatment groups, using baseline data for phases 1 and 2 (Table 2). We report the p-value of an F-test corresponding to a null hypothesis that there is no difference across the three (in phase 1) and four (in phase 2) arms – clustered at the level of treatment assignment which is the school. In phase 1, the p-values for the test are all, with 1 exception out of the 23 variables, above 0.1 suggesting that the samples are very likely balanced. In phase 2, the p-values for all but 5 variables out of the 23 variables are above 0.1. We nevertheless believe that the samples are well balanced since the absolute magnitudes of the differences across these samples are small in magnitude. For example, the share of schools with electricity is 0.313 in the control group and 0.391 in the arm in which teacher
informality would affect measurement error of GDP more compared to household final expenditure. However, as household final consumption expenditure in Syria’s NA is computed as a residual,<sup>24</sup> it could be assumed that the loss in reliability of growth estimates based on this aggregate is more severe than that based on GDP growth estimates.<sup>25</sup> Another important choice when identifying the growth rate in the per capita NA indicator to be used for poverty projections pertains the choice of the deflator. The general practice is to work with per capita NA aggregates expressed in constant terms using the GDP deflator, an implicit deflator that is meant to reflect price changes in goods and services purchased by consumers, businesses, government and foreigners (not importers). On the other hand, when assessing changes in consumption between household surveys collected at different points in time, the typical choice is that of using the Consumer Price Index (CPI), which measures the price changes in goods and services purchased by consumers (households). CPI is also used when expressing PPP conversion factors from 2011 or 2017 base years into the corresponding values in the years in which survey data are available for global poverty measurement. As shown in Figure 9, the GDP deflator and CPI trends, while generally aligned before conflict, diverge post 2011, possibly reflecting

> 23 El-Laithy, Aby-Ismail (2005). 24 Ibid.

25 Another element of concern regarding the reliability of Household and NPISHs Final Consumption Expenditure (HFCE) from NA pertains the lack of information on how its constant price estimates are computed by CBS. In fact, while GDP constant price estimates provided by CBS and included in WDI can be replicated using the GDP series in current prices and deflating it using the GDP deflator, the same does not hold in the case of HFCE. In fact, the constant price HFCE series provided by CBS and included in WDI cannot be replicated starting from the corresponding series in current prices neither using the GDP deflator (as