rafmacalaba/datause-encoder-data huggingface dataset:
these outcomes are well balanced across treatment arms. Out of the six schools that dropped out of the sample, two schools were in the control group, two were in the _Original_ group, and two in the _Plus_ group. The _p_ -values of the Komolgorov-Smirnoff statistic for the equality of the distributions of work experience in the school of the community instructors in each treatment arm and the control group are 0.773 and 0.892, respectively. The _p_ -value of the _Plus_ - _Original_ difference in the share of mentors who drop out from the program during the experiment is 0.957. There is no evidence of composition changes between the _Original_ and _Plus_ groups induced by mentor turnover (see Table B-3).

The effective sample size of the second experiment is 1,045 children/parents and 224 schools (see Appendix A.2 for further details on the sampling design). We use the Early Grade Reading Assessment (reading score) and the Early Grade Math Assessment (math score) as our main measures of children’s cognitive achievement. Those are individually administered student assessments that have been conducted in more than 40 countries and in a variety of languages (Dubeck and Gove, 2015; Platas et al., 2016). While these instruments are typically applied to students in first, second, or third grade, we administer them to third through six grade students to account for the large learning gaps of the children in our sample. The school-average standardized scores in math and Spanish as measured in the school year prior to the introduction of the second experiment are, respectively, 0.5 and 0.7 standard deviations below the national averages.<sup>4</sup> To measure the impact of the intervention on socio-emotional skills, we consider a collection of thirty-two behavioral issues as reported by a caregiver, which resembles the questionnaire in the Children section of the National Longitudinal Study of Youth (CNLSY-79), such as antisocial behavior, anxiety/depression, headstrongness, hyperactivity and peer conflicts (for details, see Appendix A.2). The resulting behavioral problem index is re-scaled in such a way that higher values are associated with fewer behavioral issues (socio-emotional score). The survey also contains a module on instructors’ characteristics as well as pedagogical practices collected through an adapted version of the Stallings Classroom Snapshot (Bruns and Luque, 2015), a module on parental attitudes and investment toward children’s education, as well as information about the mentors’ activities in the communities, among others. To better interpret our results, we standardize most of the survey-based outcome variables using the mean and the standard

> 4Only 5 percent of the children in our sample score at the maximum of the scale in two or more subdomains of the reading score (out of eight subdomains) and in three or more subdomains of the math score (out of a total of seven subdomains). Unlike the first experiment, we cannot leverage the national standardized test scores for the second experiment since the test ceased to be universal during the period of interest (after 2014).

9
# **Figure 10. Government expenditure in small states and other EMDEs**

_Government expenditure in small states, relative to GDP, is substantially higher than in other EMDEs. It increased at the onset of the pandemic before easing somewhat between 2020 and 2023. Capital expenditure in small states is higher than in other EMDEs. Primary current expenditure in small states is largely driven by the public wage bill and expenditures on goods and services, both of which were significantly higher than in other EMDEs during 2011-22. Across regions, government expenditures in small states are highest in EAP. The volatility of expenditures is higher in small states, particularly commodity importers, than in other EMDEs._

## **A. Government expenditure**

<!-- Start of picture text --> Percent of GDP Small states<br>50 EMDEs excl. small states<br>40<br>30<br>20<br>10<br>0<br>2011 2019 2020 2023<br><!-- End of picture text -->

**C. Primary expenditure by region, 2011-23**

<!-- Start of picture text --> Percent of GDP<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP small LAC small Other small<br>states states states<br><!-- End of picture text -->

## **B. Composition of government expenditure, 2011-22**

<!-- Start of picture text --> Percent of GDP Small states<br>EMDEs excl. small states<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>D. Primary expenditure by trade composition,<br>2011-23<br>Percent of GDP<br>50<br>40<br>30<br>20<br>10<br>0<br>Small states: Small states: Small states:<br>Commodity Commodity Tourism-reliant<br>exporters importers<br>Capital expenditure Interest payments Wage bill Goods and services Other current expenditure<br><!-- End of picture text --> 40
lagged rainfall levels - that is, cumulative rainfall in the agricultural year preceding the period of reference. The coefficient of the lagged rainfall variable is negative 66.2, which implies that households worked 6.6 more days for the NREGS if lagged rainfall levels were 10% below average. This supports the assumption that the NREGS helps households in coping with shocks, because households use the program to smooth income ex post - for instance, after harvest and after agricultural products have been sold.<sup>39</sup> Self-reported shocks also seem to increase the number of days worked for the NREGS, although the effect is not statistically significant.<sup>40</sup> To quantify the contribution of the NREGS to households’ risk coping, agricultural losses due to rainfall shortages can be compared with income gains through the NREGS. The agricultural production function estimated in Table 2 (col. 6) suggests that a deviation from average annual rainfall by negative 25%, would reduce agricultural output by 5.6% at the optimal level of the risk index. For the average household, this implies a nominal loss of about INR 1,654 (or US$ 35.7 in constant July 2006 values). The same deviation in lagged rainfall would lead households to work about 16.5 more days for the NREGS, which would generate an additional income of INR 1,067 (US$ 23) at mean wages observed in the sample. The NREGS thus allows households to compensate about 64% of agricultural production losses caused by rainfall shortages. Since rainfall fluctuations are among the most important sources of risk for rural households, these results suggest that the NREGS could indeed have an insurance effect in Andhra Pradesh.

# **5.3 The effects of the NREGS on households’ crop choices**

Table 4 reports estimates of the effect of the NREGS on households’ input allocation decisions. As described in Section 4, all equations are estimated in the first-differenced sample, and controls for initial conditions are included in columns (2), (4) and (6). To isolate the insurance effect, controls include variables that might be affected by the NREGS and might influence a household’s crop choice through effects other than the insurance

> 39Similar evidence is provided by Johnson (2009), who finds that the number of days households work for NREGS increases if rainfall levels are lower than average.

> 40The variable is coded as one of a household reported any of 12 self-reported shocks related to agricultural production.

22
**Plc,Planning, and Research LPopulation, Health, and Nutirtion**

Africa is the geographical flashpoint for ethical issues about family planning programs. Until recently in Sub-Saharan Africa, advocacy of family planning by non-Africans was unacceptable and by Africans politically inadvisable.

This has changed in the 1980s. The health rationale for family planning is backed by strong evidence, especially in Africa, where infant and matemal mortality and morbidity rates are high. Plopulation growth in many African countries impedes development, which - howevcr - impressive cannot keep up with needs.

Earlier attempts to offer family planning aid were often politically inept and endangered the needed partnership between donor and developing countries. Theoretical arguments and abstract demographic projections are less persuasive than carefully designed programs geared to the health and well-being of communities that help plan them.

Increased cooperation between donor and - developing countries in transferring technical and financial resources so countries can set up their own sustainable and culturally sensitive - programs has helped resolve some of the ethical difficulties that beset family planning programs.

This concise and eloquent report summnarizes many of the practical, ethical, and cultural considerations in making family planning aid acceptable.

This paper, an earlier version of which was delivered at the CIOMS Conference on "Ethics and Human Values in Family Planning" in Bangkok on 19-24 June 1988, is a product of the Population, Health, and Nutrition Division, Population and Human Resources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Sonia Ainsworth, room S6-065, extension 31091 (24 pages with figures).

The PPR Working Papcr Series disseminates the Findings of work under way in the Bank's Policy, Planning, and Research Complex. An objcctive of **the** scries is to get thesc findings out quickly, even if presentations are less than fully polished. The findings. interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
# **4. Robustness of Database**

In order to get an idea of how accurate our adjusted test score database is, we first outline some examples. While the Altinok/Murseli database just provides data until 2003, we extend the series until 2009 including SERCE 2006, PISA 2006, TIMSS 2007 and PISA 2009 data. We use the adjusting method described in Section 2 in order to predict PISA 2006 and TIMSS 2007 values for those developing countries that have not participated in international achievement tests. In a further step, we adjust these values in relation to the United States international achievement scores. For predicting TIMSS 2007 scores, we use the two countries El Salvador and Colombia as _Doubloon Countries_ because they participated in both surveys (SERCE and TIMSS 2007). For PISA 2006 we have four _Doubloon Counties_ (Argentina, Brazil, Chile and Columbia). For PISA 2009 we have eight _Doubloon Countries_ (Argentina, Brazil, Chile, Colombia, Mexico, Panama Peru, Uruguay). For TIMSS 2007 8<sup>th</sup> grade scores in math and science we use SERCE values in 6<sup>th</sup> grade in math and science. For PISA values in math, reading and science we apply the respective SERCE values from 6<sup>th</sup> grade. For 4<sup>th</sup> grade Science scores we do not have an adequate SERCE value as Science is only tested in 6<sup>th</sup> grade in this study. We also do not have predicted science values for Brazil and Chile in PISA 2006 or 2009 as those countries did not participate in the SERCE science test.

For all _doubloon countries_ we can conduct a robustness check by comparing predicted values generated using the Altinok/Murseli methodology to their original TIMSS or PISA value. Such a robustness check ensures that our methodology for standardizing test scores is valid. If our predicted values align with the original PISA or TIMMS score for _doubloon countries_ we can be more confident that our predicted scores effectively predict unbiased standardized projections of regional test scores for all countries.

Table 1.0 summarizes our comparison between original and predicted values. We see that predicted scores in both reading and science always remain within 10 points from their original TIMSS or PISA value. These differences account for less than one tenth of a standard deviation, indicating that we have generated relatively accurate predicted scores. We have just one case where the difference between the original value and the predicted one is higher than 20 points. This difference exists for math scores in Columbia.

17"
in TFP across countries. Here, management practices are found to explain at least a fifth of within-country heterogeneity in firm performance, thereby suggesting that firm- and sectorspecific factors are at least as important as the general business environment in shaping firm performance (Bloom et al., 2016).

The four sets of practices that comprise an overall management score in the World Management Survey include: operations, performance monitoring, incentives, and target-setting (Bloom and Van Reenen, 2007). Questions in the operations category seek to understand the production process and the adoption of lean manufacturing techniques, while those in monitoring category solicit responses on the types of production and input data that are collected as well as how the performance of capital and workers is measured. Questions on practices relating to the hiring and firing of workers as well as the criteria for bonuses and promotions comprise the incentives category. Finally, the target-setting category evaluates practices relating to defining and communicating organizational goals, setting a time frame for implementation, whether or not these goals are demanding, and if there are consequences for failing to achieve them. In prior studies, these scores are averaged to obtain an overall management score.

In addition to focusing on the composite management score, studies have also considered specific categories of management practices. Weaknesses in management practices, for example, relating to data driven performance monitoring cripples the overall management score in Croatia (Grover et al., 2019). Monitoring includes specific practices that help managers define their key performance indicators (e.g. inventory, sales, absenteeism) and track them periodically over time. By comparison, Croatia firms’ average score on practices relating to target setting and incentives is relatively closer to that of United States firms. Incentive practices relate to how managers and non-managers are awarded bonuses and promotions. The findings for Croatia and the Russian Federation regarding the weaknesses of these subindices are similar, that is, monitoring practices are weaker (Grover et al., 2019; Grover and Torre, 2019). By comparison, in the United States and Mexico manufacturing firms 6
relations and potential entry points. Figure 3 summarizes available tools and their usefulness across the measurement continuum. The next section includes a discussion on key quantitative and qualitative tools, what they can adequately measure, their appropriateness for different measurement purposes, and associated challenges.

**Figure 3: Measurement Tools and Their Use Across the Measurement Continuum**

|**Tool/Measurement**<br>**Purpose**|**Context**<br>**Setting**|**Diagnosis**|**Monitoring**|**Anticipatory**<br>**Analytics**|**Evaluation**|
|---|---|---|---|---|---|
|||**Quantitat**|**ive**|||
|**Indexes Tracking**<br>**Perceptions**|X|X|X|X|X|
|**Tracking changes in**<br>**behaviors and**<br>**incidents**|X|X||||
|**Survey Experiments**||X|X|X|X|
|**Behavioral Games**||X|||X|
|**Randomized Control**<br>**Trials**|||X||X|
|||**Qualitati**|**ve**|||
|**Focus Groups **|X|X||X|X|
|**Memory Workshops **|X|X|||X|
|**Key Informant**<br>**Interviews**|X|X||||
|**Participatory**<br>**Approaches**|X||X||X|
|**Mapping of**<br>**constituencies/actors**|X|||||

# _I NDICES RELATED TO S OCIAL C OHESION PERCEPTIONS_

Composite indices that integrate multiple dimensions of social cohesion are a widely used tool of measurement. These indices use multiple indicators that capture perceptions and attitudes using data collected via primary surveys designed to track respondents’ perceptions of and attitudes towards intra- and inter-community dynamics, as well as local institutions. Many will combine questions regarding opinions or values with hypothetical situations. Indicators capturing intra-community, inter-community and citizen-state dynamics are then aggregated to develop a composite understanding of the state of social cohesion in a specific context. The data for these exercises are often already widely available and already collected, and when not available, there are pre-designed tools of data collection available which can be deployed or customized for projects.

# _E XISTING S OURCES_

A number of survey-based indices tracking social cohesion indicators exist at the global, regional and in some cases national level. At the global level, two of the best-known indices are the World Values Survey and Gallup polls, which are value-based representative surveys capturing data on social capital, trust, organizational membership, perceptions of corruption, ethical and religious values, political participation, and political culture. In addition, the regional barometers (Africa, Latin America, Asia etc.) capture perceptions of state institutions, local government, justice system, security forces, corruption, or access to services, as well as attitudes towards out-groups.

14 | P a g e
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in accessing and adopting more sophisticated technologies, including efforts to address the systemic barriers that hinder women’s access to financial resources. Second, our results highlight the importance of technology in shaping the relationship between female management and firm performance, indicating that promoting technology adoption could effectively reduce the gender gap in firm performance.

The remainder of the paper is structured as follows. Section 2 describes the data. Section 3 describes the methodology used to identify the association between firms with female managers, performance, and technology. Section 4 provides descriptive statistics and initial correlations between female-managed firms and productivity as well as technology. Section 5 shows the main results and examines the role which these technological differences may have in explaining productivity differences. section 6 explains women’s higher returns to the use of technology. section 7 explores the differences in the determinants of technology adoption between businesses managed by women and those managed by men. section 8 concludes with policy implications.

# **2 Data**

Our study is based on the new data from the Firm-level Adoption of Technology (FAT) survey developed by Cirera et al. (2020). The survey was implemented in 11 countries, including both developing and developed economies. It includes a nationally representative random sample of firms with five or more employees in agriculture, manufacturing, and services. Our main dataset includes information on firms in advanced economies (the Republic of Korea, Poland, Georgia, and Chile), middle-income countries (Brazil,<sup>1</sup> Ghana, Kenya, Senegal, and Viet Nam), and low-income countries (Ethiopia and Burkina Faso).<sup>2</sup> Unlike most studies exploring the gender gap in general purpose technologies, the FAT survey offers detailed information on the adoption of several technologies across different business functions associated with general business or sector-specific business tasks.

## **2.1 Technology measure**

The FAT survey covers over 300 technologies across almost 50 business functions, divided into general business functions (GBF) and sector-specific business functions (SBF). GBF

> 1In the case of Brazil, the survey is only representative for the state of Cear ́a.

> 2The survey was also implemented in India and Bangladesh. However, the survey in Bangladesh was implemented only for the firms in the manufacturing sector, and the survey in India excluded firms in agriculture and firms with fewer than 10 employees. Due to these differences in sample criteria and their correlation with women’s participation, we have excluded these countries from our analysis.

3
Policy Research Working Paper 6918

# **Abstract**

This paper poses a question: do firms in developing countries not innovate because they are unwilling to? The question moves away from the conventional focus on the obstacles (such as the lack of access to finance) that hinder firms’ innovation ability. The World Bank’s Enterprise Survey is used first to estimate the return to firms’ innovation across many developing countries, in terms of sales and sales per worker. Then the return to innovation is compared across countries with different levels of institutional quality. In countries with lower institutional quality (specifically, rule of law, regulatory quality, property and patent right protection), the return to firms’ innovation is lower. This suggests that poor institutional environment lowers firms’ return to innovation and hence discourages them from investing in researching and adopting new products.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at hanguyen@worldbank.org.

_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 the governments they represent._ Produced by the Research Support Team
Policy ReseaRch WoRking PaPeR 4594

# **Abstract**

In many poor countries, the recent increases in prices of staple foods raise the real incomes of those selling food, many of whom are relatively poor, while hurting net food consumers, many of whom are also relatively poor. The impacts on poverty will certainly be very diverse, but the average impact on poverty depends upon the balance between these two effects, and can only be determined by looking at real-world data. Results using household data for ten observations on nine low-income countries show that the short-run impacts of higher staple food prices on poverty differ considerably by commodity and by country, but, that poverty increases are much more frequent, and larger, than poverty reductions. The recent large increases in food prices appear likely to raise overall poverty in low income countries substantially.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to understand the implications of trade policies and world price developments for poverty. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at wmartin1@worldbank.org.

_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 the governments they represent._ Produced by the Research Support Team