rafmacalaba/data-use-mentions-extended huggingface dataset:
2 time-cost basis for certain services (United Kingdom); and (g) negotiated lump-sum per an agreed project-based activity by the extensionist (Queensland, Australia).

Most of the existing studies provide information on the structure and operation of the different paid-extension arrangements, and, in some cases also some anecdotal results on the costs and benefits associated with these operations. However, there is not enough evidence and analysis that can help the reader in reaching a conclusion on the degree of success of certain paid extension arrangements.

The literature includes several studies that address the economic aspects of paid-extension. Hone (1991), in a theoretical analysis attempts to estimate the implications of recent use of direct charges to finance various rural extension networks in Australia. Dinar (1996) applies an approach that determines how much to charge for extension services, using an illustrative example from Israel. Schwartz (1994) reviews several concepts (amended by case study analysis), such as public vs. private goods, information transfer, and externalities, which are associated with paidextension.

However, several questions still remain to be answered. For example, can one arrangement for paid-extension that was successfully experienced in one country be duplicated in another country? How does one select the appropriate paid-extension arrangement for a given set of conditions? These questions suggest that a methodology to compare between different paidextension arrangements, is desirable.' This paper develops a framework for comparing the cost and several performance indicators of various paid-extension programs by using actual data from two types of paid-extension programs in Nicaragua, and assessing their performance.<sup>2</sup> The analysis focuses on selected performance indicators and their application using available data from the experience in Nicaragua. The next section develops a framework for comparison between the two types of paid-extension.

> The debate on public vs. private extension continues to rage among experts in the field. The debate includes issues such as should extension be publicly funded? Who should pay and how much should be paid for extension services, and which segments
responsible did not overlap with the 95 percent confidence interval of the differences in any of the other sensitive or non-sensitive questions in columns (2)-(8).<sup>25</sup> Although it is difficult to make strong inferences from a single question, the potential differences in responses based on the source of that sensitivity is an issue that needs to be further investigated.

Importantly, the results survive an important robustness check. It is important to further illustrate that the differences in the modalities in sensitive questions is not being driven by the observable differences in the internet and mobile phone samples. Table 5 illustrates that when restricting the samples in the internet and mobile phone surveys to be more comparable using all the demographic information available, the sensitive questions continue to have much larger differences between the modalities than the nonsensitive questions.

Specifically, Table 5 illustrates that these results survive restricting the sample to those groups that are underrepresented in the internet survey- to only those with above the median age in the internet survey (above 26), to only female respondents, to only those who do not live in the two largest cities in the country, to those whose subjective income levels are not sufficient to live comfortably, to those who worked less than three weeks in the past month, and to those whose highest school level completed was either primary school or below.<sup>26</sup>

# **Section 6b. Inferring Potential Sources of Sensitivity and Discussion**

Although the results illustrate strong differences between internet and mobile phone surveys in the responses to sensitive questions, the differences between the surveys can potentially be further used to make inferences about the sources of sensitivity, which potentially has implications for the conflict itself and the type of country that Yemenis might want to see emerge from the conflict. Specifically, we investigate differences in the magnitude of the baseline estimates across regions where the sources of sensitivity

> 25See Appendix 1 for a formal specification illustrating the larger difference by adding a triple interaction term to
27% said that they would not take the lower offer. On the other hand, among the 244 respondents in the leader survey who said no to the first offered price, 83% said yes to the second (see Appendix Tables A7 and A8). The fact that many individuals did change their answer suggests that they were engaged with the interviewer and understood the question.

Table 4 supports the assumption that leader votes are representative of community preferences, which indicates that they are acting as community representatives. The table shows the distribution of calculated median voter prices, both for the full sample and for the sample for which there 24
results from our current study and earlier report (World Bank, 2020) add to the growing evidence that demonstrates a smaller, consistent estimate of around three million foreign workers in Malaysia.

**Our results in this paper indicate that the number of foreign workers in Malaysia was between 2.99 million and 3.16 million in 2017–18.** State and nationality distributions of foreign workers in our estimates are consistent with MOHA data, indicating that our estimates are robust. Nevertheless, we note that the BNM remittance data could potentially underestimate the number of workers in states with low access to money service providers (MSPs), as well as nationalities that have access to alternative money transfer mechanisms such as commercial banking and informal transfer channels. This underestimation could be mitigated over time by BNM’s greater efforts to lower remittance costs and enhance the financial literacy of residents, including foreign workers. In this challenging time of the COVID-19 global pandemic, an expansion of online money transfer services is crucial as a mitigation measure, simultaneously lowering remittance costs and addressing issues of limited accessibility of MSPs among foreign workers.

This paper is organized as follows: the next section briefly paints the landscape of foreign workers, including a survey of available foreign worker estimates. Section 3 presents the data and methodology. Section 4 discusses the results and Section 5 concludes with a discussion on a framework to measure foreign workers in a systematic manner.

# **2. The foreign worker landscape**

**Official statistics suggest that the foreign worker population is hovering around 2.4 million – 3.2 million, but limitations exist.** MOHA reports that regular foreign workers are 1.7 million as of March 2018, based on work permit issuance. However, its estimates of irregular foreign workers are based on its ad hoc enforcement activities, suggesting that four out of ten foreign workers are irregular. Taking these two together leads to a foreign worker population of 2.93 million, including 1.3 million irregular workers,
a greater potential for economic growth and poverty reduction than a focus on large cities, which are further away from where the poor live. For their case region Kagera, in Tanzania, De Weerdt and coauthors (2021) thus find that the deterring effect of distance by far outweighs the attraction of greater earning opportunity at the urban destination, with both effects reinforced for lesser educated and poorer populations. It explains why many rural–urban migrants end up in towns rather than cities.

Ultimately, these are empirical questions which require understanding how rural populations “gravitate” around different city types and, subsequently, integrating this information with spatial information on poverty. Regression-based poverty mapping using census data already represented a paradigm shift in the level of detail with which geographical patterns of poor populations could be mapped and understood. Combined with the URCA database, the link between a country’s urban hierarchy, with its rising gradient in earning opportunities and amenities, and poverty, mostly concentrated in the rural areas, can now be better understood and poverty reducing efforts better targeted across different URCAs.

To illustrate this, Figure 5 compares for Nigeria, the new disaggregated poverty map developed by the World Bank (2014) with the URCAs (Figure 5) and examines how this can support improved geographical targeting of poverty alleviation programs in the country. At the national scale, the broad pattern of poverty distribution is of predominately lower poverty rates in southwestern regions, with a general progression to higher poverty rates to the north and east. Almost a fifth of the population lives in large cities, and more than 40 percent in intermediate and small cities, or towns. The remainder mostly live 1-hour away from an urban center.

> 7 Gollin, Kirchberger and Lagakos (2021) are a notable exception.

19
<!-- Start of picture text --> Height-for-Age: Treatment vs. Control<br>oO<br>Positive expected effect ("Ist 1,000 Days") Zero expected effect<br>3 ; |<br>3. ° \ ; ||<br>g<br>a2ioT \\ ;:;:‘ |||I<br>& \ : {=> e S<br>rs \ Z —~! voTMN ~<br>g ~+~—TY |<br>Bn | |<br>cr : |<br>| |<br>3<br>a |<br>9;<br>020 40 60 76 80 100 120<br>Age in Months at 2016 Survey<br>—-—- Treatment ——— Control<br><!-- End of picture text -->
Informal employment in manufacturing changes significantly across income groups, dropping from 77 percent in low-income to 11 percent in high-income countries.<sup>19</sup>

**Figure 1. Average Share of Informal Employment by Income Groups and Sectors.**

<!-- Start of picture text --> All Sectors Agriculture<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>High Income Upper Middle Lower Middle Low Income High Income Upper Middle Lower Middle Low Income<br>Income Income Income Income<br>Manufacturing Services<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>High Income Upper Middle Lower Middle Low Income High Income Upper Middle Lower Middle Low Income<br>Income Income Income Income<br><!-- End of picture text --> _Source: Authors’ computations using the I2D2 dataset._ Other indicators from the WDI and Penn World Tables are included as control variables in the econometric analysis. Among these variables are shares of sectoral value added, shares of import and export in GDP, human capital, dependency ratio, population, and real interest rates. The sectoral shares of the value added, import and export shares in GDP, and human capital index are obtained from the Penn World Table database (version 8). The human capital index is based on the average years of schooling from Barro and Lee (2013) and an assumed rate of return to education is based on Mincer equation estimates from Psacharopoulos (1994).<sup>20</sup> Data on dependency ratio, population, and real interest rate are taken from the WDI.

>
GDIM, we do not include in our specification any control variable other than a dummy indicator for the survey in which the data were collected (to control for differences in the data collection process).<sup>6</sup> An additional measure of relative mobility is the intergenerational correlation (IC) in education— the correlation between and . IC is defined similarly to IP but in terms of the standard cc pp deviation of schooling. It measures the increase in standard deviations of a child’s education ii ii ss ss associated with a one standard deviation increase in his or her parents’ education. A higher correlation indicates a lower degree of mobility. **4. Educational mobility and economic development: Empirical framework** To analyze the relationship between educational mobility and economic development, we rely on a framework similar to that of Neidhofer and others (2023). Our main empirical specification is the following: c c c c c c , (7) cc cc xx hh ii where _Yctct_ is the level of income in country YY = ωω + ττ + ββMM _c_ in year + δδ _t_ ; ω are country fixed effects; τ are year fixed XX + δδ HH + δδ II + εε where _Yctct c t_ effects; _Mct_ is the aggregate level of mobility in country _c_ in year _t_ , calculated using maternal education;<sup>7</sup> **_X_** _ct_ , **_H_** _ct_ , and **_I_** _ct_ are vectors of control variables; ε is a residual; and and are parameters to be estimated. As control variables, we include contemporary country-level variables ββ δδ ( **_X_** _ct_ ), such as income inequality (measured by the Gini index) and the log of population size (which we include with a quadratic polynomial). We also include a set of cohort-level variables ( **_H_** _ct_ ), such as the average years of education of the workforce and its standard deviation
for moving among women were school (7%), following parents/relative (29%), and marriage (49%). Among men, following parents and relatives were also frequently reported (35%), but compared to women, marriage was less so (21%).

Using our three rounds of survey data, we can also examine sequencing of moves further from the baseline community. Table 2 shows the composition of our sample with respect to residence in Round 2 (2008) and Round 3 (2009), compared to the baseline location. Among those who moved to a nearby village from 2007 to 2008, 90% continued to reside in a nearby village. That is, 10% of these young adults moved even further away from the baseline between 2008 and 2009.

Table 3 compares characteristics of migrants and non-migrants at baseline and at the end of the survey, during the final round in 2009. Several differences among the two groups are apparent. First, respondents living in a household headed by one of their parents are significantly less likely to move, compared to those who are other relatives of the head. This is this case for both men and women. Among men, 77% of non-movers were the child of the head, compared to 59% of movers. Among women, these numbers are 79% and 67%, respectively. Other relationships to the household head seem to matter for men, and not to the same degree as to women. For instance, men who are nephews or siblings of household heads are significantly more likely to move than those who were not, but the effects of being a niece or sibling are not statistically significant for women. Young men with both parents alive are more likely to be stayers compared to those with at least one parent deceased, but there is no difference in orphanhood rates by migration status for women.

Among both women and men, those in the wealthier households at baseline (the highest wealth quartile) are more likely to
less so as they age. This decrease in flexibility is due to a drop in energy, loss of brain tissue, disengagement, and less interest in events distant from one’s immediate life, as well as to the accumulation of friends with similar world views. Spear (2000) states that a younger brain in a transitional period differs anatomically and neurochemically from an adult brain. In a young brain, the grey matter in the cortex gradually increases until about the age of adolescence, then sharply declines as the brain sheds neuronal connections superfluous to adult needs.

We examine the effects of the COVID-19 pandemic in Colombia on native altruism and attitudes towards migrants. It is challenging to identify the effect of COVID-19 on attitudes because a simple comparison of individual views beforehand and afterward could confound the effects of the pandemic with unrelated but concurrent events. For instance, in addition to the pandemic and its economic consequences in 2020, many nations implemented policies that same year to restrict immigration, which could arguably also have affected attitudes towards migrants.

To isolate the effects of COVID-19, we conducted an online survey experiment with 3,400 Colombian nationals.

> 1See the evolution of the Migrant Acceptance Index from the Gallup report collected in 140 countries between 2016 and 2019 at: https: //news.gallup.com/poll/320678/world-grows-less-accepting-migrants.aspx

2
in the minimum wage would not lead to a violation of the fiscal rule from increases in public sector wages.<sup>30</sup> This is not the case for social protection spending linked to the minimum wage, such as the war veteran scheme. Any decisions on the adjustment of the minimum wage need to account for the budgetary implications of the war veteran scheme being based on the minimum wage.<sup>31</sup>

**The expected poverty impact is modest.** Using HBS data over the period 2012-2017, we perform Shapley decompositions to quantify the contributions to poverty reduction in recent years stemming from changes in labor income, as well as other factors, including demographics, changes in the share of employed working-age individuals, and changes in non-labor income. The results show that a 1 percentage point increase in the share of occupied adults (relative to the working age population) has been associated, on average, with a 1.39 percentage point reduction in the national poverty rate over the period 2012-2017. On the other hand, a 1 percent increase in average wages per employed adult has been associated with a 0.26 percentage point reduction in the poverty rate. We combine these estimates with our minimum wage simulations to quantify the potential contributions of the simulated changes in employment rates and labor earnings to poverty reduction. We assume that self-employment income does not change, so all increases in labor earnings are derived from higher earnings among salaried workers. These results imply that the lower employment rate and small increases in labor earnings (Table 3) can translate into a 0.1 percentage point increase in poverty in the short run, and a 0.3 percentage point increase in the long run.

# <mark>Sensitivity checks: Wage elasticity of labor demand</mark>

**<mark>Up to now, we have assumed a value of labor demand elasticity equal to -0.5 in the short run and -0.8 in the long run for
the rural–urban continuum (Murali et al., 2019). Access to local provisioning of these services is relatively limited in urban areas and should be expanded (Bolund & Hunhammar, 1999). On the rural side, although access is more guaranteed, there is also variability, as not all rural households can rely on the direct use of ecosystem services either because of spatial variability or inequality in access to resources. An important research endeavor would be to match geo-located environmental data with socio-economic data in URCAs to analyze the impact of rural–urban linkages in determining ecosystem service provision and use.

27
Datastream, Bloomberg and hand collected data from 20-F filings.

> 13 Consistent with the MSCI Emerging Markets Free Index, we define the following 30 countries as emerging markets: Argentina, Brazil, Chile, China, Colombia, Czech Republic, Egypt, Ghana, Greece, Hungary, India, Israel, Jordan, Malaysia, Mexico, Morocco, Pakistan, Peru, Philippines, Poland, Russia, Slovakia, South Africa, South Korea, Sri Lanka, Taiwan, Thailand, Turkey, Venezuela and Zimbabwe.

14
# **2 Data and Measures**

Measurement is very important for a proper diagnosis of the longer run constraints to reducing chronic malnutrition in any given context. Each of the three clusters of underlying causes of malnutrition is inherently multidimensional making measurement difficult and costly. As a consequence the main concepts underpinning the original UNICEF framework regarding the interrelationship between and synergies among food security, environment and health, and child care are usually taken for granted or mistakenly assumed to have been investigated by other earlier studies.

Table 1 lists the countries and sources of data used in this study. Two countries were purposefully chosen from each of the four regions where malnutrition is a problem, (i.e. South Asia (SAR), Latin America and the Caribbean (LAC), East Asia Pacific (EAP), and Sub‐Saharan Africa (SSA). Additional criteria applied included: (i) the survey contained reliable information of children’s height (and weight) which are the widely accepted measures of chronic and short‐term malnutrition. This criterion limited the analysis to the Demographic and Health Surveys (DHS) as these are the only surveys is most countries with child height (and weight) measures. (ii) Data were available for at least two years in the last decade; (iii) malnutrition rates were stable or slowly declining over time; (iv) there was parallel analytic work in the Bank on different dimensions of poverty and nutrition taking place in some of these countries (e.g. Ethiopia); and (v) there were more than one survey available in the same country (e.g., Bangladesh). The samples in most countries are mainly rural except for Bolivia, Peru, Indonesia, and the Helen Keller survey in Bangladesh where urban households make up between one‐third and one‐half of the samples. The IFPRI survey for Bangladesh includes only rural households.

**Table 1: Countries and Data Sources**

|**Region **|
# **Introduction**

Many countries are struggling with the quality of their education systems. Efforts to reform education are often met with resistance and the lack of a model to follow. Many of the top performers in the world are high-income countries with many years of development of systems. Many middle and even high-income countries are only just starting to undertake important reforms. Resource-rich countries, such as Gulf Cooperation Countries (GCC), are making significant investments in their systems but have yet to see results. What is often lacking is experience from middle-income countries that have made progress. A useful starting point is the entry of many GCC countries into national student assessments that offer them a benchmark on results. Few examples exist however of countries making use of such assessments to inform their reform efforts. Even scarcer still are success stories. Jordan provides a useful case of a country that used an international assessment to benchmark and reform its system; more importantly, Jordan made great strides not only in the implementation of the program but also in improving the system.

The literature on the effectiveness of education initiatives in developing countries is scarce. It is also not clear how assessments themselves affect the improvement of national educational policies. Jordan is one of few developing countries that have been taking student assessment seriously. It is a small country that invests extensively to improve its education system because human capital is the major resource Jordan has, especially in comparison to neighboring oil-rich countries. The role of education is important in producing students equipped with the knowledge and skills crucial for Jordan’s growth and development, especially as the country is actively attempting to attract foreign investment. Policymakers in Jordan have always wanted to know what works in their education system and have been experimenting with different educational interventions, including comprehensive enhancements to the curricula, assessment tools, technology, and restructuring the education system and its institutions.

Jordan’s investments in improving the quality of education in past decades seem to have paid off. There is a noticeable impact on student learning since the