rafmacalaba/data-use-mentions-extended huggingface dataset:
capacity; and some are technical, relating to the informational inputs required.

Figure 10. Sequence of the IPF <!-- Start of picture text --> I<br>Criteria • Deliberation and consultation with decision<br>Identification makers and experts<br>• SEI and FEI based on existing appraisal data<br>II<br>• PCA when objectivity in criteria weighting is preferred<br>SEI and FEI<br>• CBA elements incorporated when available / calculable<br>calculation<br>• Sensitivity analysis<br>• (SEI, FEI) coordinates<br>III<br>Visual interface • Quadrants defined by budget<br>constraint<br>• Based on informed political<br>IV and technical debate<br>Selection • Quadrant A projects high<br>priority to invest<br><!-- End of picture text --> 23
|**Max to min ratio of per capita**<br>**DAU allocation **|145.15|652.39|7,998.02|5.72|
|---|---|---|---|---|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|27,022,350|21,877,864|35,392,980|12,559,045|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|551,144|1,244,735|194,263|1,124,271|
|**Max to min ratio of per capita**<br>**revenue after transfer **|49.03|17.58|182.19|11.17|
|**Coef. of variation**|1.041|0.783|1.531|0.621|
|**Weighted coef. of variation**|0.963|0.725|1.732|0.606|
|**Relative mean deviation**|0.342|0.274|0.466|0.230|
|**Gini Index**|0.465|0.361|0.603|0.309|
|**Weighted Gini Index**|0.411|0.299|0.600|0.279|

Notes:

*) Excluding non-receivers, inequality measures calculated for after transfer per capita revenue

21
Policy Research Working Paper 10419

# **Abstract**

Natural resources management (NRM) helps protect forests and promote sustainable development. Although women are key in strengthening activities in NRM, they are dramatically underrepresented in public funding for forest projects in many countries, such as Mexico, limiting their participation and impact. While structural barriers, such as land tenure and low capacity, cause this problem, this is exacerbated by barriers such as lack of information, complex application processess, gender norms, and rural women’s low aspirations and limited agency and self-efficacy to participate in NRM projects. This paper tests whether additions and modifications to the standard outreach strategies of a call for proposals for NRM grants in Mexico increase the number of applications submitted by localities and the share of women participating. The study uses a randomized controlled trial in 113 rural localities, where the standard outreach approach (control) is complemented with additional information channels and simplified materials (treatment 1), aiming to appeal more directly to inexperienced populations. A second treatment group further modifies the informational materials using insights from behavioral science (loss aversion, norms framing, and others) and adds proactive text message reminders to prompt behavior (treatment 2), hoping to address the barriers to women’s participation. The results suggest that treatment 1 localities had, on average, 2.3 more applications per locality than the control group (increasing the participation of both men and women). Treatment 2 complemented this, having, on average, 6.4 more women per locality participating of these applications than in treatment 1. This shows that women manifested interest in participating in these activities. A representative survey of women in the study localities (1,485 women in 52 localities) suggests that women in treatment localities were more likely to recognize the name of the project or informational materials. The analysis also suggests that the complementary strategies had no effect on the likelihood of being selected to receive a grant under the project, suggesting that additional support is needed to translate this
least developed nations and one of the poorest countries in its region. The IMF (2009) estimates that gross domestic product (GDP) per capita was $350 in 2007 and $457 in 2008 (current US$).<sup>14</sup> Based on the broader set of development indicators used in the UNDP human development index (e.g., health, education, living standards), Afghanistan ranks 181<sup>st</sup> out of 182 countries.<sup>15</sup> And UNICEF (2009, p. 11) reports that Afghanistan has the highest prevalence of stunting in the world among children under five years old.<sup>16</sup> The Afghan economy is largely based on agriculture; major crops include wheat, rice, maize, barley, vegetables, fruits, and nuts. Approximately 70% of cultivated crop area is devoted to wheat and about 15% is devoted to rice, barley and maize.<sup>17</sup> Wheat is both a major production crop and the main staple of the Afghan diet, with wheat flour contributing 57 percent to the total caloric content of the average bundle of food items of the relatively poor (i.e., the 20<sup>th</sup> to 50<sup>th</sup> percentile of the total consumption distribution, which is the basis for the official Government of Afghanistan poverty line). Due to large fluctuations in weather and insecurity, however, wheat production is highly volatile and the country is dependent on its trading partners to meet any shortfalls.<sup>18</sup> Pakistan is the major supplier of wheat (mostly in the form of flour) due to close historical ties and a shared 1,600 kilometer border; estimates of Pakistan’s share of the Afghan wheat and flour import market range from 59% (Chabot and Dorosh, 2007) to 79% (Maletta, 2004
(Janzen and Michler, 2021). The use of a pre-analysis plan allowed us to pre-define the sources of data for inclusion in the study, what metrics would be tested using what functional forms, and how we would compare results across models in the absence of formal statistical tests. Second, we adopted a blinding strategy to help ensure objectivity in the implementation of the pre-analysis plan. As such, the authors were divided into two groups: the Data Generating Group and the Data Analysis Group. Authors Kilic and Murray were in the Data Generating Group and had full responsibility for extracting the EO data and matching it to the household records in the household survey data to create a number of different paired EO-survey datasets. In these datasets, the source of the EO data was anonymized prior to sharing with the Data Analysis Group. Authors Josephson and Michler made up the Data Analysis Group and had full responsibility for cleaning the agricultural productivity data, running the regressions, and conducting and writing the analysis. The pre-specified analysis was carried out on the blinded datasets and these results were posted to arXiv.org prior to unblinding (Michler et al., 2021a). The generation of datasets in this manner preserves the objectivity of any findings regarding differences in outcomes between different EO products.

The following analysis, and the associated results, were all pre-specified. If methods, approaches, or inference criteria differ from our plan, we highlight these differences. Results arising from these deviations in our plan should be interpreted as exploratory.

# **4.1 Estimation**

We follow the basic model of Deschˆene and Greenstone (2007): where _Yht_ is our outcome variables from the LSMS-ISA-supported household surveys, described above, for household _h_ in year _t_ , log transformed using the inverse hyperbolic sine. We control for year fixed-effects ( _γt_ ) and include household fixed-effects ( _αh_ ) in some specifications. The function _fj_ ( _Wjht_ ) represents our weather variables of interest where _j_ represents a particular measurement of weather. Finally, _uht_ is an idiosyncratic
br>_9) F = LESPT_<br>_+ PBV_<br>_10) F = LDIF_|In this study, quarterly data for Egypt<br>covering the period 1981:IV to 1993:IV is<br>used to analyze the current episode of<br>dollarization. As expected, given that<br>foreign currency deposits in Egypt earn a<br>competitive rate of return, using the<br>coefficient of the expected rate of depreciation<br>(_LE, DEP_or_LESPT_) as a measure<br>of currency substitution, the results of the<br>error correction models reported in<br>The study suggest that currency substitution is<br>essentially absent in the short-run in<br>estimates of Egyptian foreign currency<br>denominated deposits held in Egyptian<br>economy|

92
own money. At the same time, expenditure may increase if they spend more on nutritious foods rather than calorie-based staple foods. When the type of aid is cash, it is most likely to increase food expenditure if they were previously food insecure.

# **Non-food expenditure**

> 12 All dollar amounts are US dollars unless indicated.

> 13 In the winterization cash assistance program for Syrian refugees in Lebanon, both the treatment and control group also received a restricted food voucher of approximately $30 per person per month. In this review, we focus on the winterization cash transfer of $575 that was only received by the treatment group.

> 14 These effects on food expenditure are similar to those in the general literature on social protection. For example, a recent meta-analysis of social assistance programs, including 48 studies of 39 social protection programs, found that transfers increased monthly food expenditure by 17 percent on average (Hidrobo et al. 2018).

15
- <mark>Claessens, S., and S. Djankov. 2002. Privatization benefits in Eastern Europe.</mark> _<mark>Journal of Public Economics</mark>_ <mark>83(3), 307-324.</mark>

- <mark>Cull, R., W. Li, B. Sun, and L. C. Xu. 2015. Government connections and financial constraints: evidence from a large representative sample of Chinese firms.</mark> _<mark>Journal of Corporate Finance</mark>_ <mark>32(1), 271-294.</mark>

- <mark>Dinc, I. S., and N. Gupta. 2011. The decision to privatize: finance and politics.</mark> _<mark>The Journal of Finance</mark>_ <mark>66(1), 241-269.</mark>

- <mark>Djankov, S., and P. Murrell. 2002. Enterprise restructuring in transition: a quantitative survey.</mark> _<mark>Journal of Economic Literature</mark>_ <mark>40(3), 739-792.</mark>

- Dong, X. Y., and L. C. Xu. 2008. The impact of China’s millennium labor restructuring program on firm performance and employee earnings. _Economics of Transition_ 16(2), 223-245.

- <mark>D'souza, J., and W. Megginson. 1999. The financial and operating performance of privatized firms during the 1990s.</mark> _<mark>The Journal of Finance</mark>_ <mark>54(4), 1397-1438.</mark>

- <mark>D'Souza, J., W. Megginson, and R. Nash. 2005. Effect of institutional and firm-specific characteristics on post-privatization performance: evidence from developed countries.</
[0.0128]|<br>0.0897***<br>†[0.00706]|0.0583***<br>†<br>[0.00684]|<br>0.0799***<br>[0.00633]|0.0794*** ‡<br>[0.00666]|0.0103 ‡<br>[0.0132]|
|Common border|-0.389***<br>[0.114]|<br>-0.380***<br>[0.136]|-0.453***<br>[0.0334]|-0.327***<br>[0.0581]|-0.494*** ‡<br>[0.0566]|-0.356***<br>[0.0541]|
|Common<br>language|-0.166***|-0.0878*** †|-0.186***|-0.117***|-0.145***|-0.273*** ‡|
|Observations|504|668|102|802|450|408|

# Source: World Bank.

Note: * p<0.05, ** p<0.01, ***p<0.001,. Robust standard errors are shown in parentheses. † indicates statistically significant difference of the coefficient estimate from the coefficient estimate for a sample of advanced economies only. † indicates statistically significant difference of the coefficient estimate from the coefficient estimate for a sample of commodity importers only. The table shows estimated coefficients from a gravity panel regression estimated for up to 95 countries using annual data for 2007-18 where the dependent variable is the log of bilateral trade costs. The
## **Box 5.  Ethnicity, Earnings, and Occupational Segregation**

An issue of great political importance in the Baltic countries is that of discrimination against ethnic minorities. A critical problem in evaluating this issue is that the available data do not contain information on state language skills, which are likely to be correlated with ethnicity. Bearing this in mind, the average differentials are sizable nonetheless. According to LFS 2002 data, in which ethnicity is self-reported, ethnic Latvians’ average net earnings were 10 percent higher than those of other ethnic groups (mainly Russians). This can be compared to a 16 percent differential found in Estonia and 9 percent - in Lithuania (using 2000 data).

Despite the low levels of occupational segregation amongst ethnic groups in Latvia, segregation of ethnic groups by economic activities is substantial. According to a dissimilarity index measure of segregation, at 0.21 segregation in Latvia is less strong than in Estonia but larger than in Lithuania. Table 8 summarizes the most pronounced horizontal segregation patterns. All three countries having a relatively low share of minority employees in public administration, and relatively high share in transport and communications (Appendix Table 27 provides details for Latvia). In contrast with occupational segregation, there is no clear evidence that minorities are concentrated in lowwage sectors. Segregation with respect to ownership sector is found only in Latvia, where 35 percent of nonLatvians and 49 percent of Latvians are employed in the public sector.13 Finally, in all three countries a much larger proportion of minorities than of native employees have the advantage of working in the capital city, where the wages are higher.

It is of policy interest to compare gross ethnic wage differentials and unexplained wage differentials with those experienced in the other Baltic countries (see Table 10 and Appendix Table 31). The gross differential and unexplained gap are largest in Estonia, while the Latvian and Lithuanian gaps are comparatively modest. Only in Lithuania can part of the gross ethnic wage differential be explained by observed employee characteristics, and this part is
wage elasticity of labor demand. Our calculations assume that the distribution of earnings (wage rate times employment) does not change above the Basic Income Guarantee. This will hold if the wage elasticity of labor demand is -1. This seems to be a reasonable assumption for the average wage elasticity based on past research on labor demand in the US.<sup>7</sup> However, there is undoubtedly variation around this mean elasticity, so we can expect (positive and negative) effects on earnings for many “non-Basic-Income-Guarantee” workers not captured by our analysis. Third, it is of interest to see how the size of this market compares with the current count of undocumented migrants. However, this count is uncertain, with current estimates in the (wide) range of 11 million to 22 million. The lower bound is derived from the Census Bureau’s survey-based estimates of the total migrant population (foreign-born people who have not become naturalized citizens) after deducting the count of legal migrants based on administrative data.<sup>8</sup> There are naturally concerns that illegal migrants will be reticent to reveal this fact, and may instead report that they are citizens; some illegal migrants may also be outside the sample frame of the household surveys. The upper bound of 22 million is based on Fazel-Zarandi et al. (2018), who used a time-series accounting of flows with demographic modeling. There is a continuing debate on the size of the undocumented migrant population in the US.<sup>9</sup>

**Calibrating migration and other costs** Demand curve (3) depends on parameter Γ _id_ , which represents the out-of-pocket cost of moving to the US (travel expenses to the US and back and visa fees). A reasonable assumption for the out-of-pocket cost of moving (and returning) is US$4,000, covering the costs of obtaining a visa as well as travel and relocation costs.<sup>10</sup> In addition,
13 Finally, the impact of government policy on health status is assessed with regression analysis using the cross-sectional country level data. In each country, a panel of cross-section (provinces or regions) and different time-series data were utilized. The results in several studies indicate uniformly favorable impact of immunization and water supply on health status in the Philippines, Malaysia and Viet Nam, while sanitation and immunization are more effective in China. Poor country such as Viet Nam faced stricter economic constraints than others in the sample during the period under review, and so improved health status is a result of rising income growth than public health spending. The dissimilar effects of public health spending in Viet Nam also reflects greater importance of other health promoting interventions for reducing infant deaths in a period of deteriorating health infrastructure, high incidence of infectious and water-borne diseases and poor quality of services. (See Technical Annex A).

# Emerging Problems

# Health Transition:

China is now completing its demographic transition from high fertility and mortality rates to low ones. The demographic transition has profoundly influenced the age structure of population and the cause of mortality which has set the stage for epidemiological change. As the population continues to age, the relative frequency of various causes of death changes, causing more chronic diseases to occur among the older population and fewer infectious diseases among the younger population. Figure 7 illustrates that the proportion of older population will rise from 9.5 percent in 1995 to 16 percent in 2020, and the proportion of deaths among this age group will increase from 55 to 65 percent because mortality rates from infectious and childhood diseases are decreasing disproportionately during the same period. By way of illustration, China's close Asian comparators--South Korea, Malaysia, and Thailand--will generally experience similar shifts in the age structure of their population and age pattern of mortality.

# **Changing Risk Factors**

Lifestyle choices for example, cigarette consumption, alcohol intake, as well as such factors as urbanization, environmental pollution, and differences in access to
- 2 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

# **1 Introduction**

In 2007 the global urban population exceeded the global rural population, and it is projected that urban dwellers will double the rural dwellers by 2050 (United Nations, 2014). Latin America and the Caribbean (LAC) is the most urbanized region on the planet, with 80% of its population living in cities (UN-Habitat, 2012). This growth has not stopped, according to the World Development Indicators, LAC cities added 206 million people between 1960 and 1990, and 191 million people between 1990 and 2015 (The World Bank, 2017). This rapid level of urbanization imposes challenges for local authorities, as many cannot provide services and infrastructure for the new urban areas at the same speed as the urban growth. Urban form has an important influence on the emergence of agglomeration economies and congestion costs, and on cities’ economic productivity, as well as quality of life and environmental sustainability (Squires, 2002). The economies of agglomeration is strongly associated with intraurban connectivity levels, the level of compactness, and land use patterns (Ciccone and Hall, 1996; Cervero, 2001; Rosenthal and Strange, 2004). LAC cities are still spatially growing despite the deceleration in population growth (UN-Habitat, 2012).

However, there is a lack of empirical studies about the characteristics of LAC cities and their changes over time. The challenges of topography may have prevented dense development in some cases, as many Latin American cities are located in rugged topographies with natural barriers. Some of the urbanization in the region is in fact occurring over steep terrain, with slopes greater than 15%, and we verified the trend of urbanizing even steeper terrains in recent years. According to Gencer (2013), these levels of urban concentration of population increase the potential for disasters and most of the cities exposed to at least
Doctor Voucher treatment to have a positive RDT. Malaria-positive patients are more likely to get treated for malaria and especially severe malaria, so this difference would bias results _against_ patient-driven and _towards_ doctordriven demand. This is the opposite of what we find.

Panel C of Table 2 shows that patients come from relatively poor families: just under one-third of survey respondents (either the patient or the caregiver accompanying the patient to the clinic, in the case of minor children) are literate; households are quite large, with 10.3 members on average, and monthly per capita income is just under CFA 22,000 ($36). Forty-one percent of household members are children under the age of 15, and households own 0.49 mosquito nets per capita.

Appendix Table B6 shows that the characteristics of individuals selected for the home survey versus not, and interviewed versus not (conditional on selection), are balanced. There is, however, significant selection into taking the home RDT: patients who consented to take the home test are sicker and more likely to have purchased an antimalarial than patients who did not give consent.

# **3.4 Predicting Malaria Risk**

We would like to study how prescription and treatment outcomes vary with patients’ underlying malaria risk ( _π_ in the model). We follow Cohen, Dupas and Schaner (2015) and estimate the relationship between RDT result and observed patient characteristics in the clinic survey with the following probit specification: where _posict_ is a dummy variable equal to one if a patient tests RDT positive and **xict** is a vector including dummy variables for symptoms, days since onset of illness, patient age, a dummy equal to one if the patient is under age 5, the interaction between age and the under 5 dummy, patient gender, and patient pregnancy status.<sup>26</sup> The results of this regression, shown in Appendix Table

> 26We expect malaria risk to change discontinuously at age 5 because the Malian government had a
As these approaches evolve independently, they portray an unprecedented opportunity to sidestep the challenges associated with traditional enumerator-based price data surveillance, including extensive temporal lags (sometimes >5years gap), coarse spatial resolution, and poor market segment coverage (Zeug, et al., 2017). Further, as the data systems mature, they can complement enumerator-led efforts by enriching national data systems with high-frequency price monitoring. However, for the data outputs to be considered useful, it is imperative to validate against standard enumerator-submitted data across market locations and over time. Due to the nascence of these innovative data approaches and sources, it is uncertain if these alternative estimates of commodity prices are credible reflections of market signals and actual price levels. Both approaches are characterized by inherent limitations. For instance, the crowdsourcing approach can arguably be influenced by the vagaries of independent volunteers who may submit spurious or outdated price data at will, defying the inbuilt controls to enforce the integrity of the data collection system (Solano-Hermosilla, et al., 2022; Arbia, et al., 2023). Similarly, model-based price estimates from AI algorithms can be subject to various accuracy issues and uncertainties due to parameterization and bias (Aldoseri, et al., 2023; Andrée, 2021a). Therefore, independent ground-truthing against enumerator-led data, and evidencing [dis]agreement between these data sources, is imperative to guide further innovation and engagement of stakeholders to advance real-time assessment of food price changes, affordability, and food security.

# _Study Area_

We focused this comparative study on three (3) major states in the core northern region of Nigeria (Figure 1), where the geographical and time-range coverage of the World Bank’s RTP (henceforth, AI-estimated) food prices overlap with crowdsourced price submissions. With a combined population of 30.5 million, the focal states account for 15% of the national population (UNFPA, 2023) and approximately 6.5% of the national GDP (BudgIT, 2022). Characterized by low literacy and historically high