rafmacalaba/data-use-mentions-extended huggingface dataset:
Bennedsen and Feldman (2002) extend these models of informational lobbying with a single decision maker, to address the fact that ultimately the purpose of lobbying is to influence legislation, which is made by Congress. Hence, any complete theory of informational lobbying must take into account the process by which majorities are formed during legislation of policy. They develop a theory whose main conclusion is that the ability to _create_ majorities in Congress provides the necessary incentives to lobby groups to carry out their activities.

The findings in Wright (1990) empirically motivate the relevance of the BennedsenFeldman model. Wright conducted a survey of lobbies that had contacted Ways and Means committee members and Agriculture committee members of the U.S. House of Representatives. His inference that money contributions _plus_ informational lobbying influenced voting in Ways and Means affirms the first view that informational lobbying does matter. Further, Wright found that lobbying mattered more than money to Ways and Means committee members (that is, money bought access in order to informationally lobby). Wright’s findings about lobbying of Ways and Means member have two implications: (i) taking note of the fact that decisions are made not individually but in committees of the Congress, rather than target one individual lobbyists allocate their scarce resources over a set of influential decision makers, and (ii) lobbying is informational. The Bennedsen-Feldman contribution is thus more relevant than previous theoretical models that presumed informational lobbying of an individual decision maker.

Notably, Wright finds very weak evidence of informational lobbying of the House Agriculture committee, the most influential policymaking committee in agriculture. The specific content of the five-yearly Farm bills are determined in this committee. In Wright’s data strong collinearity among regressors precluded reliable inferences about informational lobbying of the Agriculture committee. Wright attributes the weak finding to the fact that the Agriculture committee deals with a narrow and well defined set of issues on a periodic basis (as different from Ways and Means which attacks a range of diverse issues, often on an ad hoc basis). Both, leaders and
In sum, the true influence of pupil background on achievement **can** be better **estirated** if we take into account: (a) those family background characteristics -- **garental** values **and** press to achievement **and** the child's own effort and perceived efficacy -- that appear to operate independently of conventional aspects of class (for **the** few countries in which **theme** factors have been studiedi, **and** (b) better indicators of **material and** *octal **aspects** of class.

# STUDY **1:** THAILAND

Our first analysis **examines** family effects on Grade 8 mother **s achievement** gain in Thailand, and focuses on a broader range of background indicators, specifically motivational variables.

# Method

Sample. Data **were** drawn from the Second Interntional Mathematics Study, conducted under the auspices of the International Association for the Evaluation of Educational Achievement. The IEA SIMS sample comprised 99 mathemathics teachers and their 4030 eighth-grade students and was derived from a two-stage, stratified random sample of classrooms. The primary sampling units were the twelve national educational regions of Thailand plus Bangkok. Within each region, a random sample of lower-secondary schools was selected, with replacements. At the second **stage,** a random sample of one class per school was selected from a list of all eighth grade mathematics classes within the school. The esulting sample represented a 12 sample of eighth grade mathematics classrooms within each region.

At both the beginning and end of the school year, students were administered a mathematics test covering five curriculum content areas

-8-
factors. This paper investigates the relationship between temporal and geographic variation in food security and variation in each of the potential drivers described above. Conflict data were obtained from the Armed Conflict Location & Event Data Project (ACLED), which provides information on the date, location, event type, actors involved, and the number of people killed in each

> 11 See (accessed July 2017): <u>http://www.bbc.com/news/world‐middle‐east‐29319423.</u>

> 12 For example, the Central Statistical Organization has stated in informal discussions that much of their staff has departed following the inability to pay their salaries regularly.

4
through purchase but store the foods without consumption during the reference period. In these cases, expenditure data fails to reflect the actual amount of consumption.

The EAT Lancet planetary health diet’s composition as used for this paper is one specific selection that has been made based on the quantified contribution from each food group as proposed by the EAT Lancet publication, with the contribution from different foods within each food group as observed in the Bangladesh HIES. Another contribution from different food groups, so as to select a vegetarian or vegan diet or to adapt to specific dietary patterns in different countries is also possible, but as no quantification for those options was provided, we used the quantified option.

# **5 Conclusion**

Many developing countries (48 lower middle-income countries) have a severe double burden<sup>14</sup> (Popkin et al. 2020) where both undernutrition and poor diets are equally important to consider. For ensuring access, links need to be drawn between social protection programs and food systems to ensure that sustainable approaches are taken to reduce undernutrition and poverty, which at the same time do not harm these populations (i.e., by resulting in overweight/obesity and not resolving micronutrient deficiencies) and the impact on the environment is appropriately balanced with the right food production policies and approaches.

> 14 Defined as a prevalence of wasting of >15%, stunting of >30%, and thinness in women of >20%, and an adult or child overweight prevalence >20%.

19
|

Source: Eurostat - <u>https://ec.europa.eu/eurostat/statistics explained/index.php?title=Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE)</u>

**Other multidimensional poverty indicators are commonly used in other countries/regions.** These include the Unmet Basic Needs, traditionally used in Latin American countries, the UNDPOPHI Multidimensional Poverty Index (MPI) developed by the Alkire-Foster Approach to Multidimensional Poverty, and the Global Multidimensional Poverty Measures (MPM) developed by the World Bank.<sup>19</sup> These measures might include different dimensions of deprivations and/or indicators not included in the AROPE, including education, health, infrastructure, etc. (see Annex 2 for differences in dimensions and indicators). The World Bank MPM is made publicly available for EU countries, and some experimental multidimensional indices following UNDP-OPHI have also been published, as mentioned before. Recent evidence proposes a conceptual framework for social exclusion and a multidimensional measure (Cuesta et al. 2024).

**This paper primarily follows the AROPE 2030 framework, which is the primary framework for analyzing multiple deprivations among vulnerable groups, given its prominence as the standard measure for policy formulation and monitoring within the EU.** The AROPE framework has several advantages among the multidimensional poverty measures. First, AROPE is situated within the extensive body of research on multidimensional poverty measurement,<sup>20</sup> and adheres to globally recognized criteria when crafting indicators and dimensions to assess household wellbeing in the EU. Moreover, the material deprivation index, which leverages a methodology pioneered in 1999 and further refined in 2012<sup>21</sup> ,  generates a deprivation measure for the EU that is appropriate and exhibits validity, reliability, and additivity. Second, the AROPE indicators can be constructed with microdata from the European Union Statistics on Income and Living

> 19 For more details, see World Bank (2018a).

> 20 See World Bank, UNDP & UNICEF (
year as reported in the WBES data. Second, we match the annual temperature data to each firm’s geocoded location. Third, we calculate historical averages for each firm’s location by considering the panel of weather data from 1980–2008.

# **5 Empirical framework**

## **5.1 Empirical specification**

Our main metric of temperature shocks is the average annual temperature, although in principle our approach works with any weather metric. For each weather variable, we compute the difference between the variable in the fiscal year in which we observe a firm in the WBES and the historical average of the variable between 1980 and 2008, denoting this difference _CDiff_ . Using differences allows us to identify shocks, or deviations from “business as usual” weather conditions. The deviations remove the impact of fixed factors such as local geography which can affect local temperatures and economic activity—for example, coastlines face milder climates and proximity to ports can imply lower transport costs.

Our core specification includes country by fiscal year by sector fixed effects. As such, we compare the performance of firms in the same sector in the same country over the same fiscal year who face different weather shocks. We control for the historical average of the variable between 1980 and 2008, denoted _CHist_ , so that we draw direct comparisons between locations with similar historical climates.

The WBES provides weights that can be used to construct nationally-representative statistics for firms within a fiscal year. However, as Solon, Haider, and Wooldridge (2015) point out, using survey weights in regressions does not resolve mis-specification stemming from weighted data.<sup>2</sup> The authors show that it is preferable for to model the important sources of heterogeneity instead of using sample weights. We follow their recommendation

> 2The general intuition is that weighting data in regressions resolves issues stemming from over- or undersampling certain groups but cannot resolve bias stemming from differences in the variance of the main explanatory variable between groups.

12
creating additional incentives for wives to increase their precautionary savings.<sup>5</sup> Two recent papers, in the very different contexts of Ireland and the United States, indeed suggest that women who are faced with a higher risk of divorce both save (González and Özcan 2013) and work more (Papps 2006).

However, the expected impact of an increase in the risk of polygamy on monogamous wives’ saving behavior is theoretically ambiguous since it depends on the possibility for women to protect their own savings from being seized by their husbands. Indeed, if the wife’s savings can be used by the husband to finance the cost of a second wife, the risk of polygamy is expected to have a negative impact on wives’ savings (Antoine 2002). Note that in Senegal data from the PSF survey suggest that the cost of marrying a second wife is high: in addition to the wedding ceremony and the work to set up a room for the new cowife, the husband has to pay a

> 3 Or the risk of late marriage (Kureishi and Wakabayashi 2013). Note that other channels may create different incentives to save for married men and women. One in particular is explored by Browning (2000): based on a two-person household model best suited to the context of developed countries, he shows that the age difference between spouses and the higher longevity of women generates different incentives to save for men and women.

4 Indeed, 34 percent of polygamous wives of rank 2 were either divorcees or widows before remarrying, while this is the case of only 11 percent of first wives.

5 Note, however, that in the Senegalese context, divorcing is all the more costly as divorced women are pressed to remarry and are most often constrained to marry as a second- or higher-rank wife. Moreover, divorced women may be constrained to leave some of their children to their husband, which represents a further cost of divorce.

5
consumption and economic growth in Bangladesh. Mozumder and Marathe (2007) find a unidirectional causality from per capita gross domestic product (GDP) to per capita electricity consumption, using annual data from 1971 to 1999. Along the same line, Asghar (2008) examines the causality between energy consumption and income from 1971 to 2003 and concludes that in Bangladesh, a unidirectional Granger causality runs from GDP to electricity consumption. In contrast, Sarker and Alam (2010) and Hossain and Saeki (2011) find a reverse causal relationship running from electricity generation to economic growth. More recently, Hossen and Hasan (2018) find unidirectional causality running from electricity consumption to GDP in Bangladesh from 1972 to 2011. Finally, Alam et al. (2012) and Ahamad and Islam (2011) find bi-directional, long-run causality between Bangladesh’s economic activity and energy consumption.

Our study contributes to the literature in three ways. First, it reconsiders evidence on the causality between electricity consumption and economic activity in Bangladesh at annual frequency and adds monthly analysis. Second, this is the first study to analyze electricity consumption as an high-frequency economic indicator for Bangladesh, with lessons for other emerging markets and developing economies. Third, it uses the indicator to compare the costs 3
<!-- Start of picture text --> 600<br>500<br>400<br>Mean<br>300 Ratio<br>Regression<br>Linear<br>EGRA LLECE PASEC PISA SACMEQ<br>SUPPLEMENT FIGURE V<br>Comparison of Scores Across Linking Methods by Test<br><!-- End of picture text --> _Notes:_ We compute Harmonized Learning Outcomes scores using multiple methods including regression, linear, mean and ratio linking functions. This figure compares scores using a density plot. We only compute scores using the regression method for LLECE, EGRA and PISA since SACMEQ and PASEC only have a single country used to make score comparisons.

# _C. Potential Limitations_

A potential limitation is the representativeness of the data of the total stock of cognitive skills in a given country. While the tests used are nationally representative, they are conducted at the school. To this end, learning data might be affected by enrollment patterns, and we advise users of the data to analyze learning outcomes alongside enrollment trends. For example, as marginal students enter the schooling system, average test scores might be driven by selection rather than true learning progress. While this is a potential concern, it is mitigated for a few reasons. First, primary enrollment rates are relatively high, reaching 90 percent on average, and above 75 percent even in the furthest behind regions, such as Sub-Saharan Africa. Second, the direction of the bias is likely to yield a conservative upper bound of learning in a given country. If all students enrolled, the average test score would be even lower, since the marginal students would pull the average down. Since most countries at the bottom of the distribution of learning are also those with relatively lower enrollments, it is unlikely this will alter substantive conclusions – the lowest preforming countries will be revealed to be even lower performing. In addition, data at the primary level should be largely unaffected, since at this level students are being
# **References**

- Agnolucci, Paolo, Carolyn Fischer, Dirk Heine, Mariza Montes de Oca León, Kathleen Patroni, Joseph Pryor, and Stéphane Hallegatte. Forthcoming. “Measuring Total Carbon Pricing.” World Bank, Washington, DC.

- Ahluwalia, M. S., & Patel, U. 2022. Climate Change Policy for Developing Countries (CSEP Working Paper 23). New Delhi: Centre for Social and Economic Progress.

- Bastin, J-F., Y. Finegold, C. Garcia, D. Mollicone et al. 2019. The global tree restoration potential. Science, 365(6448): 76-79.

- Dasgupta, S., Lall, S, and Wheeler, D. 2021. Urban CO2 Emissions : A Global Analysis with New Satellite Data. Policy Research working paper,no. WPS 9845 Washington, D.C. : World Bank Group.

- Dasgupta, S., Lall, S, and Wheeler, D. 2022. Who is Accountable for Climate Change?  History and Equity in Carbon Budget Allocation, mimeo, World Bank.

- Hallegatte, S., Wolburg, P., and Mahler, D. 2023. The Climate Implications of Ending Global Poverty. World Bank.

Mattoo, A. and Subramanian, A. 2013. Greenprint: a new approach to cooperation on climate change. Center for Global Development.

Stern, N. 2007. The Economics of Global Climate Change: The Stern Review. Cambridge University Press.

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R</u><sup>2</sup> <u>of the targeting methods</u>

|**Ran**|**k correlations**|**AUC**|**R-squared**|
|---|---|---|---|
|Partial registry (10% of the census sample)|0.75<br>|0.89|0.57|
|PMT scores|(0.02)<br>|0.50|0.00|
|IHS training sample|0.13<br>|0.53|0.01|
|RWI|0.20<br>|0.60|0.04|

**Figure 2.** Rank correlations, AUC, and R<sup>2</sup> of the targeting methods

15
# **1 Introduction**

Accurate measures of welfare for spatially disaggregated areas are valuable inputs into the design and evaluation of effective development policies (Atkinson, 2019; Blumenstock, 2016; Ravallion, 2015; Merfeld and Morduch, 2023; McBride et al., 2022). Yet, most estimates of welfare are derived from household surveys that can only produce reliable statistics at higher levels of aggregation, mostly because of the high cost of data collection (Fujii and van der Weide, 2020; Kilic et al., 2017). Small area estimation combines survey data with more comprehensive auxiliary data to obtain estimates for small areas, in this case defined as one administrative level below that which survey estimates are considered to be sufficient reliable to publish. These more granular estimates can improve geographic targeting (Elbers et al., 2007) as well as program and policy evaluation (Ratledge et al., 2021). While some countries can draw on rich administrative data such as income tax records to serve as auxiliary data, developing countries do not typically maintain accurate and up-to-date administrative data sources. It is quite common to predict welfare with census data and a contemporaneous survey – following Elbers et al. (2003) or Molina and Rao (2010). But in poorer countries, census data are usually collected infrequently. As a result, official statistics on welfare in small areas tend to be dated.

Against this backdrop, recent advances in machine learning and the growing availability of non-traditional data sources have led to the proliferation of new options for small area estimation. For example, Blumenstock et al. (2015) use mobile phone records to infer the socioeconomic status of phone owners in Rwanda and Aiken et al. (2023) use mobile phone call data records to predict targeting performance of programs in Togo. However, one drawback of mobile phone data is that – like banking records – the population of mobile-phone owners may be systematically different from the population of those without phones. Satellite-derived geospatial data do not suffer from this
bounded away from total inequality unless the maximum group size becomes sufficiently small.  This implies that although the expected value of between group inequality might increase as the number of groups increases (Shorrocks and Wan (2004), proposition 3), this value may be well below total inequality if one of the groups remains very large.  For example, with a lognormal (0,1) distribution and one group occupying 70% of the population while every other individual constitutes a separate group (implying effectively an infinite number of groups), the maximum possible between group inequality (measured by GE(0)) would be 0.373 – well below the inequality level of 0.5 in this distribution. For related results, see Davies and Shorrocks (1989) and Shorrocks and Wan (2004).

> 18 Starting from a BGI-maximizing group order, split-up a group in such a way that mean incomes in the new groups are equal to the old parent group.  BGI among these groups is equal to the old BGI.  Obviously, if the incomes of the two new groups are non-overlapping, then BGI will increase.  Hence a refinement of a particular grouping will generally lead to an increase in BGI.  See also Shorrocks and Wan (2004), proposition 2.

> 19 Ordering population groups by their mean incomes using, say, a household survey would introduce a possible difficulty due to sampling variability.  In other words, our ability to order groups by mean income (or consumption) could be limited by the fact that some of the group means are statistically indistinguishable from each other.  For the time being, we ignore the standard errors associated with the observed group means.

15
to the year 2019. Data are available for all 133 jurisdictions reported to have used trade measures in the first half of 2020 in the GTA trade policy data set (Appendix Table 1).<sup>15</sup> PP processes in richer countries tend to require both a smaller number of steps and less time for completion on average (Figure 3, bottom panels). The total number of steps range from a low of 12 (Singapore) to a high of 21 (Honduras, Hungary, the Islamic Republic of Iran, the Lao People’s Democratic Republic, Myanmar and Oman). The total time taken to complete a procurement process ranges from 270 (Korea) to 2062 (the República Bolivariana de Venezuela) days. The sample mean is 18 steps and 717 days, respectively. Richer countries also tend to use e-procurement more on average (Figure 3, top right panel). The share of e- procurement in total procurement ranges from less than 25% for most African countries to more than 75% for EU and ASEAN Member States.

_GPAj_ is constructed using information on membership of the WTO’s Agreement on Government Procurement as of July 2020; and _Num_DPAj_ is constructed using data from Shingal and Ereshchenko (2020), which cover all PTAs in effect until March 2017.<sup>16</sup> On average richer countries tend to be members of more DPAs (Figure 3, top left panel). The number of non-zero DPAs ranges from 1 for West Asian countries to 26 for the EU. There are 32 WTO GPA members in the sample, mostly comprising high-income countries. E The control variables are sourced as follows: population and GDP data are from the World Bank World Development Indicators; market penetration ( _MPj_ ) is computed using bilateral distance data from CEPII (Head et al. 2010); and government effectiveness ( _GEj_ ) is sourced from the Worldwide Governance Indicators (Kaufmann et al. 2011). Trade data to construct the import share and RCA
|**Table 2.4: Fertility rate by region in rural Mexico,**|
|---|
|**1980 and 2000**|

||**1980**|**2000**|
|---|---|---|
|**Total Mexico**|2.8|2.6|
|**North**|2.8|2.5|
|**Capital**|2.6|2.2|
|**Gulf**|2.7|2.5|
|**Pacific**|3.0|2.7|
|**South**|2.8|2.9|
|**Center North**|3.2|2.8|
|**Center**|3.0|2.7|
|Source: Census 1980 and 2000.|||

The demographic change that demands the most urgent policy response is the growth in the economically active population in rural areas. During 1990–2000 the number of those aged 12 to 64 rose by more than 300,000 (0.6 percent). The growth rate is low primarily due to out-migration and some due to reduced fecundity. Migration is important in rural Mexico. Mostly young people leave their village in search of employment and find work in a wide variety of economic sectors, either in Mexico or the U.S.

**Table 2.5: Female and male labor market participation** **<u>rate in Mexico, 1980 and 2000, Percent</u>**

||**Male**|**Female**|**Male**|**Female**|
|---|---|---|---|---|
||**1**|