rafmacalaba/data-use-mentions-extended huggingface dataset:
.

Breiman, L. (2001). Random Forests. _Machine Learning_ , _45_ (1), 5–32. https://doi.org/10.1023/A:1010933404324 Breiman, L., & Cutler, A. (2006). _randomForest_ . Retrieved from http://stat-www.berkeley.edu/users/breiman/RandomForests Butler, D. (2013). When Google got flu wrong. _Nature News_ , _494_ (7436), 155. https://doi.org/10.1038/494155a Carlson, S. (2017). Dynamic Incentives in Credit Markets: An Exploration of Repayment Decisions on Digital Credit in Africa.

## _Working Paper_ .

Demirguc-Kunt, A., Klapper, L., Singer, D., & Van Oudheusden, P. (2014). The Global Findex Database. World Bank. Retrieved from http://www.worldbank.org/en/programs/globalfindex Francis, E., Blumenstock, J., & Robinson, J. (2017). Digital Credit: A Snapshot of the Current Landscape and Open Research Questions. _CEGA White Paper_ .

FSD. (2016). FinAccess Household Survey.

22
0436)||
|avetar|||0.890***|||0.798***|
||||(0.0525)|||(0.0532)|
|SPS/TBT NTMs|||-9.221***|||-8.764***|
||||(1.302)|||(1.272)|
|Other NTMs|||-3.621***|||-3.598***|
||||(1.267)|||(1.256)|
|Observations|256,711|250,432|153,084|251,934|245,615|150,582|
|Importer Fixed E¤ects|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter Fixed E¤ects|Yes|Yes|Yes|Yes|Yes|Yes|
|Product Fixed E¤ects|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Robust standard errors in parentheses are clustered by importer-product;

*, ** and *** indicate that coe¢ cients are signi...cant at 90%, 95% and 99%, respectively.

42
Mohanan, P.C., “Differentials in the Rural-Urban Movement of Workers,” _The Journal of Income and Wealth_ , 30(1), (2008): 59-67.

- Moretti, Enrico, “Human Capital Externalities in Cities,” in Henderson, J.V. and J.F. Thisse (eds.), _Handbook of Regional and Urban Economics Volume 4_ (Elsevier, 2004): 22432291.

- Mukim, Megha, “Industry and the Urge to Cluster: A Study of the Informal Sector in India,” SERC Discussion Papers 0072, (2011).

- Nataraj, Shanthi, “The Impact of Trade Liberalization on Productivity: Evidence from India’s Formal and Informal Manufacturing Sectors,” _Journal of International Economics_ , forthcoming (2011).

- Nelson, Andrew and Hirotsugu Uchida, “Agglomeration Index: Towards A New Measure of Urban Concentration,” World Bank WDR Paper 2009 (2008).

Planning Commission, “Eleventh Five Year Plan 2007-2012,” Government of India (2008).

- Rossi-Hansberg, Esteban and Mark Wright, “Establishment Size Dynamics in the Aggregate Economy,” _American Economic Review_ , 97(5), (2007): 1639-1666.

- Schneider A., M. Friedl and D. Potere, “A New Map of Global Urban Extent from MODIS Satellite Data,” _Environmental Research Letters_ , 4(4), (2009).

- Shaw, Annapurna, and Kavita Pandit, “The Geography of Segmentation of Informal Labor Markets: The Case of Motor Vehicle Repair in Calcutta”, _Economic Geography_ , 77(2), (2001): 180-196.

- Sivaramakrishna, K.C., A. Kundu and B.N. Singh, _Handbook of Urbanization_ (New Delhi: Oxford University Press, 2005).

- Sridhar, Kala Seetharam, “Impact of Land Use Regulations: Evidence from India's Cities,” _Urban Studies_ , 47(7), (2010): 1541-1569.

- Srivastava,
, fi nd that variation due to teacher effects explains about 25 percent of variation in test scores. More traditional regression-based studies also validate this fi nding. Rockoff (2004) using a 12-year panel of teacher-student data from two school districts in New Jersey fi nds signi fi cant teacher fi xed effects. A one standard deviation change in the teacher fi xed effect (unobserved quality) is associated with gains in Mathematics and reading of 0.26 and 0.16 standard deviations respectively. Less is known about the speci fi c attributes of teachers that affect student learning. Limited evidence on teacher experience and training is provided by Rockoff (2004) and Angrist and Lavy (2001), who fi nd that both experience and training have a positive impact on learning achievement.

Closer to the results presented here are the studies by Jacobson (1989) and Ehrenberg and others (1991). Jacobson (1989) describes an interesting policy experiment in which a pot of money 4
Sundarban _upazila_ s using 2010 poverty incidence estimates provided by the World Bank (2014a) and 2011 population estimates from the Bangladesh Bureau of Statistics. Following World Bank (2014a), we use two standards to determine poverty incidence: the upper poverty line, for households whose food expenditures are at or below the food poverty line established by the Bangladesh Bureau of Statistics;<sup>15</sup> and the lower poverty line, for extremely poor households whose total expenditures are at or below the food poverty line.

Table 4 and Figure 6 provide summary statistics for the five _upazila_ s. Although all five are poor by national standards, their national percentiles vary from 71.5 to 92.3 for lower-line poverty and 77.0 to 91.0 for upper-line poverty. Koyra and Shyamnagar are the poorest (by both standards) and most populous. Koyra’s lower-line and upper-line poverty incidences -- 29.1% and 49.1% -- are in the national 86th and 89th percentiles, respectively. Its lower- and upper-line poverty populations are 56,400 and 95,200. Shyamnagar’s lower- and upper-line poverty incidences are in the national 92nd and 91st percentiles, with poverty populations of 107,600 and 159,800.

**Table 4:  Poverty status of Sundarbans** **_upazila_ s**

|Upazila|Lo<br>%Poor|wer Poverty <br>National<br>Percentile|Line<br>Population<br>(‘000)|U<br>%Poor|pper Poverty <br>National<br>Percentile|Line<br>Population<br>(‘000)|
|---|---|---|---|---|---|---|
|Mongla|22.7|71.5|31.0|41.9|77.0|57.2|
|Dacope|24
|dispute to the VH’s court if they had one (0/1)|(0.446)|(0.029)|(0.025)|(0.032)||

Table displays standard errors in parentheses. *, ** and *** indicate significance at the 90, 95 and 99 percent confidence levels respectively.

However, they beg the question of how civil society leaders had this impact. We turn to this in the next section.

# **7 Mechanisms**

The inclusion of civil society leaders in the training sessions for village heads could have resulted in reforms to village governance through a variety of different mechanisms. In this section, we consider the evidence in support of three mechanisms, first outlined in figure 1. In particular, we examine whether civil society leaders caused reform through (1) competition between the two sets of leaders, (2) capacity upgrading or (3) increased monitoring. We probe the plausibility of each of these mechanisms using both quantitative and qualitative evidence.

## **7.1 Quantitative evidence**

First, we use our survey data to test observable implications of each mechanism, drawing on evidence from our survey of village heads, households and especially civil society leaders. It is important to note that 29
(alternatively measured by employment share or fraction of facilities) of these six sectors in each Brazilian municipio is used as a proxy for the concentration of dirty industry.

The Brazilian data suggest that the share of these six dirty industries -- no matter whether based upon - share of employment or share in number of enterprises declines smoothly across deciles. This may be seen in columns 1 and 2 of Table 2, which show the share of the dirty six industries falling from about 55% in the poorest regions to 30-35% in the richest.

# T'able 2: **Pollution Intensity and** Plant **Scale by Income DeCile**

<!-- Start of picture text --> Dirty Industry Small Plant Large Plant<br>________________ Share Share Share<br>No. Employ No. Employ No. Employ<br>Plants ment Plants ment Plants ment<br>Income<br>Decile N6/NT E6/ET Ns/NT ES/ET NU/NT EL/ET<br>1 0.56 0.55 0.90 0.83 0.06 0.10<br>2 0.55 0.56 0.85 0.71 0.08 0.16<br>3 0.51 0.51 0.85 0.67 0.05 0.16<br>4 0.45 0.46 0.84 0.63 0.06 0.18<br>5 0.44 0.46 0.83 0.60 0.06 0.21<br>6 0.43 0.44 0.82 0.55 0.06 0.23<br>7 0.37 0.39 0.80 0.46 0.06 0.28<br>8 0.34 0.37 0.75 0.35 0.08 0.36<br>9 0.33 0.40 0.
|
|Czech Rep|41|11|0|
|Estonia|31|17|9|
|Hungary|55|16|9|
|Latvia|34|9|7|
|Lithuania|12|6|2|
|Poland|19|4|2|
|Romania|13|5|3|
|Slovak Rep|41|13|0|
|Slovenia|47|4|4|
|_EU (potential ) candidates_|_30_|_4_|_2_|
|Albania|18|2|1|
|Bosnia|15|4|1|
|Croatia|37|7|6|
|Macedonia|33|2|0|
|Serbia|21|4|3|
|Turkey|58|3|0|
|_EU neighborhood_|_10_|_2_|_1_|
|Armenia|9|4|1|
|Georgia|7|2|1|
|Moldova|2|1|0|
|Ukraine|21|1|1|
|**Mean**|**26**|**6**|**2**|

Source: LITS survey and authors‟ calculation. Note: This table reports means for each variable by country. Observations are weighted to account for the varying size of the sampling units within countries. Credit Card is 1 if any member of the household has a credit card and 0 otherwise. Mortgage is 1 if the household owns its dwelling and currently has a mortgage and 0 if it owns its dwelling but has no mortgage. FX Mortgage is 1 if the household owns its dwelling and currently has a foreign currency mortgage and 0 if it owns its dwelling but has no FX mortgage.

> 11 See Beck and Brown (2010) for a detailed discussion of the LITS survey. Information on the 2006 survey is available from: http://www.ebrd.com/pages/research/economics/data/lits.shtml

30
26<br>0.11<br>0.25<br>0.10<br>0.24<br>0.09<br>0.23<br>0.22 0.08<br>0.21 0.07<br>0.20 0.06<br>Source: Household budget survey<br>1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> Table 2 decomposes the overall inequality – measured by the Theil mean log deviation index - into two components: (i) differences between groups and (ii) within group inequalities. The focus is on variations within (and between) (i) regional groups (96 groups = 16 voivodships crossed with 6 town sizes), (ii) educational groups (3 groups: higher, secondary, other) and (iii) age categories. If, for example, mean consumption was the same for all regions (i.e., the region variation was 0), then the numbers in the respective row in the table would be 0 and all inequality could be entirely attributed to inequalities within each region. Analogously, if everyone within a given region had equal consumption (i.e., within-region variation was zero) but regions differed in mean consumption, the “between” inequality component would be 100 percent. The table shows that inequality caused by variation within groups is the main component of total inequality for all groups analyzed. However, differences between different educational groups are significant and growing component of overall inequality and are differences between different regions. On the other hand, differences across different age groups were and remain minimal.

Thus, regional inequalities clearly increased during 1994-2002, not only in absolute terms<sup>6</sup> but also in relative terms. Or, equivalently, regional inequalities rose even more than total inequalities and the role of regional inequalities in overall inequality increased.
where<sup>1</sup> _/_ (1 _−θ_ ) is the elasticity of substitution between capital and skilled labor while<sup>1</sup> _/_ (1 _−ρ_ ) is the elasticity of substitution between capital (or skilled labor) and unskilled labor. Under this specification, the elasticity of substitution between capital and unskilled labor and between skilled labor and unskilled labor is the same. This production function exhibits capital-skill complementarities when _ρ > θ_ (when the elasticity of substitution between capital and unskilled labor<sup>1</sup> _/_ (1 _−ρ_ ) is higher than the elasticity of substitution between capital and skilled labor<sup>1</sup> _/_ (1 _−θ_ )).

There are other potential combinations for the nesting of _K_ , _S_ , and _U_ , but we follow Krusell et al. (2000) who have used this production function (distinguishing in addition between equipment and physical structures) to quantify the role of capital-skill complementarities (as opposed to skill-biased technical change) in explaining the increase in the skill premium in the US that accompanied the increase in the supply of skilled labor. They estimate the parameters in Equation 5 using US data, and find that the values for _ρ_ and _θ_ are consistent with complementarities between skill and capital. Krusell et al. (2000) in turn cite the evidence in Goldin and Katz (1998) and Hamermesh (1996) for a nesting consistent with Equation 5. More recently, Duffy et al. (2004) document additional evidence of complementarities between capital and skilled labor using the specification in Equation 5.

We divide Equation 5 by _L_ to obtain output per worker, and use _s_ and _u_ to denote the shares of skilled and unskilled workers, respectively, with _s_ + _u_ = 1: We also assume a law of motion for physical capital as in the Solow-Swan framework and assume that there is no accumulation of skill (the
|0.35 to 0.75||14.25 (479)|
|0.76 to 1.27||14.25 (479)|
|≥ 1.28||14.55 (489)|
|Perceived Child Intelligence Ladder|||
|1||0.92 (31)|
|2||3.01 (101)|
|3||5.98 (201)|
|4||13.86 (466)|
|5||22.97 (772)|
|6||25.59 (860)|
|7||27.67 (930)|
|Caregiver Characteristics|||
|Age (years)|28.57 (7.78; 15-83)||
|Education|||
|Did not attend school||25.02 (841)|
|Primary or less||52.93 (1,779)|
|Secondary or higher||22.05 (741)|
|CESD Depression score|17.15 (3.40; 10-29)||
|<br>Perceived influence on child's intelligence|||
|None||14.67 (493)|
|Some||25.59 (860)|
|A lot||59.74 (2,008)|
|Household Characteristics|||

11
the second contribution of our paper to the literature. In recent years, machine learning (ML) techniques have made their way into economics research and have proved their utility. Athey (2018), as part of an assessment of the early contributions of ML to economics, indicates that ML techniques have been most successful when applied to problems that require predictions as in our case. Furthermore, applying ML in agricultural economics research makes sense to model key variables such as crop yield, which is determined by a complex combination of soil quality, weather, input timing and other management choices, replete with non-linearities and interactions (Storm et al., 2020).

Our analysis showcases (a) the utility of ML techniques for predicting CC yields as a function of SR yields and complementary survey and geospatial data, and (b) the reliability of the resulting predicted CC yields in the analysis of the relationships between crop yields and agricultural inputs. We show analytically and empirically that predicted CC yields, although informed in part by SR yields, have attenuated NCME, which is uncorrelated with the intensity of agricultural input use. Only one-third of the sample is found to be sufficient for implementing crop cutting alongside self-reported survey data collection, such that an ML model can generate predicted CC yields for the rest of the sample in a way that leads to reliable parameter estimates of the relationships between yields and inputs. The findings reveal the importance of using SR yields as a predictor variable, and hence, underscores the need to continue to collect and improve the quality of self-reported survey data on crop production. Our analysis is first conducted with the data from a small-scale methodological survey experiment and is then replicated with the data from a nationally-representative survey to verify the external validity of 3
# **Changing demographic structure**

As countries develop, they go through a demographic transition (Figure 6). The initial phase of development is accompanied by improvements in health, which reduces infant and child mortality and increases life expectancy (Pritchett and Summers 1996). Fertility decline typically follows, but with a lag (Lee 2003) for a host of reasons, including improved survival of children, preference for fewer children, focus on _quality_ rather than _quantity_ of children, higher opportunity cost of childbearing and childcare, and potentially the availability of contraceptives. This means that countries undergo a youth bulge as they develop. Developed countries are far ahead in this transition, and their populations have stabilized (or are even declining) with the trend expected to continue. By contrast, developing countries are experiencing various stages of a demographic transition with some experiencing a youth bulge and others nearing a fall in their populations.

## **Figure 6 The share of children falls and the share of the elderly increases with development**

_Sources_ : Original calculations based on demographic data from UN DESA 2022 and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figure show the relationship between the population shares of children (ages 0–14) and the elderly (ages 65+) and GDP per capita. Each line on the background traces the evolution of population shares and GDP for each country between 1960 and 2020. The solid lines in the foreground plot the smoothened locally linear estimate pooling all countries and years between 1960 and 2020. CI = confidence interval.

The demographic transitions that accompany development, by themselves, can create demand for and supply of migrant labor. Developed countries tend to have an aging population and workforce, which increases the demand for younger workers in the economy. In addition, an aging populace increases the demand for care services, which can further fuel migrant demand. On the other hand, less developed countries, early in
asymmetric and incomplete updating of existing beliefs among smallholders in Africa (Lybbert et al., 2007) and households and firms (Kohlhas and Walther, 2021).

# **5. Spillover Effects on Farmer Self-Reporting on Non-Land Inputs**

If farmers exhibit misperceptions of plot sizes that vary little over time and adjust only incompletely to corrective information, one might then likewise expect inattention to inputs that vary more over time, and are often worth less, such as fertilizer applications or agricultural labor time allocation. One economical way for people to conserve on attention to details would be to hold a single belief about a central value – e.g., plot size – and then estimate other variables' values based on a simple, stable prediction rule that takes the single belief as its argument – e.g., a seeding or fertilizer application density or a rate of labor use per unit land cultivated – consistent with the notion of optimal prediction error (Hyslop and Imbens, 2001).<sup>19</sup> For example, fertilizer applications are usually recommended per unit of land, farmers may easily use that information to predict their applications.<sup>20</sup> Our experimental design allows us to test whether the randomized provision of plot size information indeed propagates to farmers' self-reports of other input application rates – that could not have actually changed – thereby signaling information spillovers because the new information on one production input (land) affects respondents' reporting of other production inputs (e.g., fertilizer, labor).<sup>21</sup> This would also signal that NCME in such variables are likewise correlated.

We focus on farmers' reported use of labor and fertilizer prior to and during planting, information that was collected from treatment group farmers immediately after they received the corrective information of the GPS plot size measure. Behavioral adjustments could cause real adjustment in later season input application rates in response to corrective plot size information, hence our focus on input application that
Policy Research Working Paper 10631

# **Abstract**

This paper aims to understand the existing gaps in microlevel data on forcibly displaced people—refugees and internally displaced persons. The paper undertakes a comprehensive review of all existing micro-level data sets in the United Nations High Commissioner for Refugees Microdata Library and the World Bank Microdata Library. It first identifies a corpus of micro-level data sets that are designed to have a representative sample of refugees and/or internally displaced persons and assesses gaps in geographical and thematic coverage. The paper then evaluates whether the data sets contain a core set of questions that are essential for the proper identification of refugees and internally displaced persons. The findings show that microdata on forcibly displaced people are comparatively rich in Sub-Saharan Africa in contrast to other regions. However, data scarcity is notably pronounced in countries facing fragility and conflict. Scarcity is also evident among internally displaced persons and on topics such as labor and employment, finance (for instance, credit, debt, and banking), agriculture/livestock/ fishery, and education. The paper also highlights that many of the existing micro-level data sets on forcibly displaced people do not contain the core set of questions needed for proper identification of refugees or internally displaced persons according to international statistical standards.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at tmasaki@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
br>age age<br>1991 1997 2000 2009 1991 1997 2000 2009<br>Rural Male Rural Female<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>45 55 65 75 45 55 65 75<br>Age Age<br>1991 1997 2000 2009 1991 1997 2000 2009<br>Lowess Plot bw=0.3<br> Working / Total<br> Working / Total<br><!-- End of picture text --> Notes: Employment rates by age cohort are calculated using non-parametric locally weighted regression (LOWESS) with a bandwidth of 0.3. Panel A and B use data from the common provinces surveyed across waves of the China Health and Nutrition Survey (CHNS) conducted from 1991 to 2009. Panel A also includes data from the 2008 China Health and Retirement Longitudinal Study (CHARLS) Pilot.

29
Figure 31: _p_ -values for Temperature Metrics by Weather Product <!-- Start of picture text --> Mean Daily Temperature Median Daily Temperature<br>1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>Variance of Daily Temperature<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0 p>0.90 p>0.95<br>p>0.99 95% C.I.<br>MERRA-2 ERA5 CPC MERRA-2 ERA5 CPC<br>MERRA-2 ERA5 CPC<br>Share of Significant Point Estimates Share of Significant Point Estimates<br>Share of Significant Point Estimates<br><!-- End of picture text --> _Note_ : The figure displays the share of coefficients on the temperature variables that are statistically significant for three temperature metrics for each product, aggregated over country, outcome variable, and specification. Only one extraction method is included. Each panel includes 216 regressions, with each column representing 72 regressions.

92