rafmacalaba/data-use-mentions-extended huggingface dataset:
611|
|Annual growth, 2006-2012|14%|11%|-11%|8%|13%|
|Annualgrowth,2006-2011|6%|8%|-10%|10%|7%|

Source: UNPS 2006-2012

**Figure 5: Regional differences in per capita crop income growth, 2006 to 2012**

<!-- Start of picture text --> 600,000<br>500,000<br>Central<br>400,000<br>Eastern<br>300,000 Northern<br>200,000 Western<br>100,000<br>0<br>2005 2009 2010 2011<br><!-- End of picture text --> Source: UNPS 2006-2012 To assess the role of different types of income growth in reducing poverty in Uganda we correlate the real per capita income aggregates with household consumption. This way, it is possible to ascertain whether growth in one of the income sources has been more important for increasing consumption than other income sources, particularly among the bottom 40 percent. Specifically, a fixed effects model was estimated using the log of per capita consumption and the log of per capita income (for each source of income), allowing an analysis of the relationship between changes in income and changes in consumption. Interview year and month fixed effects were also included. The analysis was conducted only for 2006 and 2010 as there is a marked reduction in the consumption aggregate after 2010 that is hard to explain and is inconsistent with the national poverty trends.<sup>9</sup> 9 One possible explanation is the methodological differences in the collection of consumption data in the 2011 and 2012 survey rounds. Computer Assisted Personal Interviews (CAPI) was introduced in the UNPS for the 2011 and 2012 rounds 13
Can Environmental Policy Reduce Infant Mortality? Evidence from the Ganga Pollution Cases<sup>_∗_</sup> Quy-Toan Do Shareen Joshi Samuel Stolper World Bank Georgetown University University of Michigan _Keywords:_ environmental policy, development, pollution, infant mortality. _JEL Codes:_ Q53, Q56, C36

> _∗_ We are grateful to Prashant Bharadwaj, Jishnu Das, Ram Fishman, Garance Genicot, Mark Giordano, Susan Godlonton, Rema Hanna, Hanan Jacoby, Remi Jedwab, Guido Kuersteiner, Shomik Lall, Arik Levinson, Rohini Pande, Martin Rama, Martin Ravallion, John Rust, Simone Schaner, George Shambaugh, Shinsuke Tanaka, and Jennifer Tobin for useful comments. Many thanks to Mr. Jairam Ramesh and Mr. M.C. Mehta for helpful discussions. Thank you as well to Michael Greenstone and Rema Hanna for sharing their pollution data, to Sam Asher and Paul Novosad for sharing their weather data, and to Shreya Shah for valuable qualitative research. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent.
In addition, the analysis incorporates data from other sources on market prices of agricultural products, weather and conflict. These data provide objective measures of shocks that households experienced during this time. Objective measures are preferred to subjective measures, in which the households themselves report whether they face one type of shock or another (Thomas et al. 2013, Anttila-Hughes and Hsiang 2014, Hill and Porter 2016). Households are more likely to recall shocks that resulted in large losses of income and not report those that did not. As such, there is likely to be significant reporting bias in the occurrence of shocks, which will bias the estimate of the impact of shocks on income and welfare. However, the cost of using objective measures of shocks is that our analysis is restricted to covariate shocks. That is, shocks experienced by everyone in a given area (weather, prices, conflict) that can be observed in data other than the household survey. This means that we are unable to say much about the idiosyncratic shocks that affect welfare in Uganda. In particular, we are unable to look at the impact of health shocks on welfare despite a literature that suggests un-insured health shocks have a substantial impact on welfare in Uganda (Bridges and Lawson 2008, Helbert et al. 2013, Aliga 2013).

More specifically, the following sources of secondary data were included:

- Monthly price data collected at eight wholesale markets across Uganda as part of the Uganda Bureau of Statistic’s Consumer Price Index (CPI) data collection exercise. A 12-month moving average was assigned to each household according to the date in which they were interviewed. All prices are deflated by the national CPI before being used in the analysis such that the prices used capture relative price changes, i.e. the degree to which the maize price increased or decreased more than the price of the basket of consumption goods represented in the CPI. Each household is assigned the prices of the closest market, measured by geographical distance.

- Water Requirement Satisfaction Index (
# **5.3 Ownership**

Another interesting question is whether the ownership, private or public, tends to influence the compliance of firms to environmental regulations. Table 5 presents the results keeping also the air category variable as a control. One finds that in general, privately-owned firms seem to be more in compliance than publicly owned firms when firms are inspected. Selfreported data, on the other hand, suggest that public firms tend to report to be more in compliance. This analysis requires considering that in some industries most of the firms are either privately or publicly owned (as the descriptive statistics showed). For this reason, it is important to carry out the same study at the industry level.

||(1)<br>Self mon.|(2)<br>Self mon.|(3)<br>Inspection|(4)<br>Inspection|
|---|---|---|---|---|
|Air category|0.829<sup>_∗∗∗_</sup>|0.829<sup>_∗∗∗_</sup>|-0.551<sup>_∗∗∗_</sup>|-0.551<sup>_∗∗∗_</sup>|
||(0.087)|(0.087)|(0.068)|(0.068)|
|Private||-0.534<sup>_∗_</sup>||1.535<sup>_∗∗∗_</sup>|
|||(0.305)||(0.513)|
|Observations|5624|5624|3638|3638|
|Pseudo _R_<sup>2</sup>|0.165|0.165|0.117|0.117|

Standard errors in
Figure 1. Legal status of Syrian refugees upon arrival <!-- Start of picture text --> Female Principal Applicants Male Principal Applicants<br><!-- End of picture text --> <!-- Start of picture text --> No data Smuggled Born in No data Born in Smuggled<br>0.02% 0.09% CoA 0.03% CoA 0.27%<br>0.04% 0.03%<br>Legal<br>43.81% Legal Informal<br>Informal 48.86% 50.81%<br>56.04%<br><!-- End of picture text --> _Source_ : Own calculations using Home Visits data for Syrian refugees that arrived in Jordan after 2011. _Note:_ CoA = City of arrival.

# Demographic Characteristics

<mark>Women and children left Syria in large numbers and therefore, as is often the case for refugee populations, the demographic characteristics of the refugee population differ from that of pre‐crisis Syria. Figure 2 shows that before the refugee crisis, the Syrian population was very young, with the largest population cohorts being children under 15 (Panel a), but the Syrian refugee population in Jordan is even younger (Panel b). Children under 15 accounted for 36 percent of the population in Syria in 2010 but represent 45 percent of the refugee population living in Jordan. In pre‐crisis Syria, 3.5 percent of the population was 65 years or older, but among the Syrian refugees, this group only accounts for 2 percent of the population.</mark> Figure 2. Age pyramid by sex of Syrians in Syria and Syrian refugees in Jordan <!-- Start of picture text --> a. Syrians pre‐crisis b. Syrian refugees in Jordan<br>75‐79 75‐79<br>60‐64 60‐64<br>45‐49 45
_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 7_ reflect that initial imputations are uncertain. When the price imputation process is deployed in real-time, prior knowledge about heteroskedasticity can be obtained by modeling a Generalized Autoregressive Conditional Heteroskedastic (GARCH) error process. The initial disturbance term can then be drawn using observationlevel standard deviations. This means that when the price monitor is active in real-time, time-varying variance can be filtered and used to control the stochastic part of the initialization. See the Appendix for notes.

When a monitoring system is first deployed, and no previous results exist, a multi-step spatial time series interpolation approach can be taken to generate the initialization following the original implementation of Andr ́ee (2021).<sup>1</sup>

# Prediction Model

The regression models _f_<sup>_A_</sup> _, f_<sup>_B_</sup> _, ..._ involved are specified as follows (using the first as an example): The price data vectors are in long format, so that _A_ stacks the sub-vectors of market-level time series _A_ = _{a_ 1 _,_ 1 _, a_ 1 _,_ 2 _, ..., , a_ 1 _,T , ..., aJ,_ 1 _, aJ,_ 2 _, ..., , aJ,T }_ , with _aj,t_ being the price quote at location _j_ and time _t_ . Specifically, apart from the other prices items, a matrix of covariates is added. These include the spatial coordinates of markets, so that the model is able to interpolate spatially, as well as any prices vectors contained in ( _B, C_ ) that are at least 95% observed (minor gaps imputed using a structural time series model) so that the model has an accurate representation of observed temporal trends. Finally, the institutional exchange rate is added to inform the model about currency depreciation effects.<sup>2</sup>

>
in their social relationships are more likely to have children who are in the correct school grade for their age.

# **3.  The Peruvian Context**

According to the last National Households Survey (2010), Peru is a country where 35 percent of the population is poor and 10 percent is extremely poor.<sup>7</sup> There are important geographical disparities across regions and between urban and rural areas. In 2010, about 54 percent of rural population were living in poverty against 19 percent of urban population. Similarly looking at the three climatic zones, in the Amazonian jungle ( _selva_ ) the poverty rate was 37 percent, and in the highlands ( _sierra_ ) 49 percent of the population were poor and 20 percent were extremely poor. The poverty rate was 18 percent in the Pacific Coast ( _costa_ ), which is the richest part of the country, where just 2 percent of the population were extremely poor. Poverty is highly correlated with a low level of education. In 2010, more than 55 percent of the poor population had no education or just primary education.

As expected, poverty particularly affects the most vulnerable segments of population and in particular children. In the period 2000-2009 about 8 percent of newborns had a low birth weight and 30 percent of Peruvian children under 5 years old were stunted (WHO, 2010) with strong disparities between rural (44 percent) and urban areas (16 percent) (INEI, 2008).<sup>8</sup> The Peruvian context is particularly interesting to study social capital. The country has a long-standing tradition of social participation. The bloody wave of terrorism by the armed group called “Shining Path” ( _Sendero Luminoso_ ), which shook up the country in the 1980s, boosted the rise of spontaneous organizations at the community level, especially but not exclusively, in rural areas where terrorism was stronger (Wiig, 2005). In this period which lasted over 10 years, the “Committees of Self Defence” (or
.81|0.38|0.86|-0.05|0.41|0.94|-0.53|
|Georgia|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Ghana|-0.02|0.18|0.32|-0.34|-0.10|0.10|-0.20|
|Guatemala|0.75|0.26|0.85|-0.10|1.09|0.41|0.68|
|Guinea|0.54|0.21|1.07|-0.52|0.55|0.43|0.12|
|Guinea-Bissau|1.92|0.70|1.97|-0.05|2.31|1.00|1.31|
|Indonesia|-0.00|0.03|0.03|-0.03|-0.02|0.00|-0.03|
|Iraq|-0.03|0.08|0.16|-0.20|-0.17|0.03|-0.20|
|Jordan|0.00|0.00|0.00|-0.00|0.00|0.00|0.00|
|Kenya|0.26|0.59|2.23|-1.97|-0.28|0.55|-0.83|
|Kyrgyz Republic|0.08|0.04|0.11|-0.03|0.12|0.05|0.07|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the (country by country) elimination
showed some sign of physical damage (51 percent of which was damage to the screen). Analyzing usage data from 86 functioning devices stored on the cloud, we find that average number of books read - in the 6-8 months duration of treatment - was 4.6. Only 6 percent of devices with extracted data showed no book read. There were no systematic differences across treatment groups in the average number of books read. Just over 85 percent of devices with extracted data had at least one highlight; 56 percent had at least one note.

# **5. Impacts of eReader treatment**

The results of estimating specifications (1) and (2) (described above) are presented in **Tables 5-9** . The first pair of columns in each table correspond to specification (1) with and without controls. Controls are drawn from a set of baseline student characteristics<sup>15</sup> as well as baseline student performance in reading and average school performance at baseline (de Ree et al. 2017). The coefficients of interest capture contrasts between adjacent treatments (T1 vs control, T2 vs T1, T3 vs T2). We then present results of estimating specification (2) where the overlapping treatment indicators are interacted with three student-level attributes: student having above median reading at baseline, student owned a paper textbook at baseline, student had aspirations for high level of education at baseline. All specifications cluster standard errors at the school level.

> 14 Oliver Twist was the most frequently cited book, followed by students’ English textbook, Five Little Friends, and students’ math textbook.

> 15 Age, educational attainment of mother and father, household wealth, access to electricity, mobile phones, and proxies for frequency of paper-textbook use.

9
34

**References.**

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Bourguignon, F., F. Ferreira and N. Lustig (1998): "The Microeconomics of Income Distribution Dynamics in East Asia and Latin America", World Bank Research Proposal, (Washington, DC: World Bank, April).

Buhmann, B., L. Rainwater, G. Schmaus and T. Smeeding (1988): "Equivalence Scales, Wellbeing, Inequality and Poverty: Sensitivity Estimates Across Ten Countries _using_ the Luxembourg Income Study Database", _Review of Income and Wealth,_ **34,** pp. 1 15-142.

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vary by region. Table 4 provides the first stage of the IV regressions. Locality-clustered bootstrapped standard errors in parentheses. ***, **, * - statistically significant at 1%, 5%, and 10%, respectively.

Source: Enterprise Surveys and authors’ calculations.

Taken together, these estimates suggest that policy measures that ease access to bank credit specifically for green investment might not suffice to see a substantial increase in green investments. This appears to be the case particularly for MENA and may be relevant for other countries at a similar level of development and climate change awareness. Governments and development banks should also consider measures that could strengthen green management practices. These may include disseminating information on best green management practices, requirements to measure and report environmental impacts or credit lines contingent on the adoption of better green management practices by firms.

28
# **6.4 Parental Time Investments**

We explore the impacts of the DICIPE program on other outcomes related to parental time investments, motivated by the large bodies of work that highlight the role of parental investments in complementing early childhood interventions (Cunha and Heckman, 2007; Doepke et al., 2019; Attanasio et al., 2020; Ravindran, 2021; Bos et al., 2024) or education interventions in general (see, for example, Yedomiffi 2024; de Walque and Valente 2023; Andrabi et al. 2017; Cerdan-Infantes and Filmer 2016; Avvisati et al. 2013). In our setting, parental investments can be directly linked to program parenting education sessions, as these meetings were used to provide information to teach parents how to enhance their children’s growth and development. Indirectly, the program also induced parents to be significantly engaged in the preschool activities.

First, we show that parents in treated communities improved their parental practices related to stimulation and home play relative to parents in control communities. We present our results in Table 6, where we study two common indices of parental practices at home constructed using the UNICEF Multi-Indicator Cluster Surveys (MICS) and 29
; 16 countries to the structure of the local economy, mostly referring to the presence or predominance of non-agricultural activity, and 11 countries to a minimum population density threshold. The remaining 99 countries do not make use of any explicitly stated criteria. Rather, they either simply list their urban areas by name, or state a designation of administrative units that constitutes cities.

Compared to the overall sample of WUP countries, a smaller share of the 40 LAC countries covered by the WUP database use a specific criterion or criteria to define urban areas (i.e. 42 percent of countries versus 57 percent). LAC countries are also less likely to employ a minimum population threshold in their definitions of urban than the sample overall (30 percent versus 44 percent). These differences between the LAC countries and the overall sample are mainly driven by the Caribbean countries. Conversely, LAC countries are more likely to include reference to the provision of infrastructure or services in their definition (i.e. 20 percent versus 13 percent). In this case, the difference between LAC and the global sample is driven by the Latin American countries.<sup>15</sup> Rwanda produced by the US Census Bureau as part of its international _Demobase_ project (http://www.census.gov/population/international/data/mapping/demobase.html). 13 This sub-section draws, in part, on a previous conference paper by two of the authors (Deuskar and Stewart, 2016). 14 For all countries (except Austria) urban criteria fall into at least one of these four broadly defined categories. However, every country’s definition is slightly different, and may include particularities not fully reflected among these four. For example, the definition for Honduras (see Annex 2), was counted in the ‘population size’ and ‘urban services or characteristics’ categories, but also has elements that do not fit neatly in either (e.g. “ _communication by land (road or train
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<mark>Hurd, Michael D., and Susann Rohwedder (2013). "Expectations and Household Spending."</mark>

- <mark>Kilic, T. and Sohnesen, T. P. (2017). “Same Question But Different Answer: Experimental Evidence on Questionnaire Design's Impact on Poverty Measured by Proxies”.</mark> _<mark>Review of Income and Wealth</mark>_ <mark>, doi:10.1111/roiw.12343</mark>

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- <mark>Pradhan, Menno (2009). "Welfare analysis with a proxy consumption measure: evidence from a repeated experiment in Indonesia."</mark> _<mark>Fiscal Studies</mark>_ <mark>30.3‐4: 391-417.</mark>

- <mark>Rolstad, S., Adler, J., & Rydén, A. (2011). “Response burden and questionnaire length: is shorter better? A review and meta-analysis.”</mark> _<mark>Value in Health</
Castaneda Aguilar, R. Andres; Fujs, Tony; Jolliffe, Dean; Lakner, Christoph; Gerszon Mahler, Daniel; Nguyen, Minh C.; Schoch, Marta; Vargas Mogollon, David L.; Viveros Mendoza, Martha C.; Baah, Samuel Kofi Tetteh; Yonzan, Nishant; Yoshida, Nobuo. 2020. September 2020 “PovcalNet Update : What’s New. Global Poverty Monitoring Technical Note; No. 14.” World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/34451 License: CC BY 3.0 IGO.

Christiaensen, L., P. Lanjouw, J. Luoto, and D. Stifel. 2012. “Small area estimation-based prediction methods to track poverty: Validation and applications.” Journal of Economic Inequality, 10(2), 267–297. <u>https://doi.org/10.1007/s10888-011-9209-9</u> Dang, H. A. H., P. F. Lanjouw, and U. Serajuddin. 2017. “Updating poverty estimates in the absence of regular and comparable consumption data: Methods and illustration with reference to a middle-income country.” Oxford Economic Papers, 69(4), 939–962. https://doi.org/10.1093/oep/gpx020 Deaton, Angus, 2005. “Measuring poverty in a growing world (or measuring growth in a poor world).” The Review of Economics and Statistics, 87(1), pp.1-19.

Douidich, Mohamed, Abdeljaouad Ezzrari, Roy Van der Weide, and Paolo Verme. "Estimating quarterly poverty rates using labor force surveys: a primer." The World Bank Economic Review 30, no. 3 (2016): 475-500.

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time increasing village income by triggering adoption of better agricultural technology.

In order to control for contemporaneous effects of weather shocks, we include a village level rainfall variable in all of our specifications. Additionally, to account for the possibility that these first period shocks could have an effect on future realizations of the control variables (good rainfall in the first period leading to better agricultural technology, higher fertility and higher incomes in the subsequent periods), we need instruments that are correlated with the change in income, wages, land prices and population, but are uncorrelated with the shocks. Village population density in 1991 and road density in 1975 provide us with a valid set of instruments for the equilibrium equation.<sup>15</sup> For the reduced form equation, we use population density in 1991, road density in 1975 and an artificial

> 15The equilibrium equation is under-identified right now as I don’t have enough pre-period variables I can use as instruments.

22