rafmacalaba/data-use-mentions-extended huggingface dataset:
Jurado Nacional de Elecciones, the body which supervises elections in Peru, keeps records on electoral outcomes for elections held since 1990. Some of these data have also been compiled and published (Tuesta 1994, Jurado Nacional de Elecciones). Results are generally available for all provinces in the country, and for all districts in Metropolitan Lima.

The level of aggregation used for the analysis in this paper was dictated by theoretical considerations and the availability of data. Section 4 uses national aggregates of FONCODES expenditures to analyze the relationship between the timing of expenditures and the electoral calendar. Sources of funding and the administration of the community-based and special projects are different, so the analysis is broken down by project type. FONCODES has assigned resources to individual provinces since 1993, and to individual districts since 1996. Section 5 uses a province-level data set to analyze the

> 5 Note, however, that even without political interference actual expenditures in a given province would rarely be equal to allocations because of the demand-drive **n** ature of the program.

5
-<br>Formal|Services-<br>Informal|Services-<br>Formal|Not<br>working|All|
|Agriculture|11.1|0.8|0.0|0.6|0.2|0.5|13.2|
|Industry - Informal|0.5|11.2|0.4|1.1|0.3|0.8|14.3|
|Industry - Formal|0.1|0.4|4.3|0.1|0.5|0.3|5.8|
|Services - Informal|0.3|1.1|0.3|12.9|1.3|0.8|16.6|
|Services-Formal|0.2|0.0|0.5|1.4|15.0|0.9|18.0|
|Not working|0.9|1.4|0.3|1.6|0.6|27.4|32.1|
|All|13.0|14.7|5.8|17.7|18.0|30.7|100.0|

Note: Unweighted percentages based on a total sample size of working-age males of 36,126.

Table 2.b: Two-year transition matrix used to update HIECS (females)

|||Updated<br>|HIECS FY20(no COVID)<br>||
|---|---|---|---|---|
|HIECS 2018|Informal|Formal|Not working|All|
|Informal|6|0|4|10|
|Formal|0|7|1|8|
|Not Working|2|1|79|82|
|All|8|8|83|100|

Note
(CP), People of India (PoI), Aspiration India (AsI), and Income Pyramids (InP). This study uses the CP, InP, and PoI sections. The CP is a household-level monthly survey reporting household expenditures on various kinds of goods and services, without a breakdown of individual members of multi-person households. Specifically, it asks households about their monthly expenditure on all types of transport, including expenses on a combined category of “buses, trains, and ferries” (BTF). Our study period for the CP is from November 2020 to September 2021. The InP is a monthly survey that tracks the income of each household member. We use the same study period as in the CP data. The PoI is an individual-level survey conducted every four months. There are three waves a year: January-April, May-August,

> 28The CMIE started to collect household members’ time usage information in the wave of SeptemberDecember 2019, while the government of Delhi started the _Pink Slip_ scheme in November 2019. We do not include Delhi in the analysis since we do not have pre-period information on time usage.

5
scope for rapid growth of private capital. Starting from a very low base in the 1970s, private sector capital increased at a remarkable pace but remains considerably below advanced country levels (Figure 27).

> 20 Government investment in the IMF data set accounts for capital spending by central and subnational governments, so that comparable data are available for a large number of countries. The total infrastructure stock for China is possibly underestimated, as infrastructure investment by state-owned enterprises (SOEs) is included in the private capital stock. For details refer to https://www.imf.org/external/np/fad/publicinvestment/data/info.pdf.

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child was breastfed. The _z_ -scores were computed using the US Centers for Disease Control (CDC) growth charts. On average, children have a height-for-age _z-_ score that is 1.2 standard deviations below the US norms, and 23.4 percent of children in the sample are stunted (heightfor-age _z-_ score less than –2). Consistent with other evidence on Ecuador, stunting (low heightfor-age) is more common than wasting (low weight-for-height). Hemoglobin levels were measured in the field using pin-prick tests, and values were corrected for elevation by merging our sample with data on the mean altitude of the parish. Elevation-adjustment was done with the CDC guidelines (CDC, 1989). The average elevation-adjusted hemoglobin level is 11.1, exactly equal to the threshold value that is often used to define anemia for children in this age range. Using this threshold, 48.0 percent of children in the sample are anemic. Breastfeeding duration is based on retrospective questions asked of the mother for each of her children; the average duration of reported breastfeeding is 5.7 months.

We use three measures of parenting quality in our analysis. The first is based on the widely-used Home Observation for Measurement of the Environment (HOME) scale (Bradley, 1993; Caldwell and Bradley, 1984; Bradley et al., 2001). This is constructed from 11 items that are assessed by interviewers at the close of the interview, and measures punitiveness (for example, whether parents yelled at or hit children during the interview), and responsivity to children (for example, whether they responded to and encouraged children in a positive way during the interview). Each item is scored as a dichotomous variable; the final scale ranges from 0 to 11, with higher values corresponding to less responsive and harsher behavior. Low values are more common than high values: The mean HOME score is 2.3, 31.8 percent of children have parents with a score of 0, and 85.9 percent have scores
for establishing broad patterns, we caution that any observed misperceptions might be attributable to sample selection rather than pluralistic ignorance.

# **2.4 Comparison of the Gender Equality at Home Survey with Other Data Sources**

The Gender Equality at Home Survey represents a general population of Facebook users and should not be considered representative of a country’s population. For example, demographic characteristics of the respondents on Facebook might differ from the average characteristics of a representative individual in the same country; or they may have different gender attitudes.

While our sample is not nationally representative, we examine how closely the gender attitude statements from our data correlate with comparable attitude statements collected in nationally representative global data sources: the World Values Survey (WVS)<sup>16</sup> and

> 16For our analysis, we utilized data from Wave 7 of the World Value Survey, spanning from 2017 to 2021.

10
But beginning in the Soviet era and extending over nearly three decades following independence, policy has strongly resisted both migration and urbanization in Uzbekistan. As a Soviet Republic, Uzbekistan experienced a rapid fall in the urban population share, declining from 34 percent in 1959 to less than 26 percent in 1989. This eventually left the country as or less urbanized than Soviet Tajikistan or Kyrgyzia (at 25.7 and 37.5 percent in 1989, respectively).<sup>4</sup> No population census has been completed in the intervening years, and the current urban population share is imprecisely measured.<sup>5</sup> However, administrative records suggest that the pace of urbanization stagnated in 2009, leaving the country more than 50 percent rural (using new rural/urban classifications), and substantially less urbanized than otherwise comparable countries.

This paper focuses on two key impediments to achieving the urbanization and growth targets set out by the Government of Uzbekistan: legal limits on internal migration, and the high cost of housing in the most dynamic urban centers. Detailed information on the domestic registration system (commonly referred to as the _propiska_ system), domestic migration, and housing costs are drawn from the Listening to the Citizens of Uzbekistan (L2CU) study, which began in 2018 and remains ongoing as of this writing. The discussion then moves to the views of citizens on how to resolve these challenges. Reform preferences of a nationally representative sample of households are described, and an embedded survey experiment provides additional validation.

This analysis comes in the context of comprehensive national economic reforms. Under new presidential leadership since September 2016, the government set out to transform the prevailing stateled growth model and rapidly become a high-income country. A central objective of these reforms is ensuring that the labor market opportunities generated under the new economic model are widely shared among the population. This paper describes key bottlenecks to these goals. Without action to address the forces slowing domestic migration, theory and international experience suggest that output and productivity growth
|Table 1: Illustrat<br>A|ion of the Sensitivity of th<br>ssumptions about the Ref|e Estimated Po<br>erence Group|verty Rate to|
|---|---|---|---|
|Assumption|Mid point of reference<br>(Rp/month)|Poverty line<br>(Rp/month)|Poverty Rate<br>(%)|
|Reference group<br>centered on 15%|69,645|77,265|21.78|
|Reference group<br>centered on 30%|86,159|84,550|28.48|

# _D) Poverty incidence across regions_

Our approach for the inter-regional comparison has been to keep the quantities in the food basket constant. Theoretically, we want the poverty line to represent the same in utility. This approach guarantees that the poverty line suffices to purchase this national basket in each region. A disadvantage is that the applied basket is not necessarily optimal for every region. In a region with a very different set of relative prices compared to the national average, the same welfare (in utility terms) can generally be reached with a lower total expenditure than the poverty line would imply. This is the argument in favor of using region specific food bundles.<sup>4</sup> Chesher (1998) finds that moving to a regional poverty basket increases the extremes in measured poverty, raising provinces that are already high and lowering provinces that are already low.

Using Susenas February 1999 data, the resulting regional poverty rates from our iterative method are presented in table 2, while the associated poverty lines are .4 There are a number of compelling arguments against, which are discussed in length in Ravallion and Bidani (1994) or Ravallion (1994).

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# 4.4. Planning the multi-mode data collection

The operational costs of the survey limit the tracking rules; thus, movers who leave the sampled area (i.e. enumeration area, the parish, district, etc.) cannot be tracked in all cases. To obtain synthetic information on the variables of interest for movers and to reduce panel attrition due to movers, a cost-effective strategy is to conduct a telephone interview. In this case, a shortened questionnaire is preferable or, in more sophisticated cases, the survey design may include a CATI (Computer Assisted Telephone Interview) interview and the panel survey will rely on multi-mode data collection.

Finally, the standard questionnaire needs to ask for the telephone number to avoid nonresponse of movers in future waves.

# 5. SHORT DESCRIPTION OF THE LSMS-ISA PANEL SURVEYS

We test this methodological framework through a simulation using real data from the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA), conducted with the support of the World Bank. The purpose of the simulation is to verify the properties and usability of the proposed estimator and to compare its estimates with those currently produced in a specific application case. The LSMS–ISA surveys are quite heterogeneous when considering the entire estimation process, including the sampling plan, the tracking protocol, the target parameters, the set of auxiliary variables, and the estimators used. For the sake of simplicity, the proposed estimator does not include all the specific issues and technicalities affecting the survey used in the simulation. Instead, we focus on the main steps of the process that are common to all longitudinal surveys to draw general conclusions that are not survey specific.

LSMS-ISA surveys are nationally representative, longitudinal household surveys designed to understand the relationship between living standards and agriculture in Africa. Since its launch in 2008, the LSMS-ISA project has worked with statistical offices in eight partner countries in Sub-Saharan Africa, providing technical assistance to design and implement multi-purpose household panel surveys with a strong focus on agriculture, while promoting innovation and efficiency in data collection methods
in the lower panel of Table 1, with standard errors clustered at the primary sampling unit (village in REDS data, and county in CFPS data). The evidence from both IGRC and IRC estimates shows that, in rural China, the null hypothesis of separability cannot be rejected at the 10 percent significance level; the F statistic for IGRC estimates is 0.014 with a P-value of 0.90, and the corresponding numbers for IRC are 0.13 (F statistic) and 0.72 (P-value). In contrast, in rural India, the null hypothesis of separability is rejected at the 10 percent level for IGRC (F=3.80, P-value=0.052), and at the 5 percent level for IRC (F=6.42, P-value=0.012). Since the estimated effect of parental schooling is larger in the nonfarm households in rural India, the evidence suggests complementarity between nonfarm occupation and father’s education in determining a son’s schooling.

# **Relative Mobility and Long-Term Variance in Schooling**

When interpreted as a dynastic model of the evolution of schooling across generations, a higher IGRC implies a higher long-term variance in schooling.<sup>31</sup> To see this, note that for the IGRC equation (12), we can write the long-term variance of education as: where _σs_<sup>2isthelong-termvarianceofeducationand</sup><sup>_σ_</sup> _ε_<sup>2isthelong-termvarianceof</sup> the error term capturing all other factors unrelated to father’s schooling such as market 1 luck, and macro and trade shocks.<sup>calledthe‘familybackgroundmultiplier’</sup> (1 _− ψ_ 1<sup>2)is</sup> by Emran and Shilpi (2019), which amplifies the impact of the shocks to education. Using equation (16) and the estimates of _ψ_ 1<sup>_f_and</sup><sup>_ψ_<
**Figure 2. Share of households owning animals and cultivating fields, by country and urban-rural**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Burkina Faso Mali Niger Senegal Chad All<br>Any animals Any fields<br>Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total<br>Share of households (percent)<br><!-- End of picture text --> Note: Household-level sampling weights applied. ‘Any animals’ measured at the time of the interview. ‘Any fields’ refers to whether fields were cultivated in the previous agricultural season. Source: EHCVM and World Bank estimates.

We also observe that diversification of income sources is relatively low among Sahelian households. Across the EHCVM sample, just under half of households (46.8 percent) had any working-age members primarily engaged in activities outside of agriculture, livestock, or fishing, with households as a whole engaging in 1.5 different sectors on average (Figure 3). Income-generating activities also appear to be less diversified in rural areas than urban areas.

**Figure 3. Diversification of household employment sectors, by country and urban-rural**

<!-- Start of picture text --> 2.5 100<br>90<br>2.0 80<br>70<br>1.5 60<br>50<br>1.0 40<br>30<br>0.5 20<br>10<br>0.0 0<br>Burkina Faso Mali Niger Senegal Chad All<br>Number of sectors (LHS) Any non-agriculture/non-livestock (RHS)<br>Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban Total Share of households with a worker<br>Mean number of sectors of primary
. Even so, household expectations of inflation consistently exceed actual inflation, and the deviation has not declined. Figure 7 shows that the average of professional forecasts has been close to actual inflation, while household expectations have often exceeded actual inflation. In the last few years, and particularly since the shift to IT, expected inflation has declined, in line with the decline in actual inflation. However, households’ expectations have declined less than actual inflation and continue to be higher (by about 3 percentage points).

> a statistically significant reduction in the response of inflation expectations (12-month-ahead) to shocks in oil prices and observed inflation. 38 Benes et al. (2017) and Patra and Ray (2010) similarly found that lagged inflation, as well as current and lagged changes in fuel and food prices significantly affected inflation expectations prior to inflation targeting. 39 We restrict our analysis to a quarterly frequency in order to have comparable results across sectors. 40 We use the CPI combined inflation series from 2012 onwards and prior to that the CPI-IW. Professional forecasters’ expectations are for the CPI-IW prior to 2014, and for the combined series from 2014 onwards.

22
provide the exact individual years of migration, but only the year interval.

11 A person is considered “US educated” if he arrived in the US before he would have normally acquired the declared education level. For example, if a university graduate arrives at the age of 23 or older, then he is considered “foreign educated”. Likewise, a high-school graduate arriving at 18 is assumed to have completed his studies abroad. Time since migration is reported as intervals of 5 years in Census 1980 and as smaller intervals for Census 1990. As such, to construct the age at migration variable for those Censuses, we make the imperfect assumption that migrants reported by Censuses 1990 and 1980 arrived in the middle year of the reported interval. We use different ages at which individuals complete their education. For example, we use 23 years Bachelor’s and higher Degree holders. On the other hand, it takes into account inadvertently those individuals who have completed their Master’s degree, Professional degree or PhD in the US after the age of 23. This is because Censuses 1980 does not allow identification of individuals with Bachelor’s degree, Master’s degree, Professional degree and PhD, so we use the more restrictive education 1980 classification for cross-Census comparability purposes.

12 Censuses report occupation only for people aged 16 or more. When following the members of the 1980s arrival cohort through 1990 and 2000 censuses, we set the minimum age to 16 for Census 1990 and 26 for Census 2000, whereas when analyzing individuals arriving in the 1970s, whose performances can be captured by all three censuses, the minimum ages are defined as 16, 26 and 36 for Censuses 1980, 1990 and 2000, respectively. To reduce the effect of cohorts shrinking across censuses because of older individuals, we also place upper limits on the age intervals used in the samples: 40 for Census 1980, 50 for Census 1990 and 60 for Census 2000.

8
differences in financial development. Leuz, Nanda, and Wysocki (2003) also establish a link between a country‟s legal and institutional environment and the quality of accounting earnings reported to investors.

While accounting standards are a measure of information quality at the country level, a second stream of literature has focused on measuring the information environment at the firmlevel. Three approaches are of note here. First, papers by Durnev and Kim (2005) and Klapper and Love (2002) use data on the quality of governance practice compiled by Credit Lyonnais Securities Asia (CLSA) across 27 countries, and compiling indicators of a firm‟s disclosure 31
related research on the effects of aggregation raises concerns for data quality implications and motivates the need for methodological validation. Research by Beegle et al. (2012), for example, explored the impacts of the level of detail in a consumption questionnaire on the resulting consumption estimates. The a priori assumption is that greater levels of detail, specifically a longer list of specific consumption items, trigger respondents’ memories and prevent

> 3 For reports on the LSMS+ surveys in Tanzania, Malawi, Ethiopia, and Cambodia, see Hasanbasri et al. (2021a) and Hasanbasri et al. (2021b).

3
The overall number of death events reported by the GBD was compared with national records to test consistency. According to Geostat, 50,771 people died in the country in 2016 (Geostat 2016).

> 16 Hence, 2,972 smoking-attributable death events for the population between 30 and 64 years old are considered. This is equivalent to only 44 percent of all smoking-attributable deaths. Currently, life expectancy in Georgia is 69 years for males and 78 years for females (WDI 2017). An analysis based on the Labor Force Survey (LFS) 2017 determined that there is not a clear age cutoff in labor force participation in Georgia. (Results are available upon request to the authors.) Hence, the analysis applies the standard assumption of retirement at age 65. This threshold represents a conservative assumption, as the WYLL would be higher (and hence, the effects of taxing tobacco higher) if workers continue earning labor incomes after age 65.

13
://doi.org/10.1093/qje/qjx040 Duflo, Esther, Pascaline Dupas, and Michael Kremer. 2015. “School Governance, Teacher Incentives, and Pupil-teacher Ratios: Experimental Evidence from Kenyan Primary Schools.” _Journal of Public Economics_ 123: 92-110. https://doi.org/10.1016/j.jpubeco.2014.11.008 Glewwe, Paul and Michael Kremer. 2006. “Schools, Teachers, and Education Outcomes in Developing Countries.” _Handbook of the Economics of Education_ 2: 945-1017. <u>https://doi.org/10.1016/S15740692(06)02016-2</u> <mark>Fairlie, Robert W. 2005. “The Effects of Home Computers on School Enrollment.”</mark> _<mark>Economics of Education Review 24</mark>_ <mark>(5): 533-547. https://doi.org/10.1016/j.econedurev.2004.08.008</mark> <mark>Fairlie, Robert W., Daniel O. Beltran, and Kuntal K. Das. 2010. “Home Computers and Educational Outcomes: Evidence from the NLSY97 and CPS.”</mark> _<mark>Economic Inquiry</mark>_ <mark>,</mark> _<mark>48</mark>_ <mark>(3), 771-792.</mark> <u><mark>https://doi.org/10.1111/j.1465-7295.2009.00218.x</mark></u> <mark>Fairlie, Robert W., and Rebecca A. London. 2012. “The Effects of Home Computers on Educational Outcomes: Evidence from a Field Experiment with Community College Students”.</mark> _<mark>The Economic Journal</mark>_ <mark>,</mark>