rafmacalaba/data-use-mentions-extended huggingface dataset:
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Data on military employment include conscripts (400,000), but excludes personnel in paramilitary units, i.e., the Frontier Forces (100,000), directly subordinate to the President, the Forces for the Protection of the Russian Federation (20,000), and the MVD (180,000).

GDP estimate is taken from Statistical Handbook 1995: States of the Former USSR and relates to 1992. Wages and salaries as percent of per capita GDP is from Barbara Nunberg's Towards a new Civil Service in Russia, Current Issues and Future Prospects, September 1995 and relates to 1992. The wage bill corresponds to the civilian budgetary sphere wage bill (5.2%) less wages in Health and education (3.5%).

# Tajikistan

Unemployment come from the Country Economic Memorandum of August 12, 1994. Data on paid employment in non-agricultural activities are taken from Statistical Handbook 1995: States of the Former USSR and are for 1993. Data on Central, Education and Health employment is taken from Country Economic Memorandum of August 12, 1994 and relates to 1992. Central Government corresponds to General Administration and defense, to which we have subtracted the number of military. Tajikistan has not yet formed any military units. A number of potential officers are being trained.

# Turkmenistan

Unemployment rate reflects only official unemployment for 1994. Data on paid employment in non-agricultural activities are taken from Statistical Handbook 1995: States of the Former USSR and are for 1993. Central Government employment, which is drawn from Statistical Handbook 1995: States of the Former Soviet Union. Central Govemment corresponds to 'Public Administration and defense". However, it is unclear whether this includes all Central Government employees or just Centrally located Central Government employees. Education and Health employment data are drawn from the Country Economic Memorandum of 7 February, 1994, Education corresponds to "Education Culture and arts" for 1992, while Health is "Health care, social security, physical culture and sports." Military employment
|
|**Panel B: Private**||||||
|Private * Eligible|0.001*|0.002**|-0.000|-0.000|0.000|
||(0.001)|(0.001)|(0.000)|(0.001)|(0.001)|
|Private Preschools|-0.000|-0.003**|-0.000|-0.000|0.002|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|
|Eligible Child|-0.004|-0.007|0.002|-0.038***|0.012*|
||(0.004)|(0.005)|(0.002)|(0.006)|(0.006)|
|FDR q-value|0.204|0.204|0.992|0.992|0.992|
|Observations|225,985|226,400|226,400|226,400|226,400|
|Mean|0.077|0.172|0.037|0.185|0.125|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dependent variable indicated in column heading on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, a dummy for having a preschool-aged eligible child, and their interaction. Comparison group includes all mothers of children of the wrong ages and non-mothers. All regressions include mother’s age fixed effect, urban dummy, district,
conditions were enforced; because the program‘s target was to alleviate poverty, beneficiaries included poor families even when the household-head or someone else in the household had some labor activity. In addition, as the federal government provided the funds while municipalities were responsible for program implementation –local governments had little incentive to promote formal jobs and reduce beneficiaries or to enforce requirements strictly.

The dataset consisted of two short rotating panels from the national household survey (Encuesta Permanente de Hogares Continua –EPHC) covering 70% of Argentinean urban populations in 28 large urban areas and representing 60% of the population. Individuals are interviewed in two consecutive rounds, left for two other rounds and followed again in the following 2 quarters (rounds). The variables of interest are whether a specific individual transferred to formal employment in period t to t+1 and whether the individual is a beneficiary of PJH in t. The authors employ a difference in difference estimation to study whether program beneficiaries are more reluctant to seek/accept formal jobs than otherwise equal counterparts, particularly when the relative value of the transfer with respect to formal wages dropped.

They find some evidence on the informality bias of the PJH in the period when the value of the transfer was relatively high compared to wages in the formal labor market. The effect of the distortion vanished when earnings in registered jobs went substantially up. The results, however, are not robust to all the specifications.

Camacho et al. (2009) measure the effect of a subsidized regime (SR) for health insurance for the poor in Colombia on informality. The period of study is 1992-05. SR was introduced in 1993 and informality increased steadily in the period 1996-2002.

The SR is designed to reach the poor and the unemployed. The SR is financed with a one percent transfer from the formal sector program, CR, and with local and central government funds. The package is less comprehensive than the CR, but unlike the CR, covers every member in an eligible household regardless of their relationship to the
*|0.116***|0.169***|
|(constant 2010 IDR)|(0.371)|(0.109)|(0.006)|(0.022)|(0.022)|
|Growth in GRP per capita|-0.010|-0.014|0.001|-0.002|-0.000|
||(0.043)|(0.009)|(0.001)|(0.002)|(0.003)|
|Number of observations|272|272|272|272|272|
|R-squared|0.766|0.737|0.792|0.741|0.770|
|Year dummies|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

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is known both at the beginning and the end of the period.<sup>6</sup> An extension of the Datt and Walker method can be used to force the distribution at the end of the simulation to closely match that of the survey data. Specifically, extend the notation from above to reference each household’s quantile, with n the number of fractions in which the distribution is broken down. For instance if n equals five, the distribution would broken down in quintiles.

These quantiles are defined over individuals by household consumption per adult equivalent and can be in terms of the full national distribution, or in terms of each sector’s distribution. The simulation is carried out using these growth rates:

> 5 In fact, the 2002 Census report shows that children made up a slightly smaller percentage of the total population in 2002 as they did in 1988. This means that the growth rate of consumption per adult equivalent was slightly larger than the growth rate of consumption per capita.

> 6 More precisely, _estimates_ of the distribution are known from the household surveys.
**Table 2. Correlations between the Self-Reported Food Security Indicator and Other Indicators of Income, Poverty, Hunger, and Malnutrition, Circa 2005**

|**Alternative poverty/hunger indicator**||**Self-reported**|
|---|---|---|
|**(source)**||**hunger**|
|GDP per capita, purchasing power parity, log|Correlation|−0.71***|
|(World Bank)|No. of obs.|44|
|Household income per capita, USD, log|Correlation|−0.68***|
|(World Bank Povcal)|No. of obs.|59|
|Prevalence of hunger|Correlation|0.58***|
|(FAO)|No. of obs.|62|
|Prevalence of poverty, USD 1/day|Correlation|0.77***|
|(World Bank Povcal)|No. of obs.|58|
|Prevalence of poverty, USD 2/day|Correlation|0.67***|
|(World Bank Povcal)|No. of obs.|49|
|Prevalence of low-BMI women, excluding outliers|Correlation|0.73***|
|(DHS & WHO)|No. of obs.|17|
|Prevalence of underweight preschoolers, log|Correlation|0.55***|
|(DHS & WHO)|No. of obs.|45|
|Prevalence of stunted preschoolers, log|Correlation|0.48***|
|(DHS & WHO)|No. of obs.|45|

_Source_ : Dependent variable is from the GWP (Gallup 2011) . The sources of the independent variables are as follows: _World Bank_ , World Bank (2010b) _WDI_ ; _World Bank Povcal_ , World Bank (2010a); _FAO_ ; Food
in remittance-dependent households, wives and mothers are generally viewed as more trustworthy and responsible than sons or brothers (ACAPS 2021). Importantly, women encounter obstacles in directly receiving transferred funds, often depending on male guardians, while also facing challenges in accessing necessary identification documents.

# III. Literature review and conceptual framework

The methodology used in this paper closely follows the approach adopted by Bargain et al (2019), who find a significant improvement in women’s final say in decision making in regions most affected by the Arab Spring in the Arab Republic of Egypt. Using data from the Demographic and Health Surveys from 2011-13, the authors apply a double difference analysis that exploits

> 2 According to UN data for 2020, of the 1.3 million Yemeni migrants, about 454,000 were women and 847,000 men.

5
Helen Keller survey in Bangladesh where urban households make up between one‐third and one‐half of the samples. The IFPRI survey for Bangladesh includes only rural households.

**Table 1: Countries and Data Sources**

|**Region **|**Country ‐data source**|
|---|---|
|SAR|Bangladesh(Helen Keller 2010,2011andIFPRI 2011)|
||Nepal(DHS2001and2011)|
|LAC|Bolivia (DHS 2003 and 2008)|
||Peru (DHS2005 and2012)|
|EAP|Cambodia (DHS2005 and2010)|
||Indonesia (Riskesdas2010)|
|SSA|Ethiopia (DHS2000 and2011)|
||Zimbabwe (DHS2005 and2010)|

Given these criteria the sample of children used is between 0 and 24 months in Bangladesh (Helen Keller data), Cambodia, and Zimbabwe, between 0 and 25 months in Bangladesh (IFPRI data) and Indonesia, and between 0 and 36 months in Bolivia, Ethiopia, Nepal, and Peru.<sup>6</sup>

> 6 In spite of recent findings that catch‐up growth occurs without interventions (Prentice, et al., 2013), or as a result of interventions, much of stunting occurs before the age of 24 months (Victora, et al., 2010).  In addition recent research has found that catch up growth in school aged children is not associated with improvements in cognitive ability (Sokolovic, Selvam, Srinivasan, Thankachan, Kurpad and

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_9_ (3), 347–383.

- Doepke, M., & Tertilt, M. (2016). Chapter 23—Families in Macroeconomics. In J. B. Taylor & H. Uhlig (Eds.), _Handbook of Macroeconomics_ (Vol. 2, pp. 1789–1891). Elsevier. https://doi.org/10.1016/bs.hesmac.2016.04.006

- Enke, S. (1971). Economic Consequences of Rapid Population Growth. _The Economic Journal_ , _81_ (324), 800– 811. https://doi.org/10.2307/2230318

- Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The Next Generation of the Penn World Table. _American Economic Review, 105_ (10), 3150-3182.

- Ferrant, G., Pesando, L. M., & Nowacka, K. (2014, December). _Unpaid Care Work: The missing link in the analysis of gender gaps in labour outcomes._

- Filipiak, E., Kolev, A., & Arbache, J. S. (2010). _Gender Disparities in Africa’s Labor Market._ Washington D.C.: Agence Française de Développement and the World Bank.

- Galor, O., & Weil, D. N. (1996). The gender gap, fertility, and growth. _American Economic Review_ (86), 374387.

- Garenne, M. (2008). _Fertility Changes in Sub-Saharan Africa. DHS Comparative Reports No. 18._ Calverton, Maryland, USA: Macro International.

- Government of the Republic of Liberia. Pro-Poor Agenda for Prosperity and Development (PAPD). A Five-Year National Development Plan Towards Accelerated, Inclusive, and Sustainable Development (July 2018 – June 2023) (2018). Monrovia, Liberia.

- Hill, T. (2009). Time Use,
Saharan Africa (Gascoigne et al., forthcoming). For drought we consider a much less frequent and presumably more severe event, so the loss in consumption may be higher.

Although there are productivity losses associated with extreme heat (Kjellstrom et al., 2018, Foster et al., 2021), here we focus on the loss of life and morbidity associated with extreme heat (without considering the potential major effect of higher temperatures and heat on quality of life and average labor productivity). A WBGT threshold of 33 degrees Celsius corresponds with the reference upper limit for healthy, acclimatized humans at rest to keep a normal core temperature, based on international standard ISO 7243 used to assess heat stress on workers (International Organization for Standardization, 2017). Heat-related mortality and hospital visits increase significantly around this level, disproportionately affecting outdoor workers and the elderly for whom WBGT does not have to exceed 33 degrees Celsius to reach a dangerous level (Cheng, Lung, and Hwang, 2019; Tuholske et al., 2021).

To determine the number of people exposed to any one of the four shocks considered we take the maximum number of people exposed in each 1km grid cell to either of the four hazards (Figure 5). This is done to avoid double-counting. Note that we do not assume everyone in a 1km grid is either exposed or not exposed to flooding by overlaying the data in this way since we first calculate the population exposed to flood using the relevant higher resolution flood and population data.

# 4 MEASURING VULNERABILITY

In this section we motivate the choice of indicators used to identify households that are vulnerable and discuss the data and methods used to measure them.

## 4.1 CHOICE OF INDICATORS

As highlighted above we consider two key aspects of vulnerability: (i) the physical propensity to experience severe income, asset or health loss and (ii) the inability to cope with and recover from the losses. Multiple dimensions are considered within each of these. We introduce the proposed indicators for measuring each
to interpolate and extrapolate household surveys such that we cover all 218 countries in every year. This approach is similar to the methodology used for reporting global poverty by the World Bank (see Prydz et al. 2019 and Ferreira et al. 2016). Previous studies, such as Lakner and Milanovic (2016), Milanovic (2002, 2016, 2021) and Ravallion (2014), on the other hand, used the household surveys directly. As a result, these papers were able to provide estimates only for selected benchmark years (e.g. every 5 years). For instance, Milanovic (2021) has 131 countries in the global distribution benchmarked to 2013, which includes surveys conducted between 2011 and 2015. While our approach requires additional assumptions, it allows us to provide (i) global inequality measures that are consistent with the global poverty measures, and (ii) annual estimates of global inequality.

Our estimates of global inequality measure the inequality between all citizens of the world, following _Concept 3_ inequality as defined by Milanovic (2005). In this concept of global inequality, individuals are ranked according to their personal income no matter the country they reside in.<sup>2</sup> Other authors have looked at the cross-country dispersion in GDP per capita – unweighted or weighted by population ( _Concept 1_ and _2_ , respectively, in the Milanovic taxonomy). For example, Deaton (2021), using per capita GDP growth rates, finds that _concept 1_ inequality decreased whereas _concept 2_ inequality increased. The difference in his findings is due to accounting for population size in the latter concept. These studies do not account for the withincountry inequality which is accounted for by our paper. Another difference is that we measure

> 2 Anand and Segal (2008) review the evidence on global inequality and its measurement issues. In a recent paper, Kanbur et al. (2022) focus on the future direction of global inequality, in particular between-country differences.

3
that thinks they have freedom of speech|42|X|X|X|X|X|X|Barometers|
|**Social accountability**|||||||||
|<br>Voice and accountability|204|||Nati|onal level|||WGI|
|<br> <br>Government effectiveness|209<br>|||<br>Nati|<br>onal level|||WGI<br>|
|Control of corruption<br>Rule of law|209<br>209|||Nati<br>Nat|onal level<br>onal level|||WGI<br>WGI|
|**Citizen engagement**||||Nati|onal level||||
|Civil societ participation (0-10)<br>|137|||Nati|onal level|||BTI|
|Civil rights (0-10)<br> <br>y|137<br>|||Nati<br> <br>|onal level<br> <br>|||BTI<br>|
|Civil society participation index<br>CSO women’s participation|179<br>179|||Nati<br>Nati|onal level<br>onal level|||VDEM<br>VDEM|

13
the bias of our imputation procedure compared to the average parameters using fully observed data as if they were the asymptotic population parameters. For both conditional and unconditional quantile regressions, the bias observed using the multiple imputation data is small when the homoscedastic error is assumed to follow a symmetric bell curve distribution, regardless of the type of heteroskedasticity implied by the dgp.

When the errors follow a chi2 distribution or uniform distribution, we observe some bias, especially for the lower quantile coefficients. The bias, however, is considerably smaller if the data generating process assumes an functional form with exponential heteroskedasticity. Finally, the results using the varying coefficient structure reveal low bias in both cases. In all simulations, the bias magnitude did not depend on the sample size (see appendix).
000s passenger-kilometers per carriage; wagon productivity = ̳000s net tonne-kilometers per wagon. Source: AICD railways database.

According to the 2010 LPI, the quality of East Africa’s rail infrastructure is remarkably low compared with that of the other parts of Africa. This highlights the need for facilitation services to further develop and promote the use of railways (World Bank 2010b).

# Ports

East African ports (figure 2.7) substantially increased their container and general cargo traffic between 1995 and 2005. The average annual rate of growth of container traffic and general cargo traffic through East African ports is 10.7 percent and 10.8 percent per year, respectively (table 2.5). Though not as high as the growth of container traffic in West Africa or of general cargo traffic in southern Africa, this expansion is nonetheless substantial. The overall growth in traffic was propelled by rapid economic growth in Sub-Saharan Africa, a rising tide of global trade, the privatization of ports, and the advent of modern container vessels.

Owing to the rapid expansion of traffic, a few of the region’s ports are beginning to experience capacity constraints (figure 2.8). This is most notable in the case of Mombasa (Kenya) and Dar es Salaam (Tanzania), where the volume of general cargo and container traffic significantly exceeds design capacity. Port Sudan (Sudan) is also experiencing capacity constraints with respect to container traffic. All three of these ports are also reaching their limits with respect to dry-bulk cargo. There is some scope for easing capacity constraints by improving the efficiency of port performance, although ultimately new investments will be required.

The ports of Mombasa and Tanzania have traditionally been the most important players in the container sector on the east coast of Africa. Double-digit growth in this sector has now pushed both ports to their limit, however, and new capacity is essential.

21
Our source for Latin American within country data is Bruhn and Gallego (2010). This source provides data on income per capita, the Gini index, temperature, rainfall, and altitude, as well as a landlocked dummy, for different regions within fourteen Latin American countries<sup>17</sup> . We collected additional within-country information on political parties, electoral outcomes, and on

> 15 The countries included in the panel dataset are Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, Honduras, Peru, Uruguay and Venezuela.

> 16 See Marshall, Gurr and Jaggers (2010).

> 17 The countries covered in this within country dataset are Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Panama, Paraguay, Uruguay, and Venezuela.

10
match across datasets, although it is recommended it follows a hierarchical structure similar to the one proposed by Zhao (2006). Note that in this step, the default is to use the defined areas from the simulation step. In this option the user is given the opportunity to change this grouping.

- The hierarchical id should be of the same length for all observations for example: AAMMEEE.

- `weight()` : The `weight` option indicates the new variable which corresponds to the expansion factors to be used in the target/ydump dataset. The default option is to use the weight variable saved in the ydump file, if a variable is specified here all results will be obtained with this new weighing. The user must have added the variable to the target data imported `(sae data import)` .

## **6.5.2 Profiling**

This sub-routine aids researchers in creating a profile from group classifications that are the outcome from the simulated census vectors. For example in the context of poverty it allows researchers to break down characteristics of the poor and non poor. In the context of anthropometric Z-scores it would allow researchers to obtain characteristics for the individuals who fall below the indicator’s threshold. The command syntax is as follows: ``` saeprocessstats,matasource(string)aggids(numlistsort)[contvar(string)catvar(string) plinevar(string)plines(numlistsort)area(string)weight(string)] ```

- `contvar()` : The `contvar` option indicates the continuous variables for which the user wants to estimate the mean/distribution based on poor and non-poor groups defined from the defined poverty lines in either `plines` or `plinevar` . Those statistics will be aggregated at the aggregation levels indicated in the `aggids` option. The user must have added the variable in the `sae data import` command when preparing the target dataset.

- `catvar()` : The `catvar`
both programs apply only to persons living in rural areas, I restrict the analysis to persons in rural areas and persons aged between 18 and 60. The sample includes 440 districts within the 17 largest states of India. I use data from Jul 2004 to Jun 2005 (round 61) to form the pre-NREGA period. For the post-NREGA period, I use data from Jul 2007 to Jun 2008 (round 64). Consistent with the methodology used in Imbert and Papp (2015), all statistics and estimates computed using the NSS data are adjusted using sampling weights.

> 8 To check the reliability of the SGRY district-level data, I aggregate district-level expenditure estimates to state-level. Apart from minor differences, the state-level figures match with the official state-level estimates published in MoRD reports.