rafmacalaba/data-use-mentions-extended huggingface dataset:
2015), ‘On intergenerational (im) mobility in Latin America’, _Latin American Economic Review_ **24** (1), 1–29.

- Deaton, A. (1997), _The analysis of household surveys: a microeconometric approach to development policy_ , World Bank Publications.

- Emran, M. S. and Shilpi, F. (2011), ‘Intergenerational Occupational Mobility in Rural Economy Evidence from Nepal and Vietnam’, _Journal of Human Resources_ **46** (2), 427–458.

- Emran, M. S. and Shilpi, F. (2015), ‘Gender, geography, and generations: Intergenerational educational mobility in post-reform India’, _World Development_ **72** , 362–380.

- Ferreira, F. H., Messina, J., Rigolini, J., L ́opez-Calva, L.-F., Lugo, M. A., Vakis, R., L ́o, L. F. et al. (2012), _Economic mobility and the rise of the Latin American middle class_ , World Bank Publications.

- Fields, G. S. and Ok, E. A. (2000), Income Mobility: Concepts and Measures, _in_ N. Birdsall and C. Graham, eds, ‘New Markets, New Opportunities? Economic and Social Mobility in a Changing World’, Brookings Institution and Carnegie Endowment Press, Washington, DC.

- Fosu, A. K. (2015), ‘Growth, inequality and poverty in Sub-Saharan Africa: recent progress in a global context’, _Oxford Development Studies_ **43** (1), 44–59.

- Francesconi, M. and Nicoletti, C. (2006), ‘Intergenerational mobility and sample selection in short panels’, _Journal of Applied Econometrics_ **21** (8), 1265–1293.

- Girdwood, S. and Leibbrandt, M. (2009)
are depicted in Figure 1. For reasons discussed in Section 4.2 of this paper he works with a stock market volatility based measure of uncertainty. Rather than working with stock market volatility explicitly he chooses to only work with periods of exceptional uncertainty during which the measured volatility exceeded the mean by at least 1.65 times the standard deviation. The identification of the uncertainty shock is achieved through a standard Cholesky decomposition with the uncertainty shocks ordered after the stock market index so that the effect of the stock market is already accounted for when looking at the effect of the uncertainty shocks.

In order to extend Bloom’s analysis, which only focuses on the impact of uncertainty of production, we augment this baseline VAR in several ways. To assess the impact of uncertainty shocks on consumption we add consumption expenditures in the model. To further disentangle the effects of consumer uncertainty, we split the consumption expenditures into a durable and non-durable component. Finally, as the focus of this paper is on international trade, we augment the VAR with monthly import and export levels. Given that many uncertainty periods occur at times of major financial constraints and stock market crashes we also take a brief look at the impact of these effects on our variables of interest. We use the same dataset as in Bloom (2009). Additional variables, not included in Bloom’s dataset, are taken from the Federal Reserve Economic Data (FRED) of the St. Louis Fed.

9
8 variables (with metropolitan region omitted) and ETH is eight ethnic dummy variables (with indigenous omitted).

In equation (2) the dependent variable is per capita household expenditure (excluding remittances), rather than per capita income (excluding remittances).<sup>10</sup> There are at least three reasons for using expenditure rather than income data here. First, the purpose of this paper is to estimate the impact of remittances on poverty, and most poverty economists prefer to use expenditure rather than income data to identify poverty. Since people tend to use savings to smooth fluctuations in income, poverty economists generally believe that expenditures provide a more accurate measure of an individual’s welfare over time. Second, in developing country situations like Guatemala, expenditures are often easier to measure than income because of the many problems inherent in defining and measuring income for the self-employed in agriculture, who represent such a large proportion of the labor force. Third, the poverty line that will be used in this study to separate poor from non-poor households is based on expenditure rather than income data.<sup>11</sup> In order to keep the analysis consistent, it is therefore preferable to work with expenditure data in the predicting equation.

Since the results of the predicting equation (2) depend to a large extent on the choice of regressors, it is important to discuss the reasons for including each independent variable in the model. Four household composition variables appear in equation (2). The first of these, household size, captures the impact of family size on household expenditure and is expected to be negative. The other three household composition variables relate to life-cycle factors:<sup>12</sup> number of males over age 15, number of females over age 15 and number of children under age 5. It is expected that the first two of these
TABLE I: Progresa Transfer Amounts (bimonthly, in pesos)

||1998, sem. 1|1998, sem. 2|1999, sem. 1|1999, sem. 2|
|---|---|---|---|---|
|Type of beneft|||||
|Nutrition support|190|200|230|250|
|Primary school|||||
|3|130|140|150|160|
|4|150|160|180|190|
|5|190|200|230|250|
|6|260|270|300|330|
|Junior secondary: girls|||||
|7|400|410|470|500|
|8|440|470|520|560|
|9|480|510|570|610|
|Junior secondary: boys|||||
|7|380|400|440|480|
|8|400|400|470|500|
|9|420|440|490|530|
|Maximum support|1170|1250|1390|1500|

_Source_ : Attanasio et al, 2011. Sem. denotes semester.

# _V.B Key Facts about Child Labor Supply and Income_

As will be discussed in our identification strategy below, we estimate income and price effects for children of secondary school age only (children aged 12-15 years, inclusive), as nearly all children below the age of 12 attend school. Before doing so, we first investigate the extent to which children in this age range work, their earnings relative to their parents, and evidence that schooling induces a discrete loss in household consumption. Table II summarizes the labor supply statistics of children aged 12-15 years in 1997 (pre-Progresa grants). It can be seen that of those children not in school, 63% of boys report having a job, while 20% of girls report having a
16 determination models which make fertility a choice variable for a household unit are unlikely to b8 very useful. Analysing people's choices at an individual level makes sense only if we know that they have the information and ability to consider the event they face as a choice. This may also be seen at the level of definition of the decision unit. Models which focus on child costs and benefits often presume the exlstence of a person or group of persons who both bear costs and receive benefits, and also take decisions about fertility. In a post-transitional, regulated fertility population, this is often the Western-nuclear -vily. But in both historical and contemporary pre-transitional societies, there is plenty of evidence that such groups are often the exception (Goode (1963), Smith (1981), Caplan (1984), Kreager (1980)).

The cost of trying to produce a general model is having to explain wide variation, for developing countries encompasses a range of fertility regimes from the almost completely natural to the thoroughly regulated<sup>8</sup> . What is clear however, is that the key shift from natural to regulated fertility regimes which is a necessary component of permanent fertility dxecline is only possible with the thorough dispersion of information about contraception and of the cultural values that lead to its adoptin. This dispersion has, in the modern era, been intimately linked to education (Caldwell (1982), Cleland and Wilson (1987)).

8See, for example, the U.N. Demographic Yearbook and the WFS studies for information on fertility rates and contraceptive prevalence.
market access commitments WTO members exchanged at the negotiating table. Facilitating investment may prove challenging in the continued presence of a host of measures that deter market entry, raise the costs of firms, reduce market competition and restrict consumer choice.

Regulatory costs in services show considerable modal and sectoral variance. Figure 16, which draws on the OECD’s Services Trade Restrictiveness Index (STRI), documents the restrictiveness of services policies by sectors and modes of supply. Overall, cross-border supply (Mode 1) is found to face the least trade restrictions of all service sectors. Commercial presence (Mode 3), which involves setting up an office, branch or subsidiary in a foreign country, is the most restricted mode of services supply. Restrictions on foreign direct investment are particularly high in air transport, rail freight transport, as well as in legal and accounting services. The movement of natural persons (Mode 4) also faces high trade costs owing to stringent qualification and licensing requirements, especially in regulated sectors such as engineering, architecture, legal, medical and accounting services. Because of the close links between foreign investment and the deployment of key personnel, impediments to Mode 4 compound Mode 3 establishment and operating costs (Shingal, 2019).

In sum, despite the far-reaching liberalization of host country investment regimes pursued in recent decades, investment in services remains, relative to investment in primary (agriculture, mining) or secondary (manufacturing) sectors, subject to a wider range of entry and post-establishment barriers.

32
framing, conducting robust research is akin to an optimization problem in which the researcher tries to maximize credible knowledge subject to budget and data quality constraints (Dillon et al. 2020). Improvements in data quality provide opportunities to improve the quality of inference conducted. In line with Dillon et al. (2020), we argue that research on vaccination has paid insufficient attention to the sensitivity of data quality to design choices. Our study addresses this gap.

Secondly, for vaccination data specifically, we contribute to a body of research documenting and analyzing misalignment between survey-based and administrative sources (Wolter et al. 2022; Nguyen et al. 2021; Galles et al. 2021; Bradley et al. 2021; Sandefur and Glassman 2015; Miles et al. 2013; Murray et al. 2003; Lim et al. 2008; Burton et al. 2009). This literature stresses that both data sources are subject to potential errors. The strength of administrative data is its spatial granularity and frequency, but it often suffers from numerator (number of people vaccinated) and denominator (size of the target population) issues.<sup>1</sup> Survey data, for example from large household survey programs such as the Demographic and Health Surveys (DHS), Multiple Indicators Cluster Survey (MICS), or Living Standards Measurement Study (LSMS) is independent of public record keeping and can also capture vaccination obtained through private or non-governmental providers

> 1 For example, numerator issues may occur when administrative data misses out on vaccinations conducted through the private health care sector and or when aggregating figures from lower administrative levels. Denominator issues may relate to outdated census data or inaccurate population projections (Burton et al. 2009).

3
# **1. Introduction**

Gender gaps in earnings and in other economic outcomes have been documented extensively by economists (Blau and Kahn 2000). While gaps favoring men exist in virtually every country, disparities in income, health, education, and decision-making power are more pronounced in developing countries (Jayachandran 2015). Understanding the causes of these gaps has received a lot of attention in the literature, with most of the empirical work focused in developed countries.<sup>2</sup> The early literature documented cases of discrimination against women in the market as a source of disparity in outcomes (Altonji and Blank 1999), while a more recent literature has increasingly focused on gender differences in innate preferences, attitudes and ability as underlying causes, inspired by insights from psychology and experimental research (Bertrand 2011).

Research documenting innate gender differences in risk preferences or attitudes towards competition relies heavily on lab experimental approaches (e.g. Gneezy et al 2003, Niederle and Vesterlund 2007), with very few field experiments (e.g. Flory et al 2015). The literature on discrimination has developed in parallel, and somewhat independently, using observational and experimental data in field settings, employing methods such as audit studies (Neumark 1996), blinded natural experiments (Goldin and Rouse 1996, Moss-Racusin et al 2012), and field experiments with fictitious resumes or emails (Bertrand and Mullainathan 2004; Riach and Rich 2006; Petit 2007; Milkman et al 2012).

The central thesis of this paper is that the two literatures and classes of explanations may be intricately tied to each other: even if women have innate characteristics (raw ability and attitudes) that are comparable to those of men, they may perform differently in the market if the task requires them to interact with others in society, and they are subject to discrimination in those societal interactions. We set up a field experiment and data collection strategy to explore this thesis, and then add a cross-cutting experiment to explore whether the provision of
would appear to have an enormous gender disparity of 2. It hardly seems right that medical progress that leads to better survival chances be called an increase in gender disparity.

A second possibility is to norm to recent mortality rates in a richer, low mortality country. This represents the mortality when medical resources are effective and plentiful, which might represent "natural" mortality. As Svedberg points out, in one setting where medical technology is as advanced as possible (Sweden in the 1980s), "the child mortality rates are almost identical [for boys and girls], .29 and .28 respectively" (Svedberg, 1990, fn. 3). But this is not always the case. In the U.S., the death rate in ages 1 to 4 is .52 for boys and .41 for girls, so the gender ratio is .78. Hence, a country with equal male and female death rates normed to the U.S. ratios would have a gender disparity of 1.27. However, 62 percent of the gender difference in mortality is due to a greater frequency of accidental death from accidents, as the gender mortality ratio for accidents is .68 versus .86 for all other causes of death. If a country with equal death rates were normed to the U.S. death rates from nonaccidental causes alone, the gender disparity would be 1.15. It seems extremely odd to argue that the reported gender disparity for a country with gender equal death rates should be so strongly influenced by the propensity of a reference population of U.S. boys to die of fatal accidents.

_Results._ The recent exchange between Svedberg and Klasen (Svedberg, 1990, 1996, Klasen, 1996) highlights the potential importance of normalizing. When normalizing mortality rates using the Northern European countries from 1820-1963 as the reference, Klasen finds that within Sub-Saharan Africa DHS samples, nine out of 14 countries exhibit excess female child mortality. Using a collection of 32 surveys from 20 countries
We find that skewness and kurtosis of returns are similar between global and simulated global funds. For example, according to the largest number of funds (longest time series) simulations, the skewness of global fund returns is -0.69 (-0.81) whereas that of the simulated global funds is -0.84 (-0.94) or -0.70 (-0.74), depending on whether the variance is minimized or expected returns are maximized. The kurtosis of global funds is 10.69 for the largest number of funds simulation and 12.85 for the longest available sample simulation. The kurtosis of the simulated global funds is 10.04 (11.96) when the variance is minimized and 7.74 (8.60) when expected returns are maximized, using the largest number of funds (longest available sample) simulations. Overall, the evidence suggests that, despite the differences in the mean and variances reported above, higher moments of the distribution of returns are not considerably different across global funds and simulated global funds. If anything, the kurtosis estimates are lower for the simulated global funds than for global funds, indicating that global funds do not appear to be robust portfolios as their distribution of returns has fatter tails.

We now consider the ability of global funds to move away from turbulence-hit countries or regions, and thus actually avoid realized risks. Given the limited information on portfolio holdings at a high frequency, we focus the analysis on the incidence of negative returns during turbulent times. For instance, conditional on large negative returns on the MSCI Emerging Market Index (our proxy for turmoil periods), we compare the realized returns of both actual global funds and simulated global funds.<sup>38</sup> The results reported in Table 16 show that their performances are typically not statistically different from each other. For example, the average return of global funds is -3.42% per week (p.w.) when the
5.6)|(8.5)|
|All other|47.9|30.3|17.6|
||(7.9)|(20.8)|(18.2)|

Notes: Weighted to national level with weights provided by the Nepal DHS in 1996. Standard errors in parentheses. The notation<sup>***</sup> is p<0.01,<sup>**</sup> is p<0.05,<sup>*</sup> is p<0.10.

Source: Authors‟ calculations based on MHP/NE/MI (2007), MH/NE/ORC (2002), and Pradhan _et al._ (1997).

37
economic sectors<br>in which the production total is consistent with that of the macro-framework. Individuals from the<br>household surveys are joined in representative groups of households defined by the labor categories of<br>the heads of household. For each labor category, labor demand depends on sectoral output and real<br>wages. Wage-income levels by economic sector and labor category can thus be determined. In<br>addition, different income tax rates and different levels of budgetary transfers across labor categories<br>can be added to wage income. Third is a model that uses the labor-model results for each labor<br>category to simulate the income growth for each individual inside a group, which is assumed to be a<br>representative group. After projecting individual incomes, PAMS calculates the incidence of poverty<br>and the inter-group inequality.|
|**Data/information:**|The model requires national accounts with a breakdown by sector, household survey data with income<br>or expenditure data by unit, and a wage and employment breakdown by sector.|
|**Time:**<br>**ements**|With a macro-model, the time needed to build a PAMS would be about three months: (1) one month to<br>select or extract categories of households from the household survey and match the economic sectors<br>from the macro-model, (2) one month to link the macro-model to the household survey data, and<br>(3) one month to run the macro- and household-module together and adjust.|
|**Skills:**<br>**Requir**|Knowledge is required of (1) national-accounts-based macroeconomic models, (2) basic labor-demand<br>models, and (3) the structure of household surveys.|
|**Supporting**<br>**software:**<br>|EViews, Excel.|
. https://doi.org/10.2788/253582

- Pinkovskiy, M (2013). Economic Discontinuities at Borders: Evidence from Satellite Data on Lights at Night. Working paper, Massachusetts Institute of Technology, Boston MA.

- Quigley, J (1998). Urban Diversity and Economic Growth. _Journal of Economic Perspectives_ , 12: 127-138.

Rauch, J (1993a), Productivity gains from geographic concentration of human capital: Evidence from the cities. _Journal of Urban Economics_ , 34: 380-400.
.5<br>1<br>0.5<br>0<br>Source : World Bank 2010d<br>Note: SSA = Sub-Saharan Africa.<br>Logistics Performance Index<br>Senegal Benin Guinea Togo Nigeria Niger Cote d'Ivoire Gambia, The Ghana Liberia Mali Burkina Faso Guinea-Bissau Sierra Leone SSA<br><!-- End of picture text --> operators on the ground (global freight forwarders and express carriers), providing feedback on the logistics ―friendliness‖ of the countries in which they operate and those with which they trade. Such operators combine in-depth knowledge of the countries in which they operate with informed qualitative assessments of other countries with which they trade as well as the experience of a global logistics environment. By this ranking, Senegal’s capacity to move goods and connect manufacturers and consumers with international markets is the highest in West Africa.

But there is only so much a well-designed network can do, if it is not properly maintained over time. Road conditions are eroding due to the poor allocation of resources to maintenance and periodic rehabilitation (based on the roads’ natural life cycles), and this hinders Senegal’s connectivity. The 10
version of Lane and<br>Milesi-Ferretti (2007) dataset|
|Political risk|Risk ratings range from a high of 100 (least risk) to a low of 0<br>(highest risk)|Political Risk Services (PRS)|
|Exchange regime|de facto exchange rate regime classification|Ilzetzki,Reinhart,Rogoff,2008|
|Investmentgrowth|Quarterlyinvestmentgrowth|IFS|
|Nominal GDP|QuarterlyNominal GDP|GEM,World Bank|
|Real GDP|QuarterlyReal GDP|IFS|
|Foreign reserves|Foreign Exchange Reserves in Million USD(End ofperiod data)|IFS|
|Exchange rate|Official exchange rate local currency per USD(Monthlyaverage)|IFS|
|Stockprice index|National Stock Price Indices,monthlyaverage in currentprices|IFS and Haver|
|Current account<br>balance|Sum of net exports of goods and services, net primary income, and<br>net secondaryincome,% of GDP|National Sources|
|Domestic credit to<br>private sector|Financial resources provided to the private sector by financial<br>corporations|WDI|
|Real effective<br>exchange rate|Nominal effective exchange rate index adjusted for relative<br>movements in national price or cost indicators of the home<br>country,selected countries,and the euro area|JPMorgan Real Broad Effective<br>Exchange Rate Index|
|Nominal effective<br>exchange rate|Ratio (base 2010 = 100) of an index of a currency's period-average<br>exchange rate to a weighted geometric average of exchange rates<br>for currencies of selected countries and the euro area|JPMorgan Nominal Broad<br>Effective Exchange Rate Index|
|Real exchange rate|Computed as nominal exchange rate*US consumer price index/<br>consumerprice index|exchange rate from IFS; CPI<br>from WDI|
|Inflation|CPI inflation calculated as % change overpreviousyear.(%yoy)|IFS|
|Inflation targeting|dummy variable takes