rafmacalaba/data-use-mentions-extended huggingface dataset:
., p_4)</sup><sup>_′_ofthemodelinSection5byus-</sup> ing method of simulated moments, which minimizes the distance between moments **_m_** _m_ simulated from the model, and the corresponding data moments **_m_** _d_ from the HIES and BRMS samples.<sup>29</sup> Specifically, we minimize the estimation criterion _crit_ ( **_θ_** ) = ( **_m_** _d −_ **_m_** _m_ ( **_θ_** ))<sup>_′_</sup> **_W_** ( **_m_** _d −_ **_m_** _m_ ( **_θ_** )) _,_ where **_W_** is a diagonal weighting matrix with the inverse variances of the data moments on the diagonal.

The moments targeted are listed in Appendix Table A6, and Figure 12 shows the model’s good overall fit. The figure plots moments simulated by the model against their empirical counterparts. For better visibility, we use log scales, and indicate the different groups of moments in the graph. The precise magnitudes for all moments are shown in Appendix tables A7-A9.

> 29The simulation draws individuals from the sample-specific, age-education distribution, and mirrors the empirical sampling scheme of the BRMS in selecting former (simulated) migrants who have returned since 2010.

39
13

**Table 4.1: Results for Targeting Performance of Zonal Subsidies in Panama**

||**Household**<br>**zonal**|**s receiving**<br>**subsidy**|**Household**<br>**receive zon**|**s failing to**<br>**al subsidy**|**To**|**tal**|
|---|---|---|---|---|---|---|
||**'OOOs**|**%**|**'OOOs**|**%**|**'OOOs**|**%**|
|Below extreme poverty line|1.0|68.6|16.2|3.8|17.2|4.0|
|Above extreme poverty line|0.4|31.4|408.2|96.2|408.7|96.0|
|Total|1.5|100.0|424.3|100.0|425.9|100.0|

The key explanation for these results is that the vast majority of extremely poor people in the IDAAN customer base were living in zones that were predominantly inhabited by people above the extreme poverty line. In other words, extreme poverty is comparatively diffuse in the urban areas of Panama.

This level of targeting performance was thought to be unacceptable for a water subsidy and the use of zonal subsidies was consequently ruled out in the case of Panama.

# **Individual Eligibility**

An alternative approach is to base the eligibility for the subsidy on an assessment of individual household circumstances. While reliable information about poverty levels at the zonal level may be obtained based on census or survey data, it is much harder to
14. Yamada, Go and Palma, Stefania. Is China's Belt and Road working? A progress report from eight countries. Asia.Nikkei.com. [Online] Nikkei Asian Review, March 28, 2018. https://asia.nikkei.com/Spotlight/Cover-Story/Is-China-s-Belt-and-Road-working-A-progress-report-from-eight-countries.

15. Pozzebon, Stefano. Mongolia Has To Change Its Railroads To Account For An 85 Millimeter Difference In Track Spacing. BusinessInsider.com. [Online] Business Insider, October 1, 2014. http://www.businessinsider.com/mongolia-extends-trans-border-railway-to-china-russia-2014-10?r=UK&IR=T.

16. Farchy, Jack, et al. One belt, one road. s.l. : Financial Times, September 14, 2016. FT.com.

17. Shepard, Wade. Aktau: A Look Inside Aktau, Kazakhstan's Other Hub On The 'New Silk Road'. Forbes.com. [Online] Forbes, April 15, 2017. https://www.forbes.com/sites/wadeshepard/2017/04/15/a-look-inside-aktau-kazakhstans-other-hub-on-the-new-silk-road/#358f2eea7e1c.

18. Kazakhstan Railways Press Service. _Елбасы дал старт работе новых железных дорог Жезказган_ - _Бейнеу и Аркалык_ - _Шубарколь_ . August 22, 2014. TemirZholy.kz/press-tsentr. "President commences work on new Jezkazgan-Beyneu and Arkalyk-Shubarkol railways." In Russian..

19. _Протяженность железнодорожных путей Казахстана на начало года составила_ 15,3 _тыс_ . _км_ . _Ж_ / _д_ - _линии_ , _проходящие по территории страны_ , _занимают_ 14,5 _тыс_ . _км_ , _еще_ 275 _км проходят по территориям других государств_ . s.l. : EnergyProm, July 12, 2016. EnergyProm.kz. "Total length of railways in Kazakhstan at the beginning of the year was 15.3 thousand kilometers. Railways that traverse the country's territory cover 14.5 thousand kilometers, while 257 km pass through other countries." In Russian. Cites Kazakhstan Ministry of Transport web portal as data source..
27 Table 1: 300 most-cited publications in health economics, by topic

|Topic|Year|Title|First author|Journal / Edited volume|Citations|Rank|
|---|---|---|---|---|---|---|
|Health and its value|2001|The Colonial Origins of Comparative<br>Development: An Empirical Investigation|Daron Acemoglu|American Economic Review|4355|1|
|Health and its value|1993|<br>The RAND 36-Item Health Survey 1.0|R. D. Hays|Health Economics|826|28|
|Health and its value|1993|The Value of Risks to Life and Health|W. Kip Viscusi|Journal of Economic Literature|790|29|
|Health and its value|2003|The Value of a Statistical Life: A Critical Review<br>of Market Estimates throughout the World|W. Kip Viscusi||627|48|
|Health and its value|1995|<br>Multi-attribute Health Status Classification<br>Systems: Health Utilities Index|David Feeny|PharmacoEconomics|489|83|
|Health and its value|1985|<br>The Value of Safety: Results of a National<br>Sample Survey|M. W. Jones-Lee|Economic Journal|412|105|
|Health and its value|1997|<br>Disability-Adjusted Life Years: A Critical<br>Review|Sudhir Anand|Journal of Health Economics|399|111|
|Health and its value|2008|How to Value a Life|W. Kip Viscusi|Journal of Economics and Finance|367|127|
|Health and its value|1990|Utility Functions That Depend on Health Status:<br>Estimates and Economic Implications|<br>W. Kip Viscusi|American Economic Review|319|169|
|Health and its value|1992|<br>Quality-of-Life Assessment
**Table 4: Implicit** **<u>tax rate on consumption (</u>** in %<sup>*</sup> <u>)</u>

||**1995 - 1999**|**2000 - 2004**|**2005 – 2007**<br>**(ranking in brackets)**|
|---|---|---|---|
|Hungary|28.6|26.4|26.4(1)|
|Bulgaria|-|20.2|25.1(2)|
|Slovenia|24.2|23.7|23.8 (3)|
|Estonia|19.6|19.9|23.3(4)|
|Czech Republic|20.2|19.8|21.6 (5)|
|Slovak Republic|23.8|20.5|21.0(6)|
|Poland|19.9|17.9|20.4(7)|
|Latvia|19.4|18.1|20.0 (8)|
|Romania|15.9 (1999)|16.5|17.9 (9)|
|Lithuania|18.9|17.3|17.0 (10)|
|EU-25 average|21.3|21.1|22.0|

<u>* The implicit tax rate is calculated by dividing consumption tax revenue by the macroeconomic tax base.</u> _Source:_ EU Commission.

**Table 5: Implicit tax rate on labor (** in %<sup>*</sup> <u>)</u>

||**1995 - 1999**|**2000 - 2004**|**2005 – 2007**<
exception: while Uruguayan banks may have ultimately felt the adverse effect of bank failures in Argentina, they did benefit from the deposit run across the border at earlier stages of the crisis, as indicated by the positive and significant impact of the decline in deposits in the neighboring country (see the negative and significant coefficient in column 9 of Tables 4 and 5).<sup>15</sup>

# **3.2 Dynamic Estimates**

While the systemic variables used in the previous section explained reasonably well the evolution of deposit and interest rates at a monthly frequency, it is well known that during a crisis financial variables tend to display extreme high frequency volatility that is the result of sudden changes in risk perception, either directly reflected in the systemic risk variables used in the previous tests or associated with specific events or news that are taken as indicators of new developments in the unraveling of the crisis. At any rate, unlike bank-specific fundamentals, the incidence of systemic factors is likely to be apparent also (and perhaps even more so) at high frequencies.

In this section, we use daily data to examine the incidence of systemic factors from this different perspective. Specifically, we look at depositor reactions to daily events by estimating the following vector autoregressive regression (VAR) model, where _yt_ is a vector including daily system-wide deposits or interest rates (the latter, due to data availability, only in the case of Argentina) along with measures of country risk, exchange rate risk (again, due to data availability, only in the case of Argentina), and a dummy capturing

> 15 Similar results were obtained using Argentina’s sovereign risk. However, given the strong trade links between the countries, Argentina’s sovereign risk may have affected Uruguayan banks both (positively) through its incidence on the neighbor’s financial sector and (negatively) through its adverse real impact on the local economy. In this sense, the decline of deposits lends itself to a clearer interpretation as a substitution effect
**Using Satellite Imagery and a Farmer Registry to Assess Agricultural Support in Conflict Settings: The Case of the Producer Support Grant Program in Ukraine**

**Klaus Deininger**<sup>1</sup> **Daniel Ayalew Ali**<sup>1</sup>

JEL Codes: Q10, O13, H56, R14 Keywords: Ukraine, conflict/war, food security, agricultural production, cash grants, remote sensing 1 The World Bank, 1818 H Street NW, Washington, DC, USA The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Funding support from the European Union (ENI/2017/387-093 and ENI/2020/418-654) is gratefully acknowledged. We thank Denis Bashlyk, Eduard Bukin, Markiyan Dmytrasevych, Taras Gagalyuk, Ben Hell, Thea Hilhorst, Roman Hrab, Sergyi Kubakh, Nataliia Kussul, Vasyl Kvartiuk, Guido Lemoine, Daria Manzhura, Andrii Martin, Sevara Melibaeva, Roman Neyter, Oleg Nivievskyi, Martin Petrick, Irina Schuman, Andrii Shelestov, Stefan von Cramon-Taubadel, Taras Vysotskyi and Sergyi Zorya for helpful discussions and insightful comments and as well as Tania Khorzovskaya for outstanding coordination and administrative support.
three fiscal<br>years ago (or two fiscal years ago in some<br>countries) from the date of the Enterprise<br>Survey.<br>Source: Enterprise Surveys.<br>|
||www.enterprisesurveys.org|
|Population (lagged, logs)|Total population in the country. Values of the<br>variable used are one year prior (that is, lagged<br>by 1 year) to the fiscal year covered by the ES.<br>In cases where the fiscal year covered by the<br>ES spans more than one calendar year, the<br>population figure is the weighted average over<br>the calendar years covered (lagged by 1 year),<br>where the weights used are the proportion of<br>months covered in the calendar year.<br>Source: World Development Indicators,<br>World Bank.|
|Inflation (CPI, annual %)|Annual percentage rate of inflation based on<br>the consumer price index. Values of the<br>variable used are for the fiscal year covered by<br>the Enterprise Survey. In cases where the fiscal|

57
# **II.3 Education**

Why is education a suitable variable by which to measure IGM? Education as a measure of socioeconomic status relative to income in the context of developing countries has at least three advantages: (1) it contains less measurement error, reducing potential attenuation bias (see Solon, 1992); (2) it is fixed early in the life cycle, which avoids the life-cycle bias found in studies that use income (see Haider & Solon, 2006), in addition, education is closely linked to income and it is important by itself in terms of human development; and (3) it can be completely attributed to a specific individual, while income sometimes is hard to assign within a household (e.g., a household with multiple persons and home production, which may be specially relevant in the case of rural populations in poor countries).

There are two questions about educational attainment in the data set. The first one reports the total years of schooling completed by each individual (formal schooling regardless of the track or kind of study), and the second one is re-coded by IPUMS to capture educational attainment in terms of the level of schooling completed<sup>7</sup> and contains four categories: (1) less than primary completed, (2) primary completed, (3) secondary completed, and (4) university completed. In the main analysis of the paper, I use the latter variable, which has a lower number of missing values and it is available for more countries than the former.<sup>8</sup> This variable applies, to the extent possible, the United Nations standard of six years of primary schooling, three years of lower secondary schooling, and three years of higher secondary schooling.

In the sample, a majority of individuals report levels of education that correspond to less than completed secondary and near 50% less than primary (see Figure 3a), which supports the focus on primary completion
the lack of labor supply effect at endline, no income gains or extra intra-household transfers are observed several months after the program ended.

Did having their own bank accounts enable women to manage their own earnings? The administrative data that we obtained from the paying agency show that women do not use their accounts to save their income. Instead, female beneficiaries withdraw almost all of their earnings shortly after deposit. This finding is not necessarily surprising, since the participating households were cash-poor. In addition, participating women are not likely to be familiar with financial institutions, so merely having a bank account might not be a sufficient reason for them to use other financial services or strategies like saving money. This explanation is more compelling because, even though the participating households were poor, their expenditures do not increase by nearly the full amount of their public works income. This result echoes Field et al.’s (2021) findings that providing individuals with access to a bank account alone, without training them on how to use it, does not result in greater use of a bank account. This is especially salient in our setting where women have very low literacy levels.

Next, we documented the program’s effects on women’s perceived decision-making power, which we proxied by five alternative indicators (see Table 8). We do not find that public works employment changes women’s decision-making power. While we detect some marginal program effects contemporaneously with public works employment, such as increases in expenditures on women’s personal goods like clothing, these increases are dwarfed in comparison to the share of income spent on items like khat and tobacco for men. Therefore, the program’s insignificant impacts on women’s decision-making power are consistent with the temporary nature of the employment offer.

## **5.6. Well-being**

We use two indicators to assess the program’s impacts on the well-being of the female beneficiaries and their husbands. The five-item Mental Health Inventory (MHI-5) developed by Veit and Ware
- End of picture text --> zero, per equation **(1).** Manager-Only momentum controls for investor redemption effects as in equation (2). Investor-Only momentum reflects only investor redemption effects as in equation **(3). All** t-statistics are corrected for heteroskedasticity across fuinds. Full sample: quarterly data from April **1993** to January **1999.** The crisis portion of the sample is December 1994-June **1995,** July 1997-March **1998,** August 1998-October **1998,** and January **1999.** The non-crisis portion is the rest of the sample. The total of roughly 4400 observations is **13** funds times an average of about **35** stocks per fund, times an average of about **10** quarters of available data per fund. For robustness, results in each cell are based only on observations within three standard deviations of the mean.

- Statistically Significant **at** the 10-percent level

- ****** Statistically Significant at the 5-percent level

****** Statistically Significant at the 1-percent level

**26**
analyzing earlier VHLSS data covering the early 1990s to the early 2000s, Le and Booth (2014) and Nguyen _et al._ (2007) found widening urban-rural gaps in inequality. Benjamin _et al._ (2017) examine household income inequality during 2002-2014 through several dimensions, including urban-rural, ethnicity, and work sectors. Bui and Imai (2019) examine urban-rural gaps in Viet Nam during 2008-2012 and find education and remittances to be important factors. For other studies that focus on the ethnic gaps in living standards, see Dang (2012) and Fujii (2018).

3
the move is unrelated to other determinants of primary completion, I also estimate these effects within households, using plausibly exogenous migration outflows, and instrumenting destinations with past migration patterns. All these exercises produce results that are qualitatively similar. Moreover, I explore potential heterogeneity by estimating childhood exposure effects by sub-populations and I explore an alternative outcome that computes upward mobility with secondary education as the level of interest. I do not find much evidence of heterogeneity and the results of the estimates using secondary education show similar patterns to the baseline exercise but with larger convergence rates.

This paper is related to three strands of the economic literature. First, it is related to the recent literature that estimates intergenerational mobility (IGM) in education within countries and documents important variation across places in developing countries (see for example, Alesina, Hohmann, Michalopoulos, & Papaioannou, 2020; Alesina et al., 2021; Asher, Novosad, & Rafkin, 2021; Munoz, 2021a, 2021b; Van der Weide, Ferreira de Souza, & Barbosa, 2020). This is an extension of the literature on intergenerational income mobility within countries initiated by Chetty, Hendren, Kline, and Saez (2014) that is mostly focused on high-income economies, and the intergenerational mobility in education at the countrylevel (see Torche, 2019, for a survey focused on developing countries).<sup>2</sup> Second, it adds to the literature that studies how the place in which children grow up matters for medium or long-term socioeconomic outcomes (see Chyn & Katz, 2021, for a recent survey about place effects). This includes studies that use random or quasi-random variation to identify the effects (see for example Chetty, Hendren, & Katz, 2016; Chyn, 2018; Damm & Dustmann, 2014) and those using observational data exploiting samples of movers (Alesina et al., 2020, 2021; Chetty & Hendren, 2018a; Deutscher, 2020; Lalibert ́e, 2021; Ward, 2020). Third, this
followed by Portugal (9.7/12.1%) and Spain (over 9%), but _the deteriorations_ in Ireland and Spain were dramatic, the current account deficit worsening by over 100%. In Ireland, a 2003 surplus changed to a 6% deficit.<sup>11</sup> Increases in domestic demand, accompanied by rising wages and prices and consequent real effective exchange rate appreciation (Figure 5b) was associated with rising external imbalances.

**Figure 6: External debt to GDP (%)**

<!-- Start of picture text --> 400 1200<br>350<br>1000<br>300<br>800<br>250<br>200 600<br>150<br>400<br>100<br>200<br>50<br>0 0<br>2002 2004 2006 2008 2010 2012 2014<br>Greece Portugal Spain Ireland<br><!-- End of picture text --> Values for Ireland are on the right axis.

Source: The World Bank, Quarterly External Debt Statistics and World Development Indicators (for GDP data).

High private or public borrowing led to large increases in external debt (Figure 6); in Greece and Spain the gross external debt/GDP ratios rose by 50%, to reach about 150% of GDP in 2007. Portugal, 10 Transfers dropped from 6.5% of GDP in 1995 to 1.5% in 2008 in Portugal representing a secular trend; as Portugal became richer, remittances and transfers dropped (Felke and Eide, (2014).

> 11 Net interest income/workers remittances and current transfers were important‐ the net outflow was large enough to turn the positive trade balance into a current account deficit.

7
tax records is not without caveats. For example, we need to make assumptions about the functional form of the relationship between the house price and household income, and about the functional form of the upper tail of the house price distribution. In addition it is assumed that one house constitutes one household and that all houses are domestically owned. Therefore, in cases where tax record data are available these should undoubtedly be considered first. However, we certainly believe that our approach provides more reliable estimates of inequality than estimates obtained using survey data alone. The perfect should not be the enemy of the good.

This paper is related to a number of other studies which have tried to correct household surveys for the problem of missing or underreported top incomes.<sup>3</sup> Korinek et al. (2006) exploit geographic variation in response rates to correct for selective non-response in the United States. Lakner and Milanovic (2015) exploit the gap between household surveys and national accounts to adjust the top end of the income distribution.<sup>4</sup> This paper is structured as follows. The methodology is presented in Section 2. In Section 3 we introduce the data used in the empirical application to Egypt. The empirical application itself is presented in Section 4. Finally, Section 5 concludes.

# **2 Methodology**

## **2.1 Combining income survey with top income data**

The objective is to estimate the level of income inequality for a given population. We will refer to database 1 (DB-1) as the primary data source for the estimation of inequality. It is assumed that top incomes are mostly missing from this database. Database 2 (DB-2), which we will refer to as the secondary data source, primarily contains data on top incomes but generally not on lower incomes. Estimates of income inequality will be biased if computed using any single one of these databases. It takes a combination of the two to obtain consistent estimates of inequality. DB-1
source of omitted variable bias. Further, following the strategy of Rajan and Zingales (1998) for guarding against endogeneity concerns, we test for some theoretical predictions about how credit supply affects the choice of woman vs. man top manager. Fourth, we focus on developing countries, which is a novelty in the literature (see Section 2). Fifth, we use firm-level survey data which allows us to account for several potential omitted variables such as firm size and productivity, industry, and gender composition of workers and firm owners.

# **2. Conceptual framework and literature review**

# 2.1 _Gender gap in top management_

Gender gaps favoring men over women exist in most countries and in diverse areas like labor market outcomes, health, education, and political empowerment (see Klasen 2020, World Bank 2011). Likewise, the share of women among top managers of firms is also low when compared to their share in total population and in lower-level jobs. See Fernandez and Campero (2017), European Union (2016), Coric (2018), Peng and She (2020), Islam and Amin (2016), Islam et al. (2019), and Sekkat et al. (2015). For example, Coric (2018) for 45 countries in Europe and Islam and Amin (2016) for 73 developing countries report that only 26 percent and 19 percent of the top management positions, respectively, are held by women.

Direct evidence on the drivers of the gender gap in top management positions is limited. However, the broader literature on gender gaps offers several potential explanations. First, studies report that compared to men, women are more risk averse (see Croson and Gneezy 2009, Dohmen et al. 2011), and prefer less competitive environments (see Niederle and Vesterlund 2007, Gneezy et al. 2003) and more flexible work conditions (see Goldin 2014, Bertrand 2018). Thus, women may shy away from managerial work that is more risky, competitive,