rafmacalaba/datause-encoder-data huggingface dataset:
where F(zt) is a dummy variable on various fiscal policy and business cycle variables (zt) where 1 is defined as an adverse outcome. For example, the impact of climate shocks may differ in economies with high levels of government debt, those in recession, or those that generally implement procyclical policy (government spending tends to rise in good times and fall in bad times).

# 3.2. Data

We focus on natural disasters (such as floods, drought, earthquakes, etc.) from the EM-DAT database. While natural disasters are only a subset of broader climate shocks and are not always directly related to climate change, they are nonetheless informative on some aspects of the process of climate change as the frequency of many of these natural disasters increase (most clearly droughts and extreme temperature).

Floods stand out as one of the most recurrent natural calamities worldwide. In recent years, the incidence of such disasters has surged. Among the regions most impacted, East Asia and Pacific (EAP) bears the brunt, closely trailed by Sub-Saharan Africa. However, it is not solely the frequency that alarms but also the severity. The proportion of the population affected annually has notably escalated, averaging around 3 percent (Figure 1.A). Particularly concerning is the EAP region, where an average of about 6 percent of the population faces the consequences of natural disasters each year (Figure 1.B.).

EM-DAT compiles data on total persons affected, total deaths, and total economic damages of natural disasters from various sources, including UN agencies, non-governmental organizations, reinsurance companies, research institutes, and press agencies. It records human and economic losses at the country level for disasters meeting at least one of the following criteria: 10 fatalities, 100 affected people, a declaration of a state of emergency, or a call for international assistance. In reviewing event-specific data, we note a decreasing prevalence of missing values (as a fraction of events reported) over time. Missing observations for total affected/deaths hover around 25 percent, while missing observations for damages range from 50 to 75 percent. Consequently, we opt to concentrate solely on population affected and deaths in our intensity measure. Acknowledging that data availability has expanded since 2000, thanks to technological advancements allowing wider 8
<!-- Start of picture text --> 100 - -o- Bulgaria<br>_-_- Hungary<br>J-.- Poland lolal<br>ffi < , _ _ ~~~~-&- Romania-}<br>a' 20- - ~ ~ -Foie zehulvlci<br>1968 19i17 1968 1989 1990 1991i 1992<br>Your<br><!-- End of picture text --> Figure 5: Share of CMEA Trade in the Exports **of** Eastern Europe

# **3. The Framework**

Two elements are important in the analysis. First, given the severe data constraint in Eastern Europe, the framework (a tax model) needs to be simple in the sense that it makes use only of available national income accounts and revenue infornation, while capturing at **t.e** same time the minimum, essential features of an open economy. The model employed in this paper is a modified version of the 1-2-3 general equilibrium model presented in Devarajan, Lewis and Robinson [1990 and 1993]. It is a simple onecountry, two-activity and three-commodity model. In the original setup, a single open economy engages in the production of two goods, a pure domestic good and a pure export good, with a constant elasticity of transformation between the two. The domestic good also competes with impors in the domestic market _Go: Revenue Uzceriainry_ _9_
To incentivize participation, all respondents were paid (see appendix table B1). All participants received 3 cedis ($1.36) for completing the baseline survey, 3 cedis ($1.36) for completing the endline survey, and 4 cedis ($1.82) worth of airtime credit for completing the three-month followup. Participants who were interviewed once a week (by phone or in-person) received 3 cedis ($1.36) for each completed interview, and participants who were interviewed three times a week received 2 cedis ($0.91) for each completed interview. All payments, except the follow-up survey payment, were made at endline. Individuals assigned to the weekly phone, and triweekly phone arms received phones (whether or not they already owned one) with a SIM card and 1 cedi ($0.46) of phone credit.

The sample was drawn from respondents in three cities (Accra, Cape Coast, and Kumasi) of the Ghana Urban Household Panel Survey (GUHPS).<sup>5</sup> A baseline in-person interview was conducted with all respondents before the high-frequency survey was initiated. It served multiple functions, including collecting baseline information on key variables of interest, collecting contact information, distributing cellphones to participants, familiarizing respondents with the survey questions, and cultivating trust between enumerators and respondents. All respondents provided their phone numbers (often more than one) and were asked to provide their preferred phone number (typically their existing number) for completing phone interviews.

Subjects were allocated to one of the three arms. The arms were balanced on a range of observable characteristics, including gender, education, age, occupation, marital status, dependency ratio, and asset ownership. Randomization was applied at the household level, so that everyone in a household was assigned to the same arm, in order to avoid intrahousehold spillovers arising from assigning members from the same household to different arms.

A number of strategies helped reduce attrition and enhance the quality of the data. The inperson baseline interview was key to this process. At baseline, enumerators were paired with 5 The Ghana Urban Household Panel Survey (GUHPS) is a panel labor market survey administered by the Centre for the Study of African Economies at the University of Oxford. The sampling frame for the experiment consisted of respondents who had been interviewed in the GUHPS, excluding individuals under the age of 20 or above the age of 60 in 2013, individuals not contacted in either 2010 or 2012, and individuals located in Takoradi-Secondi (to cut costs, the number of locations in which the survey experiment was conducted was limited). The resulting sample frame consisted of 2,251 individuals from 720 households.

7
There is also plenty of empirical evidence available in the literature on the association between a mother’s education and different dimensions of child health such as infant mortality, the number of immunizations (e.g., Desai and Alva, 1998), child nutrition measured by height-for-age (HAZ) or stunting (e.g. Alderman and Heady, 2017), and fertility (e.g. Pop-Eleches, 2010; Kim, 2010; McCrary and Royer, 2011; Monstad et al., 2008). Yet, causal evidence on the effect of a mother’s education on ECD net of its effect of stunting is rather scarce. A better understanding of the channels through which maternal education affects ECD outcomes is essential for the prioritization of policies related to child health. Evidence of a direct effect of maternal education on ECD, separate from its potential effects on child stunting, would suggest that maternal education has the added advantage of compensating for the potentially negative effects of stunting on ECD and thus provide even stronger justification of the need to increase the quantity and quality of female education as an especially important factor in the development and economic growth of Sub-Saharan African countries undergoing a demographic transition.

With this background in mind, the analysis in this paper takes advantage of the variation across households in indicators of early childhood development and applies recently developed methods for more robust inferences based on observational data (Chernozhukov et al., 2018). The results from the conventional ad-hoc specification of a model, i.e., a regression model with the usual suspect confounding factors, chosen based either on custom or economic intuition, are contrasted to a more sophisticated approach, called double machine learning (DML). DML employs data-driven methods to select controls from a much wider set of variables available in the survey along with statistical learning methods that yield information that would not be available from fitting the model only once using the original sample (e.g. Hastie et al., 2009; James et al., 2013; Athey and Imbens, 2019).

# **Data**

The analysis relies on the cross-sectional variation across households in two Multiple Indicator Cluster Surveys (MICS): the 2014-15 MICS in the Republic of Congo and the 2014-15 MICS in São Tomé and Príncipe. The MICS is an international household survey program that collects information about health, nutrition, education, and early development of well-being of children and their families in developing countries.

4
Policy Research Working Paper 9080

# **Abstract**

This paper offers for the first time a global picture of gender discrimination by the law as it affects women’s economic opportunity and charts the evolution of legal inequalities over five decades. Using the World Bank’s newly extended _Women, Business and the Law_ database, the paper documents large and persistent gender inequalities, especially with regard to equal pay and treatment of parenthood. The paper finds positive associations between improvements in the law and several labor market outcomes, and establishes a small, but over time increasing, causal impact of more equal laws on higher female labor force participation.

This paper is a product of the Development Economics Vice Presidency (DEC) and the Office of the Chief Economist. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at mhyland@worldbank.org, sdjankov@worldbank.org, and penny.goldberg@yale.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> 42 7 45 12 omar repr &0<br>to) teen Siento L 4 o —_— 30<br>cas ae<br>_<br>= 8 recroationalcetac ilies q|e= B+ ] oga<br>& 25 8 z Hy<br>Ss conor oe lt * Mbeya (2 2 = Moritewwst Tanzania 8<br>e = Peale tice General Population = 8 pil” oa - 30<br>att ~~ = = 14 an —— V4<br>2+ —" MaasPp preparesenfox —fat _i B24 CST er svcrratisnal fecthian<br>oh1990 1995Maton 15-445 2000J 2005 2010— o.58 ao ()2000Sasa| Hees) -_Earle2002esGener!MteyaPopul2004* 2006 Re 20084 0erasPolice officersSalen2010 2012- i)10<br>+ <1% ART Coverage<br><!-- End of picture text -->

“Horizontal error bars denote time period in which prevalence ar incidence was measured. Value is at midpoint of time period. mms Modelled incidence (%), adults 15-49, from Spectrum software Prevalence (*), females 15-24 (source: national DHS/AIS reports) men Prevalence (%), females 15-24 (source: national ANC reports) mms Prevalence (%), young people, UNAIDS Indicator 1.6 (source: AlIDSinfo database) eens Prevalence (7%), young females (source: published studies, see Annex) een Incidence rate (per 100 person years), adults (source: published studies, see Annex) os ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013
# **1. RATIONALE**

Accurate and representative HIV incidence estimates are imperative for policy makers and program managers to be able to assess progress in meeting HIV programmatic goals and make decisions in regard to allocation of financial and health resources. While modeled UNAIDS Spectrum incidence estimates are valuable, it is important to remember that these estimates are derived from ANC and DHS prevalence data and rely on multiple assumptions, which are imperfect for the reasons to be discussed further in this document.

Triangulation of multiple forms of incidence data, including from published studies and trials, provides a comprehensive illustration of HIV incidence trends, though few reports have done so. Some reports of incidence trends at the country level exist, but are restricted to those using sentinel surveillance and DHS/AIS data, with little, if any measured incidence data from clinical trials or other studies. Those that have done this for multiple countries within Sub-Saharan Africa have concentrated on comparing ANC, DHS, and Spectrum data (Mahy, Garcia-Calleja, and Marsh 2012; Montana, Mishra, and Hong 2008). None of these reports have also described these incidence trends in relation to ART scale-up. In a technical meeting on the large estimated declines in HIV incidence in countries with generalized HIV epidemics in 2013, the importance of going back to observational HIV data and drawing comparisons between such real-world data and modeled incidence trends was emphasized (HIV Modelling Consortium, 18-19 Sept 2013 London).

As such, the rationale for this data review was to triangulate multiple types of HIV incidence data and describe trends prior to and through ART scale-up within a select group of Sub-Saharan countries. Specifically, this document focuses on assessing incidence trends between 1990 and 2012, comparing directly measured incidence from clinical and observational cohort studies, indirectly measured incidence proxies (prevalence among females age 15-24), and modeled incidence estimates (Spectrum).

10
# **Appendix Table I. 2: Effect of Reforms on Organized Sector Log (Real Output)**

||(1)<br>(2)|(3)|
|---|---|---|
||De-licensing<br>Trade Liberalization|FDI Liberalization|
|Hitl Ott Sill f|_Other-sector Output Spill_|_overs_|
|orzona upu povers rom|||
|Unorganized Sector|0.1571***<br>0.1919***|0.1244***|
||(0.0281)<br>(0.0225)|(0.0185)|
|Backward Output Spillovers from|||
|Unorganized Sector|-0.5854**<br>-0.4467*|-0.3623*|
||(0.2596)<br>(0.2703)|(0.2184)|
|Forward Output Spillovers from|||
|Unorganized Sector|0.1151<br>0.0706|0.2926|
||(0.3163)<br>(0.3222)|(0.2715)|
|Reform|-17.1911**<br>0.0664|9.9640|
||(7.9290)<br>(0.0896)|(12.3753)|
|Reform*Horizontal Spillovers from|||
|Unorganized sector|-0.0593**<br>0.0015***|0.0019|
||(0.0279)<br>(0.0003)|(0.0327)|
|Reform*Forward Spillovers from|||
|Unorganized sector|0.5175*<br>-0.0041+|0.1346|
||(0.2696)<br>(0.0026)|(0.4132)|
|Reform*Backward Spillovers from|||
|Unorganized sector|0.2702++<br>-0.0036+|0.0607|
||(0.1938)<br>(0.0023)|(0.2934)|
||_Other-sector Productivity Sp_|_illovers_|
|Average TFP of Horizontal-linked|||
|Organized Plants|0.8420***<br>0.8553***|0.8348***|
||(0.1042)<br>(0.1050)|(0.1066)|
|Average TFP of Backward-linked|||
|Organized Plants|1.2035***<br>1.2542***|1.2650***|
|<br>|(0.3143)<br>(0.3024)|(0.3097)|
|Average TFP of Forward-linked|||
|Organized Plants|1.3701***<br>0.9954***<br><br>|1.3844***<br>|
||(0.3347)<br>(0.3302)|(0.3339)|
||_Own-sector Output Spill_|_overs_|
|Forward Output Spillovers from|||
|Organized Sector|1.5278*<br>2.1071**|1.7179**|
||(0.8429)<br>(0.8189)|(0.7875)|
|Backward Output Spillovers from|||
|Organized Sector|1.7593***<br>2.5071***|2.1933***|
||(0.4942)<br>(0.5328)|(0.4660)|
|Reform*Forward Spillovers from|||
|Organized sector|-0.2928<br>-0.0030|-0.5286|
||(0.2906)<br>(0.0033)|(0.5463)|
|Reform*Backward Spillovers from|||
|Organized sector|0.3815++<br>0.0044+|-0.0130|
||(0.2709)<br>(0.0029)|(0.3891)|
|Constant|-65.5925***<br>-99.1099***|-88.6925***|
||(20.7742)<br>(19.3816)|(18.5293)|
|Observations|5888<br>5888|5888|
|R-squared|0.1650<br>0.1673|0.1577|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. All specifications also include state-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 55
them, would have generated the counterfactual rate of protection against which the UR should be evaluated. To measure the extent of liberalization, the estimated post-UR border protection is compared with two hypothesized counterfactual rate of future protection without the Round. The first is based on the average of the longest available sample, 1982-93. This is based on the argument that border protection in agriculture is inherently unstable, reflecting variations in world prices and stable (politically fixed) internal prices. This also assumes that there has been no underlying upward trend in the level of protection and that the distribution of protection during 1995-2000 would be the same as in the historical sample. The second baseline is based on the average protection in recent years (1989-93). This is based on the argument that the recent decade has seen structural shifts in trade policies with many developing countries and some industrial countries undergoing unilateral and/or bilateral reforms, while others, particularly most industrial countries, have shown the tendency to maintain or increase protection in agriculture.

The use of the average protection over the the longest sample rather than more recent or current levels as a counterfactual has important implications for the liberalization estimates depending on whetller the long-term average is above or below the current levels of protection. In oeneral, in countries where protection levels have increased over time, such as in several OECD countries (e.g. EU, Japan and EFTA) and some East Asian developing countries (e.g. Republic of Korea). the long-run average protection is below more recent levels. Hence, lower estimates of liberalization may result when the long-run average is compared with the phased-in post-UR protection levels, than when a more recent average is used as benchmark. Also, the actual advalorem equivalents of the post-UR specific tariffs will be influenced by future world price clhanges. The estimates presented in this section are based on current World Bank world price projections.

43
# Introduction

This paper describes the challenges of accurately measuring the upper tail of the income and wealth distributions in low- and middle-income countries. We first review the seminal contributions in the literature on measuring the top of the income distribution and then more recent work, focusing on the challenges in doing so and the solutions that have been proposed. We focus initially on incomes, but then devote a specific section to examining measurement of the top of the wealth distribution and the key differences compared with measuring income. We then identify gaps in the literature and the implications for those undertaking research in this area. Finally, we propose a research agenda to close the identified key gaps for practitioners, distinguishing between those that can be undertaken with current datasets and those that require new data.

Kuznets and Jenks’s (1953) landmark study brought together tax data, newly created national accounts and population estimates for the United States to estimate shares of income received by the top percentiles. Similar studies were subsequently undertaken in other countries, but the top of the distribution was then neglected, until being revived in the last 20 years thanks to the work of Piketty (2001, 2003). Following Piketty’s work, researchers have used tax tabulations from official reports, but the availability of tax microdata, also used by Piketty, has also generated further impetus to the research agenda of investigating the top of the income and wealth distribution. A 2022 special issue of the Journal of Economic Inequality, entitled “Finding the Upper Tail”, shows that this is an area of active research. We draw from several of the articles from that special issue below, as well as from Lustig (2020), when discussing the challenges that arise when measuring the top tail of the income and wealth distributions and the solutions.

Atkinson (2007) provided two motivations for better measurement of the top tail of the income distribution - to improve knowledge of the capacity of states to raise extra revenue through increased taxation levels and to measure the extent to which those with high incomes have command or power over other people, including the ability to pay for what Atkinson calls “elite separation”, which he emphasized was a bad thing.

Another motivation for better measurement of the top tail of the income distribution is that while much research acknowledges that the top tail is not well measured in surveys, the extent to which it is under-captured is not known. One common but very approximate statistic that is indicative of the problem that is available for many countries is the difference between total income or consumption in surveys compared to national accounts (Lakner and Milanovic, 2016). Prydz et. al. (2022) show that these differences are substantial, and argue that under-estimates of income or consumption in the top tail are thus part of the explanation, although how much of the explanation under-estimates of 2