rafmacalaba/data-use-mentions-extended huggingface dataset:
6 The standalone category includes country-specific household surveys such as a Household Budget Survey (HBS) or a Household Income and Expenditure Survey (HIES) (their scope is typically rather similar). Some countries also have a dedicated survey capturing health expenditures and other health-related concerns: the US, for example, has a Medical Expenditure Panel Survey. Access to the microdata from these standalone surveys tends to vary. Some countries provide authorized users with access to the microdata, though in many cases the publicrelease microdata are modified to ensure respondent anonymity. Countries also vary in their policies on who may access the microdata and how: OECD countries like Ireland, the UK and the US have very liberal policies, as do several middleincome countries, like Peru and South Africa, allowing any bona-fide researcher to download the public-release microdata. By contrast, many OECD countries have much tighter access policies, e.g. authorizing only nationals of the country access to the data, and sometimes requiring the user access the data on-site.

The other category in Figure 1 covers multi-country survey collections. These are of two types. The first are genuine multi-country surveys – surveys where there is a fairly standardized questionnaire used in several countries. The World Bank’s Living Standards Measurement Study (LSMS) is an example. The WHO’s World Health Survey (WHS) is another. The former is a multipurpose survey with rich information on household consumption and expenditure across a range of areas, including health, while the latter is a health survey with extensive information on out-of-pocket health expenditures but only very limited information on other expenditures. Access rules to these multi-country data sets vary, sometimes (e.g. the LSMS) varying even across surveys within a collection depending on permissions granted by the relevant government.

The second type of multi-country survey collection consists of country-specific surveys where the questionnaires differ across countries but the data have been harmonized ex post by an agency, such as Eurostat, the Luxembourg Income Study (LIS) (Luxembourg Income Study (LIS) 2019
dence of a significant change in women’s attitudes toward IPV in response to increased exposure to female leaders. In addition, we do not observe any significant impacts on more subjective evaluations of IPV, including women’s likelihood of reporting being subjected to psychological violence.<sup>3</sup> Our study contributes to a growing literature that documents the effects of female leaders on a range of health and economic outcomes, including infant mortality and health-seeking behaviors (Bhalotra and Clots-Figueras 2014), local public goods and female-oriented policies (Chattopadhyay and Duflo 2004; Clots-Figueras 2011; Bardhan et al. 2010), attitudes toward female leaders (Beaman et al. 2009), and aspirations and education of girls (Beaman et al. 2012; Clots-Figueras 2011).<sup>4</sup> We complement this literature by showing that female leaders improve village-level public health infrastructure, especially for services that benefit women directly. However, unlike prior studies, we also find that female leaders may have unintended negative consequences for their female constituents if their policy formulations lead to intrahousehold conflict.

Our work also relates to studies documenting that female leaders induce an increase in the reporting of crimes against women, such as rape and kidnapping, to the police (Iyer et al. 2012) and a decrease in women’s acceptance of spousal violence (Kuipers 2020). Although our findings complement these studies, our work differs in several ways. First, we focus on women’s experience of violence based on survey data collected by female surveyors in a confidential setting rather than data from police reports. Given the social norms and stigma surrounding IPV in our context, women are more likely to report IPV in a survey context than to the police. Second, we focus on IPV rather than crimes against women more generally. IPV is not only more prevalent than other types of gender-based violence, but the mechanisms underlying IPV are also likely to be different. Our study sheds light on several channels through which IPV against women may
ACS) and providing a detailed discussion of the questionnaire. In the process of data collection, businesses that operate without registration are considered to be informal. Throughout the paper, we refer to the unit of analysis as 'business' rather than 'firm' to avoid the legal connotation of the latter. Importantly, as noted above and discussed in detail in the companion paper, Aga et al. (2022), the samples of respondents of the Informal Sector Enterprise Surveys are representative of the underlying population of informal businesses at the city level. With proper weighting, which is well established in the literature and is applied in this paper, population estimates are unbiased.

Descriptive findings from the Informal Sector Enterprise Surveys data are enriched by comparing informal businesses with formally registered micro-enterprises (employing fewer than 5 employees) interviewed in some of the same countries and around the same time through the World Bank Micro-Enterprise Surveys. Section 3 presents these findings, organized as the following set of stylized facts across a wide range of topics and contributing to several branches of literature on informality: (1) Owners of informal businesses are generally young, having started the business out of necessity rather than as an opportunity for growth, and less skilled than their formal counterparts (consistent with Maloney, 2004; La Porta and Shleifer, 2014, 2016). Representation of women among the owners varies considerably across cities, with women generally experiencing a sizeable productivity gap compared to male-owned informal businesses.

(2) A demographic analysis of informal businesses reveals that they are very small, largely one-person operated, younger than their formal counterparts, and mostly in the retail sector (consistent with Kanbur, 2017; La Porta and Shleifer, 2014).

(3) External finance is difficult to access for informal businesses, perhaps due to the very nature of informality (see, for instance, Koeda and Dabla-Noris, 2008), with widespread use of mobile money (consistent with Islam and Jolevski, 2019).

(4) Informal businesses frequently operate from within households
asset index, whilst confirming the earlier conclusion that the two approaches (imputed welfare v asset index) are measuring different dimensions of welfare and that the asset index may be reflecting ownership of assets laid down in the past rather than current welfare.

**Table A2: Result of household economic index, urban area.**

||_(2)_<br>_Mean_|_(3)_<br>_Standard_<br>_Deviation_|_(4)_<br>_Eigenvectors_<br>_of first_<br>_principal_<br>_components_|_(5)_<br>_Scoring_<br>_factor/_<br>_Std._<br>_Dev. _|_(6)_<br>_Score if_<br>_they_<br>_have_<br>_asset_|_(7)_<br>_Score if_<br>_they don’t_<br>_have asset_|
|---|---|---|---|---|---|---|
|1. Gas Line|0.792|0.405|0.353|0.872|0.181|-0.690|
|2. Gas Cylinder|0.139|0.346|-0.277|-0.801|-0.689|0.111|
|3. Electric Oven|0.188|0.390|-0.233|-0.597|-0.485|0.112|
|4. Heating System (private, public)|0.110|0.313|0.116|0.371|0.330|-0.041|
|5. Stove|0.171|0.376|-0.294|-0.782|-0.648|0.134|
|6. Water|
<mark>Hatchondo, J.C., Martinez, L., 2012. Debt Dilution and Sovereign Default Risk. Working Paper 10-08, Federal Reserve Bank of Richmond.</mark> <mark>Hatchondo, J.C., Martinez, L., Sosa Padilla, C., 2014. Voluntary sovereign debt exchanges. J.f Monet. Econ. 61, 32–50.</mark> Kiyoung Jeony and Zeynep Kabukcuogluz (2014). “Income Inequality and Sovereign Default” mimeo University of Pittsburg <mark>Kaminsky, G. L., Vega-García, P. 2016. Systemic and idiosyncratic sovereign debt crises. Journal of the European Economic Association 14(1): 80-114. https://doi.org/10.1111/jeea.12165 Kraay, A., 2006. When is growth pro-poor? Evidence from a panel of countries. Journal of Development Economics 80(1):198-227. DOI:10.1016/j.jdeveco.2005.02.004</mark> <mark>Kuvshinov, D., Zimmermann, K., 2019. Sovereigns Going Bust: Estimating the Cost of Default. European Economic Review 119: 1-21. DOI: 10.1016/j.euroecorev.2019.04.009</mark> <mark>Levy-Yeyati, E., Panizza, U., 2011. The Elusive Costs of Sovereign Defaults. Journal of Development Economics 94(1): 95-105.</mark> <mark>https://EconPapers.repec.org/RePEc:eee:deveco:v:94:y:2011:i:1:p:95-105</mark> <mark>McDonald, J., 2008. Some Generalized Functions for the Size Distribution of Income. In: Chotikapanich, D. (eds) Modeling Income Distributions and Lorenz Curves. Economic Studies in Equality, Social Exclusion and Well-Being, vol 5. Springer, New York, NY
**Figure 1: Descriptive statistics across industries in SAR**

<!-- Start of picture text --> Annual relative growth<br>Wholesale and Retail 6.5%<br>Fabricated Metal Products and Machinery and ... 15.7%<br>Non Metallic Mineral Products and Basic Metals 9.4%<br>Chemicals, Plastics & Rubber 8.7%<br>Textiles and Garments 7.1%<br>Real Estate Renting & Business Activities (70-74) 27.3%<br>Transport (60-64) 14.3%<br>Hotels & Restaurants 3.5%<br>Services of Motor Vehicles 11.7%<br>Construction 22.5%<br>Electronics (31-32) 8.1%<br>Food 3.8%<br>Other Manufacturing 5.1%<br>0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%<br><!-- End of picture text --> _Source: INTERIM ES, Own calculations Industries with fewer than 30 observations in the Region were excluded; growth rate over three years; unweighted_<sup>_6_</sup>

**The share of expanding firms dominated shrinking or stagnant companies.** With the exception of food, the share of firms increasing their size was the largest across all industries, which also implies that more firms were growing than shrinking. Particularly broad was the expansion in real estate and business renting, chemicals, plastics and rubber and fabricated metal products and machinery and equipment. The largest share of companies that were decreasing in size was in textile and garments (see table 1).

> 6 In longitudinal establishment-level data studies, a pervasive finding is that idiosyncratic factors dominate the distribution of growth rates of output, employment, investment, and productivity across establishments. Seemingly similar plants within the same
GVC participation data are from the OECD’s Trade in Value Added database (TiVA). We find that a unit standard deviation (SD) increase in domestic value added (DVA) in exports of intermediate products (an index of participation in GVCs) is associated with a 0.1 SD decline in the _Zipf_ coefficient. Spatial concentration is strongly and significantly correlated with GVCs involving knowledge-intensive business services and high-tech manufacturing. Even with low-tech manufacturing, the correlation with spatial dispersion is negative, albeit not statistically significant, implying that participation in GVCs does not help with the spatial dispersion of economic activity. The findings on the service sector are particularly important for public policy given their rising shares in GVCs.<sup>3</sup> These findings are consistent with alternative measures of participation in GVCs (DVA in foreign exports; ratio of forward to backward linkages) and spatial concentration (e.g. inverse of the Gini coefficient).

An important econometric challenge is to address the potential endogeneity of GVCs with the urban size distribution. The point here is that changes to the urban system come from unobserved sources which are associated with country level decisions to integrate in global supply chains. Consider “infrastructure readiness”, which is a key driver of participation in GVCs. In China, the government pumped four trillion yuan (about US$ 520 billion) in 2008 towards promoting the development of inland regions through rural infrastructure projects including construction of roads, railroads and airports, as well as the supply of affordable housing (WTO, 2011). Such investments that deeply alter the spatial allocation of domestic economic activity also make global production more attractive in these locations, suggesting the possibility of reverse causality. Using real effective exchange rates and net FDI inflows as instruments to address the endogeneity between GVCs and the urban size distribution, our results confirm the strong and positive relationship between spatial concentration and GVC participation.

Our findings are consistent with an emerging body of evidence showing that economic integration across borders is associated with greater spatial concentration within
assumptions. While this approach can be used for analyzing interval censored data, it only focuses on estimating conditional quantile regressions, requiring specialized software that are not readily available. In contrast, the method we propose can be applied not only for the estimation of conditional quantile regressions, but also for the estimation of unconditional distribution statistics.

Other studies, like the one proposed by Han et al. (2020), construct new measures of the income distribution and estimate poverty in the U.S. using data from the monthly Current Population Survey (CPS). They address the problem of censored income data using draws from the empirical income distribution observed in the last March supplement. A similar method is proposed by Parolin and Wimer (2020), who produce monthly updates of the Supplemental Poverty Measure (SPM) rates with demographic data from the CPS and poverty data from the previous March supplement of the CPS. However, these studies seek to obtain income estimates using the uncensored distribution of previous years, which is not always available with other data sources, like the ones analyzed in this paper.

Buutner and Rassler (2008) propose a multiple imputation approach, similar to ours, to analyze wages from the German Institute of Employment Research (IAB) employment survey. While their method focuses on the analysis top coded data, we expand the approach to analyze data with a more generalized censoring structure.

The paper is organized as follows. Section 2 introduces the model and the econometric issues associated with the imputation method; Section 3 provides a Monte Carlo simulation exercise to analyze the performance of the methodology; Section 4 uses the methodology to analyze labor income distribution changes in Grenada using the 2013-2020 series of the Labor Force Survey. Section 5 concludes.

# 2. Methodology

To address the problem of interval-censored data, we propose a multiple imputation approach based on a heteroskedastic interval-regression model. An interval-regression model is a generalization of the Tobit model that allows using a mixture of censored and completely observed data, even if the censoring thresholds are unique to
# **I. INTRODUCTION**

# **_Context_**

Over the past decade, the production of data has grown exponentially around the world, and now more than ever, complex data are available at the touch of a button—from the grand, such as DNA sequencing and macroeconomic modeling, to the more mundane, such as weather maps, trash collection schedules, and public transport timetables. Expanding at a rapid pace, experts estimate that there will be a further 4,300 percent increase in annual data generation, reaching a level of 35 zettabytes (equivalent to 35 trillion gigabytes) by 2020.<sup>2</sup> As the sheer amount of available data in the world increases—driven by advances in technology and computing—terms such as “Big Data” and “Open Data” have become common parlance, reflecting the importance of data use in our everyday lives.

In particular, Open Data—that is, “digital data that is made available with the technical and legal characteristics necessary for it to be freely used, reused, and redistributed by anyone, anytime, anywhere.”<sup>3</sup> —has attracted much attention as a means to innovate, add value, and improve outcomes in a variety of sectors. Based on its attributes of “availability and access, reuse and redistribution, and universal participation”,<sup>4</sup> it has been argued that Open Data has the potential to deliver a number of benefit streams. In particular, Open Data’s potential to foster innovation, efficiency, jobs, profits, and economic growth has been touted by the private sector, which has widely emphasized the potential financial and economic returns of Open Data. For instance, McKinsey estimates that “Open Data can help unlock between US$3 trillion to US$5 trillion in economic value annually across seven sectors, including education, transportation, consumer products, electricity, oil and gas, health care, and consumer
cleaning and cooking; fetch water if there is no water pump or tap; feeding and bathing children; help the children in their studies at home; look after ill persons. The index is the share of questions to which the answer was both/same. Therefore, higher values represent more egalitarian gender norms. Time allocation data was collected both at baseline and endline. Respondents were provided a set of 25 beads and a board with six circles representing: "Education", "IGA", "Leisure", "Household Chores", "Sleep" and "Other". The Education category includes schooling, vocational training and study time. "IGA" includes paid and unpaid work of any kind. Respondents were then asked to allocate beads into each circle in a way that represents time allocation in an average week. Data on leisure time allocation was collected in a similar way. The recorded categories for leisure are: "Friends", "Men", "Alone", "Church", "Volunteer" and "Other". The exact phrasing for the "Men" category is "With boys or men you have a sexual relationship with". Respondents were then asked to allocate beads into each circle in a way that represents time allocation in an average week. The data points were later converted into weekly hours using recorded total leisure time from the first exercise. Unwanted Sex is defined as, "Been involved in any sexual intercourse that you were not willing to do". Transactional Sex is defined as, "Receiving anything such as money, gifts, help with schoolwork or something else in exchange for sexual intercourse". Literacy is assessed based on respondent's ability to read simple things like labels on containers (basic), and reading comprehension and writing of complete sentence (advanced). The score representing respondent's proficiency with these tasks is then rescaled to range between 0 and 100. Pregnancy and HIV knowledge represent the number of correct answers to the following true/false
Note: BLR = Belarus; CE = Central Europe; RUS = Russian Federation; TUR = Türkiye; UKR = Ukraine; WBK = Western Balkans.

2023 indicates forecast.

A. C. D. Sample includes 13 ECA countries (A) or 2 Western Balkans and 4 Central Europe economies (C,D). E. Estimates of infrastructure investment needed to halve the infrastructure gap with the euro area by 2030. Estimates for ECA are from Global Infrastructure Hub, IMF (2020), Rozenberg and Fay (2019), and the Three Seas Initiative. Central Europe, the Western Balkans, and Russia and Türkiye are as estimated by IMF (2020). Bars show median, and orange whiskers show minimum and maximum range. F. Reconstruction costs are converted into real 2015 U.S. dollars using the U.S. Bureau of Economic Analysis GDP deflator series. Ukraine costs are based on July 2022 estimates by the European Investment Bank, Kyiv School of Economics, and Ukraine Government. Under the Marshall Plan, the U.S. provided about $13.3 billion in aid, or close to $1.1 trillion in real 2015 U.S. dollars, with 16 economies signing up for assistance.
**Figure 1:** Sectoral development in GDP (1965 - 98)

<!-- Start of picture text --> 24. Agriculture -<br>24 : .... ..~~~~~~~~~~~~~~~~~~~~~~~. ......<br>23.<br>1965 1970 1975 1980 1985 1990 1995 2000<br>25 - Industry . . ..<br>24,<br>1965 1970 1975 1980 1985 1990 1995 2000<br>25.5 Services<br>25<br>24.< ---------. |<br>1965 1970 1975 1980 1985 1990 1995 2000<br>Source: Banco del Ecuador<br><!-- End of picture text -->

# 3. **Data and Methodology**

# **Data Description**

The data used in this study consists of quarterly data for real GDP in the industrial _(Ind),_ agricultural (Agr) and service _(Ser)_ sectors from 1965 to 1998. The data was provided by the Banco del Ecuador. The three series are depicted in log-levels in Figure 1 and show an increase over the whole sample.

The industrial GDP series is marked by one large jump, caused by the rapid increase in industrial output after the foundation of a national oil industry in 1972. In March 1987 industrial GDP fell temporarily due to a major earthquake, which destroyed the export oil pipeline and halted oil exports for _5_ months. The drop in the agricultural GDP series in 1983 is caused by the adverse impact of the natural phenomena of El Ninlo.

Table 1 presents the contribution of the different sectors in percentage shares of total GDP at various points in time. When comparing the contribution of the sectors to the total GDP across time, it appears that the weight of the agricultural sector
to nonpoor people. The _elasticity of the marginal utility of consumption_ is the parameter that describes how $1 in consumption loss affects differently poor and non‐poor people. Implicitly, it sets _distributional weights_ , i.e. the weight attributed to poor people vs. the rest of the population in the aggregation of costs and benefits in an economic analysis (Fleurbaey and Hammond, 2004). We use a standard value of 1.5 but, as this parameter is a normative choice, we explore in the Online Technical Note how results change for different values. Higher values give more importance to poor people, lead to higher estimates of welfare losses, and make it relatively more important to use policy instruments targeted towards poor people to reduce welfare risks at the country‐level.

Average losses for poor and non‐poor people may not capture the full impact of the disaster: in each category, losses are heterogeneous and some households may lose everything, and experience long‐term effects or fall into _poverty traps_ . In Mumbai, household surveys show that the median asset and income loss per capita was approximately Rs. 9,300, while the average loss was substantially higher at around Rs. 13,700. Losses across the population follow a lognormal distribution with a long tail: median losses are moderate, but some households lost almost all their income. For the people experiencing large losses, the welfare impact of the shock is not only related to the net present value of the flow of consumption losses, but also to possible long‐term effects, such as reduction in food intake, health effects and disability, and exclusion from job markets, which can lead households to fall into poverty traps (Barnett et al., 2008; Carter et al., 2007; Kraay and McKenzie, 2014; Maccini and Yang, 2009). The risk of poverty traps is particularly acute for children, as severe health impacts or interruptions in education can have lifelong impacts on earnings.<sup>5</