rafmacalaba/data-use-mentions-extended huggingface dataset:
Principles represent an international consensus on best practices in insolvency and creditor rights. The World Bank uses them for, _inter alia_ , benchmarking the effectiveness of countries‟ insolvency and creditors‟ rights system. In addition, they offer guidance to policymakers who want to establish a functional system for healthy debtor-creditor relations. The UNCITRAL Legislative Guide on Insolvency Law provides further supporting details for implementation and design of systems.

_Sources of information, gaps_

**Several sources were consulted in the preparation of this chapter** _._ The information contained in this report consists largely of information gathered from a May 2009 survey, coauthored by the World Bank, together with the Hawkamah Institute, the OECD and INSOL International, with the significant assistance of international expert, Neil Cooper. Field research was also conducted by the author of this report in Jordan, Saudi Arabia, Morocco, Egypt and the UAE between 2008 and 2010. Other written information that was available on individual countries is cited herein where used.

# _1.3.Scope of chapter_

**This chapter covers non-bank, non-household, commercial insolvency** _._ The focus is on the way that MENA countries address companies who are in financial distress, those who cannot pay their debts. There is a discussion of features of legal infrastructure that may prevent insolvency filings, and the

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for the attacks of the other aggressor group. Therefore, we control for both types of attacks in the same regression model (Specification C). The third concern is that the results may be driven by endogenous sorting of the local population. On the one hand, the population residing in areas closer to conflict may decide to relocate to safer areas. On the other hand, Boko Haram attacks led to the displacement of over 2 million people; thus, the results could be driven by the responses of displaced households included in the DHS sample, which may have different gender norms (e.g., regarding employment, fertility choices, or violence within the household). As the DHS data do not contain a variable that indicates whether a household is displaced, we use as a proxy variable the number of years of residence in the cluster, taking five years or more—the highest category—as a threshold. We conduct the analysis focusing on households that have resided in the cluster for at least five years (Specification D).

# **5 Results**

## **5.1 Boko Haram and Fulani Violent Events**

We start by providing the results on Boko Haram violent attacks in Table 2 for women (top panel) and men (bottom panel). This table displays the results of the estimated effects according to the baseline specification and the four alternative specifications.

Concerning the financial outcomes, we observe a positive effect of distant Boko Haram attacks on the employment of women and men for all regression models (Column I). The coefficients are statistically significant at the 5 percent level, except in various model specifications for the estimated effect on female employment, in which they appear statistically significant at the 10 percent level. Consistent with this overall increase in economic activity at the household level, the point estimates of the effects on the probability of independently owning a house or land are positive and statistically significant among women and men (Column II).<sup>15</
N 50,000 (about $366 as of 2003). There is no co-insurance and membership to the fund is mandatory. The premiums are assessed at a flat rate of 0.94%.

_Sources_ : Alawode (1992), Garcia (1999), IADI Survey: Nigeria (2003), Kyei (1995), Talley and Mas (1990).

**Norway.** ( _Deposit Guarantee Fund_ ) The scheme of Norway was established in 1961. There are separate funds for commercial banks and savings banks. Both of these funds are privately administered and jointly funded. In Norway there was actually a guarantee fund for savings banks with voluntary membership in 1921 which became obligatory in 1924, whereas a guarantee fund for commercial banks was first introduced in 1938. However, Norway’s guarantee fund at the time is not considered a pure deposit insurance scheme so they had no official explicit deposit insurance until 1961. The government and central bank have borne losses in the past. There are seven members on the boards of the funds. One of the members is from the Central Bank, and the other is from the Banking and Securities Commission. Deposits of other banks and deposits of companies in the same group with the depository bank are excluded. The Commercial Banks Guarantee Fund provided unlimited coverage between 1962 and 1997. Since 1997, the maximum coverage amount allowed has been NOK 2 millions per depositor per institution.

_Sources_ : Own survey of deposit insurers, Garcia (1999), Gerdrup (2003), IADI Survey: Norway (2003), Kyei (1995).

**Oman.** The scheme of Oman was established in 1995. It is officially administered and jointly funded. The central bank matches half of the member banks’ premium contributions. The fund can borrow from the government, central bank and the member banks. Deposits of significant shareholders, directors and senior managers, illegal deposits and the deposit of auditors, parent, subsidiary and affiliated companies are excluded. The coverage is up
36 °<br>38 °<br>u<br>a<br>D li<br>dg e<br>28 °<br>30 °<br>32 °<br>34 °<br>36 °<br>38 °<br>y<br>m<br>i<br>n<br>bi<br>M<br>N<br>w r<br>m<br>g<br>r<br>o<br>u<br>G<br>i<br>r<br>Edwa<br>D<br>Mur rd<br>Lachlan<br>ra<br>a<br>y<br>lr<br>r<br>i<br>ie<br>n<br>y<br>W<br>rie<br>M<br>qac<br>u<br>a<br>a<br>g<br>r<br>r<br>u<br>M<br>JANUARY 2005 IBRD 33726<br>oL<br>d<br>d<br>o<br>n<br><!-- End of picture text -->
in eligible areas and advertised the program through campaigns. Interested households had to go to these offices and provide the information to determine eligibility, a costly process that resulted in much lower take-up rates and led to significant self-selection in program participation among potential beneficiary households. Administrative data suggests that, initially, only about 50 percent of eligible households were enrolled in the program.<sup>6</sup>

# **3. Data**

The main data source of this paper is the Mexican Family Life Survey (MxFLS), a longitudinal survey that collects a rich set of information on demographic and socioeconomic characteristics of individuals, households, and communities. At baseline, the sample is representative at the national, rural-urban and regional level. The first wave (MxFLS1) was conducted in 2002 and interviewed 35,677 individuals in 8,440 households. These households reside in a total of 150 communities located across 16 different states.<sup>7</sup> The second wave (MxFLS2) was conducted during 2005-2006 and achieved a 90 percent re-contact rate at the household level. This wave consists of 36,946 individuals in 8,434 households, who due to migration decisions are located across 247 localities in 21 states throughout Mexico. A third wave (MxFLS3) spanned through 2009-2013. Due to the timing in the roll-out of Oportunidades, this paper draws on data collected in the first two rounds of the survey.

The two key pieces of information in our analysis are: height – our outcome of interest, and the timing in the roll-out of the program in MxFLS localities. With respect to height, MxFLS collects

> 6 More specifically, households interested in the program had to go to the registration offices on specific dates and answer an inclusion questionnaire. With that information households were immediately classified as qualified for the program or not. If qualified, they had to answer a second questionnaire and were visited later in their dwellings to confirm their eligibility status.

> 7 Mexico is divided into 31 states and
consumed (because this VAT cannot be captured by an analysis based on data from a single year).<sup>25</sup> Because savings rates tend to increase with income, this biases income-based VAT burden results downwards at higher income levels – driving the finding that the VAT is regressive.

In contrast, studies that measure VAT burdens as a proportion of expenditure (across either the income or expenditure distribution) tend to find that VAT systems are relatively proportional, or even slightly progressive (see, e.g., Thomas, 2022a; Bird and Smart, 2016; IFS, 2011a; Metcalf, 1994). The largest crosscountry study favoring the expenditure-based approach is Thomas (2022a), who finds the VAT to be either roughly proportional or slightly progressive in 23 of 27 OECD countries examined. However, results for four countries show that broad-based VAT systems with very few reduced VAT rates or exemptions can produce a small degree of regressivity.

Most recently, Bachas et al. (2023) have considered the impact of informality in developing countries on the distribution of VAT burdens. They estimate informality Engel curves (relating the informal budget share to log total expenditure) for 32 low and middle-income countries. They find that informality decreases as household expenditure increases, and conclude that this will create a degree of progressivity in the VAT in developing countries. Similarly, Jenkins et al. (2006) find that informality creates progressivity in the VAT in the Dominican Republic.

# 4.1.2 The ability of reduced VAT rates to target poorer households

A number of empirical studies examine who benefits from reduced rates, finding the VAT to be a poorly targeted tool for supporting poorer households. For example, two large scale studies consider who benefits from reduced VAT rates in nine European (IFS, 2011a) and 20 OECD countries (OECD/KIPF, 2014). Both studies find that reduced VAT rates as a whole have a progressive impact, but that richer households benefit more in aggregate terms than poorer households.
mark>

- <mark>Daveri, F. and C. Jona-Lasino. 2008. “Off-shoring and Productivity Growth in the Italian Manufacturing Industries,” Working Paper No.2288, CESIfo, Munich.</mark>

- <mark>Dolan, C. and J. Humphrey. 2000. “Governance and Trade in Fresh Vegetables: The Impact of UK Supermarkets on the African Horticulture Industry,”</mark> _<mark>The Journal of Development Studies</mark>_ <mark>, 37:2, 147176.</mark>

- <mark>Du L., Harrison A., and G. Jefferson. 2011. “Do Institutions Matter For FDI Spillovers? The Implications Of China's Special Characteristics,” NBER Working Paper 16767, February 2011.</mark>

- <mark>Egger, H. and P. Egger. 2006. “International Outsourcing and the Productivity of Low-skilled Labour in the EU,”</mark> _<mark>Econonomic Inquiry</mark>_ <mark>, 44, 98-108.</mark>

- <mark>Egger, P., M. Pfaffermayr and Y. Wolfmayr-Schnitzer. 2001. “The International Fragmentation of Austrian Manufacturing: The Effects of Outsourcing on Productivity and Wages,”</mark> _<mark>North American Journal of Economics and Finance</mark>_ <mark>, 12, 257-72.</mark>

- <mark>Eichengreen, B., D. Park, and K. Shin. 2013. “Growth Slowdowns Redux: New Evidence on the MiddleIncome Trap,” NBER Working Paper 18673.</mark>

- <mark>Farole, T. and D. Winkler. 2014. “The Role of Mediating Factors for FDI Spillovers in Developing</mark>

- <mark>Countries: Evidence from a Global Dataset,” in Farole, T. and D. Winkler (eds.),<
inputs, and provider entry—as well as a smaller number of possible indicators of service delivery quality.

To advance this agenda, the next step will be to begin gathering the data for these indicators more systematically. To do so effectively, it will be necessary to develop new instruments, and to make use of multiple types of instruments rather than trying to rely on one or a handful. Governance involves a complex set of processes that require different measurement approaches; moreover, much can be gained from cross-checking different sources. There are many promising instruments for collecting data: facility surveys such as Public Expenditures Tracking Surveys (Reinikka and Smith 2004), expert surveys on system variables, household surveys, secondary sources, administrative data, and qualitative studies. Describing these instruments is beyond the scope of this paper, but two recent surveys—Lindelow and Wagstaff (2008) and Amin and Chaudhury (2008)—offer excellent detailed discussions of instruments and methodologies appropriate for the health and education sectors, respectively.<sup>19</sup> A second set of questions concerns who should carry out the data collection, and how often. Is it better to have the government collect data for these indicators, or to rely on an NGO or other outside actor? And should data collection and indicator production and dissemination be occasional one-off events, to raise awareness and set some benchmarks, or institutionalized elements of regular monitoring activities? While there are tradeoffs in each case, what is most

> 19 The edited volume by Amin, Das, and Goldstein (2008) reviews the instruments and lessons from their application to various cases.  See also the working-paper version of this paper, which discusses the issue in more detail.

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<mark>what was previously found in the literature, but still of comparable magnitude with a more robust econometric method. Furthermore, the synthetic control method allows for time-variant effects which previously used methods in the literature were unable to estimate. We show that the cost of default is heavily frontloaded.</mark> <mark>More importantly, to our knowledge, this is the first paper to systematically assess some of the key social costs of sovereign default in a large panel. We find significant effects of sovereign default on poverty, nutrition, electricity and health outcomes, indicating that the most vulnerable segments of the population are heavily impacted by sovereign defaults and the decade that succeeds them. Whether the effects on poverty are predictably only caused by plummeting growth is a question that will need better data and further inquiry, but our study showed that there may be considerable heterogeneity in results. Additionally, the main association stands: Sovereign defaults impose a great burden on a society, pushing about 30% more people into poverty and depriving the population of access to basic needs like nutrition and energy.</mark> <mark>The social cost results are informative despite data limitations of some of the outcome variables. In particular, using data for the 8 countries where we observe output collapses after default and where annual data on the income distribution is available, we find that on average, lower income segments of the population bear the brunt of the default cost. This result is consistent with the results from the less granular poverty data. In other words, just like all roads lead to Rome, all variables here point to a decline in standards of living of those whose standards of living were already sub-standard.</mark> <mark>The link between social outcomes and sovereign default has been under-researched. A notable exception is Andreasen et al. (2019), as well as Jeon and Kabukcuoglu (2018). Both works develop a theoretical model to predict sovereign default based on the level of inequality. In their
Argentina leads to the reallocation of women from formal to informal wage employment (Viollaz, 2018).

_Have gender gaps in time use narrowed over time?_ To analyze the evolution of time use patters during the last years, we compare our results with those from the 2012 WDR (World Bank, 2011) for 10 countries: Argentina, Ghana, Guatemala, Iraq, Mexico, South Africa, Uruguay, USA, Timor-Leste and West Bank and Gaza.<sup>7</sup> We calculate changes in the gender gap in time allocated to market work, unpaid domestic work and care activities, and personal activities—including leisure. Survey instruments are different in some cases; thus, caution should be used when making comparisons. For example, while we use a time use module for Argentina and a time use survey for West Bank and Gaza, the 2012 WDR employs aggregate data for these two countries. Similarly, for Ghana we use a time use survey and the WDR employs a household survey module.

Progress in reducing gender gaps in time use has been uneven. Some countries have made small improvements in the last years, while differences persist or even widened in others. Figure 4 presents the change in gender gaps over time.<sup>8</sup> Panel A shows that the gender gap in time allocated to market work narrowed in 4 of the 10 countries (Ghana, Guatemala, USA and Timor-Leste); it increased in 5 countries (Argentina, Iraq, Mexico, South Africa and West Bank and Gaza), but the change was rather small in two of them (Argentina and South Africa); and in one country (Uruguay) the gender gap remained unchanged. With respect to the time allocated to unpaid domestic work, Panel B indicates that in 2 out of the 10 countries (Argentina and Uruguay) the gender gap increased (in absolute value); in 2 countries, the gender gap barely changed (Mexico and Guatemala); and in the remaining 6 countries
.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>Sub-Saharan Africa SSA Oil Rich Countries SSA Metal Rich SSA Non-Resource<br>(SSA) Countries Rich Countries<br>Physical Capital Human Capital TFP Output<br>percent<br><!-- End of picture text --> _Notes: Regional and sub-regional averages presented in this figure are population weighted averages. The human capital index_ H�exp�φ�s���� _is characterized by the relationship between human capital (H) and the years of schooling (s) as_ φ�s����φ� ∙s�� _for each country i in period t — where the returns to education (_ φ� _) are heterogeneous across countries. The returns to education are measured from the estimation of Mincerian wage regressions on schooling. We use the Mincerian returns estimated by Montenegro and Patrinos (2014) and the total years of schooling for each country and year to construct the human capital index H. Source: Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._ Figure 2 displays the long-run average growth rate of output per capita as well as its sources (physical capital, human capita, and TFP) over the past half-century. However, it does not capture whether the contribution of the factors of production vis-à-vis TFP growth remained constant over time. The proneness to shocks affecting growth in the region over the medium-term justifies assessing the sources of growth in the region across different time periods; namely, 1961-1977, 1978 – 1995, and 1996-2014, respectively. Figure 3 depicts the dismal performance of the region _page 6_
83.8% vs. 87%; p=value<0.01) to own a smartphone. Similar modest gains in asset ownership over a number of household durables could plausibly add up to explain a substantial part of the unaccounted transfer income. However, without data on these expenditures, this claim remains speculative. The study team added questions on asset ownership to the 24-month follow-up phone survey, which was interrupted by the pandemic and is not used in this paper.

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frequency data can supplement infrequent HBSs to help identify rapid political and social-economic changes. For example, Lebanon and the West Bank and Gaza could benefit from this type of data collection given the high frequency of shocks people in each country face.

# **Public Opinion Surveys (Gallup, Arab Barometer, World Values surveys)**

# <u>Method</u>

Some firms and organizations perform household surveys at a higher frequency than national statistical offices. The Gallup World Poll (GWP) conducts surveys every year in nearly every country, for example. Importantly, this includes surveys in some very challenging settings, such as Yemen. These surveys can supplement traditional household surveys to better understand how welfare is evolving in data-scarce environments.

Each type of organization has its own sampling strategy in each individual country and year. For example, the GWP outsources sampling and actual surveying to survey firms in each country. Depending on the capacity of the firm and the security situation, the surveys can be either face-to-face or conducted via mobile phone. In each year, the GWP surveys 1,000 nationally representative households. Importantly, each survey is comparable across countries and can be particularly useful for region-wide analysis.

One problem with public opinion surveys is that they are short, with questions typically focusing on subjective welfare and household opinions. However, each survey collects information on employment and access to food and shelter, all of which strongly correlate with poverty. We can use these data to identify welfare trends in a country over time, even in settings with no traditional welfare metrics.

# <u>Data requirements and pitfalls</u>

There are three primary drawbacks to using public opinion surveys as a source of welfare information. First, similar to phone surveys, one cannot construct traditional welfare metrics, such as poverty. Furthermore, the survey collects very little information on household characteristics, location, and living conditions. Given these important gaps, these surveys are best used to supplement household surveys. That said, in countries with
steeper among older children, consistent with an unfolding effect of accumulating exposure. Finally, section 2.3 considers the average height difference between children in South Asian and Sub-Saharan African countries, and shows that much of this gap is accounted for by sanitation.

# **2.1 Data**

All data used in this paper are publicly available free of charge on the internet. Demographic and Health Surveys (DHS) are large, nationally representative surveys conducted in poor and middle-income countries. DHS surveys are collected and organized to be internationally comparable. In some countries, several rounds of DHS data have been collected; in others only one or two. I use every DHS survey which recorded household sanitation and measured child height.<sup>2</sup> This creates a maximum sample of 140 country-years and 65 countries, ranging in frequency from 26 countries that appear in the survey once to 10 that appear in four separate DHS surveys. The earliest survey in the dataset was collected in Pakistan in 1990; the most recent are from 2010.

I match data from other sources to the collapsed DHS surveys. GDP per capita and population are taken from the Penn World Tables. “Polity” and “Democracy” scores of democratization are taken from the Polity IV database. A measure of calorie availability produced by the World Food Program is used in some specifications. All other variables are from DHS surveys.

Using these data, the basic regression I estimate is where observations are collapsed DHS surveys, _c_ indexes countries, and _y_ indexes years. _Open defecation_ is a fraction from 0 to 1 of the population reporting open defecation without using

> 2I use published summary statistics available online at www.measuredhs.com. DHS surveys do not include rich countries, such as the U.S. One important omission is China, where there has not been a DHS survey.

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