rafmacalaba/data-use-mentions-extended huggingface dataset:
neighborhood (Figure 1). These results corroborate the idea that not only perceptions of criminal incidents but also actual knowledge of members of the community having been victimized is very low. These results are almost exactly corroborated by all 32 focus groups held in different parts of the country. Interestingly, the incidence of politically motivated violence seems to be higher especially in urban areas.

<!-- Start of picture text --> Figure 1: Bangladesh: Very high perception of harmony in the community<br>Source: World Bank Gender Norms Survey 2006 - respondents sayingpeople in<br>their locality live in harmony v/s fight a lot<br>100<br>90<br>80<br>70<br>60<br>Rural<br>50 Urban<br>40<br>30<br>20<br>10<br>0<br>Men 25-50 Women 15-25 Women 45-60 Community Total<br>Leaders<br>%<br><!-- End of picture text --> Less than 13 percent of the respondents had heard of any violent incident in the community<sup>4</sup> in the last year. At the bivariate level (table 1), residents of Chittagong and Khulna had a much higher probability of reporting such incidents in their community and this was driven to a large extent by politically motivated violence. The multivariate analysis, controlling for a range of individual and household characteristics including wealth quintile, land holding size and geographical area corroborates the bivariate results<sup>5</sup> . Essentially, region of residence and exposure to the media really determines whether you have heard of violent incidents in the last year. This commonsensical conclusion is nonetheless illuminating. What it says to us is that no other characteristic of the household such as poverty level (although individuals in the second quintile are slightly more likely to report knowledge of violence), landownership or religion or characteristics of the individual such
treatment of outliers, the inclusion of benefits beyond food and non-food (i.e., rent, housing, durables), and adjustments for both adult equivalent, and spatial and temporal price differences. For all three designs, the final consumption aggregate is represented by the sum of all its components: i) food consumed inside the home – whether purchased, self-produced, or received in-kind as gift; ii) food consumed outside the home; iii) non-food expenditures of various reference periods – one week, one months, 12 months in our case; iv) education expenditure; v) health expenditure – excluding hospitalization costs; vi) the imputed value of consumer durables; and vii) the imputed value of dwelling benefits.

Construction of the food component was guided by three principles. First, that all possible sources of food consumption were included (i.e., purchases in market, produced by household, received as gift or payment, etc.). Second, non-purchased food that was consumed must be assigned a value. Finally, only food that was consumed – not necessarily the total amount of food purchased – is included. Non-food consumption is slightly more difficult to measure, and we relied on a few widely accepted assumptions to do so. First, that the costs of acquisition of a non-food item is equal to its consumption, and second, that the value of consumption of non-food goods and services from own production or gifts is negligible. Finally, unlike food, non-food expenditures were valued at the purchase or self-reported acquisition cost, as most non-food items are too heterogeneous to collect quantities and calculate prices. Expenditure on some non-food goods and services was excluded from the calculation of the consumption aggregate, including payments of mortgages or debts (financial transactions), losses to theft (neither consumption nor expenditure), expenditure on marriages, dowries, births, and funerals (too irregular). Given that consumption data from household surveys is both extensive and complex, data errors and outliers are expected regardless of data
rural inequality, as measured by the Gini coefficient. However, there are no discernible changes in inequality within the urban and rural areas.

Using the $2/day poverty line, Chen and Ravallion (2004) estimate that 45.2% of the rural population in China and 4.1% of the urban population are in poverty. Applying these figures to our benchmark data for the year 2005, we obtain the poverty line of 4730 ~~Yu~~ an (1997 prices) for urban and 3580 ~~Yu~~ an (1997 prices) for rural households. By assuming a uniform distribution of the population within each of the vingtiles, we are able to estimate how the poverty headcount changes in the wake of these reforms. This information is also reported in Table 5. In the _Full-Lib_ scenario, the monetary poverty line increases by 2.4%, following the change in the CPI (Table 4). Nevertheless, higher factor earnings mean that the poverty headcount ratio declines for all household groups.

Since transfer incomes are assumed to be constant in real terms and are indexed by the consumer price index, the urban transfer specialized household group experiences only a modest decline in its poverty headcount, reflecting a decline in non-transfer income, which comprises less than 5 percent of this group’s total income. The aggregate urban poverty headcount decreases by about 2.1 percent. Rural households enjoy a 2.7 percent reduction in poverty headcount, which amounts to a 1.2 _percentage point_ reduction in rural poverty (i.e., the proportion of the entire rural population in poverty falls by 1.2 percent). Given the large population base in rural China, this translates into a rural poverty reduction of 10.6 million people. The Doha scenario shows a similar pattern of poverty reduction across households, but with lesser absolute reductions. Overall, the impoverished share of the national population falls from 31.3 percent of total population to 30.5 percent in the scenario
Evaluating evidence from administrative data and urban household surveys, mimeo, The World Bank.

- Giles J, Wang D and Zhao C (2010) Can China’s rural elderly count on support from adult children? Implications of rural to urban migration, Journal of Population Ageing 3(3): 183-204.

- Giles J, Park A and Cai F (2006a) How has economic restructuring affected China’s urban workers? China Quarterly 185 (March 2006): 61-95.

- Giles J, Park A and Cai F (2006b) Reemployment of dislocated workers in urban China: The roles of information and incentives, Journal of Comparative Economics 34(3) (September 2006): 582-607.

26
# **_Section II: Does it matter for growth who borrows?_**

This section analyzes the relationship between borrower type and economic growth. The growth regressions are of the following form (using either share of household credit over total credit or using household credit to GDP):

- (1) GDP p.c. growth=a+ b (initial GDP p.c) + c (household credit/GDP) +d (household credit/GDP)^2 +e X

Where X encompasses the usual variables used in growth regressions, including foreign direct investment, secondary school enrollment, inflation, institutions, and the quality of banking supervision, among others. Dummy variables for the proportion of years in a banking crisis and to indicate the region of the world where the country is located, are also added.

These regressions are run using annual data as well as five-year averages. The growth regressions use the standard set of control variables and various specifications (not all shown) and are estimated using OLS, 2SLS and GMM.<sup>6</sup> Similar to the results in Beck et al (2008, 2012), I find that per capita growth is not significantly related to the share of household credit in GDP. This is true in OLS, 2SLS, and fixed effects analysis (not all reported here). In the GMM analysis, it is significant, but enters with a negative sign. In contrast, the linear relationship between the share of firm credit in GDP and per capita growth is significant and positive in the 2SLS specification.

In addition, the paper explores the possibility of a nonlinear relationship between household or firm credit and GDP growth. Tables 5a and 5b present some key results. A number of additional control variables are used to check the robustness of the results. The IV regressions use legal origin and religion as instruments. GDP per capita growth is associated with household credit- in the nonlinear specification in OLS, FE and 2SLS estimations. GMM
major international (including regional) arbitration and dispute settlement mechanisms.

**Table 2. Encouraging and protecting foreign investment: Policy objectives and performance indicators**

||**Policy objectives**|**Performance indicators**|
|---|---|---|
|**Rem**|**oving restrictions to foreign investment**|•<br>GATS commitments (WTO), regional commitments, and domestic<br>laws|
|•<br>•<br>•<br>•|Allowing more foreign equity/ownership/partnership<br>Facilitating the movement and employment of key personnel<br>Relaxing domestic content rules<br>Relaxing rules on foreign exchange and repatriation of benefits|•<br>Services Trade Restrictiveness Indexes (WB, OECD)|
|**Rem**<br>**of fo**|**oving barriers to foreign investment and increasing the protection**<br>**reign assets**|•<br>Arbitration awards (ICSID and other arbitration bodies statistics)<br>•<br>Protecting investors (ADI)|
|•<br>•<br>•|Strengthening investor protection, including rights to challenge<br>domestic regulations/decisions<br>Development of alternative dispute resolution mechanisms<br>available to foreign investors (e.g. recognition of international<br>arbitration, bolstering of domestic arbitration capacities)<br>Adjustment of the laws pertaining to nationalization, expropriation,<br>foreign ownership, stability clauses, etc.||

Source: Authors based on OECD, 2012a • _Level of standards and corporate social responsibility (CSR)_ Although respect for standards might vary depending on the maturity of the GVC’s lead firm and the final market (see Kaplinsky, Terheggen and Tijaja, 2010), it is a key element of the functioning of GVCs: “Today, lead
find that a higher female income share is associated with a better nutritional status of children (as measured by height for age), suggesting that at least some of the higher food expenditures are child related.

Other authors have documented similar findings for other countries and time periods. Engle (1993) provides cross-sectional data from Guatemala to show that a higher female budget share is associated with better children's nutritional status (measured by height for age, weight for age, and weight for height). Phipps and Burton (1998) use data from the 1992 Expenditure Survey in Canada and focus on married-couple households where both spouses work full time. The authors find that a higher share of wives' income is correlated with higher expenditures on child care, children's clothing, women's clothing, and food. Kennedy and Peters (1992) compare female headed with male headed households in Kenya and Malawi and find that in female headed households (typically headed by a widow, who is often the grandmother of the children in the household), a larger share of the budget is spent on food. The paper also documents better anthropometric outcomes (weight for age and height for age). For Malawi, the paper also documents smaller expenditure shares on alcohol. The econometric specification does not control for income, but interestingly female-headed households have better child anthropometric outcomes despite overall lower incomes. Thomas (1990) uses Brazilian survey data collected in 1974/75 to study gender differences in the impact of non-wage income on health and nutrition in Brazil. He finds that maternal income increases family nutrition by four to seven times more than income of men. Both total calory intake as well as protein intake is affected more by female than by male income. Child survival is highly positively related to unearned income of mothers, and the effect is 20 times larger compared to unearned income of men. Maternal income also has a larger effect on two anthropometric outcomes (weight for height and height for age).

# **2.2
social protection scale‐up. More in‐depth analyses of the post‐disaster _financing gap_ have been performed using specific models, investigating the various mechanisms available to finance post‐disaster actions, including budget reallocation, domestic and international borrowing, and specific instruments such as catastrophe‐bonds, insurance contracts, or reserve funds (Cardenas et al., 2007; Hochrainer‐ Stigler et al., 2014). In a next phase, results from these more sophisticated models can be used to refine our estimates of the ability of each country to fund crisis management and provide post‐disaster support.

Our methodology to assess asset risk is a simplification of state‐of‐the‐art catastrophe modeling (Aerts et al., 2014; Michel‐Kerjan et al., 2013; UN‐ISDR, 2015), applicable with global data. As a result, our estimates for flood probability, exposure and asset vulnerability have to be considered with caution. Our simplifications are acceptable as we focus on how asset losses translate into welfare losses (socioeconomic resilience), not specifically on asset losses. The sensitivity analysis presented in the Online Technical Note concludes that our estimates of socioeconomic resilience and our ranking of policy options are largely independent of aggregate asset exposure and vulnerability. Our main results are thus robust to the simplicity of the hydrological modeling and to errors in exposure and asset vulnerability data.

We assess the resilience of 90 countries to floods, by calculating the ratio of expected asset losses to expected welfare losses. Resilience averages 61% across our sample, ranging from 25% to 86%. Resilience in Malawi is 53%, which means that $1 of asset losses in Malawi has the same impact on welfare as a reduction of Malawi’s national income by almost $2.<sup>6</sup> All 90 countries are represented in Map 1, and the list of countries with their risk and resilience information is in the Appendix below. Risk to wellbeing (expected welfare losses in percent of
which is allocated to each education level depending on the share of the budget spent in each level. Benefits are assigned to households who have members attending a government school, as reported in HEIS. We present benefits net of user fees, which are the self-reported household expenditures in education services in government facilities. This means that fees are treated as a tax paid to the public provision system on the service acquired.

However, households may not value these services at the cost of provision. For example, poor quality services may mean that households do not benefit as much and equally from ostensible services provided. Compared to the amount spent, Jordan’s achievements in education and health are substandard (Rabie _et al.,_ 2017; Abu-Ghaida, 2016). In both sectors, workforce performance (teachers and health professionals) does not meet standards of effort (Rabie _et al.,_ 2017). In education, overcrowded classrooms, high student-teacher ratios, low performance in international learning tests have also driven richer households towards private education (Abu-Ghaida, 2016). In the health sector, the absence of a national policy for quality improvement and patient safety, the lack of performance-based indicators for monitoring, benchmarking and accreditation, among others, limit recent efforts to improve quality of service provision (El-Jardali and Fadlallah, 2017). In a sensitivity analysis, we introduce a value-tohousehold adjustment which accounts for within-country differences in education and health outcomes across socioeconomic status. We use information from the Socioeconomically Disaggregated Human

> 36 Some cost might be included in the monthly rent, or the service might be provided as part of employer-supplied housing, for instance.

> 37 Electricity and water inputs to the production of final goods and services means consumers pay higher or lower prices depending on whether the utility tariffs to the producers are higher or lower than the cost of production. The difference is an implicit or indirect tax or subsidy.

> 38 The administrative data includes non-Jordanian students. Syrian children are mostly integrated into Jordanian schools (Abu-Ghaida, 2016)
Economics_ 36(C):99–111.

- Goldin, Claudia. 1991. “The Role of World War II in the Rise of Women’s Employment.” _The American Economic Review_ 81(4):741–56.

- Goldin, Claudia. 2014. “A Grand Gender Convergence: Its Last Chapter.” _American Economic Review_ 104(4):1091–1119.

- Goldin, Claudia. 2021. “Career and Family: Women’s Century-Long Journey toward Equity.” _Princeton University Press_ .

- Goldstein, Markus, Paula Gonzalez and Sreelakshmi Papineni. 2019. “Tackling the Global Profitarchy: Gender and the Choice of Business Sector.” _World Bank Working Paper Series_ .

- Google. 2020 and 2021. “Community Mobility Reports.” _2020 mobility reports retrieved in November 2020 and 2021 mobility report retrieved in October 2021 from: https://www.google.com/covid19/mobility/_ .

- Halim, Daniel, Elizaveta Perova and Sarah Reynolds. 2021. “Childcare and Mothers’ Labor Market Outcomes in Lower- and Middle-Income Countries.” _World Bank Policy Research Working Paper_ .

33
capital regulations and official supervisory agencies over this period (following Basel guidelines), the reforms are not likely to improve bank stability or efficiency.

The rest of the paper is organized as follows. Section II introduces the 2011-12 BRSS, describing how it has been designed and implemented. Section III examines differences between the regulatory and supervisory framework of crisis and non-crisis countries. Section IV examines recent changes in regulation and supervision distinguishing between crisis and non-crisis countries. Section V concludes. Appendix I presents more detailed survey results, going through every section of the survey, providing an update on the state of regulation and supervision around the world across advanced economies and emerging and developing countries.

# **II.   THE DESIGN AND IMPLEMENTATION OF THE 2011-12 BRSS**

The survey provides data for 143 countries<sup>3</sup> , of which 37 are advanced economies and 106 are emerging markets and developing economies (EMDEs).<sup>4</sup> These include most major economies (all G-20 countries except for Japan and Saudi Arabia) and countries from all regions (Figure 1). Overall, the survey sample provides a broadly balanced representation of countries in terms of level of income and population size.

In terms of topical coverage, the survey is quite comprehensive, providing a unique and valuable set of information on a wide range of issues related to bank regulation and supervision. It contains over 270 questions, some with sub-questions. In total, the survey covers about 630 features of bank regulation and supervision, in the following 14 broad areas: (1) entry into banking, (2) ownership, (3) capital, (4) activities, (5) external auditing requirements, (6) bank

> 3 The 2011–12 Bank Regulation and Supervision Survey is available at http://go.worldbank.org/WFIEF81AP0. Data from previous iterations of the survey are available at http://go.worldbank.org/SNUSW978P0.

> 4 See Table 1 for
0.0036)|(0.0018)|(844.9)|(0.84)|(0.0020)|(0.0007)|
|Mekong River Delta|14858.2|18.71|0.0425|0.0143|14497.9|17.45|0.0359|0.0112|
||(265.8)|(1.10)|(0.0033)|(0.0015)|(280.7)|(1.08)|(0.0029)|(0.0011)|

Standard errors are in parentheses. <u>Source: Estimation from the 2009 VPHC and the 2010 VHLSS.</u> 3 We conduct two exercises to shed light on the question how well the models do in predicting per capita expenditure. Firstly, within the 2010 VHLSS, we randomly split the six regions into two samples, and estimating the regional models in the first sample, we predicted expenditure in the second sample and compared the results to the actual per capita expenditure. We find that the observed poverty rates are in all regions’ subsamples similar to the predicted ones. Secondly, we also examine the sensitivity of the expenditure poverty rate of districts and provinces to different expenditure models. We estimate two expenditure models: one with a large number of explanatory variables and another with a smaller number of explanatory variables. Both models give very similar estimates of poverty indexes at the district and province level. For interpretation in this paper, we will use the estimates from the large model, which give lower standard errors of welfare estimates.

9
whole<br>apartment|Private person<br>rented|Rental of apartment - free<br>market rent|Leased from a<br>private person,<br>private legal entity|
|4=Free or<br>other|Held for<br>free|Lives with no<br>charge in a<br>house one<br>does not own|Other|Sub-renting part of apartment;<br>at parents’ or other person’s;<br>other type of residence|Other form of<br>ownership, difficult<br>to answer|

Housing cost primarily included rent, heat, gas, and electricity. The survey in each country posed the questions differently, and in some cases, common utilities might include items that are impossible to separate.

|**Country**|**Costs included**|
|---|---|
|Armenia|Rent, heat, electricity, gas|
|Serbia|Rent, water supply, garbage disposal, central heating, building maintenance, electricity|
|Romania|Rent, water, sewerage and garbage collection, heat, electricity, other utilities|
|Poland|Rent, electricity, gas, district heat and other fuels|
|Russia|Rent,hot water, cold water,heat, gas, electricity|

In addition to survey analysis, the study also draws on published census results, official statistics, and data cited in secondary sources.

14
Accurate measurement of the land rights held by individuals in customary systems as well as their impact on investment and productivity is thus essential for policy.

This paper focuses on the case of Malawi, a poor and predominantly agricultural country with widely varying institutional arrangements, to provide substantive and methodological insights on the link between individuals’ land rights and investment. To do so, we rely on two nationally representative surveys, the Malawi Integrated Household Panel Survey (IHPS) and the Fourth Integrated Household Survey (IHS4), that were conducted in parallel in 2016/17 by the Malawi National Statistical Office. Both surveys elicited parcel-level gender-disaggregated data on reported, documented, and economic ownership and on rights to bequest, sell, rent out, use as collateral, and invest in it to make improvements. The data allow us to (i) rely on within-household variation in rights for identification and (ii) to use application of organic manure and planting of cash crops as indicators for investment and diversification, to explore the extent to which such rights affect economic outcomes.

While the two surveys’ instruments are virtually identical, they adopted fundamentally different approaches to respondent selection that allow us to explore impacts of data collection methodology on substantive results. The IHPS aimed to conduct private interviews with each adult household member to elicit selfreported information regarding their personal ownership and rights for each parcel, a higher cost approach that is likely to result in higher data quality. Conversely, the IHS4 followed the traditional approach to survey data collection using proxy respondents, i.e. interview the self-identified most knowledgeable household member for each parcel to identify owners and right holders within the household. By comparing results from analysis of both data sets, we are able to draw rigorous inferences on the impact of data collection methods on survey responses and measurement of land rights. At a descriptive level, the standard practice of obtaining data on individuals’ land rights from proxy respondents -even if they profess to be knowledgeable- rather than directly from (co-)owners may under-