rafmacalaba/data-use-mentions-extended huggingface dataset:
- 16 -

33. As a source of funds, chonsei typifies the value of housing as a collateral. However, instead of borrowing from a bank against this collateral, the owner is extracting a loan from his tenant (or tenants). In terms of bargaining power, the lack of adequate housing finance and the scarcity of housing favor landlords. With chonsei, tenants assume all default risk since the landlord receives the total deposit in advance. On the other hand, the imputed rents tend to be competitively priced and reflect the cost of capital on the curb (UMM) markets. The chonsei system may be widespread but it is not financially efficient either for the landlord or the tenant compared to the possibilities offered by financial institutions under liberalization of the financial system.

# C. Financial Strategies to Achieve Housing Ownership

# 1. Sources of Funds for Housing Purchases: Overview

34. The 1986 KRIHS survey of household assets directed by Kyu-Bang Lee provides the first reliable view of general housing finance conditions in Korea. Table 2, originally published by KRIHS, reports the share of the total funds used to finance housing by 13 household income groups. This table shows how regressive the present housing finance system is, with the lowest income categories relying the most extensively on costly and short-term informal sources: kye (rotating credit associations), curb market loans, and chonsei from renting out part of the unit. Lower income households also have little access to loans from their work place or other institutions, unspecified sources are also large. The minimal role of the housing bank is also clear: as much financing comes from direct chonsei rental (column 10) asfrom KHB loans (column 6). Tne total volume of chonsei claims is of course greater (see columns 2 and 10).

# 2. Households Receiving Partial Bank Financing

35. The lack of mortgage lending in Korea forces households into complex funding strategies as shown by the annual surveys of units partially financed by the Korea Housing Bank. This subset covers a more fortunate minority
could then fall into a poverty trap.<sup>20</sup> For this reason, poor households will prefer to smooth their assets instead of smoothing their consumption.<sup>21</sup> An asset-smoothing behaviour might be a desirable strategy for households to avoid falling into poverty traps. As pointed out by Zimmerman and Carter (2003) who build on Dercon (1998)’s approach by incorporating the role of endogenous asset price risks, portfolio strategies can bifurcate between rich and poor households. In this setting, poor agents respond to shocks by using consumption to buffer assets when they get close to a critical asset threshold.<sup>22</sup>

# **_Econometric Framework_**

Let us quantify vulnerability to poverty by considering the probability to be poor in the future that is having predicted future income or assets below a pre-defined threshold, conditional on household characteristics and exogenous shocks. This probability can be stated as follows: where _ait_  1 is household _i_ welfare (using per capita asset index as a proxy) at time _t+1_ ,<sup>_x_</sup> _it_ and<sup>_x_</sup> _it_  1 are vectors of household characteristics at time _t_ and _t+1_ respectively that are not used in the definition of cohort _c_ , and _z_ is a given threshold. This probability is modelled using pseudo panel data. Indeed, in the absence of panel data, repeated cross-section data can be grouped together by age cohort, education, and geographic groups in order to implement the methodology. So, the welfare index can be modelled in logarithm as follows:<sup>23</sup> where superscript _c_ denotes cohort group. It is assumed that the residual term  _itc_ can be decomposed into an individual specific effect  _ic_ and an error term  _itc_ as follows:

> 20 Zimmerman and Carter (2003) and Carter and Barrett (2006), among others, have analyzed the existence of poverty traps when
Table 7: Heterogeneity by gender

||Psycho|social Index|P|HQ|Value to|Family|
|---|---|---|---|---|---|---|
||Female|Male|Female|Male|Female|Male|
|Work|0.165|0.233|0.120|0.276|0.091|0.121|
||(0.068)|(0.051)|(0.124)|(0.089)|(0.114)|(0.089)|
|Cash|0.086|0.045|0.089|-0.031|0.080|-0.084|
||(0.088)|(0.057)|(0.146)|(0.101)|(0.142)|(0.109)|
|Test: Cash = Work|0.268|0.000|0.799|0.001|0.921|0.021|
|Shrp. q-val Cash=Work|1.000|0.001|1.000|0.008|1.000|0.026|
|Test: Male = Female|0.247|0.247|0.047|0.047|0.223|0.223|
|Shrp. q-val Male = Female|0.396|0.396|0.396|0.396|0.396|0.396|
|Observations|223|503|223|503|223|503|
||Stab|ility|Household P|ower Index|Work Rig|hts Index|
||Female|Male|Female|Male|Female|Male|
|Work|0.241|0.284|0.323
points. These results are consistent with the framework, as poorer children may estimate lower benefits from continued enrollment, and face relatively higher direct and opportunity costs particularly at the secondary level.

Access to public education services reflects these same differences.<sup>12</sup> As shown in Figure 9, of all individuals enrolled in primary public schools, a majority live in households at the bottom of the income distribution. Between 12 and 17 percent of public primary students are from households in the first decile, while only 6 percent or fewer are from the richest 10 percent of households. These results would suggest that, in general, primary services are progressive, benefitting the poor more than the rest. Along the same lines, public LS services could be characterized as focusing on the poor and middle classes, since the shares of students living in households in deciles 1 to 7 are somewhat similar, while fewer than 6 percent of those enrolled belong to households in the richest decile. In contrast, US services appear to be regressive, since the proportion benefitting from these services generally increases along the income distribution up to decile 9. Extreme cases are Nicaragua and Honduras, where the proportion of students enrolled in public US services is three times larger in decile 9 compared to decile 1. In Costa Rica and El Salvador, where the differentials are less pronounced, the proportion of all US public students that belong to the poorest and richest households, respectively, is practically the same. The role of access in dropout will be discussed further in Section 3 below.

# **1.6 Net coverage rates and overage**

The previous discussion described school participation based on which levels individuals would be expected to be enrolled in at each age. Figure 10 summarizes the data on “net coverage” rates from the most recent household surveys, which refer to the proportion of individuals actually enrolled at the level that corresponds to their age, according to the structure of each country’s education system. In general, these
0.06|(0.83)|1,123|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022-2023 phone survey data. Each row is its own dependent variable.

- Domestic work hours reports the number of hours FR spent in the past 7 days on household chores like cooking and cleaning. Childcare hours including time spent taking care of children while not doing other chores. Hours spent outside the home is the sum of hours spent running errands or shopping, visiting friends or family, or working for pay. Hours children alone at home reports hours in the past 7 days children were at home without adults present. Hours of children by friends/family outside the home is the total hours cared by family or friends/neighbors (without pay). Non-family childcare by Jordanians/Syrians is the total non-family childcare weighted by whether they reported with was provided by a) jordanians, b) syrians or c) both. If both, the hours are split 50/50 between syrian and jordanian. Any paid childcare in 2020 is an indicator. (2020 is when the program was active). Months of paid childcare reports the number of months in 2020 paid childcare was used, including zeros. Hours spent outside the home without children report the number of hours spent running errands or visiting friends/family without bringing children. Childcare hours and chore hours report the number of hours spent by all other household members on these tasks, defined above. Paid childcare hours reports the number of hours of paid childcare the household used in the past 7 days.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number
Our data consist of primarily the date of the trip, the class of the trip (divided into coach, business, or first), the origin city, the destination city, and the number of flight miles. It is important to note the data do not consistently include the origin or destination airports (rather than cities), and also do not include aircraft types. Also, instant upgrades upon check-in are not reflected in the data.

A second data set was obtained from the World Bank’s Diio’s SRS database subscription, comprising most scheduled flights globally for CY-09, including the equipment code for each flight. The annual data has 2,122,509 records, reflecting some 26 million scheduled flights world-wide.

In order to obtain the equipment code for an AMEX-booked flight, the origins and destination cities needed to be mapped to their relative origin and destination airport codes, which would then allow mapping the scheduling data with the actual flights using the date, origin, destination, airport code, airline code, and flight number. About 30 cities in the AMEX data (out of 1,049) could not be matched with the SRS data. A total of 108,344 exact matches of flight records could be accomplished, representing 157,297 trips as the sample was prepared for Phase I. These flights were than matched with tables provided by ICAO mapping most of the flights to an aircraft code that had a total fuel consumption value for a given distance.

# **5.2 Features of HQ-Based Travel in CY-09**

Three steps can be identified in the calculation of the carbon footprint of headquarters (HQ)based WBG staff air travel for CY-09, as follows:

**Step I:** Calculations were first made using a random sample of the cabin class configurations of aircraft used for the relevant flights. By using detailed information from several websites where such data are available, a fairly accurate match between the flights and the configuration of the aircraft used by the specific airline was
be equally important, and all indicators within a dimension receiving an equal share of the total weight. The cross-dimensional poverty cut-off is defined as _k_ =50%, with those deprived in half or more of the weighted indicators identified as multidimensionally poor.

# **4. Data**

Data on forcibly displaced populations are scarce, with many household surveys excluding refugees and IDPs from the sample framework. To ensure that MPI results are representative of these communities and that they can be disaggregated for comparative analysis, an initial review of possible data sets was conducted. Feasibility was determined based on the availability of sufficient sample sizes for forcibly displaced persons for quantitative analyses, as well as inclusion of many of the indicators (on health, education, living standards, etc.)

> 14 A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘feeling safe from crime and violence when at home’ was not available, and the indicator only considers answers to the questions on safety when walking alone.

> 15 Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe water sources according to international guidelines. See https://washdata.org/monitoring/drinking-water. 16 Pit latrine without slab, bucket, hanging toilet, and no facility (open defecation) are considered as unimproved sanitation facilities according to international guidelines. See https://washdata.org/monitoring/sanitation.

> 17 According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to) are actively looking for work and are available to start, or those currently waiting to start work are - classed as unemployed. See https://www.ilo.org/ilostat <u>files/Documents/description_UR_EN.pdf.</u>

10
lies are asked. This method was used in the South African National Income Dynamics Study. A simpler version of this is one question where individuals are asked which bracket their incomes fall into. This at least provides some indication of the level of income. It is used by Statistics South Africa in the Quarterly Labour Force Surveys and General Household Surveys. In 2020 quarter 1 around 20% of employed individuals give bracket responses, around 50% give amounts and 30% refuse or don’t know.

# Ex post Solutions for the Challenges Encountered When Using Household Survey Data

Despite the best efforts of survey organizations to lessen item and unit non-response and sparsity, the income or wealth data from household surveys may still be missing part of the top of the distribution. In this case ex-post methods are required to solve missing top incomes. Hlasny and Verme (2022) characterized ex-post solutions to under-capturing of the top tail of the income distribution in 13
||(0.032)|(0.029)|(0.029)|||||
|---|---|---|---|---|---|---|---|
|Families without children|0.633***|0.648***|0.352***|||||
||(0.034)|(0.030)|(0.030)|||||
|**Female headship**||||||||
|_De facto_FHH||||-0.061**|0.591***|0.203***|0.267***|
|||||(0.024)|(0.031)|(0.032)|(0.026)|
|_De jure_FHH||||0.228***|-0.054|0.517***|0.309***|
|||||(0.027)|(0.033)|(0.035)|(0.028)|
|Observations|4,749|2,359|2,359|2,359|2,359|2,359|2,359|
|R-squared|0.556|0.872|0.714|0.504|0.782|0.297|0.145|

_Source:_ Own elaboration based on Somali HFS 2017-18. _Note:_ ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively. Standard errors in parentheses.

# **7. Conclusions**

Our findings show that even when poverty rates are very high, there are important differences between poverty
implemented along with the implementation dates. The second is monthly data from January 2008 until August 2012 on the number of firms registering as formal entities in each municipality. This includes registration of firms under all registration categories except as individual microenterpreneurs (MEIs). In particular, it includes firms who register for SIMPLES, the type of registration which is the focus of the one-stop shop. The third type of administrative data is the monthly total municipal tax payments received in each municipality from January 2008 to October 2012. The tax paid is the _Imposto Sobre Serviços de Qualquer Natureza (ISS)_ – a tax on all firms in the service sector, which is paid as part of the SIMPLES payment whereby a firm makes a single payment that covers a range of different government taxes.

There are 853 municipalities in Minas Gerais. Thirty-one of these, including Belo Horizonte, the state capital, have a physical Minas Facil office and are generally much larger in size. Our dataset consists of the monthly formalization and tax data by municipality for the remaining 822 municipalities, along with the details of which of these received a Minas Fácil Expresso office.

# **4. Estimation Methodology**

Figure 2 plots the mean monthly number of registrations for the 55 municipalities that had a Minas Fácil Expresso office implemented at some point during our study period (treated municipalities), as well as the monthly mean number of registrations for the 767 municipalities in which it was not introduced. Vertical lines show the period during which the Minas Fácil Expresso offices were introduced into the treated municipalities in a staggered fashion, as shown in Figure 1.

Measuring the impact of this reform requires coming up with a counterfactual of what registration rates would have been in treated municipalities had this reform not occurred. A simple before-after comparison would use the registration rates in the period before the reform as the counterfactual for what rates would have been in the post-reform period had this reform not occurred. There are two reasons to not believe this. First, Figure 2 shows considerable month-to-month variation
2|26|20|14|12|14|15|
|High income|45|6|31|21|10|14|24|24|
|REGION|||||||||
|East Asia & Pacifc|64|1|24|18|20|14|6|13|
|Europe & Central Asia|65|4|18|17|15|15|31|16|
|Latin America & Caribbean|55|2|17|40|15|21|26|17|
|Middle East & North Africa|77|12|9|21|8|10|10|7|
|South Asia|65|8|34|23|22|16|9|9|
|Sub-Saharan Africa|81|4|7|36|31|30|16|7|

_Note:_ Respondents could choose more than one reason. Regions exclude high-income economies. See the annex to the methodology section for regional and income group classifications. Data by education level exclude Zimbabwe; data by income quintile exclude Morocco; and data by rural or urban residence exclude Guatemala and Morocco.

_Source:_ Demirguc-Kunt and Klapper 2012.

**56**

MEASURING FINANCIAL INCLUSION
food aid and cash support in response to such a disaster (Table 2). Drought reduces the consumption of households, on average, by 3 percentage points, compared to those that are not exposed to drought. Of course, the magnitude of the impact varies based on the severity of drought (as measured in the Palmer index ranging from 2 to 4). The extent of this decline in per capita consumption increases from 5 percent to 12 percent as the intensity of drought increases. This range of impact magnitude is what we find when considering the coefficient in Table 2 and the Palmer Index ranging from a minimum of 2 to a maximum of 4 (as in Table 1).<sup>13</sup> Excess rainfall in the first two months of the growing season does not decrease consumption per capita after harvest, while excess rainfall in the following months of the growing season appears to increase consumption by 1 percent. When we exclude transfers from the food consumption basket, the negative coefficients on weather shocks decrease further and the positive ones become insignificant (see Table 3). Therefore, the coefficients on excess rainfall in Table 2 seem to be upward biased due to

> 13 We find similar conclusions and impact magnitudes as the ones drawn from the linear model. In the primary regression, we opted to use the drought intensity indicator, instead of a dummy variable for drought, to be able to leverage this variation in drought intensity in the simulations.

16
that both terms in equation (18) are zero, indicating that structural change in favor of the non-farm sector during the decades of 1970s-1990s is unlikely to contribute to the cross-sectional variance of schooling. As noted earlier, this, however, does not imply that the nonfarm sector did not play any role in the increasing income inequality in rural China during this period, only that the nonfarm sector’s effect is not mediated through intergenerational educational persistence.

> 32The estimate of the steady state is based on the combined farm and nonfarm sample. Although they are not statistically different, the point estimates differ numerically across farm and nonfarm subsamples.

20
7%_|_Yes_|
|_in which:_|||||
|_Medical Assistance_|47|0.2%|0.1%|Yes|
|_Social Insurance, Social Assistance &Labor_|_2701_|_12.2%_|_3.0%_||
|_in which:_|||||
|_Dibao Transfers_|146|0.7%|0.2%|Yes|
|_Tekun Transfers_|30|0.1%|0.0%|Yes|
|_Temporary Relief_|16|0.1%|0.0%|Yes|
|_Natural Disaster Relief_|13|0.1%|0.0%|Yes|
|_Complements for Basic Pension_<br>_Insurances_|827|3.7%|0.9%|Yes|
|_Pension for Admin. Institutions Staffs_|853|3.9%|0.9%|Yes|
|_Housing Assistance_|_681_|_3.1%_|_0.8%_|_No_|
|**Non-Social Expenditure**|**13183**|**59.7%**|**14.6%**|**No**|
|**_Debt Servicing_**|746|3.4%|0.8%|No|

_Source_ : Statistical Yearbook for China 2019, National Bureau of Statistics 2019; Financia Yearbook of China 2019, Ministry of Financ3, 2019 Compared to other upper-middle-income countries, China’s social spending is relatively low, especially on direct transfers. China’s spending on direct transfers amounts to 0.5 percent of GDP, which is the lowest share among several upper-middle-income countries and below the average of some lower-middle-income countries. (Figure 2). China’s health expenditure as a share of GDP is also one of the lowest among several upper-middle-income countries. However, its expenditures on education and on contributory pensions are closer
Pakistan had made little progress until recently on **_security of supply_** due to an absolute shortage of generation capacity, made worse by the failure to maintain and operate the nationally-owned generation, transmission and distribution capacity in the optimal fashion. Although IPPs have played an important role in adding new generation plant, their efforts have fallen short, since the government has not yet been able to make the sector sufficiently attractive to investors. Further, the government has no integrated energy plan that would identify the best energy mix. Renewables, although apparently encouraged by the regulatory system, have in practice found it difficult to enter the sector because of planning delays, and the recent unexpected fall in renewable energy prices has acted as a further brake on entry.

On **_access,_** the picture is clouded by uncertainty arising from contradictions between alternative data sources. There has been no obvious electrification plan designed to increase access during this period, nor were the distribution utilities in a strong enough position to undertake this on a large scale without government support. The evidence appears to support the view that little was done to improve access, and a current value of 70 percent grid access based on company connections data and a recent census is more plausible than the 98 percent rate based on household surveys. Accepting this view leads to the conclusion that there is a large amount still to do in connecting remote or poorly located households.

**_Affordability_** is strongly influenced by the subsidies provided to the distribution utilities to bridge the gap in revenue they incur by charging subsidized tariffs. There has been a clear trend of setting the rate of increase of tariffs for the lowest consumption bands well below that on the higher use bands, thus providing a relatively larger support to the poorest users. Calculations for the 2008 and 2011 tariffs indicated that the tariff structure was becoming more progressive. At the earlier date all households were receiving some subsidy on all the units they consumed, while by 2011 households consuming more than 300 kWh/month paid
335|0.346|0.347|
|Country-Year FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Cell FE||Y|Y|Y||Y|Y|Y|
|Controls X Time|||Y|Y|||Y|Y|
|Climate Controls||||Y||||Y|
|Mean Conflict|0.0181|0.0181|0.0181|0.0181|0.0856|0.0856|0.0856|0.0856|

Each observation corresponds to a 50km x 50km grid-cell observed in a given year between 1998 and 2017. The outcome variable is a dummy variable indicating that the grid-cell experienced a battle event in a given year, as reported in the ACLED dataset. Irrigated area is measured as the percentage share of an average plot of land that is equipped for irrigation, as documented by Siebert et al (2015). Controls include log of population at the start of the period as well as dummies to indicate the presence of wetlands, croplands, pasture lands, rivers and the homelands of ethnic groups that historically relied on agricultural livelihood strategies. Standard errors in parentheses clustered at the grid-cell level. Climate controls include mean temperature and log of total rainfall in the wet and dry seasons. * p<0.1, ** p<0.05, *** p<0.01.

25