rafmacalaba/data-use-mentions-extended huggingface dataset:
were far from their quota limits in the QFII/RQFII or the Stock Connect program. Those limits were removed in 2020 and 2016, respectively.

Domestic investors seemed to have provided bridge financing for domestic firms before international investors increased their participation. Foreign equity inflows were substantially smaller than domestic equity issuances during 2014-17. This suggests that domestic rather than international investors bought most of the new shares issued during those years. Foreign equity inflows surpassed domestic equity issuances during 2018-20.<sup>37</sup> Next, we analyze the evolution in firms’ foreign ownership structure. We compute the percentage of foreign ownership, the value of shares held by investors outside mainland China over the total value of shares outstanding per firm. We plot the average foreign ownership ratio across domestic listed firms over time (Figure 8, Panel B). The figure shows how the foreign ownership ratio substantially increased during 2018-20 relative to previous years. By 2020, the average percentage of foreign owned shares per firm had almost tripled compared to 2016, from 1.3 percent to 3.8 percent.

Overall, the evidence suggests that most of the increase in foreign equity investment in China occurred with the addition of domestic listed stocks to the MSCI Emerging Markets Index.<sup>38</sup> China’s weights in foreign equity holdings (from CPIS data) and in the MSCI Emerging Markets Index followed a similar trend (Figure 10, Panel A). However, the CPIS weight lagged behind the

> 37 Some minimum degree of bridge financing had to occur because, by regulation, firms could sell the shares of the primary issuance activity (IPOs or SEOs) only to domestic investors. However, after purchasing them from firms, domestic investors could have immediately sold those shares to international investors. Thus, this regulation does not explain the years of bridge financing domestic investors provided.

> 38 Comparing portfolio equity inflows with foreign direct investments (FDI) into China shows that the former grew relative to the latter during the internationalization process
>1.177.85|
|1 11064 Wheat flour<br>|1|3.048.093|4.420.735|<br>1.450.33|
|411072 Oat flour and meal|ton|11.383|<br>41.111|<br>3.611.61|
|11073 Composite flours and "pecula" products|kg.|3.282.208|<br>23.373|<br>7,121.12|
|8 11080 Corn glucose (dextrose)|ton|105.044|<br>191.745|<br>1,825.38|
|11082 Improved grainsorts (rice. corn etc.)|ton|2.383|<br>9.927|<br>4.165.76|
|:1083 Coconut milk<br>|liter|<br>1.826.100|<br>27.521|<br>15.070.92|
|10 11092 Cracked corn|ton|78.964|<br>90.899|<br>1.151.14|
|11106 Corn stalks||1.150|<br>817|<br>710.43|
|11108 Stalks. n.e.s.||355|<br>82|<br>230.99|
|2 11114 Wheat semolina|ton|355.516|<br>539.400|<br>1.517.23|
|TOTAL SPECIFIED|||7.001.996||

Source: 1975 Censo Industrial do Brasil, Producao Fisica:
also involves deduction of service inputs (column (6)). In table 2.8 total value added (USS 487,203 million) is the national accounts definition, the US census definition would be more or less equivalent to 659,622 million US dollars (i.e. the national accounts value added - US$ 487,203 million - plus service inputs - US$ 172,419 million-). The ratio of the national accounts to the US census concept of value added would therefore be 73.9 per cent for 1977, i.e. not too different from the 1975 ratio of 76.6 in the bottom right of table 2.6.

In the case of our detailed comparisons involving the USA in chapter III, it was not operationally possible to use a national accounts concept of value added, as we would have preferred. This was due to the impossibility of reconciling the US census material with the national accounts and the input-output table. However, we were able to put our comparison of Brazil/USA and Mexico/USA on an national accounts basis at the branch level and for manufacturing as a whole.

A reconciliation of the census and national accounts approach for major branches of manufacturing for 1977 and 1982 can be found in Board of Governors of the Federal Reserve System (1986), pp. 52-7.
important implications not only for end-user safety, efficiency, and convenience, but also for the ability for account holders to access credit.

Historically, financial institutions managed the risks of lending by only issuing loans to people with a proven history of paying loans back as evidenced by their credit history, and by requiring collateral to secure the loan. These methods can be effective if a potential borrower has a credit history and valuable collateral to offer. Yet many low-income adults and women do not. The practical consequence of relying on credit histories, therefore, is that low-income adults, women, migrants, and other excluded groups are shut out of formal credit markets (Demirguc-Kunt, Klapper, and Singer, 2017).

Establishing a pattern of payments in and out of an account via digital payments can function as a form of “alternative data” that lenders can use to assess borrower risk. Using mobile money services to make digital payments creates an official record of income and spending. Users with a limited financial history can leverage this payment record to obtain credit under more favorable loan terms.

Examples of alternative data sources include bank deposits and withdrawals, mobile money transaction histories, airtime top-ups, utilities payments, payroll, rent, and taxes, among others (World Bank, 2022). Evidence shows that using alternative data and risk assessments results in lenders extending more loans to low-income borrowers. For example, studies in the United States suggests that including data on past utility and telecom payments in risk assessments reduced the share of adults that were assessed as having no credit score from 12 percent to just 2 percent (Turner et al., 2012; Turner and Varghese 2010). In China, an alternative data model resulted in increased credit access for borrowers who either would not have been offered loans or who would have been required to put up collateral (Feyen et al., 2020). Similarly, another study in the United States found that more individuals were offered loans at an affordable interest rate when the lender incorporated previous transaction data
.........<br>..............<br>.<br>.<br>.**.**.**....**. .**....**<br>_ .**...**<br>Other<br>|2<br>|1316<br>|30.4<br>|
|,<br>........<br>..<br>. .. ...Average<br>.<br>.|~~~~~|29-5:.23,5|14.7|

Sources: The Moscow Longitudinal Household Survey, January 1996; Vladinir and Gorodets Longitudinal Household Survey,

## Decemnber 1996.

At the other pole are pensioner households (20-30% of all urban households), who are largely satisfied with their housing situation. Only 2-6% of these households are planning to move in the near future. The rest (single people, one-parent families) occupy an in-between stand.

In all three cities, similar trends were identified in the effect of the household's demographic type on potential housing mobility. The type of household, which is correlated both with the level of the household's provision with housing and the household average per capita income, is a strong determining factor in the decision to purchase housing.

# 2.2.3 Sources of fmancing housing purchases

In November 1996, only 11.4% of households in Vladimir and 7.9% in Gorodets were able to specify the sources of money for the purchase of housing (Table 2.5); this is much less than the proportion of households having such plans. Surveys conducted in 1994 (Urban Institute, 1995; Guzanova, 1994) indicate that the share of such households was noticeably higher then (
Index`|<br>`-0.0333`<br>`(0.0252)`|<br>`0.00194`<br>`(0.00323)`|<br>`0.000398`<br>`(0.00177)`|<br>`-0.0186**`<br>`(0.00888)`|
|`Private Monitoring Index`|`0.0564**`<br>`(0.0267)`|`-0.00743**`<br>`(0.00295)`|`-0.00697***`<br>`(0.00162)`|`-0.0299***`<br>`(0.00787)`|
|`Official Supervisory Power Index`|`0.00701`<br>`(0.0151)`|`0.00217`<br>`(0.00196)`|`0.000176`<br>`(0.000950)`|`0.00511`<br>`(0.00486)`|
|`Entry Requirements Index`|`0.00440`<br>`(0.0223)`|`0.00493*`<br>`(0.00269)`|`0.00405**`<br>`(0.00176)`|`0.00431`<br>`(0.0104)`|
|`Restrictions on Activities Index`<br>`------------------------------------`|`-0.0621**`<br>`(0.0246)`<br>`
guide future research but also to set the stage for the empirical results presented below. Some are difficult if not impossible to address, such as the inherent differences in the surveys, while others such as computation are easy to harmonize.

## <u>Surveys</u>

Computing the measures in Table 1 requires microdata from nationallyrepresentative household surveys that contain data on out-of-pocket health expenditures as well as total household consumption or income. An invaluable resource for identifying and exploring access to potentially suitable surveys is a microdata catalog, such as those maintained by the International Household Survey Network and the World Bank. These catalogs include ‘standalone’ surveys as well as survey ‘collections’ – see Figure 1.
(3,450.451)||(5,742.526)|||

If the table includes missing values (.n, .o, .v etc.), see the Missing values section in the help file for the Stata command `iebaltab` for definitions of these values. Significance: ***=0.01, **=0.05, *=0.1. Full user input as written by user: [ `iebaltab pay_trend_3 pay_trend_5, grpvar(treatment) savetex("C:/Users/wb589463/OneDrive - WBG/World Bank/poverty/Taxes/replication package/admin/tables/balance.tex") replace]` The analysis was conducted at two levels: while randomization was implemented only at the ward level, the administrative data was not randomized. The primary objective was to assess whether the intervention had an impact on compliance among firms in the surveyed wards. Ideally, compliance behavior would have been observed directly through the survey. However, due to the inability to link administrative tax records to survey respondents, the next best option was to aggregate administrative data within the surveyed wards.

13
methodology as of the time of the 2024 WGI annual update. While the core methodology of the WGI has not changed over the lifetime of the project, the set of underlying data sources continues to expand and evolve as new data become available, and in some cases as data sources cease to exist. The resulting aggregate indicators cover six dimensions of governance for over 200 countries since 1996, synthesizing the wealth of information in the component data that feed into the WGI. The WGI dataset also includes margins of error that clearly indicate the unavoidable imprecision in measures of governance, reflecting differences in views across the multiple data sources on which the WGI are based.

that it creates a very transparent link between the parameter estimates of the baseline UCM and this alternative model, as given in Equations (8) and (9).

29
*Notes:** ***, ** and * denote significance at the 1%, 5%, and 10% levels. Leisure time allocation data was collected both at Baseline and Endline in a similar way. The leisure categories recorded were: "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. The main results are estimated with an ANCOVA specification. Control variables include: age, PPI score, household size, illiteracy, village size (nr of dwellings), village average PPI score, distances form key facilities (clinic, secondary school and market), a dummy equal to one if the village is a political stronghold (i.e. has a resident paramount and/or section chief), the number of NGOs active within the village before the Ebola outbreak (excluding BRAC), share of Christian households, distance from Freetown and distance from Kailahun. All specifications include dummies for the randomization strata (district) and errors are clustered at the unit of randomization (village).
The Brazilian Context**

A middle-income economy, Brazil is the fifth most populous country in the world, and, similar to many middle-income countries, women are more well educated than men. Brazil has a relatively high female labor force participation rate, which is closer to that of the United States than that of Mexico, for example. Perhaps because of this economic participation, 24 percent of women ages 15–49 interviewed in a household survey were household heads.<sup>1</sup> Repartnering is common, indicating wide cultural acceptance of union dissolution. In the same survey, about 15 percent of women ages 15–49 separated and

> 1 Data from the 2015 round of the National Household Survey (Pesquisa Nacional por Amostra de Domicílios, PNAD).

5
to groups of households. Second, after data collection, consumption of optional modules is imputed for all households. Then, the resulting consumption aggregate is used to estimate poverty indicators.

# Module construction

Food and non‐food consumption for household _i_ are estimated by the sum of expenditures for the full list of consumption items<sup>7</sup> � � where y� and y� denote the food and non‐food consumption of item _j_ in household _i_ . As the estimation for food and non‐food consumption follows the same principles, the upper indices _f_ and _n_ are neglected in the remainder of this section. The list of items can be partitioned into M+1 modules each with _mk_ items: Item assignment to the core module was designed to maximize the core module’s share of total consumption, so that a large share of consumption would be enumerated from each household. Important items were identified by their average food share across households from wave 1 of the SHFS.<sup>8</sup> This strategy relies on the fact that, in Somalia, a few dozen items capture the majority of consumption. The core modules captured 94 percent of food consumption and 79 percent of nonfood consumption, respectively (Table 2). Optional modules were constructed such that items are orthogonal within modules and correlated between modules, using an iterative algorithm (Pape and Mistiaen, 2018).

**Table 2: Item partitions and consumption shares in SHFS wave 2.**

||Number of<br>items<br>|**Food Items**<br>Share Wave 2|Share Wave 2<br>Imputed|Number of<br>items<br>|**Non‐food Item**<br>Share Wave 2|**s**<br>Share Wave 2<br>Imputed|
|---|---|---|---|---|---
><br><br><br><br>|Tax revenue of the central government projected to increase slightly over the medium term<br>from 7.8 percent of GDP in 2017/18 to 8.2 percent of GDP in 2023/24<br>Direct budget support and relief projected based on data from IMF and World Bank<br>Privatization proceeds assume to reduce the outstanding debt of SOEs<br>Expenditure of the central government projected constant as a share of GDP to 12 percent of<br>GDP as in the 2018/19 budget<br>The fiscal deficit is contained below 3.5 percent of GDP throughout the period<br>Net external borrowing of the government grows by 10 percent annually in line with IMF and<br>World Bank projections<br>Annual government net borrowing from NBE remains at 1.1 percent of GDP|
|_Mone_<br><br><br><br>|_tary and exchange policy_<br>The nominal exchange rate of birr per US$ depreciates at the annual rate of 6 percent,<br>consistent with current NBE policy<br>Net foreign assets are projected to increase marginally, consistent with the balance of<br>payments projections from the IMF and the World Bank. The NBE reserves accumulate by an<br>average of around U$300 million per year during the period<br>Domestic deposits and credits in commercial banks continue to grow on their current trends<br>(23 percent and 22 percent on average during the period, respectively)<br>Broad money grows at a slightly higher pace than nominal GDP based on an estimate of the<br>change in money velocity over time. Money velocity projected to decrease from 3 in 2017/18<br>to 2.1 in 2023/24|
||Commercial bank reserves at the NBE grow in line with deposits and
|-14.3|-29.4|
|Armenia|-14.2|-16.5|-11.9|-19.4|
|Russia|-7.9|-14.1|-4.7|-30.4|
|Romania|-7.1|-12.6|-5.5|-20.6|
|Moldova|-6.5|-16.4|-7.8|-19.3|
|Hungary|-6.3|-11.5|-9.1|-15.4|
|Croatia|-5.8|-9.3|-16.2|-20.7|
|Bulgaria|-5.0|-15.0|-9.8|-22.3|
|Turkey|-4.7|-7.2|-5.3|-14.3|
|Georgia|-3.9|-11.6|-0.9|-19.4|
|Bosnia & Herzegovina|-3.1|-6.7|-4.0|-11.0|
|FYR Macedonia|-0.8|-2.5|-7.9|-10.5|

Source: IMF WEO Database, October 2010. Note: Albania, Azerbaijan, Belarus, Kazakhstan, and Poland are excluded as they have not suffered a decline in real GDP. Latvia and Estonia are the only two countries that reached their GDP peak in 2007; all others did so during 2008.

Comparing countries across multiple dimensions is never straightforward. Debt-to-GDP ratios might increase, but banking sector developments might be a source of stability; or, on the contrary, add to already weak balance sheet developments. This paper looks at these indicators simultaneously using a methodology known as cluster analysis (CA). The advantage of
the child to provide more responses; and unlike EGRA, the Uwezo assessment is not timed. In our sample, children performed better for the competence of letter recognition using Uwezo assessments, however, the patterns across literacy levels found in EGRA and Uwezo are very similar. Thus, this paper reports EGRA scores to be comparable with other education technology (edtech) studies.

Because of the brain activity that the m-Learning add-on may encourage while playing and learning, we also assess potential spillovers to numeracy outcomes for the target child and sibling (the printed materials provided at the community screenings and the m-Learning apps only included literacy lessons), we used modules from the Early Grade Mathematics Assessment (EGMA) to test both children. Overall, while we observe children doing better at recognizing numbers than letters, the baseline data shows that numeracy skills are very low. On average, 0.43 numbers are recognized out of 20 and 1.6

> 12More details about Uwezo assessments can be found at `http://nada.uis.unesco.org/nada/en/ index.php/catalogue/179` .

18
(0)|(0)|2013, 201<br>(3)|4, 2015|

Among these data sources, DHS provided the most comprehensive combination of gender-relevant outcome variables across all coutnries and time periods. WVS, though not available for all countries in the region, was selected due to availability of gender attitide variables that could serve as proxies for relevant norms. A brief description of each is included below.

> + Special DHS data type and not used.

> * Special DHS data type.

> ** Not used in the analysis.

50
# **Table A20. Price elasticity of fertilizer use (incidence).**

|Variable|(1)<br>Burkina Faso|(2)<br>Malawi|(3)<br>Nigeria|(4)<br>Tanzania|
|---|---|---|---|---|
|Log fertilizer price, winsorized|0.1036*|-0.00850|-0.0108|-0.349***|
||(0.0607)|(0.0378)|(0.0709)|(0.0991)|
|Constant|0.663*|0.661**|0.472|3.246***|
||(0.359)|(0.266)|(0.414)|(0.729)|
|Observations|882|1,772|122|541|
|R-squared|0.019|0.031|0.001|0.054|
|Survey Wave FE|YES|YES|YES|YES|

Note: Results from OLS regression of extensive margin fertilizer use on log fertilizer price. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

41
records to predict targeting performance of programs in Togo. However, one drawback of mobile phone data is that – like banking records – the population of mobile-phone owners may be systematically different from the population of those without phones. Satellite-derived geospatial data do not suffer from this selection bias and have become increasingly popular in economics (Donaldson and Storeygard, 2016). Previous research has demonstrated that geospatial data is a promising source of data to estimate economic growth (Henderson et al., 2012), labor force participation (Merfeld et al., 2022), and welfare more generally (Jean et al., 2016; Yeh et al., 2020; Chi et al., 2022; Engstrom et al., 2022; Van der Weide et al., 2024).<sup>1</sup> In this paper, we evaluate four main methods to estimate welfare at low levels of aggregation in developing countries. Importantly, we propose and implement a two-stage weighted residual bootstrap procedure to estimate uncertainty for the set of machine learning methods; estimates of uncertainty are an ongoing, but current unsolved, issue with machine learning predictions (Chi et al., 2022) and our results are encouraging.

> 1Newhouse (2024) provides a recent review of the literature of applications of geospatial small area estimation to wealth and poverty.

2
POLICY RESEARCH WORKING PAPER 2333

# Summary findings

Herrera, Perry, and Quintero explain Latin America's growth slowdown in 1998-99. To do so, they use two complementary methodologies.

The first aims at determining how much of the slowdown can be explained by specific external factors: the terms of trade, international interest rates, spreads on external debt, capital flows, and climatological factors (El Nifio). Using quarterly GDP data for the eight largest countries in the region, the authors estimate a dynamic panel showing that 50-60 percent of the slowdown was due to these external factors.

The second approach allows for effects on output by some endogenous variables, such as domestic real interest rates and real exchange rates. Using monthly industrial production data, the authors estimate country-specific generalized vector autoregressions (GVAR) for the largest countries.

They find that during the sample period (1992-98) output volatility is mostly associated with shocks to domestic factors, but the slowdown in the subperiod 1998-99 is explained more than 60 percent by shocks to the external factors.

- This paper a product of the Economic Policy Sector Unit and the Poverty Reduction and Economic<sup>Management Sector</sup> - Unit, Latin America and Caribbean Regional Office is part of a larger effort to understand output fluctuations and growth in the region. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Cristina Palarca, room 18-165, telephone 202-473-5328, fax 202-522-2119, email address cpalarca@worldbank.org. Policy Research Working Papers are also posted on the Web at<sup>www.worldbank.org/research/</sup> workingpapers. The authors may be contacted at gperry@worldbank.org or nquintero@worldbank.org.<sup>May 2000. (62</sup> pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange_<sup>_of ideas ahout_</sup> _development issues. An ohjective of
, maritime transport, and intermodal transport.

# _<u>International Road Federation (IRF) World Road Statistics</u>_

The International Road Federation (IRF) publishes _World Road Statistics_ , available online for its members. Data 1997 to 2001 covering up to 189 countries: road networks; production & export of vehicles; first registration and import of vehicles; vehicles in use; road traffic; multimodal traffic comparisons; motor fuels; road accidents, rates & basis of assessment of road user taxation; examples of taxation for five common categories of motor vehicle; annual receipts from road user taxation; road expenditure.

# _<u>US Bureau of Transport Statistics (USBTS)</u>_

BTS is the only source in the U.S. government for data on U.S. merchandise trade with Canada and Mexico transported by surface modes of transportation. This will include air and water data on the searchable online database in the future. U.S. International Trade and Freight Transportation Trends features over 80 figures and tables and analysis, covering topics from modal trends to security to trade in transportation goods and services. It reviews the linkage between transportation and international travel, examines changing patterns in modal shares, geography and purpose of travel, and identifies key influencing factors. Particular attention is paid to air travel and security issues.

25 ____________________________________________________________________________________________________________