rafmacalaba/data-use-mentions-extended huggingface dataset:
The sample is therefore nationally representative for grade 3 and 4 teachers in public Frenchspeaking schools in Guinea.

Out of this sample, 140 schools were randomly assigned to each of the two treatment arms —recognition or in-kind performance rewards— and 140 schools were randomly assigned to the control. Random assignment was carried out publicly in the context of a program launching workshop, after the baseline data collection. Names of schools were put in bowls (per strata) and picked (blindly) by young children to be assigned to one of the groups. The public nature of the randomization process aimed at maximizing trust in the transparent and impartial nature of the allocation process.

# **2.3 Data**

Data were collected on schools, teachers, and students using the following survey instruments: (i) a questionnaire administered to the school’s principal, (ii) a teacher questionnaire administered to targeted teachers (grade 3 and 4), (iii) a Math test and French test (with different parallel booklets) administered to students in the targeted teachers’ classrooms, and (iv) an inspection bulletin administered to targeted teachers in the context of two lessons, one in French and one in Math. Table A.4 presents a more detailed description of the various instruments.

All grade 2 and 3 students in the sampled schools were assessed at baseline (May 2012), and all students in grades 2 to 4 were assessed at midline (May 2013). Because of budget constraints, a random sample of up to 25 students were assessed from each class in grades 3 and 4 at endline (May 2014). The surveys reached a varying number of schools across the different rounds (Table A.5). For example, we have student assessment data for 408, 417, and 391 schools for baseline, midline, and endline rounds of data collection, respectively. It is unclear the source of the variation in number of schools; however, it is likely that, at least for the endline
syndicated lending, and lower loans-to-deposits and equity-to-assets ratios tend to be associated with a greater propensity to become the lead bank. The IV regression 5 fails the overidentification test, but IV regressions 6-8 pass the reported specification tests. Overall, Table 6 suggests that stricter capital regulation and greater supervisory power discourage a lead bank role consistent with regulatory arbitrage, while such a role is facilitated by a greater rule of law.

# **5.   Conclusions**

In this paper, we investigate how international regulatory and institutional differences affect lending in the cross-border syndicated loan market. The syndicated loan data enable us to see whether a multinational bank provides a cross-border loan directly from the parent bank or indirectly through a foreign subsidiary. Lending provided through a foreign subsidiary is subject to subsidiary-country regulation and institutional arrangements. International banks thus can engage in regulatory arbitrage by originating loans through foreign subsidiaries located in countries with relatively lax regulation rather than through their parent banks.

We find evidence that international banks’ usage of foreign subsidiaries is in part driven by international regulatory differences. In the case of loans aggregated at the borrowercountry level, we find that stricter capital regulation in the borrower country negatively affects the share of loans provided through foreign subsidiaries. When considering syndicated loan data at the bilateral level, we find that the subsidiary loan share is positively related to capital regulatory stringency and the strength of official supervisory power in lender countries. In addition, we find that stricter capital regulation and greater supervisory power discourage a lead bank role in an international loan syndicate. These results suggest that international banks prefer to operate in locations with relatively lax bank regulation and 26
, the RIM approach is often limited in its ability to measure housing affordability in low income and developing country contexts due to the lack of reliable income data as well as appropriate budget standards (i.e., the minimum food and non-food expenditure that is necessary to maintain a good standard of living). As with most developing countries, in Pakistan data on incomes is difficult to collect and not particularly informative to gauge welfare, owing to high informality, seasonality (relevant for large agriculture sectors) and irregularity (such as reliance on foreign remittances) of incomes, as well as widespread production for home use. Therefore, consumption expenditure is used as a proxy for household welfare in Pakistan.

The REM approach in Pakistan relies on two key components to estimate housing affordability: (i) households’ non-housing consumption<sup>17</sup> aggregate, as a proxy for residual income, and (ii) a minimum socially acceptable standard for non-housing residual expenditures, or a ‘non-housing poverty line.’ This differs from the traditional RIM method in two ways. First, to determine households’ current level of wellbeing, it uses survey-based household consumption data as a proxy for household residual income. Second, in place of a predetermined budget standard, the Cost of Basic Needs (CBN) method is adapted to establish a minimum threshold of non-housing needs. Putting the two together allows us to identify households living in unaffordable housing conditions. Households are considered to be living in

> 16 The logic behind this is that the economies of scale embedded in general household consumption patterns (housing and nonhousing) are not necessarily the same as those with only housing consumption or only non-housing consumption. For example, a couple arguably needs less than twice the amount of a single person to achieve the same living standard, because the couple can share some costs, notably of housing. Further, a single person and a couple are likely to have the same (or similar) housing needs/costs, whereas the non-housing consumption for a
(CP), People of India (PoI), Aspiration India (AsI), and Income Pyramids (InP). This study uses the CP, InP, and PoI sections. The CP is a household-level monthly survey reporting household expenditures on various kinds of goods and services, without a breakdown of individual members of multi-person households. Specifically, it asks households about their monthly expenditure on all types of transport, including expenses on a combined category of “buses, trains, and ferries” (BTF). Our study period for the CP is from November 2020 to September 2021. The InP is a monthly survey that tracks the income of each household member. We use the same study period as in the CP data. The PoI is an individual-level survey conducted every four months. There are three waves a year: January-April, May-August,

> 7The CMIE started to collect household members’ time usage information in the wave of SeptemberDecember 2019, while the government of Delhi started the _Pink Slip_ scheme in November 2019. We do not include Delhi in the analysis since we do not have pre-period information on time usage.

5
productivity differences across countries.

Another assumption of neoclassical trade models is that the number of goods equals the number of factors of production. The existing literature interprets the constant in the empirical equation as capturing the average effect of all omitted factors of production. The inclusion of country fixed effects consequently is then equivalent to assuming that the number of omitted factors of production can vary across countries. We estimate the model with and without fixed effects (see Fitzgerald and Hallak (2004) for a discussion of this issue).

We are interested in exploring differences in _β_ s between different subsets of countries: high income vs. low income, capital or skilled labor abundant vs. unskilled labor abundant. To account for a common error structure, we interact all the regressors with the indicator variable for the each such group. This is essentially a test of the assumption that all countries utilize the same technologies for each industry (after adjusting for cross-country TFP differentials as in Fitzgerald and Hallak (2004)) or they belong to the same diversification cone.

# **2.2 Data**

The data set used in this paper contains information on net exports in 28 three-digit ISIC manufacturing industries, endowments of capital and arable land, employment of skilled and unskilled workers for a sample of 129 countries over the 1975-2010 period. The Data Appendix provides details on the data set construction and highlights the limitations on the country and time coverage of the sample imposed by data availability. One such limitation is the availability of educational data in five-year increments. Thus, the data set is a panel of countries by industry over 5-year periods, with factor endowments measured at the initial year of each period, and net (gross) exports averaged over 5-year period.

We construct employment of skilled and unskilled workers using population and educational attainment data from Barro and Lee (2010). Skilled labor refers to workers between ages 25 and 64 who completed high school (secondary) education. The data on trade 8
consolidated codes and concordances are available at http://econ.worldbank.org/exporter-dynamics-database. 22 The number of HS 6-digit codes not affected by the consolidation process is 4559, obtained as the total number of codes 6065 minus the 1104 codes that disappeared and the 402 codes whose content changed. Despite this consolidation of HS codes, for the countries for which the data providers allow the sharing of the raw data, the country input database with the original HS 6-digit classifications is also available upon request.

11
income on the utilities. The implication is that any social policy aimed at subsidizing the cost of a subsistence basket of services need probably only focus on the poorest of the poor.

2.15 Regarding expenditure on public transport services, it is important to note that comparisons between the 1997 and 2002 household expenditure surveys are complicated by the fact that the former survey lumps together expenditure on public and private transportation (Figure 3). Nevertheless, since vehicle ownership is concentrated in the upper end of the income distribution, this need not represent a major problem for comparing the expenditure patterns of the poor. A more serious limitation is that the 2002 survey only reports transport expenditure for commuting journeys of the head of household, and thereby represents only a lower bound on public transport spending. A comparison between the two surveys shows that the budget share of public transport services more than doubled between 1997 and 2002. It rose from an average share of 4.3% in the 1997 survey to 8.1% in the 2002 _(even though the latter only includes commuting journeys for the head of household)_ . This jump is largely explained by the fact that public transport tariffs rose between 35% and 40% in real terms since 1997, while per capita household income fell by a third over the same period.

**Figure 3: Affordability of public transport in the Metropolitan Area**

_Source:_ Family Expenditure Survey, May 1997 and OPSM, November 2002 <!-- Start of picture text --> 70.0 12.0<br>Public Tranport<br>Expenditure,<br>60.0 Buenos Aires<br>10.0 1997<br>50.0<br>8.0 Public<br>Transportation<br>Expenditure,<br>Buenos Aires<br>40.0 2002<br>6.0<br>30.0 Public<br>Transportation<br>Expenditure as %<br>4.0
the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38. Permanent job is defined as working fulltime/parttime with permanent contract (the reference group is fixed-term contract and casual employment). Skilled occupation is defined as jobs with occupational scales above medium.

28
forthcoming).<sup>12</sup> Rather than indicating policymakers are doing a bad job, it could show that nature makes their job harder than elsewhere.

Furthermore, various composite indicators of the trade environment are closely correlated with the level of development. This is most clearly reflected in Figure 5 which plots the 2009 World Economic Forum Enabling Trade Index (ETI) against log GDP per capita – a proxy for development. The fitted (quadratic) line is also included. Similar plots based on logistics quality and time delays are presented in Appendix 2.

Analogous to our benchmarking of trade flows earlier in this paper, we benchmark SADC‟s trade environment against other countries with a similar level of development. For example, Figure 5 reveals high ETI values in many SADC countries relative to their GDP per capita. This suggests that these countries are not unusually constrained by their environment relative to their peers.

**Figure 5: Overall Enabling Trade Index against log GDP per capita**

<!-- Start of picture text --> Overall Enabling Trade Index<br>Mauritius<br>Botswana<br>Na mi bia South Africa<br>MalawiMadagascar<br>Zambia<br>Moza mbiq Tanz Le sotu a e nia ho<br>Zimbabwe<br>4 6 8 10 12<br>ln(GDP per Capita), 2005 PPP<br>6<br>5<br>4<br>3<br><!-- End of picture text --> Source: Team calculations using the World Economic Forum Enabling Trade Indices (ETI) obtained from the WEF Global Enabling Trade Report (2009) and GDP data from the World Bank World Development indicators. Notes: ETI indicators range from 1 (worst outcome) to 7 (best outcome). Values for Botswana are obtained from the 2010 edition Table 8 presents the results of a benchmarking exercise to identify the relative performance of SADC in terms of its institutional and other obstacles to
# **TABLE 2. VAT PRODUCTIVITY a/**

|Country|ProductivityRate|
|---|---|
|Peru(1994-1998)|0.32|
|México(1990-1997)|0.26|
|Uganda(2000)|0.25|
|KRA(1996-2000)|0.19|

_Source: Author’s calculations. a/ Calculated net of VAT collections on imports._ The cases show impressive increases in tax effort in Peru and Uganda, coinciding with the introduction of the revenue authorities there, and some progress in Venezuela and South Africa, which also coincides with the ARA reforms. They also show that tax effort declined in Kenya, though the data would suggest that this occurred primarily as a result of tax policy changes. In Mexico collections stagnated and then declined; the data suggest that tax administration rather than tax policy were to blame. Though revenue collections is a very crude indicator of administrative performance, owing to the large number of exogenous factors influencing collections, the cases show that the ARA reforms are associated with significant improvements in revenues in some cases.

# _Taxpayer Registration and Compliance Management_

The foundation of compliance is the ability of the tax agency to keep a current registry of taxpayers to ensure that they are submitting their tax declarations and making their tax payments according to schedule. Equally important is the ability of the agency to broaden the tax base to cover all the taxpayers who legally ought to be paying taxes. The cases show that Peru and South Africa have done well here, and that Kenya has made some strides in the area of taxpayer registration. The other cases demonstrate less progress.

Peru was one of the most successful in broadening the tax net, as the number of active registered taxpayers increased from 895,000 in 1993 to 1,766,000 in 1999. At the same time, the quality of information taxpayer registries is often a major problem. Peru, however, continually cleaned its registry, and, from 1995 to 1998
lack detailed expenditure or income information required for producing estimates of poverty. Small area methods attempt to exploit each data’s attributes to obtain estimators that can be used at dis-aggregated levels.

Poverty at lower geographical levels can be used to identify areas that are in need of attention, or that may be lagging behind the rest of the country. For example, the Government of Ecuador after an earthquake that occurred on April 16, 2016 relied on small area estimates of poverty to decide where help was needed most. The small area estimates of poverty for Ecuador, which were released not long before the earthquake, proved to be an invaluable resource for the rebuilding effort in the country.

One of the most common small area methods used for poverty estimates is the one proposed by Elbers, Lanjouw, and Lanjouw (2003, henceforth ELL).<sup>3</sup> This methodology has been widely adopted by the World Bank and has been applied in numerous poverty maps<sup>4</sup> conducted by the institution. In its efforts to make the implementation of the ELL methodology as straightforward as possible, the World Bank created a software package that could be easily used by anyone. The software, PovMap (Zhao, 2006),<sup>5</sup> has proven to be an invaluable resource for the World Bank as well as for many statistical agencies, line ministries, and other international organizations seeking to create their own small area estimates of poverty. The software is freely available and has a graphical user interface which simplifies its use. Nevertheless, more advanced

> 1For example districts, municipalities, migrant populations, or populations with disabilities.

> 2Poverty is a nonlinear function of welfare, consequently small area estimation methods of linear characteristics are invalid (Molina and Rao, 2010). A proposed solution to this problem is to use Monte Carlo simulation to obtain multiple vectors of the measure of interest (Elbers, Lanjouw, and Lanjouw, 2003).

> 3For a detailed discussion of
<!-- Start of picture text --> 35<br>i ey AeSs<br>~ e—_——""_J —_—_—_<br>pe © Low Income<br>%Se Oe eee<br>i ‘. ~~ a‘\VS —t—High Upper-Middle Income Income<br>-10 YX<br>-15<br>8 4 108<br>6 4<br>; 106<br>“Tt ars —_ if + 104<br>Gee “i, J 102<br>»205g% 2006q1 2007q1 2008q1 2009q1 2010q1 100<br>; 98<br>8 - 94<br>Change in GOP (left axis)<br>——— GDP Volume, Quarterly, base 100 in 2005<br><!-- End of picture text --> Source: BEA and IFS, International Monetary Funds. GDP % change based on chained 2005 USD, Quarterly, Seasonally adjusted annual rates
while varying this threshold. The point estimates on aid disbursements, illustrated by the blue bars in Figure 4, suggest a strong positive correlation between aid-dependence and aid diversion. One the one hand, when we lower the threshold to 1% (sample of 46 countries), the estimate falls to around 1.8%, which is not significantly different from zero. On the other hand, raising the threshold to 3% (sample of 7 countries), the estimate increases to a highly significant 6%. The implied leakage rates, illustrated by the red line in Figure 4, exhibit an even stronger gradient: from a leakage rate of around 4% with a threshold of 1% to more than 15% with a threshold of 3%.<sup>18</sup> The steep gradient in leakage rates has several important implications. First, it suggests that our estimate of leakage out of aid disbursements to the main sample of highly aid-dependent countries is a poor estimate of leakage out of aid disbursements more generally. The 22 highly aid-dependent countries in our main sample account for around 10% of the aid disbursed by the World Bank and the results in Figure 4 suggest that leakage rates are much lower (if not zero) for less aid-dependent countries. Second, it constitutes novel evidence that aid capture may be more salient in underdeveloped and poorly governed countries, which are also most in need of development assistance (Alesina and Weder, 2002). While this association may simply reflect that the combination of poor development and bad governance stimulates foreign aid, it is also consistent with the view that very high levels of aid may foster corruption and institutional erosion (Knack, 2000; Djankov et al., 2008).

# **4.5 Publicly available data**

Up to this point, we have conducted the analysis with a restricted dataset from the BIS that allows us to break down each country’s total foreign deposits, which is public information, into deposits in havens and deposits in
of the increase in women‟s labor force participation (over and above that which is generally true in similar circumstances) is due to conflict alone.

# **IV. Conflict and Women’s Employment**

# _<u>Data and Descriptive Statistics</u>_

This study‟s employment data come from the Nepal Demographic and Health Survey, a large nationally-representative sample of women aged 15-49 and the members of their households. We used the three most recently-available waves of the Standard DHS for Nepal: 1996, 2001, and 2006. These waves correspond with the beginning, middle, and end of the civil war. The DHS surveys provide detailed information on the variables we required including woman‟s employment status, education, age, marital status, region and terrain of residence, religion, and ethnicity; her husband‟s education and his presence in the household; and indicators of household composition, household access to electricity, and household amenities. Our working sample retains all ever-married women aged 15-49 with measured values for employment status and for the other indicators in the empirical analysis, leaving us with approximately 25,700 observations in the pooled sample. Note that the indicator for whether or not a woman is employed includes employment for cash earnings, in-kind payments, and nonremunerated work; the data do not allow us to separate these types of work. Among the employed women in the pooled data, a large proportion worked either for themselves or for their family (about 21,800 observations). We classified such women as self-employed and estimated separate regressions for the decision to be self-employed.<sup>4</sup> Sample statistics in Table 2 indicate that a very high proportion of women in Nepal were employed throughout the period, and especially in 2001, when 83 percent of women were employed in some sort of job, paid or unpaid. Table 2 further indicates that over time, a growing 12
The restriction measures described above were implemented in different times and combinations in the two urban areas and therefore had different degrees of impact on the mobility indicators calculated based on CDR data for the three study periods (October 2019, 2020, and 2021). In Bogota, none of the study periods were affected by direct mobility restriction measures: there were no direct restrictions on all trips (peak and license plate), although there were peak and license plate restrictions on private vehicle trips between Monday and Friday. The hours of application of the measure are from 6:00 to 8:30 in the morning and from 15:00 to 19:30 in the afternoon. The maximum allowed occupancy in public transport vehicles was still only 70 percent in October 2020 but already 100 percent in October 2021, and there were some restrictions on non-essential gatherings, capacity of large events, and in-person studies in both October 2020 and 2021. In AMBA, the October 2020 study period was still affected by both travel restriction measures (last weeks of enforced social, preventive and mandatory isolation (ASPO)) and restriction on the use of public transport, which was only allowed for essential workers. The October 2021 study period was no longer affected by these constraints.

# _4.3. Planning and transport supply adjustments_

Bogota's public transport system significantly reduced its supply during the periods of mandatory isolation. The first weeks of confinement saw the greatest reductions, with Transmilenio's programmed fleet values reaching 20-25 percent of the usual levels. However, supply did not fall as much as demand, with the objective of maintaining the occupancy conditions established by the public transport distancing measures mentioned above. Public transport supply grew in the second half of 2020, and by October 2020, the number of buskilometers traveled on the zonal, feeder, and trunk lines were already higher than in October 2019. In 2021, the supply of the zonal component continued to grow (in October 2021 being 20 percent above the October 2019 levels in terms of the