rafmacalaba/data-use-mentions-extended huggingface dataset:
, looking at residential and employment densities, wages, land-rents, and building heights We investigate the way these vary with parameters of the model and may produce different urban configurations (mono- or polycentric). A number of hypothetical policy experiments are undertaken, illustrating the effects of transport improvements, height restrictions, and zoning controls. An illustration of the calibration of this model on data for Dhaka, Bangladesh, is given in Sturm et al. (2021), which discusses the data needed for such an application.

# **2.  The benchmark model**

In this section we set out the benchmark model – with a single production sector – first outlining the economic relationships and determination of equilibrium and then (sub-section 2.1) turning to details of implementation and functional form.

# **2.1 Model structure**

_Geography:_ The city occupies area within a geographical space which is divided into a large number of cells (745 in the simulations and figures that follow). We assume that each cell is a hexagon and the space is the lattice of such hexagons.<sup>2</sup> Cells will be referred to by subscripts _i_ , _j_ , and each of these cells has the same land area, . The distance between the center points of two cells is i , and this will underpin all spatial interactions. Within each cell we set ii i = 0. The cells form an (approximate) circle and the city occupies a mm = mm dd subset of these cells. The size of this subset (i.e. number of these cells occupied as places of residence, work, dd or both and hence inside the city) is endogenous, and this set of urban cells is denoted _I_ .

> 1 See Henderson et al. (2021) for a dynamic urban model in which formal sector construction is durable and incurs sunk costs, while informal sector construction is malleable, and achieves high population density by ‘crowding’ rather than by building tall
5.5|5.3|5.5|
|Aryankor<br>|INDEP|5.6|5.2|4.9|6.4|7.0|7.0|6.8|
|Életút<br>|INDEP|4.7|6.2|5.9|6.0|6.0|6.0|6.0|
|Évgyűrűk|INDEP|7.0|6.3|6.6|7.7|8.0|7.6|7.1|
|Weighted Average|MKT|5.6|5.7|5.5|6.4|6.8|6.8|6.5|
|Weighted Average|EMP|6.1|5.9|5.8|6.4|6.6|6.6|6.6|
|Weighted Average|FIN|5.5|5.6|5.5|6.4|6.7|6.7|6.5|
|Weighted Average|INDEP|6.3|6.0|6.0|7.2|7.5|7.3|6.9|

Source: HFSA raw data 38. Administration and record keeping account for the largest share of operational costs, as shown in Table 16. This is followed by wages of MPF personnel, the supervisory fee paid to the HFSA, and the guarantee fee. In contrast to most systems in Latin America, the share of marketing-related costs in total costs has been extremely small, although it is possible that some marketing activities are performed by other companies in the same financial group (i.e., the asset manager or the insurance company) and the associated costs are hidden in the asset management fee. It is also possible that the costs associated with contribution collection are not fully reflected in administration
the districts in Punjab and the Cantonment of Lahore. Sampling for 2003 was stratified by rural, urban, and large city areas. In the follow-up survey in 2007-08 the sample size was vastly

> 12 The sample of eligible schools includes only girls’ schools that offered grades 6-8 at both baseline and follow-up (less than 1 percent of middle schools are mixed). Schools that were nonfunctional upgraded to middle level or above or downgraded to primary or lower level during the program implementation period were excluded, as these would not have been eligible for the stipend at both baseline and follow-up. Of the remaining schools, only those schools that had non-zero enrollment in grades 6-8 at both baseline and follow-up were retained. Finally, although in theory all public schools in Punjab are single-gender, the data shows that a small number of mixed schools exist. These schools were also excluded from the final sample.

> 13 It is important to note that, although lower-level data provide more precise estimates, the questionnaires are significantly shorter; thus, some key economic welfare indicators are excluded.

9
the different sub-samples are stable to this change (results not shown).

I test if reported infant mortality is connected with debt relief at Decision Point of the HIPC Initiative by using DHS data on infant mortality from earlier surveys.<sup>8</sup> Here, I apply a test developed by Kudamatsu (2012) in which the average infant mortality rate of mothers born in the same year, in the same country, and who give birth in the same year is compared between the used DHS and an earlier DHS. If recall bias is a serious problem, the infant mortality rate would change systematically in relation to HIPC at Decision Point. Judging from the results in Table 7, this does not appear to be the case.

Second, if fertility choices vary by women’s socioeconomic status and if this is systematically related to HIPC, one concern may be that the results on debt relief at Decision Point are driven by differences in fertility rather than infant mortality since the women who identify the effect of debt relief in the withinmother estimations are different from other mothers in terms of age, poverty levels, formal schooling, and residency. The comparison of infant mortality between women in HIPCs and non-HIPCs giving birth in the same years in the within-mother estimations would consequently be less credible. Again, I follow Kudamatsu (2012) to investigate if fertility of mothers changes depending on socioeconomic Completion Point before or in 2006 and in the year of Completion Point for HIPCs which reached Completion Point after 2006.

> 8Earlier surveys available for 44 countries in the sample.

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consultancy with some key experts ( _e.g._ , the World Bank team and country experts). We model the disruption duration as a linear interpolation between the three values, as is shown in Figure E2. We can thereby link the amount of damage an asset endured (Section 3.2) to the time needed to restore infrastructure services. We ignore that in some cases, temporary or emergency measures are taken to provide essential services while assets are being repaired or reconstructed ( _e.g._ , emergency health, education or latrines provision).

Taken together, we estimate the _disruption risk_ per asset, expressed in the terms of average expected number of days per year an asset cannot provide services. By multiplying this disruption risk output by the number of dependent households, we provide the _service disruption risk_ , expressed in units of Household Disruption Days (HDD) per year.

Apart from the aggregate service disruption risk, we can also model the service disruption risk to every individual household. That is, we can approximate for every individual household whether it loses access to water, sanitation, electricity (if connected in the first place), schools, health facilities and hospitals. We will utilize this in Section 3.5 to model who loses access to (some of these) essential services, which will ultimately affect their level of MDP poverty during the time it takes to restore infrastructure services.

# **3.4 Impacts on multidimensional poverty**

To estimate the welfare impacts to disaster-affected households, we look at the relationship between disaster impact and MDP in two ways.

- 1) Exposure of multidimensionally poor households: by overlaying the flood maps with the location of households that are considered multidimensionally poor, we can estimate the exposure of multidimensionally poor households. We define exposed households as those experiencing at least 0.5 meter of flood depth at their house. In general, households that are multidimensionally poor are considered to have a higher socioeconomic vulnerability, and hence may suffer disproportionally.

- 2
Chart 19 Share of Countries in Easing and Tightening Phases, including Holds <!-- Start of picture text --> (Percent of sample)<br>100<br>80<br>60<br>40<br>20<br>0<br>1975 1979 1984 1989 1994 1999 2004 2009 2014 2019 2024<br>% Tightening in Tightening Phase % Holding at End of Tightening Phase<br>% Easing in Easing Phase % Holding at End of Easing Phase<br><!-- End of picture text --> Sources: Authors’ calculations based on data from January 1970 through May 2024. Notes: Chart shows share of sample in easing and tightening phases, including the portion of each for which policy is “on hold” before a turning point. “On hold” is the period after the last rate increase during a tightening phase, or after the last rate decrease and end of any QE programs during an easing phase. Movements in the policy interest rate <0.1 percentage point do not qualify as a change.

Is this longer holding period at the end of the current tightening phase unusual? Or is it typical of historical periods when a large share of economies simultaneously tightens monetary policy, and the shocks behind the tightening have a larger global component (as shown in Section IV.2)? To better place today’s long holding period in context, the right side of Chart 18 compares holding periods during the Highly Synchronized Tightening periods (initially introduced in Section IV1.1) to all other holding periods before the pandemic (with results for the pandemic already shown).

Before the post-pandemic tightening, the median length of holding periods did not vary based on the degree of global synchronization (although the bottom panel shows that these holding periods comprised a shorter fraction of the overall tightening phase for the more synchronized periods, which tend to be shorter). The most recent post-pandemic, Highly Synchronized Tightening continues to stand out as
<u>Total 6999</u> Source: Own elaboration based on ES dataset ES data encompass information on selected dimensions of innovation outputs: product and process innovation, organizational innovation and marketing innovation. Figure 9 plots the (sampling adjusted) share of firms that report innovative activities within the selected set of developing East Asian countries. Results suggest that innovation incidence varies substantially across types of innovation outputs both within and across countries.

**Figure 9. Innovation rates across developing East Asian countries ( percent of all firms)**

<!-- Start of picture text --> 50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>Thailand Cambodia Lao Vietnam Indonesia Philippines Malaysia Myanmar<br>Product OR process Product innov_prodNEW (among all firms) Process Organizational Marketing<br><!-- End of picture text --> Source: Own elaboration based on ES dataset Note: tabulations are adjusted by sampling weights In addition, there seems to be a surprisingly negative association between share of firms reporting innovation activities and income level (Figure 10). This might reflect systematic differences in what is reported as innovation across the income spectrum, which could be explained by the fact that innovation outcomes are likely to be subject to biased interpretations, since firms in lower income countries are likely to report marginal innovation or even imitation as innovation.<sup>11</sup> This negative association seems to disappear after accounting for the novelty of reported innovations; when restricting the sample of firms that report product innovation, data suggest that the proportion of those reporting product innovation that are new to the market is positively correlated with income level (Figure 11).

> 11 See Cirera and Maloney (2017) as well as Cirera, Lopez-Bassols and Muzi (2016) for further discussion about these measurement issues.

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linked to noise in the CR data. This is hardly surprising since CR does in fact require a good deal of precision that, no matter how careful the training, will be hard to reach for survey enumerators that are not professional land surveyors. In terms of specific suggestions for CR measurements, we do observe an increase in discrepancy between CR and GPS when the CR closing error is above 3 percent. Translated into recommendations for survey work, this means that 3 percent offers a good rule of thumb for instructing enumerators to re-take CR measurement.

Finally, little research is to date available on the use of remote sensing imagery for area measurement in household surveys. As technology advances and image resolution improves along with affordability, the use of this method becomes more feasible, and is likely to hold promise particularly for the measurement of large plots. Future research on how to effectively integrate remote sensing and household survey data for plot area measurement, including fieldwork challenges and respondent ability to identify plots, is highly encouraged.

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while in contrast, cities like Dar es Salaam and Nairobi have large areas of industrial land or irregular residential (see figure 6 and 8).

This rapid expansion has also impacted the quality of the housing stock. Fast construction has led many buildings to be made with low quality and nondurable material. The UN Habitat estimated in 2007 that some 75% of all buildings had been constructed with mud and wood, and lacked a proper foundation. Furthermore, a startling 97% of all buildings were single story, and only 17% could be considered in good condition. Currently it is estimated that between 70% and 80% of housing units are considered slums, with a bulk of these dwellings located in prime central locations, thus preventing its use for more productive activity.<sup>5051</sup> The land cover classification map of Addis created in this study reflects many of the trends mentioned above. In Figure 14 irregular residential settlements are colored red, and appear to be highly concentrated in and around the city center. In contrast to Nairobi, Dar es Salaam and Kigali, a significant proportion of land in the city center is being occupied by irregular residential <mark>(Figure 14X)</mark> . Within the first 4 kilometers from the city center about 26% of the built‐up land is residential Irregular, with this number dropping off dramatically around to around 8% around 6 kilometers outside of the city center. Pockets of Irregular residential continue to appear towards the city’s periphery, but there are no single large sized slums like Kibera in Nairobi. Industrial and Commercial land (shown in purple in Figure 14) is also found in small pockets throughout the city rather than concentrated in large zones like in Dar es Salaam and Nairobi. This pattern of small pockets of land cover might be indicative of the ‘urban village’ environment described in the literature.

> 50 Un Habitat. 2008. Ethiopia: Addis Ababa Urban Profile.

> 51 UN Habitat. 2007. Situation
identifying those with highest risk of non-compliance. Our findings suggest they should also think carefully about how to target their remaining tools of enforcement. Sending – emails or letters to severe non-compliers individuals or firms with large predicted tax liabilities and who do not file or severely misreport their income – might be innocuous at best or counterproductive at worst, if threats are not followed by enforcement actions. In order to improve targeting, new methods that combine experimental variation, the rich administrative data often available to tax authorities, and machine learning tools are a promising avenue for exploration.

20
The results described above highlight the importance of risk-weights and quality of capital and the effect they may have on bank risk. As capital essentially preserves solvency by absorbing liquidity, information and economic shocks, the quality of capital determines how effective it performs as a buffer. Because lower quality capital is made up of components that are more opaque and subject to information asymmetries, lower quality capital can be severely undervalued during times of distress and may not be able to absorb shocks. Risk weights on banks’ assets are also an important element of risk-based capital ratios. Banks can meet capital requirements by either increasing capital or decreasing risk-weighted assets. Since the current regulations provide substantial discretion to banks in determining risk-weights, a key concern has been that individual banks may try to minimize capital requirements by underestimating the risks of assets on their balance sheets.<sup>23</sup> Consistent with the notion that there is significant discretion in the computation of regulatory capital ratios, Demirguc-Kunt et al. (2013) find that stock returns of banks during the financial crisis were more sensitive to simple leverage ratios using common equity than to regulatory risk-based ratios.

To test these tenets more formally, we examine how bank risk is related to the quality of capital and risk-weights using bank level data. We obtain bank level financial information by merging the now discontinued Bankscope database (1999-2014) with the Bank Focus (2015-2017) database. We limit our sample to commercial banks with at least $10 million USD in assets. Our sample includes more than 20,000 banks in 158 countries. We use the following regression models:

> 23 Jones (2000) discusses techniques that banks can use for regulatory capital arbitrage and provides evidence on the magnitude of these activities in the Unites States.  Le Leslé and Avramova (2012) provide evidence of heterogeneity with respect to the calculation of risk-weights.  Recent growth in financial innovation and financial engineering may have also made it easier for financial institutions to manipulate regulatory risk measures. Acharya, Schnabl, and Suarez (2013)
using India’s applied MFN tariffs in 1990, inconsistent with the theory for applied MFN tariffs _only_ in 2000-2002, but consistent again with the theory when using India’s TTBs _in addition to_ the applied MFN tariffs in 2000-2002. This evidence suggests that, over time, India unwound some of its applied tariff reductions by substituting toward antidumping and safeguard policies.

The last of our thematic questions is whether, when countries apply import protection through TTBs, their applied policies discriminate among different trading partners. While there are different ways to examine this issue, Table 6 presents two measures - the trade-weighted share of the exporting country’s total exports to the G20 economies over which the G20 economy had a TTB imposed, and the estimated value of those TTB-impacted exports to the G20 economy.<sup>56</sup> We compute these two measures both in 2013 for the G20 economies and then, for rough comparison purposes, also in 1995 for the ‘G4’ economies of Australia, Canada, the EU and the US - the major TTB users at the time.

To interpret Table 6, consider an exporter like China. In 2013, 7.1 percent of China’s exports to the G20 economies were subject to a TTB, and this is estimated to cover roughly $100 billion of

> 56These data are derived from dynamic import coverage ratios following the methodology described and applied in Bown (2011b, 2013b). The main requirement is an assumption on counterfactual import growth for products from trading partners subject to an imposed TTB during the period that the TTB was in effect. The current data relies on the relatively conservative assumption that TTB-impacted products would have grown at the average rate of import growth for non-TTB impacted products.

32
8 assets are utilized and combined and include both institutional as well as economic factors. In this study, we consider the following institutional indicators, which capture the efficiency with which production can take place; as well as economic indicators, which also capture the efficiency of economic organization:

- a. _institutional development indicators_ - indices on: voice and accountability (VA); political instability and violence (PIV); government effectiveness (GE); regulatory burden (RB); rule of law (RL) and control of corruption (CC). An increase in a given index measures an improvement in the relevant indicator. Hence, they are expected to have a positive impact on income and possibly growth.  These indicators were estimated by Kaufmann, et al, 2005.<sup>7</sup>

- b. _economic indicators_ - trade openness (TOPEN) is calculated as the ratio of exports and imports to GDP (World Bank, 2005b); and the country’s domestic credit to the private sector as proportion of GDP (PCREDIT), which represents private sector investments (Beck, _et al._ , 1999)<sup>8</sup> .

Two methods of incorporating the impact of these institutional and economic indicators were investigated. The first method involved the derivation of residuals from the regression of a nested CES production function. The residuals are the part of income not explained by the wealth components – physical capital, human capital, land resources and energy resources, and are regressed on the identified institutional and economic indicators. By using this method, however, a statistically significant correlation between the residuals and any indicator would imply that relevant variables have been omitted in the estimation of the nested CES production function. Thus, the estimated coefficients of the nested CES production function earlier derived will be biased and inefficient (Greene, 2000). Hence, another method is considered to be more appropriate. The influences of the institutional and economic indicators on income will be incorporated into the efficiency parameter of
; and (4) how the findings from the _LAYS_ approach, which relies on a multiplicative combination of quantity and quality, would compare with those of a linear approach that has been used on subnational data. The section concludes by comparing what these different approaches—existing and potential—would imply for the size of cross-country human capital gaps.

> 17 The value of learning achievement in the numeraire country L� (the Grade 8 average mathematics score for Singapore) used in Figures 2.1 and 3.4 is 621, so replacing that by 625 would barely change the results presented.

25
sampling to save the travel cost [cancel out the cluster-specific errors]. Further, because _rJ_<sup>_∗_[</sup><sup>_r_</sup> _J_<sup>+]</sup> is an increasing [a decreasing] monotonic transformation of _ρ_ , the signs of its partial derivatives with respect to _c_ , _wc_ , _τ_ , and _wh_ are the same as [the opposite of] those in Lemma 16.

It is also interesting to note that the number of households to be sampled in each cluster is the same between the main and dual problems as the following equation shows: Both equations show that the cluster size tends to get smaller when the errors due to the cluster-level variations become more pronounced. On the other hand, the cluster size tends to get larger (and the number of clusters to be visited get smaller) when the travel cost is larger. The ratio _K_ 2 _/K_ 1 = 1 _/_ ~~�~~ (1 + _wh_ ) _τ_ , which is no less than one when eq. (21) is satisfied, represents the change in the cluster size when one switches from optimal single sampling to optimal double sampling. When the parameters are unfavorable to double sampling (i.e., when _wh_ and _τ_ are high), the ratio of cluster sizes tends to be smaller.

# **4.2 Realistic estimates of gains from double sampling**

Let us now evaluate the potential benefits of double sampling using a realistic set of parameter values ( _c, τ, wc, wh_ ) taken from existing surveys. While the parameter values are clearly context-dependent and cannot be readily extrapolated to other contexts, this exercise is still useful as it gives practitioners a sense of how much they could reasonably expect to gain from double sampling. It also facilitates comparisons between the sample direct estimator and the optimal single sampling estimator.

Ideally, all the parameter values should come from a single survey. However,