rafmacalaba/data-use-mentions-extended huggingface dataset:
5 Conclusion In this paper, we provided new estimates of the evolution of city form along three dimensions -- horizontal expansion, infill development and vertical layering. Our main finding is that the evolution of city form follows the broader processes of economic development. As cities go through economic development, they attract people, expand their footprint, and build upwards. Not only do rising incomes increase demand for built-up area and floor space among families and firms, but also rising incomes are associated with stronger land and market institutions, construction capabilities, as well as financial maturity to pay for the lumpy upfront cost of infrastructure.

We have used new sources of satellite data to describe devolution of city form over the past 25 years as well as provide the first glimpse at an exciting new data set that can tell us about the height distribution of cities. We have also provided a new measure of population density which we called the floor area adjusted population density (FAPD) that takes into account floor area based on heights of buildings. We find that distinguishing between FAPD and naive population density is important as one can distinguish between cities that are crowded and those that are livable. More broadly, we find that densities in general decline with economic development as people and firms demand more space and amenities and transportation improvements allow for the city to increase its spatial reach.

Our econometric analysis shows that incomes are a central driver for providing floor space. In fact, the heights of buildings are very sensitive to income growth. In the absence of income growth, cities with rising populations will accommodate people by crowding. The evidence we provide here is a first step towards understanding how new satellite data can help us understand the evolution of city form. These data can form the cornerstone of new and exciting research looking at the impact of urban regulations, construction costs, and transport improvements on city form and its implications for productivity, livability and sustainability.

25
labor, and business-government relations. It also collects basic information on the firm, including age, size, and geographic coordinates.

Notably, the most recent Enterprise Surveys included a new Green Economy module. This unique module gathered information on key aspects of firm behavior related to the environment and climate change, including green management practices and green investments. In most economies, the response rate for the Green Economy module was over 95%. We thus have a representative snapshot—stratified by sector, firm size, and region—of firms’ green credentials in each of these countries.

We obtain data on our dependent variables, number and type of climate mitigation measures, as well as green management practices, credit constraints and firm-level controls from the Enterprise Surveys. We discuss these in more detail in the next subsection.

# _Bank-level data: Banking Environment and Performance Survey and Bureau van Dijk’s ORBIS database_

The geographical coordinates of 137,407 branches, operated by 1,788 banks across the countries in our sample, were collected by specialized consultants as part of the second round of the EBRD Banking Environment and Performance Survey (BEPS II). Data collection took place by contacting banks or by downloading data from bank websites. All information was double-checked with the banks as well as with the SNL Financial database. The 1,788 banks represented more than 95% of all bank assets in these 30 countries in 2013, so we have a near complete bank branch footprint. For each branch, we know the bank it belongs to. We merge this information with bank balance sheet information from Bureau van Dijk’s (BvD) ORBIS database. We download information about each bank’s pre-financial crisis assets in 2007.

# Variables

# _Dependent variable: Climate mitigation measures_

The Enterprise Surveys asked firms whether they adopted any of the ten different, potentially green, measures over the three years prior to the survey. The list included: machinery and equipment upgrades; vehicle upgrades; more climate-friendly energy generation on-site; heating and cooling improvements
Working Paper No. 6961, World Bank, Washington, DC.

Prydz, Espen Beer, Dean Jolliffe, Christoph Lakner, Daniel Gerszon Mahler and Prem Sangraula., "National Accounts Data used in Global Poverty Measurement", World Bank Group Global Poverty Monitoring Technical Note, no. 8., March 2019. Prydz, Espen Beer, Dean M. Jolliffe, Christoph Lakner, Daniel Gerszon Mahler, and Prem Sangraula. “National Accounts Data used in Global Poverty Measurement.” Global Poverty Monitoring Technical Note 8. Washington, DC: World Bank. 2019. <u>https://ideas.repec.org/p/wbk/wbgpmt/8.html.</u> Ravallion, M., 2003. “Measuring aggregate welfare in developing countries: How well do national accounts and surveys agree.” The Review of Economics and Statistics, 85(3), pp.645- 652.

Takamatsu, Shinya; Yoshida, Nobuo; Ramasubbaiah, Rakesh; Fatima, Freeha. 2021. “Rapid Consumption Method and Poverty and Inequality Estimation in South Sudan Revisited.” Global Poverty Monitoring Technical Note;No. 18. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/36540 License: CC BY 3.0 IGO.” 27
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE medium term to attract a more significant volume of private finance, particularly in the energy sector (for the development of a gas-fired plant), thereby helping to liberate public funds for other pressing social needs.

**Figure 27. Angola needs to attract more private investment, in particular beyond the ICT sector**

<!-- Start of picture text --> 3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power ICT Transport Water<br>% GDP<br><!-- End of picture text --> _Source:_ PPI Database, World Bank, 2010. _Note:_ GDP = gross domestic product; ICT = information and communication technology.

Selecting optimal technology choices could reduce the funding gap by three-quarters. Adopting lower-cost technologies could substantially reduce the cost of meeting infrastructure targets, and reduce the funding gap. If Angola could strategically expand its power trade, this would reduce the resource deficit of the power sector, lowering power needs from $785 million per year to $485 million per year, leading to savings of $300 million annually. Similarly, meeting transport connectivity standards using lower-cost road-surfacing technologies (such as single-surface treatment) could reduce the associated price tag from $423 million to $241 million. The overall savings from these measures would amount to $647 million, and would eliminate Angola’s funding gap (table 21).

**Table 21. Potential savings from innovation**

<u>$ millions</u>

||Before<br>innovation|After innovation|Savings|Savings as % of<br>sector funding<br>gap|Savings as % of<br>total<br>funding gap|
|---|---|---|
in the labor force. This is further confirmed by the low expected sanctions and lack of association between women’s work and expected sanctions, as shown in **<u>Table A.10, Annex A</u>** <u>. These findings are consistent with</u> the descriptive results that show unanimous high acceptance among respondents for women’s mobility and being in the public space, and less so for norms that challenge the status quo related to traditional gender roles. However, these results also show that when considering all elements of social norms together, the relative strength of the relationship between these elements and women’s work outcomes can vary.

We further contextualize findings in Table 5 by unpacking the two key items within the gender roles within the household index ( **Table 6** ). For the female caregiver norm, women’s conservative beliefs are negatively associated with their own employment outcomes, but not men’s beliefs on this issue. On the other hand, for the male breadwinner norm, men’s conservative beliefs negatively correlate with women’s work outcomes. In addition, women’s beliefs that their reference group holds conservative views on this issue also negatively correlate with their work outcomes. **<u>Tables A.12, Annex A</u>** include these results for each norm within the public space index. When looking at the full sample, we find no association of beliefs and social expectations about each norm that forms the public space index with the outcome variable – women’s paid work outside, which in line with results of Table 5.

**Table 5. Individual-level regressions of social norms indices predicting women’s work outcomes**

|||**Full samp**|**le**||**Females o**|**nly**||**Males on**|**ly**|
|-
and large categories that would also potentially reflect differential in their costs of raising non-deposit external funds. The six largest commercial banks"<sup>0</sup> were classified as banks in the large category and ten regional banks" as banks in the small category. The next step is to compare the responses of primary assets (bank loans, securities) of each bank group to a monetary contraction using the impulse responses in the standard vector autoregression (VAR).

In our case, the VAR approach was applied to monthly Korean data from January 1993 to May 1998 ( 65 observations) because of the paucity of relevant data. The shorttern RP rate over which the Bank of Korea has direct leverage in the short-term may be a

- 10 The six largest commercial banks include Cho Hung Bank (39,551 billion Won), Commercial Bank of Korea (32,498 billion Won), Korea First Bank (31,771 billion Won), Hanil Bank (34,470 billion Won), Bank of Seoul (29,655 billion Won) and Korea Exchange Bank (38,594 billion Won). The amount in parenthesis indicates the total assets of relevant bank as of the end of 1997.

Ten regional banks include Daegu Bank (10,713 billion Won), Pusan Bank (10,168 billion Won), Chung Chong Bank (4,909), Kwangju Bank (6,182 billion Won), Bank of Cheju (1,475 billion Won), Kyungki Bank (8,258 billion Won), Jeonbuk Bank (2,532 billion Won), Kangwon Bank (3,435 billion Won), Kyongnam Bank (7,034 billion Won), Chungbuk bank (2,970 billion Won). The amount in parenthesis indicates the total assets of relevant bank as of the end of 1997.

_15_
</mark> hare of each occupation in total provinceindustry-year employment, we link the skill measures of each occupation obtained from STEP to the Sakernas database: � <mark>In equation (1), ω</mark> ��� <mark>is the share of occupational group</mark> _<mark>i</mark>_ <mark>in province</mark> _<mark>p</mark>_ <mark>, industry</mark> _<mark>j</mark>_ <mark>, and year</mark> _<mark>t</mark>_ <mark>total</mark> �� <mark>employment, and s is the value of skill measure</mark> _<mark>k</mark>_ <mark>for occupational group</mark> _<mark>i</mark>_ <mark>. When constructing skills measures by demographic groups –women, men, high-educated workers and low-educated workers—we use the occupational shares of each specific demographic group in each province, industry and year total employment:</mark> �� In equation (2), <mark>ω</mark> ��� is the share of occupational group _i_ in province _p_ , industry _j_ , and year _t_ total <mark>employment of demographic group</mark> _<mark>g</mark>_ <mark>.</mark> <mark>Table 1 provides a description of the skill content of each occupational group. Professionals and technicians, and administrative and managerial workers have the highest content of non-routine analytical and non-routine interpersonal skills. On the other end of the scale, production workers, transport and equipment operators and laborers, and sales and services workers have the lowest content. Sales and services workers, and agriculture, animal husbandry, forestry workers, fishermen, and hunters use non-routine manual
(those who enrolled and finished the bootcamp) with individuals in the control group who took an alternative course. Figure 5 shows the distributions of such scores. When we compare the scores, the treated group shows a score that is about one point higher (18 percent, 0.3 standard deviations) relative to the control group ( _p_ = 0 _._ 011). This can be taken as a piece of evidence that may indicate that the bootcamp evaluated in this study is of higher quality than the alternatives taken by some individuals in the control group.

# **4.4 Employment in Technology**

Having shown that the program has strong positive effects on skills, the next step is to investigate whether beneficiaries actually use the program as a mean to help them transition into the technology sector. In order to understand this, we analyze the program effects on three outcomes of interest. First, a dummy variable that indicates whether, in February 2020 (5 months after the intervention ended in Argentina, 2 months in Colombia) the individual is working on a job that requires coding (we call this variable "In _tech_ working"). Second, a dummy that indicates whether at follow-up the individual is acquiring formal tertiary or university education on a computer science related field (we call this variable "In _tech_ studying"). Last, we create a dummy that subsumes the previous two by taking a value of 1 if the individual is either working or studying in tech, 0 otherwise (we call this variable "In _tech_ (working or studying)").<sup>29</sup> Data Appendix A.1 explains in detail how these variables were measured and constructed. As described in section 3.1, the employment outcomes that we analyze in this section and in section 4.5 use retrospective data collected in May 2020 but referring to February 2020. This is to identify the bootcamp impacts on the main employment outcomes before the COVID-19 outbreak affected labor markets in Argentina
possible, but woreda targeting can be compared to some other alternatives. A data-driven national targeting strategy requires data for the entire nation that is correlated with undernutrition. The profile of undernutrition, based on correlates is well-documented [9,14,30] and useful, as some of these can be used as targeting instruments, and some can even be argued to have a causal relationship. Some of these might be more efficient targeting instruments as they can be observed locally and do not require a full household survey and imputation models. For instance, access to health facilities could be argued to have a causal impact and could, potentially, be used as a targeting mechanism. Traveling distance to the 10
,827 kcal. This suggests a substantial disparity in calorie consumption between the most impoverished and least impoverished individuals in rural settings. The trend shows a gradual increase in calorie intake across the deciles, with each decile having a higher average intake than the previous one.

**Table 1: Distribution of calorie intake (kcal/person/day) by per capita consumption deciles and urban/rural location, 2021-22**

|**PCER deciles**|**Rural**|**Urban**|**Madagascar**|
|---|---|---|---|
|1|707|669|696|
|2|1,120|1,051|1,098|
|3|1,380|1,211|1,330|
|4|1,596|1,415|1,535|
|5|1,793|1,540|1,700|
|6|1,983|1,709|1,868|
|7|2,203|1,776|2,010|
|8|2,487|2,046|2,253|
|9|2,859|2,309|2,543|
|10|3,827|3,451|3,572|
|Madagascar|2,112|2,227|2,167|

Source: Staff calculations based on Enquête Permanente Aupres des Menages 2021-2022.

3 | P a g e
level of productivity, and a similar share of forecast error variance explained of productivity. This demonstrates similar circumstances to the model-based scenario which simulated data in the case of technology taking the form of a unit-root plus stochastic trend growth specification (Section (3.4). The differenced Spectral VARs are therefore likely producing the least biased IRFs for the response of productivity to technology.

# **_Only the Spectral identifications produce consistent estimates when estimating on data in levels and differences_**

Comparing the impulse responses across the two specifications (productivity estimated in levels versus differences), shows that only the spectral estimators produce consistent impulse responses in each case (the long-run identification is always estimated in differences). In response to a positive technology shock, productivity, consumption, and investment rise, while hours, inflation, and the long rate fall. The confidence bands around the spectral estimates are also tighter than their counterparts. While the Max-Share impulses are mainly consistent, we see differences in the impulse of hours and long rates: both hours and the long rate are negative with productivity in levels and positive when estimated in differences.

NAMS produces contradictory or non-informative impulse responses across all variables except for consumption and investment when estimated on labor productivity in differences. Additionally, the accompanying wide confidence bands of the difference specification precludes us from drawing any concrete conclusions and again manifest themselves in the very low explained FEVD (Table 6). This is to be expected, given the impact of a technology shock on productivity _growth_ after 10 years is understandably small and uncertain.

29
All national research and federal universities have been directed to become autonomous institutions.<sup>13</sup> However, start-ups from such not-budgetary institutions lose the special privileges and government support under Federal Law 217. Start-ups that fulfill MOES requirements<sup>14</sup> to belong to the Register of Notifications on Established Companies can use, for example, the Simplified Taxation System and get a potential reduction of the insurance contribution from 34 percent to 14 percent. This reduction would help start-ups lower costs to hire skilled labor.

The Russian legal regime for IP and technology transfer is developing incrementally as the Ministry for Economic Development and the MoES work on amendments to address inconsistencies and imprecision. According to government officials, Federal Law 217 is seen as a test to gauge the demand for publicly funded IP to start new companies. Yet according to experts, the market for IP in Russia is very small, there is no expertise for IP valuation and there is virtually no demand for IP from the market.

# **III.1. Research Commercialization through Technology Transfer Offices**

International experience shows that technology transfer offices (TTOs) may play a central role in accelerating the commercialization of public research. Their main role is to match the “universities’ supply” of potentially commercial ideas with businesses’ “demand” or needs. As such, TTOs are responsible for managing the IP that emerges from public research – evaluating its commercial potential, identifying potential users in the business community, and defining the best form of research commercialization (spinoffs, licensing, etc.).

To address the lack of information on the particularities of Russian TTOs, the World Bank and the MoES engaged in a joint initiative to survey existing TTOs in the Russian Federation. The survey was conducted between January and May 2011 covering the 112 registered TTOs by the MoES, with a response rate of 61 percent of the TTOs providing accurate contact information. The following paragraphs present the survey results.

The majority of TTOs were created between 2003 and
to use the same “financial diaries” method to examine the finances of poor Americans, also finding that they had precarious lives that were not captured in existing data (Morduch and Schneider, 2017). Both books draw on the diary technique to construct “hard facts” but couple this with qualitative analysis of the interview transcripts to provide powerful picture of how poor people in very different parts of the world cope with uncertainty and risk.

Sociologists routinely draw on narrative data from open-ended interviews to address important questions. An important example is Michele Lamont’s book on _The Dignity of Working Men_ (Lamont, 2000). One of her goals is to examine the “inner logic of racism” by understanding the “grammar of evaluation” used by employed, lower-middle class, white and black men in the United States, and white and North-African immigrants in France. Her data consisted of 150 two-hour open-ended interviews – 30 each with blacks in the US and North-Africans in France, and 45 with whites in both countries. She conducted all the interviews herself to avoid interviewer bias, and to reduce her distance from her respondents. Her key finding is that “workers judge members of the other group to be deficient in respect to the criteria they value the most.” They do this “boundary work” by constructing “mental maps” of similarity and difference. Working class white American distinguish themselves from those from higher social classes by seeing them as less disciplined and lacking in “integrity and straightforwardness.” And they see blacks as “lazy” and with “wrong values.” African Americans, on the other hand, consider the upper-class as lacking the criteria they value the most - “the caring self.” And they see whites as domineering and less compassionate. Lamont argues therefore that “moral criteria can generate strong intergroup boundaries,” though, at the same time, it can help bridge differences with statements like “there are good and bad people in all races.” In France, on the other hand
** **_Mehta vs. Union of India_ on river pollution**

The Ganga Pollution Cases therefore seem to have had a beneficial effect on infant health. Did they do so by reducing river pollution? One necessary condition for this explanation to hold is that the verdict is associated with reduced pollution levels. We test this condition in our reduced-form DD framework, estimating equation 2. Panel A of Table 4 reports regression results with our primary pollutant, BOD, while Panel B shows results for other relevant pollutants.

The BOD results displayed in columns 1-6 uniformly imply a significant drop in pollution. Our baseline estimate, shown in column 1 and corresponding to the full time-period of available pollution data, implies a 53.4 percentage-point drop in the likelihood of exceeding the bathing class BOD standard after the Supreme Court rulings. Prior to the ruling, the likelihood of exceedance in treated districts was 100 percent. The drop in exceedance is consistently observed throughout the post-verdict period, as shown in columns 2-4. It is also robust to an expansion of the control group to include all of India (column 5). Meanwhile, downstream impacts in column 6 mirror those from Table 3, column 7: the nearest group of districts downstream of the treatment group is associated with a large and 17