rafmacalaba/data-use-mentions-extended huggingface dataset:
basis, the capital-output ratio for the infrastructure and government sector in China had reached the same level as in the United States by the beginning of this decade (Figure 15, Panel A). Since then, it has moved steadily above the level in the United States.

# **Figure 15 Capital-output ratios in the United States and China**

A. Infrastructure and government B. Housing <!-- Start of picture text -->
1.2 1.4<br>1.0 1.2<br>1.0<br>0.8<br>0.8<br>0.6<br>0.6<br>0.4<br>0.4<br>0.2 0.2<br>0.0 0.0<br>United States China United Sates China<br><!-- End of picture text -->

_Source:_ BEA Fixed Asset Database and author estimates for China 22
# **2.1 Multiple switching and never-switching: Common issues with MPL data**

Despite their widespread use, there are a number of issues with MPLs that many, if not most, researchers have encountered. One common issue is inconsistent choices, often termed multiple switching behavior (MSB). Take a (hypothetical) subject who chooses “no report” in row 2, but “report” in row 3 of the Allcott and Kessler (2019) MPL. This subject has the preferences ($5, no report) ≽ ($10, report) ≽ ($9, no report) and, by transitivity, could be inferred to prefer $5 to $9. The term “multiple switching” refers to the fact that consistent choices require that the subject switches between the two options at most once, from the MPL option with decreasing (relative) value to the option with increasing value.

In addition to MSB, many subjects exhibit never-switching behavior (NSB). NSB occurs when a subject chooses the same option through the entire price list. Since the researchers typically vary the value difference between the two options across the full support of a reasonable distribution, this behavior often appears implausible. For example, take a subject in Alphonce and Alfnes (2017) (see Figure 1) who chooses to buy con– ventional tomatoes at all prices up to TZS 1000 this would be implausible given that the market price at the time of the experiment ranged between TZS 300 and 400. In the MPL from Burchardi et al. (2021) (also shown in Figure 1), NSB would be an even clearer indicator of irrational choices, since valuations are elicited for a “card” that is directly redeemable for its face value of UGX 1400 in cash, and all subjects should be willing to buy the card for a price below 1400 but not for a price above; in fact, the authors use this as a rationality test when comparing different elicitation methods. In general, however
# 4. Empirical Strategy and Data

## 4.1 _Identification of Sample_

Selection of the sample has been guided by the need to identify schemes that are very similar in characteristics but under different types of institutional arrangements so that the differences in performance across scheme types can be solely attributed to their respective institutional arrangements. Accordingly, Jalanidhi schemes have been matched with KWA-BM schemes and other community managed schemes based on four characteristics – water source, age of the scheme, size of the schemes defined by population coverage, and distance between the treatment and control schemes. Since geographical coordinates were not available for the majority of the schemes, latitude and longitude data for the GPs where the schemes are located have been used to calculate the distance between the schemes. In the first step of the matching exercise, schemes from different institutional arrangements that use similar type of water source (river, open dug well, bore well, ponds, lakes) and have similar age profile have been identified. In the next step, matched pairs have been identified by selecting schemes that are closest to each other with a similar population coverage Using the matching exercise, a total of 200 similar and comparable water schemes from the above mentioned three groups have been identified which formed the final matched sample. The final sample consists of 90 Jalanidhi schemes, 44 KWA-BM schemes and 66 other community managed schemes. The distribution of the final matched sample by institutional arrangement is presented in the table below.

**Table 1: Distribution of Final Matched** **<u>Sample by Scheme Type</u>**

|**Scheme**|**Identified**|**Out of**|**Built**|
|---|---|---|---|
|Jalanidhi-I|90|3,710|2001 to 2009|
|KWA-BM|44|395|2004 & 2005|
|Other CommunityManaged|66|750|1999 to 2010|
|**
income elasticity of migration.</mark> _<mark>American Economic Journal: Applied Economics</mark>_ <mark>,</mark> _<mark>9</mark>_ <mark>(2), 219-55.</mark> **<mark>[</mark> 1]** <mark>Beauchemin, C. (2020).</mark> _<mark>Towards Migration Modules for the 50x2030 Survey: Literature Review and Recommendations</mark>_ <mark>. Mimeo.</mark> **<mark>[</mark> 2]** <mark>Becerra-Valbuena, L. G., & Millock, K. (2021). Gendered migration responses to drought in</mark>

> * To help readers, we report, next to each reference, a number in bold and square brackets depending on the specific topic to which each reference belongs. The legend is as follows: [1] Migration and climate change; [2] Migration data sources, gaps, and methods; [3] Climate change impacts and weather data; [4] Other topics.

38
revenue, most credibly through property tax, tends to be unintendedly compensated by the increased magnitude of equalization or other types of redistributive transfers.<sup>14</sup> The broader policy relevancy that we would venture to highlight here is: The efficiency in property taxation is directly related to or dependent upon the structure of the fiscal decentralization.

Second, economic and population growth exerts both push and pull effects on tax policy makers. As countries climb up the development ladder, the overall restructuring and rebalancing of the tax mix would inevitably pull in the vision for better designing and administering a property tax, an efficient tax. Higher population, combined with higher state income, would extend the base for property tax on the one hand, and push for revenue-enhancing measures to finance rising demand for public services, most visibly at local levels.

A third policy issue relevant to property taxation is emerging from demographic trends observed around the world; the one most pertinent to this research is the decline in household sizes around the globe.<sup>15</sup> Estimated trends indicate that in most countries the average household size has

> 14 See, for example, Vigneault (2007) and Smart (2007) for more discussion of the disincentives from soft budget constraints and distributive transfers.

> 15 Patterns and trends in household size and composition: Evidence from a United Nations dataset, United Nations, 2019.

11
41

**Annex Table 2: Forty-seven Nation Survey of Firewalls* Applicable to Securities Underwriting and Dealing Activities**

|**Securities Activities**<br>**Permissible Without Fir**|**ewalls**|**Securities Activities Su**|**bject to Firewalls**|
|---|---|---|---|
|_Securities Activities_<br>_Permissible in the Bank_||_Securities Activities_<br>_Permitted Only in a_<br>_Nonbank Affiliate_|_Securities Activities_<br>_Permitted Only in a_<br>_Nonbank Affiliate_|
|Argentina'<br>Australia<br>Austria<br>(Bahrain)<br>Belgium<br>Bermuda<br>Cayman Islands<br>Chile|Luxembourg<br>Netherlands<br>New Zealand<br>Nigeria<br>Norway<br>Pakistan<br>Peru<br>Poland<sup>2</sup>|Bolivia<br>Brazil<br>Canada<br>Colombia<br>Indonesia<br>United States<sup>4</sup>|Czech Republic<br>Japan<sup>5</sup><br>Korea<sup>6</sup><br>Philippines<sup>7</sup>|
|Demnark<br>Estonia<br>Finland<br>France<br>Germany<br>Greece'|Portugal<br>Romania<br>Singapore<sup>3</sup><br>Spain<br>Sweden<br>Switzerland|||
|Hong Kong<br>India'<br>Ireland<br>Italy<br>Latvia|Turkey<br>United Kingdom<br>Uruguay<br>Venezuela'|||

***These "firewall" restrictions do not include so-called "chinese walls" which restrict the dissemination of non-public, confidential
# **Explaining levels of urbanization across countries**

Many economists and demographers have used the factors mentioned above to model rural-urban migration. These models are usually probit-type models which attempt to determine the probability of an individual agent deciding to move to an urban area from a rural area. These models, therefore, look not only at the characteristics of the individual (culture, education, wealth, family support, etc), but also the individual's environmental factors, both economic (rural-urban wage differential, returns to education, land availability, etc) and social (the presence of violence in the rural areas, a lack of civil freedoms).

Our study, however, focuses not on the individual, micro-economic level, but rather uses national macroeconomic and social conditions to determine a country's urbanization process. We look at national urbanization levels and at changes in these urbanization levels, which to a large extent are the aggregate or the result of these individual migration decisions. Our focus remains Africa, but to be able to identify whether Africa is different, we look across the world to see what has been the general experience with urbanization and economic development.

**Figure 3. Urbanization and income,** 1960-857

<!-- Start of picture text --> Urbanisation and income,<br>1960-85 all countries<br>1 00 -- - - - - --- - - - - - - --<br>90<br>80<br>70<br>4 0<br>1 0<br>30<br>0 2000 4000 6000 8000 10000 12000 14000 16000 18000<br>Gdp per capita<br><!-- End of picture text --> 7 Data: Summers & Heston GDP; Urbanization: World Development Indicators. Based on Ingram, 1998.

10
14. Colmer, J., Hardman, I., Shimshack, J. & Voorheis, J. Disparities in PM2. 5 air pollution in the United States. _Science_ **369,** 575–578 (2020).

15. Rosofsky, A., Levy, J. I., Zanobetti, A., Janulewicz, P. & Fabian, M. P. Temporal trends in air pollution exposure inequality in Massachusetts. _Environmental research_ **161,** 76–86 (2018).

16. Hajat, A., Hsia, C. & O’Neill, M. S. Socioeconomic disparities and air pollution exposure: a global review. _Current environmental health reports_ **2,** 440–450 (2015).

17. Wang, Y., Wang, Y., Xu, H., Zhao, Y. & Marshall, J. D. Ambient air pollution and socioeconomic status in China. _Environmental Health Perspectives_ **130,** 067001 (2022).

18. Gupta, S., Das, S. & Murty, M. Quantifying Air Pollution Vulnerability and its Distributional Consequences: Some Perspectives from Delhi. _Ecology, Economy and Society-the INSEE Journal_ **2,** 93–125 (2019).

19. Dasgupta, S., Lall, S. V. & Wheeler, D. Traffic, air pollution, and distributional impacts in Dar es Salaam: A spatial analysis with new satellite data. _World Bank Policy Research Working Paper_ (2020).

20. Dionisio, K. L. _et al. Air pollution in Accra neighborhoods: spatial, socioeconomic, and temporal patterns_ 2010.

21. Rooney, M. S. _et al._ Spatial and temporal patterns of particulate matter sources and pollution in four communities in Accra, Ghana. _Science of the Total Environment_ **435,** 107–114 (2012).

22. Liu, Q., Wang, S
and cluster family.<sup>14</sup> About a third of the total skills in the Burning Glass Malaysia data are digital, cognitive, or socioemotional according to our classification. The 20 most frequently cited skills are a mix of cognitive (6), socioemotional (4), and other (7) skills, as well as three basic digital skills (computer literacy, Microsoft Office, and Excel) (Figure 3). We identify 2,622 unique digital skills: 24 are basic digital skills, 794 are intermediate digital skills, and 1,804 are advanced digital skills. Among the nondigital skills, 24 are cognitive and 29 are socioemotional. The remaining two-thirds of skills are

> 12 We acknowledge that the skills listed in vacancies may diverge from those actually employed by workers in firms; indeed, enterprise surveys from the region show large gaps between employers’ notional skills demands and workers’ supply of skills – digital and otherwise – in local labor markets (Cirera et al. 2021).

> 13 To assist with the categorization of digital skills, we also use the O*NET-based methodology for calculating digital occupation scores in Muro et al. (2017). However, we only use this measure as an initial categorization, followed by a manual review the categorization of every skill.

> 14 To infer the cluster and cluster family for the 2,592 skills that were not assigned to a cluster by Burning Glass, we implemented a nearest neighbor search of similar skills with available information to the skills with missing information. For each skill with missing information, we assign the most common cluster and cluster family of the top 30 skills similar to it. Similarity is defined as the cosine similarity of the vectors representing the skills. The skills are assigned a 100-dimensional vector by training a Word2Vec model where we represent the job postings as “documents” and the skills as the “words” within the document. The Word2Vec model learns semantic relationships between skills as they co-occur across job posts and
concerns were heightened by the age of the most recent available poverty data from India, which was collected three and a half years before the 2015 target estimate. This paper describes an alternative method for estimating poverty, which utilized a more recent nationally representative survey from 2014-15 containing some of the same demographic and socioeconomic characteristics collected in previous expenditure surveys. These previous surveys were used to estimate a model, which was then used to predict expenditure and poverty into the 2014-15 survey. This type of statistical method has generated plausible headcount poverty estimates in other countries and appears to work well in India as well, based on two validation exercises and a variety of robustness checks. The model’s predictions imply an elasticity with respect to growth that is in line with past experience, in contrast to the traditional

> 34Information on other lines can be obtained on the Povcalnet website.

20
|_93%_|71%|

_Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform._ The performance of the two utilities with respect to **Human Resources** (HR) is along similar linesLESCO struggles to meet average scores while KE scores much above those. Notably it is able to pay bonuses to reward good performance. However, even at KE managers cannot hire or fire staff without higher level approval (see annex).

For LESCO decisions concerning middle management and above are taken by PEPCO, as well as decisions pertaining to all staff hired prior to WAPDA unbundling. Poor scores on management autonomy and performance review of employees contribute to an overall performance score of 43 percent for HR practices. The difference between KE and LESCO is important since it relates to the abilities of the two utilities to respond to poor performance and the need for change. After the 2013 decision to start the privatization of LESCO, new senior management were appointed on competitive private sector pay-scales.

KE scores highly (89 percent) on **Information and Technology (IT)** , well above the average, using the latest energy management, customer care, and corporate care IT solutions. LESCO scores well below average and importantly does not have a SCADA system or a call center to manage customer complaints (see annex).

Putting together the evidence from these various indicators shows that LESCO’s corporate governance environment falls short in many ways relative to that enjoyed by KE. Comparisons with the Indian state-owned utilities are also mixed. Much of the poor institutional performance is linked to the way in which LESCO (and the other DISCOs in Pakistan) are managed by the state— big decisions on finance, employment and pricing are all taken out of the DISCOs’ hands and instead are taken at a higher level. The incentives for management to improve performance are weak. Management is restricted in what it
|0.118|5591.061|6030.615|-0.045|
|Obs|206|151|.|287|218|.|

> 1 The table shows means for enrolled and non-enrolled communities and normalized in means.

> 2 For control communities in the 2011-2012 cohort, payments per capita are those that would have been awarded had they been successful. For control communiites in the 2013-2014 cohort, CONAFOR did not calculate these payments, so we inputed the median payment per hectare for that state, year and modality, and multiplied it by the area they submitted.

37
1 if firm _i_ exports to destination _j_ in year _t_ and did not do so in _t_ -1, equal to 0 if the firm did not export to destination _j_ in year _t_ -1 and does not start in year _t_ , and it is missing otherwise.<sup>13</sup> The explanatory variables that capture firm _i_ ’s export experience in markets other than destination _j_ are constructed as follows: where _Exp Other Gij_ ( _p_ ) _,t−_ 1 indicates whether firm _i_ was exporting (product _p_ ) to any destination _k_ that has an extended gravity relationship _G_ with country _j_ . We construct different variables to identify the spillover effect of RTAs in country _j_ on export participation and entry of Costa Rican firms. We also construct variables to account for spatial exporting patterns that capture if firm _i_ was exporting (product _p_ ) to any country sharing a border, common language, or colonial history with country _j_ .

Tables 1 and 2 provide descriptive statistics of the main variables from our expanded firm-level and product-level data sets, respectively. The first two rows in both tables report unconditional statistics, showing (as expected) that the expanded data set is largely filled with zeros and exporting is a rare event. Export entry, by definition, is a less frequent event than export participation and has fewer observations due to missing values. The subsequent rows report descriptive statistics conditional on exporting.

Table 1: Descriptive statistics: Firm level data set

|**Variable**|**N**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|
|---|---|---|---|---|---|
|**Unconditional (N=22,283,550)**||||||
|Export participation dummy|22,283,550|0.005|0
researcher. This raises the concern that, absent pre-analysis plans, researchers may engage in data dredging or _p_ -hacking in order to find the weather metric that generates the results they want. This issue is particularly problematic in instrumental variable (IV) estimation (Brodeur et al., 2016, 2020), which is a common use of weather data in economics research.

A final potential source of concern in terms of mismeasurement is that the process of spatially linking remote sensing data with public use data on plots, households, or communities creates another source of measurement error. This source is introduced by the obfuscation of GPS coordinates of sampled households in order to protect the privacy of survey respondents. Figure 3 visualizes the mismeasurement introduced by a number of different obfuscation methods, which may shift a unit record’s true GPS coordinates from a location experiencing high rainfall to one experiencing drought conditions. While this is not an issue when the unit record is the country, or when the researcher conducted the data collection herself, the economics literature increasingly relies on census, administrative, or third-party data in which the true location of the unit records are obscured. Obfuscating coordinates for the sake of privacy sacrifices accuracy and precision, with respect to estimates, and may result in Type II errors.

In relation to the above, we investigate three specific hypotheses:

1. _H_ 0<sup>1- different obfuscation procedures implemented to preserve privacy of farms or households</sup> have no impact on estimates of agricultural productivity.

2. _H_ 0<sup>2- different weather metrics have the same impact on estimates of agricultural productivity.</sup>

> and thus a valid instrument. This latter question has been addressed by Deaton (2010) and Sarsons (2015).

4
<u>Table 6: Ad recall at endline</u>

||All resp|ondents|Solid|house|Non-s|olid house|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|T|0.030<sup>_∗∗_</sup>|0.033<sup>_∗∗_</sup>|0.049<sup>_∗∗_</sup>|0.049<sup>_∗∗_</sup>|0.011|0.009|
||(0.014)|(0.013)|(0.024)|(0.023)|(0.023)|(0.023)|
|Individual controls||✓||✓||✓|
|Cluster controls||✓||✓||✓|
|Observations|1492|1492|728|728|764|764|
|Adj. R-squared|0.000|0.002|0.003|0.004|-0.001|0.001|
|Mean in control|0.114|0.114|0.101|0.101|0.126|0.126|

Each observation is an individual. Responses collected in the endline survey between December 15, 2020 and January 31, 201 are included. T is the treatment indicator. Solid house is a binary indicator equal to 1 if the individual lives in a dwelling made of stones, bricks, cement or concrete. State fixed-effects are included in all specifications. Individual controls include gender, age, household size, caste, education, distance to the medical center, employment and student status,
predict the applicant’s ability and willingness to repay a loan. The IDB and EFL co-financed the implementation of the psychometric tool during the pilot with our partner bank.

SMEs that applied for a working capital loan (up to 18 months in duration with an average loan size of $3,855) between March 2012 and August 2013 were screened by the

> 1 All statistics on market share and credit volume are from the Peruvian Bank Supervisor (SBS) for February 2012.

5
Now suppose the government invests in transportation infrastructure. In Table 8, we compare the effect on total urban income of investments favoring big cities versus small cities. The first column is the total urban income relative to the baseline income when infrastructure investments favor largest cities, specifically a 1⁄2 standard deviation (.4) decrease in inter-city transport cost of largest cities. The baseline income is the predicted value of Table 2 (3). The second column is the total urban income when the same amounts are invested in the smallest cities to decrease those cities’ transport cost by the same magnitude (.4). We experiment with several combinations of cities in Table 8.

The simulation results show that there are very small differences in total urban income from favoring small cities vis-à-vis large cities. These income differences range around 0.3 ~ 0.7%p of total urban income growth in 2000. The difference is highest when we favor the 104 smallest cities vis-à-vis than the largest two cities (.698%p). These results tell that there are no major gains in terms of overall urban income from diverting investments from the largest cities to secondary cities.

# **5. SUMMARY AND CONCLUSIONS**

In this paper, we have examined the determinants of Brazilian city growth between 1970 and 2000. For the analysis, we constructed a dataset of 123 agglomerations, and examined factors that influence wages and labor supply. Our main findings are the following. (1) Increases in rural population supply is a major driver of city growth. (2) Inter-regional transport improvements that lead to increases in the market 24