rafmacalaba/data-use-mentions-extended huggingface dataset:
Singh, S.K. 2007. ―Use of Gamma Distribution/Nash Model Further Simplified for Runoff Modeling,‖ _Journal of Hydrologic Engineering_ , 12: 222.

Smakhtin, V.U. 2001. ―Low flow hydrology: a review,‖ _Journal of Hydrology_ , 240: 147–186.

- Stahl, Kerstin. 2005. ―Influence of Hydroclimatology and Socioeconomic Conditions on Water Related International Relations,‖ _Water International_ , 30(3): 270-282.

- Stinnett, Douglas and Jarolsav Tir. 2011. ―International Institutions and the Management of Transboundary Water Conflicts,‖ _Paper presented at the International Studies Association Annual Convention_ , Montreal, Quebec, Canada, March 16-19, 2011.

- Susskind, Lawrence. 1994. _Environmental Diplomacy: Negotiating More Effective Global Agreements_ , New York, NY: Oxford University Press.

- (TFDD) Transboundary Freshwater Dispute Database. ―Product of the Transboundary Freshwater Dispute Database, Department of Geosciences, Oregon State University.‖ Additional information about the TFDD can be found at: <u>http://www.transboundarywaters.orst.edu,‖ accessed 22 November 2008</u>

- Tir, Jaroslav, and John Ackerman. 2009. ―Politics of Formalized River Cooperation,‖ _Journal of Peace Research,_ 46(5): 623-640.

- Toset, Hans Petter Wollebæk, Nils Petter Gleditsch, and Håvard Hegre. 2000. ―Shared Rivers and Interstate Conflict,‖ _Political Geography_ , 19(8): 971-996.

- Turton, Anthony. 2003. ―A Southern African Perspective on Transboundary Water

- Management,‖ _Environmental Change and Security Program Report_ , Issue 9: 75-79.

- Vinnokov, K.Y. , A. Robock, S, Qui, J.K. Entin, M. Owe, B.J. Choudhury, S.E. Hollinger, E. G. Njoku. 1999. ―Satellite remote sensing or soil moisture in Illinois, United States,‖ _Journal of Geophysical Research_ , 104:
_Saving on Investment_|+[+]|+[+]|+**|
|_Investment on Saving_|-[-]|-[-]|+**|

Source: Author’s estimations. Results in brackets for difference-estimations. Significance at 10, 5, and 1 percent levels are denoted by ***; **; *. The dynamic tri-variate model by Attanasio et al. (2000) was OLS-based with a sample of 38 countries over the period 1960-1994 — controlling for simultaneity and country heterogeneity. See Orazio et al. (2000) for another comparative study.

All except one relationship for Brazil in Table 1 confirm the findings by Attanasio (2000) — thus making the case even stronger that private saving causes growth.<sup>1</sup> The finding is robust to the measure of saving being either gross or private. Cumulative impulse response functions from the VAR model further support the positive growth-implications over time of an increase in private saving.<sup>2</sup> Private saving is found to positively but insignificantly affect investment; growth significantly increases investment; and if investment increases in the short term, growth may fall since output is used for long term investment rather than consumption—over the long term investment positively affects growth. These relationships for Brazil conform well to the established macroeconomic literature.

# **_Econometric Evidence on Aging-Saving Relationship_**

Standard life cycle models generate a strong link between the age composition of the population and private saving. Also, cross-country econometric studies using aggregate time-series data reveal correlations between saving rates and demographic structure broadly confirming LCH predictions; countries with more elderly populations tend to have lower saving rates (Graham, 1987; Koskela and Viren, 1992; Masson et al., 1998; and Miles, 1999).

The international evidence on whether saving follow the LCH is mixed if low and middle-income countries are also considered — and highly dependent on how income is estimated. Households do not seem to run

> 1
||||
||||Tajikistan||Russian Federation||||
||||Tonga||Samoa||||
||||Uruguay||Sri Lanka||||
||||Vanuatu||St. Vincent and the Grenadin|es|||
||||Venezuela, RB||Suriname||||
||||||Syrian Arab Republic||||
||||||Tajikistan||||
||||||Turkmenistan||||
||||||Uzbekistan||||
|Sourc|e: Authors’ calcu|lations based on|ata from the Wo|ld Development|Indicators datab<br>Yemen, Rep.|se|||

Source: Authors’ calculations based on data from the World Development Indicators database.

**_Although many more developing countries are off track than on track to achieve the targets, two thirds or more of developing countries are actually on target or close to being on target,_** thanks to more than a decade of better policy and growth (figure 2). Many countries are making substantial progress in several MDGs: gender parity in primary education (89 of them), gender parity in secondary education (82), access to safe drinking water (66), primary completion rate (55), and extreme poverty (47). For instance, about 70 percent of developing countries have achieved or are on track to achieve the targets for gender parity in primary and secondary education. Although half the monitored countries (57) are off target for the primary education completion goal, two thirds of them (38) are very close to being on track.

Progress is mixed or poor on access to sanitation, maternal mortality, and child mortality. Unfortunately, more than 40 percent of low-income to upper-middle-income countries in the sample (58 countries) are significantly off target for access to sanitation.<
47

**Table A1**

# **List of variables, acronyms and sources of data**

|**Variable**|**Acronym**|**Data source**|
|---|---|---|
|Current account balance, US dollars||IMF, Balance of Payments Statistics|
|GDP, US dollars||World Bank, World Development Indicators|
|Current account balance (% of GDP)|CA||
|Workers’remittances (credit), US dollars||IMF, Balance of Payments Statistics|
|Workers’remittances (% of GDP)|REM||
|Workers’ remittances: dummy equal to 1<br>if the country level of workers’<br>remittances over GDP is above the<br>median or mean or 75<sup>th</sup>percentile value<br>of the yearly distribution|DREM||
|Workers’ remittances: dummy equal to 1<br>if the country mean of workers’<br>remittances over GDP is above the<br>median or mean or 75<sup>th</sup>percentile value<br>of the distribution of country means.|DREMave||
|International Reserves, US dollars||World Bank, Global Development Finance|
|International Reserves (% of GDP)|RES||
|External debt (% of GNI)|EXTD|World Bank, Global Development Finance|
|Short term debt (% of external debt)|SHORTD|World Bank, Global Development Finance|
|Domestic credit (Monetary Survey)||IMF, International Financial Statistics|
|Domestic credit annual growth rate|CREGRO||
|GDP per capita, PPP 1995 US dollars|PPPGDP|World Bank, World Development Indicators|
|Trade in goods (% of GDP)|OPEN|World Bank, World Development Indicators|
|Net official transfers, US dollars|OT|
whole, India’s manufacturing sector became more urbanized, with the share of workers in urban areas rising from 33% of employees in 1989 to 41% in 2005 (Figure 3a). Urbanization growth was most dramatic from 1989 to 1994, but slowed down from 1994 to 2000. The urbanized employment share was basically flat from 2000 to 2005. This pattern of increasing urbanization was also present when looking just at manufacturing plant counts, but the opposite trend is observed for manufacturing output. The latter has increasingly moved towards rural areas”.

The differences in the relative movements of the organized and unorganized sectors, shown in Figures 3b and 3c, are striking. Specifically, “Throughout the 1989-2005 period, the organized sector moved from urban to rural locations, with its urban employment share declining from 69% in 1989 to 57% in 2005. On the other hand, urban employment share for the unorganized sector increased from 25% to 37%. Since the unorganized sector accounts for about 80% of employment in India’s manufacturing sector, the total urbanization level increased for the employment measure. Likewise, the organized sector accounts for over 80% of India’s output, such that the aggregate output series instead becomes more rural” (Ghani et al. 2012).

The authors conclude that large firms in India with high capital and land intensity are more likely to locate in rural areas in districts. There is evidence to support this from both within- district and betweendistricts movements. They both tend to work in the same direction for the urbanization of the unorganized sector and the de-urbanization of the organized sector.

Could these changing patterns in the urbanization of informal sector and the de-urbanization of formal sectors be explained merely by changing definition of urbanization? The definition of an urban setting in India has been mostly stable since the 1961 census. India uses a more demanding set of criteria than most countries to define what is ‘urban’. For example, substantial parts of U.S. metropolitan areas like Atlanta or
United Arab Emirates 30. Dominica 65. Namibia 100. Uruguay 31. Dominican Republic 66. Nauru 101. Venezuela 32. Ecuador 67. Netherlands Antilles 102. Vietnam 33. Egypt 68. Nicaragua 103. Zimbabwe 34. El Salvador 69. Nigeria 35. Feather 70. Niue

88. St. Vincent and the Grenadines 93. Tokelau 94. Tonga 95. Trinidad and Tobago 98. Turks and Caicos Is.

99. United Arab Emirates 100. Uruguay _Source:_ UNCTAD TRAINS Database.

## **Eligible Least Developed Countries (duty-free access)**

1. Afghanistan 18. Haiti 2. Angola 19. Kiribati 3. Bangladesh 20. Laos 4. Bhutan 21. Lesotho 5. Burkina Faso 22. Liberia 6. Burundi 23. Madagascar 7. Cambodia 24. Malawi 8. Central African Rep. 25. Maldives 9. Chad 26. Mali 10. Dem. Rep. of Congo 27. Mauritania 11. Djibouti 28. Mozambique

28. Mozambique 12. Equatorial Guinea 29. Myanmar 30. Nepal 13. Eritrea 31. Niger 14. Ethiopia 32. Republic of Benin 15. Gambia 16. Guinea 17. Guinea-Bissau 33. Ruanda 34. Samoa 35. Saô Tomé and Prìncipe 36. Senegal 37. Sierra Leone 38. Solomon Islands 39. Somalia 40. Sudan 41. Tanzania 42. Timor-Leste 43. Togo 44. Tuvalu 45. Uganda 46. Union of Comoros 47. Vanuatu 48. Yemen 49. Zambia _Source:_ UNCTAD TRAINS Database.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 42
2.8 million of them are registered as living in Colombia (UNHCR 2025). Relative to Venezuela’s 1990 census population of 19.83 million, the last census before Ch ́avez’s election, this represents a 14 percent reduction in population that emigrated from Venezuela to Colombia (= 2.8 / 19.83). A municipality that lost 14 percent of its 1990 population to Colombia would therefore have experienced a GDP contraction of approximately 29.4 percent (= 2.1 _×_ 14).

27
cost per calorie, we use information on food purchases (expenditures and quantity consumed) by food item from POF. POF collects information on food purchases for consumption at home as well as food consumed away from home (FAFH). Food purchases for consumption at home are collected in the household expenditure diary, which collects expenditures on frequent purchases over a reference period of one week (POF 3). It collects expenditures and quantities purchased by food item, containing 4,549 different food items. Although accounting for FAFH in the calculation of the cost per calorie is advisable, we cannot include it since it is not possible to assign calorie intake values to FAFH as observed in the data. FAFH is collected in a different questionnaire (POF 4) that collects expenditures by food item but does not have the associated quantities consumed.<sup>8</sup> 8 We assessed the possibility of addressing this shortcoming by using data from another questionnaire (POF 7) that registers quantities of items consumed, location, time, and its calorie intake. However, it was not possible to match the data from POF 7 (quantities) and POF 4 (expenditures) in a reliable way. First, item specifications differ across the two questionnaires. Food items in POF 4 are mostly vague. The item with highest expenditure is "takeaway meal (lunch/dinner).” In contrast, POF 7 has items as detailed as white rice, beans, eggs, potatoes, and so on consumed during lunch or dinnertime. Second, reference periods and data collection methods are different. POF 4 expenditures are collected via recall and have a reference period of one week. POF7 is a diary and collects information on all food items consumed within two nonconsecutive days in that reference week. Finally, POF 7 is captured only for a random subset of the sample and only for household members ages 10 years and above.

6
# Models for can Using Large Language Qualitative Analysis Introduce Serious Bias<sup>∗</sup>

Julian Ashwin Aditya Chhabra Vijayendra Rao<sup>†</sup> Maastricht University World Bank World Bank

**Keywords:** Large Language Models, Qualitative Analysis, ChatGPT, Llama 2, Text as Data, Aspirations, Rohingya, Bangladesh

**JEL Codes:** C63 C89 O1

## **1 Introduction**

Large Language Models (LLMs) are increasingly being used in social science research to, among other things, analyze and annotate text data (Gilardi et al., 2023). As LLMs become more accessible and popular we can expect that there will be a temptation to use them to analyze open-ended interview data such as those used by qualitative researchers (Small and Calarco, 2022) who follow an interpretative analytical approach. This relies on careful, nuanced, coding conducted by trained social scientists (Detering and Waters, 2018). Qualitative analysis of this kind lies at the core of fields like anthropology and sociology, and there is now a rapidly expanding literature on the use of Natural Language Processing (NLP) methods to analyze qualitative data in sociology (Bonikowski and Nelson, 2022), and qualitative analysis and NLP are also now being increasingly employed in more quantitative fields such as economics (Rao, 2023).

Data generated from open-ended, in-depth, interviews is potentially very different from the benchmark datasets often used in the NLP literature to validate modelling approaches such as English language tweets and news, or product reviews. This is because qualitative research is often conducted in a

> ∗The authors are grateful to the World Bank’s Knowledge for Change Program, and the World Bank-UNHCR Joint Data Center on Forced Displacement for financial support. Sudarshan Aittreya provided valuable research assistance for the project.

> †Corresponding author: vrao@worldbank.org
- Fourth, the effects land market regulations had on the efficacy of specific

- projects.

The first research finding – that housing demand across the world was highly regular – provided some perspective on how the Bank could most effectively structure many of the components of the housing projects. In retrospect, this finding may now seem almost naïve, but it is important to remember that it was only 10 years prior to that review that the first cross-country empirical study of housing investment was undertaken, by Burns and Grebler (1977). In addition, and more importantly, in the year that study was undertaken public housing production and ownership was the main vehicle of housing assistance throughout much of the developing world. For instance, at that time, China, the 15 countries established since the 1991 dissolution of the former Soviet Union, as well as India, Korea, Mexico, Argentina, and many African countries all had very active public housing programs.<sup>13</sup> Many of these countries also proscribed or discouraged private production of housing.

The work on housing demand also provided a basis for understanding what kind of standards and units were affordable to low-income families. It sought to demonstrate how and why the unaffordable standards pursued in most places left the public sector not only unable to meet housing demand, but also drove so much housing supply into the informal sector and illegality. It also showed why it was so important to focus on the details about the kind of housing units that the poor could afford without the aid of subsidies. That is, the work emphasized why the units produced could be sustainably financed only if modest housing standards appropriate to beneficiaries’ lower income levels were used.

> 13 At about the time of the last review, Tolley in work subsequently published in 1991, demonstrated the importance of rents based on a cost of capital perspective in China, contributing to China’s initial housing reform program. Shortly thereafter, in 1992, Renaud wrote  “Why the Former Soviet Union Needs Housing Markets.”
.55|0.49|0.24|0.35|0.30|0.25|
|Share of labor with computer|3.072***|2.849***|3.331***|3.085***|3.338**|3.493**|
||(0.885)|(0.911)|(1.063)|(1.131)|(1.378)|(1.538)|
|F-frst stage|14.9|13.6|12.1|10.6|10.6|6.4|
|Sargan|0.29|0.28|0.78|0.96|0.84|0.87|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|730|730|730|730|730|730|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects.

Source: Authors’ calculations using data from ENTIC, INEGI 24
for the accounting system, REMITA), infighting between the state and primary health care providers (the SPHCDAs) and rank opportunism and interference, as exemplified by the Abia case (Annex 8.1). In this context, ADG’s TA was required to be not only an agent of socialization into the brave new world of adaptive implementation _but also_ into PfR. Delays in fund disbursement contributed to a misalignment of TA and state implementation,<sup>22</sup> a somewhat unbalanced emphasis on planning, and what was perceived by various actors as an abrupt (and unfortunate) exit of ADG’s support.

World Bank staff and federal level officials have also been learning and adjusting to the PfR instrument as they go, and they readily refer to aspects that, given a second chance, could be designed differently. For example, although the SMART survey, on whose results performance payments were to be made, was expected to take place every year, in practice it was used to form a 2015 baseline but then did not take place in 2016 or 2017 and has only recently been undertaken in 2018.<sup>23</sup> The use of an alternative survey tool in 2016 raised significant outcry and has affected perceptions of overall fairness.<sup>24</sup> The fact that this 2016 survey, against which performance was measured and payments 21 There is a possibility that what federal level officials saw as low technical capacity, ADG saw as a case of letting state implementers lead in the design of hybrid, state‐relevant solutions.

22 Initially it was planned that the TA would take place in a fairly linear model – an initial preparation, sensitization and planning phase that would include obtaining political buy‐in and would result in completion and acceptance (by government) of state‐level action plans in eight states; an immediate implementation phase with constant monitoring, adaptation and learning over a yearlong period; and a completion phase characterized by reflection on results and capturing of
not pay workers). To guard against outliers in the wage rates or ratios, we use the wage rate of the median employee in the district (and its urban and rural areas). We do not directly observe the full wage distribution, but instead calculate the metrics through plant averages and their relative employment levels. Wages are expressed in 2005 U.S. dollars. When discussing our results, we describe in greater detail the extent to which this factor may model other costs beyond labor factors for districts. We jointly model the average wage and urban-rural wage ratio variables as their correlation is small at 0.12.

Our remaining variables are covariates to contrast with the above factors. We first consider district land area. This metric is calculated by the World Bank using Geographic Information System (GIS) data files of district boundaries in India. Conditional on the other variables specified, we would not anticipate district scale playing an important role. Our focus is instead on the urban build-up of a district. Following Schneider et al. (2009), we consider the share of land in the district that is built-up for urban use. This fraction is calculated using 2001 MODIS satellite information at 500 meter spatial resolution. Built-up area, focusing on building structures, is a good proxy for urban area. Greater built-up area in a district can allow for stronger urbanization growth and is our best proxy for local land markets. A second proxy, termed land use intensity, is calculated as total land value of organized sector plants per unit of shipments.

For our core estimations, we do not model the overall level or change in population urbanization for a district, as this may be endogenous/follow the industry movements we consider. Robustness checks do consider, however, the change in population urbanization from the 1991 Census to the 2001 Census as a control. Thus, these regressions will be considering the movement of industry over-and-above that of the underlying population base. Section 2 noted that some sub-areas of districts change their urbanization status in 2001
of the wealth distribution. The authors used the data described in Alstadsæter et al. (2019), discussed above, to improve the estimates of the shares of wealth held by the top 0.01% for 10 countries, although the only LMIC included was the Russian Federation, where the share held by the top 0.01% more than doubled once offshore wealth is included. This implies that government-collected administrative data may have important weaknesses when measuring the top tail of the wealth distribution.

Xie and Jin (2015) provide an example of improving the measurement of the top of the wealth distribution using replacement and reweighting. The authors assume that the bottom 99.9% of the Chinese wealth distribution is well represented by the household survey data they use. They then use a Chinese rich list of the top 1,000 wealthiest Chinese and, assuming that the top can be represented by the Pareto distribution, estimate the Pareto parameter from the rich list and then use this to replace the top 0.1% with the values implied by the estimated Pareto parameter. This adjustment means that the estimated share of wealth held by the top 1% rises from 16% to 35%.

24
changing investor base for their debt. In this respect, improving market monitoring, the timely availability and quality of ownership statistics, and good investor communication and outreach to establish solid links with key participants (wherever they may be), will be ever more important.

**Table 2: Summary of inclusion criteria for some widely tracked EM / global local currency bond indices**

||**Rating/**<br>**income**<br>**level**|**Market**<br>**size/**<br>**liquidity**|**Issue**<br>**size/**<br>**liquidity**|**Coupon**|**Redemption**|**Embedded**<br>**options?**|**Maturity**|**Other**|
|---|---|---|---|---|---|---|---|---|
|**GLOBAL IN**|**DICES**||||||||
|**Citi World**<br>**Govt.**<br>**Bond**<br>**Index**|Min. A-/A3<br>by S&P or<br>Moody’s|Total of<br>all<br>eligible<br>issues<br>≥$50bn|Market<br>specific<br>(e.g.<br>MXN:<br>≥MXN<br>10bn)|Fixed or<br>zero only|Bullet or<br>sinking fund|Yes|≥1 year|Sovereign<br>must actively<br>encourage<br>fgn. investor<br