rafmacalaba/data-use-mentions-extended huggingface dataset:
real base money associated with increased demand for money due to increased economic activity and other factors, have risen sharply as monetary authorities in reserve currency-issuing countries  have substantially increased their balance sheets through quantitative easing and bank support.  IMF estimates that these revenues may have reached 8 percent of GDP (Fiscal Monitor, April 2012); however, it is not clear what portion of  this burden is born by non-residents.

Other authors use the excess returns on _net-foreign assets_ approach to analyze the benefits (Hausmann and Sturzenegger, 2006, Gourinchas and Rey, 2005, Lane and Milessi-Ferretti, 2007 and 2008, and Habib (2010)). Habib arrives an excess return of 330 basis points, which if applied as “savings” on the current US negative Net Foreign Asset Position of $2.5 trillion (2010), one gets an annual savings of $82 billion.

> a The term “seigniorage” dates back to the time when the artisan minting specie would take a small amount of the metal as payment for this service.  In more modern times, it refers to the value of goods and services that can be purchased from the simple creation of currency—without having earned income from sale of goods or services or assets.

b Another approach to measuring international seigniorage would be to look at the growth of US currency in circulation and assume that half ends up in the hands of foreigners: free imports. Since 2000, the total currency in circulation increased by $484 billion. If half was absorbed externally, that would imply cumulative “international” seigniorage of about $240 billion or about $20 billion per year.

11
**Table 1** : Government domestic spending data sources

|**Sector**|**Source**|**Variables**|
|---|---|---|
|Health|WHO GHED|•Domestic government health expenditure as % of GDP_(current only)_<br>•Domestic government health expenditure as % of total government<br>expenditure_(current only)_|
|Education|UIS, IMF|•Domestic government education expenditure as % of GDP_(current +_<br>_capital)_<br>•Domestic government education expenditure as % of total government<br>expenditure_(current + capital)_|
|Social<br>protection|ASPIRE|•Domestic government spending on social protection (defined above) as<br>a % of GDP_(current + capital)_|

The main source of information on country-specific human capital outcomes is represented by the HCI, which measures the human capital that a child born today can expect to attain by age 18, given the risks of poor health and poor education that prevail in the country where she lives (Kraay 2018). The index has three components:

- 1) **Survival** : measured as the complement of the under-5 mortality rate.

- 2) **School** : measured both through the _quantity_ and _quality_ of education. The quantity of education is measured by the number of Expected Years of School. The Quality of education is measured by the Harmonized Learning Outcomes (HLOs), that harmonize test scores from major international student achievement testing programs. A recent paper provides an in-depth discussion on the HLOs (Altinok, Angrist, and Patrinos 2018). The two measures are aggregated into the Learning Adjusted Years of School that vary between 0 and 14.

- 3) **Health** : measured by Adult Survival Rate (ASR), the share of 15-year-olds who survive until age 60, and the complement
|0.338|0.600|0.177|
|Mean for other poor|0.084|0.397|0.507|0.119|
|Mean for middle class|0.065|0.268|0.721|0.182|
|Mean for upper class|0.091|0.329|0.584|0.373|
|<br>Observations|32594|32594|32594|32594|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (3) estimated with OLS. The sample includes observations from non-ultra-poor households. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. In Panel A, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household received any transfers from any ultra-poor, other poor, middle class or upper class households (within her community) respectively. In Panel B, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household gave any transfers to any ultra-poor, other poor, middle class or upper class households (within her community) respectively. In the pooled regressions (Panels A1 and B1) each observation is weighted using sampling weights calculated as the fraction of households surveyed from each wealth class (lower, middle and upper) relative to the number of households from the relevant wealth class in the community census.

41
two developed economies for which comparable data exists, the cost composition is the reverse, with a higher fraction accounted for by fixed costs. This finding resonates with firm-level models of technology choice that posits a trade-off in operating with a low marginal cost technology at a high fixed cost, or vice versa.

One concern in asking truck operators about their variable and marginal costs is that economic agents do not always explicitly or accurately calculate these economically relevant variables. When asked about them on the spot, they may give noisy guesses. They are, however, more likely to be well-informed about the levels of separate cost components. Therefore, information collected through surveys can be leveraged as an input to imputing transport costs. For example, a survey can simply ask operators about hourly wages of drivers, the cost of vehicle maintenance and the fuel efficiency of their fleet with the goal of precisely measuring each component. Putting all this information together, under assumptions on the importance of each component—which itself can be informed by surveys or interviews—the researcher then imputes freight costs per ton-mile. Since this approach goes one step beyond surveys, the next section 8
of regulation if these firms have obtained control over the regulatory processes. Examining if there are differential effects in who bribery affects among young and old firms will empirically test the regulatory capture view. The business transactions surveyed in Enterprise Surveys also refers to activities involving public workers or officials.

# **4.1 Estimation Results**

Among firms who conducted business transactions, those who were bribed have lower sales growth (0.9%) than those who were not (3.2%). Note that the variation in annual sales growth is high. Among firms who were bribed, sales growth one standard deviation from the mean ranges from -24.3 percent to 30.9 percent. In the sample, less than half of the firms have negative growth rates. Firms who participate in business transactions on average have higher sales growth than firms who do not, and this may be correlated to higher entrepreneurial abilities or plans to expand. In the surveys, bribery data is only available for firms who conducted at least one business transaction in the past two years. The analysis is restricted to these firms, 74 percent of the sample. In the main specification, firms who did not have any transactions are excluded because it is not possible to identify whether bribery was the reason why they refrained from participating in a transaction. Although this sample restriction could lead to a selection bias, the effect of the selection is not clear. Large and efficient firms with growth potential are more likely to apply for permits and have business transactions. Yet they may also be better suited to compensate for bribery distortions. In this case, any negative impact of bribery we find would be the lower bound of negative impact of bribery. On the other hand, increased visibility in the market could lead to more frequent solicitations and higher distortions. We also present results with the sample including firms without any transaction to show whether the relationship between bribery payments and sales growth is sensitive to the composition of the sample. We make the assumption
# **Introduction**

The process of financial liberalization was expected to increase the volatility of interest rates and asset prices, to have distributional consequences in the form of reduced or relocated rents, and to have increased competition in the financial services industry. In this paper we examine the available data on money market and bank interest rates for evidence on these propositions.

WVe show that, as more and more countries liberalized, the level and dynamic behavior of developing country interest rates converged to industrial country norms. Liberalization did mean an increased short-term volatility in both real and nominal money market interest rates. Treasury bill rates and bank spreads were evidently the most repressed, and they showed the greatest increase as liberalization progressed: this shifted substantial rents from the public sector and from favored borrowers. Whereas quoted bank spreads in industrial countries contracted again somewhat during the late 1990s, spreads in developing countries remained much higher, presumably reflecting both market power and the higher risks of lending in the developing world.

Sections 1 and 2 review the global pattern of long-term and short-term dynamics in interest rate levels and spreads. Section 3 proposes an approach to judging when the _de facto_ liberalizaLtion of wholesale rates occurred, and Section 4 measures the speed of adjustment of developing country interest rates to external interest rate shocks before and after these dates. Section 5 examines the way in vwhich changes in wholesale rates passes through to bank lending and deposit rates. Using the date of _de facto_ wholesale interest rate liberalization, Section 6 compares overall economic performance before and after. Section 7 contains concluding remarks.
7|7|4|10|2|0|0|0|0|86|
|RATIO|11.3%|18.4%|25.6%|13.2%|15.4%|11.7%|18.9%|12.5%|12.9%|15.9%|3.7%|0%|0%|0%|0%|10.9%|

# Source: DBT SBIRI database.

Note: Bioinformatics and Food biotechnology areas are tracked separately from the 8<sup>th</sup> batch onwards (prior to that, they were combined under ̳Others‘). Proposals received under batches 11 onwards are still under consideration, with 2 additional proposals under batch 11 and 4 proposals under batch 12 going through the final process of approvals, bringing the approved total to 92 as of September 30, 2011.

There has been an increase in the number of applications in the areas of bio-informatics and food biotechnology over time, leading to these areas being separately monitored as of June 2008. There is typically a time lag in granting final approval for some of the proposals due to the applicants not possessing recognition by the Department of Scientific and Industrial Research (DSIR) for their research facility, which is a mandatory requirement for SBIRI

> 17 Since 1992, funding under the US SBIR phase I has typically been up to $100,000 while phase II awards have ranged up to $750,000, though it is not uncommon for awards to exceed these thresholds. US SBIR awards in fiscal year 2005 totaled $1.85 billion, with almost $1 billion being awarded by the Department of Defense. The average award size was roughly $315,000, with phase I awards averaging $110,000 and phase II awards averaging $760,000; see Table 1 in Link and Scott (2010).

22
is even larger in essential jobs that also have the lowest wages and the lowest levels of education. We find that 40.4% of migrants with low levels of education have essential jobs, compared to 34.1% of native workers. Finally, immigrants are less likely to be employed in income-safe jobs (53.9% versus 65.3% of native workers) and in health-safe jobs (77.1% versus 84.4) though the difference between the immigrants and the natives is narrower in the latter (84.4% versus 77.1%).

**Figure 3: Share of foreign workers in key essential occupations by destination country**

<!-- Start of picture text --> Doctors<br>Nurses<br>ICT<br>UK Personal care<br>Drivers<br>Cleaners<br>All occupations<br>Doctors<br>Nurses<br>ICT<br>Personal care<br>Germany Drivers<br>Cleaners<br>All occupations<br>Doctors<br>Nurse s<br>ICT<br>France Personal care<br>Drivers<br>Cleaners<br>All occupations<br>Doctors<br>Nurses<br>ICT<br>Personal care<br>Spain Drivers<br>Cleaners<br>All occupations<br>Doctors<br>Nurses<br>ICT<br>Italy Personal careDrivers<br>Cleaners<br>All occupations<br>0 .1 .2 .3 .4 .5<br>Share of foreign workers<br><!-- End of picture text --> Source: Own calculations based on EU-LFS 2018 data.

16
# REFERENCES

Ahmed, Sarah. "Women left behind: Migration, agency, and the Pakistani woman." _Gender & Society_ 34, no. 4 (2020): 597619.

Bicchieri, Cristina, Jan W. Lindemans, and Ting Jiang. "A structured approach to a diagnostic of collective practices." _Frontiers in psychology_ 5 (2014): 1418.

Bicchieri, Christina. “Norms in the wild: How to diagnose, measure, and change social norms.” Oxford University Press (2017).

Bernhardt, Arielle, Erica Field, Rohini Pande, Natalia Rigol, Simone Schaner, and Charity Troyer-Moore, “Male Social Status and Women’s Work.” AEA Papers and Proceedings, (2018), 108, 363–67 Bursztyn, Leonardo, Alessandra L. González, and David Yanagizawa-Drott. "Misperceived social norms: Women working outside the home in Saudi Arabia." _American economic review_ 110, no. 10 (2020): 2997-3029.

Bushell, Brenda. "Women entrepreneurs in Nepal: what prevents them from leading the sector?" _Gender & Development_ 16, no. 3 (2008): 549-564.

Buchmann, Nina, Carl Meyer, and Colin D. Sullivan. _Paternalistic discrimination_ . Working Paper, 2023.

Bussolo, Maurizio, Jessy Amarachi Ezebuihe, Ana Maria Muñoz Boudet, Stavros Poupakis, Tasmia Rahman, and Nayantara Sarma. "Social Norms and Gender Disparities with a Focus on Female Labor Force Participation in South Asia." _The World Bank Research Observer_ 39, no. 1 (2024): 124-158.

Bussolo, Maurizio, Ana Maria Munoz Boudet, Chinmaya Holla, Thomas de Hoop, Tasmia Rahman, Garima Siwach, Nele Warrinnier. “Measuring Gender Norms in the Field”. The World Bank (2024, Forthcoming).

Central Bureau of Statistics (CBS). "Report on the Nepal labour force survey 2017/18." (2019).

Chakravarty, Shubha, Mattias Lundberg, Plamen Nikolov, and Juliane Zenker. "Vocational training programs
China Agriculture Press (in Chinese).

- Chang S.Y., Zhao L.L., Zhang T., et al. 2012. Liquid biofuel. In: China Automotive Energy Research Centre. China automotive energy outlook 2012. Beijing: Science press (in Chinese).

- CNPC Research Institute of Economics and Technology. 2011. Domestic and international oil and gas industry development report 2010. Beijing: CNPC Research Institute of Economics and Technology (in Chinese).

- CRES (China Renewable Energy Society). 2010. Yearbook of China new energy and renewable energy. Guangzhou: GuangZhou Institute of Energy conversion, Chinese academy of sciences (in Chinese).

- DRST (Department of Rural Science and Technology, Ministry of Science and Technology). 2009. Yearbook of China agricultural products processing industries. Beijing: China Agriculture Press (in Chinese).

- DSERE(Department of Science, Education and Rural Environment, Ministry of Agriculture, et al. 2011. Summary table of rural renewable energy 2010 (in Chinese).

- EBSDEC(Editorial Board of Sustainable development of energy crops in the People's Republic of China). 2009. Sustainable development of energy crops in the people’s republic of China. Beijing: China Agriculture Pressing (in Chinese).

- ERI(Energy Research Institute, National Development and Reform Commission). 2008. Study on large-scale development of biofuels in China (in Chinese).

- Energy Sector Management Assistance Program (ESMAP), 2011. China’s Envisaged Renewable Energy Target: The Green Leap Forward, The World Bank, Washington, DC.

- Fang J., Pu W.H., Zhang H. J.. 2010. The development status of cassava industry at home and abroad.  Chinese agricultural science bulletin, 26(16):353-361(in Chinese).

- Fu Q.Y., Wang Z.B., Bi Y. P.. 2011. Effects of bioethanol development on grain production of China
# 4. Discussion

Of course, these numbers should be used with caution because they largely underestimate the total impacts of climate change on poverty. First, disaster risk data are not available in all countries, and the impact is calculated only in the countries and for the disasters for which data are available.

Second, these estimates do not include macroeconomic impacts or indirect impacts, and they assume that each disaster happens independently. As such, they disregard some of the elements discussed in Hallegatte et al. (2016, 2017).

And finally, because we rely on household surveys that report only household‐level consumption, we cannot explore the intra‐household distributional issues, and especially the question of whether children or women are disproportionally affected by natural disasters. Gender inequality has been   widely reported see, for example, Hoddinot et al. (2006) and Rose (1999) and a disproportionate   impact on children is well‐identified see Kousky (2016) but these important effects cannot be included in this analysis. The estimates proposed here are therefore a lower bound to the impacts of natural disasters on poverty.

# 5. References

- Devarajan, Shantayanan, S. Delfin Go, Maryla Maliszewska, Israel Osorio‐Rodarte, and Hans Timmer. 2013. “Stress‐Testing Africa’s Recent Growth and Poverty Performance.” Policy Research Working Paper 6517, Development Prospect Group & Africa Region, Office Of The Chief Economist, The  World Bank.

- Hallegatte, S., M. Bangalore, L. Bonzanigo, M. Fay, T. Kane, U. Narloch, J. Rozenberg et al. 2016.

- Shock Waves: Managing the Impacts of Climate Change on Poverty. Climate Change and Development Series. Washington, DC: World Bank.

- Hallegatte, S., A. Vogt‐Schilb, M. Bangalore, and J. Rozenberg. 2017. Unbreakable: Building the Resilience of the Poor in the Face of Natural Disasters. Climate Change and Development Series.
.2%|11.9%|
|Share Electricity, Gas , Steam, Water and Sewerage|-0.1%|0.3614|1.0%|1.1%|
|Share Construction|1.2%|0.0005|9.7%|8.4%|
|Share Commerce and services|-1.4%|0.0055|23.6%|25.0%|
|Share Others|0.7%|0.1739|20.8%|20.1%|
|Registered with Any SS institution related to your main job|8.0%|0.0000|63.5%|55.5%|
|Share of workers registered with any social security<br>|6.3%|0.0000|52.4%|46.1%|
|institution related to your main job|||||

_Notes_ : 2011 N = 21,460, 2013 N = 21, 467 _Source_ : LFS 2011 & 2013. World Bank author’s calculations.

31
68

**Figure 15: A Complete Decomposition**

<!-- Start of picture text --> o ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ . . .<br>-0.5<br>ct:~~~~~~~~~~~~~~~p~ --- ------<br>-2.0<br>Percentiles<br>-<br>-nUalphas and betas alphas, betas, gammas<br>-<br>-*-mu(d), alphas, betas, gammas mu(d), mu(e), alphas, betas, gammas<br>---<br>1996-1976<br><!-- End of picture text --> Source: "Pesquisa Nacional por Amostra de Domicilios" (PNAD), 1976 and 1996.
reduction in consumption and hence vulnerability to poverty. Demissie and Kassie (2017) study vulnerability in rural areas by cross-sectionally estimating the probability of households’ consumption falling below the poverty line in the future and defining a household as vulnerable if the probability is higher than a certain threshold. Using the 2011 Household Consumption Expenditure Survey (HCES) and Welfare Monitoring Survey (WMS), they find that vulnerability to poverty in 2011 was 54 percent in rural Ethiopia as opposed to a poverty rate of 31 percent.

> 4 For a detailed discussion on the shocks in the drought-prone lowlands, refer to World Bank and DFID (2019).

> 5 Vulnerability to poverty is related to the concept of “resilience” which concerns the longer time-path of well-being in the face of shocks, and especially the likelihood that any adverse outcomes of either risk avoidance or a realized shock do not persist for an extended period. For example, a nonpoor household may be vulnerable to becoming poor due to job loss yet be quite resilient if the prospects for finding follow-on employment offering similar compensation are high and/or formal or informal safety-net programs reliably provide adequate support reasonably promptly. In statistical terms, a nonpoor household with high conditional variance of income might be both vulnerable (to becoming poor) and resilient (because the poverty is sufficiently low in duration, intensity, and/or likelihood) (Barrett and Constas, 2014).

3
respondents and 223 low- and middle-income neighborhoods. The qualitative and quantitative data provide revealing insights into the workings of the drug economy. Gangs did not provide public goods, the government does, but gangs provide excludable goods such as security services, informal contract enforcement, and dispute resolution which complement the services that governments provide. Consistent with this, the quantitative data show that the services provided by drug gangs tend to be more frequently closer to _communa_ headquarters. Blattman et al thus challenge the received wisdom that organized crime “fills a vacuum left by weak states” but argue instead “criminal governance can also be a strategic response to strong state presence.” 15
place of origin</sup><sup>_j_</sup> in year _t_ . All models are estimated using the PPML estimator on PR’s import data pooled across observations at the year, HS6 digit product, and place-of-origin level, with year fixed effects included in the estimation model. _σ_<sup>_k_</sup> estimates from Fontagné et al. (2022) are interacted with geographic frictions, and enter separately in the regression. IHST denotes the Inverse Hyperbolic Sine Transformation. Pharmaceutical products are excluded from the estimation.

28
University of California, San Diego (UCSD), and Wadonda Consult. The project spanned the period of November 2016 to June 2017 and yielded a unique dataset which includes a measure of physical activity of 415 adults (>15 years old) sampled from 215 rural households across 20 enumeration areas in the two districts (Pratt et al., 2020; Friedman et al., 2023).

The physical activity tracking data collection was spread over the period of February-April 2017 and was piggybacked onto the weekly visits that were paid to these 215 households for collecting weekly individual-disaggregated data on plot-level household agricultural labor inputs throughout the 2016/17 rainy season. The field staff were trained intensively on the activity tracking component prior to the start of the fieldwork. This training was led by a team of technical specialists on physical activity tracking from the Active Living Research laboratory at UCSD, who also monitored the incoming physical activity data daily and provided feedback to the field staff on the quality of the incoming data.

During fieldwork, each adult household member that was engaged in agriculture wore a researchgrade ActiGraph physical activity tracker for a two-week period during non-sleeping hours of each day. The enumerators visited the sampled households three times during the 17 days when the accelerometers were with the participants, typically days 1 (deployment), 9 (check in) and 17 (pick up). On day 1, the enumerators explained the study objectives to the participants, obtained their consent and demonstrated how to wear the accelerometers (with a belt around their waist). Day 9 was a checkin visit, during which data were downloaded and enumerators checked on battery status and for any unusual patterns in the data (which could signal that the devices were not worn correctly). On day 17 the remaining data were downloaded, and the accelerometers returned for reassignment to subsequent participants.

The participants in the study wore an accelerometer around the waist. Physical activity can be detected using accelerometers worn on various parts of the body, such as waist (hip), ankle,