rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Fraud is an ancient crime of deception that aims to obtain money or other benefits from people. In Dante’s description of inferno in the Divine Comedy, fraud is considered as one of the worse sins--people who committed fraud would endure punishment at the _Bolgia_ eight of _Malebolge_ (eighth circle of hell).

Fraud victimization has profound economic and social implications. With the widespread leakage of private information, especially via the internet, and the everevolving fraud schemes, people frequently encounter fraud schemes, yet find it increasingly difficult to avoid victimization. Millions of people suffer from fraud victimization with enormous economic losses every year. As much as 11 percent of Americans (about 25.6 million) in 2011 and 4 percent of Australians in 2012 were victims of fraud schemes (Anderson, 2013; Jorna and Hutchings, 2013). A Federal Trade Commission (FTC) survey reveals that the individual/household level economic losses due to fraud victimization ranged from $40 to $40,000 in 2005 (Reisig and Holtfreter, 2007). In 2011 alone, 2.7 percent of Americans (i.e., 6.4 million people) paid at least $1.9 billion in total to fraudsters in various forms (Anderson, 2013), and the direct monetary costs incurred by victimization could reach $50 billion (Brenner et al., 2020).

The issue is of greater importance in developing countries. In China, for instance, fraud incidents have grown at an annual rate of 20 to 30 percent in the past decade.<sup>2</sup> According to a report by the Government of China, nearly half of internet users encountered fraud schemes in 2018, and 28 percent of them suffered economic losses.<sup>3</sup> Based on judicial data, fraud is one of the most frequent crimes, accounting for 32 percent of cybercrime that involves 46,000 fraudsters.<sup>4</
80<br>14<br>-40 -40<br>60<br>12<br>-60 -60<br>10 40<br>-80 -80<br>-100 8 -100 20<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>Stringency of<br>Mobility around<br>Period of interview lockdown<br>transit stations<br>restrictions<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br><!-- End of picture text --> Data from Google (2020) for the mobility trends and Hale et al. (2020) for the lockdown restrictions. The mobility trends are smoothed using 7-day moving averages.

38
errors and check if (i) the log-likelihood is bimodal, and (ii) the frequency with which the maximum of the log-likelihood corresponds to the true value of the parameter of interest -- the credit effect, δ. The results of this simulation (Table 4) demonstrate that (i) bimodality was found in all 100 simulations, and is likely to be a “common” feature of PK-type models; (ii) all 100 simulations used data that were constructed with normal errors, so that non-normality is not an implication of the finding of bimodality, as RM suggest; (iii) the global maximum was tightly centered around the true value of δ, so that bimodality is innocuous in the simulations because the true estimate is recovered in every case; (iv) “weak” instruments are not necessary for bimodality, as RM suggest, as the instruments in the simulations were sufficiently “strong” to identify δ with tight bounds; and (v) the inferior mode was always in the direction of the least squares bias, that is, the mode had a smaller credit effect δ. In no case did the mode associated with the smaller value of δ have a higher likelihood. RM do not dispute that the PK estimates are the global maximum. Although the data-generating process of the PK data is, of course, unknown, this is suggestive evidence that the global maximum in PK, the one with a significant and positive credit effect, is the “true” maximum.

The simulated data-generating process has two genders and one program. Forty variables Xi were generated from independent normal (0,1) random variables. The treatment chosen by those who are offered treatment is cf = 0.5∑x - 7μ + 5εf for females, and cm = 0.5∑x - 7μ + 5εm for males, where μ , εf and εm, are independent normal (0,1) random variables. The outcome is y = 1.0*cf + 1.0*cm
target scenarios: girls have the same inactivity rate as adult women (Target 1) and girls have the same inactivity rate as boys (Target 2).The following equation is used to calculate the cost as foregone output to the economy: where _IRy_ is young girls’ inactivity rate; _IR_<sup>_*_</sup> is the target inactivity rate; _WAPy_ is the working age population of young females; and _wy_ is real annual female youth wage. The opportunity cost scenario is where girls’ inactivity rates are the same as that of a counterfactual group, where IR* is the counterfactual rate. Thus, (IRy – IR*)xWAPy gives us the number of additional girls who would be working if girls’ inactivity rates were the same as IR*. Since most girls are not searching for jobs, we assume that the lost productivity per year is equivalent to the annual wage of all those girls who ―could‖ be working. _Cost_ is then divided by total GDP in order to obtain an estimate of the cost of youth inactivity for males and females as a share of GDP.

Data for the number of female youth labor force participants is drawn from the ILO’s KILM database, which projects employment data based on country-specific models. GDP is drawn in current US dollars from the WDI database (World Bank). As there are no cross-country comparable wage data for youth, the methodology relies on using the GDP per labor force participant (PPP adjusted). The computation assumes that the share of wage earnings is 60% of GDP.<sup>9</sup> Since females earn less than males and this ratio differs widely across countries, we introduce three adjustment factors to simulate the gender wage gap: 25%, 50%, and 75% of average wages (based on Saba Arbache, Kolev, and Filipiak, 2010). Further, since youth earn less than adults, we introduce three adjustment factors to the average wage to account for the age
In this paper, we analyze risk coping behavior by firms in Cambodia following the global financial crisis. First, we develop a simple theoretical framework to analyze the welfare cost of imperfect risk coping in the presence of a large negative demand shock. Firms would prefer to reduce (‘smooth’) the downward adjustment of their use of inputs conditional on a negative shock in sales if they anticipate the downturn in sales to be temporary and if they face adjustment costs when changing input use. However, the actual ability of a firm to smooth (or hoard) inputs is adversely affected by the presence of liquidity or credit constraints, and the additional adjustment costs incurred by a liquidity constrained firm (relative to an unconstrained firms) can be used as an indicator of the welfare loss of incomplete risk coping. Next, we use the basic insights of the theoretical framework to analyze the adjustments in inputs by firms in Cambodia during the global financial crisis using panel data on firms for the period 2007-2009. We show that firms differed in terms of the degree to which they adjusted inputs, but that firms without credit constraints and expectations that the demand shock was temporary were much more able to smooth their use of inputs. Firms facing credit constraints, however, reduced inputs significantly even if they expected the downward shock to be temporary only.

The paper is organized as follows. In section 2, we discuss the evidence on the impact of the global financial crisis on firms in Cambodia and their coping strategies. The impact of and response to the crisis has been highly heterogeneous at the firm-level. In section 3, we introduce a theoretical framework to explain why firms would like to smooth their input use in response to negative demand shocks, but are restrained to do so in the presence of credit constraints. In section 4, we analyze empirically the smoothing behavior of firms in Cambodia and show that firms differed in terms of the degree to which they adjusted inputs, particularly depending on the presence of credit constraints and their expectations on the duration of
Table 2. Descriptive statistics of variables of the production frontier model 20022005

|**Variable**|**Year**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|GDP (in millions)|2002|456684|1419363|9997600|2371|
||2003|466695|1451176|10249800|2703|
||2004|483326|1503767|10651700|2987|
||2005|497255|1547630|10995800|3402|
|Gross Fixed Capital|2002|91219|274765|1835000|448|
|Formation (GFKF) (in|2003|93539|280281|1890700|600|
|millions|2004|98363|293680|2004600|637|
||2005|103604|309418|2132800|664|
|Labor Force (LF)|2002|20738|55685|408342|168|
|(in miles)|2003|21005|56640|416285|169|
||2004|21353|57546|422759|173|
||2005|21656|58537|430607|177|
|Research and|2002|10336237|38943279|265884666|215|
|Development Expenditure|2003|10553995|39853975|272239510|354|
|(R&D)|2004|10714079|40311619|275048835|988|
|(in miles)|2005|11210290|42355334|287770196|1168|
|New Businesses|2002|49496|101292|650843|31|
|Registered (NBR)|2003|52404|104156|618503|57|
||2004|57433|111332|657195|24|
||2005|59459|114565|676830|23|

Notes: Number of observations = 61 countries. Source: Authors’ calculation from the World Bank’s World Development Indicators
with business regulations ( _Time Tax_ ) as reported by firms in the ES, and the Freedom from Business Regulations measure from Heritage Foundations Economic Freedom of the World ( _Freedom From Regulation_ ). We also experimented with the Distance to Frontier (DTF) measure due to World Bank’s Doing Business project. However, this did not any effect on our main results.

For tax rates, we use the tax rate on firms’ profits as measured by the World Bank’s Doing Business project. For corruption, we use the Control of Corruption measure taken from the World Bank’s Worldwide Governance Indicators ( _Control of Corruption_ ). Similarly, for the quality of enforcement of rules and laws, we use the Rule of Law measure from the World Bank’s Worldwide Governance Indicators ( _Rule of Law_ ). Except for Time Tax, which is available only for the current year, all other business environment indicators are lagged by two years to allow for possible delays in their impact on R&D activity. Last, our baseline specification controls for human capital using gross enrollment rates in primary and tertiary education. Enrollment in secondary education was not included because it is highly correlated with enrollment rate in primary education. However, in the robustness section, we show that including the control for the gross enrollment rate in secondary education does not impact our main results much. The data source for the secondary education enrollment rate is World Development Indicators, World Bank.

# 2.5 _Endogeneity checks_

To further raise our confidence against endogeneity concerns, we test for several predictions about how informal competition affects R&D activity by formal sector firms. The tests are based on how the effect of informal competition is moderated by the business environment and certain firm characteristics. These are as follows.

22
significantly. Following Uruguay’s loss of the investment grade, the spread on sovereign bonds as measured by the Uruguay Bond Index (UBI) jumped from less than 300 to almost 2,000 basis points by July 2002. Similarly, the interest rate spread between peso and dollar time deposits almost doubled over the period, going from 2,310 to 4,520 basis points. Deposit withdrawals throughout July 2002 also reflected the resignation of the minister of economy on July 22.

On July 30, 2002, the government declared a four-day bank holiday, during which it negotiated financial support from the multilateral agencies (for a total of 1.5 billion dollars) to fund a program designed to avoid a general deposit freeze, such as the one imposed in Argentina six months earlier. On August 4, 2002, a law created a special purpose fund (Fund for the Stabilization of the Banking System, or FESB in Spanish) to provide full backing for dollar sight and savings deposits at state-owned banks and those financial institutions that had been suspended. The same law extended the maturities of all dollar time deposits held at state-owned banks. An amendment to this law was approved in December 2002, with the purpose of further strengthening the banking system.<sup>11</sup>

# **3. Systemic Risk and Depositor Responses**

To evaluate depositor responses to systemic risk, we follow two distinct approaches, using two different types of data for the recent crises in Argentina and Uruguay. We begin by running regressions similar to those in the traditional market discipline literature discussed in the introduction. For each country, we conduct panel regressions using monthly bank-level

> 11 The amendment imposed reporting obligations on bank employees that acquire knowledge of irregularities, authorized bank regulators to fine state-owned banks, and created a public register for bank shareholders. The new law also provided the basis for the liquidation of the four private banks (whose operations had been suspended during the bank holiday), expanded the powers of the central bank in connection
(2019). “Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data‐Scarce Environments.” _Journal of Economic Surveys_ , 33(3): 757-797.

- <mark>Dang, Hai-Anh; Dinc, Mustafa; Diaz, Juderica; Maeda, Hiroko; Pullinger, John; Serajuddin, Umar; Stacy, Brian; Wolde, Dereje.  (2021).</mark> _<mark>Measuring the Statistical Performance of Countries: An Overview of Updates to the World Bank Statistical Capacity Index.</mark>_ <mark>World Bank, Washington, DC.</mark>

- Devarajan, Shantayanan. (2013). "Africa's statistical tragedy." _Review of Income and Wealth_ , 59: S9-S15.

- Fantom, N. and N. Watanabe (2008). “Improving the World Bank’s Database of Statistical Capacity”. _African Statistical Newsletter_ , 2(2): 21-22.

Food and Agriculture Organization of the United Nations, Asia and Pacific Commission on Agricultural Statistics (2012). _Report on Initial Country Assessments_ . <u>http://www.fao.org/fileadmin/templates/ess/ess_test_folder/Workshops_Events/APCAS_24/ Paper_after/APCAS-12-7-__Report_Country_Assessment.pdf.</u> 21
Policy Research Working Paper 9269

# **Abstract**

This paper crowdsources information on ~22,000 train rides undertaken by 357 women commuters in Rio de Janeiro to study sexual harassment in the public space and its effects on demand for a women-reserved space. Women in the public space experience harassment on average once a week and randomly tasking them to ride in the reserved space reduces harassment by 50%. A revealed preference experiment shows that demand for the reserved space is heterogeneous, with top-tercile users making up 80% of the demand. These high-level users experience half of the harassment in our sample. The authors find that perceptions of norms around the reserved space may limit women’s agency. Data on commuters’ attitudes show over half men and women commuters associate women in the public space with more sexual openness; women who perceive this attitude to be the prevailing norm are 79% more likely to report using the reserved space.

This paper is a product of the Development Impact Evaluation Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fkondylis@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
**|{0.37}***|{0.21}***|
|No. Observations|16957|17022|33979|16957|17022|33979|
|||**Estima**|**tes of Mobility**|**from Quadrati**|**c CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|6.32|4.16||**_IGME0 _**|0.52|0.64|
|**_ES5 _**|8.75|7.09||**_IGME5 _**|0.454|0.533|
|**_ES10_**|10.82|9.46||**_IGME10_**|0.384|0.426|
|**_ES16_**|12.85|11.58||**_IGME16_**|0.300|0.298|

Notes: (1) The data used are IHDS 2012 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; standard errors clustered at district-level in square brackets; standard errors clustered at statelevel in braces; *** p<0.01, ** p<0.05, * p<0.1.
well-documented fact that crime is far more common among men than women and it tends to rise during their late teens, peak at around the early twenties and then decline rapidly (Hansen 2003, McCall 2013, and Chioda 2015, among others). There is also growing evidence that high school drop outs have a higher probability of engaging in criminal activities (Anderson 2014 and Chioda, de Melo, and Soares 2012). In Mexico, information from administrative records on all alleged criminals in 2007 shows that 90 percent of those undergoing the judicial process were men, 2
) when matching by procuring entity, and Tk. 60.0 crore (US$ 7.06 million - model 2a in Table 7) when matching by financial year of tender. The increased cost is obtained because the effect of higher number of bids overshadows the lower bid preparation costs.

c. Security Cost Savings: One of the primary reasons for the introduction of the electronic government procurement system was frequent coercive incidents while physically submitting the bid at the procuring entity office. This was especially true when the bid was being submitted by a non-local firm. These firms would therefore spend to ensure their safety while submitting these bids. The security cost savings is estimated by multiplying the number of bids had the tender been a manual tender with the average security cost incurred for submitting a bid, as obtained from the responses to the bidders’ survey. The average security cost for submitting a bid was Tk. 2,950 (US$ 35). The bidder security cost savings for about 69,000 tenders was estimated to be Tk. 105.9 crore (US$ 12.46 million – using model 1a in Table 7) when matching by procuring entity, and Tk. 97.2 crore (US$ 11.4 million – using model 2a in Table 7) when matching by financial year of tender.

We observe that the security costs are on average higher for firms that submit or win a larger proportion of their bids, which indicates that security expenses are higher for non-local firms.

76
Table 3: Matched sample balance: SBM subsidy eligibility criteria (SBM data)

||.<br>**All**|**N**|**Control**|**N**|**Treatment**|**N**|
|---|---|---|---|---|---|---|
|HH owns BPL card|0.29<br>(0.45)|959|0.34<br>(0.037)|551|0.23<sup>_∗∗_</sup><br>(0.035)|408|
|Landless with homestead|0.076<br>(0.27)|959|0.094<br>(0.032)|551|0.051<br>(0.017)|408|
|Physical disability|0.0021|959|0.0036|551|0|408|
||(0.046)||(0.0025)||(0)||
|Scheduled Caste|0.34<br>(0.47)|959|0.33<br>(0.049)|551|0.36<br>(0.059)|408|
|Scheduled Tribe|0.030<br>(0.17)|959|0.033<br>(0.014)|551|0.027<br>(0.015)|408|
|Small and Marginal Farmers|0.23<br>(0.42)|959|0.19<br>(0.031)|551|0.29<sup>_∗_</sup><br>(0.050)|408|
|
cultivation survey to measure trends in input costs at the state level.

**SHRUG Data:** To measure baseline conditions in the districts in the primary analysis, I use data from the 2001 Census compiled in the new Socioeconomic High-resolution Rural-Urban Geographic panel for India (SHRUG) dataset (Asher et al., 2019). I use the night lights data provided in SHRUG in robustness checks as well.

**Pollution Data:** Previous work on the relationship between agricultural fires and pollution has relied on data from air quality monitoring stations (Pullabhotla, 2018). Like with weather data, this has the disadvantage of limiting the analysis of pollution to areas with monitors that have been active over the full time period. To get around this issue, I use satellite re-analysis data from the Modern-Era Retrospective analysis for Research and Applications (MERRA) database provided by NASA (Rienecker et al., 2011). This is a satellite based product used by economists to study air pollution from coal fired power in India (Barrows et al., 2018) that provides data on the monthly average emissions rates for black carbon, organic carbon, and sulfur dioxide (SO2) on a 0.5<sup>_◦_</sup> _×_ 0.625<sup>_◦_</sup> grid. Importantly for my study, MERRA separately identifies the emissions of the above pollutants by source, including biomass burning. I also collect concentrations of black carbon, organic carbon, sulfur dioxide, and sulfate, which allows me to calculate the concentration of PM2 _._ 5 (He et al., 2019).

# **3.2 Empirical framework**

Following the difference-in-differences approach of Shah and Steinberg (2015), I estimate the effect of MNREGA on fire use as the change in fires before and after implementation of MNREGA within a district, controlling for month by year and district fixed effects. In the primary framework, districts are treated when MNREGA becomes
. The MICS, though representative at the state level, allows only the calculation of deprivation levels, as it does not include information to infer about monetary poverty.

> 14 Though a panel survey, the data can be used as an individual level panel across visits within a wave, but only as a household level panel across waves. The GHS wave 4 sample only includes a subset of the earlier waves’ sample households in addition to a refresh sample. (For a discussion see NBS and World Bank (2019).). Though a representative long panel is kept in wave 4, the part of the analysis relying on panel data, only uses data up until wave 3 as sample sizes in the long panel up until wave 4 are considered too small for the purpose of this analysis.

> 15 Due to the unbalanced nature of the panel and household members joining and leaving the household between post-planting and post-harvest, thus leading to slightly different household sizes, whereas the mean per capita consumption figure of the household remains constant, results in slightly different individual level post-planting versus post-harvest poverty rates. Hence, these also slightly differ from the aggregate poverty rates based on household level data.

> 16Health deprivation can only be estimated from waves 1 and 2, as the survey module is missing in waves 3 and 4. Education deprivation is based on the education module in the post-planting surveys in waves 1 and 2 and the post-harvest survey in waves 3 ad 4. For information deprivation household asset ownership data, only available in the post-planting survey, is assumed to be a proxy for access to these assets. While an access question exists, in wave 3, tv and radio access questions were dropped, so that this not allow consistent comparisons. Thus, household ownership of these assets was chosen to be a good proxy. Charity and tanker water were assumed to be protected, but not directly drinkable water sources. Drinking water source, sanitation and shelter data was in waves 1 and 2 only available in the post-harvest surveys and in