rafmacalaba/data-use-mentions-extended huggingface dataset:
respondent with two questions simultaneously: the sensitive or embarrassing question, and a completely innocuous question. For example, question A might be, "Have you ever bribed a government official?" Question B might be, "Is your birthday in December?" The respondent then flips a coin (not revealing the result to the interviewer) and on seeing heads answers question A and on tails answers question B. The surveyor simply records the response. As a result, the respondent can give a completely truthful response knowing that the surveyor has no way of knowing which question she is answering.

Determining the percentage of a sample that has bribed a government official then becomes simple. Assume that 400 of 1200 respondents answered "yes" to the question. We would expect 50 percent of the respondents to answer the birthday question, and 1/12 of those to answer the question affirmatively. That is, 1200(0.5)/12 = 50 of the "yes" responses were to the birthday question. The expected number of people answering "yes" to the bribery question is therefore 400 - 50 = 350. Because 600 (i.e., 1200*0.5) people answered the bribery question we could conclude that 350/600 z 58.3 percent of the sample had bribed a government official. This method complicates hypothesis testing because of the measurement error it introduces, but World Bank surveys intent on measuring particular behavior in populations could potentially use this method to great effect.

# V. **CONCLUSIONS**

This review has attempted to examine existing World Bank firm surveys and the state-ofthe-art in questionnaire design. World Bank surveys have dramatically improved our understanding of firm behavior in developing countries. In addition, most of the data and measurement requirements we spell out in this paper are fulfilled in some surveys, such as the use of panel data, and detailed measurements about various themes. Needless to say, _not_ all Bank surveys should pay heed to the specific themes and hypotheses this paper
one-time opportunity for accelerated economic growth.

The current age distribution of Morocco’s population represents such an opportunity, but it also presents the major challenge of harnessing that human capital and putting it to the best use (HCPWB, 2017). Until now, the Moroccan labor market has not been able to absorb all of its new entrants in an optimal way. Calculations based on subsequent rounds of the Moroccan Labor Force Survey (see data section) indicate that of some 390,000 new entrants in the national workforce every year, barely a third manage to find employment in the formal or informal sector.

2
.78)|(374.529)|(264.849)|(0.45)|(0.39)|||
|Machinery and equipment|5.009|152.354|43.198|0.668|0.143|12,704|1,707|
||(0.791)|(326.021)|(256.078)|(0.471)|(0.35)|||
|Electrical, comm., medical|5.227<br>(0.949)|202.276<br>(424.024)|71.201<br>(379.659)|0.709<br>(0.454)|0.231<br>(0.421)|9,619|1,262|
|Motor vehicles, other transport|5.419<br>(0.989)|354<br>(806.788)|152.26<br>(728.611)|0.783<br>(0.412)|0.288<br>(0.453)|8,348|1,056|
|Other|4.612<br>(0.874)|202.387<br>(442.729)|39.12<br>(153.547)|0.83<br>(0.376)|0.196<br>(0.397)|8,004|1,150|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. The table reports means and standard deviations (in parentheses) for the estimation sample of manufacturing firms with more than 10 employees over the period 1995-2013 (except 2000-2002). A firm is foreign-owned if foreign investors hold at least 50% of capital. TFPR is estimated
suggests that both tariff cuts in PTAs and IPRs reforms, undertaken on a non-discriminatory basis under such agreements, encourage technology transfers to middle-income and emerging economies (Maskus, 2012).

Thus, there may be remaining concerns about endogenous selection. To alleviate the problem, we estimate specifications in which we eliminate from the sample trade with the major partner in each of the treatment agreements, generating estimates of the trade impacts with respect to all other countries, both in the aggregate and bilateral trade. We regard this as the most rigorous specification, in that it extracts the possibility that the intent of the major partner was to increase trade in IP-sensitive goods with treatment countries, including through trade in intermediates, leaving just residual trade effects with third countries.

Our selection of treatment IPAs (and treatment countries as members) is as follows. In case 1 we designate treatment IPAs as those legally enforceable agreements involving the United States, the EU or EFTA as a partner country.<sup>11</sup> This selection is made in part because these are the main _demandeurs_ of elevated IPRs standards. However, we also wish to compare results using the World Bank database with the same specification of IPAs using the DESTA database in Maskus and Ridley (2020). In case 2 we consider all PTAs in which IPRs are designated as legally enforceable and in case 3 we use such PTAs where the United States, EU or EFTA are not partners.<sup>12</sup> In the final two cases we shift from permitting the nature of full agreements to determine the treatment selection to considering, in a basic fashion, whether specific BTRIPS provisions matter for trade sufficiently to serve as meaningful selection devices. We do this in part to explore the explanatory power of the World Bank database in its considerable specificity. However, this focus is of more general interest because of the importance placed on TRIPS-Plus standards by negotiators and the attention they draw in the policy debate. If, for example, American negotiators place heavy emphasis on
) and other advanced countries (e.g., UK, 70%). India desperately needs to unlock this workforce to reach not only sustainable development goals on gender equality, human development, and poverty eradication but also to foster economic growth and shared prosperity.

If gender disparity in India is a result of discriminatory hiring practices in the labor market, then economists, dating back to Gary Becker, would argue that competition should drive out such discrimination.<sup>3</sup> The idea behind this theory is that people prefer to work with their own “types.” In lax environments, or when firms have market power, managers may engage in discriminatory behavior to hire their own “type” and still be able to remain in business. However, competition is thought to put a brake on the scope of discrimination and crony capitalism. If a firm is in a fierce battle for survival, then it must optimize to stay in business, and firms with managers who are willing to give up (or never had) tendencies to discriminate over employees will be more likely to succeed. Evidence from the United States suggests that following deregulations in the banking sector, discrimination within banks and among product markets declined due to greater competition.<sup>4</sup> However, a systematic study on the impact of pro-competitive reforms on gender discrimination and/or segmentation in developing countries is rather scarce.

> 1 See Albrecht, Björklund and Vroman (2003) for evidence on Sweden.

> 2 India ranks among the top 10 countries in the WEF’s political empowerment index, which is one of the pillars of the Gender gap index.

> 3 Becker (1957).

> 4 Black and Strahan (2001) and Levine, Levkov and Rubinstein (2014).

2
Development Board (BWDB). These three agencies were also the first to introduce e-procurement in Bangladesh, starting in FY2012-13.

9. Our primary data source is administrative data on public procurement. We obtained detailed electronic procurement data for all electronic tenders of BWDB, LGED and RHD from Bangladesh’s Central Procurement Technical Unit between FY2011-12 and FY2017-18, a total of 69,240 tenders for 185 district-level procuring entities.8 To obtain equivalent data for paper-based tenders, we surveyed paper-

> 6 In 2014, Bangladesh ranked 145th out of 175 on TI’s Corruption Perception Index. Numerous high-profile, high-value corruption scandals in public procurement further support the perception that corruption is a widespread problem. 7 Based on the number and value of contracts.

> 8 Bangladesh has 64 districts, but each agency does not have an office in each of these districts. For example, BWDB does not have offices in districts without any significant rivers or canals.

3
refugee crisis, if any, is limited to a subset of low-skilled workers, a result that Peri and Yanesov (2017) and Clemens and Hunt (2017) also disputed.

Interstingly, all these papers ignore the question of household well-being. Whether the decrease in wages for a subset of low-skilled workers ultimately results in a decrease or increase in average household wellbeing depends on production and productivity. Therefore, these results do not provide any evidence on whether living standards of the local residents of Miami have improved or not in the aftermath of the Mariel boatlift. This has been an important gap in this specific debate.

The only paper that looks at outcomes beyond the labor market is Saiz (2003), who finds that rental prices increased in the short and medium run (1979-1983) by between 7 and 11 percent more in Miami than in comparison municipalities. These findings were limited to lower-quality housing and show that demand for this type of housing remained strong over the period. The literature also offers some explanations for the lack of impact of the Mariel boatlift on the labor outcomes of the host community. The structure of the industry in Miami (notably a relatively high share of textile and apparel industry) offered jobs for lowskilled non-English speaking labor (Card 1990). Using a confidential micro data version of the Annual Surveys of Manufacturers, Lewis (2004) finds little evidence that these industries expanded their production of labor intensive goods relative to other productions (like a Heckscher-Ohlin open economy model would have predicted). However, he also shows that these industries opted for labor intensive technologies in the aftermath of the crisis and adopted computers more slowly than industries in comparison cities expanding employment at the extensive rather than intensive margin. The new arrival of Cubans also helped to compensate for a net decline in internal migration to Miami (Card 1990) whereas the overall increase in population increased local consumer demand, which in turn increased labor demand. Bodvarsson, Van den Berg, and Lewer (2008) find that the positive impact of Cuban immigration on
to informal workers that was paid starting in April 2020. The grant of EGP 500 per worker was paid monthly for three months to around 2 million informal workers registered in the workforce databases of the Ministry of Manpower across governorates.<sup>13</sup> A relative allocation using HIECS survey weights indicates that the grant was paid to about 12.1 percent of Egypt’s informal workers. Since the distribution rules are not clear, we randomly selected informal workers in the updated HIECS dataset to reach the size of the informal worker population that benefited.<sup>14</sup> 12 The targeting rules are applied using the Fiscal Incidence Tool constructed with the Ministry of Finance. The PMT targeting formula relies on a set of household demographics and assets to identify poor households.

13 The first payment was processed in April 2020 via post offices (4,000 branches), the Agriculture Bank of Egypt (1,100 branches), and 600 schools that also served as payment sites – a total of 5,700 outlets. Beneficiaries received a free ATM card with their first payment to cash their second and third payments at post offices and/or banks. To avoid overcrowding and ensure safety, accepted beneficiaries were notified via SMS regarding the location and time to visit to collect their first payment and ATM card.

14 The following caveat concerning the analysis should be kept in mind. It is unclear from LFS data whether there could be some double counting of the targeted transfers to workers. This means that the income loss reported in LFS could have been bigger if respondents included those transfers, and the reported results may be overestimating the importance of the cash transfers in smoothing the impact.

10
<!-- Start of picture text --> Global Music Industry Turnover (1973-2009)<br>In Billions Source: RIAA year-end shipment statistics; Bain analysis<br>$15<br>$10 \ Digital<br>\<br>co<br>$5<br>LP/EP; Tape<br>so1973 1977eS1981 1985 1989 1993 1997 2001 2005 2009<br><!-- End of picture text -->
central credit registries, or other improvements in enforcement) could be to increase demand for insurance.

We note that reciprocity may provide an alternative interpretation for our experimental results. Since the cash is given to the farmer by the ICRISAT representative, the former may feel

> 21 To calculate these values, we multiply our price elasticity estimates by the percentage estimated difference in price between the Indian rainfall insurance contracts (from Table 1) and US insurance data (provided by David Cummins of Temple University). The US contracts provide an average payout-to-premia ratio of 70%, compared to 46% for the Indian rainfall insurance contracts; implying the price per unit of payout is 34% lower for the US contracts. Our average elasticity estimate of 0.79 implies that cutting the price of the Indian contracts by 34% would increase demand by 27% (or around 25%). The upper bound of 50% is calculated in a similar way, except comparing the price of the lowest-value Indian insurance contract to the highest-value US contract.

18
) on trade and production that are expected to be reasonably short lived.<sup>13</sup>

# 2 DATA AND CONTEXT

**Firm-to-firm trade.** Our primary data source is daily establishment-level transactions with distinct information on establishment locations.<sup>14</sup> This data is provided by the tax authority of a large Indian state with a diversified production structure, around 50% urbanization rates, and high levels of population density. To benchmark the size of this Indian state to other firm-to-firm trade datasets, the population of this state is roughly three times the population of Belgium, seven times the population of Costa Rica, and double the population of Chile.

The data contains daily transactions between all registered establishments in this state and

> 11Prior work has highlighted the aggregate implications of supplier churn (Baqaee et al., 2023; Khanna et al., 2022). We focus on the substitution among suppliers given a fixed set of suppliers as in Baqaee and Farhi (2019).

> 12As firm-to-firm data become common (Panigrahi, 2021; Demir et al., 2024; Dhyne et al., 2021; Alfaro-Urena et al., 2022; Chacha et al., 2022), our methods can be used to quantify shock propagation through complex networks. 13We may hesitate to use these elasticities for exercises on long-term structural transformations.

> 14While we use the term “firm" throughout the paper, our data is actually at the more granular establishment level, and we can identify the parent firms for each establishment as well.

6
Bazzi, Samuel, Lisa Cameron, Simone Schaner and Firman Witoelar (2021) “Information, intermediaries and international migration”, Mimeo. UCSD.

Beam, Emily, David McKenzie and Dean Yang (2016) “Unilateral Facilitation Does Not Raise International Labor Migration from the Philippines”, _Economic Development and Cultural Change_ , 64(2): 323-68.

Becker, Sasha and Andreas Ferrara (2019) “Consequences of forced migration: a survey of recent findings”, _Labour Economics_ 59: 1-16.

Beegle, Kathleen, Joachim De Weerdt and Stefan Dercon (2011). “Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey.” _Review of Economics and Statistics_ , 93, 1010–1033 Bewley, Truman (2002) “Knightian Decision Theory: Part 1”, _Decisions in Economics and Finance_ 25: 79–110 Borjas, George (1987) “Self-selection and the earnings of immigrants”, _American Economic Review_ 77(4): 531-553.

Bryan Gharad, Shyamal Chowdhury and A. Mushfiq Mobarak (2014). “Underinvestment in a Profitable Technology: The Case of Seasonal Migration in Bangladesh.” _Econometrica_ , 82(5): 1671–1748 Bryan Gharad and Melanie Morten (2019) “The Aggregate Productivity Effects of Internal Migration: Evidence from Indonesia”, _Journal of Political Economy_ , 127(5), 2229-2268.

Bundervoet, Tom (2018) “Internal Migration in Ethiopia : Evidence from a Quantitative and Qualitative Research Study” World Bank, Washington, DC. <u>https://openknowledge.worldbank.org/handle/10986/32097</u> Cai, Shu (2020) “Migration under liquidity constraints: Evidence from randomized credit access in China”, _Journal of Development Economics_ 142: article 102247.

Chapela, Jorge González (2022) “Is there a patience premium on migration?”, _Empirical Economics_ <u><mark>https://doi.org/10.1007/s00181-021-02196-z</mark></
org/10.1787/9789264091450-en)

- OECD. 2014. PISA 2012 Results: What Students Know and Can Do (Volume I, Revised edition, February 2014): (https://doi.org/10.1787/9789264208780-en)

- OECD. 2019. Education at a Glance 2019: OECD Indicators. OECD. <u>https://doi.org/10.1787/f8d7880d-en</u>

- Pennings, Steven. 2019. The Utilization-adjusted Human Capital Index (UHCI). World Bank (mimeo)

- Perera, Marcelo & Aboal, Diego, 2019. "The impact of a mathematics computer-assisted learning platform on students' mathematics test scores," MERIT Working Papers 2019-007, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT). https://ideas.repec.org/p/unm/unumer/2019007.html

- Psacharopoulos, George, and Harry Anthony Patrinos. 2018. “Returns to Investment in Education: A Decennial Review of the Global Literature.” Education Economics 26 (5): 445–58. <u>(https://doi.org/10.1080/09645292.2018.1484426)</u>

- Psacharopoulos, George, Harry Patrinos, Victoria Collis, and Emiliana Vegas. 2020. The COVID19 cost of school closures. Available online at <u>https://www.brookings.edu/blog/educationplus-development/2020/04/29/the-covid-19-cost-of-school-closures/</u>

- Rodrigues, Margarida, and Federico Biagi. "Digital technologies and learning outcomes of students from low socio-economic background: An Analysis of PISA 2015." Publications Office of the European Union, Luxembourg, <u>http://dx.doi.org/10.2760/415251</u> (2017). https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-

38
.5%|52.7%|49.9%|
|Area-level model withgeospatial covariates|39.1%|41.8%|40.0%|
|Direct estimates with WorldPopweights|48.2%|N/A|N/A|
|Direct estimates with surveyweights|46.8%|N/A|N/A|

Looking at the MSE estimates in Table 3, the household-level model generates estimates with lower MSEs on average compared to the estimates under the grid cell-level and area-level models. A further comparison between the estimates produced with geospatial covariates and the census-based ones is to compute coverage rates by treating the assumed gold standard census-based estimates as the truth. The coverage rate is the share of Communes for which a 95% normal confidence interval for headcount poverty, defined as the estimate plus/minus 1.96 times the square root of the estimated MSE, contains the census-based estimate. Overall, the coverage rate for estimates under the household-level model with geospatial covariates is 92.3%. Of course, this is not an ideal test because the census-based estimates are themselves estimates, derived from the same sample data as the geospatial based estimates. Nonetheless, the high coverage rate alleviates concerns about the validity of the estimates produced under the unit context model.

Recent research suggests that machine learning methods that allow for more flexible functional forms can improve small area prediction (Krennmair and Schmid, 2022, Merfeld and Newhouse, 2023). Exploring whether the use of machine learning methods improves prediction for out-ofsample areas is an area of current research focus. The performance for out-of-sample prediction will depend on the focus country and how well geospatial data predict poverty. Therefore, out-of-sample estimates under the unit context model in the four countries that lack recent census data should be interpreted with great caution and are likely to change when the next round of census-based estimates becomes available.

# **5. Assessment of experimental SAE estimates of head count poverty in Burkina Faso, Chad, Guinea, Mali
15 To explore the replacement effects of conflict-related deaths on fertility, we conduct crosssectional analyses with each of the post-genocide RDHS waves. The 2000 and 2005 RDHS provide insights into the short-term (five-year) and long-term (ten-year) effects of genocide on fertility, respectively. The dependent variable of interest is the number of children born alive in the aftermath of the genocide (while controlling for the number of children previously born). This estimation strategy builds on the idea that women may adjust their fertility in the post-conflict period, depending on the exact channel through which they were affected by the genocide. We exclude children conceived during the genocide (potentially through rape) from our analysis. Rather, the focus is on understanding how fertility decisions are made during the transition to peace. In the 2000 RDHS, the period of interest is May 1995 (when the first cohort of children conceived after the genocide is born) to June 2000 (the start of interviews for the 2000 RDHS). Similarly, in the 2005 RDHS, the dependent variable is the number of children born to a woman between May 1995 and February 2005 (when 2005 data collection began).

The determinants of fertility are estimated as a reduced-form equation, in line with the existing literature, as discussed above, with: where fertility _K_ of woman _i_ is a function of a constant α, a vector of the woman’s socioeconomic characteristics _Xi_ , district-level and province-level characteristics _D_ , region fixed effects _R_ , a proxy for conflict exposure _Conflicti_ (as discussed in Section 4.2), and a normally distributed error term _ui_ . The estimated coefficient _β4_ measures the impact of _actual_ conflict exposure during the genocide on fertility, relative to _all women after the genocide_ .

To explore the marriage market effects of conflict on fertility, we exploit the fact that the conflict variables are available for all data waves, including the 1992 RDHS collected before the genocide. We pool the three cross-sectional RDHS waves pair-wise, combining the 1992 and 2000 waves and the 1992 and 2005 waves
# **B Variable**

Table B1: Rider audits: demographic survey

|**Variable**|**Defnition**|
|---|---|
|Age|Median age in years of the rider’s age category when demographic<br>survey was responded|
|Employed|= 1 if rider had part-time or full-time job when responded to<br>demographic survey|
|High self-reported socio-economic status|= 1 if rider reported being a member of classes A or B|
|Low education (middle school or less)|= 1 if highest degree obtained by the rider at the time the demo-<br>graphic survey was responded is middle school or lower|
|Number of Supervia rides in typical a week|Number of times rider would normally ride the Supervia in a<br>typical week during which they are not taking any app rides|
|Single|= 1 if rider was not married when responded to demographic<br>survey|
|Years of schooling|Number of years equivalent to rider’s highest level of education<br>when demographic survey was responded|
|Young (18-25 years-old)|= 1 if rider was between 18 and 25 year-old when responded to<br>demographic survey|

Table B2: Rider audits: rides

|**Variable**|**Defnition**|
|---|---|
||All rides|
|Afraid of harassment|= 1 if rider reports being afraid that a stranger might touch them<br>intentionally during the ride in an inappropriate way|
|Against trafc|= 1 if rider is going in direction to the Central Station at the<br>evening or away from it in the morning|
|High crowding|= 1 if rider report of share of passengers standing in the car they<br>boarded is above the median|
|Moved within space|= 1 rider moved inside a car during the ride, but