rafmacalaba/data-use-mentions-extended huggingface dataset:
|
|_Cereals and roots_|3,207.0|350.9|948.9|385.7|232.3|82|430.9|776.4|
|_Sugars and oils_|2,625.0|108.5|561.6|125.7|1,046.3|40.5|405.6|336.8|
|_Pulses_|67.3|13.4|10.1|5|6.6|1.8|17.6|12.7|
|**Livestock, 2010, mt**|24,884.6|1,997.1|9,503.5|1,638.0|3,599.1|2,020.2|1,983.5|4,143.5|
|_Poultry_|20,451.1|1,137.5|8,363.1|1,355.6|3,008.5|1,777.2|1,322.1|3,487.1|
|_Pigs_|939.3|31.0|538.9|64|82.4|0.1|12.6|210.4|
|_Ruminants_|3,494.1|828.6|601.6|218.4|508.2|243|648.8|445.6|

Note: Since urban expansion data use 2015 as the starting year, any crop or livestock that overlap with the 2015 urban extent are not counted toward the initial agricultural production.

In Table 3, we present descriptive statistics on the percentage of grids that have overlap in urban extent, crop production, and livestock production. For example, 30 percent of grids that have cereal production also have fruit production, 90 percent of grids have both pig and chicken production, 35 percent of grids have both cereal
**POLICY RESEARCH WORKING PAPER 6062**

# **Abstract**

This paper uses plant-level data to examine the impact of industrial and trade policy reforms on the geographic concentration of manufacturing industries in India from **1980** to **1999.** First, the research shows that delicensing and liberalization in foreign direct investment significantly reduced spatial concentration, but trade reforms had no significant effect on spatial concentration.

Second, plants respond differently to policy reforms based on their size. Liberalization in foreign direct investment and de-licensing caused small plants to disperse, while trade liberalization had the opposite effect. However, for large plants trade liberalization led to lower spatial concentration.

Tbis paper is a product of the Trade and Integration Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at afernandes@worldbank.org and sharmag@missouri.edu.

> r _he Policy Research Working Paper Series disseminates thefindings ofwork inprogress to encourage the exchange ofideas about development issues. An objective ofthe series is to get the findings out quickly, even ifthe presentations are less than fully polished. The papers carry the names ofthe authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those ofthe authors._ **_They_** _do not necessarily represent the views ofthe International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those ofthe Executive Directors ofthe World Bank or the governments they represent._

Produced **by** the Research Support Team
phone numbers and email address for some alumni. Given the interest in observing impacts on long term outcomes, the 2008 NYSC cohort was selected for the phone survey as it is the earliest year for which there is significant contact information for alumni.

The alumni survey includes questions on respondents’ current and previous locations, migration preferences, attitudes towards Nigeria and other ethnic groups, close relationships, demographic information as well as a quiz on knowledge of other states. To avoid priming respondents and to minimize experimenter demand bias, the survey was introduced as an alumni survey and there was no mention of NYSC until the last section of the survey.

The university sent out emails and mobile phone text messages to the contact list describing the alumni survey, requesting their current phone numbers and alerting respondents that they would be contacted by the research team. Trained enumerators attempted to contact all potential respondents with available phone numbers for a survey using Computer Assisted Telephone Interviewing (CATI) software. After successfully interviewing participants, interviewers asked them for names and contact information for their exclassmates. In addition, the survey team sent emails to all respondents to thank them for their participation and to request further referrals that may not have been provided during the phone interview. The team also used social media (Facebook, Linked In and webpages) to identify and contact individuals and request their current contact information but these avenues were less successful than the phone referrals.

Using all the contact information compiled, enumerators completed interviews with 644 participants, 51 percent of the 1,256 in the cohort. Only 4 percent of the attempted interviews were refused. Most unsuccessful attempts were due to a lack of current contact information for participants.<sup>13</sup> Among applicants the survey team could contact (i.e. 13Although most individuals in Nigeria have mobile phones, their phone numbers change frequently.

11
# **The Impact of Recall Periods on Reported Morbidity and Health Seeking Behavior**<sup>**1**</sup>

Jishnu Das Jeffrey Hammer Carolina Sánchez-Paramo Keywords: Health Surveys, Inequality, Recall Periods JEL Classifications: I10, I11, I14, I15

> 1 Das: World Bank, Washington DC and Centre for Policy Research, New Delhi; Hammer: Princeton University; Sanchez: World Bank, Washington DC. This paper is based on a survey of morbidity and health seeking among households in an urban location by The Institute of Socio-Economic Research in Development and Democracy (ISERDD) in Delhi. We especially thank Rajan Singh, Purshottam, Charu Sharma and Simi Bajaj, and all members of ISERDD for their long-term involvement and investment in this project. We thank Veena Das and Ranendra Kumar Das for helpful discussions and Maria Ines Berniell, Natalie Bau and Aakash Mohpal for excellent research assistance. Participants at the conference on ―Survey Design and Measurement in Development Economics provided invaluable inputs for revising an earlier draft. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent. Contact information: Jishnu Das: jdas1@worldbank.org.
same background or training. Notwithstanding the explanation for the lack of teacher effects, our key finding is that peer effects are robust to the inclusion of variables that control for teacher influence.

An alternative mechanism that might be related to our findings is that the nature of instruction in the course might differ depending on the background of the students who are enrolled. For instance, even with a rigid syllabus and random assignment, in a class where some students have positive returns, the teacher could be more affirming of the virtues of active trading. If investors with trading background dominate the class discussion, inexperienced students might get less instruction that is tailored to their needs. In turn, there would more participation after the course, but less success among new investors. Under such conditions, we should expect to find complementarities between the teacher and the students’ trading outcomes. For example, an instructor with high portfolio returns teaching in a class where experienced students also have positive returns could be more encouraging of stock trading. In column 4 of Table D.1, we include interaction terms between teacher returns and peer returns, but we do not find any evidence of complementarities between teachers’ and students’ outcomes. Our evidence, including our results using the survey sample, is most consistent with the idea that direct communication between classmates are driving the market participation decision.

# **6.4. EXTERNAL VALIDITY**

A final important concern regards the external validity of the findings. There are several important qualifications to the generality of the impact from selective communication on financial decision making. First, the type of social learning on which we focus differs from classic models such as Banerjee (1992) and Bikhchandani, Hirshleifer, & Welch (1992), in that peer choices and outcomes are revealed imperfectly. Many individual choices are accurately observed by members of a social group; for example, peer effects have been documented in automobile purchases (Grinblatt, et al., 2008); choice of workplace (Bayer, et al.
|
||Gov. affairs|4%|3%|
||Other|2%|4%|

Statista attributes the large expenditure in banking to the high risk posed by their data processing activities as a bank data breach runs the risk of handing over the financial information and resources of data subjects, therefore requiring heavier investments in cybersecurity.<sup>81</sup>

**Total Compliance Cost for FTSE 100 in Millions of U.S. Dollars**

|**Research Entity**|**Industry**|**Cost**<br>**of**<br>**Compliance**|**GDPR**|
|---|---|---|---|
||Banks|$93.8M||
|**Statista, 2018**|Technology & Telecoms|$28.5M||
||Energy & Utilities|$27.3M||
||Retail|$21.4M||
||Health care|$15.4M||
||Travel & Leisure|$14.2M||
||Financial Services|$11.3M||

> 81 Johnson, _supra_ note 61.

> 80 _Id._ at 28

14
# **7. Policy implications**

Analysis conducted as part of this study reveals that adoption of e-commerce could replace up to 9.1 percent of the trips made on weekdays in Bogota and between 7 percent and 12 percent of trips on weekdays in Buenos Aires. This corresponds to all trips for shopping purposes that are round trips and half of the trips for shopping purposes that are part of travel chains.

The reduction potential is greatest in the morning (though after peak hour), late afternoon, for short distance trips (< 10 minutes), and for trips made by non-motorized modes. The reduction potential among private vehicle trips is lower than among total trips, at 5.6 percent in Bogota and 8 percent in Buenos Aires. More than half of the trips by private vehicles for shopping purposes are part of travel chains, limiting their reduction potential. This implies that e-commerce has limits as a tool for reducing private vehicle use and externalities such as congestion during peak hours, given the concentration of shopping trips outside peak hours.

There is no evidence that the growth of e-commerce is having a significant substitution effect on shopping trips in either Bogota or Buenos Aires. Although the demand for trips to shopping areas in Bogota during 2019-2021 decreased more than to other areas of the city, the analysis by demand segments suggests that this was not due to the growth of e-commerce but that other phenomena (fall in employment in shopping areas, fall in consumption, etc.) are more explanatory of this evolution. Moreover, trips to shopping areas decreased less among the socioeconomic strata that are higher adopters of e-commerce. In Buenos Aires, the demand for trips to shopping areas decreased more than to other areas of the city in 2020, suggesting a certain substitution effect, but this trend reversed in 2021, suggesting that the steeper drop in 2020 was strongly influenced by the enforcement of mobility restrictions. Moreover, consumer surveys by CACE indicate that among online shoppers there was an _increase_ in visits to physical stores.<sup>
girls.<sup>15</sup> The results of the simulation (table 7) show only minor changes in the aggregate height-for-age z-scores when the mortality rates are increased from 5 – 8 percent (table 1) to 15 percent. The changes are statistically significant for the samples of boys and girls in 1998 and for the sample of boys in 2005, but even in these cases the magnitudes of the changes have not exceeded 3 percent.

# **V. Conclusion**

This paper used data from three rounds of a nationally representative health survey in India to assess the magnitude of the bias in children‘s anthropometrics due to selective mortality. The nutritional status of the child population was simulated under the counterfactual scenario that all children who had died in the first three years of life were alive at the time of measurement. These simulations imputed various values of heightfor-age z-scores to the sample of dead children. The simple simulations, with imputed z- scores that are independent of the child‘s characteristics, show that, at the rates of child mortality prevailing in India in 1992–2005, the selective mortality could have only a moderate impact on overall anthropometric measures. The imputations based on the literature on the association between child mortality and nutrition result in only a 5 percent difference between the counterfactual and the actual height-for-age z-scores.

The simulations based on the hazard model that takes into account differences in mortality and anthropometrics related to child characteristics are consistent with the observation that malnourished children are less likely to survive and thus to contribute to anthropomorphic measurements. However, the results also show that with the current low (and declining) mortality rates by historical standards, improved survival rates have an insignificant impact on overall height-for-age z-scores. The _changes_ in mortality between 1992 and 2005 imply that some malnourished children who would previously have died instead survived and are measured in the 2005 survey. To the degree that selective mortality affects overall malnutrition levels or rates, the reductions in child mortality mask some of the improvement in nutrition. However
Statistics_ , forthcoming.

- Garin, A., and F. Silv ́erio (2024): “How Responsive are Wages to Demand within the Firm? Evidence from Idiosyncratic Export Demand Shocks,” _Review of Economic Studies_ , forthcoming.

- Gopinath, G., E. Boz, C. C. F. Diez, P.-O. Gourinchas, and M. Plagborg-Moller (2020): “Dominant Currency Paradigm,” _American Economic Review_ , 110(3), 677–719.

- Grigsby, J., E. Hurst, and A. Yildirmaz (2021): “Aggregate Nominal Wage Adjustments: New Evidence from Administrative Payroll Data,” _American Economic Review_ , 111(2)(2), 428–471.

- Helpman, E., O. Itskhoki, M.-A. Muendler, and S. J. Redding (2017): “Trade and Inequality: From Theory to Estimation,” _Review of Economic Studies_ , 84, 1–36.

- Hummels, D., R. Jorgensen, J. Munch, and C. Xiang (2014): “The Wage Effects of Offshoring: Evidence from Danish Matched Worker-Firm Data,” _American Economic Review_ , 104(6), 1597–1629.

- Irarrazabal, A., A. Moxnes, and K. H. Ulltveit-Moe (2013): “Heterogeneous Firms or Heterogeneous Workers? Implications for Exporter Premiums and the Gains from Trade,” _Review of Economics and Statistics_ , 95(3), 839–849.

- Juhn, C., K. McCue, H. Monti, and B. Pierce (2018): “Firm Performance and the Volatility of Worker Earnings,” _Journal of Labor Economics_ , 36(S1), S99–S131.

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# **3.3 Sample Characteristics by Firm Type**

All analysis splits the sample into firms with and without workers during the weekly panel data period of March and April, 2015. Of the 342 firms in the analysis sample, 184 report no worker activity during any week in that seven week period. The remaining 158 firms, categorized as with workers, reported non-owner working hours for at least one of the seven weeks in the weekly monitoring data. Note that this measure of firms with and without workers is time-invariant, as it is defined as employing non-owner labor inputs at any time during the seven week panel.<sup>12</sup> Baseline characteristic comparisons between one-person and with-worker firms are presented in Table 1.<sup>13</sup> Firms with workers are predictably larger as measured by revenues, profits, and capital assets. They are also more likely to do things like keep formal business records (as measured by the adoption of management practices), register with the municipal government, and become a member of a trade association, evidence that these are more formal firms. Readers should interpret the findings with caution in light of these gravity differences between firms with and without workers; firms with workers are almost tautologically larger than firms without workers.

Perhaps reassuringly, there is no statistical difference between access to any manual or electric sewing machine across firms with and without workers, though firms with workers predictably have more of both. Firms with workers are more likely to report having any electric equipment (including electric sewing machines, but also other electric equipment like electric irons or embroidery machines), though this difference would tend against the finding that one-person firms are differentially negatively affected by unreliable electricity. It is also reassuring that most of the demographics are balanced across the two firm types, including gender, age, ethnicity, educational background, and Ravens (a measure of abstract cognitive ability).

During the panel data collection period, firm owners in one-person firms and in firms with workers
Defining household welfare is challenging and complex, and there is no perfect welfare indicator to measure it. Nevertheless, absolute poverty is usually a reasonable approximation of household welfare, correlating with vulnerability to natural hazards. Thus, we approximate household welfare using a monetary poverty measure, and we measure households' vulnerability to absolute poverty (poverty from now on) in a setting where it is possible that households suffer damage from natural hazards. In LAC, monetary poverty is usually computed over household incomes and not consumption since all countries in the region collect income information more widely and periodically, and only a few collect frequently consumption information. In addition, it is essential to recognize that the vulnerability concept encompasses the future well-being of the household, which is dynamic. One approach to achieving this is by utilizing long panel data. However, periodic panel datasets are not available in the region, and to overcome this, we propose a model based on a crosssectional study.

Since the concept of vulnerability includes the susceptibility to be harmed and the capacity to cope and adapt, we measure household vulnerability as the propensity of being poor conditioned to observed natural hazards' potential damage, community exposure, and current coping and adaptation strategies. We extend Chaudhuri's (2003) and Skoufias et al. (2021) approach, where shocks are unobserved, and explicitly introduce aggregate shocks through observed natural hazard variables in the model. We also incorporate some elements of Hill and Porter's (2017) study working at the household level. And we exploit the spatial variability of the cross-section data as much as the survey design allows (i.e., for some countries, we go down at the district level, and for others, at the state/province level). Thus, we propose to estimate the probability of being vulnerable to poverty using the following Probit model: P (P P H ), h,dd,rr dd,rr dd,rr = 1 | HH, EE, CC, DD) = ΦΦ
sup>9</sup> In other words, they are better-off than 60 percent of world population. The same interpretation applies to any other national ventile. Figure 1, using the example of BRIC countries (Brazil, Russia, India and China) and the US, illustrates vast differences in incomes which exist between countries, and in particular between the poorest ventiles of the population. While the poorest Americans are (as we have just seen) at the 60<sup>th</sup> global percentile, the poorest Brazilians and Indians are at the 3<sup>rd</sup> or 4<sup>th</sup> global percentile, that is among the poorest people on the planet. The poorest Chinese are around the 16<sup>th</sup> global percentile, the poorest Russians around the 37<sup>th</sup> percentile. Even more striking is the comparison of the income of the

> 8 All 2005 global data derived from nationally-representative household surveys conducted around year 2005 (see World Income Distribution (WYD) database available on http://econ.worldbank.org/projects/inequality).  In order to keep comparability with the precedent calculation for mid-19<sup>th</sup> century, I use price levels and PPPs that are commensurate with 1990 PPPs rather than the most recent version of PPPs  (year 2005). The latter gives an overall higher global inequality (70 Gini points rather than 65.4; see Milanovic 2011, and Milanovic 2012).  The share of the between-country component is also about 85 percent if we use more up-to-date PPPs.

> 9 The position of the poorest ventile of the rich country is indicated by a broken line in this and following graphs.

7
internalized social norms that justify abuse.  Similar to the surveys analyzed for PAHO 2012, the share of women who agreed with statements justifying wife-beating was higher among women who had experienced abuse than among those who had not, and may indicate either that women experiencing violence learn to “accept” or rationalize this abuse, or that women are at greater risk of violence in communities where a substantial proportion of individuals accept and tolerate abuse (Garcia-Moreno et al. 2005).

_15_
Gerber, Alan S., Donald P. Green, and Ron Shachar. 2003. ―Voting May Be Habit-Forming: Evidence from a Randomized Field Experiment.‖ _American Journal of Political Science_ 47 (3): 540–50.

Green, Donald P., Alan S. Gerber, and David W. Nickerson. 2003. ―Getting Out the Vote in Local Elections: Results from Six Door-to-Door Canvassing Experiments.‖ _Journal of Politics_ 65 (4): 1083–96.

Gerber, Alan S., Donald P. Green and Christopher W. Larimer. 2008. ―Social Pressure and Voter Turnout: Evidence from a Large-scale Field Experiment.‖ _American Political Science Review_ 102 (1): 33-48.

Harbaugh, W. T. 1996. ―If People Vote because they Like to, then Why do so Many of them Lie?‖ Public Choice 89 (October):63–76.

Horiuchi, Yusaku, Kosuke Imai and Naoko Taniguchi. 2007. ―Designing and analyzing randomized experiments: application to a Japanese election survey experiment.‖ _American Journal of Political Science._ 51(3):669–687.

Imai, Kosuke. 2005. "Do Get-Out-the-Vote Calls Reduce Turnout? The Importance of Statistical Methods for Field Experiments." American Political Science Review, 99 (2): 283-300.

Jacoby, Hanan and Ghazala Mansuri, 2011, ―Crossing Boundaries: Gender, Caste and Schooling in Rural Pakistan‖, mimeo Kling, Jeffrey, Jeffrey Liebman, and Larry Katz. 2007. ―Experimental Analysis of Neighborhood Effects‖ _Econometrica_ , 75 (1): 83-119.

Krackhardt, David. 1988. ―Predicting with Networks: Nonparametric Multiple Regression Analysis of Dyadic Data.‖ _Social Networks_ , 10(4): 359–81.

Kremer, Michael and Edward Miguel, 2001, ―Worms: Identifying Impacts On Education And Health In The Presence Of Treatment Externalities,‖ _Econometrica_ , Vol. 72, No. 1, 159–217 Lott, John R. Jr
to make an assessment of whether Indonesian teachers are up to standard. For the analysis we select two items from the 20-item math component of the subject-matter test of the Midline study (fielded in April-May 2011). Midline data of teachers is arguably the most reliable source of information we have, as both baseline and endline had to rely on smaller budgets for the field work. Especially, for midline, we managed in most cases to test teachers in the same classroom as their students, thereby minimizing the risk of collaboration among teachers. The two test items we look at are the 5