rafmacalaba/data-use-mentions-extended huggingface dataset:
were sensitive to fuel price increases remained the same across scenarios. However, the population living in these areas were much more sensitive to fuel price increases than their Rio counterparts, resulting in much higher travel budgets required to reach 10% of total jobs at higher fuel prices. This highlights the sensitivity of Kinshasa residents to fuel price increases, as transport is relatively expensive against incomes and all modes are dependent on fossil fuels. The only area that was relatively unaffected was parts of Socimat and Binza, where some of the wealthiest residents that cannot walk to 10% of jobs live.

# 4 Discussion and conclusion

This study evaluated how transit-based access to economic opportunities are impacted by carbon pricing in the form of a fuel-based levy in Kinshasa and Rio de Janeiro with varying travel time and budget thresholds. The goals of this study were to assess how average accessibility would be impacted in two cities of different economic status and with different spatial layouts, to understand which were the heaviest hit communities both in terms of their locations within the urban area and their economic status, to investigate how carbon pricing would impact inequality in accessibility, and shed some light on who and where the households excluded from the use of transit services are located. An overarching goal of the study was to understand whether the most commonly discussed climate policy, a carbon tax, would be regressive or progressive on accessibility to job opportunities, keeping in mind the fact that regressive policies tend to be resisted, regardless of their aggregate economic impacts.

The usage of Pareto frontiers to identify the optimal different itineraries between different origindestination pairs provides a computationally efficient way to conduct this analysis using both travel time and travel budget thresholds and could be used to assess the impacts of different pricing mechanisms or proposed new services in different cities. While GTFS feeds and socio-economic survey information that 28
Candidate variables were selected from the NHBPS 2014/2015 based on a review of the existing literature, hypothesized correlations to consumption, and the following criteria important for the field implementation of the PMT (Coady, Grosh, and Hoddinott 2004; Brown, Ravallion, and Van de Walle 2018; Sebastian et al. 2018):

1. It should be feasible and cost-effective to collect the required data on a variable for a significant share of the population in a timely manner.

2. Each variable in the PMT should be easy to measure and observe.

3. Each variable in the PMT should be difficult for the household to manipulate.

While it may be the case that surveyed household consumption is a more accurate measure of welfare, it is often difficult and costly to obtain such information from a large number of program applicants.<sup>10</sup> Additionally, if variables in the PMT are not observable and verifiable, households may have an incentive to hide or understate their assets and wealth in order to qualify for public assistance.

Based on these criteria, a broad range of candidate variables were selected from the household survey covering household head characteristics, household population characteristics, asset ownership, and employment. Table A1.1 in Annex 1 provides a list of the candidate variables included in this analysis along with their definitions. Summary statistics of for all candidate variables are presented in Table A1.2 in Annex 1.<sup>11</sup> In this paper, we propose a PMT and show how the use of a PMT could improve program targeting. The final design of the PMT targeting system and final administration, including the final choice of variables and survey forms, should be developed in consultation with the government agency tasked with managing the targeting system. Certain variables may be more easily manipulatable or difficult to verify in different country contexts.

Guided by the expected program design in Sudan and robustness analyses, we exclude state and area (e.g., rural and urban) indicator variables
# **5. Conclusion**

Notwithstanding the explorative nature of this study, the preliminary findings derived from the survey of industry actors appear to largely confirm a number of earlier findings reported in the literature concerning Singapore’s business environment for high-tech innovation and KIBS (see e.g. Wong 2004 and Wong and He 2004). The finding that Singapore is highly rated in terms of business efficiency but only moderately so in terms of innovation is also consistent with other international benchmarking indicators provided by sources such as the Global Competitiveness Report and the World Competitiveness Forum. Overall, the fact that the perception of Singapore’s chance to become an innovative city is only moderately favorable underlies the awareness of growing regional competition, particularly from China and India. Indeed, the need to become more innovation–driven vs. investmentdriven has been recognized by the Singapore government as a major strategic shift in the city-state’s future economic development as outlined in the recent Report of the Economic Review Committee (ERC 2002). However, the drive for innovation does not appear to imply a lesser need for efficiency; indeed, many of the industry actors indicated that an efficient business infrastructure is a pre-requisite for their locating their innovative activities in Singapore, suggesting a complementary relationship rather than a substitutional relationship between innovation and efficiency. Still, a few respondents did voice a need for Singapore to allow for a bit more “chaos” and to be more tolerant of non-conformance behavior (Florida 2002) to foster more creativity.

While the importance of innovation and the need to translate it into protectable intellectual property (IP) appears to be widely recognized by industry actors in all three sectors covered in our study, interesting differences can be discerned. On the whole, development of IP and its effective protection appear to be of greater importance to the high-tech, R&D-intensive manufacturing sector and the creative contents sector than to the KIBS sector. Reflecting perhaps the more tacit or process know-how nature of the knowledge in use in many KIBS activities, which are
# **3.3 Descriptive Statistics**

Table 1 reports the summary statistics of the BRMS sample of return migrants.<sup>16</sup> A clear majority of migrants are men. Women represent only 4 percent of returnees in the survey sample; this level is consistent with statistics from administrative data from the Bureau of Manpower, Employment and Training (BMET). The low level of female migration can be explained by the low labor-force participation of women in Bangladesh, and by difficult work conditions in the destination countries (International Labour Organization 2015; Farole, Cho, Bossavie, and Aterido 2017). In addition, there is social pressure on women to stay behind because they bear household responsibilities, and because low-skilled temporary migrants are not allowed to take their families with them. Given the small share of female migrants in the sample, combined with the fact that post-migration patterns are likely to differ between genders, we restrict our working sample to men.

Most temporary migrants in the sample have some secondary schooling, while 17 percent have never attended school and only 2 percent have some tertiary education. The average years of schooling in the male sample of return migrants is 6.5 years, which is low compared to average educational attainment across the world (Barro and Lee 2013). Returning migrants have a higher level of schooling than the non-migrant, male, working-age population (5.8 years), but their schooling levels are lower than those of migrants who are currently overseas (7.6 years) according to the HIES 2016. These differences are largely explained by the older age of returnees compared to current migrants and increasing education levels across cohorts.

About 75 percent of temporary migrants in the sample have returned from GCC countries. Saudi Arabia, with about 25 percent of migrants, is the largest destination and closely followed by the United Arab Emirates. Malaysia represents 13 percent of temporary migrants, and is the largest destination in Southeast Asia. Only a small minority of migrants in our sample were
Amare _et al._ , 2020; Ceballos _et al._ , 2020; Mahmud and Riley, 2020). Using nationally representative pre-pandemic household survey data and a follow-up phone survey from Mali, our difference-in-difference estimates highlight a dramatic increase in food insecurity. These difference-in-difference estimates rely on sub-national variation in the intensity of pandemic-related disruptions between urban and rural areas. Although we aim to validate our understanding of the variation in intensity of these disruptions with data on recorded COVID-19 infections, Google mobility data, and our phone survey data, it remains likely that the pandemic also disrupted food systems in rural areas. Therefore, our results in this paper are likely an estimate of the lower bound of the effect of the pandemic on food security in Mali. Despite this limitation, we estimate effects with meaningful magnitudes that are substantially larger than existing predictions of the effect of the coronavirus pandemic on food security globally.

Existing predictions of the effect of the coronavirus pandemic on food security globally—based on expected changes to income, prices, and food supply—point to an increase in the number of undernourished people in the world of 10–16 percent in 2020 (Baqueano _et al._ , 2020; FAO _et al._ , 2020). Our estimates document important heterogeneity in this global aggregate prediction. In Mali our difference-in-difference 16
# **VI. Growth Incidence Curves**

Average consumption in Africa as a whole increased by approximately 15% (or 0.92% per annum) between 1993 and 2008 (Table 3, Panel H), which is a very low growth performance. In terms of average consumption expenditure, resource-rich countries have performed better than the overall African average. Average consumption in upper middle/high income countries grew faster than in lower middle income countries, while there was a decline in low income countries.

The African (anonymous) growth incidence curve (GIC) (Figure 4) shows how the growth in mean consumption expenditure of less than 1% per year was distributed along the distribution.<sup>37</sup> For example, it shows how the average consumption of the bottom 5% in 1993 compares with the average consumption of the bottom 5% in 2008. Growth was particularly high for the top 5% of the distribution, whose average consumption grew at around 2% per year. The GIC is upward sloping throughout, so higher income groups benefited relatively more from growth between 1993 and 2008, and inequality increased. Consumption expenditure declined for the poorest 10% of Africans. The growth rates for all ventiles except the very top ventile were less than the growth of the mean, shown by the dashed red line. Furthermore, the bottom 30% of the distribution had a negative consumption growth and there were nearly no gains between the 30<sup>th</sup> and 40<sup>th</sup> percentile groups. The GIC drawn over the original decile groups without imputing the Lorenz curve also exhibits the same upward sloping shape (not shown here). When we use the balanced sample (i.e. the same set of countries in 1993 and 2008), the growth rate for the bottom 5% becomes positive, but it is important to note that the result of stagnation for the bottom 30% remains unchanged (not shown here).
**Figure 1: Number of Job Listings on Babajob and Average Number of Applciations per Listing by**

**Occupational Category, 2015**

<!-- Start of picture text --> Managers<br><!-- End of picture text --> <!-- Start of picture text --> Professionals<br><!-- End of picture text --> <!-- Start of picture text --> Clerical support workers<br><!-- End of picture text --> <!-- Start of picture text --> Skilled workers<br><!-- End of picture text --> <!-- Start of picture text --> machine operators, Assemblers,<br><!-- End of picture text --> <!-- Start of picture text --> Elementary occupations<br><!-- End of picture text --> <!-- Start of picture text --> Applicants per ad<br><!-- End of picture text --> Source: Authors’ calculations using Babajob data

# **5. Using Big Data to Analyze Labor Market Dynamics in India: the Case of Babajob**

A review of the existing literature and a thorough analysis of Babajob data revealed five ways in which online job-portal data can be used to formulate and refine labor market policies. These include (i) labor market monitoring and analysis, (ii) assessing demand for workforce skills, (iii) observing job-search behavior and improving skills matching, (iv) predictive analysis of skills demand, and (v) experimental studies.

# **5.1. Labor Market Monitoring and Analysis**

Online job-portal data can be used to monitor and analyze labor market trends in real time. Big data can complement official government statistics and other traditional forms of employment information-gathering, such as sample-based labor force and enterprise surveys
.” In _International Handbook on the Economics of Migration_ , edited by A. F. Constant and K. F. Zimmermann, 121–33. Cheltenham, Edward Elgar.

Friebel G., M. Manchin, M. Mendola and G. Prarolo (2018), “International migration intentions and illegal costs: Evidence using Africa-to-Europe smuggling routes.” _CEPR Discussion Paper_ , 13326.

Frouws, B., and C. Horwood (2017), “Smuggled South. Danish Refugee Council and Regional Mixed Migration Secretariat (RMMS).” <u>http://regionalmms.org/images/briefing/Smuggled_South.pdf.</u> Hunter D. R., Goodreau S. M., and M. S. Handcock. 2008, “Goodness of Fit of Social Net-work Models,” Journal of the American Statistical Association, 103(481): 248-258 Gathmann C. 2008. “Effects of Enforcement on Illegal Markets: Evidence from Migrant Smuggling along the Southwestern Border.” _Journal of Public Economics_ 92 (10–11): 1926–41.

Global Initiative against Transnational Organized Crime (2017). _The Human Conveyor Belt_ . _Assessing the Collapse of the Human-smuggling Industry in Libya and the Central Sahel._ Geneva, GITOC Hatton, T. 2004. “Seeking Asylum in Europe.” _Economic Policy_ 19 (38): 5-62.

Ibáñez A. M., and Andrés Moya. 2010. “Vulnerability of Victims of Civil Conflicts: Empirical Evidence for the Displaced Population in Colombia.” _World Development_ 38 (4): 647–63.

Ibarraran P. and D. Lubotsky. 2007. “Mexican Immigration and Self‐Selection: New Evidence from the 2000 Mexican Census.” in _Mexican Immigration to the US_ , edited by G. Borjas. University of Chicago Press, pp.159-192 IOM 2015. “Migration Trends Across the Mediterranean: Connecting the Dots. Prepared by Altai Consulting for IOM, MENA Regional Office.” https://publications
38 BiblingraphY

- Ajuha, Vinod, 1996, "Land Degradation, agricultural productivity and common property: Evidence from Cote d'Ivoire," Mimeo: University of Maryland.

- Alesina, Alberto, Reza Baqir and William Easterly, 1997, "Public goods and ethnic divisions," mimeo.

- Besley, Timothy and Anne Case, 1994, 'Diffusion as a learning process: evidence from HYV cotton," Princeton University Woodrow Wilson School of Public and International Affairs Research Program in Development Studies, Discussion Paper No. 174:1-19.

- Binswanger, Hans P, Shahidur R. Khandker, and Mark Rosenzweig, 1993, "How infrastructure and financial institutions affect agricultural output and investment in India," _Journal of Development Economics,_ 41:337-66.

- Borjas, George J., 1994, "Ethnicity, neighborhoods, and human capital externalities," _American Economic_ Review, 85(3):365-390.

- Case, Anne and Lawrence Katz, 1991, "The company you keep: The effects of family and neighborhood on disadvantaged youths," _NBER Working Paper_ #3705 (May).

- Coleman, James Samuel, 1990, Fonundatinns. nf soCial thenry, Cambridge, Mass.: Harvard University Press.

- Deaton, Angus, 1997, The Analysis of fTmtiRebhnId Surveys Mirmrecnnnmmetrin Analysis for flevelnpment Policy, forthcoming.

- Easterly, William and Ross Levine, 1996, 'Africa's growth tragedy: Policies and Ethnic divisions,' Mimeo (World Bank).

- Foster, Andrew, and Mark R. Rosenzweig, 1995, "Learning by doing and learning from others: human capital and technical change in agriculture," _Journal of Political Economry,_ 103:1176-1209.

- Fukuyama, Francis, 1995, Trtiit the sacial virtuieg and the rre2tinn nf pmsperity, New York: Free Press.

- Greif, Avner, 1993, "Contract enforceability and economic institutions in early trade: the Maghribi traders' coalition,' _American Economic Review,_ 83:525-48.
and sell more. They use more energy both during and post-intervention, with this increase statistically significant when measured in levels during the intervention. The log results suggest firms are using 17 percent more energy, although this is not statistically significant. In contrast, the pattern is more mixed for the individual treatment group, which has a statistically insignificant increase in energy costs when measured in levels, but statistically insignificant decrease when measured in logs.

Column 6 examines whether the improvement in management has resulted in higher labor productivity (measured as real sales per worker). The percent increase in employment for group treated firms is slightly higher than the percent increase in sales, and the result is a small, and statistically insignificant, drop in labor productivity (3 percent). The individual treatment also has a small and negative point estimate on labor productivity. These results contrast with the 17 percent improvement in productivity found in India by Bloom et al. (2013). However, since the improvement in management in our experiment is only one-third that found in India, a proportional improvement in productivity would be only 5.7 percent.<sup>15</sup> The confidence intervals of approximately [-13%, +8%] for the productivity effect found here include both the possibility of a productivity improvement of this magnitude, commensurate with what the existing literature would predict from a management improvement of the size, but also include the possibility that labor productivity fell.

Finally, columns 6 and 7 examine the extent to which any increase in sales came through exports. We use administrative data on exports, which have the advantage of being available for all firms and all months. Sixty percent of firms exported in at least one month between January 2013 and December, but on average, only 21 percent of firms export in a given month. As a result, most sales are domestic: exports are 0 percent of monthly sales for the median firm, and 3.8 percent for the mean; and even conditional on exporting
uence patient expectations regarding drug treatments is a difficult but urgent topic for further research. It could be as simple as public service announcements cautioning against the use of too many drugs or it might be as complicated as improving patients’ abilities to search for reliable and trustworthy care.

More data, a larger sample or a modi fi ed sampling strategy could have addressed the lack of information on patient attributes (matched to practitioner effort) and selection into the public sector. We explored using “standardized patients” (fake patients), who would mitigate the matching problem but discarded this possibility for ethical reasons. Because the doctor would need to be informed prior to sending such patients, it was unlikely that permission to carry on would have been approved. A second option, conducting exit surveys of patients on the day of the observation, turned out to be operationally difficult given the urban scenario and the crowded clinics. Concurrent work in Paraguay implements the matching exit survey (Das and Sohnesen 2005). The preliminary results are very much in line with what we fi nd here–practice patterns vary considerably across doctors, but very little across patients for the same doctor.

The differences between the public and private sector are the joint effects of selection and moral hazard. (altruistic doctors are in the public sector versus lazy doctors are in the public sector). A number of

> 27 MBBS degrees are offered mostly by public institutes where the budget is not itemized. Our fi gure of $8,000 is based on the fee for the MBBS degree in a private institute (Manipal Academy of Higher Education). This overestimates the true cost if governments operate as non-pro fi ts. It underestimates the true cost if (a) efficiency is lower in the government sector and/or (b) the quality of training is higher in the private sector.

19
to have an occupation in the agricultural stratum and are more likely to be in a NM occupation (for any stratum of origin), in comparison to men and non-migrants, respectively. The results of the relative mobility analysis confirm that both women, in comparison to men, and migrants, in contrast to non-migrants, have more social fluidity or equality in labor opportunities. Women experience less strength from occupational inheritance, and they do not face a barrier in longdistance upward mobility from the base of the stratification. Migrants, for their part, do not experience occupational inheritance, nor barriers in long-distance upward mobility from the agricultural stratum.

A comparison of ethnic backgrounds shows a small difference in absolute mobility rates. There is only a slightly higher probability of indigenous youths of having an agricultural destination when their provider had an occupation in the four strata with a lesser position in the hierarchy is observed, while they are more likely than those not-indigenous of inheriting their provider’s occupation if their origin is NM. Paradoxically, this would suggest that, although it is more likely that indigenous people, in comparison to non-indigenous, occupy the stratum of lower hierarchy, it is also more likely that they inherit their provider’s occupation when they come from a higher stratum. Nonetheless, the relative mobility analyses did not provide evidence that suggests differences in the social fluidity or equality in labor opportunities between indigenous and nonindigenous youths.

Regarding the comparisons of different treatment intensity, no substantial differences in the absolute mobility rates are observed between both groups, which may be partially due to the relative similarity in intensity with which our comparison groups participated of the program. In terms of relative mobility, it is important to note that the two groups experienced intergenerational inheritance, which has a differentiated strength for the different strata of origin, being stronger at both extremes. Nonetheless, youths of the high intensity group experience a greater probability of intergenerational inheritance when their origin is in the MN stratum and less probability of inheritance when they stem from the agricultural stratum. Therefore,