rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Discussion_

In an agrarian economy, as is the case in some LMICs under study, many jobs are in the primary sector (agriculture, forestry, mining) and may involve heavy manual labor, which people with physical difficulties may not be able to do. People with hearing or cognition difficulties, on the other hand, may not experience barriers to physical labor. The effect of disability on employment will also depend on the workplace, its accessibility, available accommodations and transport, and whether there may also be discrimination that might prevent access to employment and/or might lead to lower wages. The policy context is also relevant; for instance, vocational rehabilitation, disability insurance or social assistance programs could facilitate, limit, or not affect access to employment for disabled people depending on how they are designed and implemented.

Results in this paper are consistent with those from several studies in LMICs finding that persons with disabilities are less likely to be employed and are more often in the informal sector (WHOWorld Bank 2011; Mizunoya and Mitra 2013)<sup>5</sup> and that the relative employment rate tends to be higher in low-income countries compared to middle-income countries (Mizunoya and Mitra 2013). People with disabilities may be more often in the informal sector due to barriers they face in the formal labor market. This study further adds to the literature by showing the functional difficulty gradient in the disability gap and the higher idle rate among youths with disabilities.

Finally, this study shows that the unemployment rate, although it is part of SDG indicator to be disaggregated by disability status (indicator 8.5.2), should be used with caution and should be complemented with other labor market outcomes such as the employment population ratio. The unemployment rate is significantly higher among persons with any difficulty in six countries but lower in eight countries while the EPR was lower for persons with disabilities in almost all countries. Persons with functional difficulties may disproportionately drop out of the labor force by stopping to search for work and hence
**<u>│</u> 21**

Looking specifically at China, the developing country example of using FDI for export upgrading _par excellence_ , evidence has shown that a one standard deviation increase in foreign MNE presence is associated with a 6.3 percent increase in transaction unit values and a 1.3 percent increase in exports (Chen and Swenson, 2014). For high-income economies there is often a smaller technology gap to close, and so the presence of foreign MNEs has a smaller effect on the sophistication of domestic production (Harding and Javorcik, 2012).

# Innovation input

The strong association of foreign investment with productivity improvements suggests a direct link with innovation – both by the foreign multinational firm and its domestic competitors and suppliers. Channels of such effects could take place through adaptation of foreign multinational firm practice to local context and exposure of domestic firms to foreign production processes and practices.

Yet, empirical work on the relationship between FDI and host economy innovation, whether measured by input or output (R&D intensity or patents), is scarce. Most of the existing literature on China’s experience has found a positive relationship between FDI and domestic firm innovation. Looking at Chinese provincial-level data from 1995 to 2000, evidence shows positive effects of FDI on the number of domestic patents across three different kinds of patents (Cheung and Lin, 2004; Xian and Yan, 2005), though these effects are strongest for minor rather than major innovations. Separate evidence, from Mexico, finds that the entry of a foreign multinational retailer caused market share reallocations, exit patterns, and adjustments in innovative effort that increased both productivity and the rate of innovation (Iacovone et al., 2009).

The Enterprise Surveys confirm that the innovation input intensity of foreign multinationals is higher than that of domestic firms in most countries, and even more often in countries highly integrated in global value chains with significant upgrading taking place over the last years (see Fig. 6). Innovation intensity is also associated with
# **Acknowledgements**

I would like thank Todd Pugatch, Alison Johnston, Paul Thompson, Patrick Emerson, Jennifer AlixGarcia, and Olga N. Shemyakina for their invaluable comments, guidance, and support. I am thankful to John Ashton Loeser, Marcus Holmlund, Daniel Rogger, and Arianna Legovini from the World Bank for their support, insightful feedback, and suggestions. I am also very grateful to DIME Analytics at the World Bank for their incredible support in verifying the computational reproducibility package of this study. I am equally thankful to the workshop/seminar participants at the OSU SPP Brownbag, OSU Dev Econ group, AMIE Workshop in Applied Microeconomics 2022, AEFP Doctoral Students Series 2022, PNW Labor Day Workshop 2022, NWDW 2023, PacDev23, Development Econ Series at University of Georgia, SEA 2023, SEEDS 2023, and the anonymous reviewers for insightful comments. All errors lie solely with the author.

# **Funding**

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

# **Data availability**

The dataset used in this study comprises the Kankor exam results for all test-takers from 2013–2018, collected from publicly available online sources. The author compiled a six-year individual-level dataset and constructed concentration- and major-level datasets. The author is willing to make the aggregate data and the Stata codes used for analysis available online upon publication. Individual-level de-identified data can be shared on a case-by-case basis. Computational reproducibility verified by DIME Analytics. Please see details of the reproducibility in the online appendix or this LINK.

# **Disclosure statement**

None

# **Declaration of Generative AI and AI-assisted technologies in the writing process**

During the preparation of this work, the author used ChatGPT-4 in order to identify grammatical errors in some sections of the draft version of the paper before rigorous proofreading. After using this tool/service, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.

33
services warrants special focus. The access to services through allowing foreign affiliates to enter and sell services in the domestic market (Mode 3) and reforming FDI restrictions are important for the effective provision of network services that connect the various stages of the production process. India’s level of FDI restrictions in services is relatively high compared to the OECD average and even some non-OECD countries.<sup>9</sup> Reducing services policies’ restrictiveness could potentially improve the country’s participation in services GVCs.

Additional focus on other types of policy barriers in services is also needed. An enabling environment is essential to accompany regulatory policy reforms. Generally, strong factor endowments can enable participation in more sophisticated GVCs as they allow for more skills and capital to be exploited in the production process. Once regulators achieve this goal, services are most likely to be used as inputs further downstream by firms, which in turn makes these firms more productive (van der Marel 2016; Beverelli et al. 2017).<sup>10</sup> The role of domestic institutions becomes even more important for services when it comes to regulators and the regulatory quality.<sup>11</sup> Services markets often suffer from market failures, such as

> 9 These include Brazil, Cambodia, China, the Lao People’s Democratic Republic, South Africa, Vietnam etc., as per the OECD FDI restrictiveness index.

> 10 Besides regulations and domestic institutions, additional factors such as human capital, particularly know-how and managerial capabilities are also important to foster better linkages of services with other sectors and facilitate integration into global markets. For a discussion on the role of these enabling factors, we refer the interested reader to Huria et al. (2019).

> 11 While the reforms introduced in 1991 in India considered the establishment of specialized regulators, the required institutional modernization of the country, which plays a critical role in a market economy, has been lagging (Lateef 2017). Roy et al. (2019
(Table 2). Only 12.1 percent of MENA firms have a loan or line of credit, compared to over 40 percent in the Eastern Europe and Central Asia and Latin America and Caribbean regions respectively.

Figure 3. GCI Financial Markets Indicator (2005-2009), MENA vs Non-MENA <!-- Start of picture text --> ISR<br>BHR<br>MLT<br>QAT<br>KWT ARE<br>JOR OMN<br>SAU<br>TUN<br>MAR<br>EGY<br>SYR<br>DZA<br>7 8 9 10 11<br>Log GDP per capita (2005,USD)<br>Source: GCI, WDI<br>6<br>5<br>gci8finmkt<br>4<br>3<br><!-- End of picture text --> Table 2. Finance <u>Characteristics, By Region</u>

||% Firms with a checking<br>orsavings account|% Firms with a bank<br>loan/line ofcredit|Proportion of loans<br>requiring collateral(%)|
|---|---|---|---|
|MENA|59.3|12.1|75.2|
|EAP|87.7|37.6|76.6|
|ECA|90.2|40.1|80.3|
|LAC|92.9|47.6|72.4|
|SAR|78.9|34.8|84.3|
|AFR|88.1|23.8|79.7|

_Notes: Surveys were implemented following the GLOBAL sampling methodology. However, the MENA average includes nonGLOBAL surveys since region has not had a complete survey roll-out. MENA average includes Algeria 2007, Egypt 2008, Iraq 2011, Jordan 2006, Lebanon 2009, Morocco 2007
June 30, 2022.<sup>15</sup> As such, approximately three in each four Ukrainian refugee students in secondary school age were not enrolled in Italian schools in academic year 2021-22.

In the following years, enrollment rates have shown improvement despite still remaining low. As shown in Figure 1, in academic year 2022-2023 around 32% Ukrainian refugees were enrolled in Italian secondary schools,<sup>16</sup> with the percentage becoming 36% in academic year 2023-24.<sup>17</sup> These numbers remain low compared to the enrollment rates for other foreign students. According to the data from the MoE, the enrollment rate of students with non-Italian citizenship, except for preprimary schools, is close to that of Italians. In particular, in grades 1-8, the enrollment rate is close to 100 percent; in grades 9 to 11, it reaches almost 90 percent; on the other hand, in the last three years of secondary school, corresponding to the start of non-compulsory school years, the enrollment rate of students with non-Italian citizenship decreases to 78 percent compared to 83 percent for Italian students (MoE, 2023).<sup>18</sup> Analysis of MoE data covering academic years 2022-23 through 2023-24 highlights a substantial dropout trend from the Italian school system among displaced Ukrainian learners.<sup>19</sup> Only 58% of Ukrainian refugee students initially enrolled in Grades 6 to 12 during the 2022-23 academic year continued their education within the Italian system the subsequent year, suggesting a 42% drop-out rate.<sup>20</sup> Figure 2 shows that the dropout rate among Ukrainian refugees is more than twice as high as that of other foreigners and six times higher than that of Italian students. These numbers are in line with observations from previous academic years, when the dropout rate of foreigners was three times higher than that of Italians (30.1 percent vs. 9.8 percent) (ISTAT, 2023). No
Central Role of The Propensity Score In Observational Studies For Causal Effects”, _Biometrika_ 70: 41-55.

- Ruggles, Steven, Matthew Sobek, Trent Alexander, Catherine A. Fitch, Ronald Goeken, Patricia Kelly Hall, Miriam King, and Chad Ronnander (2004) _Integrated Public Use Microdata Series: Version 3.0_ [Machine-readable database]. Minneapolis, MN: Minnesota Population Center, http://www.ipums.org

- Sobek, Matthew, Steven Ruggles, Robert McCaa, Miriam King, and Deborah Levison (2002) Integrated Public Use Microdata Series-International: Preliminary Version 1.0. Minneapolis: Minnesota Population Center, University of Minnesota..

- Smith J and Todd P (2005). “Does Matching Overcome Lalonde’s Critique of Nonexperimental Estimators.” _Journal of Econometrics_ 125 (1-2): 305-353.

- Woodruff, C. and  Zenteno, R.  (2001). “Remittances and Microenterprises in Mexico.” Working Paper, UCSD and ITESM-Guadelejara, December.

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consumable income, after all taxes (direct and consumption taxes), _cash or near-cash_ transfers and subsidies have been accounted for, the estimated poverty rate drops to just less than 24 percent.<sup>45</sup>

**Figure 10. Fiscal Policy’s Impact on Inequality (bars and right axis) and Prefiscal Inequality (dots and left axis), Select Countries/Years**

<!-- Start of picture text --> 0.00 0.0<br>‐2.0<br>0.10<br>‐4.0<br>0.20<br>‐6.0<br>0.30<br>‐8.0<br>0.40<br>‐10.0<br>0.50<br>‐12.0<br>0.60 ‐14.0<br>Gini index<br>Difference in Gini index<br><!-- End of picture text --> _Sources:_ Egypt: based on HIECS 2015 and budget figures from FY14/15 and FY15/16. Georgia: Cancho and Bondarenko 2017. Iran: Enami, Lustig, and Taqdiri 2017. Mexico: Scott 2014. Tunisia: Jouini, Lustig, Moummi, and Shimeles 2018. Armenia: Younger and Khachatryan 2017. El Salvador: Beneke, Lustig, and Andres Oliva 2018. Sri Lanka: Arunatilake and Lustig 2017. Indonesia: Jellema, Wai-Poi, and Afkar 2017. Guatemala: Icefi. 2017a. Jordan: Alam, Inchauste, and Serajuddin 2017. _Note:_ Fiscal impact on inequality is plotted with bars and on the right-hand axis as the difference in Gini Index of the market income distribution and the consumable income distribution. Prefiscal inequality is plotted with dots and measured as the Gini index on the left-hand axis.

**Figure 11. Fiscal Policy’s Impact on the Poverty Headcount Ratio**

<!-- Start of
men than for women. It is important to note that these findings could be driven by the positive impact that Venezuelans have on the labor market outcomes of Peruvians.

Overall, we find that increased Venezuelan migration leads to improvements in both objective and subjective measures of the lives of Peruvians living in the same locations. This may explain why, even though some media has discussed Venezuelans in a negative light, there has been little political backlash against them in Peru up to this point in time.

# **6 Conclusion and Policy Implications**

In this paper, we study the economic underpinnings of hostility and discrimination against immigrants. In our analysis, we first use a specialized survey of Venezuelan immigrants in Peru to identify the causal effect of local labor markets on discrimination against immigrants. Then, we studying the flip-side of this analysis, namely, how does the presence of Venezuelan 18
**Figure 4. Distribution of pre-earthquake asset index**

<!-- Start of picture text --> Asset Index Density Before Earthquake<br>0 20 40 60 80 100<br>Asset Index<br>EFSA I<br>EFSA II<br>kernel = epanechnikov, bandwidth = 3.7832<br>.04<br>.03<br>.02<br>.01<br>0<br><!-- End of picture text --> Source: Own computations using February 2010 (EFSA I) and June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. Asset index weights are calculated using EFSA I pre-earthquake assets.

To compare the retrospective answers in both surveys, we have built an asset index, obtained from a multiple component analysis using pre-earthquake housing characteristics and household durable goods. This asset index is thus a proxy for household wealth. Weights are estimated with the February survey and are used to build asset index in both surveys. Figure 4 shows that pre-earthquake asset index distributions are very similar. So, recall bias might be small in the June survey.

# _(c) Assets_

Based on the June survey, an asset index is calculated using a wider set of pre-earthquake dichotomous variables, namely some durable goods not declared in the February survey and access to basic utilities.<sup>14</sup> Table 2 (in Appendix) reports both weights and contributions to inertia. Weights have signs consistent with interpretation of the first component as an assetpoverty index. In directly affected areas, contribution to inertia of lighting appears to be particularly high (26.7%). Water source also contributes in a large extent to inertia (18.9%).

> 14 Note that all our results have been replicated using a more restrictive definition of the asset index for which water source, water filtration
growth was shared along the distribution, we find that the growth incidence was broadly pro-rich – in line with the observed increase in inequality. The pattern of growth was such that the richest 5% of East Asians received more than 20% of the gain in regional income, compared with less than 1% for the poorest 5%. In the most recent period, however, the upward trend in regional inequality appears to have reversed, as within-country inequality has stopped increasing.

The paper is structured as follows. First, we explain the construction of our database from household surveys. Second, we study the regional distribution, its inequality and the contributions of within- and between-country differences. Third, we examine both relative and absolute gains along the distribution using growth incidence curves. Fourth, we analyze relative movements of specific country groups in the regional distribution, and the resulting changes in the regional composition. The Appendix includes further methodological details, and robustness checks.

> 3 The literature on global inequality is reviewed in Milanovic (2005), Atkinson and Brandolini (2010), Anand and Segal (2015) and Milanovic (2016). Similar to our paper, Ravallion (2014a) estimates inequality in the developing world.

3
and cassava migration and the respondent died in 2 1-member households. There was no other loss of sampled households due to refusal and all diary households kept their diaries for as long as they had cassava standing on their plots during the fieldwork period.

13 The experiment used 15 enumerators, and each enumerator covered 3 EAs. EAs were divided among the staff in such a way that enumerators could reside within one of their EAs or a nearby district center, and bicycle to their assigned households.

> 14 A garden ( _munda_ ) is defined as a continuous piece of land that is not split by a river or a path wide enough to fit an ox-cart or vehicle. A garden can be made up of one or more plots. A plot is continuous piece of land on which a unique crop or a mixture of crops is grown, under a uniform, consistent crop management system. It must be a continuous piece of land and must not be split by a path of more than one meter in width. Plot boundaries are defined based on the crops grown and the operator.

15 After walking the perimeter of a given plot with the plot manager to identify the boundaries, the enumerators repaced the perimiter and measured the area with the GPS unit.

16 The allowable units for self-reported plot areas were square meters, acres and hectares. The majority of respondents reported in acres, with approximately 5 percent reporting in hectares or square meters.

17 Further, the enumerator obtained a leaf sample from each sub-plot for objective variety identification based on DNA fingerprinting. Each leaf sample was associated with a barcode that was scanned by the Survey Solutions CAPI platform to enable the linkage between the lab results and the household survey data. The assessment of the relative accuracy of farmer-reported variety names and attributes, in comparison to DNA fingerprinting-based cassava variety identification, is the subject of a separate research study.

18 Given the within-EA proximity of CVIP households to one another, it is possible that the enumerators and the respondents may have seen one another on a more frequent
Another important area for further work is to continue to experiment with additional forms of geographically comprehensive auxiliary data to better predict labor market outcomes. This could include, for example, information on building footprints or the presence of different types of businesses. New types of geospatial data are being released each year that can be incorporated into this type of approach.

There is room for further work on model diagnostics. The results of this study demonstrate that R<sup>2</sup> can be a misleading metric for assessing the usefulness of the model, since even models with relatively low R<sup>2</sup> s can contribute important information to reduce bias in the predictions (Marhuenda et al., 2017). At the same time, model R<sup>2</sup> s are much higher in the full enumeration sample, but the small area estimates contribute little additional information. An important research agenda for further work is to better understand which diagnostic indicators, in cases where census data is not available, can provide a rough assessment of the gains from incorporating geospatial data.

Another open question is how optimal sample design changes in the presence of free, predictive geospatial data that can be linked to surveys. The evidence presented above indicates that estimates for sampled municipalities are substantially more accurate than non-sampled municipalities, suggesting that surveys should try to cover all target areas if possible. It is not clear, however, how the benefit of small area estimation relates to the sampling structure, and what this implies for optimally structuring household surveys.

Importantly, it remains to be seen how well the results here will generalize to other contexts. As such, it will be important to conduct studies to evaluate the methodology in other settings before applying it generally. This is easier said than done, however. In order to validate the methodology, it is best to use a census – or an unusually large survey –as a measure of ground truth. Furthermore, either the survey or ground truth must
# **4.3 State household IT-use**

As mentioned before, in order to construct our instrumental variable, we combine IT sectoral intensity for the US obtained from Bloom et al. (2012b) with household IT use.

Information regarding household IT use at the state level is from the National Population and Housing Censuses 2000 and 2010. We also obtained information from the Module on Information Technology Availability and Use in Households (MODUTIH 2010 and 2013) in order to compare it to the census data.

Considering the great differences between the rural and urban sectors in some states, alternative household IT use variables were constructed restricting the sample to localities with more than 15,000 inhabitants (urban IT use).

# **4.4 Descriptive Statistics**

As mentioned before, the random sample design of the ENTIC surveys, made it possible to match only 701 firms and around 88% of them are big firms, as they are the ones that must be in both samples in order to achieve statistical representativeness. This fact has important implications regarding the interpretation of our results given that previous studies suggest that small firms are the ones more likely to exit and be negatively affected by the increasing competition (Iacovone et al., 2013). In this case, due to these data limitations we are only able to analyze the extensive margin.

Consistent with the size of the firms in our sample, approximately 50% of the firms have a share of FDI higher than 80%. Accordingly, the mean of exports/total sales is much higher than the one observed for the whole ENTIC. Table 1 shows descriptive statistics for the main variables used in this analysis for our sample and the whole ENTIC sample.

Analyzing IT use at the sectoral level, as Figure 1 shows the sectors that make more intensive use of IT measured as computers-per-worker are chemical products followed by computer equipment and electronics and beverages and tobacco. The second sector is not only an IT-use sector but an IT-producing sector. It is important to note