rafmacalaba/data-use-mentions-extended huggingface dataset:
psychological research: Conceptual, strategic, and statistical considerations. _Journal of Personality and Social Psychology_ 51 (6): 1173.

Black, Sandra E, Aline Bütikofer, P Devereux, and K Salvanes. 2014. This Is Only a Test? LongRun and Intergenerational Impacts of Prenatal Exposure to Radioactive Fallout. _Unpublished manuscript._ Buckles, Kasey S and Daniel M Hungerman. 2013. Season of birth and later outcomes: Old questions, new answers. _Review of Economics and Statistics_ 95 (3): 711-724.

Bullock, John G, Donald P Green, and Shang E Ha. 2010. Yes, but whats the mechanism?(dont expect an easy answer). _Journal of Personality and Social Psychology_ 98 (4): 550.

Currie, Janet and Tom Vogl. 2012. Early-life health and adult circumstance in developing countries. _NBER Working Paper 18371._ National Bureau of Economic Research. National Bureau of Economic Research.

Flores, Carlos A. and Alfonso Flores-Lagunes. 2009. Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness. _IZA Discussion Paper No. 4237._ Hanson, Robert Harold. 1978. The current population survey: design and methodology 79
operate in their jurisdiction. Moreover, a larger number of any type of reforms or licenses reforms reduced market shares of establishments operating in these municipalities. The results support our conjecture that the elimination of regulatory barriers reduced firms’ rent-seeking opportunities.

# **7. Conclusion**

We use a unique data set for Peru that captures the elimination of subnational regulatory barriers to competition across about 1,800 municipalities and departments in 2013 and 2014 to estimate the impact of pro-competition reforms on establishment productivity growth. We argue that the specific legislative changes expanding Peru’s competition authority’s mandate in 2013/14 24
and 58 million IDPs.<sup>12</sup>

# 3. Methodology

The methodology we apply to analyze micro-level datasets in the UNHCR and WB MDLs follows a procedure that is systematic, replicable, and scalable (Figure 1). First, we scrape the metadata from all the micro-level datasets found on the UNHCR and WB MDLs.<sup>13</sup> We find 412 datasets from UNHCR MDL and 1,927 datasets from WB MDL that have been collected in the sample of countries under study.

Second, we remove _false positive_ datasets from the UNHCR MDL. While a large majority of datasets in the UNHCR MDL are individual/household-level household survey datasets sampled from refugees or IDPs, some are not. Furthermore, many datasets do not have a clearly defined sampling frame from which a representative sample can be drawn, thus failing to meet our inclusion criteria for this exercise. A few datasets listed in the UNHCR MDL are indeed based on key informant interviews (instead of a 11 Venezuelan refugees and migrants are included in this analysis due to their status as Persons of Concern (PoC) under UNHCR’s mandate. The reason why migrants are included here is laid out by UNHCR: “Venezuelan [migrants] refers to persons of Venezuelan origin who are likely to be in need of international protection under the criteria contained in the Cartagena Declaration, but who have not applied for asylum in the country in which they are present. Regardless of status, Venezuelan [migrants] require protection against forced returns and access to basic services.” (UNHCR 2020). In short, since Venezuelan migrants are likely to be in refugee-like situations, they are treated like refugees and thus should be included in this analysis.

> 12 The number of refugees, Venezuelan refugees and migrants and IDPs (in 2021) is taken from World Bank _World Development Indicators_ (WDI) <u>(https://databank.worldbank.org/source/world-development-indicators), UNHCR</u> Refugee Finder
Statistics.

# **6 Results**

## **6.1 First stage results**

Table 6 presents estimates of the first-stage regressions. Actual municipal revenue is robustly positively correlated with simulated revenue. Column (1) indicates that a 1 Reais increase in simulated revenue is associated with 0.42 Reais more revenue.<sup>37</sup> The F-statistic on the excluded

> 35I have population by age data for 1991, 1996, 2000, 2007. I compute annual estimates by linear interpolation.

> 36In Portuguese, Instituto de Pesquisa Econˆomica Aplicada.

> 37Several factors can explain why this coefficient is not equal to 1.0. First, municipalities can reduce collections of tax revenue in response to an influx of money from FUNDEF/B. Second, the panel is rather long; changes in the political and economic landscape of a municipality over time may drive the coefficient up or down.

22
Gibson and McKenzie (2010) we find that 90 percent of Micronesians and 95 percent of Tongans with bachelor degrees in their sample earned these degrees abroad, as did 28 percent of Ghanaians and 49 percent of Papua New Guineans with advanced degrees such as a masters or doctorate.

# **Question 7: Do High-Skilled Workers Remit, Invest, and Share Knowledge Back Home?**

The benefits that a sending country might receive from high-skilled immigration are usually listed in terms of return flows of income, investment and expertise from migrants back to the sending country. These are the kinds of benefits that underlie the claim by India‘s prime minister that modern India is benefitting from its high-skilled out-migration and the resulting global Indian diaspora. But how common are such benefits? As we have seen, brain drain rates are highest from countries with small populations and those experiencing political instability and poor prospects for career success. Thus while India‘s annual remittances have now reached $55 billion (World Bank, 2011) and India‘s high-skilled migrants have shaped the development of its impressive information technology sector (Saxenian, 2006), the experience of a country with a population of over one billion and a fast-growing economy may have few lessons to offer the small and/or unstable countries for which brain drain is at most an issue. New research offers a more mixed picture of the potential benefits of brain drain.

Consider first remittances, the most visible channel through which migrants contribute to households back home. Global remittance data at the micro-level are not currently publicly available, so we use a database of 33,000 immigrants in 11 OECD countries put together by Bollard et al. (2010). In this sample, skilled migrants remit more than less-skilled migrants, with the average tertiary-educated migrant remitting about $1,000 per year. We take the 6,318 migrants in their sample with tertiary education who come from countries with at least 50 skilled migrants in the database. We then remove survey fixed effects, and then,
firms may have different productivity levels. PPTT Assuming monopolistic competition and CES technology, HK derive T s in terms of revenue data as: PPTT T s s σσ ~~,~~ σσ−1 s<sup>YYs</sup> s<sup>)</sup> ααss 1−ααss PPTT = AA = κκ<sup>(PPs</sup> where is a function of output quantity and price and can be normalized to 1. In line with HK, KK LL let’s define the efficient sector's productivity level (when all marginal products are equalized) as: ��� 1 κκ =(∑ s ) . MMss σσ−1 σσ−1 TFPQ is calculated from AAss ss=1 AA s , which contains elements of distortions, and is typically not observed in the data: PP )<sup>ααss</sup> = s s ) ~~�~~<sup>RR</sup> ~~�~~ ααss �<sup>wws</sup> ~~�~~ 1−ααss kks σσ (1 + ττ PP yys ss ss σσ−1 AA (1 −ττ αα 1 −αα 6
# 4. Conclusion

This paper discusses the potential of artificial intelligence for the next generation of work. While most previous economic research has focused on high-income countries, this is one of the first papers to look specifically at middle- and low-income countries. The analysis applies information from the AI occupation exposure index developed by Felten et al. (2021) to a harmonized set of representative labor force surveys for 25 countries across income groups. The total sample contains about 3 million observations and represents a population of 3.5 billion people. The results show that substantial disparities persist across income groups: High-income countries have the highest exposure to AI, with more than one in two workers exposed, 62 percent. This is followed by uppermiddle-income countries with an exposure level of 49, lower-middle-income countries with 44, and low-income countries with lower exposure levels of 37. The persistent gap underscores the uneven global distribution of the potential benefits and challenges of AI. The result suggests heterogeneity in the impact of AI on labor markets across national contexts, which may have important policy implications.

The multivariate analysis of worker characteristics on occupational exposure to AI underlines the need for a more nuanced perspective across income levels: Education has a pronounced effect in lower- and upper-middle-income countries. This suggests that better educated individuals in these regions may be able to adopt or adapt more quickly to AI-induced changes in the labor market. Gender differences in AI exposure exist and are particularly pronounced in middle-income countries. Urban workers are generally more exposed to AI than rural workers, emphasizing the role of urbanization and occupational choice for a possible effect of AI technologies on employment, productivity, and earnings. We find further support for the idea that advances in AI will disproportionately affect professionals and technicians compared to those in elementary occupations. This notion is also supported by a sectoral analysis, which shows that white-collar industries are more exposed to AI, especially in low- to upper-middle-income countries, while blue-collar industries are less exposed.

Often, analyses build on AI approaches without considering the needed infrastructure.
ICP exercise. These flaws may be an important driver of the discrepancy between the price deflator in the ICP and the deflator used in the national accounts. Methodological changes in the 2011 ICP survey have resulted in substantial re-estimations of the size of many economies relative to the 2005 ICP (Deaton and Aten, 2017; Inklaar and Rao, 2011). Some of the discrepancies between national-accounts based measures of labor productivity and PPP-based estimates may also be due to inconsistent sampling methodologies for prices over time.

43
was not possible to control directly for school fixed effects in the econometric analysis.<sup>33</sup> This, however, seems to be less problematic in the RDD since treatment and control groups are in some cases comprised of children that attend the same

> 33 It was not possible to control for school fixed effects in the econometric analysis for at least three reasons. First, high school completion is observed only for individuals in the relevant ages that registered for the ICFES test and therefore it is impossible to know the school of children that were not matched to the ICFES database. Second, information with the name and code of schools exists only for a – probably not random- subsample of the students who took the ICFES test. Third, in many cases – particularly in small municipalities where the school supply is fixed – the inclusion of municipality fixed effects is expected to remove any existing school fixed effects.

27
program placement was random and impacts common across all units then one would not be concerned, although heterogeneous impacts cloud the picture, even in an experiment.<sup>9</sup> In nonexperimental evaluations, biases can be expected even under homogeneous impacts.

However, the literature on policy or program evaluation does not contain (to my knowledge) even a single example in which this type of baseline recall has been tested against conventional survey data collected at both the baseline and post-intervention.

This paper tries to help fill this gap in our knowledge. The paper reports on an experiment that was designed to test the idea of using retrospective data as a substitute for baseline data from a contemporaneous survey. After collecting baseline and post-intervention data for treatment and comparison units to allow estimation of a standard double-difference (DD), a series of recall questions were asked on how various dimensions of welfare had changed since the time the project was introduced. This allows what I will call the ―shoestring double difference‖ (SDD) estimator. More precisely, the two estimators of mean impacts are:

> Here  _Yi_  _Yi_ 1  _Yi_ 0 is the measured change in consumption between the baseline (date 0) and post-intervention surveys (date 1) for the respondent in household _i_ , where each respondent is assigned to either the treatment (the set _T_ ) or comparison group ( _C_ ), and<sup>_R_</sup> _i_<sup>denotes the</sup> subjective recall of the change in living standards over the same period.

Two versions of the SDD estimator are studied here: SDD1: This assumes that no baseline data are available. Only an ex-post survey can be done. Thus no adjustments are made for selection bias based on contemporaneously observed pre-intervention differences that might influence subsequent trajectories.

> 9 For example, suppose that the true impacts of a project are greater for poor people, for whom recall is less reliable. (There is supportive evidence for this conjecture in Das et al.
and Sjöholm 2005), which shape both domestic and foreign firm characteristics as well as the transmission channels for spillovers (Paus and Gallagher 2008).

Using a cross-section of more than 25,000 domestic manufacturing firms in 78 low- and middle-income countries from the World Bank’s Enterprise Surveys Indicator Database, <mark>we assess how mediating factors influence intra-industry productivity spillovers to domestic firms from FDI.</mark> This paper contributes to the growing body of research on mediating factors for FDI spillovers in several ways: First, most studies are limited to examining a single mediating factor, with the majority focusing on the absorptive capacity of domestic firms. Fewer studies analyze the role of foreign investors or host country characteristics and the institutional context for FDI-enhanced productivity spillovers. To our knowledge, only Havranek and Irsova (2011) control for all three types of mediating factor. Their meta-analysis uses the t-statistic of existing FDI spillover estimates from other studies as the dependent variable. In contrast, this study estimates the impact of FDI directly on productivity, and introduces all three types of mediating factors in the form of interaction terms with the FDI presence variable. While the methodological approach of this study is similar to Blalock and Gertler (2009), their study focuses only on a domestic firm’s absorptive capacities.

Secondm related to the relatively low number of studies that take into account the characteristics of foreign investors is the fact that studies have neglected the specific dynamics within global value chains (GVCs). Global production networks are “led by large firms based typically in the industrialized countries, and relying often on complex networks of suppliers around the world” (Milberg and Winkler, forthcoming). The potential for FDI spillovers, however, is determined by the GVC in which foreign firms operate and by the specific GVC dynamics, including the FDI motive and sourcing strategy, among others (Paus and Gallagher 2008). This study addresses this gap in the literature by including two measures that proxy for foreign investors’ FDI motive and sourcing behavior
# Educational Attainment of Formal and Informal Employees In European Countries, by Region. 2004-2009

<!-- Start of picture text --> HU PL BG LV RO EE SI SK CZ UA RU DK FI SE NO PT ES IT GR IL CY AT NL FR IE UK DE CH BE<br>East North South West<br>HU PL BG LV RO EE SI SK CZ UA RU DK FI SE NO PT ES IT GR IL CY AT NL FR IE UK DE CH BE<br>East North South West<br>Basic Secondary Higher<br>100<br>80<br>60<br>40<br>20<br>0<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Work status refers to the survey week Formal: Employee with a contract; Informal: Employee without a contract. Self-employed excluded Source: Calculation with ESS data

## **Figure 8 Formal and informal employees by educational attainment**

The contrast between skill composition of formal and informal employees is especially sharp in Poland, Bulgaria, Latvia, Denmark, Spain, Italy, Ireland and the Netherlands. However, it would be wrong to claim that generally informal employees in Europe are predominantly low-educated. In all countries except Poland, Hungary, Portugal, Spain, Italy, Austria, the Netherlands and France, three-fifths to nine tenths of those working without contracts have at least secondary Moreover, in Ukraine, Russia, Norway, Sweden, Israel, France, the UK and Belgium, 20 to 30% of informal employees hold university degrees, and in seven other countries this proportion is between 15% and 20%.

**_Occupations._** The generally lower educational attainment among informal employees results of course in a substantially different occupational composition than that found in the formal sector. Generally speaking, among informal employees one finds a
Andhra Pradesh, Bihar, Chhattisgarh, Jharkand, Orissa, Punjab, and Uttar Pradesh. Between 2008 and 2013, 336,770 girls were enrolled in the scheme (see section A3, Appendix).

For their evaluation, Sekher and Ram (2015) collected household survey data from 4,000 beneficiary and non-beneficiary households in 8 blocks across 5 states (Punjab, Bihar, Orrisa, Andhra Pradesh, and Jharkand) between 2013 and 2014. They used a propensity score matching (PSM) approach to evaluate how the behavior of beneficiary and non-beneficiary households differed,<sup>10</sup> after controlling for characteristics that predict beneficiary status. They supplemented this analysis with key informant interviews and focus group discussions with surveyed household members.

The study found that girls in beneficiary households fared better. They were more likely to be enrolled in school, and their parents were more intent on them pursuing higher education. Parents in beneficiary households were also more willing to delay their daughter’s marriage, and less likely to view their daughter as a liability. However, many parents in beneficiary households also saw the terminal benefits of the scheme as means to meet their daughter’s marriage costs, and there is no evidence that the program changed families’ preference for girl children. From a program design perspective, one of the major drawbacks highlighted was that there were no incentives for girls to complete secondary education (ibid).

Impact evaluations of other CCT programs also provide important policy lessons. Anukriti (2017) conducts an evaluation of the _Devi Rupak_ program that was implemented in the state of Haryana in 2002/2003. The program had the dual aim of reducing fertility and balancing the sex ratio at birth in the state (see section A3, Appendix). For the analysis, Anukriti (2017) pooled women’s birth-history data from the NFHS and the District Level Household Survey of India (DLHS). To isolate the treatment effect of the scheme on the number and sex-composition of children, she uses a difference-in-difference
during the boom period, and then falling relative to the rest during the crisis.

We also estimate (6) using these data to estimate the effect of the transmission of shocks across borders and the rate of adjustment back to the long-run equilibrium. We merge in gravity model data to explore the relative transmission of shocks across countries that share borders relative to the rest of the world. Incorporating the border effects is one way to estimate the relative importance of neighbors.

Table 1 shows that the model (6) performs as expected in the sense that the estimated coefficient on the first term is positive. The lagged difference terms are negative as expected but extremely small. These results are consistent with the idea that on average the effects of shocks in an average country on another average country are not large and that there is very little evidence of global convergence.

When we look at the relative importance of neighbors, as measured using the border dummy variable, we find that the countries that share borders have much more strongly correlated shocks. In Table 1, we estimate that the transmission of shocks across countries that share common borders is about four times that of countries that do not share borders. This result is stronger when we use the inverse of the log distance between countries as weights, which reinforces the idea that neighborhood effects are important. The effect of borders is also very prominent when estimating the speed of convergence, as shown in columns (3) and (4) of Table 1. The rate of convergence for countries that share a border is an order of magnitude larger than that of a random (noncontiguous) pair of countries.

Using GDP per capita data for 28 Latin American countries covering the period 19902013,<sup>5</sup> Figure 2 illustrates both the standard deviation and the average value of the absolute value

> 5 The data are PPP-valued GDP per capita in constant (2011) dollars from the World Development Indicators.

11 | P a g e
the regional road network. More recent data will reflect this change.

**Table 2.3 . Condition of East Africa’s regional road network by member country, based on 2008 data**

|Percent||**Co**|**ndition**|||**Type**||
|---|---|---|---|---|---|---|---|
||Good|Fair|Poor|Unknown|Paved|Unpaved|Unknown|
|Burundi|42.4|27.9|24.2|5.6|94.4|5.6|0.0|
|Kenya|40.5|38.4|21.1|0.0|79.5|20.5|0.0|
|Rwanda|66.7|33.3|0.0|0.0|100.0|0.0|0.0|
|Tanzania|44.9|35.7|3.7|15.7|60.0|40.0|0.0|
|Uganda|13.5|47.4|4.3|34.8|69.9|21.9|8.2|
|**EAC**|**38.0**|**38.7**|**13.2**|**10.2**|**73.3**|**25.3**|**1.3**|
|Ethiopia|43.5|10.9|11.3|34.4|69.9|30.1|0.0|
|Sudan|0.0|15.3|9.1|75.6|11.6
<br>urban rural urban rural urban rural<br>Months of household cons. Months of household cons. Months of household cons.<br>Months of household cons. Months of household cons. Months of household cons.<br><!-- End of picture text -->

# **7. Conclusion**

The combined forces of population aging, weakening family and village risk-sharing networks, and low formal pension coverage will make financing elderly consumption a major challenge for the future. Life expectancy (and with it, the number of unproductive years to be covered) has increased significantly since the early 1990s. In Pakistan, it went from 60.1 years in 1990 to 67.11 in 2018 (World Bank, World Development Indicators, 2018). While still relatively high compared to peer countries, the fertility rate in Pakistan has almost halved over the last thirty years, from 6.2 births per woman in 1990 to 3.5 in 2018, increasing the dependency ratio and reducing the availability of family or network members for support. Formal pension coverage is likely to remain low as a fraction of the labor force, as large cohorts of young workers enter the labor market, exceeding the availability 18
0.0058)|
|||||[0.0067]|[0.0075]|[0.0037]|
|Intercept|7.91|7.37|-0.54|7.90|7.31|-0.60***|
||(0.13)***|(0.15)***|(0.20)***|(0.14)***|(0.16)***|(0.22)|
||[0.34]***|[0.41]***|[0.27]*|[0.38]***|[0.47]***|[0.26]*|
|No. Observations|2833|2814|5647|2833|2814|5647|
|||**Estima**<br>|**tes of Mobility**|**from Quadrat**|**ic CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|7.91|7.37||**_IGRC_**|0.27|0.32|
|**_ES6 _**|9.53|9.29|||||
|**_ES9_**|10.34|10.25|||||
|**_ES16_**|12.23|12.49|||||

Notes: (1) The data used are CFPS 2016 with children aged 18-30. (2)