rafmacalaba/data-use-mentions-extended huggingface dataset:
Living Standards Survey|Oct 2016|Oct 2017|$1,605|$899|$1,294|
|**Kenya**|Integrated Household Budget Survey 2015-2016|Sep 2015|Aug 2016|$1,373|$621|$950|
|**Lesotho**|Household Budget Survey|Jan 2017|Feb 2018|$1,280|$721|$942|
|**Liberia**|Household Income and Expenditure Survey 2016|Jan 2016|Dec 2016|$1,114|$597|$864|
|**Malawi**|Fourth Integrated Household Survey 2016-2017|Apr 2016|May 2017|$764|$280|$379|
|**Mauritania**|Enquête Permanente sur les Conditions de Vie des Ménages (EPCV)<br>2014|Apr 2014|Jan 2015|$1,555|$1,097|$1,325|
|**Mauritius**|Household Budget Survey 2017|Jan 2017|Dec 2017|n.a.|n.a.|$4,066|
|**Namibia**|Household Income and Expenditure Survey 2015-2016|Apr 2015|Mar 2016|$5,148|$2,046|$3,726|
|**Niger**|National Survey on Household Living Conditions and Agriculture<br>2014, Wave 2 Panel Data|Sep 2014|Mar 2015|$1,082|$526|$620|
|**Nigeria**|Nigerian Living Standards Survey 2018/19|Oct 2018|Sep 2019|$1,025|$622|$782|
|**South**<br>**Africa**|General Household Survey 2018|Jan 2018|Dec 2018|n.a.|n.a.|n.a.|
|**Sudan**|National Household Budget & Poverty Survey 2014/2015
br>(0.151)|
|||Panel B: School Characteristics|
|Average test score (Spanish) 2010|425.173|431.340<br>0.158|
||(57.245)|(60.810)|
|Average test score (Math)|415.998|421.333<br>0.363|
||(76.967)|(80.895)|
|Number of students|14.023|14.770<br>0.205|
||(8.403)|(7.069)|
|Number of local instructors|1.216<br>(0.449)|1.279<br>0.054<br>(0.514)|
|Share students over-age|3.125|3.620<br>0.264|
||(6.285)|(5.629)|
|Observations|1,475|230|

_Notes_ : Means and standard deviations in parentheses for various characteristics collected before the introduction of the API program. The last column shows asymptotic _p_ -values for mean differences between the overall population and the experimental sample. Panel A shows community-level characteristics from the population census (2010), whereas Panel B displays school-level variables from the school census (2010). See Appendix A.1 for more details on the data sources.

high “marginality,” as defined according to the National Population Council or CONAPO (Poverty 1), and whether the community was targeted by an anti-poverty program (Poverty 2).<sup>13</sup> In the sample of schools in the second experiment, approximately one-third satisfy the Poverty 1 criterion, 70 percent satisfy the Poverty 2 criterion, and 25 percent satisfy both criteria. We run separate regression models for three summary outcomes of the intervention on both students and parents: the overall index of student achievement, the indicator
# **1 Introduction**

An increasing number of regional trade agreements (RTAs) have been signed during the last decades, with many of them covering provisions going beyond purely trade-related clauses. In particular, an increasing number of RTAs contains provisions regulating and easing the bureaucratic procedures to obtain a visa, and the cross-border movement of individuals. Such a reduction in the administrative cost of moving across countries, i.e. red tape for migration, may affect the optimal migration choice for potential migrants at origin. Conditional on other _push_ and _pull_ factors of migration – see Hatton (2005) and Mayda (2010) – the reduction in the administrative cost to obtain a visa is expected to affect the potential migrant’s choice about his/her optimal destination country, and hence may favor the settlement of migrants among an RTA’s signatory countries.

This paper benefits from the recent World Bank data on the content of RTAs and studies the effect of visa-related provisions in RTAs on the bilateral settlement of migrants. Using UN data on bilateral migration stocks in the period 1990-2020, we show that including visa provisions in RTAs stimulates bilateral migration stock by 5.9%. An Instrumental Variable (IV) approach addresses the endogeneity concerns and confirms the positive effect of visa provision on bilateral migration. Interestingly, we show a strong heterogeneous effect of visa provisions. This is magnified among countries having different income levels (i.e. North-South); and dampened in destinations with a strong anti-immigration sentiment of voters (i.e. destinations having a large share of votes for the extreme right party).

Visa provisions in RTAs may target specific types of individuals and regulate a wide spectrum of migration-related frictions. For example, visa provision may apply to a restricted set of professionals (i.e. specific job profiles), or to all individuals of RTAs’ member countries. Also, in some cases visa provisions grant the free mobility of individuals across signatory countries, or just simplify the administrative burden to obtain a visa
richest quintile. The poorest households are also most reliant on the remittances that they receive from abroad: remittances account for 11 percent of household income in the poorest quintile households, and about 8 percent in the richest quintile. The two most important uses of remittance income are for food and to buy or improve housing (combined, these account for about 82 percent of the primary use of remittance income). Expenditure on food is substantially more common among poorer households. It is important to note however that as remittance income tends to relax household budgets in general (the income is “fungible”) this is a rather subjective measure of the welfare impact of remittance income.

**Figure 2: Migration and Use of Remittances**

<!-- Start of picture text --> 30%<br>Primary use for remittances<br>25% EducationOther<br>Health<br>20% Pay debt<br>Wedding<br>15%<br>Impr./buy<br>10% housing<br>5%<br>0%<br>Poorest 2 3 4 Richest<br>Food<br>Share with Migrant Household Member<br>Average Remittance Share of Income<br><!-- End of picture text --> _Notes: Listening to the Citizens of Uzbekistan Baseline Survey, Author’s calculations_ These relationships can be comprehensively summarized using a simple ordinary least squares (OLS) regression framework, though such cross-sectional comparisons are only descriptive. Table (4) reports such a set of descriptive regressions for which the dependent variable is equal to one when a household member is a current migrant, and zero otherwise. The coefficient in column (1) is negative and significant, indicating that households with current migrants abroad have lower per capita consumption on average than those that do not. This comparison controls for which province the household lives in, whether the household resides in an urban location, and the size of the household. Column
Working Paper No. 221/2008.

- Herrmann, Sabine and Dubravko Mihaljek, 2011. ―The Determinants of Cross-border Bank flows to Emerging Markets: New Empirical Evidence on the Spread of the Financial Crisis.‖ Bank of Finland, Institute for Economies in Transition (BOFIT) discussion paper3/2011.

Iarossi, Giuseppe. 2006. _The Power of Survey Design_ . Washington DC: World Bank.

- Kamil, Herman and Kulwant Rai, 2010. ―The Global Credit Crunch and Foreign Banks‘ Lending to Emerging Markets: Why Did Latin America Fare Better?‖ IMF Working Paper WP/10/102.

- Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi, 2007. ―Governance Matters VI: Governance Indicators of 1996-2006.‖ World Bank Policy Research Working Paper 4280. Washington, DC.

- Mihaljek, Dubravko, 2010. ―Domestic Bank Intermediation in Emerging Market Economies during the Crisis: Locally Owned versus Foreign-owned Banks.‖ In BIS papers No. 54, The Global Crisis and Financial Intermediation. pp. 31-47.

37
rate translates to 15.2% using the 2015 data. Given this, we first update the 2015 data to create a new distribution of consumption, and observe a pre-covid 19 (2019) poverty rate. We then use growth projections to identify the distribution of post-pandemic consumption and assess impacts on poverty and inequality.

> 6 We do not observe the evolution of prices over the whole year. Thus, we use the inflation rate observed up to the date of the simulation (September) and we assume that for the rest of 2020 we observe the same trend.

> 7 These are recent World Bank projections estimated by the Macro, Trade and Investment team at the World Bank Group using the MFMod model and information from government and other sources to inform the forecast.

> 8Consider a sector that will lose 20% of jobs (according to the estimated elasticity). Thus, we assume that 20% of those with the lowest predicted probability will lose their jobs. The imputed level of consumption for those who lost their job will be equal to the average observed consumption of an unemployed person. The calculation is made for workers in the household but adopting the simplifying assumption that they contribute equally to household consumption.

11
In addition to authorized dealers, Sri Lanka Post (SLP), the National Savings Bank (NSB), which is a licensed specialized bank, and one private company, MMBL Money Transfer Ltd. (MMBL), which is a subsidiary of a Merchant Bank in Sri Lanka, are permitted to engage in money transfers as restricted dealers. Of these, NSB and MMBL are permitted only to engage in inward money transfers. Although provisions under the Exchange Control Act prohibit any other entity to participate in the remittance business without the permission from the central bank, some informal remitters are in operation. The identification of these informal dealers becomes difficult as their operators and localities are unknown and they hardly keep readily accessible records of their transactions (Hettiarachi, 2005). Estimates based on a recent World Bank study (2005a) suggest that the share of private remittances channeled through informal dealers could have been close to 45 percent in the mid-1990s.

_The potential of the formal remittance infrastructure is not being maximized_ . The country remains largely cash dominated. Although the banks report a relative good (checking) account-to-per-capita ratio of about 1:6, the usage appears low, with only 4 cashless payments transactions per capita, or approximately 25 per account per year. On the other hand, many Sri Lankans keep large amounts of cash at hand. An average Rs. 5,000 per capita were in circulation with individuals in mid-2004 (Boon 2005). This suggests that access to basic payments services is in practice difficult. There is close to one branch per 14,000 inhabitants, but this ratio is inadequate to serve the nation given that the financial sector is predominantly cash-based and paper-based.<sup>9</sup> Lessons from other countries show that this ratio should at least be 1:6,000 and ideally better than 1:3,000, as is the case in most industrialized economies (Boon 2005).

The remainder of this section examines the state of the formal and informal infrastructure for remittances in Sri
could be that a more stable economic environment in the EU may leave less scope for those who perceive to have moved downward in the past to expect a reversal of their fortunes in the immediate future. It is not possible, however, to confirm this with the LiTS data. Willingness to take risks is also positively associated with future mobility expectations outside of the EU, whereas this is not the case in EU countries. On the other hand, the belief that need is the product of laziness is positively associated with future mobility expectations only in the EU.

17
As is now standard in the literature, we include domestic trade flows (Yotov 2021) as well as zero-trade flows, and estimate the model multiplicatively with the Poisson Pseudo Maximum Likelihood (PPML) estimator (Santos Silva and Tenreyro 2006) using a panel of consecutive years (Egger, Larch, and Yotov 2022); standard errors are clustered three-way (importer, exporter, and year) (Larch et al. 2019).<sup>2</sup> We regress exports _Xijst_ of sector _s_ from source country _i_ to destination _j_ on dummy variables that capture the different types of trade agreements, i.e., _EnClijt_ , _shallowijt_ , and _deepijt_ , while controlling for the effectively applied tariffs _ln_ (1 + _τijst_ ). Including tariffs allows us to disentangle between the effects of tariff cuts and changes in non-tariff trade costs due to RTAs. Additionally to the measures for policy changes, we include exporter-sector-time and importer-sector-time fixed effects _νist_ and _νjst_ , which control for multilateral resistances Ω _ist_ and Ω _jst_ (Anderson and Van Wincoop 2003) as well as for any other country-sector specific trends such exchange rate fluctuations or sector-specific demand or technology shocks. These terms also capture all multilateral policy changes (both of tariffs and of NTBs). Lastly, the country pair-sector fixed effects, _μijs_ , absorb all possible time-invariant bilateral trade costs consequently controlling for omitted variables such as bilateral distance or common language.

# **2.2 Accounting for Omitted Variables**

Next, we describe how our empirical approach departs from the traditional identification strategy to account for confounding factors, that are especially caused by measurement error in the standard sources for tariff data as well as general trends in trade policy. Both omitted variables substantially bias the NTB effect of deep trade agreements upwards.

The advantage of the indirect approach is that it is less demanding in terms of necessary data: instead of gathering information on changes in NTBs that are hard to quantify and to compare across different types of barriers, we only need information on tariffs and the
drawn in this paper related to teaching are constrained by limitations in the available data. For example, the TIMSS variable describing teachers’ qualifications at grade 4 was omitted from analysis due to an error identified in the Saudi Arabia data set for TIMSS 2019. TIMSS collects some data related to teachers and classroom practices but more detailed analysis on teacher quality would be possible with research studies and, thus, data sets more focused on this topic.

Finally, although the highly gender-segregated structure of the education system in Saudi Arabia presents an opportunity to examine the educational environments experienced by boys and girls in 31
analyze to what extent skilled migrants from various countries are employed in unskilled jobs in the US labor market. Their study finds evidence of significant variation in immigrants’ occupational placement which persists even after accounting for education and experience accumulated in the US. However, a large part of the variation is explained by country of origin attributes that affect the quality of human capital on one hand, and by US immigration policy affecting selection of migrants on the other. Therefore, the authors conclude that the “underplaced” immigrants suffer primarily from low (or poorly transferable) skill levels rather than skill underutilization. Chiswick et al. (2005) design and test on Australian longitudinal data a model of immigrants’ occupational mobility. The model predicts that occupational mobility from the last job in the country of origin to the first and then the subsequent ones in the destination countries follows a U shape, the depth of which depends on the similarity between origin and destination countries, but also on migrants’ skill levels. Evidence of downward occupational mobility is also found, for the United States, by Akresh (2006b), a study relying on data from the US New Immigrant Survey-Pilot. Exploring unique dimensions of the same database, such as information on migrants’ occupational placement before, immediately after, and several years post-migration, Akresh (2008) assesses how performance paths differ across various immigrant admission categories.

# **3. DYNAMIC PATTERNS IN OCCUPATIONAL PLACEMENT OF SKILLED MIGRANTS BY COUNTRY OF ORIGIN**

Our estimation focuses on occupational placement as an indicator of performance and aims to identify how migrants’ performance patterns differ by country of origin. We control for some important human capital characteristics, and especially account for the part of the assimilation effect that is common to migrants irrespective of origin. Finally, we attempt to disentangle the inferred country of origin effects by identifying various country attributes explaining it.

The evaluation of assimilation patterns uses the “synthetic cohort” approach introduced by Borjas (1985) for earnings and implemented by Green (1999) for occupational choice
o Is my country’s level of spending explained by:

- Size of the economy or economic growth?

- Size of government (share of government revenues in GDP)?

- Prioritization of education in the government budget?

-

- How fast can my country mobilize additional resources:

- If I follow the average country?

- If I follow countries with the fastest spending growth?

-

- What are the policy levers that I can move to increase spending?

- Is prioritization of education in the budget low, thus requiring only a change in priorities?

- o Does the increase in spending depend on the overall government budget and thus needs a fiscal reform?

-

- What is my country likely to achieve in terms of outcomes from this increase in spending:

- If I compare myself to the countries with average returns?

- If I compare myself to the countries with the highest returns?

- If I compare myself to the countries with the lowest returns?

Jointly, all these questions guide the decision of how much education spending should increase. They also require accountability from the education sector by asking: What should I expect from this increase in resources?

The approach outlined in this section is not expected to provide a complete country-level education financing strategy. It uses broad public finance indicators to identify whether a country has the fiscal space to mobilize additional public resources for education. Ideally, the results from this exercise would serve as a starting point for a more detailed country-level assessment of whether and how additional funds should be diverted to the education sector. For example, the approach outlined will identify whether a country has the potential to increase public spending as a share of national income, based on a comparison with similar countries. However, a more detailed country assessment would be needed to determine whether increases in public spending are feasible and whether they could be financed through a growth in tax revenues, borrowing, or increased external assistance (see Figure 13).

The approach has some limitations due to the poor quality of existing spending
any certainty that stagnation has occurred in the regional trends in income inequality, both regional aggregates in figure 2 represent evidence of a slower pace in inequality reduction since 2010, more marked in the case of the weighted average because of the upward inequality in Mexico and the slowdown in the decline in inequality experienced in Brazil (Cord et al. 2016).

The decline in income inequality observed in the region during the period is the most synchronized of these three trends. It is robust to the inequality measure<sup>25</sup> and to the aggregation method, both weighted and unweighted averages of country values. Moreover, the decline occurred in 16 of 17 countries in the region,<sup>26</sup> and it holds once the comparability issues of the country-specific trends are taken into account. In contrast, the increase in regional inequality during the 1990s and the slowdown after 2010 hides a certain amount of heterogeneity at the country level. For instance, during the 1990s, Brazil, Mexico, and Chile experienced a slight fall in total income inequality, in contrast with other countries such as Argentina, Uruguay, and Honduras, which witnessed sharp increases. Moreover, the still recent slowdown in the 2010s has meant different trends in some countries such as Bolivia, Costa Rica, and Mexico, where inequality has increased, while other countries experienced a slowdown, such as Chile and Brazil.

> 25 Our analysis also reaches this conclusion based on the Theil index, the mean log deviation, the Atkinson index, and the 90/10 ratio. 26 Only Costa Rica exhibited an increase in inequality over the first decade of the 2000s (figure C.1, panel b).

8
; Yuen, Fung, & Ziegler, 2017). To assess the complexities of this sustainable enterprise, we recommend a follow-up cost-benefit analysis study to evaluate the net benefit of bamboo for sustainable shelter and climate change mitigation in Cox’s Bazar. For example, the land-cover type to be substituted with bamboos could be studied in detail using high resolution remotely sensed data.

Another critical policy question is whether there would be significant forest cover loss if refugee hosting in Bangladesh took a more integrated approach. Past studies suggest that integrated refugee populations are less likely to rely heavily on natural resources, more likely to find alternative sources of income and livelihood strategies, more likely to get assistance from members 34
not control for expenditure under SGRY. Third, I estimate NREGA’s impact on employment outcomes: private sector work and public works. The effect on both employment outcomes is again underestimated by 25-34% without SGRY. Finally, I reassess child labor and human capital outcomes with and without SGRY.

> 9 As Shah and Steinberg (2021) report, 71 percent of adolescents aged 13–17 attend school (as main activity), while 25 percent report working. Younger children aged 5–12 are much more likely to attend school (85 percent) and much less likely to report working (about 3 percent).

> 10 Thanks to Thiemo Fetzer for sharing the rainfall data.
*|**1.0**|
|The agricultural group|1.2|0.6|1.3|0.6|1.1|0.5|0.8|0.4|
|The low-skilledgroup|2.4|1.5|2.5|1.6|2.2|1.3|1.8|0.8|

Source: Own calculations based on ENIGH96 survey 1/ Following OECD, it is assumed that teachers work on average 41 weeks per year and non teachers work on an average of 49 weeks per year.

As shown in Table 8, real monthly labor earnings have increased substantially for primary public school teachers. Such earnings almost doubled from 1988 to 1994. In real terms, the teachers' salary increase was significantly above the increase obtained by other groups. _The mixed-skilled_ group has lost purchasing power; losses have been even more severe for people employed in _the agricultural_ group and _the low-skilled_ group. There is considerable variation in teacher’s labor earnings but significantly less than variation in earnings from other occupations. Basic public school teachers clearly earn higher earnings than _the agricultural_ group, _the low skilled group_ and _the mixed-skilled_ group. In 1988, moreover, teachers were underpaid with respect to other _Professionals with an University degree in Education not teaching_ ; after 1994 this trend changed. In fact, _University Teachers in Public School_ were earning a higher salary.

14
- <mark>Dixit, Kshitija, and Rupayan Pal. (2010) "The impact of group incentives on performance of small firms: Hausman–Taylor estimates."</mark> _<mark>Managerial and Decision Economics</mark>_ <mark>31, no. 6: 403-414.</mark>

- <mark>Dollar, David, and Aart Kraay. (2004) "Trade, growth, and poverty."</mark> _<mark>The economic journal</mark>_ <mark>114, no. 493: F22-F49.</mark>

- <mark>Duda-Nyczak, Marta, and Christian Viegelahn. (2018) "Exporting, importing and wages in Africa: Evidence from matched employer-employee data."</mark> _<mark>ILO Working Paper, International Labour Organization, Geneva</mark>_ <mark>.</mark>

- <mark>Dutz, Mark A., Ioannis Kessides, Stephen O’Connell, and Robert D. Willig. (2011) "Competition and innovation-driven inclusive growth."</mark> _<mark>Promoting Inclusive Growth</mark>_ <mark>: 221.</mark>

- Engel, Jakob, Deeksha Kokas, Gladys Lopez-Acevedo, and Maryla Maliszewska. Trade and Development. _Distributional Impacts of Trade: Empirical Innovations, Analytical Tools, and Policy Responses_ . Washington, DC: The World Bank, 2021. ISBN 978-1-4648-1704-5.

- <mark>Frankel, Jeffrey A. and David Romer (1999) “Does Trade Cause Growth?” American Economic Review, 89(3): pp. 379-399.</mark>

- <mark>Fox, Louise, and Ana Maria Oviedo. (2008) "Institutions and labor market outcomes in subSaharan Africa."</mark> _<mark>World Bank Policy Research Working Paper</mark>_ <mark>4721.</mark>

-