rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9605

# **Abstract**

An unprecedented number of Venezuelans have left behind the worsening economic and social crisis at home to look for better future prospects. Brazil is hosting about 261,000 Venezuelans as migrants, asylum seekers, or refugees, which, at 18 percent, constitutes the largest share of Brazil’s 1.3 million refugees and migrants population (as of October 2020). Although previous literature on other host countries found that Venezuelan refugees and migrants are struggling to secure high-paying jobs that are commensurate with their education, little is known about their access to education and social protection. This paper fills this gap by analyzing various administrative and census data to explore whether Venezuelan migrants and refugees face differential access to education, the formal labor market and social protection programs. It finds that even though there is minimum legal constraints and work permits are relatively easy to obtain, Venezuelan refugees and migrants face challenges integrating into the education system, social protection programs and the formal labor market. The results suggest that Venezuelan refugees and migrants have faced downgrading in grades at school and occupations at work. They are more likely to attend overcrowded schools than their host community counterparts and more likely to do inferior jobs characterized by temporality, lower wages and higher hours worked. Overall, the results suggest that improvement in school capacity, accreditation of Venezuelan education or degrees and relocation to places with favorable employment opportunities may facilitate integration.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pacosta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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mechanisms that cause the disparity in overall life satisfaction between refugees in and outof-camps. I analyzed them directly from the ATT of propensity score specification. The result on household earnings (income per capita) shows that camp refugees are significantly more likely to have lower income per capita than out-of-camp refugees. Camp refugees typically earn an average of JD33.65 per capita per month, whereas out-of-camp refugees earn about JD48.53. After controlling for pre-crisis characteristics, refugee households living in camps earn on average JD14.77 per household member less than refugees living out-of-camps. Ginn (2020) showed that camp residents' low income is not associated with assistance dependency as male camp residents are more likely to show readiness to work than out-of-camp counterparts. But the challenge is that camp residents often lack access to jobs compared to out-of-camp residents. Although camp officials provide some assistance for camp refugees to secure resident permits and help them in job search, Ginn showed that males living in camps are less likely to have worked at least one hour in the last 30 days. There is also concern that out-of-camp refugees' additional income may be canceled by the high rent they pay; but as I will show, this additional income helped many out-of-camp refugees meet basic needs.

Material deprivation is a function of whether households meet their basic or psychological needs such as food, water, clothing, and a comfortable place to sleep. The abject poverty measure allows estimation of the share of households at the risk of deprivation with respect to these basic needs. About 62 percent of households living in camps are at risk of living in abject poverty compared to 28 percent for those living outside of camps. The difference after controlling for pre-crisis characteristics is 36—a very significant finding. Indeed, refugees are entitled to some form of blockchain cash transfer, but even after controlling for amount of aid received, the result still shows that camp refugees are more at risk of deprivations in basic needs. The camp refugees are also less likely to say they are able to lend up to JD150 to friends. The estimate shows that households living out of camps possess 7 of the 17 included assets on average. Although this asset level is generally low, it is double that of camp households.

Furthermore, I assessed the shelter adequacy and satisfaction with accommodation services by the two groups. The number of people per shelter is one of the impact indicators UNCHR uses to measure shelter adequacy or overcrowding (UNHCR, 2014a). It is common practice to consider that children will pair in rooms. Hence, I measure shelter adequacy, which I called "risk of overcrowding", by the number of rooms per adult equivalent, with children below 12 20
**Figure 2.26 Morocco: Trade balance with China, 2001‐2017 (US$ thousand)**

<!-- Start of picture text --> 5000000 400000<br>4000000<br>350000<br>3000000<br>300000<br>2000000<br>250000<br>1000000<br>0 200000<br>‐1000000<br>150000<br>‐2000000<br>100000<br>‐3000000<br>50000<br>‐4000000<br>‐5000000 0<br>Imports Trade Balance Exports<br><!-- End of picture text --> _Source_ : International Trade Centre.

Morocco’s trade with Sub‐Saharan Africa has also increased in recent years, especially after 2011; Moroccan exports to the region largely exceed imports and Morocco generates a sizable surplus (Figure 2.27). Most of that trade is with West Africa, especially with countries belonging to ECOWAS (Figure 2.28).<sup>1</sup> But the composition of trade is highly skewed: Moroccan exports to the region consists mostly of food, drinks and tobacco, as well as semi‐finished products, whereas imports consist mainly of food, drinks and tobacco, as well as energy (Figure 2.29). In addition, trade with some of the bigger countries of the region (the Democratic Republic of Congo, Nigeria, and South Africa) remains limited. Overall, trade in goods with Sub‐Saharan Africa has not yet achieved its potential.

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<!-- Start of picture text --> + h G (pre) fan) \<br>ay yl NIA A Oxpput (si _ ) \ Nino<br>= ! \ = ° 7 \ IN<br>~x 0 v cS i\\ Vv<br>g { ~ 4 , \/<br>l — GDP ip<br>+ + --- Output (si)<br>1960 1970 1980 1990 1995 2000 2005 2010<br>ou ‘ at \<br>\ es AW) AVE<br>.‘ Gons.\j (pr Mi ' — Cons.o\v,(pds)<br>+<br>--4 Cons. (sir) + --- Cons. (si<br>1960 1970 1980 1990 1995 2000 2005 2010<br>wHKil 4 ° h A<br>\ \/ \ i<br>Z Mol Nate Vy ge adi RR A<br>a o I \ \v | ' ! = i! v \; \ /<br>= --#4 Inv.Inv. (pref(sim) °° y| \ --\/ Invnv. (sim)(post)<br>1960 1970 1980 1990 1995 2000 2005 2010<br><!-- End of picture text -->
Fernandes and Paunov (2012) demonstrate the causal impact of FDI penetration in services on manufacturing TFP in Chile. They find a positive impact of services FDI on TFP for Chilean manufacturing industries, which explained 7 percent of manufacturing’s contribution to total Chilean TFP growth. Duggan et al. (2013) provide evidence that reform of restrictions on FDI in services in Indonesia increased TFP of manufacturing firms.

**A wide set of regulatory restrictions in services are important to consider for reform.** Services restrictions usually take on different forms and encompass a wider set of barriers than in the case of goods. For example, services restrictions can prevent foreign investors from entering the domestic market. Regulatory policy barriers in services can be broken down into those that affect _entry of firms_ into a market and barriers that have an impact on the _operations of firms_ . Barriers to entry restrict foreign and domestic service providers from bringing competition to the market. If entry barriers are high, domestic incumbents will be sheltered from competition and less incentivized to perform better, eventually leading to higher prices. Barriers to operations, on the other hand, are barriers that firms encounter after entry has taken place. Both types of barriers can be discriminatory or non‐discriminatory for foreign service providers. Table 4.1 shows examples of each type of regulatory barrier.

**Table 4.1: Typology of services regulatory barriers**

||**Firm entry**|**Firm operations(conduct)**|
|---|---|---|
|**Discriminatory**|E.g., nationality quotas for<br>managers of affiliates; minimum<br>equity stake required for<br>national investors; economic<br>needs tests|E.g., foreign insurance firms not<br>permitted to offer certain types<br>of coverage or product<br>innovations; price controls|
|**Non‐discriminatory**|E.g., A limit of three mobile<br>phone providers permitted to<br>operate in the country|E.g., all retail banks required to<br>maintain a minimum level of<br>capital, independent of type of<br>legal entity|

Source: Francois and Hoekman (2010)

**Both entry and conduct regulations in services have a knock‐on effect on the economy.** As Figure 4.1 shows, both types of barriers are negatively correlated with firm‐level TFP. A recent study by Van der Marel et al. (2016), using data from millions of European firms, shows that reducing service restrictions to the level of the three least regulated EU member states (UK, Denmark, Sweden) would increase the productivity of firms operating in both services and manufacturing by up to 5.3 percent within two years of implementation. While removing both entry and conduct barriers in services increases firm productivity, the effect is larger for conduct barriers. Barriers to operations prevent services firms from growing in the post‐entry phase and therefore prevent the Schumpeterian dynamic of ‘creative destruction’.

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# Annex 2: Impact of global conditions on nominal lending rates

<!-- Start of picture text --> Figure 7: Impact of global conditions (global interest rates and liquidity) on nominal lending<br>rates<br><!-- End of picture text --> <!-- Start of picture text --> %<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>-1.2<br>2006 2007 2008 2009 2010* 2011* 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Note:_ Year fixed effects from a panel regression using the same framework as in Table 3, Panel A, equation 14. Year fixed effects is an indicator of the impact of global conditions (i.e., global interest rates and liquidity) on nominal lending interest rates in EMDEs. Years in asterisk are the effect coefficients that are significantly different from zero. Panel data regression with annual information between 2005 and 2017 for 50 EMDE countries, country and year fixed effects included, and robust errors.

Source: Authors’ calculations.

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**Table 1. Summary of vehicle travel price sensitivity studies**

|**Study**|**study type**|**study scope**|**Major results**|
|---|---|---|---|
|Johansson and Schipper (1997)|summary of previous studies|International|-0.2 long run|
|Goodwin, Dargay and Hanly (2004)|Summary of various fuel price<br>and income elasticity studies|1929 to 1991, mostly North<br>America and Europe|-0.1 short run; -0.3<br>long run|
||Elasticity of vehicle travel with|<br>1950 to 1994 time series and||
|Schimek (1997)|respect to fuel price|1988 to 1992 pooled data, US|-0.26|
||||1966 to 2001 ( -<br>0.047 short run and -|
|Small and Van Dender (2010)|||0.22 long run); 1997|
||Comprehensive model using||to 2001 ( -0.026|
||vehicle travel elasticity with||short run and -0.121|
||respect to fuel price<br>Comprehensive model using<br>|1966-2001, US|long run)|
|Hymel, Small and Van Dender (2010)|state-level cross-sectional<br>time series gasoline price||-0.026 short run, -|
||elasticity<br>Comprehensive model of|1966 to 2004, US|0.131 long run|
|Li, Linn and Muechlegger (2011)|vehicle travel with respect to|||
||fuel price|1968-2008, US|-0.24 to -0.34|
||||-0.12 to -0.17 short|
|Brand (2009)|||run; -0.21 to -0.3|
||Gasoline price elasticity|2007-2008,US|long run|
||||-0.15 to -0.20|
|Gillingham (2010)|comprehensive model using<br>odometer and fuel||medium run, with<br>variation by vehicle|
||consumption data|2005-2008, California|type and location|
||||-0.67 short run, with|
|Spiller and Stephens (2012)|Comprehensive model of||variations by|
||monthly state-level fuel price||household income|
||and VMT data|2009 US travel survey data|and location|

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growth may have important aggregate effects on economies. This is particularly true with regard to employment growth. Compared with a larger literature on capital and entrepreneurship training, there is a paucity of research on the functioning of the informal labor markets most relevant to these firms. We use a shock to wages, implemented through temporary subsidies offered when enterprises hire new workers, to measure frictions in urban informal labor markets in Sri Lanka.

Data from the experiment suggest that labor markets function with modest frictions for these firms. The effects of the subsidy are consistent with well-functioning neo-classical labor markets. Firms respond to the subsidy by hiring additional workers, but the excess hiring dissipates completely within a year or so of the removal of the subsidies. Complementary experiments loosening credit constraints and providing entrepreneurship training do not change the effects of the temporary subsidies. We conclude that hiring frictions are not an important constraint to firm growth in our setting.

An obvious question is how much the results from this experiment generalize to the broader development context. We suspect that many characteristics of this labor market hold in most urban areas in developing countries. Workers remain unregistered and hiring is generally unregulated. The work performed by employees in small enterprises involves relatively more brawn and less creative energy. Employees are hired from local areas and there is usually a low degree of separation between employer and employee before hiring. There are other aspects of the context which are likely less usual. Our experiment coincided with the end of a long civil war in Sri Lanka, and a period of rapid growth. Compared with other low- and middle-income countries, our samples of both microentrepreneurs and wage workers show relatively high levels of generalized trust.<sup>15</sup> How much these more distinctive features of the context drive the results is difficult to say. We would hope this might be resolved by future work examining labor market frictions in different contexts.

# **References**

> 15 In April 2009 surveys. 32 percent of wage workers and 27 percent of entrepreneurs said that “most people can be trusted.” Though this may seem low, it compares with rates in the general population in most Sub-Saharan African and Latin American countries, where less than 20 percent of the population respond similarly. [Calculations by authors using WVS data downloaded from <u>http://www.worldvaluessurvey.org .]</u>

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changes, and there are usually provisions for resetting prices according to actual costs and demand from time to time—but the approaches attempt to ensure the private firm bears significant risks.

If the approach works, it should lead to lower costs than cost-plus regulation and, in the long term, lower prices for customers. For the present purposes, the key feature of this regulatory approach, however, is that it allocates more risk to the firm and, for any given leverage, increases the risk of bankruptcy.<sup>3</sup>

# **2.2 Leverage**

Available data on private infrastructure firms’ leverage are limited. Below, we present some data on infrastructure companies listed in the United States and project-finance firms around the world.

To begin, Figure 1 shows information on average leverage for listed firms in the United States categorized by industry over a 20-year period ending in 1981. It shows the three industries with the highest leverage—telephone companies and electric and gas utilities (the only infrastructure industries in the sample) and airlines—and descriptive statistics for the entire sample.

**Figure 1: Leverage by industry in the United States, 1962–1981**

<!-- Start of picture text --> 0.70<br>0.58 0.58<br>0.60<br>0.53<br>0.52<br>0.50<br>0.40<br>0.33<br>0.30 0.26<br>0.20<br>0.20<br>0.09<br>0.10<br>0.00<br>Minimum 1st Median 3rd Telephone Electric Airlines Maximum<br>quartile quartile and gas<br>utilities<br>Leverage<br><!-- End of picture text --> Source: Bradley and others 1984: 870.

Figure 2 shows data for selected infrastructure industries from July 2002. The data are for companies listed on stock markets in the United States and are therefore dominated by

> 3 Of course, firms may change their leverage according to the regulatory regime. In particular, the move from cost-plus to fixed-price regulation may encourage them to reduce their leverage.

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to standing area for bus is about 2 to 1. Similarly, there are 206 passenger standing inside a double tram in a single journey. The double tram is 65 meter by 2.65 meter which has a standing area of 84 square meters. The average standing passenger density for the double tram is 2.45. The tram has more space for standing relative to seating which makes the ratio of seating to standing to be around 1.

In the calculation of the monetary cost of the fuel for bus, we are using average distance of a bus journey as<sup>_D_</sup> 4<sup>=15.6 km. And the bus rider’s average travel distance is 8 km which corresponds</sup> to an in-vehicle travel time of 32 minutes. A bus rider’s average distance and the in-vehicle travel times are then used to calculate the speed of the bus which is around 15 km/hr. The tram rider’s average distance is 6 km and the reported operational speed of 18 km/hr is used to calculate an average in-vehicle travel time of 20 minutes.

**Table 1:** Mode characteristics and calibrated parameter values (2014 baseline)

|**Mode characteris**<br>|**tics**<br>||||
|---|---|---|---|---|
|Private car|Motorcycle|Taxi|Bus|Tram|
|Modal shares,<br>_m_<sup>_P_</sup><br>33|13|40|12|2|
|Utility constants,<br>_m_<sup>_E_</sup><br>13.6|11|11.8|9.5|0|
|Travel distances,<br>_m_<sup>_d_ (km)</sup><br>13.4|9.6|11.69|8|6|
|Fuelconsumption, ��(liters/km)<br>0.088|0.06|0.09|0.43|0|
|Fuel efficiency factor,<br>_m_<sup>_e_</sup><br>1.09|0.63|1.09|3.26|0|
|Relative slowness,<br>_m_<br><br>0.43|0.60|0.45|1|0|
|Vehicle occupancy,<br>1<br>_m_<sup></sup><br><br>1.4|1|5.5|106|324|
|Car equivalent load of a vehicle,l�<br>1|0.75|1.4|2|0|
|Trip load factor,<br>_m_<br>_m_<br>l<br>0.714|0.75|0.254|0.019|0|
|In-vehicle time/trip,<br>,<br>_m inv_<br>_G_<br>23|23|21|32|20|
|Speed,��(km/hour)<br>35|25|33|15|18|
|Waiting time/trip,<br>,<br>_Gmwait_<br>0|0|5|11.5|6|
|Monetary cost/trip,<br>_m_<sup>_g_</sup><br>42.1|54.1|7.09|3.45|5.7|
|Journeys per taxi, bus and tram:��, ��,��<br>0|0|29|7.8|10|

The congestion parameter values in equation (9) are partially taken from the literature, and the rest are calibrated to provide reasonable congestion estimates. The value of the congestion 14
# **TABLE 12** (continued)

||1989|1990|1991|1992|1993|1994|1995|1996|1997|1998|1999|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Asia**|**1,982**|**2,128**|**1,856**|**1,381**|**1,307**|**1,548**|**2,078**|**2,757**|**2,420**|**2,221**|N.A.|
|Korea|71|113|63|41|26|41|91|198|68|73|154|
|Taiwan|90|69|76|43|21|23|44|42|43|50|91|
|Hong Kong|66|43|87|62|62|174|504|434|283|217|178|
|Israel|196|215|164|187|103|3|11|10|30|18|38|
|Japan|1,394|1,506|1,241|793|700|1,001|979|1,393|1,156|851|777|
|Singapore|11|33|37|104|131|67|173|235|387|449|480|
|China|0|9|63|20|45|42|37|38|46|106|126|
|Other|155|139|126|131|219|197|239|407|407|456|N.A.|
|**Africa**|**73**|**61**|**70**|**42**|**14**|**16**|**47**|**81**|**120**|**94**|**NA.**|
|**Oceania**|**_53_**|**57**|**76**|**57**|**_56_**|**_69_**|**75**|**75**|**88**|**123**|**N.A.**|
|Australia|38|37|51|49|48|54|63|58|76|109|N.A.|
|Other|16|20|25|8|8|15|12|17|12|14|N.A.|
|**Rest of the World**|**38**|**10**|**0**|**0**|**22**|**52**|**42**|**_25_**|**60**|**52**|**N.A.**|

SOURCE: State of the Nation Report from 1989-98, Secofi for 1999, both with Banxico data.

*Exports includes transportation and insurance expenses.

**Figures may not add up due to rounding off.

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