rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Operationalizing an Investment and Mode 3 Trade in Services Facilitation Agenda**

|**Trade in services**<br>**cost component**|**Market Failure/**<br>**Problem to be**<br>**addressed**|**Trade facilitation**<br>**agenda dimension**|**Diagnostic**<br>**indicator**|**Impact Indicators****|
|---|---|---|---|---|
|Information costs|Information<br>asymmetry|Transparency|WBG<br>governance<br>indicators|Trade/investment<br>generated|
|Transaction costs|Red tape|Process streamlining|WBG/IAB<br>indicators|Private time/cost<br>savings|
|Governance quality|Uncertainty and un-<br>predictability from<br>regulatory risk|Consistency and<br>predictability in the<br>application of<br>regulatory<br>frameworks|Political risk<br>indicators|Investment<br>retained/expanded|
|Discriminatory Policy<br>Barriers|Distortions to<br>competition|Standstill of existing<br>discriminatory<br>measures affecting<br>commercialpresence|STRI|Trade/investment<br>generated|
|Regulatory<br>Heterogeneity|Economies of scale|Inter-governmental<br>cooperation<br>promoting regulatory<br>convergence through<br>negotiation|RH Index|Trade/investment<br>generated|

** Methodologies to operationalize these impact indicators have already been developed by the WGB.

While the substantive remit of the WTO investment facilitation discussions is arguably sub-optimal in scope, it nonetheless targets a range of issues where the dissemination of best practice examples in policy design, internationally-agreed rules and strengthened coordination and implementation efforts at both the international and domestic levels (including within-country (i.e. sub-national) levels of investment governance) clearly stand to enhance host country investment climates. Such rules and the norm diffusion associated with international cooperation can be expected to improve investment governance, help host countries to attract and retain higher volumes of service sector FDI and to reap the attendant spillover benefits flowing from such investments.

36
**Table 9. Contribution to the Pension System in December** 1999- **January** 2000 Employed Men of Working Age (14 **-** 65)

||P|ROBITI|PRO|BIT2|MEAN|
|---|---|---|---|---|---|
|CONT99|dF/dx|Std.Err|dF/dx|StdErr.||
|AGE99<br>|**0.035**|**(0.013)*****|**0.035**|**(0.013)*****|37.66|
|AGE<sup>2</sup>|-3.9E-04|(1.6E-04)***|-3.9E-04|(1.5E-04)***|<br>1546 63|
|HEAD|-0.077|(0067)|-0.046|(0.064)|074|
|MARRIED|-0041|_(0.050)_|-0.057|(0.048)|059|
|SPCONT|-0012|(0.047)|-0.026|(0043)|0.19|
|YEDU|-0.005|(0.006)|-0.003|(0005)|9.62|
|RURAL|0.022|(0.053)|0.017|(0.050)|0.20|
|FSIZE|0.001|**(3.2E-04)***|4.9E-04|(3.OE-04)|66.68|
|WRKHRS|4OE-04|(0001)|0.001|(0 001)|**50.49**|
|SELFPR|**-0.555**|**(0.088)*****|**-0.540**|**(0.093)*****|0.25|
|INFWPR|-0.542|(0.083)***|**-0.531**|(0.089)***|016|
|AGR|0.116|(0.082)|0 109|(0.078)|006|
|MINES|-0.059|(0.109)|-0.122|(0.106)|0.05|
|UTIL|0238|(0.145)|0.214|(0.135)|007|
|CONST|-0059|(0.082)|-0082|(0080)|0.09|
|RETAIL|0027|(0096)|-0.012|(0090)|007|
|TRANS|0034|(0071)|0.015|(0067)|0.23|
|FIN|-0026|(0079)|-0.062|(0075)|015|
|SOC|0.076|(0076)|0.047|(0072)|0.21|
|CREDCON|-0.106|(0.044)**|-0.101|(0.043)**|0.26|
|SHYKIDS|4.2E-04|(0.001)|0001|(0001)|18.18|
|SHKIDS|1.8E-04|(0.002)|-1.2E-04|(0002)|678|
|SHOLDM|**0.007**|**(0.003)*****|**0.006**|**(0.003)****|141|
|SHOLDW|**-0.004**|**(0.002)****|**-0.005**|**(0.002)*****|271|
|NYFEM|-0004|(0027)|-0003|(0.026)|1.25|
|RESFAM|-0.008|(0.085)|-0.012|(0077)|0.04|
|EXIUDS|-0.007|(0.013)|-0.007|(0.012)|2.59|
|LEDKIDS|-0 005|(0.004)|**-0.007**|**(0.004)***|532|
|FAMEX|**-0.083**|**(0.039)****|**-0.088**|**(0.037)****|053|
|GIVES|0.072|(0.039)*|0.049|(0.037)|0.34|
|GETS|0 052|(0.047)|0 034|(0.045)|017|
|LRENT|-0.001|(0.005)|0.003|(0.004)|637|
|COLATT|0.092|(0.047)**|0.113|(0.046)***|046|
|FINASST|0017|(0113)|0023|(0.101)|002|
|WRKASST|-0.031|(0.045)|-0.013|(0043)|0.30|
|SLIFEX|0.001|(0002)|0001|(0001)|37.63|
|LJUB|**-0.019**|**(0.007)*****|**-0.016**|**(0.006)*****|0.96|
|LPASIS|0.016|(0.013)|0.017|(0.012)|030|
|CONT98|**0.179**|**(0.042)*****|**0.175**|**(0.042)*****|060|
|CONDEN|**OA20**|(0.093)***|0.401|(0.092)***|052|
|MPG|**-0.125**|**(0.067)****|0151|(0.101)|016|
|MPG*LRENT|||**-0.040**|**(0.013)*****|1.13|
|Numberofobs|741|||741||
|Loglikelihood|-124.441|||-117749||
|LRchi2(41)|698540||LRchi2(42)|711920||
|Prob>chl2|0.000|obs.P. 0.663||0000|obsP 0.663|
|PseudoR2|0.737|predP0.894||0751|pred.P0906|

48
**Table 9: Supplier Premia by Absorptive Capacity**

|**Variable**|**Thresholds**|**Measure of FDI**|**Linkage:****_outp_**|
|---|---|---|---|
||supplier = 1 if ... and 0 otherwise|**Difference**|**p-value**|
|_gap_|_gap_= 2|-7.2833|(0.448)|
||_gap_>= 3|-2.8160|(0.713)|
|_soph_|_soph_= 2|0.7105|(0.941)|
||_soph_= 3|5.7639|(0.516)|
||_soph_= 4|23.1604*|(0.072)|
|_emp_ter_|20% >=_emp_ter_< 50%|12.6626|(0.112)|
||50% >=_emp_ter_< 80%|-5.5474|(0.541)|
||_emp_ter_>= 80%|-8.9682|(0.526)|
|_emp_sec_|20% >=_emp_sec_< 50%|18.2152**|(0.042)|
||50% >=_emp_sec_< 80%|15.5753*|(0.095)|
||_emp_sec_>= 80%|8.4187|(0.484)|
|_exper_|3 >=_exper_< 10|20.9871|(0.139)|
||10 >=_exper_< 20|14.4016|(0.296)|
||20 >=_exper_< 30|6.4514|(0.647)|
||_exper_>= 30|27.5507*|(0.080)|
|_man_educ_|_man_educ_= 2|3.3842|(0.841)|
||_man_educ_= 3|-10.1846|(0.493)|
|_man_exper_|_man_exper_= 1|7.3526|(0.314)|
|_emp_|10 >=_emp_< 50|-18.1670|(0.157)|
||50 >=_emp_< 250|-24.1310*|(0.072)|
||_emp_>= 250|-23.7696|(0.118)|
|_export_|_export_= 1|9.8261|(0.121)|
|_dist_|20 >=_dist_< 100|-19.9154*|(0.056)|
||100 >=_dist_< 500|-18.0726*|(0.057)|
||_dist_>= 500|-26.1891***|(0.005)|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01 (p-values in parentheses). Note: All variables refer to FY 2012. Each panel is estimated as a separate regression. All regressions control for country-sector fixed effects. Standard errors are robust to heteroscedasticity.

# **4.4 Regression Results**

## **_Overall Results_**

Table 10 reports the regression results based on the specification of equation (3). Given the differences between supplier sectors and countries, all regressions control for country-sector fixed effects. Standard errors are robust to heteroscedasticity. A more sophisticated production process ( _soph_ ) has a significantly positive impact on suppliers’ output to foreign firms, supporting the positive role of R&D for local firms in the literature. Firm location also matters for FDI linkages. A larger distance to the foreign firm (ln _dist_ ) reduces the supplier’s output share going to foreign 23
- 25 -

# Table 2.4. Percent of Manufacturing Establishments Without Own Radio Equipment or Motorcycles in Each Employment Size Category

||**_Ni_**|**_h_ria**|**Ind**|**onersia**|**Th**|**eiland**|
|---|---|---|---|---|---|---|
|EpLoyment<br>|||||||
|**size**|Radio<br>Equfpment|Oan<br>Motorcycle|Radio<br>Equfpment|_Own_<br>Motorcycle|**Radio**<br>Equipment|**own**<br>Notorcycle|
|1- 19|100.0|100.0|80.0|80.0|100.0|90.9|
|20 - 49|94.3|82.9|95.7|76.6|97.3|90.5|
|50 - 99|77.3|75.0|92.9|75.0|95.6|72.1|
|100 - 199|45.7|48.6|85.5|77.4|98.2|66.7|
|200 - 499|42.3|38.5|77.3|68.2|89.4|61.7|
|500 - **999**|6.7|26.7|84.2|79.0|93.8|81.3|
|1000&over|25.0|37.5|60.0|60.0|87.5|37.5|
|AUl|63.1|62.6|87.9|74.8|°95.7|75.7|

NOTE: Fron Appendix Tables _87,_ ea, C7, CB. D7, and D8.

Source: World Sank Nigeria Infrastructure Project Establishment Survey 1988. World Bank **Research** Project (RPO 676-71) Establishment Survey for Indonesisa nd Thailand 1992.
Table 3 (Contd.)

# Wage Premium for Formal Sector Jobs

|Study|Estimation<br>technique|Burkina<br>Faso|Cameroon|C6te<br>d'Ivoire|Mali|
|---|---|---|---|---|---|
|Vallee and<br>Thomas<br>(1994; Table<br>5.12, page 170)|OLS<br>with sample<br>split between<br>regulated and non-<br>regulated firms||34.3 %<br>[RPED, 1993;<br>N = 740]|||
|Vijverberg and<br>Van der Gaag<br>(1993b; Table<br>6.2, page 34)|OLS<br>with factor<br>analysis<br>for job<br>coverage|||65.2 %<br>[CILSS, 1985;<br>N = 295]||

- Note: All studies control for individual characteristics, but the set of controls varies across studies. The wage premium is based on the coefficient (c) multiplying the formal sector dummy variable. In the case of Lachaud (1993b), it is based on the difference between coefficients for covered and uncovered wage jobs. In the case Vijverberg and Van der Gaag (1990), the formal sector dummy is the first of the principal components of a set of job characteristics. For large values of the c coefficients, in absolute terms, the percent premia is approximated as 100[exp(c)-1]. Data sources and sample sizes are reported in brackets. CILSS is the Cote d'Ivoire Living Standards Survey, a household survey with national coverage. IIES is a series of labor force surveys carried out by the Institut International d'Etudes Sociales in the capital cities. RPED is the Regional Program for Enterprise Development, a plant-level survey which includes data on up to ten workers in each firm.

18
instrumental variable (IV) may be available in some cases. This requires a believable exclusion restriction (an alternative conditional independence assumption).

A second approach might rely on a household panel that includes the same detailed information as above, and exogenous changes in access to water for at least some households. Then the key assumption is that changes in access to infrastructure are exogenous (conditional on observables). This is more likely to hold if the panel was expressly collected to deal with the likely endogeneity of the changes in access to water, or the panel contains more than two waves or one has good data on the initial conditions jointly influencing infrastructure changes and subsequent outcome changes. A double difference (possibly with matching) can then be used to see the effect of access to water infrastructure on the outcomes of interest.

Here we propose a third approach that has not (to our knowledge) been used before. We have an outcome variable _Yij_ for individual _i_ in area _j_ , an indicator of the individual’s access to infrastructure _Z ij_ , a vector of exogenous individual and household characteristics _X ij_ , and exogenous community characteristics _G j_ (which can include community means of _X ij_ , denoted _X j_ ). The aim is to estimate the causal impact of _Zij_ on _Yij_ . The problem is that the observed variation in _Z_ reflects latent factors that also influence _Y_ . Within a _ij ij_ given locality, some households will have latent preferences, knowledge or unobserved resources that lead them to have better access to infrastructure than other (observationally similar) households. This is particularly worrying when talking about certain kinds of basic infrastructure such as access to water. Thus there must be a strong presumption that the individual-specific differences in _Zij_ are endogenous to outcomes. The standard solution is to find an IV that is correlated with _Zij_ but uncorrelated with outcomes given _Zij_ . However, this is a demanding requirement, as one can reasonably question whether any observed household characteristic that might influence whether that household has a higher level of access to household-specific infrastructure would not also be a relevant factor in determining the overall outcomes, independently of infrastructure.

We show below that we can address this problem without an instrumental variable by exploiting the geographic differences in infrastructure placement and outcomes.

11
<!-- Start of picture text --> Variations of maximum FSI in commercial areas<br>25 =<br>. a<br>17.5: a<br>15<br>o<br>2<br>© 12.5<br>uw<br>: a ee<br>75 \ = —_<br>Ne eee<br>-0 ————_—<br>CBD 5 km 10 km 15 km 20 km 25 km 30 km<br>Distance from CBD —<br>12<br>Variation of maximum FSI In residential areas with distance from CBD<br>10 New York<br>2a.<br>6 gapo<br>zcsCS<br>=<br>GBuw<br>4 _ l<br>. So; ="<br>0OS——<br>CBD 5km 10 km 15 km 20 km 25 km 30 km<br>Distance from CBD<br><!-- End of picture text -->
# **Methodology**

We design the forward-looking scenarios by employing the World Bank’s LINKAGE and GIDD models. LINKAGE is a global general-equilibrium model with an adjustable number of countries, regions and production sectors, linked by global trade flows and finance (van der Mensbrugghe 2013). Its current database is the Global Trade Analysis Project (GTAP) 8 with 2007 as the base year.<sup>2</sup> At its core, LINKAGE is essentially a neo-classical growth model, with aggregate growth predicated on assumptions regarding the growth of the labor force, savings/investment decisions (and therefore capital accumulation) and total factor productivity. Unlike more simple growth models, LINKAGE has considerably more structure and the full dimension of its data covers a total of 226 countries aggregated into 129 regions or country groupings, and 57 sectors or commodities. This multi-sector and multi-regional configuration permits the analysis of global development and structural transformation, incorporating the complex interactions of productivity differences at the country, sector or factor level, shifts in demand as income rises, demographic and skill dynamics in the factor markets, as well changes in comparative advantage and trade flows from globalization. In practice, aggregation is employed to emphasize the countries or sectors underscored in the analysis. For this study, the list of country or regional units (Table 1) includes all 18 individual African countries currently available in the database at the time of the study, several large countries (such as United States, China, India, Brazil, and Russia), plus various regional subgroups (such as rest of SSA, OPEC, other less developed countries, EU countries, and other high income countries). In addition, 7 sectors are delineated – agriculture, natural resources (excl. oil), oil, food, manufacturing, infrastructure, and services.

**Table 1:  Country and regional aggregation in the study**

|United States of America|Ethiopia|
|---|---|
|EU countries|Kenya|
|Other high income countries|Madagascar|
|China|Malawi|
|India|Mauritius|
|Brazil|Mozambique|
|Russia|Tanzania|
|OPEC countries (excl. Nigeria?)|Uganda|
|Other less developed countries|Zambia|
|Cameroon|Zimbabwe|
|Côte d’Ivoire|Botswana|
|Ghana|Namibia|
|Nigeria|South Africa|
|Senegal|Rest of SSA|

> 2 See www.gtap.org for details.

3
(Bendixen 2004). In order for Banks to play a significant role in money transfer services, they have to inspire trust in their services.

Respondents are also asked how often they receive remittances during the year. Among those that receive remittances monthly, the informal sector is very small—only about 1-2 percent of total, as compared with 5-10 percent for all remittances. We also find that the informal sector is more heavily _dollarized_ , about 70 percent of transactions were in foreign currency, as compared with just over 50 percent in the formal sector on average over this period. In addition, as the model predicts, the transaction size is smaller, on average about $90 as compared with about $200 in the formal sector.<sup>9</sup>

# _El Salvador_

According to national statistics, El Salvador is among the top 15 developing country recipients of remittances and of remittances/GDP, receiving approximately $2.3 billion per year. Stratified surveys are available for a number of years from 1989-2003. Only the surveys in 1995-1997 record detail about the channel by which remittances were received.

Results in Table 7 show that inflows to El Salvador through informal channels are about 15-20 percent of total recorded flows in 1995-1997. Of interest, there appears to be a large shift away from money transfer providers to banks and post offices in 1997.

As predicted by the model, flows through informal channels tend to be smaller.

On average, formal transactions were about double informal transactions. The median

> 9 Excludes data for third quarter 2001, because of idiosyncrasies due to September 11.

25
hypothesis parameter estimate is significant in virtually all specifications, although it appears to add less explanatory power to the model in the sample of developing economies, especially for secondary completion. In contrast with the tail hypothesis, its role in the gender gap dynamics appear to to be stronger in high-income countries, while the tail hypothesis predominates among developing economies.

Our findings indicate that the respective contributions of the mean and tail hypotheses to the gender gap reversal somehow differ in high-income and developing economies. Explanations for the gender gap reversal suggested by previous literature have been confined to the mean hypothesis, and have been proposed in the context of high-income countries, primarily in the US. In this context, Goldin, Katz and Kuziemko (2006) and Chiappori, Iyigun and Weiss (2009) invoke the progressive removal of female career barriers over time. Goldin, Katz and Kuziemko (2006) associate the removal of barriers to female employment to a progressive change in fertility and marriage patterns, driven by the access to reliable contraception (Goldin and Katz, 2002). Chiappori, Iyigun and Weiss (2009) argue that technological progress has freed women from many domestic tasks, which disproportionally increased the labor market and marriage market returns of schooling for females. Similarly, Black and Spitz-Oener (2010), Olivetti and Petrongolo (2016) and Ngai and Petrongolo (2017) have argued that technological change and secular labor demand shifts favored occupations and industries disproportionately employing female college graduates. This would in turn increase the returns to higher level of schooling for women.

Such shifts in technology and gender norms have not taken place to the same extent in developing countries (World Bank, 2012). They may have occurred only in recent years or, in some case, are yet to take place. Therefore, the conditions under which the reversal occurs according to the mean hypothesis may not necessarily hold in developing countries. This could explain why the mean hypothesis appears to play a prominent role in the context of high-income countries relative to developing countries.

29
<!-- Start of picture text --> Ceray<br>[ey<br>Japa ee<br>United Kingdom<br>France<br>United States [x<br>Spi) as<br>orca<br>Braz)<br>Australia<br>South Africa [x<br>Tukey<br>Canada<br>India<br>Argentina xg<br>China<br>Indonesia _x<br>Saudi Arabia<br>Russia I<br>Mexico Jf<br>0 5 10 15 20 25 30 35 40 45<br>m Revenue and expenditure measures mLoans, equity injections, and guarantees<br><!-- End of picture text -->
**<mark>Box 3. The complementarity of road infrastructure and the road vehicle fleet in generating energy demand with its associated carbon emissions: The case of the US Interstate Highway System</mark>**

<mark>29.</mark> **<mark>The Interstate Highway System in the United States provides an example of a major demandside lock-in but with partial decoupling between capital stock and emissions.</mark>** <mark>This example also illustrates the concentration of investments in a short period and the shift in vehicle miles traveled and</mark> energy consumption that it entailed.

<mark>30. The U.S. Interstate Highway System program, enacted in 1956, created a 42,700 mile network of high quality highways linking major U.S. cities across the country. This massive undertaking was a lumpy investment in a structural (not marginal) change in transport patterns. The network was built in a relatively short period of time, with</mark> **<mark>two thirds of it completed in less than two decades</mark>** <mark>between</mark> 1965 and 1985 (Figure 1).

<mark>31. The interstate network represents only one percent of the total U.S. road network, but about one quarter of the total U.S. national highway system. The interstate network also accounts for nearly a quarter of total vehicle miles traveled (VMT) in the U.S. (24.4% in 2004), and nearly</mark> **<mark>half</mark>** <mark>of all heavy truck traffic associated with interstate and global commerce. The networks share of total traffic grew in proportion to the completion of the network: 10% of total VMT by 1966, 15% by 1971, and 20% by 1982. For all practical purposes the network was completed by 1990 and its share of traffic has</mark> remained more or less constant since then.

**<mark>Figure 1. U.S. Interstate Highway System Mileage Increment over 5-year periods. 1950-2005.</mark>**

<!-- Start of picture text --> U.S. Interstate Highway - Miles increment<br>over 5 year period<br>12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>1950 1960 1970 1980 1990 2000<br><!-- End of picture text -->

# _Source: Cox and Love (1996) for 1950 to 1965 data, and Weiss (2008) for 1970 to 2005 data_

<mark>32. The rapid increase in the Interstate network’s share of traffic can be explained in part by a shift in vehicles away from older roads. More significantly, by reducing transportation costs, the Interstate Highway System also made it possible for firms to both expand their reach and decentralize their production over several sites, and to eventually reduce warehousing by adopting the Japanese innovation in logistics of “just in time” delivery. We observe a doubling in the annual increment in vehicle miles traveled after the mid-1960s (Figure 2) which can be attributed to additional demand for transportation</mark> _<mark>induced</mark>_ <mark>by the Interstate Highway System.</mark><sup>27</sup> 27 We also observe that GDP and total VMT increase at nearly the same rate over the 1955-1965 period, but that total VMT increase faster than GDP in the 1965-1973 period.

13/39
# **LIFE OUT OF THE SHADOWS: THE IMPACTS OF REGULARIZATION PROGRAMS ON THE LIVES OF FORCED MIGRANTS**<sup>*</sup>

Ana Mar ́ıa Ib ́a ̃nez<sup>†</sup> Andr ́es Moya<sup>‡</sup> Mar ́ıa Adelaida Ortega<sup>§</sup> Sandra V. Rozo<sup>¶</sup> Maria Jos ́e Urbina<sup>||</sup>

Originally published in the Policy Research Working Paper Series on _February 2022._ This version is updated on _March 2023._ To obtain the originally published version, please email prwp@worldbank.org.

## **JEL Classification:** F22, O15, R23

**Keywords:** Forced Displacement, Venezuelan Migrants, Social Inclusion, Latin America.

> *This project was approved by the IPA’s IRB protocol 15396. Ib ́a ̃nez acknowledges financial support from the Inter-American Development Bank. We would like to thank Marisol Rodr ́ıguez-Chatruc for her work during the initial stages of the project. Rozo acknowledges financial support from the University of Southern California and the World Bank’s Research Support Budget grant. We are also grateful for comments by participants in seminars at the fourth CEGA/IPA/JDC Webinar on Forced Displacement in the Global South, the Pearson Institute at the University of Chicago, WEI-CSWEP, Universidad de los Andes, EAFIT, Universidad del Rosario, RIDGE Labor Forum, Williams College, University of Illinois, ITAM, LACEA, CGD, NEUDC, LEAP, LAC Chief Economist Office at the Word Bank, Princeton University, Lille University, the World Bank Research Group, and the 2nd JDC Research Conference on Forced Displacement. We are also grateful to Anna Maria Mayda, Cevat Aksoy, Yang-Yan Zhou, Michael Clemens, Thomas Ginn, C ̧ a ̆glar Ozden,<sup> ̈</sup> Berk Ozler,<sup> ̈</sup> William Maloney, Xavier Gine, Leopoldo Fergusson, Marcela Eslava, Ana Mar ́ıa Mayda, M ́onica Hern ́andez, Mauricio Romero, David P ́erez, and Felipe Mu ̃noz for useful suggestions. We would also like to thank IPA Colombia for their support in collecting data for this project.

> †Inter-American Development Bank. E-mail: anaib@iadb.org

> ‡Universidad de Los Andes. E-mail: a.moya@uniandes.edu.co

- §University of California Davis.

- ¶World Bank, Development Research Group. Corresponding author, E-mail: sandrarozo@worldbank.org

- ||World Bank. E-mail: murbinaflorez@worldbank.org