rafmacalaba/datause-encoder-data huggingface dataset:
- <u>Table 1. Educational attainment of native Danes and immigrants, 1995 2002, percent</u>

||Primar<br>y|Secon-<br>dary|Voca-<br>tional|Higher<br>education,<br>short|Higher<br>education,<br>medium|Higher<br>education,<br>long|All|N|
|---|---|---|---|---|---|---|---|---|
|NativeDanes|23.1|3.8|47.5|5.3|12.4|8.0|100|520,677|
|Immigrants,<br>all non-Western|25.9|7.8|33.9|7.9|14.2|10.3|100|65,909|
|Turkey|58.9|4.6|20.9|4.4|8.7|2.5|100|9,841|
|Pakistan|39.2|18.5|24.7|4.0|7.7|5.7|100|3,774|
|Vietnam|23.9|2.9|47.5|8.8|12.4|4.5|100|4,791|
|Iran|5.6|4.9|25.5|12.2|31.5|20.4|100|7,357|
|Iraq|11.6|9.8|25.7|10.5|22.3|20.2|100|2,413|
|Ex-|||||||100||
|Yugoslavia|22.1|7.3|48.4|6.3|9.7|6.3||12,347|
|Somalia|16.3|19.9|34.3|9.3|8.6|11.6|100|594|
|Stateless|15.9|8.5|37.1|9.0|16.0|13.6|100|1,819|
|Other non-<br> Western|21.1|9.0|33.8|9.0|14.0|13.1|100|22,973|

Note: The data set covers male wage earners aged 30-57, with what amounts to at least 2 months of fulltime employment in a given year. Individuals undertaking education and individuals employed in the military or in management positions are excluded. The data set includes first-generation non-Western immigrants with either a Danish education or a foreign acquired education, for which Statistics Denmark has survey-based information. Immigrants for whom Statistics Denmark has calculated imputed levels of education because they have not responded to the education surveys conducted in 1999 and 2003 are not included.

- <u>Table 2. Educational attainment of native Danes and immigrants, 1995 2002, percent</u>

||Primar<br>y|Secon-<br>dary|Voca-<br>tional|Higher<br>education,<br>short|Higher<br>education,<br>medium|Higher<br>education,<br>long|All|N|
|---|---|---|---|---|---|---|---|---|
|NativeDanes|23.1|3.8|47.5|5.3|12.4|8.0|100|520,677|
|Immigrants,<br>all non-Western|25.9|7.8|33.9|7.9|14.2|10.3|100|65,909|
|– Immigrants<br>with Danish<br>education|21.9|2.6|34.1|9.2|19.8|12.4|100|30,740|
|– Immigrants<br>with foreign<br>education|29.4|12.2|33.8|6.8|9.3|8.4|100|35,169|

Note: See note for Table 1.

35
The extent of the reversal is however smaller than for secondary school completion and tertiary enrollment. Among advanced economies in particular, the share of countries that experienced the gender gap reversal in primary school completion has hardly changed from 1950 to 2010.

The fact that the gender gap reversal is not as clear cut at the primary level could be explained by several factors. First, in many countries in our sample, primary school completion has been close to universal already for some time. As a result, there has been little variation in the gender ratio which remained around one over our period of study. This is the case for advanced economies, Europe and Central Asia and Latin America (Figure I). A related explanation is that primary schooling has been mandatory for some time for many countries in the sample, which leaves little room for variation in the gender ratio in primary school completion over time. As result, primary school completion is mainly driven by institutional features rather than optimal decisions of investment in schooling by gender.

# **3 Theoretical Framework**

## **3.1 Micro Foundations**

We now develop a conceptual framework that allows to formulate alternative hypotheses for the reversal. The economy is assumed to be populated by of mass individuals that differ in their level of scholastic performance _z_ , which is continuous and perfectly observed by individuals.<sup>11</sup> For the sake of simplicity, a single-period model is assumed where individuals receive the benefits of their investment in schooling in the same period as they invest. Individuals choose years of schooling _s_ to maximize their level of utility _U_ . Building on Card (1994), we simply express the utility function of individuals as:

> 11Scholastic performance can be interpreted as the product of a complex combination of individual ability as well as other inputs such as effort and motivation, which can empirically be measured by test scores (Heckman and Kautz, 2012)

9
# **1. Introduction**

The global market for labor has some of the largest distortions of any factor market (Clemens, 2011). The same worker can earn very different wages depending on the country in which they work (Clemens et al, 2009; McKenzie et al. 2010). As a result, moving from a poor country to a rich country to work is perhaps the single act most likely to succeed in dramatically increasing an individual’s income, as well as that of remaining family members (e.g. Cox-Edwards and Ureta, 2003; Yang 2008; Gibson et al. 2011). In recognition of this fact, a number of developing countries have put in place policy measures to help their citizens work abroad. Until recently, the Government of the Philippines was at the forefront of facilitating overseas temporary contract work and making emigration part of its national development strategy, and many other developing countries are now seeking to emulate the Philippines in this regard.

However, the recent global financial crisis has highlighted the potential vulnerability of migrant jobs to economic conditions in destination countries. Emigration to Ireland from the new European Union states fell 60 percent from 2008 to 2009, while overall European Union flows to Spain fell by two-thirds. Inflows to the United States fell in almost all legal temporary work categories, including a 50 percent decline in visas issued to low-skilled seasonal workers (Papademetriou et al, 2010). Net migrant outflow from Mexico to the U.S. was only 0.09 percent of the Mexican population in 2010-11, compared to 0.53 percent in 2006-7 (Rodriguez, 2011). Moreover, despite these responses at the extensive margin (the number of migrants), immigrant employment rates among those who do migrate or remain abroad are more sensitive to the business cycle than the employment rates of natives (Orrenius and Zavodny, 2009).

A key contribution of this paper is to show that the high vulnerability of migrant jobs to economic shocks is intimately tied to the large gains in wages that migration offers. The extent to which migration flows respond to shocks at destination depends on the output elasticity of demand for migrant labor and on the extent to which wage adjustment can occur through movements along the migrant labor supply curve. However, estimating this responsiveness in the context of bilateral migration flows is complicated by concerns that economic shocks also affect the migrant origin country, thereby also shifting the labor supply curve and preventing identification of the labor demand impact. In addition, reliable microeconomic data on migrant flows and the wages these migrants earn are extremely rare. We overcome both issues by using a 2
**Figure 26: Speed plots approach – impact of short term price change on social costs of congestion (in billion LE)**

_Source:_ Authors’ elaboration based on World Bank (2010) and Litman (2012)

**Figure 27: Volume to capacity ratio approach – impact of short term price change on social cost of congestion (in billion LE)**

_Source:_ Authors’ elaboration based on World Bank (2010) and Litman (2012) 26
Policy Research Working Paper 10396

# **Abstract**

Work-related migration has many potential drivers. While current literature has outlined a theoretical framework of various “push-pull” factors affecting the likelihood of international migration, empirical papers are often constrained by the scarcity of detailed data on migration, especially in developing countries, and are forced to look at few of these factors in isolation. When detailed data is available, researchers may face arbitrary choices of which variables to include and how to sequence their inclusion. As male and female migrants tend to face occupational segregation, the determinants of migration likely differ by gender, which compounds these data challenges. To overcome these three issues, this paper uses a rich primary household survey among migrant communities in Indonesia and employs two supervised machine-learning methods to identify the top predictors of migration by gender: random forests and least absolute shrinkage and selection operator stability selection. The paper confirms some determinants established by earlier studies and reveals several additional ones, as well as identifies differences in predictors by gender.

This paper is a product of the Gender Global Theme and the Office of the Chief Economist, East Asia and the Pacific Region.. 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 dhalim@worldbank.org and sseetahul@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
This very detailed plan includes intervention strategies for a Safe Motherhood program in the Americas. The general strategy includes promotion of good health for women, including education, nutrition supp'ements, and family planning; training of health personnel; research on maternal mortality; improvement of information systems; and general improvement of health services. This document advocates universal coverage of health services for maternal care, a risk screening approach, a strengthened referral system, and better transportation facilities. In addition, for the poorest countries in Latin America, the plan recommends networks of homes for high-risk pregnant women. The report provides an annex with detailed estimates of the costs of the first five years of the plan.

Royston, E. and Armstrong, S., Preventing Maternal Deaths, World Health Organization, Geneva, 1989.

This well written and very readable book provides documentation of the problem of maternal mortality, delving into detail on the reliability of official statistics, the causes of mortality, consequences of the status of women in many developing countries, and the particular problems associated with abortion. Later chapters deal with maternal morbidities, and explore the role that health services and family planning can play in reducing mortality. The work is well documented and provides a comprehensive overview of the problem. There is, however, almost no discussion of the cost of maternal health interventions.

Starrs, A. and Measham, D., Challenge for the Nineties: Safe Motherhood in South Asia, World Bank and Family Care International, 1990.

This is the renort of a conference held in Lahore, Pakistan in March, 1990. The report discusses the status of women's lives in South Asia, possible interventions for reducing maternal mortality, and resource mobilization and utilizat on requirements. In considering the costs of maternal health programs, the report quotes from the works by Herz and Measham, Walsh, et al., and Maine, et al. which are also listed here.

Walsh, J., Feifer, C., Measham, A., and Gertler, P., Maternal and Perinatal Health, Ch. 11 _of_ Disease Control Priorities in Developing Countries, forthcoming, World Bank.

This chapter in the forthcoming World Bank study presents a detailed analysis of the causes of maternal mortality, as well as perinatal mortality. It also discusses actual and recommended practices in treatment. This study uses the same costing assumptions as the Herz and Measham study, but incorporates assumptions about the program impact on neonatal mortality. With increased contraception and the higher level of care implicit in the Safe Motherhood program, perinatal mortality is assumed to fall by 43% in the limited effort scenario from Herz and Measham, and by 79% in the more moderate scenario. The result is that the number of deaths averted increases by 24 times and 13 times in the two scenarios respectively.

Ward, V., Maine, D., McCarthy, J., and Wray, J., Indicators of Success in Programs Designe; to Improve Morbidity and Mortality, Center for Population and Family Health, Columbia 36
|_hired_labor_value_LCU_|Vale of hired<br>labor, in LCU|Hired labor value valued using<br>median prices per in a defined<br>geographical area. The area in<br>question is the EA (or admin 1 in<br>some surveys) under the<br>condition that there are more than<br>10 observed wages in the area. If<br>there are less than 10 wages,<br>prices are calculated at a higher<br>geographical level. When<br>possible, separate wages were<br>calculated for men, women and<br>children.|TZA: overall<br>median wages<br>calculated, then<br>applied to total<br>number of labor<br>days (no<br>man/child/woman<br>wage distinction).<br>Moreover, “free”<br>labor is not<br>distinguished from<br>paid labor, and<br>wages may<br>therefore appear<br>lower than reality.|
|---|---|---|---|
|_hired_labor_value_USD_|Vale of hired<br>labor, in USD|Hired labor values<br>(_hired_labor_value_LCU)_are<br>converted to current USD and<br>deflated to 2020 dollar values,<br>using exchange rates and a<br>deflator extracted from World<br>Bank databases (Azevedo et al.,<br>2011)||
|_total_labor_days_|Total labor days<br>on the plot||Family labor days<br>+ hired labor days<br>+ “other” labor<br>days (non-hired<br>and non-family)<br>NGA: no PP<br>harvest in wave 1.<br>No “other” labor<br>until wave 4.<br>TZA: no “other”<br>labor.<br>UGA: no family<br>labor in wave 7. No<br>“other” labor after<br>wave 3|
|_irrigated_|Is the plot<br>irrigated?|||
|_tractor_|Did the household<br>use a tractor in<br>this season?||ETH: absent in<br>wave 1<br>UGA: absent in<br>waves 1 and 2|
|_plot_owned_|Farmer declares<br>owning the plot||Owned=<br>purchased,<br>inherited, granted<br>by local leaders...|
|_plot_certificate_|Possession of a<br>certificate for the<br>plot||<br>MWI: absent in<br>wave 1 and wave 4|
|_erosion_protection_|Is the plot<br>protected from<br>erosion?||NGA: absent in<br>w1, w2. Only for<br>plots with erosion|

34
°° ° Figure 13: Actual and Synthetic Debt Series <!-- Start of picture text --> 4 , \ r ey<br>// \\, \ NS // NYa<br>ry // \\ NM~ /ca ,\<br>- / WALTJ oN\ TM\Nene OL” N.S S<br>= y!iIIii “wo NS 7SA. N,<br>7 x<br>a \\\\\\\ \\<br>\<br>‘<br>‘ \<br>Ty,Ax.mA ///vA<br>+ w o s ~ SSN,\Na - _- a///<br>a | \\,NU -n. TM\Noe ..N . S SS. S NS //ui/ / // / y va“-a<br>4 NSN. ‘ \— N /a,//<br>ZY<br>VVa<br>“<br>I/ \<br>= = == “ e w ane fd/"/ti AN\\ NS \\ \ \\\\<br>\ N<br>N RNo<br><!-- End of picture text -->
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Bibliography and references

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic. All of these can be downloaded from the project Web site: www.infrastructureafrica.org. For papers go to the document page (www.infrastructureafrica.org/aicd/documents), for databases to the data page (www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (www.infrastructureafrica.org/aicd/tools/models), and for maps to the map page (www.infrastructureafrica.org/aicd/tools/maps). The references for the papers that were used to compile this country report are provided in the table below.

## General

- AICD (Africa Infrastructure Country Diagnostic). _Africa’s Infrastructure: A Time for Transformation_ . www.infrastructureafrica.org.

- Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank.

## Financing

Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

## Growth

Calderón, César. 2009. ―Infrastructure and Growth in Africa.‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa’s Infrastructure

- Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

49
21 TRADE, TRANSPORT, AND TERRITORIAL DEVELOPMENT secondary region. In that case, the observed differences in regional growth rates could simply be driven by difference in growth rates across industries and would be uncorrelated with increased import competition. On the contrary, we find that there is no correlation between the post-liberalization growth in three-digit industries (1994-2000) and their pre-liberalization share of output in the secondary region (Figure 5 in Appendix C). Given the pre-reform trends in spatial concentration of activity in primary regions, we are less concerned about the spatial element that may be contributing to the endogeneity of reforms and rely on Topalova and Khandelwal (2011) who have convincingly argued that tariff cuts in India during 1989-2000 were rather exogenous.

To check for the sensitivity of our results, we repeat the specification in (11) with alternative samples, region classification and methodology for clustering standard errors. Our results remain robust to using unweighted regressions and a sample with unbalanced panel of districts (Tables 7 and 8 in Appendix D); alternative distance thresholds for defining regions (Tables 9 and 10 in Appendix D); classification of industries based on the absolute changes in the import tariffs rather than the rate of change of the import tariffs between 1993 and 1997 (Table 11 in Appendix D). In all cases, our main result on the reallocation of manufacturing activity away from the primary towards the secondary region in the period immediately following trade reforms and a reversal in the following period continue to hold across various robustness checks. We believe that this may be driven by the larger internal trade cost across districts in India that we examine next.

# **5. Evidence on Decline in Internal Trade Costs**

India went through massive highway upgrades starting from the 2000s.Conceived in 1999, the Golden Quadrilateral (GQ) highway upgrades began in 2001, and was 95 percent complete by the end of 2006. The GQ network connects four major cities of Delhi, Mumbai, Chennai, and Kolkata (the nodal districts) and is the fifth-longest highway in the world. Several studies, such as Ghani et al. (2016), have subsequently documented the importance of the GQ upgrades for the development of Indian manufacturing along the highway system; none, however, has examined how the decline in internal trade costs – brought about by GQ upgrades– affects regions with differential access to ports.

## **5.1 Specification**

The model in Section 3 predicts that as internal trade costs fall, economic activity may shift towards port locations. A decline in domestic trade cost reduces protection from trade com-
Chart 12. Trade Integration Export plus import volumes of merchandise, ratio to GDP <!-- Start of picture text --> Pettent<br>50<br>45<br>40<br>35- LMIC ___<br>30 -<br>25 - High Income OECD _ ,<br>20 , -----------------_,- _-<br>20<br>74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04<br>Percent Percent<br>74767880828486889092949698000204 74767880828486889092949698000204<br><!-- End of picture text --> Source: International Economnics Department, World Bank, February 1995.

20
# Figure 5. EMDE growth outcomes

<!-- Start of picture text --> A. Growth in EMDEs B. Primary balance C. Average 2021-22 growth in<br>sustainability gap in EMDEs, EMDE regions<br>Percent Baseline Downside/upside range 20222 Percent Baseline Downside<br>6 Percent of GDP 7<br>0 6<br>4<br>5<br>2 4<br>-1<br>3<br>0<br>2<br>-2 -2<br>1<br>-4 0<br>2020 2021 2022 -3 EAP ECA LAC MNA SAR SSA<br>Upside Baseline Downside<br><!-- End of picture text --> Source: Oxford Economics; World Bank. Note: Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates. C. F. A negative gap indicates a primary balance that would set government debt on a rising path. Gaps calculated as in Kose,Kurlat et al. 2020.

16
we reduce tariffs by 50 percent and improve the export prices of seven key sectors. Foreign direct investment in our model is based on a finite number of firms that only makes sense with imperfect competition. Thus, we know from our results above that the most important impact (liberalization of barriers against FDI in services) will be ignored in this scenario. Moreover, the productivity impact from additional varieties of goods in imperfectly competitive goods sectors will also be ignored.

We find (see table 13e) that the overall welfare gains to the economy drop to only 0.1 percent of GDP, from 4.5 percent of consumption with our full model. This demonstrates that a traditional model that focuses on Harberger triangle type resource allocation gains from trade liberalization and terms of trade effects would miss the most important aspects of the impact of WTO accession on Russia.

## **Calibration to the National Accounts or NOBUS Household Survey for Factor Shares**

In our central scenario, when we reconciled the national accounts and the household factor share data, we choose equal weighting between the two data sources. In table 13g we reconcile to the household survey data; that is, by choice of weights in our objective function, we allow the national accounts factor payment data to change and hold the household data unchanged to the extent possible.<sup>33</sup> In table 13h, we present results from the opposite approach. The household data is allowed to change to be consistent with the national accounts data.

The estimated welfare gains are 4.5 percent of GDP or 8.1 percent of consumption. That is, the overall welfare impacts are virtually unaffected by the data source that is chosen.

# **Piecemeal Sensitivity Analysis**

In table 19, we present the impact on welfare of varying the value of key parameters. In these scenarios, we retain the central value of all parameters except the parameter in question. In general, the gains to the economy (welfare gains) increase with an increase in elasticities, since higher elasticities imply that the economy is able to more easily shift to sectors or products that are cheaper after trade and FDI liberalization.<sup>34</sup> There are two parameters in the table that have a strong impact on the results: the elasticity of substitution between value-added and business services (esubs) and the elasticity of multinational firm supply (etaf). A liberalization of the barriers to FDI will result in a reduction in the cost of business services, both from the direct effect of lowering the costs of doing business for multinational

> 33 No change in the household data was not a feasible solution, so we allowed a slight change in the household data. 34 An increase in the elasticity of substitution between varieties reduces the welfare gain. This is because when varieties are good substitutes, additional varieties are worth less to firms and consumers.

29