rafmacalaba/datause-encoder-data huggingface dataset:
# Summary

|1.|INTRODUCTION............................................................................................................................3|
|---|---|
|2.|HOW BIG IS THE ROLE FOR THE PRIVATE SECTOR IN TRANSPORT|
||INFRASTRUCTURE?.....................................................................................................................4|
||2.1. Developed economies...............................................................................................................7|
||2.2. Developing economies..............................................................................................................7|
|3.|FORMS OF PRIVATE SECTOR PARTICIPATION IN TRANSPORT .....................................10|
|4.|HAS TRANSPORT DE-REGULATION AND RESIDUAL REGULATION BEEN|
||EFFECTIVE?.................................................................................................................................14|
||4.1. Efficiency................................................................................................................................14|
||4.2. Fiscal.......................................................................................................................................15|
||4.3. The user viewpoint..................................................................................................................16|
||4.4. Summing up............................................................................................................................18|
|5.|TOWARDS A RE-REGULATION OF THE SECTOR? ..............................................................18|
||5.1. Price regulation.......................................................................................................................19|
||5.2. Quality regulation ...................................................................................................................20|
||5.3. Access regulation and competition policy ..............................................................................20|
||5.4. Rebundling..............................................................................................................................22|
||5.5. Additional stimulus through yardstick competition................................................................23|
||5.6. The stimulus to multimodalism ..............................................................................................23|
||5.7. Local_vs._global competition issues ........................................................................................24|
|6.|CONCLUSIONS............................................................................................................................25|
|BIB|LIOGRAPHY..................................................................................................................................26|

2
We finally investigate two alternative mechanisms that could explain our results: increasing labor market concentration and increasing product market concentration as consequences of takeovers. First, we find that virtually none of the takeovers in our sample has a meaningful impact on labor market concentration (see also Arnold, 2021). Therefore, we conclude that takeover-induced changes in labor market concentration cannot drive our job loss results. Second, we also find that takeovers that are more likely to increase product market concentration (e.g., horizontal takeovers between firms in the same narrowly defined industry) do not induce larger job loss.

Our work contributes to the recent literature on the consequences of mergers and acquisitions for workers (Furlan, 2015; Geurts and Van Biesebroeck, 2019; Arnold, 2021; Todd and Heining, 2020; Prager and Schmitt, 2021). We make several contributions to this literature. First, we are able to study administratively identified takeovers, rather than relying on worker flows between firms as some prior work did, potentially missing the mergers of smaller firms. Second, we have comprehensive administrative data on a variety of worker outcomes, including employment outcomes, earnings, and benefit uptake. These data allow us to comprehensively characterize the consequences of takeovers. Third, using detailed data on wages and skills, we are able to examine potentially important mechanisms, including labor market concentration and labor restructuring behind takeover-induced job loss. In this latter aspect, the most similar papers to ours are Lagaras (2017) which uses micro-data from Brazil to characterize how workers at target firms are affected differently based on their occupational relatedness with the acquirer and Lagaras (2023) which studies long-term earnings of workers separated from target firms. In another related paper, Lee, Mauer and Xu (2018) use aggregate data in the United States to study how post-consolidation job loss is increasing in the human capital relatedness of consolidating firms. Similar to Lagaras (2017) and Lee, Mauer and Xu (2018), we find that job loss is higher among target workers at firms that have higher human capital relatedness (and therefore are more likely to have efficiency gains) and further find that this job loss is higher for the specific types of workers whose skills are replicated in the acquirer. We further are able to complement this work by demonstrating through our comprehensive income measures (including non-labor income) that even in the generous social safety net environment of the Netherlands, affected individuals tend to be underemployed and have lower total income, even four years after their firm is acquired.

We also contribute to the literature on the longer-term impacts of job loss (e.g., Couch and 3
Table 1

# PERCENTAGE OF FEMALES IN THE LABOR FORCE, BY SECTOR BETUKE 1950-1982 (For the Four Main Sectors of Employment)

|Sector<br>of Employment||Percentage|of Females||
|---|---|---|---|---|
||In Labor|Force|Among Wage|Earners|
||High<br>Income|Low<br>Income|High<br>Income|Low<br>Income|
|Agriculture|32.7|28.4|18.9|31.5|
|Manufacture|29.8|25.8|29.7|20.7|
|Commerce|43.8|27.9|45.1|22.1|
|Services|52.0|31.8|53.5|31.8|
|Total|35.6|24.9|36.6|22.5|

_Source:_ Schultz (1989), Table 4.

20
Over the last year or two, some research has begun to analyze the economic effects of the exodus of Venezuelans on the surrounding countries. The existing work has so far focused on Colombia, the main receiever of Venezulean migrants. Caruso et al. (2019) analyze the effects of labor inflows from Venezuela on the Colombian labor market. Their instrumental-variables estimates suggest that wages in the informal sector in urban areas may have fallen by about 2 percent. Penaloza-Pacheco (2019) also finds evidence of large negative wage effects in Colombia’s regions bordering Venezuela, with larger effects among low skilled workers and in the informal sector.

# **3 Context**

## **3.1 Venezuelan Crisis**

The progressive deterioration of Venezuela’s institutions and democracy has triggered one of the most severe crises in the Western Hemisphere in recent times. Between 2013 and 2018, Venezuela’s GDP per capita fell by about 50% in real terms, comparable to the contractions experienced by Sierra Leone in 1991 and Rwanda in 1994. Venezuela has also been suffering from hyperinflation since the end of 2017. The IMF’s World Economic Outlook estimates annual inflation in Venezuela for year 2019 at 200,000%.

As a result of these macroeconomic trends, poverty rates have skyrocketed: 9 of 10 Venezuelans were below the poverty line in 2018 (Encovi 2018) and child mortality has risen to 26 per thousand live births. At the same time, Venezuela has become one of the countries in the world with the highest rates of crime and violence with 89 homicides per 100,000 inhabitants. The situation is even starker for women who face increased rates of violence and soaring death rates associated to childbirth.

Not surprisingly, Venezuela’s economic and political collapse has given rise to a large exodus, mainly to other countries in Latin America and the Caribbean. It is estimated that, as of September 2019, approximately 4.3 million people have left Venezuela (i.e. about 13.6% of the country’s population in 2016). The magnitude of this exodus is similar to the refugee crises in Syria, Afghanistan, Somalia and South Sudan (OEA (2019)).

As of 2018, about 80% of the Venezuelan migrants have resettled in Latin America. Colombia is the main receiving country, with an estimated 1.4 million Venezuelan migrants (WorldBank (2018)), followed by Peru (0.9 million, WorldBank (2019)) and 6
_-_ 14-

# **BibliogmaRhv**

Allingham M. and A. Sandmo, "Income Tax Evasion: A Theoretical Analysis", Journalof Public Economics, Vol.1, November 1972 P. Beckerman, "Public Sector Debt-Distress in Argentina", World Bank working paper, N., 1992 R. Bird and M. Casanegra de Jantscher, "Improving Tax Adminisration In Developing **Countries",** IMF, 1992 R. Bird, **"** Tax Policy, and Economic Development", John Hopkins Un. Press, 1992 V. Duran, "Una estimacion de la funcion de recaudacion del IVA", mimeo, Proyecto de politica tributaria, Buenos Aires, 1989 Heller P., "Model of Public Fiscal Behavior in Developing Countries: Aid Investment, and lhxation", American Economic Review, June 1975 3. Olivera, " Money, Prices and Fiscal Lags: A Note on the Dynamics of Inflation", Banca Nationale del Lavoro. Ouarterly Review, September 1967.

S. Richuptan, " Deterninants of Income Tax Evasion", in "Supply-Side 'mx Policy, V. Gandhi, IMF, 1987.

C. Rodriguez, "The Macroeconomics of the Public Sector Deficit: the Case of Argentina", World Bank working Paper, N.632, March 1991 C. Silvani, "Improving Tax Compliance", in "Improving Tax Administrtion In pjeveloping Countries", R. Bird and M. Casanegra de Jantscher (eds), EMF, 1992 Srinivasan T.N., "'mx Evasion: A Model", Journal of Public Finance, November 1973.

V. TInzi, "Inflation, Real Tax revenues, and the Case for Inflationary Finance: Theory with an Application to Argentina", IMF Staff Papers, September 1978.

J. Vazquez-Caro, G. Reid with R. Bird, "'Tx Administration Assessment in Latin America", Regional Studies Program, Report N. 13, the World Bank, January 1992.

The World Bank, "Argentina: "Tax policy for Stabilization and Economic Recovery", A V A Bank Country Study, 1990.
earnings than it does at higher levels of education. Similarly, marginal increases in education at low levels of education (where the poor are typically concentrated) raise earnings substantially.

# **CHANGING PATTERNS OF RETURNS TO EDUCATION**

Returns to investments in schooling are estimated by computing the difference in average earnings between those with a given level of schooling and those at the level below it. Typically, the earnings function elaborated by Mincer (1974) is most often used. This estimates an earnings function where the log of average wages is explained by years of education, years of relevant work experience and its square. The coefficient on education can be interpreted as the private (that is, what the individual expects to earn as a result of their investment) return to an additional year of education, and can be calculated, using dummy variables, for each relevant level of education (Patrinos 2016). That is, when one estimates the earnings function with “no education” as the base category and includes dummy variables for each level (primary, secondary and tertiary), then the estimated coefficient on the “primary schooling” variable shows the average earnings change received by those with primary schooling over those with no education. Similarly, the coefficient on the “secondary education” variable shows the earnings change received by those with secondary education over those with no education. The coefficient on the “tertiary education” variable shows the earnings change received by those with tertiary education compared with those with no education. The average return to, say, secondary education is calculated by subtracting the coefficient on the primary education variable from that on the secondary education variable and dividing the answer by the number of years of education in the secondary school cycle. The same procedure would be undertaken for those with tertiary education.

A review of current returns patterns by level and region is presented using two large, recent databases. The analysis is based on estimates of the returns to schooling from more than 140 countries and more than 1,000 estimates from 1950 to 2014. The data come from countries all over the world, representing more than 90 percent of the world’s population, and are disaggregated by gender (Montenegro and Patrinos (2014) and Psacharopoulos and Patrinos (2018)). Montenegro and Patrinos (2014) present comparable estimates of the rate of return to investment in education in a database that uses the same specification, estimation procedure, and 6
20 applied in the design and implementation of project and sector work tasks. This section briefly reviews some of the institutional factors which may have influenced how the Bank has gone about the task of assisting Borrowers with the development of their PHN services at the periphery.

# Staffing

ProfessionEls working on PHN operations in the Bank have tended to be of two types: population and public health specialists, and economists. The three operational divisions of the former PHN department contained between them only one sociologist, one management specialist, and one communications specialist. This staffing pattern helps to explain why the Bank has focused more on the collection of epidemiological and quantitative information about clients. than on behavioral and qualitative information; and why more attention has been paid to the choice of PHN interventions to be emphasized in the programs the Bank supports, than to the detailed design of outreach systems to deliver them. The policy and research division of the former PHN department was heavily dominated by economists, and was not an effective constituency for advancing sociological, management, and institutional concerns in the work of the operating divisions.

# Beliefs

There is uncertainty among at least some PHN pro'ect officers whether the Bank should concern itself with the details of outreach system design and implementation. On the one hand, there is the argument that the Bank is a bank, and that its job is therefore to agree on broad project designs and conditionality, leaving the Borrower, as implementor, to concern itself with the detailed planning of implementation in the field. On the other hand, there is an alternative argument that, while the Bank should concern itself with these issues because of their importance to program success, the Bank is not suited for this task because of its centralized nature, compared, say, to UNICEF.

This paper suggests that the first of these arguments confuses the Bank's role in institutional development with the Borrower's role in implementation. While it is never the role of the Bank, as financial intermediary, to imp'iement projects, it is the Bank's role, as development agency, to assist with the Borrower's institutional development to ensure that projects are effective and loan funds safeguarded. This role in institutional development should include assistance with any Borrower systems and processes which are critical for success--whether they are accounting systems for tracking disbursements, or learning processes for improving services to clients. The second argument- that the Bank cannot operate effectively at the periphery--is contradicted both by PHN sector successes like the Tamil Nadu nutrition project, and by the Bank's heavy existing involvement with client/provider issues in agricultural extension and irrigation water management. More fundamentally, it can be argued that if the Bank does attach priority to its projects effectively reaching people at the periphery and is indeed not suited for the task, then it would be the Bank's job to rearrange itself institutionally so as to do better.
The situation is much different at the tertiary education level. Tertiary level enrollments have tripled since the 1970s (according to UNESCO as listed on the World Bank’s EdStats website: <u>https://data.worldbank.org/indicator/SE.TER.ENRR), from 10 percent in 1970 to 38 percent in</u> 2017. At the same time, the returns to tertiary education have increased by almost 3 percent.

The situation for men and women is similar at the primary and secondary levels. The returns to schooling at the primary and secondary levels for men decreased by one-third (Figure 5). The returns to higher education stayed roughly the same between 1980 and 2014. For women, the returns to primary only decreased by 15 percent. The returns to higher education increased by 8 percent (Figure 6).

<!-- Start of picture text --> Figure 5: Changes in retuns to education since 1980, men<br>1.20<br>1.00<br>0.80<br>0.60<br>1980 1990 2000 2014<br>Primary Secondary Higher<br><!-- End of picture text --> Source: Montenegro and Patrinos 2014 11
3

> skills. Another group of interest consisted of Burkinabe neoliterates who had become literacy teachers.

> In May 2000, the researchers tested groups of residents with the CAAS and obtained speed and accuracy data for basic reading and math tasks. Results were compared with children who were (a) students in grades 1-3 of the bilingual schools; (b) adolescents attending nonformal schools for adolescents, largely financed by UNICEF; (c) secondary school students, who had completed the bilingual education primary school program; (e) graduates of literacy classes; (f) graduates of literacy

> classes who had become literacy teachers; (g) literacy teachers with formal education; and (h) secondary school students. The results were compared with norms of U.S. students.

The comparisons indicated that the Burkinabe adults completing one phase of adult literacy training were generally performing at a level lower than that attained by 2nd grade students in both Burkina Faso and the United States (Figure 1). More specifically:

- Students at all levels tested (rural bilingual grades 2, 3, 8, adolescent and adult nonformal classes) read more slowly and less accurately than a sample of second grade students in the United States.

- Learners in the process of completing two years of nonformal adolescent and adult classes read too slowly (about 2.2 seconds per word) and inefficiently (80-87 percent correct); given the limitations of human memory, they were probably not able to use reading extensively.

- Graduates of past years who became literacy teachers have become more efficient readers with time and approximated the speed and accuracy of formally educated literacy teachers and of secondary rural school students. Some of the tested literacy teachers, however, had scores only slightly above those of their learners.

- Student scores in basic arithmetic additions and subtractions approximated U.S. scores of the appropriate grades.

These results supported the hypothesis that adult literacy training programs may not be developing adequate low-level reading skills. Suitable methods were sought to find out if it was possible to increase the speed and accuracy of the next cohorts of literacy learners.

# **Figure 1. Reaction speed and accuracy of U.S. and Burkinabe Students**

<!-- Start of picture text --> CAAS Speed (in seconds) of Burkinabe CAAS Accuracy (percent correct) of<br>and US Students on Word Recognition Burkinabe and US Students on Word<br>Task Recognition Task<br>3 ~ O 95 l O m<br>2 r > Omde 90> , | Ir~~~9 4<br>1 I L o D Aduh irAd 80 I Aduh Iad<br>Burkinabe U.S. Burkinabe U S.<br><!-- End of picture text -->
- 40 -

# Table 3.16: CENTRAL GOVERNMENT TRANSFERS, SUBSIDIES AND DOMESTIC CREDIT IN ARGENTINA, BRAZIL, COLOMBIA, MEXICO, AND VENEZUELA (as Percent of Total Expenditures and Lending Minus Repayment)

|Countries/Groupings|1978|1979|1980|1981|1982|1983|1984|1978-84|
|---|---|---|---|---|---|---|---|---|
|M6EN1INA|40.58|45.12|48.04|43.06|41.16|62.25|**56.73**|48.13|
|(Trans;ers)<br>|(4.59)|(3.72)|(4.22)|(5.58)|(5.10)|(8.45)|(5.61)|(5.33)|
|Subsidies&Transfers|32.90|36.57|39.47|32.93|31.42|39.22|41.11|36.23|
|NetLending|7.68|8.55|8.57|10.13|9.74|23.03|15.62|11.90|
|BORZIL|68.94|70.72|72.12|72.66|69.22|72.22|66.56|70.35|
|(Transfers)|8.94|9,64|(8.42)|(8.60)|(8,44)|(7.55)|(7.93)|(8.50)|
|Subsidies&Transfers|48.94|48.86|53.02|47.41|44.34|40.59|37.92|45.87|
|Net Lending|20.00|21.86|19.10|25.25|24.88|31.63|28.64|24.48|
|COLOMIA|N.A.|42.39|39.19|41.39|40.98|**44,26**|N.A.|41.64|
|(Transfers)|N.A.|(22.87)|(19.06)|(21.16)|(22.23)|(25.17)|N.A.|(22.10)|
|Subsidies|N.A.|39.43|36.35|36.97|41.07|44.40|N.A.|39.64|
|NetLending|-2.08|2.96|2.84|4.42|-. 09|-. 14|N.A.|2.00|
|MEXICO|28.64|27.60|30.67|25.50|40.77|25.25|23.59|28.86|
|(Transfers<br>(est.)|(11.06)|(11.76)|(14.34)|(14.05)|(8.12)|(12.79)|(13.43)|(12.22)|
|Subsidies&Transfers|23.87|20.98|23.41|17.35|35.91|22.71|21.82|23.72|
|Net Lending|4.77|6.62|7.26|8.15|4.86|2.54|1.77|5.14|
|VENEZ.d<br>ELA|19.18|22.49|34.52|36.87|31.84|29.73|36.45|**31.15**|
|(Transfers)|(12.19)|(16.38)|(12.44)|(12.08)|(13.00)|(15.16)|(12.16)|(13.34)|
|Subsidies|12.24|17.53|18.22|19.92|19.23|23,29|28.93|19.91|
|Net Lending|6.94|4.96|16.30|16.95|12.61|6.44|7.52|10.24|
|**UAC**COUR4IES|45.62|46.57|48.76|47.44|49.58|50.11|48.30|48.05|
|(Transfers)|(8.16)|(8.66)|(7.53)|(8.26)|(8.57)|(9.11)|(7.95)|(8.32)|
|Subsidies|33.02|33.47|36.16|32.04|<br>35.98|<br>33.51|<br>33.40|33.94|
|Not Lending|12.60|13.10|12.60|15.40|13.60|16.60|14.90|14.11|
|IND.**W.**<br>**ECONOMIES**|**62.68**|**62.61**|61.84|60.91|61.03|60.96|60.10|60.87|
|(Transfers)|(14.48)|(14.17)|(13.35)|(12.60)|(11.63)|(11.44)|(11.42)|(12.73)|
|Subsidies&Transfers|56.28|56.41|55.94|55.71|55.83|52.26|55.60|55.43|
|Net Lending|6.40|6.20|5.90|5.20|5.20|4.70|4.50|5.44|

Source: _IW_ Government Finance StatIstics Yearbook, 1986 and own elaboration.
# 1 Introduction

In the wake of the recent Global Financial Crisis (2007-2008), the impact of adverse events on families has gained renewed focus among policy makers and academics. However, adverse events are not always caused by aggregate shocks, which generally emanate from economic recessions, natural disasters, etc.; they could also be caused by idiosyncratic shocks, such as a death in the family, job loss, etc. Irrespective of the source of the shock, families adopt coping strategies to mitigate the impacts of these adverse events. These coping strategies generally consist of drawing on savings, increasing family labor supply (inserting non-working family members into the labor market or increasing the number of hours worked), accessing formal (government supported social assistance) or informal (remittances, charities, nongovernmental organizations, borrowing) safety nets, and reducing household expenditures (durable goods, food, clothing, etc.) and investments (human, financial etc.).

This paper studies whether households affected by income shocks cope by reducing human capital investments. We focus on coping strategies in human capital investments because of their potential longer term or intergenerational impact on household welfare. The paper exploits five unique Crisis Response Surveys (CRS) conducted in Armenia, Bulgaria, Montenegro, Romania, and Turkey during 2009 and 2010.<sup>2</sup> The timing of the surveys coincided with the effects of the recent global financial crisis. Despite the breadth and depth of the crisis, households were also affected by idiosyncratic shocks, as they are at any given time. However, the data available do not allow us to separate households affected by idiosyncratic and aggregate shocks.

To analyze human capital investment decisions by households in response to adverse income shocks, we adopt a propensity score matching (PSM) technique. We further focus on the health and education investment decisions of households affected by income shocks. Although the PSM technique has been used extensively in the literature, it is susceptible to hidden bias due to unobserved factors. We therefore perform sensitivity analyses using Mantel-Haenszel (M-H)

> 2 According to the World Bank Classifications, Armenia is the only Lower Middle Income Country, while the other four countries are Upper Middle Income Countries.

2