rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A3 OLS Estimations based on country specific variables**

||**1985 a**<br>|**rrival **<br>||**1975 a**<br>|**rrival**<br>||
|---|---|---|---|---|---|---|
|**EDUCATION INDEX 2**|**1990**|<br>**-2000**|**198**|<br>**0-1990**|**199**|<br>**0-2000**|
|||**(1/10)**||**(1/10)**||**(1/10)**|
||**predicted**<br>**(lnOP1990)**|**Δpredicted**<br>**(lnOP1990-2000)**|**predicted**<br>**(lnOP1980)**|**Δpredicted**<br>**(lnOP1980-1990)**|**predicted**<br>**(lnOP1990)**|**Δpredicted**<br>**(lnOP1990-2000)**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**Log of GDP per capita**|**0.0249*****<br>**[0.00496]**||0.0151<br>[0.00974]||0.00577<br>[0.00571]||
|**Openness Index**|-0.00003<br>[0.000155]|0.00000<br>[4.02e-06]|-0.00006<br>[0.000256]|-0.00001<br>[5.85e-06]|-0.00007<br>[0.000175]|0.00001<br>[6.23e-06]|
|**Log of Distance to the US**|**0.0303*****<br>**[0.0111]**|**0.000468****<br>**[0.000206]**|0.0144<br>[0.0118]|0.000449<br>[0.000394]|**0.0185***<br>**[0.0105]**|0.0000983<br>[0.000251]|
|**English**|0.0132<br>[0.0105]||0.0233<br>[0.0144]||0.00911<br>[0.0107]||
|**Military conflict**|**-0.0239****<br>**[0.0111]**|-0.0000189<br>[0.000333]|0.000991<br>[0.0248]|-0.000136<br>[0.000450]|-0.00811<br>[0.0135]|-0.000082<br>[0.000389]|
|**Communism index**|-0.00533<br>[0.0164]|**0.00124****<br>**[0.000618]**|0.00645<br>[0.0260]|-0.000495<br>[0.000491]|-0.0112<br>[0.0169]|0.000246<br>[0.000427]|
|**Predicted Log of OP in 1990**||**-0.0216*****<br>**[0.00439]**||||**-0.0197****<br>**[0.00913]**|
|**Predicted Log of OP in 1980**||||**-0.0383*****<br>**[0.00478]**|||
|**Constant**|2.204***<br>[0.110]|0.0572***<br>[0.0105]|2.389***<br>[0.132]|0.103***<br>[0.0121]|2.502***<br>[0.102]|0.0541**<br>[0.0236]|
|**Observations**|71|71|72|72|72|72|
|**R-squared**|0.617|0.579|0.216|0.669|0.167|0.248|
|***** p<0.01, ** p<0.05, * p<0.1**<br>**Robust standard errors in bracket**|**s**||||||

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interaction between human capital measures and the ability to finance physical investment.<sup>31</sup> This is not to say that growing less finance intensive sectors, such as services or the informal sector, via investments in human capital is the first best solution to a weak financial system. Manufacturing has proven to be a strong and reliable engine of growth, and the informal sector’s inability to finance via formal lending, and ability to avoid regulation and taxes are problematic (see, e.g. McKinsey (2004), and Adams (2006)). Increasing access to finance by bringing more firms into the formal economy and by promoting regional banking sector outreach is likely the first best solution, though this process can be difficult and costly.

This paper substantiates the role of the local banking sector in growth and documents a new role for human capital. While the data are drawn from a sample of Indian districts, I believe the results are more widely applicable to other regional contexts.

> 31 It seems likely that human capital is enabling economic activities which are less finance dependent. A paper by Amin and Mattoo (2008) analyzes state level data in India and finds that the presence of human capital tends to promote the transition of the economy to a higher mix of services. In Kendall and Singh (2006), we document the experiences of village entrepreneurs in India who start internet kiosks in their villages using very little or no formal finance but who rely heavily on their own education to succeed.

25
more likely to be enrolled in college (Cortes, 2013). Prior research has also shown that immigration enforcement is associated with negative school performance (repeating a grade in school) among those without legal status (Amuedo-Dorantes and Lopez, 2015). Argentina, Colombia, and Mexico have made some advances in migrant inclusion, but the effects of these programs have not been carefully evaluated despite showing some positive initial results (OAS, 2011). Thus, we can expect that:

**H3 (Financial Aid):** Migrant students who apply for financial aid will have higher enrollment in higher education compared to native students.

Although most research presents evidence that migrant status has negative educational effects, some studies on the educational performance of second-generation migrants highlight that investment in education is a priority for migrant families. For instance, Becker et al. (2020) illustrates that descendants of forced migrants in Poland have higher educational attainment than those of non-forced migrants. This helps close the educational gap between second-generation migrants who prioritize education and natives (Card et al., 2000; Alexander and Ward, 2018; Bauer et al., 2013; Borjas, 1992).

# **Research design**

## **Data**

Our analysis is based on official administrative data from the Chilean government. We obtained disaggregated and de-identified individual information, including nationality, for the entire population of enrolled students in all Chilean academic institutions between 2016 and 2020 from the Ministry of Education. We merged this data with the college admission test scores.<sup>1</sup> We therefore have information on enrollment, financial aid access, and scores for the various higher education tests for the entire graduating student population in 2018. Table 1 presents the number of observations and data sources. Table 1 shows the summary of all students in the Chilean educational system, however, we need to analyze ”regular” students, and the group of interest. In this case ”not regular” students are older students and students with special needs. We will also exclude students in the Kinder and pre-kinder grades, as grade advancement is not always associated with academic performance). These students have lower advancement

> 1Until 2019 the test was known as the PSU for its acronym in Spanish (Prueba de Selecci ́on Universitaria). It is currently referred to as the Transition Test or Prueba de Transici”on.

9
39

# **References :**

Barysch, Katynka. 2005. _The Economics of Turkish Accession_ , Centre for European Reform, London, June.

- Blonigen, Bruce A. 2001. “In search of substitution between foreign production and exports” _Journal of International Economics_ , 53: 81-104.

- Gereffi, Gary. 1999. “International Trade and Industrial Upgrading in the Apparel Commodity Chain.” _Journal of International Economics_ , 48: 37-70.

- Kaminski, Bartlomiej. 2004. “Production Fragmentation and Trade Integration in Enlarged Europe: How MNCs have succeeded where CMEA had failed.” _Papeles del Este: Transiciones poscommunistas_ . No. 9.

- Kaminski, Bartlomiej and Francis Ng. 2004. "Romania's Integration into European Markets: Implications for Sustainability of the Current Export Boom." _Policy Research Working Paper 3451_ , The World Bank, Development Research Group-Trade, Washington DC, May.

- Landesmann, M. and R. Stehrer. 2003. “Structural Patterns of East-West European Integration: Strong and Weak Gershenkron Effects, in WIIW Structural Report 2003 on Central and Eastern Europe, Vol. 1, The Vienna Institute for International Economic Studies, Vienna.

- Markusen, James R. 1998. “Multinational Firms, Location and Trade,” _The World Economy_ . Vol. 21. No. 6. August (733-756).

- Maurer, A. and P. Chauvet. 2002. “The Magnitude of Flows of Global Trade in Services,” in B. Hoekman, A. Mattoo, and Ph. English, eds., _Development, Trade, and the WTO. A Handbook,_ The World Bank, Washington, D.C.

- [MIT 2004] _Turkey 2004: Economic and Industrial Outlook_ , Republic of Turkey, Ministry of Industry and Trade, Directorate General for European Union Co-ordination, Ankara, August.

- Togan, Subidey, Husamettin Nebioglu and Saadettin Dogan. 2005. “Integration and Manufacturing Industry,” in Bernard Hoekman and Subidey Togan, eds. _Turkey: Economic Reforms and Accession to the EU._ The World Bank, Washington, D.C.

- Togan, Subidey, 2004. “Turkey: Toward EU Accession,” _The World Economy_ , vol. 27, no. 7, July, pp. 1013-45.

- Togan, Subidey. 2000. “Effects of Turkey-European Union Customs Union and Prospects for the Future.” _Russian and East European Finance and Trade_ , vol. 36, no. 4, July-August, pp. 5-25.

- Togan, Subidey. 1997. “Opening up of the Turkish Economy in the Context of the Customs Union with the EU,” _Journal of Economic Integration_ 12 (pp. 157-79).

- [UNCTAD. 2002] World Investment Report: Transnational Corporations and Export Competitiveness. United Nations: New York and Geneva.

- [World Bank 2006] _From Disintegration to Integration: Europe and Central Asia in International Trade_ , preliminary draft, The World Bank, Washington, D.C.
conjecture could be that countries with largely domestically owned financial systems could be more eager to implement Basel III for reasons of signaling and attracting future foreign direct investment into their banking systems. Another explanation may be that the variation in foreign ownership share reflects some systematic, qualitative factors behind a country’s choice to implement Basel III—such as those described by Beck, Jones, and Knaack (2018). Based on the results from table 12, we include variables that were statistically significant for both the panel and cross-section samples in our selection equation.

# <u>7.2. Baseline Results</u>

This subsection discusses the baseline results for the repeated cross-section and the panel of firms in turn. The regression results for the repeated cross-section sample address the sample selection issues. The estimated regression specifications broaden by consecutively including firm, country, and bank characteristics.<sup>13</sup> In addition, we estimate the regressions for sample splits of firms with a bank account and loan (the most financially included) and firms with a bank account and no loan (the moderately financially included).<sup>14</sup> Table 13 reports the results.

Here, we estimate the Basel III effect on SME access to financing as significantly negative in all the regression specifications, even when controlling for banking system indicators and country context. The size of the coefficient remains largely stable across the specifications. Moreover, the coefficients in the regressions split by the degree of financial inclusion are similar—that is, similar for firms that initially used payment accounts and bank credit (table 13, column (5)) versus firms that initially used only payment accounts and no credit (column (6)). This result suggests that the estimated effect of Basel III on the access to finance has not depended on the degree of financial inclusion.

> 13 The number of observations in columns (1) to (4) is identical by construction. This is to avoid that the results differ across different regression specifications merely because of changing the composition of firms included in the regression. Some explanatory variables—typically the ones for the banking systems—are not available for certain firms; for this reason, the number of countries included in the regressions decreases to 20.

> 14 Firms with no bank account or loan (the least financially included) are excluded from the sample to mitigate a possible upward bias from over-pessimistic reporting of financial constraints (see Hallward-Driemeier and Aterido (2009), on the usefulness and limitations of perception-based indicators of financial constraints).

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<!-- Start of picture text --> Energy Water<br>RoE vs CoE RoE vs CoE<br>25% 25%<br>20%<br>20% 15%<br>10%<br>15% 5%<br>0%<br>10%<br>-5% 98 99 00 01 02<br>-10%<br>5%<br>-15%<br>0% -20%<br>-25%<br>98 99 00 01 02<br>-5% -30%<br>RoE CoE RoE CoE<br>Railways Ports<br>RoE vs CoE RoE vs CoE<br>25% 25%<br>20%<br>15% 20%<br>10%<br>15%<br>5%<br>0% 10%<br>-5% 98 99 00 01 02<br>-10% 5%<br>-15%<br>-20% 0%<br>-25% 98 99 00 01 02<br>RoE CoE RoE CoE<br><!-- End of picture text -->

# **7. Conclusion**

During 1998–2002 overall shareholder returns in our sample were insufficient relative to the risks taken. Thus most infrastructure companies are not particularly attractive investment opportunities—though there are notable caveats to this conclusion. Moreover, water and railway investments did worse than port and energy investments.

Investors in East Asia and the Pacific, South Asia, and to a lesser extent Europe and Central Asia did better than those in Latin America and Africa and the Middle East. And because the former regions are less risky, they have greater potential to become more attractive to investors. In addition, lower-middle-income countries appear to have a brighter investment outlook than low-income and upper-middle-income countries.

The main question to emerge from this analysis may thus be: If things are that bad, why are companies not pulling out? A first answer has to do with the life cycle view of the projects. These results provide a snapshot of a few bad years but many investors have a 19
samples. The next section (section (5)) reports evidence from a number of alternative samples, for different age ranges of children. Section (6) provides an explanation for the findings that the coresident sample bias in the IGC estimates is small, often ignorable, especially when compared to the bias in the IGRC estimates. The following section (section (7)) discusses the implications of the results for sorting out the conflicting evidence from the existing studies on intergenerational mobility in developing countries. The paper concludes with a summary of the results and their implications for the emerging literature on intergenerational mobility in developing countries.

# **2. Related Literature**

The literature on intergenerational economic mobility in developed countries is vast, but the corresponding literature on developing countries is limited at best. The economics literature on intergenerational mobility in developed countries has focused on intergenerational income correlations, with an emphasis on the link between fathers and sons (see, for example, Solon (1992, 1999), Mazumder (2005), Corak and Heisz (1999), Bowles et al. (2005)). The relative neglect of research on developing countries is evident from the fact that, in his survey for the handbook of labor economics, Solon (1999) cites only two papers: Lam and Schoeni (1993) on Brazil, and Lillard and Kilburn (1995) on Malaysia.

Research on intergenerational economic persistence in developing countries has been constrained primarily by two types of data limitations. First, the income data on parents and children are not available for more than a few years to allow reliable estimation of permanent income across generations. As shown by a substantial body of literature on developed countries, it is necessary to have good quality income data over a period of more than a decade to address the attenuation bias in the estimate of income persistence (Solon (1992), Mazumder (2005)). The household surveys available in developing countries usually provide income information only for a single year, and estimating individual income may be a daunting task in rural areas where self employment, work sharing, and informal activities predominate (Deaton (1997)). The second challenge which constitutes the focus 5
Using this weighted average of appreciation rates, we de...ne the industry-speci...c exchange rate for imports as with Ejt normalized to 1 in the base year (i.e., 2000) or any starting year for each industry.

# 1.1.5 Computing Industry-speci...c Domestic Input Price Indices

Computing the input price indices involves two steps. First, we use the Tornqvist method to construct an industry-speci...c time-varying domestic input price indices. For each industry j (15 of them), let Ijt be the set of common sub-industries in two consecutive years, t and t � 1: Denote sub-industry s’s output price index in year t and t � 1 by Pst and Ps;t�1; and denote the share of sub-industry s’s sales in industry j<sup>0</sup> s total sales in year t and t � 1 by !sjt and !sj;t�1. Data on output price indices at the 4-digit sector level (based on China’s NBS classi...cation) are obtained from Brandt et al. (2012).<sup>55</sup> The industry-speci...c rate of output price in‡ation in year t is de...ned as Using this weighted average of in‡ation rates, the sector-speci...c output price level is de...ned as with P<sup>e</sup> normalized to 1 in 2000. jt The second step is to compute the weighted average of P<sup>e</sup> jt, with weights equal to the coe¢ cients from the Chinese IO table for 2002. The goal is to compute the average domestic prices facing processing ...rms in industry j. Speci...cally, for each industry j, the weighted

> 55http://www.econ.kuleuven.be/public/N07057/CHINA/appendix/

5
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE b. Potential contributions of infrastructure to annual per capita economic growth across various subregions of Africa, in percentage points <!-- Start of picture text --> telecom electricity roads<br><!-- End of picture text --> Source: Calderon 2009.

# The state of Liberia’s infrastructure

The spatial distribution of Liberia’s economy shows low population density, widespread poverty, and isolated pockets of economic activity (figure 2a and 2b). Mining activities are concentrated in the north and western parts of the country, while the most intensively farmed areas are along the country’s central strip and in pockets of the far northwest and southeast (figure 2d).

In terms of infrastructure, Liberia has yet to develop full national backbones of paved roads, power transmission and fiber optic cable (figures 3a-d). As a result, is not yet in a position to connect fully with broader regional networks, although a regional power transmission line linking into Guinea and Sierra Leone is planned as part of the West Africa Power Pool. The main road artery in terms of traffic is the route inland from Monrovia towards the frontier with Guinea and Cote d’Ivoire, plus a relatively short coastal section on either side of Monrovia. Elsewhere on the network, traffic is sparse. Road network condition is quite patchy; many key routes alternate between sections in good, fair, and poor condition, which adversely affects national connectivity (figure 3a). GSM coverage is largely concentrated around Monrovia and to a lesser extent along the central strip inland towards Guinea (figure 3c). Liberia is not connected to the SAT3 submarine cable that skirts the West African coastline, although it has been assured a place in one of the new West Africa cable projects. There are a few pockets of irrigated farmland, most of which lie along the northerly land barrier (figure 3d). Looking ahead, the Freeport of Monrovia has the potential to become a maritime hub for mining traffic from the eastern side of Sierra Leone due to its geographical proximity, while the port of Buchanan could potentially serve iron ore exports from neighboring Guinea.

An important component of Liberia’s infrastructure is built and operated by multinational companies that have taken on the national mining, forestry, and agricultural concessions (including rubber and palm 4
listing exercise was then conducted in each of the randomly selected EAs, followed by the systematic random selection of 15 households (2,010 in total) to be included in the main survey. Of the 2,010 households interviewed, 1,156 of them owned and operated non-farm enterprises, with about 1 – 4 enterprises per household. This gave a total of 1,180 non-farm enterprises.

The GISMS also randomly selected up to four adults 15 years old or older in each household to provide information on personality traits and gender role attitutes, with a total of 2,652 individuals responding. Given the specific objective of examining the linkage between personality traits, gender role attitudes and business performance, we restricted the sample further to only those businesses that the owner also provided all needed information on personality traits and gender role attitudes. This led to an analytical sample of 861 businesses, which is used for most of the analyses.

# **3.2. Definition and construction of main variables**

Table 1 shows the definitions and construction of variables used in the analyses. We measure business performance in two ways: labor productivity (measured as ratio of total revenue/sale to total number of workers, including household workers) (Martins, 2021); and profits in the last month of operation (McKenzie, 2017). Each outcome variable was winsorized at the top 1% to deal with outliers. Although it may be argued that neither of these measures, taken alone, represents a perfect, universally agreed upon measure of small business success, together, they provide a fairly broad picture of business performance.

The main explanatory variables of interest are the measures of business owners gender role attitudes and personality traits, which were administered to upto four randomly selected men and women in each household. Five Likert scale response options were used – strongly disagree, disagree, neutral, agree, and strongly agree. The gender role attitudes index was constructed in four steps. First, all responses were recoded so that higher values correspond to 8
21

# **_2.2.6 Administration Costs_**

Administration costs are low by international standards, but still high compared to those of the AHV. Table 6 presents the evolution of administration costs since 1970. Current administration costs amount to approximately 5 percent of total contributions or 6 percent of total benefits. It seems as if the mandatory BVG system increased the cost efficiency of pension providers. However, one has to keep in mind that the measurement of administration costs is notoriously difficult, since it is not straightforward whether to assign the costs to the fund or the employer. Especially for small employers, the operation of the pension fund offers quite some synergy gains, as the administration of the first and second pillar can be done in tandem.

In addition to the pure administrative costs, there are also the asset management costs. Over the period explored in Table 6 the relative costs have remained constant at around 0.3 percent. As a comparison, the total administrative costs of the first pillar (i.e., operating costs, pure administrative costs, and the costs of the asset management together) amount to 0.3 percent of the total contributions.

||**Table 6 A**<br>|**dministrati**<br>|**ve costs o**<br>|**fthe Seco**<br>|**nd Pillar **<br>|||
|---|---|---|---|---|---|---|---|
|BVG<br>In m. CHF<br>|1970|1987|1990|1996|1998|2000|2002|
|Total Contributions<sup>22</sup><br>|3458|15,125|20,860|24,709|26,415|25,842|28,394|
|Total Benefits<sup>23</sup>|1,317|6,450|8,737|15,350|17,443|20,236|21,698|
|Total Assets|n.a.|165,070|n.a.|348,295|428,251|490,883|440,555|
|Costs asset<br>management (AM)<br>Pure administrative<br>costs|n.a.<br>264|765<br>339|913<br>452|1,311<br>751|1,335<br>963|1,318<br>1,065|1,388<br>1,412|
|Total administrative<br>costs|n.a.|1,104|1,365|2,062|2,298|2,383|2,800|
|In %:<br>Pure admin. cost to:<br>- total contributions|7.63%|2.24%|2.17%|3.04%|3.65%|4.12%|4.97%|
|- total benefits|20.05%|5.26%|5.17%|4.89%|5.52%|5.26%|6.51%|
|Cost of AM to:<br>- total assets|n.a.|0.46%|n.a.|0.38%|0.31%|0.27%|0.32%|

Source: Bundesamt für Sozialversicherung (2004b), BV1.1. sqq. on p.125 sqq.

# **2.3 Combined Replacement Rates**

As mentioned above, there are no target replacement rates for the first pillar. The idea of introducing an occupational system was to ensure a replacement rate of approximately 60 percent relative to the pre-retirement income for lower and middle income people. The minimum contribution rates in the second pillar are in line with this goal.

> 22(employer & employee)

> 23(Annuity & Capital)