rafmacalaba/datause-encoder-data huggingface dataset:
**Table A2.CU.D: Intergenerational Persistence in Schooling in Urban China - -** **<u>(18 30 Years Age Cohorts. First born Sample)</u>**

|||**LINEAR CEF**||**Q**|**UADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft.|0.25|0.37|0.12|0.072|0.4|0.33|
||(0.022)***|(0.023)***|(0.032)***|(0.062)|(0.065)***|(0.090)***|
||[0.025]***|(0.026)***|[0.030]***|[0.066]|[0.089]***|[0.088]***|
|Quadratic Coefft.||||0.013|-0.0018|-0.014|
|||||(0.0038)***|(0.0039)|(0.0054)**|
|||||[0.0039]***|[0.0053]|[0.0059]**|
|Intercept|9.53|8.45|-1.07|9.93|8.4|-1.53|
||(0.21)***|(0.22)***|(0.30)***|(0.26)***|(0.28)***|(0.38)***|
||[0.22]***|[0.30]***|[0.34]***|[0.30]***|[0.41]***|[0.40]***|
|No. Observations|1668|1748|3416|1668|1748|3416|
|||**Estima**|**tes of Mobility**|**from Quadrati**|**c CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|9.93|8.45||**_IGME0 _**|0.12|0.37|
|**_ES6 _**|10.83|10.67||**_IGME6 _**|0.222|0.37|
|**_ES9_**|11.63|11.78||**_IGME9 _**|0.298|0.37|
|**_ES16_**|14.41|14.37||**_IGME16_**|0.474|0.37|

Notes: (1) The data used are CFPS 2016 with first-born children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
on expected value questions in Nairobi. However, it is not the case that those who are better at such calculations are less likely to violate IIA.

# **6. Does Game Behavior Predict Real-Life Migration Actions?**

We examine whether migration decisions made in the lab can help predict real-life migration actions by collecting follow-up data on actual migration decisions made by the experimental subjects who played the lab games.

We collected data on 140 out of the 154 lab participants in the Lisbon sample six months after their graduation, which closely coincided with the time of participation in the lab experiments. We found that 50% had decided to pursue graduate education, while 26% were working, and only 5.7% currently live abroad.

In the Nairobi sample, we found that most study participants took longer to graduate. For that reason, there was a longer gap between participation in the lab experiments and graduation, but students were also, on average, interviewed at about 6 months after graduation. We were able to reinterview 242 out of the 265 initial experimental subjects. Most study participants were either working (39.3%) or looking for a job (42.2%). Differently from the Lisbon sample, only 3.7% pursued graduate studies. The fraction who had migrated abroad was low at 1.7%.

In order to evaluate the predictive behavior of migration decisions made in the lab, we focused on the decisions made in the “home bias” round because this is the only round of lab decisions that did not depend on any lab assigned characteristics (namely observable and unobservable skills).

As is shown in Table 7, we find that lab decisions in this round strongly predict whether study participants had looked for jobs abroad and received job offers from abroad. Participants displaying “home bias” in the lab were less likely to look for jobs abroad by 29p.p. and 17p.p. in the Lisbon and Nairobi samples, respectively. These differences were strongly statistically significant - at the 1% and 2% levels. The same pattern holds for receiving job offers from abroad: participants who exhibited “home bias” in the lab were 2.8p.p. less likely to receive job offers from abroad in the Lisbon sample (not significant) and 4p.p. in the Nairobi sample (significant at the 5% level).

When we look at the probability of living abroad, we find that participants exhibiting “home bias” in the lab were 8 p.p. less likely to be living abroad in the Lisbon sample (significant at the 5% 23
The training covered the objectives of ENTRE, some of the key barriers to staying in school (lack of motivation, financial constraints, lack of family support, poor academic performance, and inertia), and several methods and resources to address these barriers. For instance, to address the lack of motivation to continue studying, in line with international evidence on cost-effective strategies for this barrier, the training presented information on economic returns to education in Guatemala (which are among the largest in Latin America) and recommended a set of activities to effectively communicate these returns to students. The participants, divided into small groups, then had the opportunity to practice the approach with role-play exercises, and to brainstorm about the logistics of implementation. The sixth-grade teacher and the principal of the same school were intentionally placed together so they could start discussing implementation in their own school. To address financial constraints, the participants were informed about means and merit-based scholarships available through the Ministry of Education (MINEDUC) and provided with instructions on how to apply to them (including where to locate the required forms). As highlighted by empirical evidence (see Bettinger et al, 2018), the participants were also informed about the importance of helping disadvantaged families to fill out these forms, since many times despite qualifying for this type of benefit, low-income families do not apply to them.

The training also included a section on the importance of focusing efforts on students at-risk of dropout. Participants were encouraged to use their own judgement to assess this risk, but they were also informed that some schools would receive complementary information on students at-risk as part of MINEDUC’s efforts to identify the most effective content for ENTRE (component 2).

# <u>Component 2: Information on students at high risk of dropping out</u>

The second component of ENTRE is a list of sixth-grade students at high risk of dropping out in the transition to lower secondary, providing school actors with more information on _who_ needs the most help. The list was estimated using routinely collected administrative panel data on students and combining linear regression models with a simple algorithm. These models are able to correctly identify 82% of the sixth-grade students who will drop out within the next year, performing better than other commonly used targeting approaches, and as well as models used in the U.S. (For more details on the methodology, please see Adelman et al., 2018).

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should cause prices to change. Events which are expected, presumably are built into the forecast process by rational economic agents. Economic news alters agents' expectations about the future course of economic variables which in turn changes prices of commodities.

# Impact of Money Surprises

22. The first category of economic news considered here is contained in weekly announcements of U.S. money supply. Although it is widely accepted that monetary policy is neutral with respect to coamodity prices over the long-run 1/, it is not so obvious that monetary shocks are neutral over the short-run. 2/ 23. According to the policy anticipation hypothesis about how weekly money stock announcements influence commodity prices, speculators in the commodity markets believe that the unexpected money growth in period t will be offset in period t+l as the Fed restricts the money supply, driving up real interest rates. A rise in real rates will lead to a fall in commodity prices as investors make a portfolio adjustment to hold more money and fewer physical assets. By contrast, the expected inflation hypothesis assumes that the Fed will not offset increases in the money stock but will keep on increasing the 1/ See Crennes and Lapp (1986), for instance.

- 2/ Results from Frankel and Hardouvelis (1985) and Barnhart (1988, 1989) point to the importance of monetary shocks for short-run commodity price behavior. However, the impact of monetary shocks is very sensitive to the operating procedure of the Fed.
**Table 2. Trade** indicators for Sub-Saharan African countries, 1997

||||**Single most impor**|**tant export product**|**to OECD**|
|---|---|---|---|---|---|
||**Openness**<br>|||||
||**(exports as**|**Total trade**|||**Percentage of**|
|**Country**|<br>**percentage**<br>**of GDP)**|**(millions of**<br>**US dollars)**|**In value of trade**|**Millions of U.S.**<br>**dollars**|<br>**total exports to**<br>**OECD**|
|**_West and Central_**||||||
|**_Africa_**||||||
|Benin|16.6|77.778|Cotton|32.830|42.2|
|Burkina Faso|6.5|67.725|Cotton|38.936|57.5|
|Cameroon|12.2|1,874.895|Crude petroleum|680.546|36.3|
|Centr.African Rep.|9.5|144.910|Pearl, prec-, semi-p stone|106.373|73.4|
|Chad|3.4|94.946|Cotton|82.070|86.4|
|Congo, Rep. of|64.5|1,370.287|Crude petroleum|700.809|51.1|
|Cote d'lvoire|28.0|2,859.735|<br>Cocoa|1,324.627|46.3|
|Equatorial Guinea|..|204.917|Crude petroleum|117.934|57.6|
|<br>Gabon|49.5|2,917.452|<br>Crude petroleum|2,486.791|85.2|
|Guinea-Bissau|..|14.492|Fish, fresh|11.178|77.1|
|Mali|18.2|117.389|Cotton|94.558|80.6|
|Niger|10.5|90.260|Medicinal etc. products|36.456|40.4|
|Senegal|8.4|371.742|Fish, fresh|176.746|47.5|
|Togo|22.6|126.592|Fertilizers, crude|55.820|44.1|
|<br>Gambia, The|28.1|156.753|<br>Pearl, prec-, semi-p stone|132.726|84.7|
|Ghana|17.0|1,215.887|<br>Cocoa|422.629|34.8|
|Guinea|13.5|585.667|Non fer base mtl. ore, conc.|374.396|63.9|
|Liberia|..|1,029.487|Ships and boats|660.109|64.1|
|Mauritania|26.4|505.769|Iron ore, conc.|272.401|53.9|
|Nigeria|21.0|12,516.209|Crude petroleum|11,337.293|90.6|
|<br>Sierra Leone|23.1|<br>192.108|<br>Pearl, prec-, semi-p stone|<br>129.101|67.2|
|**_East and Southern_**||||||
|**_Africa_**||||||
|Angola|38.5|3,614.481|Crude petroleum|3,153.998|87.3|
|Botswana||||||
|Burundi|5.2|80.947|Coffee|64.557|79.8|
|Congo, Dem. R|5.7|1,120.675|Pearl, prec-, semi-p stone|635.127|56.7|
|Eritrea|..|9.158|Leather|2.571|28.1|
|Ethiopia|6.5|445.808|Coffee|316.822|71.1|
|Kenya|15.3|1,138.099|Coffee|329.787|29.0|
|Lesotho||||||
|Madagascar|10.5|595.869|Clothing not of fur|203.182|34.1|
|<br>Malawi|20.0|371.890|<br>Tobacco UNMFD|304.089|81.8|
|Mauritius|36.1|1,543.916|Clothing not of fur|903.458|58.5|
|Mozambique|11.9|182.571|<br>Fish, fresh|89.032|48.8|
|Namibia||||||
|Rwanda|9.0|42.031|Coffee|33.102|78.8|
|Somalia|..|29.195|Fruit, nuts|14.139|48.4|
|South Africa|20.8|15,319.178|Silver, platinum,|1,990.128|13.0|
|Sudan|4.6|241.270|<br>Cotton|52.004|21.6|
|Tanzania|14.8|369.521|Coffee|112.765|30.5|
|Uganda|6.0|476.552|Coffee|375.522|78.8|
|Zambia|24.6|459.326|Copper|176.498|38.4|
|Zimbabwe|19.9|1,276.976|<br>Tobacco UNMFD|387.027|30.3|
|**_Sub-Saharan_**|**17.8**|**53,986.029**|**Crude petroleum**|**18,711.169**|**34.7**|

**_Africa_**

_Note:_ OECD import data are used to get a picture of Sub-Saharan African exports because of problems with missing or unreliable data when the reporting country is from Sub-Saharan Africa.

_Source:_ OECD; UN Comtrade; and World Bank 1999b,c.

5
satisfaction with education in LiTS2, the 2009 PISA survey found that 83 percent of 15-year olds were functionally illiterate (they scored below PISA level 2).

# **Complaints and grievance redress**

About 67 percent of respondents who had used health services during the previous 12 months reported at least one problem with service delivery, and for education services the figure was 50 percent. The main problems reported for health were long waiting times, lack of drugs and payment for services that should be free (Figure 5); in the case of education, the main problems were lack of textbooks/supplies, payments for services that should be free, and poor conditions of facilities.

**Figure 5: Problems with public health and education provision in the previous 12 months reported by respondents**

<!-- Start of picture text --> Public health clinic/hospital Public education<br>Long waiting times No textbooks or other supplie s<br>Payments required for services that should<br>No drugs available be free<br>Payments required for services that shouldbe free Facilities in poor conditio n<br>Treated disrespectfully by staf f Poor teaching<br>Frequent / unjustified absence of doctor s Overcrowded classrooms<br>Facilities not clean Frequent / unjustified absence of teacher s<br>0 10 20 30 40 0 5 10 15 20 25<br>Per cent of respondents Per cent of respondents<br><!-- End of picture text --> Source: LiTS2 (2010).

Grievance redress or complaints mechanisms refer to the institutions and channels that people can use to express their preferences and hold providers to account<sup>5</sup> . There are a number of specific objectives associated with the use of complaints handling in the public sector: promoting accountability in the delivery of services; measuring the quality of service provision and the effectiveness of policy; and harnessing experience to improve service delivery (Lister et al., 2008).

Grievance redress mechanisms have long traditions in Western Europe, but they are relatively underdeveloped in transition countries<sup>6</sup> . While 16 percent and 11 percent of respondents, respectively, reported that they had filed complaints for education and health in the comparator countries, only 5 percent did so in transition countries for either service. Some countries in the region have begun introducing complaints-handling mechanisms to improve service delivery. For

> 5 Complaints handling systems take diverse forms, including customer complaints procedures; administrative appeals and tribunals systems; independent complaints handlers or ombudsmen; and judicial review (and other forms of legal action).

> 6 Sweden first installed an independent ombudsman function in the late 19th century. More recently, the adoption of complaints handling systems spread in the countries of the Organisation for Economic Co-operation and Development in the 1990s as governments looked increasingly to private sector practice to improve the standards of public service provision. Studies on the private sector highlighted that the presence of a complaints system in businesses was associated with better performance (Johnson and Mehra, 2002);

7
**Table 3. C Hurricane Irma in Antigua and Barbuda (September 2017): Deviations of macro-fiscal outcomes from projections made immediately prior to the disaster (percent of GDP)**

||**Antigua**<br>**Barbuda**|**and**|**Other**<br>**SIDS**|**Caribbean**|
|---|---|---|---|---|
||2017/18|2018/19|2017/18|2018/19|
|GDP growth|4.1|3.7|-0.3|-0.3|
|Govt Revenue|-2.1|-2.8|-1.3|-0.8|
|Govt Expenditure|0.9|1.3|-1.6|-0.9|
|Net Lending|-2.9|-4.0|0.2|0.1|
|Gross Public Debt|0.9|-2.2|-4.4|-3.7|

_The figures in the table show the difference between the outcome for each variable and the projection made just before the disaster. Net lending is equivalent to the overall fiscal balance._ <u>Source: WEO database</u> Table 3.B shows the impact of Hurricane Maria, which struck Dominica in September 2017 and which caused the largest amount of damages—as a share of GDP (279 percent)—by any tropical cyclone in the Caribbean or Oceania in the last three decades. Compared to pre-disaster projections, the impact on GDP was slightly negative in the first 12 months but positive in the second. There was a huge increase in government expenditure in both periods, which when combined, amounted to almost 50 percent of GDP. Government revenues also increased, but only by about half the increase in expenditure; the difference was accounted for by larger fiscal deficits. Hence public debt also rose, although the increase in the public debt stock as a share of GDP was not as large as what would be implied by the larger fiscal deficits alone.

Table 3. C shows the impact of Hurricane Irma on Antigua and Barbuda, which struck in September 2017 and caused damages estimated at 17 percent of GDP. Real GDP growth was higher than had been projected before the disaster, but there was only a small increase in government expenditure in both of the 12-month periods following the disaster. Government revenues were lower in both periods, although by relatively moderate amounts, and there was a reduction in the fiscal balance (larger deficits) of three to four percent of GDP), but this was not reflected in higher public debt stocks, which were actually slightly lower two years after the disaster than had been projected before the disaster.

The impact of cyclone Evan on Samoa, near to the end of 2012, is shown in table 3.D. This caused damages estimated at 17 percent of GDP. Relative to pre-disaster projections, there was a small reduction in real GDP in both of the two following years and a much larger fall in government revenue, especially in 2013. Surprisingly, government expenditure also fell, almost 11
WPS3795 the German account obligors (Bakker, Klapper, and Udell 2004).

# **B. The legal, judicial, and bankruptcy environments**

A country's legal, judicial, and bankruptcy environments significantly influence the context in which loan contracting is conducted. The legal environment that affects business lending consists of the commercial laws that specify the property rights associated with a commercial transaction. The judicial and bankruptcy environments determine how well these laws are enforced in commercial disputes and in bankruptcy resolutions. This enforceability, in turn, determines the confidence of contracting parties in financial contracts. Collectively, these features constitute the rule of law as it relates to the extension of credit. Countries differ significantly on this dimension: for some, commercial laws are unambiguous and conducive to commercial transactions and enforcement is predictable; for others commercial law is ambiguous and incomplete, enforcement is problematic, and criminal and racketeering behavior block the creation of new businesses, undermine existing ones, and deter foreign investment (EBRD 2003).

Empirical studies have shown that firms in countries with greater financial development and stronger property rights have increased levels of investment funded by external finance. Firms in countries with weaker financial development and property rights, in contrast, are more likely to rely on potentially less efficient financing from development banks, the government, or informal sources (Beck, Demirguc-Kunt, and Maksimovic 2004b). Smaller firms may be particularly affected. One study found that the effect of financial, legal, and corruption problems consistently constrained the growth of smaller firms more than larger firms in a cross-country analysis (Beck, Demirguc-Kunt, and Maksimovic 2005).

Commercial laws and their enforcement of these laws also affect the ability of banks to deploy specific contracting elements that can be used to address informational opacity problems. Specifically, they can affect the deployment of contracting elements such as covenants, maturity, collateral, and personal commitments that have been shown to mitigate adverse selection and moral hazard problems (e.g., Chan and Kanatas 1985, Berlin and Loeys 1988, Sharpe 1990, Berkowitz and White 2004).

The commercial law on security interests (collateral liens) in a nation, for example, is important in determining the efficacy of collateral in a loan contract, the essential component of the asset-based lending and fixed-asset lending technologies. Key issues include whether a country’s commercial law clearly defines how a collateral lien can be perfected, how collateral priority is determined, and how notification of a lien is 16
The Economist (2008c). “Back on the Offensive.” January 26th, 2008.

- The Economist (2007a). “Saying No to Free Trade.” The Economist Intelligence Unit ViewsWire. July 18th, 2007.

The Economist (2007b). “Policing a Whirlwind.” December 13th, 2007.

The Economist (2007c). “A Change of Mind on Coca.” May 5th, 2007.

The Economist (2006). “One Step forward in a Quagmire.” March 18th, 2006.

The Economist (2004). “Always Coca.” April 24th, 2004.

The Economist (2001). “Opinion: The Case for Legalization.” July 26th, 2001.

- Thoumi, Francisco E. (2005). “Illegal Drugs, Politics and Armed Groups in the Andean Region and Afghanistan.” Unpublished Manuscript, Universidad del Rosário.

- UNDCP – United Nations International Drug Control Program (1997). “Statistics and Analysis on Supply and Trafficking in Narcotic Drugs and Psychotropic Substances – 1996.” UNDCP, Unpublished Manuscript.

- Van Ours, Jan C. and Stephen Pudney (2006). “On the Economics of Illicit Drugs.” De Economist, 154(4), 483-490.

- Wilson, Suzanne and Marta Zambrano (1994). “Cocaine, Commodity Chains, and Drug Politics: A Transnational Approach.” In: Gary Gereffi and Miguel Korzeniewicz (eds). Commodity Chains and Global Capitalism. Westport, Connecticut: Greenwood Press, 297-315.

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**Figure 1. Vulnerability Status by Department**

<!-- Start of picture text --> Chuquisaca La Paz Cochabamba Oruro Potosi Tarija Santa Cruz Beni Pando<br>Mean<br>2003 2007<br>4<br>3<br>VAM 2<br>1<br>0<br><!-- End of picture text --> Source: Authors from World Bank (2010) Agricultural Public Expenditure Database (APER).

At the municipal level, 62 percent of the municipalities were categorized in 2007 as having a moderate to very low vulnerability to food insecurity (VAM 1 to 3), while 38 percent were in the high to very high vulnerability (4 and 5) categories. The percentage of municipalities with high or very high levels of vulnerability to food insecurity decreased from 51 percent in 2003 to 38 percent in 2007 (Figure 2).

**Figure 2. Percentage of Municipalities by Vulnerability Status**

<!-- Start of picture text --> 60<br>40<br>20<br>0<br>1 2 3 4 5<br>VAM<br>2003 2006 2007<br>Percentage<br><!-- End of picture text --> Source: Authors from World Bank (2010) APER database.

However, this reduction masks important variations in the vulnerability situation across municipalities. Evidence suggests that mobility across vulnerability levels is limited and asymmetric. Transition matrices (Table 1) indicate that 80 percent of municipalities did not change either their low/moderate or high/very high vulnerability status between 2003 and 2007. For each municipality that worsened its status from low to high vulnerability, more than two improved from high to low vulnerability. This is also true comparing 2003 to 2006.

**Table 1. Vulnerability Transition Matrix by Municipality**

|||**2006**<br>Low vulnerability|High vulnerability|**2007**<br>Low vulnerability|High vulnerability|
|---|---|---|---|---|---|
|**2003**|Low vulnerability|146 (45%)|14 (4%)|148 (45%)|12 (4%)|
||High vulnerability|35 (11%)|132 (40%)|54 (17%)|113 (35%)|

Source: Authors from World Bank (2010) APER database.

6