rafmacalaba/datause-encoder-data huggingface dataset:
parents-involved matches than self matches. However, the sharable part of the marriage outcome could be lower or higher under parental matchmaking. It would be higher because parents put more emphasis on sharable market and household production than on love, and therefore the wife that is picked by parents tends to have a higher ability to contribute to the sharable productions. It could be lower when parents over-emphasize goods produced within the household and the preservation of old social structure at the expense of market productivity. In this case, the couple's income can be lower in parents-involved matches, but key inputs (or traits) for household production such as the number of children, willingness to provide old age support, and the submissiveness of the wife would increase.

Following BMS (2015), we further discuss how the need of old age support shapes the eect of parental matchmaking on marriage outcomes. For nancially constrained parents who have to rely on their children for old age support, it is dicult to commit adult children to providing old age support. One way to obtain the commitment is parental investment in shaping children's preferences (when they are young) so that they are more altruistic toward parents;<sup>2</sup> as a result, parents get more old age support, which in turn motivates parents to invest optimally on children's human capital. Though BMS (2015) deal only with the preference of the son (in our context), their basic idea can be readily applied to the situation of parent matchmaking, which can be another instrument to meddle with children's (or children-in-law's) preferences. Although it is largely impossible to change the preference of a daughter-in-law after marriage, parents can help to select the right kind of daughter-in-law: she had better be submissive to family authority, be cooperative in delivering old age support, and be diligent in doing household chores. Under the assumption that more children imply more old age support, parents may also use matchmaking to ensure that their son has more children to provide him old age support in the future. Because urban areas rely less on children for old age support (and enforce the one-child-policy more vigorously), the demand for the number of children under parental matchmaking would be lower in urban regions than in rural regions. Moreover, since urban areas present more market opportunities, urban parents may emphasize more on sharable monetary income than rural parents.

We take these predictions to a sample of about 3400 rural couples and 3800 urban couples in 1991 in seven Chinese provinces. Here 48% of rural couples and 14.5% of urban couples were married by parent-involved matchmaking; the rest by either self search or friend introduction (both of which are referred to as self match). We examine a number of marriage outcomes, including the degree of domestic harmony to capture the emotional output of marriage, the joint income of husband and wife to capture the couple's economic well-being,

> 2See Heckman (2008) for evidence on how parent inuence on children is more prominent when children are young.

4
# **Table 4: IV**

## **Dependent Variable Cols 1, 3: Completion Binary [yes=1] Dependent Variable Col 2: CS-Monitoring/Incentives Management Index, 2015 Dependent Variable Col 4: Initiation Binary [yes=1] Dependent Variable Col 5: Completion Index Standard Errors: Robust**

||**OLS**|**First Stage**|**IV:**|**Second Stag**|**e**|
|---|---|---|---|---|---|
||**(1) Baseline [IV**<br>**Sample]**|**(2) Monitoring**|**(3) Project**<br>**Completion**|**(4) Binary**<br>**Initiation**|**(5) Completion**<br>**Status**|
|**CS-Autonomy**|0.04**||0.11**|0.14***|0.35***|
||(0.02)||(0.05)|(0.02)|(0.10)|
|**CS-Incentives/Monitoring**|-0.09***|0.44***|-0.27**|-0.38***|-0.95***|
||(0.02)|(0.04)|(0.11)|(0.06)|(0.21)|
|**CS-Other**|-0.06*||-0.03|0.01|-0.04|
||(0.03)||(0.03)|(0.02)|(0.07)|
|**First Stage F Statistic**||107||||
|**Noise Controls**|Yes|Yes|Yes|Yes|Yes|
|**Project Controls**|Yes|Yes|Yes|Yes|Yes|
|**Fixed Effects**|Project Type,<br>Sector|Project Type,<br>Sector|Project Type, Sector|Project Type,<br>Sector|Project Type,<br>Sector|
|**Observations (clusters)**|2,463 (18)|2463|2,463|2,463|2,463|

**Notes:** *** denotes significance at 1%, ** at 5%, and * at 10% level. Robust standard errors are in parentheses, and are clustered by organization in Column 1. All columns report OLS estimates. In Columns 1 and 3, the dependent variable is a dummy variable that takes the value 1 if the project is fully completed and 0 otherwise. In Column 2, the dependent variable is the monitoring management index defined using baseline data. In Column 4, the dependent variable is a dummy variable that takes the value 1 if the project is initiated and 0 otherwise. In Column 5, the dependent variable is an index of project completion (that is a continuous measure between zero and one). Project Type fixed effects relate to whether the primary classification of the project is 'Advocacy and Policy Development', 'Financial & Budget Management', 'ICT Management and Research', 'Monitoring, Training and Personnel Management', 'Physical infrastructure', 'Permits and Regulation' or 'Procurement'. Sector fixed effects relate to whether the project is in the administration, environment, finance, infrastructure, security/diplomacy/justice or social sector. Project controls comprise project-level controls for whether the project is regularly implemented by the organization or a one off, whether the project is a bundle of interconnected outputs, and whether the division has to coordinate with actors external to government to implement the project. Capital controls comprise a count of the number of interviews undertaken, which is a close approximation of the total number of employees. General controls comprise organization-level controls for the share of the workforce with degrees, the share of the workforce with postgraduate qualifications, and the span of control. Noise controls are averages of indicators of the seniority, gender, and tenure of all respondents, the average time of day the interview was conducted and of the reliability of the information as coded by the interviewer. Figures are rounded to two decimal places.
foreign and state ownership as a result of the crisis. Results based on movements in the Lerner index do not reveal substantial improvement in competition as a result of this change in ownership structure (Kubo, 2006). Of course, little time had passed since the crisis and foreign banks were acquiring the most troubled domestic banks during this period. A more recent study indicates that foreign bank presence is associated with reductions in net interest margins, personnel expenses and return on assets for domestic banks and that improvement on efficiency measures was highest for banks acquired by foreign banks (Heberholz, 2008). Our overall view is that given the relatively low levels of foreign bank participation in most Asian countries, modest competitive effects on the domestic banking sector should have been expected.

# **_Foreign bank penetration and banking sector stability_**

Evidence on the effects of foreign bank presence on banking sector stability is more clear-cut than that regarding efficiency and competition. Early cross-country evidence from developed and developing countries indicates that greater foreign bank presence is associated with lower probability of systemic banking crisis in the host country (Demirguc-Kunt et al., 1998). Subsequent research on a broader sample of 107 countries shows that official barriers to foreign bank entry are associated with measures of banking system fragility (Barth et al., 2004).

Another strand of this literature focuses on the response of foreign banks during crisis in a host country. For example, a study of 1565 banks from twenty emerging markets from 1989 to 2001 finds weak evidence that foreign banks’ credit levels are less sensitive to monetary conditions in the host country while their lending and deposits rates are less volatile than those of domestic banks during crises (Arena, Reinhart, and Vazquez, 2007). Similar results are found for a sample of 250 banks from ten countries in Eastern Europe from 1993 to 2000 (De Haas and Van Lelyveld, 2006).

Other studies focus on foreign banks’ actions in particular countries and during particular crises. Case studies of Argentina, Brazil, and Mexico from 1994 to 1999 indicate that foreign banks do not pull back from host countries in the face of their economic problems, but rather view these difficulties as an opportunity to become more firmly rooted in these economies (Peek at al., 2000). And in fact, in Argentina and Mexico foreign banks had higher growth rates and lower volatility of lending than domestic banks during the crises of the mid to late 1990s (Dages 13
|||Dan|es|Immigr|ants|
|---|---|---|---|---|---|
|Wage-earner<br>Occupation category|2-digit<br>DISCO-<br>88 code|%<br>overeducated|%<br>employed|%<br>overeducated|%<br>employed|
|Precision, handicraft, printing and<br>relatedtrades workers|73|5.6|0.9|21.7|0.6|
|Other craft and related trades<br>workers|74|0.5|1.2|5.1|0.8|
|Other craft and related trades<br>workers,nec.|7|15.6|1.9|16.8|1.9|
|**Plant and machine operators**<br>**and assemblers**||||||
|Stationary plant and related<br>operators|81|6.7|1.1|10.1|1.4|
|Machine operators and assemblers|82|2.7|6.2|8.9|13.7|
|Drivers andmobile plant operators|83|26.2|5.0|28.3|4.3|
|Other plant and machine operators,<br>nec.|8|2.4|1.8|9.2|5.0|
|**Elementary occupations**||||||
|Sales and services elementary<br>occupations|91|25.2|2.8|11.6|8.6|
|Agricultural, fishery and related<br>laborers|92|11.8|0.5|26.8|0.5|
|Laborers in mining, construction,<br>manufacturing and transport|93|28.6|6.5|30.8|6.4|
|Otherelementary occupations,nec.|9|50.6|3.8|25.6|6.5|
|**All**||**11.0**|**100.0**|**16.3**|**100.0**|

Note (a): See note for Table 1.

Note (b): In the data, the length of a given education is given as the maximum number of months required to obtain that education.

Note (c): For some individuals, information about occupation is only provided at the 1-digit DISCO-88 level, and hence limits are calculated separately for these “not elsewhere classified (nec.)” categories, e.g. 2, 3, 4.

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Figure 30: Proportion of Land Cultivated Irrigated by Crop Grown, 2014/15 <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

States with a high rate of fertilizer use also have high rates of irrigation (Figure 31). Al-Gezira, the River Nile, and the Northern states all have close to universal rates of irrigation and have very high rates of fertilizer/pesticide use. States such as the Red Sea, North Kordofan, and Central Darfur are at the other end of the spectrum, with low rates of fertilizer/pesticide use and low rates of irrigation. However, there is greater variability with fertilizer/pesticide input use, as many states have low rates of irrigation, with input use ranging from 3 percent for the Red Sea to up to 41 percent for the Blue Nile state.

Figure 31: Correlation between Fertilizer/Pesticide Use and Irrigation in Sudan, 2014/15 <!-- Start of picture text --> 90%<br>Al-Gezira<br>80%<br>70%<br>River Nile<br>60%<br>50%<br>Northern<br>Khartoum<br>40%<br>Blue Nile Kassala<br>30% South Kordufan Sinnar<br>West Kordufan<br>20%<br>Al-Gadarif White Nile<br>East Darfur<br>10% South Darfur<br>Central Darfur<br>West Darfur<br>Red Sea<br>0% North Darfur North Kordufan<br>0% 20% 40% 60% 80% 100% 120%<br>Proportion of households irrigating plots<br>fertilizer/pesticide<br>Proportion of households using<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

Credit Over half the agrarian households access some form of credit, but most of this is from family and individual sources (Figure 32a). Very little credit comes from institutional sources, such as formal banks, the government, or microfinance institutions. As only 5 percent of agrarian households own a bank 33
**Table 2. CONALEP Students Compared to Students from Selected Institutions Centro Nacional de Evaluacion**

|All'<br>**Institutions**|**Option**<sup>**2**</sup><br>**Number**|**Global**<sup>**3**</sup><br>**Test Score**|**Family**<sup>**4**</sup><br>**Income**|**GPA In'**<br>**Lower-**<br>secondary|**Mother's**<br>**Schooling**<sup>**6**</sup><br>(years)|**Father's**<br>**SchooUng**<sup>**6**</sup><br>(years)|**Private Lower**<br>**Sec -1**|
|---|---|---|---|---|---|---|---|
|CONALEP||||||||
|Mean|2.27|54.2|2271.2|7.627|7.1|8.2|8.70E-03|
|N|460|460|460|460|460|460|460|
|SD|2.13|15.3|2269|.6992|4.8|5.2|9.29E-02|
|Col.Bach.||||||||
|Mean|3.05|66.4|3132|7.658|9.0|10.0|3.33E-02|
|N|421|421|421|421|421|421|421|
|SD|2.28|13.2|2845|.75722|5.2|_5.1_|.18|
|Edo.Mex.||||||||
|Mean|2.41|64.6|2721|7.931|8.452|9.9|1.76E-02|
|N|1192|1192|1192|1192|1192|1192|1192|
|SD|2.09|16.56|2436|.7600|4.9|5.2|.13|
|DGETI||||||||
|Mean|2.71|59.6|2610|7.7205|7.700|9.2|2.20E-02|
|N|682|682|682|682|682|682|682|
|SD|2.32|_15.6_|2488|.7271|_5.0_|_5.4_|_.15_|
|IPN||||||||
|Mean|1.97|80.7|3315|8.1865|9.8|11.3|5.81E-02|
|N|430|430|430|430|430|430|430|
|SD|1.61|13.9|2652|.7871|4.7|5.0|.23|
|UNAM||||||||
|Mean|1.46|88.1|3967|8.3935|9.8|11.4|9.41E-02|
|N|510|510|510|510|510|510|510|
|SD|.83|11.6|3385|.7864|5.212|5.0|.29|
|Other||||||||
|Mean|1.38|82.9|3896|8.5417|11.969|12.9|8.33E-02|
|N|48|48|48|48|48|48|48|
|SD|.96|15.8|3164|.8124|3.676|4.2|.28|
|TOTAL||||||||
|Mean|2.33|67.9|2945|7.9248|8.6|10.0|3.50E-02|
|_N_|3743|3743|3743|3743|3743|3743|3743|
|SD|2.03|18.5|2693|.7982|5.1|5.2|.18|

1. Col.Bach. refers to _Colegio de Bachilleres,_ the local answer to over-demand; Edo.Mex. to the Estado de Mexico, state-centralized high school system; DGETI is the Direcci6n General de Educaci6n T6cnica Profesional, a centralized institution; IPN is the Instituto Polit6cnico Nacional -centralized-; and UNAM is the Universidad Nacional Aut6noma de Mexico -autonomous-.

2. This is the average preference number toward each institution from students who applied and got in.

3. Out of 128 questions.

4. In net pesos per month.

**5.** Grades go from **_5_** (fail) to 10.

6. Years of schooling.

# _CONALEP's Past Evaluations_

The CONALEP system has been evaluated several times in the past. The first evaluation was done by CONALEP (1994) and CONALEP (1999) using graduate tracer surveys. These data sets are described in the next section. The other evaluations were done by Lane and Tan (1996) and by Lee (1998). CONALEP also hired international consultants (Camoy and others 2000) to assess the evolution of the CONALEP system. For this purpose the consultants used a different data set as is explained below.

18
expressed a high grievance level while 13.81 percent do not feel as strongly aggrieved. Only a low of 5.12 percent feel personally satisfied or undisturbed with the status quo. Paradoxically, there is a general positive expectation about the future (hope), with only 4.68 percent of the sampled population expressing despair or uncertainty. 77.74 percent of communities surveyed have experienced at least one violent conflict<sup>27</sup> over the past 10 years from the date of survey.

# **4.3 Data**

The **Dependent Variable** ( _rebel_participation_ ) is a dummy indicating that the respondent is willing to be engaged in an armed struggle for self determination<sup>28</sup> .

# **Independent Variables** **_Individual-level variables_**

_Ethnic_ is a dummy which takes the value of 1 if the respondent is an _Ijaw_ and 0 otherwise. About 14 million _Ijaws_ constitute the largest ethnic group in the Niger Delta and the fourth largest ethnic group in Nigeria (Catholic World News, 2004).

_Unemployment_ is a dummy measuring the state of unemployment. It takes the value of 1 if the individual is currently unemployed and is not a full-time student or apprentice, and 0 otherwise.

_Studentship_ is a dummy indicating that respondent is currently a full-time student in a formal educational institution or apprentice in a vocational training.

_Education_ is a discrete variable that measures the level of respondent’s formal educational attainment. The variable takes on a value of 3 if respondent completed tertiary education, 2 if respondent completed secondary education, 1 if respondent completed primary education, and 0 if respondent has no formal education.

_Marriage_ is a dummy which takes on the value of 1 if respondent is married and 0 otherwise<sup>29</sup> .

27 I define a violent conflict as one involving destruction of life and/or property.

> 28 The data is derived from answers to the question “ _Will you be willing to join a group that will be able to fight to ensure that your community and other communities in the Niger Delta own and manage the oil in their lands?”_ It takes a value of 1 if respondent’s answer is “ _yes_ ” and 0 otherwise.

29 It is possible to make further distinctions, e.g., if divorced or separated, if married with children etc. But I assume that the distinction between married and unmarried is adequate for the purpose of this study.

16
**Figure 11: Composition of the middle class across LAC countries (percentage shares)**

<!-- Start of picture text --> Venezuela<br>Rest of South America<br>Peru<br>Mexico<br>Colombia<br>Chile<br>Caribbean<br>and Central America<br>Brazil<br>Argentina<br>2005 2030<br>100<br>80<br>60<br>percent<br>40<br>20<br>0<br><!-- End of picture text --> _Source:_ World Bank staff calculations.

# **4.2 Description of the members of the middle class: Characteristics and position within the national distribution**

# **_From the top to the middle of the income scale: The middle class earnings position from 2005 to 2030_**

The members of 2005 middle class in LAC earn incomes well above the averages of their own countries of residence. For example, in Venezuela and Peru, households in the middle class are in the top 20 percent of the income scale within their own countries and a large majority is even in the top 10 percent. Looking at the region as a whole, most (about 80 percent) of the middle class members occupy the top two deciles of the income distribution. This situation changes dramatically by 2030. A full 40 percent of the middle class will be earning incomes in the seventh decile or lower at the regional level. For example in Chile, most of middle class members belonged to eighth decile or above in 2005, but by 2030, 75 percent of the middle class members will be evenly distributed across the third and the seventh deciles of their national income distribution, and no member of that class will earn more than 90% of the population. In short, while only the 20 percent richest households could afford the global middle class’s living standards in 2005, households just above the median income in the distribution will earn enough to enjoy these standards by 2030.

25
Figure 23 illustrates average per capita expenditures at the national level (See Figure 27 and Figure 28 for regional disaggregation). Total central expenditure spending ranges from about 1,000 to 1,400 per capita in 2005PPP. The level of local expenditures per capita is much lower.

In total, central spending is about three times higher than local spending. Therefore, in the upcoming analysis, it will not be surprising that central spending also has larger poverty reduction impacts. The largest amounts of central spending are personnel costs followed by purchases of goods and services and capital spending. In terms of local spending, purchases of goods and services is the category with the highest amounts of spending.

Across regions, the east has higher amounts of central spending but lower amounts of local spending. One aspect of this is higher personnel expenditures in the east which includes defense spending.

Figure 12. Per Capita Expenditures <!-- Start of picture text --> Central Expenditures Local Expenditures<br>2006 2007 2008 2009 2010 2011 2012 2013<br>2006 2007 2008 2009 2010 2011 2012 2013<br>Personnel Social Security Personnel Social Security<br>Purchases Interest Payments Purchases Interest Payments<br>Current Transfers Capital Spending Current Transfers<br>Capital Transfer Lending<br>Substitute Payments<br>Source: Ministry of Finance – General Directorate of Public Accounts<br>2 2<br>1.5 1.5<br>1 1<br>.5<br>.5<br>0<br>Per Capita Expenditures 2005PPP, (thousands) Per Capita Local Expenditures 2005PPP, (thousands) 0<br><!-- End of picture text --> Table 7 illustrates regression results from equation (3), and using central spending. There is low explanatory power from fiscal variables, the R-squared is very low. But the R-squared is higher by many fold with addition of sectoral growth variables. Also differences in results with and without sector controls can be due to either the control of sector variables or the difference in time frame. In regressions with the addition of sector growth variables, fiscal variables still remain significant. The impacts of total spending disaggregated by east and west also have disparate impacts on changes in poverty; the poverty reducing impacts of central spending is much stronger in the West. Figure 27 shows the average level of per capita central spending in eastern and western parts of the country. While in eastern regions, total spending is higher, this is due primarily to the category on personnel costs, which also includes defense personnel. At the grouping of three regions, total central spending is the most poverty reducing in region R2 (Anatolian Tiger). However, as already mentioned, government spending includes personnel costs that do not necessarily trickle down to the population.

25