rafmacalaba/datause-encoder-data huggingface dataset:
village with new information that helps them update their perception of your social status. In particular, if the bride is marrying a "prestigious" groom, either because he is rich or well educated, then this marriage alliance has status benefits for the wife's family. However, a lavish wedding without some "real" change in status may simply give people the impression that the spender is extravagant.

Thus, if families are rational, then this extra money should only be spent when there is new information to communicate. If there is nothing to show off it would not be rational for a father to spend anything beyond the minimum (maintenance level) expenditure required to fulfill his social obligations. Furthermore, even if the bride were marrying a rich groom there would be no new information to communicate to the village if everyone in the village already knew that the groom was rich. The very existence of the marriage alliance provides enough information for the village to update its opinion on the social standing of the bride's family. Therefore, lavish weddings would only make sense when the bride is marrying a prestigious groom _and_ when the village has no information about the groom and his family.

The variations in village exogamy provide us with a way to translate this point into a testable hypothesis. When marriage rules prohibit partners from the same village from getting married, then the bride family's home village will not know much about the groom's family. On the other hand if brides and grooms from the same village customarily marry each other, then the village should have very good information on the groom's family. Thus, if lavish weddings serve as a means of increasing status, it is only when the groom is a high status "catch" from another village that we should observe particularly lavish weddings.

This hypothesis will be tested by estimating a multivariate regression of wedding celebration expenses on the characteristics of the groom and bride and their families, the number of alternate partners in the marriage market, and whether the sub-caste that the family belongs to practices village exogamy<sup>9</sup> . I will interact the village exogamy variable with the education and wealth of the groom. If these interaction terms are positive we can infer that it is only when grooms are educated and wealthy, i.e. "prestigious," _and_ from an outside village -- resulting in a lack of information about them in the bride's village -- that marriage expenses are significantly higher. This would support the hypothesis that lavish celebrations are a signal of improved social status -- communicating new information about mobility via a costly action.

9 Note that the village exogamy variable measures the customary practices prevalent in the community to which the responding household belongs. This can be treated as exogenous to the dowry and wedding celebration decisions since they are unlikely to change in the short term.

8
||TableA2:Labor force<br>|participationbyrace,im<br>|migrationstatus, citizenship a|nd abilityin English.<br>||
|---|---|---|---|---|---|
||(1)<br>Males|(2)<br>Females||(3)<br>Males|(4)<br>Females|
|Dependent variable|Inthelabor force|Inthelabor force||Inthelabor force|Inthelabor force|
|White born out US,|-0.0090***|-0.0395***|White born out US,|-0.0197***|-0.0723***|
|citizen|[0.0018]|[0.0023]|good English|[0.0015]|[0.0018]|
|White born out US, not|-0.0542***|-0.1350***|White born out US, not|-0.1062***|-0.1915***|
|citizen|[0.0022]|[0.0026]|good English|[0.0050]|[0.0056]|
|Black born in US|-0.0805***|-0.0270***|Black born in US|-0.0804***|-0.0268***|
||[0.0009]|[0.0010]||[0.0009]|[0.0010]|
|Black born out US,|-0.0259***|0.0600***|Black born out US,|-0.0398***|0.0390***|
|citizen|[0.0038]|[0.0039]|good English|[0.0028]|[0.0031]|
|Black born out US, not|-0.0593***|0.0029|Black born out US, not|-0.0975***|-0.0496***|
|citizen|[0.0040]|[0.0044]|good English|[0.0130]|[0.0115]|
|Asian born in US|-0.0317***|0.0047|Asian born in US|-0.0315***|0.0050|
||[0.0032]|[0.0038]||[0.0032]|[0.0038]|
|Asian born out US,|-0.0524***|-0.0225***|Asian born out US,|-0.0727***|-0.0826***|
|citizen|[0.0020]|[0.0021]|good English|[0.0018]|[0.0019]|
|Asian born out US, not|-0.1045***|-0.1761***|Asian born out US, not|-0.0898***|-0.1300***|
|citizen|[0.0024]|[0.0025]|good English|[0.0034]|[0.0035]|
|Hispanic born in US|-0.0512***|-0.0344***|Hispanic born in US|-0.0512***|-0.0342***|
||[0.0012]|[0.0014]||[0.0012]|[0.0014]|
|Hispanic born out US,|-0.0567***|-0.0314***|Hispanic born out US,|-0.0594***|-0.0567***|
|citizen|[0.0018]|[0.0021]|good English|[0.0014]|[0.0018]|
|Hispanic born out US,|-0.0607***|-0.1153***|Hispanic born out US,|-0.0601***|-0.1175***|
|not citizen|[0.0013]|[0.0017]|notgood English|[0.0015]|[0.0019]|
|Observations|3472846|3649194||3472846|3649194|
|PseudoR-squared|0.15|0.10||0.15|0.10|

Robust standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%.

Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The probit regressions (marginal effects shown) also include 5 year age group dummies, region dummies, education (HS drop out, HS graduate, some college, college and above), indicators for being married, disabled and in school, the number of children under age 5 in the household and the logarithm of total personal income earned by the other household members.. The regressions use the sampling weights provided with the IPUMS data.

34
Policy Research Working Paper 6484

# **Abstract**

This paper compares the raising of external equity capital from private equity investors via private investments in public equity (PIPEs) and seasoned equity offerings (SEOs) using a sample of 456 PIPEs and 1,910 SEOs drawn from nine Asian countries. Consistent with the idea that insiders attempt to time the markets, firms issuing SEOs are preceded by a significantly higher run-up in stock price compared with those issuing PIPEs. This result is consistent with the undervaluation hypothesis that states that firms are more likely to issue PIPEs when they perceive their stock to be undervalued. In contrast to the United States where this undervaluation appears to be driven by financial distress and asymmetric information, the results show PIPE and SEO issuers to be statistically undistinguishable from each other. The announcement of a PIPE offering is on average associated with a significantly higher stock market reaction compared with an issue of a SEO, suggesting that private equity investors may play a certification or monitoring role. However, a comparison of PIPE issuers’ operating performance and stock market returns in the pre-issue and the post-issue periods does not detect any significant improvements.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lklapper@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
facilities tends to be limited, further contributing to their low utilization of health services. Compounding the situation, the poorest often have worse health status due to poor living conditions and nutrition intake as well as lack of access to health services. As a consequence, the need for care among the poorest is high. When inpatient services are offered free of charge, the poorest are more likely to utilize services.

Figure 4. The Possible Pathways of the Impact of MAP on Surgeries/Inpatient Services Utilization <!-- Start of picture text -->
1. Less or not informed about the<br>program<br>2. High travel costs or other costs  The poorest use less<br>associated with seeking care   benefits/services<br>3. Limited supply of medical professionals<br>or medical facilities<br>MAP<br>PROGRAM<br>Initial poor health due to long term lack of  The poorest use more<br>access to health services, poor diet, etc.   benefits/services<br><!-- End of picture text -->

The literature has documented different international experiences in this area. For example, Abel-Smith and Rawal (Abel-Smith and Rawal 1992) found in Tanzania that many patients had to incur substantial costs, in addition to travel costs, to use the “free” services supported by the government because of inadequate supplies of drugs and food at hospitals. Other literature shows that the poorest beneficiaries are more likely to use health services because of their poor health, which is partly due to their having had little access to health services prior to the program’s inception (Trujillo, Portillo et al. 2005; Wagstaff 2007).

# **_Method_**

In order to test whether the MAP has reached the poorest in Georgia, all MAP beneficiaries are grouped into five quintiles based on their welfare scores, and the use of services is compared between the lowest quintile and the highest. The RD analysis estimates the impact of the MAP on the beneficiaries who are right below the threshold. The following comparison between the lowest and the highest quintile among the beneficiaries will inform us whether MAP has benefited the poorest.

13
-32 -

# 5.2. Education effects

Table 15 shows the impact of education through the opportunity probabilHies. Here the wage rates and the education variable (MAXED) in the conditional profit function are kept unchanged. Thus we study the pure *opportunity effect. Contrary to the wage simulations above, e obtain a large effect from Increased education. If female education Is Increased by one year, female participation in the wage sector Increases by 9.2 percent. The change In the participatlon rate in self-employment, however, Is wlthin the simulatlon error margin. If male education is Increased by one year, participation In wage work Increases by 3.4 percent, and remains unchanged for the selfemployed. If the minimum education for females Is Increased to nine years, female participation in the wage sector Increases by 19 percent. When males' level of schooling is Increased analogously, male participatlon In the wage sector Increases by 3.9 percent. The cross effects appear to be negligible.

Table 15. Effects of Education on Sector-Specific Participation Rates When Wages Are Fixed (percentage change)

||Se|ctor-soecifi|c particiDation ra|tes|
|---|---|---|---|---|
||<br>Wac|<br>e work|<br>Self emp|<br>lovment|
||F|M|<br>F|<br>M|
|Base case ..........<br>..............|0.32|0.62|0.34|0.35|
|One year of additional schooling<br>for females .........<br>..............|9.2|-1.4|0|0|
|One year of additional schooling<br>for males ...........<br>..............|.1.3|3.4|0|-0.6|
|One year of additional schooling<br>for both males and females .....<br>.......|<br>7.6|2.4|0|-0.9|
|Nine years of schooling as a lower<br>limit for females .......<br>.............|19.0|-1.0|0|-0.3|
|Nine years of schooling as a lower<br>limit for males ........<br>.............|-1.3|3.9|0|-0.9|
|Nine years of schooling as a lower<br>limit for both males and females ....<br>....|.<br>18.0|3.5|0|-1.2|
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE time, at 48 hours, is the highest among West African ports. The global best practice for truck cycle time is one hour, and it is six hours in the Port of Cotonou (table 3). Delays in the Port of Lomé and administrative requirements are responsible for the bulk of the time consumed importing to Niger using the Lomé-Niamey corridor. This port is one of the busiest in West Africa, handling around 460,000 TEUs<sup>5</sup> per year. Crane productivity in Lomé is 23 tonnes per hour, substantially better than elsewhere, although still short of the international benchmark. The international standard for the time containers spend in the terminal (dwell time) is 7 days or less, but in the Port of Lomé it is 13 days (table 3).

Time-consuming regulatory processes related to customs clearance and technical controls further augment the total time of trading across borders. In the Cotonou-Niamey corridor, administrative procedures account for about 23 percent of the total time required to move imports, and in the LoméNiamey corridor they consume around 35 percent (figure 6b). The administrative process in Niger is particularly cumbersome; unnecessary and often redundant formalities significantly prolong the time needed for customs clearance. This results in the costly immobilization of goods and means of transport (Enhanced Integrated Framework 2008).

**Table 3. West African port indicators**

|Port|Unit|Lomé,<br>Togo|Cotonou,<br>Benin|Dakar,<br>Senegal|Abidjan,<br>Côte<br>d’Ivoire|Tema,<br>Ghana|Apapa,<br>Nigeria|Harcourt,<br>Nigeria|
|---|---|---|---|---|---|---|---|---|
|Total container cargo handled|TEU,annual|460,000|158,201|331,191|500,119|420,00<br>0|336,30<br>8|7,900|
|Container dwell time|Days|13|12|7|12|25|42|30|
|Truck turnaround time<br>|Hours|4|6|5|2.5|8|6|24|
|General-cargo vessel pre-berth<br>waitingtime|Hours||48|24|2.9|9.6|36|38.4|
|General-cargo vessel turnaround<br>time|Hours|—|48|60|2.2|48|40.8|45.6|
|Craneproductivity|Containersper hour|—|—|—|18|13|12|—|
|Craneproductivity|Tonnesper hour|23|15|—|16|14|9|8|
|Container-cargo-handlingcharge|$ per TEU|220|180|160|260|168|155|—|
|General-cargo-handlingcharge|$ per tonne|9|9|15|14|10|8|8|
|Bulk dry-handlingcharge|$ per tonne|5|5|5|5|3|—|—|
|Adequate road accesspresent|0=no;1=yes|0|0|0|—|0|0|0|
|Landlord model is used|0=no;1=yes|1|0|0|1|1|1|1|

_Source:_ AICD ports database (www.infrastructureafrica.org/aicd/tools/data); data as of 2006. _Note:_ TEU = 20-foot equivalent unit. — = Not available.

> 5 20-foot equivalent units.

11
salary increases for their performance as classroom teachers, school directors, supervisors and technicaladministrators. Teacher evaluation is based on performance (35 points), experience (10 points), professional skills (25 points), educational attainment (15 points) and completion of accredited courses. There are five levels of promotion (“A”, “B”, “C”, “D”, “E”). The salary rewards allocated to each one of these levels represent an increase but do not represent a change in post assignment. The promotion ladder attaches considerable importance to seniority within _Carrera Magisterial_ , rural posts or teaching in under-developed areas. Promotion within _Carrera Magisterial_ is complex because of the different levels ( _escalafones)._ Government is the dominant provider of basic educational services. It owns close to 91 percent of primary and secondary schools, which account for 90 percent of total enrollment.<sup>4</sup> At the university level, however, the private sector plays a much bigger role, accounting for close to half of the enrollment (46 percent). The educational system in Mexico is now so extensive that there are over 483,000 schools (excluding preschools) staffed by over a million teachers, of which 84.3 percent are in public schools.

In 1999, public schools teachers<sup>5</sup> held 43 percent of total government positions. All teachers in basic public education are affiliated with SNTE. All teachers in upper secondary and tertiary education have a union or are independent (in Autonomous or State Universities).

# **3. THE DATA**

The National Household Income and Expenditures Survey ( _Encuesta Nacional de Ingresos y Gastos de los Hogares_ , ENIGH) is collected by the National Institute of Statistics, Geography, and Information ( _Instituto Nacional de Estadística, Geografía e Informática_ , INEGI). This survey is available for 1984, 1989, 1992, 1994 and 1996. Each survey is representative at national level, and distinguishes urban from rural areas. The annex shows the sample sizes of this survey. The ENIGH surveys identify several variables such as educational attainment, personal income and number of hours-worked per week by family member. Total income is aggregated into eight broad categories: i) labor earnings; ii) income from self-employment; iii) property income and rents; iv) monetary transfers; v) other current income; vi) monetary and non-monetary financial income; and vii) non-monetary income such as imputed rent, in-kind transfers, gifts and auto-

> 4 The share of public school enrollment is about 94 percent (primary), 93 percent (lower secondary) and 78 percent (upper secondary).

5
Alkire, S. (2018). ‘The research agenda on multidimensional poverty measurement: important and as-yet unanswered questions’, _OPHI Working Paper 119_ , University of Oxford.

- Alkire, S. and Apablaza, M. (2016). “Multidimensional poverty in Europe 2006–2012: Illustrating a methodology.” _OPHI Working Paper_ 74, University of Oxford.

- Alkire, S. and Santos, M. E. (2014). ‘Measuring acute poverty in the developing world: Robustness and scope of the multidimensional poverty index,’ _World Development_ , 59, 251-274.

- Alkire, S., Apablaza, M., and Jung, E. (2014). ‘Multidimensional Poverty Measurement for EU-SILC Countries.’ _OPHI Research in Progress_ 36c, University of Oxford.

Alkire, S. and Foster, J. (2011). ‘Counting and multidimensional poverty measurement’, _Journal of Public Economics_ , vol. 95(7–8), pp. 476–487.

- Alkire, S., Foster, J. E., Seth, S., Santos, M. E., Roche, J. M., and Ballon, P. (2015). _Multidimensional Poverty Measurement and Analysis_ . Oxford: Oxford University Press.

- Alkire, S., Meinzen-Dick, R. S., Peterman, A., Quisumbing, A., Seymour, G., and Vaz, A. (2012). The Women’s Empowerment in Agriculture Index. _IPRI Discussion Paper_ 01240. International Food Policy Research Institute.

Alkire, S., Ul Haq, R. and Alim, A. (2019). ‘The state of multidimensional child poverty in South Asia: a contextual and gendered view’, OPHI Working Paper 127, University of Oxford.

Batana, Y. (2008). ‘Multidimensional Measurement of Poverty in Sub-Saharan Africa’. (2013) _Social Indicators Research,_ 112(2), 337-362.

Bessell, S. (2015) The Individual Deprivation Measure: measuring poverty as if gender and inequality matter, _Gender & Development_ , 23:2, 223-240.

29
<!-- Start of picture text --> Bangladesh: Spousal education difference<br>N<br>5<br>8_o Urban<br>35ad239eoI SS_————<br>g8 Higher education<br>oY<br>ee<br>aoe<br>¢o>) z<br>=o<br>Ce<br>a°9<br>o<br>o<br>i<br>%<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing education difference of spouses on wealth, urban, higher education<br>and interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal: Spousal education difference<br>*<br>Cc<br>a)<br>: 3<br>oon<br>33"<br>ao ;<br>ee Higher education<br>~ oy<br>23SSSve<br>5<br>g°%<br>it<br>%<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing education difference of spouses on wealth, urban, higher education<br>and interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> India: Spousal education difference<br>N<br>5<br>i<br>aagoaSe0 Urban<br>59!<br>38<br>0<br>a5<br>QSa z Higher education<br>=5<br>ce<br>2°9<br>is}<br>o<br>i<br>9<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing education difference of spouses on wealth, urban, higher education<br>and interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Spousal education difference<br>N<br>6x “ca<br>ev<br>3°<br>3a<br>a”<br>og<br>i-ie ot<br>se |<br>nee Higher education<br>ti<br>%<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing education difference of spouses on wealth, urban, higher education<br>and interactions with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
# **Table 4 Type of health care utilization by poverty status using PA poverty lines (%)**

||**Alb**|**ania**|**Bosni**<br>**Herze**|**a and**<br>**govina**|**Monte**|**negro**|**Ser**|**bia**|**Kos**|**ovo**|
|---|---|---|---|---|---|---|---|---|---|---|
||||**Non-**||||||||
||**Non-poor**|**Poor**|**poor **|**Poor**|**Non-poor**|**Poor**|**Non-poor**|**Poor**|**Non-poor**|**Poor**|
|Public ambulatory|9.53**|8.28**|36.67***|27.96***|70.41|61.6|22.74**|17.03**|15.98**|14.70**|
|Hospital (outpatient)|3.54|3.58|_na_|_na_|_na_|_na_|_na_|_na_|_na_|_na_|
|Popular doctor/alternative medicine|0.37**|0.16**|2.44***|0.95***|_na_|_na_|1.01|0.58|_na_|_na_|
|Private doctor|1.39*|0.99*|8.39***|4.34***|3.41|0.54|2.37***|0.48***|2.83|2.88|
|Private nurse|1.38|1.41|0.67|0.26|1.08|0.00|_na_|_na_|1.00|1.15|
|Health service abroad|_na_|_na_|_na_|_na_|_na_|_na_|0.13|0.00|_na_|_na_|
|Other|_na_|_na_|15.49***|10.78***|3.90*|0.00|_na_|_na_|2.75|2.84|
|Non-prescription medicines|16.32***|12.40***|42.98***|36.49***|0.02**|0.01**|22.48***|10.24***|10.28***|8.52***|
|Hospital (inpatient)*|3.93|4.37|4.99|3.93|21.20***|37.86***|5.09|3.94|5.1|4.69|
|Dentist|22.03***|12.44***|28.13***|19.00***|0.02***|0.00***|7.54***|2.72***|_na_|_na_|
||5145,33||2223.146||90.34||4111.31||106.689||
|PA PovertyLine|New Lek/pc|/per month|KM/pc/per|year|Euro/pc/pe|r month|Dinars/pc/p|er month|DM/pc/per|month|

Note: *In Montenegro, the data include outpatient care at hospitals. * significant at 10%; ** significant at 5%; *** significant at 1%