rafmacalaba/datause-encoder-data huggingface dataset:
LLSV (1998) in using the legal origin of each country’s Commercial/Company law as an instrumental variable for RIGHTS. These instrumental variable results, however, confirm the paper’s findings.

# _C. Controlling for other country-level and bank-level factors_

To assess whether the shareholder rights variable is proxying for some other country trait, we assess the robustness of the results to controlling for other country specific characteristics. Specifically, we include each country’s level of real per capita Gross Domestic Product (PER CAPITA INCOME), an index of contract enforcement (ENFORCE), an index of the degree of official corruption in the economy (where higher values indicate less corruption) (CORRUPT), an index of the law and order tradition of the country (LAW), and dummy variables for whether the country has a French legal origin (FRENCH LO) or an English legal origin (ENGLISH LO). We include these indicators to control for the level of economic and institutional development and the design and operation of the legal system beyond specific shareholder protection laws. If RIGHTS is simply proxying for “good” countryspecific traits that boost bank valuations, then these control variables should drive out the significance of RIGHTS in the valuation regressions. As shown in Table IX, we obtain the same results when including these country-specific characteristics.

We further assess the robustness of the results to controlling for additional bank-specific characteristics. In particular, bank size may influence valuations, so we include the logarithm of each bank’s total assets (SIZE). Furthermore, while we have controlled for average net loan growth over the three years prior to the valuation date (LOAN GROWTH), we also assess the robustness to including net loan growth during the year in which bank valuation is measured as well as current and lagged values of asset growth and revenue growth. Including these additional bank-level traits does not change any of this paper’s results.

28
# 3.1 Fldcto kU^Wa the Mfin=m A.af Wage^L

The simulation results associated with a permanent, 5 percent rcd-iiction in the minimum wage are illustrated in Tables 1 and 2, which display relative and absolute percentage changes from the baseline sol.ution, respcctively, for the first 10 periods after the shock. This time period is referred to below as the "adjustment period." The experiment assumes that the government borrows domestically to finance its deficit-implying therefore (as discussed earlier) an offsetting adjustment in the savings rate of capitJalists a.rd rentiers, in order to maintain the aggregate bala-ace between savings and 'nvestment (equation (82)).26 Table 1 provides data on national accounts, fiscal accounts, and the labor market, whereas Table 2 sHows changes in prices, consumption and income for each household group, and poverty and distributional indicators, both income- and consumption-based. Also shown in TLable 2 is the real exchange rate, defined as a weighted average of the domest c-currency price of exports and imports (with weights based on initial volumcs of trade), divided by a weighted average of the price of domestic sales of agricuJ.tural and private sector goods.

The impact effect of the reduction in thhe minimum wage is an increase in the demand for unskilled labor in the private sector of the ^rcder of 4.3 percent in the first year. The increase ir demand is met by the existing pool of unskilled workers seeking employment in the urban sector. As a result, the unskilled unemployment rate drops sigrificantly, by 2.8 percentage points in the first year.<sup>27</sup> The cut in the minimuum wage, by reducing the r_eiative cost of unskilled labor, leads to substitution among production factors not only on impact but also over time. Because unskilled labor has a relatively high elasticity of substitution with respect to the composite factor consisting of skilled labor and physical capital, the -lower cost of that categcry of labor gives private firms in the formal sector an incentive to substitute away from skilled labor and physical capital. In tmrn, the fall in the demand for that category of labor puts downward pressure on skilled wages, which drop by 1.6

> 26 How this "transfer" of private savings to the government takes place is not explicitly specified; one can think of a "pure" financial ;ntermediary operating in the background. Note that a more elaborate approach would involve accounting explicitly fe: the issuance of government bonds, and thus portfoiEo decisions on the part of savers.

> 27 Note that, from equation (23), the initial change in unskilled unemployment is given _by AUNEu,o =_ **_-AU3dO/U;._** It therefore depends on the initial number of workers seeking employment in the formal sector. Similarly, from _(32), AUNEs,O = -AS3,G/S._

36
- Card, D., P. Ibarrar ́an, F. Regalia, D. Rosas-Shady, and Y. Soares (2011): “The Labor Market Impacts of Youth Training in the Dominican Republic,” _Journal of Labor Economics_ , 29, 267–300.

- Card, D., K. J., and A. Weber (2015): “What Works? A Meta Analysis of Recent Active Labor Market Program Evaluations,” _NBER Working Paper No. 21431_ .

- Card, D., J. Kluve, and A. Weber (2010): “Active labour market policy evaluations: A metaanalysis,” _The Economic Journal_ , 120, 452–477.

- Chakravarty, S., M. Lundberg, P. N. Danchev, and J. Zenker (2015): “The role of training programs for youth employment in Nepal: impact evaluation report on the employment fund.” The World Bank, Washington, DC.

- Cho, Y., D. Kalomba, A. M. Mobarak, and V. Orozco (2013): “Gender Differences in the Effects of Vocational Training: Constraints on Women and Drop-Out Behavior,” _IZA DP No. 7408_ .

- Corseuil, C. H., M. Foguel, G. Gonzaga, and E. P. Ribeiro (2012): “The Effect of an Apprenticeship Program on Labor Market Outcomes of Youth in Brazil,” Presented in the 7th IZA/World Bank Conference: Employment and Development, New Delhi.

- D ́ıaz, J. J. and D. Rosas Shady (2016): “Impact Evaluation of the Job Youth Training Program Projoven,” _Inter-American Development Bank Working Paper_ .

- Groh, M., N. Krishnan, D. McKenzie, and T. Vishwanath (2016): “The Impact of Soft Skills Training on Female Youth Employment: Evidence from a Randomized Experiment in Jordan,” _IZA Journal of Labor and Development_ , 5, 9.

- Heinrich, C. J., P. R. Mueser, K. R. Troske, K.-S. Jeon, and D. C. Kahvecioglu (2013): “Do Public Employment and Training Programs Work?” _IZA Journal of Labor Economics_ , 2, 1–23.

28
30 some sort of Ul program (ISSA, 1983; ILO, 1988).<sup>A detailed comparison of the provislons of these</sup> progmrms to those of Ul systems In OECD countris that were discussed<sup>in Section C Is not Informative;</sup> but a gnemal comparison between the two groups of countries<sup>points out some Interesting dierences</sup> and similarities. In decreas-.g order of thoir<sup>starkness, these are:</sup>

* Ul systems in developing countries are more<sup>likely to rely on shared</sup>

employerAworker financing of benefits.

* Potential duratlon of bonefits Is generally shorter.

* Waiting periods are more likely.

* Programs rarely cover govemment workers.

X Net replacement rates are about the same.

Obviously a major, indeed a defining difference<sup>between labor markets in developed and</sup> developing economies is the absence of industrialization<sup>in the latter. Instead, there are large urban</sup> trdiftonal sectors characterized by casual employment<sup>and very small-scale and often short-lived</sup> operations, with the modern sector being relatively small, and with<sup>both dwarfed by traditional agriculture.</sup> Even these essential, but very broad differences offer<sup>some guidelines for structuring Ul</sup> programs. Given the small size of establishments,<sup>their rapid tumover, and the lack of standard record-</sup> keeping, covering urban traditional employees seems undesirable.<sup>Similarly, covering small firms is likely</sup> to be diflicuit administratively; and their ubiquity and high<sup>failure rates suggest that offering benefts to</sup> their former employees would add tremendously<sup>to the costs of a Ul program. Taken together, these</sup> consierations suggest that coverage be llmited to firms<sup>above some size threshold that have been In</sup> opoeation for some period of Ume. At least one year's operation<sup>should be required; and perhaps a lower</sup> llmit of 10 employees should be necessary for coverage of<sup>a</sup> fOrm's workers.

In the theoretical analysis of developing labor markets the<sup>major contribution has been</sup> the model of economic dualitm. An essential characteristic<sup>of this model is the assumption that the</sup> supply of labor to enterprises In the modem sector<sup>Is perfectly elastic (i.e., that real wages, and</sup> presumably also wages relative to traditional Industry<sup>are rigid). The source of this rigidity may be</sup>
<!-- Start of picture text --> 0.03 0.02 a<br>Ta. b.<br>0.02 ° TRUST AND 0.01 i- — tT<br>JOB CHARACTERISTICS > i]<br>0 I<br>0.01 = =<br>0 =T Se+ -0.01 ACTIVE MEMBER AND t<br>JOB CHARACTERISTICS<br>T tT -0.02 dL<br>0.01 =<br>_High Upper middle Lower middle Low High Upper middle Lower middle Low<br>income income income income income income income income<br>0.1<br>C. 0” d. _<br>0.08 — = = - ?-<br>4<br>0.06 +. + ea 0.01 _ ° + —<br>iL _ > Lt<br>0.04 can —<br>0 5<br>0.02 DEMONSTRATION AND °<br>JOB CHARACTERISTICS ? PETITION AND<br>0 001 JOB CHARACTERISTICS<br>0.02 a -0.02<br>_High Upper middle Lower middle Low High Upper middle Lower middle Low<br>income income income income income income income income<br><!-- End of picture text -->
Despite this recent progress in education spending and enrolments, by 1998 inequities in public education spending were still considerable at the secondary level. Public expenditure at secondary education levels was still against-poor because their distributions were under the diagonal line, though the Lorenz curves moved upwards between 1993 and 1998 reflecting improvement favoring the poor. The poorest quintile received a 12 percent share of lower secondary education expenditures while it consisted of 20 % of lower secondary school-age children. Similarly, in 1998 the bottom quintile received a much smaller share of public spending on upper secondary education (4%) than its share of school-age children (18 % in 1998). In contrast, the richest quintile received 22 percent of lower secondary, 41 percent of upper secondary education expenditures while it accounted for only 16 % and 20% of school-age children for these levels, respectively (Table 14). The Lorenz distribution of public expenditure at upper secondary education was most biased against the poor, compared to that at primary and lower secondary level.

**Table 14: Share of public education spending and school-age children for poorest and richest quintile 1993 and 1998**

|Educationlevel|Year|Bottom<br>|20%<br>|Top<br>|20%<br>|
|---|---|---|---|---|---|
|||Shareofpublic<br>education<br>spending<br>|Shareof<br>school-age<br>children<br>|Shareofpublic<br>education<br>spending<br>|Shareofschool-<br>agechildren|
|_Alleducation_<br>|1993|_16.5_<br>|_19_<br>|_22.9_<br>|_19_<br>|
|Primary<br>||20<br>|24<br>|17.4<br>|16|
|Lower-Secondary<br>||7.2<br>|20<br>|33.8<br>|19<br>|
|Upper-Secondary<br>||2.1<br>|16<br>|60.3<br>|21<br>|
|_Alleducation_<br>||_18.1_<br>|_20_<br>|_21.4_<br>|_18_<br>|
|Primary<br>|1998|26<br>|26<br>|12.5<br>|14<br>|
|Lower-Secondary<br>||12.4<br>|20<br>|21.7<br>|16|
|Upper-Secondary||4|18|41|20|

_Source:_ Author's estimates based on 1992-93 and 1997-98 VLSS

# _Regional benefit incidence ofpublic spending on education_

**Table 15: Per capita public spending for education by level and region, 1993 and 1998** (000' dong in constant January 1998 price)

|_1993_<br>|Northern<br>Uplands<br>|Red River<br>Delta<br>|North<br>Central<br>|Central<br>Coast<br>|Central<br>Highlands<br>|South<br>East|Mekong<br>Delta|Vietnam|
|---|---|---|---|---|---|---|---|---|
|Primary<br>|17.6<br>|16.1<br>|17.6<br>|15.0<br>|18.5|16.7|16.3|16.6|
|Lowersecondary<br>|4.9<br>|8.2<br>|6.1<br>|9.4<br>|3.8|7.8|6.0|6.9|
|Uppersecondary<br>|2.0<br>|3.2<br>|1.9<br>|4.5<br>|0.6|3.5|1.6|2.6|
|Post-secondary<br>|7.9<br>|22.8<br>|12.2<br>|15.0|0.0|22.2|6.5|13.7|
|_Total_<br>|32|50|38|44|23|50|30|40|
|_1998_<br>|||||||||
|Primary<br>|62.7<br>|44.3<br>|58.8<br>|45.5<br>|63.4|37.7|48.3|50.4|
|Lower secondary<br>|30.2<br>|33.0<br>|32.0<br>|24.2<br>|24.1|27.2|21.7|27.9|
|Uppersecondary<br>|10.0<br>|16.7<br>|13.1<br>|13.5<br>|_5.5_|15.1|7.9|12.2|
|Post-secondary<br>|16.7<br>|45.0<br>|21.0<br>|35.9<br>|7.3<br>|66.4|21.2|31.9|
|_Total_|126|144|131|120|105|143|103|126|

_Source:_ Author's estimates based on 1992-93 and 1997-98 VLSS 18
The study also confirms the evidence elsewhere that behaviors co-occur. Young people in Mexico and Chile who engage in one positive behavior tend to engage in other positive behaviors. This is not surprising since a common set of protective factors continuously emerge as important explanatory variables for each behavior. Thus, by strengthening positive factors and minimizing negative factors, it is possible to influence a range of youth behaviors.

Despite the limitations in identifying causality, our ability to determine the key factors important for a range of behaviors can be useful to policymakers. While poverty alleviation and education are common policy entry points, programs that tap into other areas of a young people’s lives to ensure the presence of positive influences have the potential for a significant impact in preventing risky youth behaviors in LAC. For example, young people without family support can benefit from mentoring programs. Or, certain models of “after” school clubs are effective for affecting a range of behaviors. Or, those from poor households can benefit from cash transfers that reward positive behaviors. In fact, there are a range of evidence-based program and policy interventions at the individual-, micro-, and macro-levels that prevent negative youth behaviors and provide second opportunities to those who have made poor decisions (Cunningham et. al. 2008).

While this paper highlights some non-traditional variables for policy attention in order to better support young people in Mexico and Chile, the causes behind youth behavior are still largely unknown. Our analysis only explained up to 24% of the variance in some youth behaviors, so three-quarters of the behavior is explained by other factors that were not captured by our data. Further, we cannot claim that the variables that we identified as important are causing each behavior in question. To further understand the challenges facing LAC’s youth, we will need time series data with explanatory variables that proxy relationships in a child’s life, both with her parents as well as her peers, to complement the more commonly included variables and more complex models that allow us to better construct and quantify the complex environment that shapes youth decision-making.

20
# III. **METHODS**

In the panel dataset we use—the Vietnam Living Standards Survey (VLSS)<sup>4</sup> —we observe most of the outcomes of interest during the first wave (fielded in 1992/93 just before the introduction of VHI), and again in the second wave (fielded in 1997/98). In what follows, _t_ =1 denotes 1992/93 and _t_ =2 denotes 1997/98.

Let _yit_ be the outcome of interest for individual _i_ at time _t_ . In our empirical analysis _y_ alternates between health status, out-of-pocket payments, nonmedical consumption and utilization of health services. We assume that the change in _yit_ between 1993 and 1998 in the case of the “treated” group (i.e. those enrolled in VHI after it was introduced) can be written<sup>5</sup> :

> where ∆ denotes the change between 1993 and 1998,<sup>_y_</sup> _it_ *<sup>is the counterfactual outcome for VHI</sup>

enrollees, _Git_ is the “gain” or impact of the program, θ _t_ is unobserved and specific to period _t_ , ε _it_ is a white noise measurement error term associated with the outcome variable, and _N_ is the number of individuals who were enrolled in VHI in period 2. For each participant (i.e. insured), we use propensity score matching (PSM) to identify a matched non-participant (cf. Rosenbaum and Rubin 1983).<sup>6</sup> This involves estimating regression (e.g. probit) equations for the participation decision, and defining a nearest neighbor for each participant as the non-participant with the closest propensity score (predicted participation probability). We assume that for the _N_ matched controls we can write: where _uit_ is a white-noise error term. Subtracting eqn (2) from eqn (1) gives the doubledifference or difference-in-differences: which, given _Gi_ 1=0 (the program did not exist at _t_ =1), can be rewritten: Taking expectations of eqn (3’) gives given ε and _u_ are assumed to have zero mean.

> 4 Further details on the survey can be found at <u>http://www.worldbank.org/lsms/.</u>

> 5  The notation and exposition in this section borrows heavily from Ravallion et al. (2003).

> 6  Crystal clear expositions of PSM with economic applications are to be found in Jalan and Ravallion (2003a, 2003b).  Blundell and Costa Dias (2000) provide a useful survey of evaluation methods, and show how PSM relates to other methods.  Applications of PSM in the health sector include Newman et al. (2002), Pradhan and Rawlings (2002), Sadaah et al. (2001) and Jalan and Ravallion (2003a).

5
7 In this paper, we use an updated version of the Krishna, Erzan and Tan (1991) data on US apparel imports from Hong Kong to provide sc.ne estimates of the extent to which quota rents are shared.

# 2. The Data

The data utilized in this study cover the time period 1981-88 and pertain to three broad areas: domestically-produced apparel, imported apparel from Hong Kong, and license holdings for apparel imports from Hong Kong. We did not attempt to obtain data on all categories of apparel. There are severe difficulties in assembling a consistent panel of data due to the different and changing classification systems used in reporting information on imports and domestic production. Therefore, we chose groups of apparel such that these consistency problems were minimized. Our objective was to get as many relatively consistently-defined, disaggregated groups that we could find or develop concordances between the different classification systems employed.

We identified ten such groups. They are: (1) dresses; (2) skirts; (3) playsuits; (4) sweaters; (5) trousers; (6) men's coats; (7) women's coats; (8) woven shirts; (9) knit shirts; and (10) underwear. We obtained data for these groups for the following variables between 1981 and 1988. In our notation, the subscript i indexes the apparel group, and t indexes the year.

- pius Unit value of US production

- p,tHK C.i.f. Hong Kong price. This includes the license price. We shall refer to this license-price-inclusive Hong Kong price as the adjusted Hong Kong price.

Qi, iK Imports from Hong Kong.

- Hi, : Numbers equivalent of the Herfindahl index of concentration in license holding.

- VI, : Quota level for imports.

U;, : Utilization ratio of imports, where U;, = Qi,IK/V;,.
arrangement will enable children to observe other activities being carried out by their peers and will allow for more frequent communication between children of different ages.

**Figure 3: Multifunctional Space in the New Kindergarten Design**

_Source_ : City Arch (2015) Combining the playroom and the sleeping room will reduce the kindergarten’s total area but will increase the space available inside the playroom from an average of 2.5 square meters to 4 square meters per child (assuming each group contains 20 children). The zones for eating and changing clothes are incorporated into the playroom common area. Increasing the size of the playroom will not only give children more space to play but will also make it possible to arrange the space in different ways. Including the multifunctional open hall, the overall active space per child will amount to 10.5 square meters.

The arrangement of the outdoor landscape in the proposed design is based on making the outside space a complementary learning and developmental environment for children. To make the playground area as large as possible, the designers have positioned the new kindergarten building close to the border of the land plot instead of in the center of the 15
Table B3: Regression results for alternative models/estimators, Uganda

||Naïve|reghdfe|_π_= 0|_π_= 0_._5|_π_= 1|UBH|UBS|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Child is female|0.06|0.02|0.03|0.02|0.02|0.04|0.04|
||(0.01)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Oldest sib|0.09|-0.22|-0.21|-0.21|-0.21|0.05|-0.23|
||(0.01)|(0.01)|(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|
|Never enrolled|-0.16|0.07|-0.02|-0.06|-0.04|-0.01|0.06|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Currently enrolled|0.48|0.35|0.34|0.34|0.35|0.39|0.43|
||(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|
|Attends private|0.43|0.18|0.19|0.19|0.20|0.12|0.25|
||(0.01)|(0.01)|(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|
|Never ×s�|||-0.48|-0.55|-0.43|||
||||(0.04)|(0.03)|(0.02)|||
|Private ×s�|||-0.03|-0.02|-0.01|||
||||(0.01)|(0.01)|(0.01)|||
|Obs.|362,128|362,128|362,128|362,128|362,128|362,128|362,128|
|R<sup>2</sup>|0.08|0.71|0.71|0.71|0.71|0.62|0.45|

Note: columns refer to different estimators/models; UBH is the household upper bound (excludes school effects); ‘Naïve’ is simple OLS excluding fixed effects; reghdfe reports results based on the (improved, accelerated) partitioned iterative algorithm due to Correia (2017) in which household effects enter first (using the Stata command of the same name); columns π = (0, 0.5, 1) are taken from the partitioned iterative algorithm set out in the text; UBH is the household upper bound model and UBS is the school upper bound models, in which household effects are proxied by observed characteristics (not shown); all reported coefficients are significantly different from zero; cluster robust standard errors reported in parentheses.

Source: own calculations.

48