rafmacalaba/datause-encoder-data huggingface dataset:
**Table 10. Contribution to the Pension System in December 1999- January 2000** Employed Women of Working Age (14 - 60)

||P|ROBITI|PROBIT|2|MEAN|
|---|---|---|---|---|---|
|CONTPR|dF/dx|Std.Err|dF/dx|StdErr||
|AGE99|-0017|(0.041)|-0017|(0042)|38.14|
|AGE<sup>2</sup>|47E-04|(0.001)|0000|(0001)|157120|
|HEAD|0186|(0.140)|0.264|(0.150)*|021|
|MARRIED|0.257|(0.134)**|0.266|<0.136)**|0.43|
|SPCONT|0.012|(0.121)|0017|(0.126)|035|
|YEDU|0.007|(0015)|0.010|(0016)|10.19|
|RURAL|-0.181|(0120)|-0.221|(0.127)*|0.15|
|FSIZE|0.002|(0.001)*|0.002|(0.001)*|60.21|
|WRKHRSA|-0.001|(0003)|-0002|(0.003)|43.96|
|SELFPR|-1.117|(0.211)***|-1.184|(0.228)***|018|
|INFWPR|-0.971|(0.168)***|-1.021|(0.181)***|0.23|
|AGR|-0.571|(0.435)|-0.768|(0.457)*|005|
|MINES|0.012|(0343)|0.011|(0.347)|010|
|UTIL|-0.288|(0.302)|-0.300|(0.305)|0.15|
|CONST|-0.088|(0312)|-0.074|(0316)|0.19|
|RETAIL|-0.084|(1.073)|-0.005|(I 158)|0.01|
|TRANS|-0.054|(0.322)|-0027|(0.328)|0.08|
|FIN|-0.042|(0374)|-0046|(0.382)|003|
|SOC|-0.136|(0306)|-0.124|(0.310)|034|
|CREDCON|-0.182|(0.094)*|-0.216|(0.099)**|028|
|SHYKIDS|-0.001|(0003)|-0.002|(0003)|15.27|
|SHKIDS|0.007|(0.004)*|0.007|(0.004)*|780|
|SHOLDM|0010|(0.012)|0011|(0012)|1 54|
|SHOLDW|0.002|(0.006)|0001|(0006)|346|
|NYFEM|**0.119**|(0.065)*|0.119|(0.067)*|164|
|RESFAM|-0.482|(0.238)**|-0.518|(0.238)**|004|
|EXKIDS|-0064|(0042)|-0.069|(0.042)*|2.21|
|LEDKIDS|-0.010|(0.011)|-0011|(O011)|554|
|FAMEX|0.099|(0.093)|0.117|(0.096)|056|
|GIVES|-0066|(0.094)|-0.059|(0.097)|0 35|
|GETS|0.024|(0.120)|0.041|(0121)|0.16|
|LRENT|0008|(0.011)|0.000|(0.012)|676|
|COLATT|-0112|(0.107)|-0104|(0.108)|045|
|FINASST|(droppedd|uetocollinearity)||||
|WRKASST|0.180|(0.141)|0211|(0.149)|0.17|
|SLIFEX|0.004|(0.004)|0005|(0.004)|35.26|
|LJUB|-0.019|(0.022)|-0021|(0.022)|1.44|
|LPASIS|0.030|(0.038)|0.033|(0040)|0.21|
|CONT98|0.210|(0.098)**|0.188|(0.102)*|0.53|
|CONDEN|0.813|(0.189)***|0.865|(0.197)***|0 40|
|MPG|-0105|(0181)|-0.459|(0.275)*|0.14|
|MPG*LRENT|||0.054|<br>(0.030)*|087|
|Numberofobs|396|||396||
|Log likelihood|-55.458|||-53.665||
|LRchi2(40)|420100||LRchi2(41)|423690||
|Prob>chi2|0.000|obsP:0606||0.000|obs.P:0606|
|PseudoR2|0791|pred. P.0.799||0.798|pred.P0.799|

49
|South Africa: 1994|1.42|1.40|↓|
|---|---|---|---|
|Mean|1.15|1.29|⇑|
|Std. dev.|0.44|0.30||

_Source_ : Datastream, author’s calculations

# **Table 2** Access to Finance

This table depicts the density of commercial bank branches and depositors with commercial banks in emerging economies in the beginning and in the end of the decade of 2000–10.

|Country|Commercial<br>bank branches<br>per 100,000<br>adults||Depositors with<br>commercial<br>banks per 1,000<br>adults||
|---|---|---|---|---|
||2004|2010|2004/2005/2006|2010|
|Chile|13|18|1410|2134|
|Colombia|||||
|Mexico|11|15|..|1205|
|Peru|4|50|340|436|
|Indonesia|5|8|||
|Malaysia|13|..|1792|..|
|Philippines|8|8|370|488|
|Korea|17|19|4279|4522|
|Taiwan|||||
|Thailand|8|11|984|1120|

41
high real effective exchange rate means that foreign earnings have less purchasing power in the home country, which we would expect to lead to less migration. For example, migration for work should be more attractive to New Zealanders who were at the key age for deciding on an initial migration in 2001 when the real exchange rate index was 98 (and when $1US = $2.46NZ) than in 2007 when it was 137 (and $1US = $1.30NZ). The second macro variable we include is the difference in GDP growth per capita in the home country relative to the average GDP per capita growth in the main destination countries during this same age range. We should expect a negative relationship of this variable with migration – the better is growth at home relative to abroad the less likely migration should be.

# **4.4 Results on the Determinants of Ever Migrating**

Table 8 presents the results of estimating a probit model for the determinants of ever migrating as a function of these variables. We first estimate the determinants separately by country, and then pool the three countries together since the signs generally go in the same directions and the sample sizes are relatively small in the single country analysis.<sup>19</sup> First of all, unsurprisingly, the likelihood of ever having migrated increases with age. However, there is no difference in migration rates of male and female top students. Also somewhat surprisingly, there is no difference in the migration rates of top students who were born abroad from those who are born in the home country. Since this group is largely immigrants to New Zealand, this might reflect that parents have moved to New Zealand so that their children can undertake education there, with this effect offsetting the lower costs of migration for immigrants.

The strongest predictors of ever migrating are from variables that might be broadly interpreted as measuring preferences. We find a strong positive association between studying foreign languages or three sciences in secondary school and subsequent migration. Between 24 percent and 39 percent of top students in each country took three science subjects, and this is associated with a 21 percentage point higher likelihood of migrating. Foreign languages other than English are rare in PNG and Tonga, with less

> 19 Note that in particular the sample size becomes small for Tongan top students, due to significant item non-response on the online survey and to some variables not being included in the abridged phone surveys.

- 29 -
representative sample of undocumented migrants in Colombia’s major cities as of 2020.<sup>10</sup> This survey encompasses information on the socioeconomic status, health, well-being, access to services, and labor market outcomes of adult undocumented migrants. Second, we compare our sample with the Administrative Venezuelan Migrant Registry (RAMV), a nationwide census of undocumented Venezuelan migrants conducted by the Colombian government in 2018.<sup>11</sup> This census surveyed Venezuelan households regarding their socioeconomic conditions and the labor market characteristics of the household head.

We the household characteristics and the labor market outcomes of the house- compare hold heads in our sample with those in VenRePS and RAMV surveys in Table A.3.<sup>12</sup> We observe that households in the VenRePS-Kids survey are smaller on average and have a greater number of children living in the household. The latter is anticipated since one of the eligibility criteria to participate in our survey is the presence of at least one child in the household. Furthermore, the household heads in our sample are disproportionately female and more likely to be married, aligning with the family structure targeted in our sampling frame. Regarding labor outcomes, household heads in our sample are more likely to be employed and engaged in the informal sector compared to those surveyed in VenRePS and the RAMV census.

Despite notable disparities in the characteristics of our sample compared to the VenRePS and RAMV samples, understanding the interaction of these attributes within a framework of self-selection among migrants into Medellin is challenging. Moreover, it is crucial to note two primary distinctions between our survey and the VenRePS and RAMV surveys. Firstly, the inherent differences in the sampling frames of each survey stem from their distinct measurement objectives. Second, both surveys were conducted at different times compared to our survey. The RAMV survey was undertaken in 2018 in response

> 10See Ib ́a ̃nez et al. (2022) for specific survey and sampling details.

> 11Refer to Ib ́a ̃nez et al. (2022) for further details.

> 12Since the VenRePS and RAMV surveys lack information regarding children and adolescents within households, our analysis concentrates only on the household and household head characteristics that are available in all three surveys.

20
40

|Ancillary analyses|18|Results of any other analyses performed,<br>including subgroup analyses and adjusted<br>analyses, distinguishing pre-specified from<br>exploratory|Results; Supp<br>Info|
|---|---|---|---|
|Harms|19|All important harms or unintended effects in<br>each group(for specific guidance see CONSORT for harms)|NA|
|**Discussion**||||
|Limitations|20|Trial limitations, addressing sources of potential<br>bias, imprecision, and, if relevant, multiplicity of<br>analyses|Discussion, 2<sup>nd</sup><br>to last<br>paragraph|
|Generalizability|21|Generalizability (external validity, applicability) of<br>the trial findings|Discussion|
|Interpretation|22|Interpretation consistent with results, balancing<br>benefits and harms, and considering other<br>relevant evidence|Discussion|
|**Other information**||||
|Registration|23|Registration number and name of trial registry|Studydesign|
|Protocol|24|Where the full trial protocol can be accessed, if<br>available|Study design|
|Funding|25|Sources of funding and other support (such as<br>supplyof drugs), role of funders|Abstract|

*We strongly recommend reading this statement in conjunction with the CONSORT 2010 Explanation and Elaboration for important clarifications on all the items. If relevant, we also recommend reading CONSORT extensions for cluster randomized trials, non-inferiority and equivalence trials, non-pharmacological treatments, herbal interventions, and pragmatic trials. Additional extensions are forthcoming: for those and for up-to-date references relevant to this checklist, see www.consort-statement.org.
or not the individual is unemployed (for migrants, the dummy represents the status before migration).

In order to proxy for the origin characteristics,<sup>31</sup> a set of economic variables aggregated at the province (and urban/rural) level is used. Since there are 64 provinces and each province consists of urban and rural areas, there are 128 sending areas in my regressions. Thus, average per capita monthly household expenditures and average per capita land areas owned by households<sup>32</sup> are computed by province (and urban/rural) from the VHLSS 2004 in order to capture the living standards and the opportunities for agricultural, forestry, and fishery activities respectively. To proxy for the chance of obtaining a job in the state sector, the proportion of state workers in total employment at each sending area is calculated from the VHLSS 2004. In terms of the proportion of workers employed by foreign firms, the number of workers in the latter sector for each location is taken from the _Enterprise Survey_ data (GSO)<sup>33</sup> and divided by the total employment.

In order to study the determinants of the migration decision for the migrants as a whole, I first run a logit model relating a dichotomous outcome of migration (migrant vs. non-migrant) to a set of personal and origin characteristics.<sup>34</sup> Table 3 demonstrates the results of logit regressions. All coefficients are reported in terms of the impacts of the variables on the odds ratios, i.e _._ , the ratios of the odds of migration occurring relative to staying. Thus, an estimated coefficient more (less) than one indicates that the independent variable is associated with a higher (lower) probability of

> 31 For the migrant sub-sample that is drawn from the VMS, information on economic conditions (such as income levels or the type of work) prior to the migration decision is not available beyond whether or not he or she was employed.

> 32 The per capita land area owned by the household members consists of “farming, forestry land and water surface area for aquaculture, residence land and garden, pond next to housing land” (the VHLSS 2004).

> 33 There are two reasons why the _Enterprise Survey_ data are more suitable to compute the number of foreign jobs than the data from the VHLSS 2004. First, the VHLSS is likely to under-represent the temporary migrants working in the foreign sector since the sampling of the latter data is based on the households’ permanent registration status. Second, the number of workers who reported working in the foreign sector (278 observations) in the VHLSS 2004 is too small to reflect the varying employment opportunities at the province and urban/rural level.

> 34 The logit model can be viewed as a special case of the multinomial logit model when J=2.
# **V.1 Survey design and administration**

Our survey design focused on three broad areas: Corporate financing and investment, ownership structure and corporate governance, and law, institutions and business environment. Based on a review of survey-based papers in the law and finance area<sup>31</sup> , we developed the survey questionnaire with special attention to the important issues in the semi-formal environment in which Indian SMEs operate, while trying to avoid biases induced by the questionnaire and maximizing the response rate. The final version of the survey included 36 questions (most with subparts) in four sections. The survey instrument and tabulated survey results (including the response rate for each question) are available at <u>http://www.prism.gatech.edu/~rc166/India-survey.zip/.</u> Given that the target of our survey are mostly small and private firms that are typically reluctant to reveal in writing their key financial and business information, the response rate is likely to be extremely low if we followed the mailed questionnaire method to administer the surveys. Further, the nature of our questions dealing with sensitive business information required us to ensure that the responses came from the owners or top executives of the surveyed units. Consequently, we deployed graduate students as field investigators in Hyderabad and New Delhi under the supervision of researchers from the Center of Analytical Finance at the Indian School of Business, Hyderabad, to administer the questionnaire to each of the respondents in face-to-face interviews. Our final sample consists of 136 SME units in and around New Delhi in North India and 76 SME units in and around the South Indian city of Hyderabad.<sup>32</sup> The sample spans several industries including engineering, chemicals, packaging and software. The firms range in age from start-ups (less than one year old) to

> 31 These papers included DLLS (2003), McMillan and Woodruff (1999a, b), Johnson et al. (2002) and AQQ (2005). 32 The firms were selected from several industrial parks in the New Delhi and Hyderabad areas that provided industrially diversified clusters of firms.  The clusters include the Mayapuri Industrial Area, Naraina Industrial Area, WHS Kirtinagar cluster in Delhi and Patanchera and Jeedimetla Industrial Development Areas (IDAs), the Katedan Industrial Estate and the Bharat Heavy Electricals Ltd. (BHEL) Ancillary Industrial Estate at Ramachandrapuram in Hyderabad. Interviews were conducted with the owners or top level executives of the firms in the sample.  On average an interview took about 45 minutes to complete.  However, given the diversity of the business practices among the surveyed firms, a number of questions in the survey did not generate 100% response.

29
# **<<B>>** Descriptive Statistics

Analysis of the 2010 HFA and other data sets involves simple descriptive statistics (percentages for categorical outcomes). Where data permit, a cross-sectional comparison is made between voucher and nonvoucher sites. Significance tests of proportions are performed to determine whether individuals from voucher and nonvoucher sites are significantly different with respect to the outcomes of interest.

# **<<A>>** Results

The following section describes aspects of program performance and study outcomes including the uptake of different voucher services among the targeted beneficiaries, reproductive health knowledge among the study respondents, voucher-subsidized health care use among the study respondents; costs of service and program management, survey respondents’ out-of-pocket costs; poverty-targeting effectiveness, and differences in quality of care at facilities in the control and intervention study arms.

# **<<B>>** Targeting Beneficiaries

Voucher sales data show that a total of 102,562 HealthyBaby vouchers were sold during the project period (2008–12). Of these, 65,590 (64 percent) were used for SD; 86,894 (85 percent) for one ANC visit; 18,131 (18 percent) for PNC; 8,887 (9 percent) for pregnancy complications; and 4,013 (4 percent) for emergency transport (figure 1). The number of vouchers used for safe deliveries represented 130 percent of the targeted 50,456 deliveries. The annual number of deliveries subsidized by the program increased from 2,837 in 2009 to 28,193 in 2010 and to 35,442 in 2011, reflecting a growing demand for the vouchers. Of the total 65,590 deliveries, 9,044 were by Caesarean section (13.8 percent), which falls within the World Health Organization–recommended rate of between 5 and 15 percent. The impact of the project was exemplified in Kasese district, where seven SPs out of a total of 36 health facilities contributed more than 50 percent of all estimated deliveries in the district.

The program started in the final quarter of 2008, but it is worth noting that there is always a lag between voucher sales and redemption, affecting the rate of increase in the number of subsidized deliveries. Another reason for the gradualness of the increase was the time required to accredit and contract facilities, identify voucher distributors, and conduct other preparatory activities.

19
|Transmission Grid StrengtheningProject|Joseph Melitauri|
|---|---|
|Ethiopia Statistics for Results Project|Tom Bundervoet|
|Ethiopia Statistics for Results Project|Tom Bundervoet|
|Ethiopia Statistics for Results Project|Tom Bundervoet|
|Ethiopia Statistics for Results Project|Tom Bundervoet|
|Ethiopia Statistics for Results Project|Tom Bundervoet|
|Ethiopia Statistics for Results Project|Tom Bundervoet|
|Ethiopia Statistics for Results Project|Tom Bundervoet|
|Ethiopia Statistics for Results Project|Tom Bundervoet|
|Ethiopia Statistics for Results Project|Tom Bundervoet|
|XinjiangTechnical and Vocational Education and TrainingProject|LipingXiao|
|XinjiangTechnical and Vocational Education and TrainingProject|LipingXiao|
|XinjiangTechnical and Vocational Education and TrainingProject|LipingXiao|
|XinjiangTechnical and Vocational Education and TrainingProject|LipingXiao|
|XinjiangTechnical and Vocational Education and TrainingProject|LipingXiao|
|XinjiangTechnical and Vocational Education and TrainingProject|LipingXiao|
|XinjiangTechnical and Vocational Education and TrainingProject|LipingXiao|
|XinjiangTechnical and Vocational Education and TrainingProject|LipingXiao|
|XinjiangTechnical and Vocational Education and TrainingProject|LipingXiao|
|XinjiangTechnical and Vocational Education and TrainingProject|LipingXiao|
|XinjiangTechnical and Vocational Education and TrainingProject|LipingXiao|
|Zhuzhou Brownfield Remediation Project|Frank Van Woerden|
|Zhuzhou Brownfield Remediation Project|Frank Van Woerden|
|Zhuzhou Brownfield Remediation Project|Frank Van Woerden|
|Zhuzhou Brownfield Remediation Project|Frank Van Woerden|
|Zhuzhou Brownfield Remediation Project|Frank Van Woerden|
|Zhuzhou Brownfield Remediation Project|Frank Van Woerden|
|Zhuzhou Brownfield Remediation Project|Frank Van Woerden|
|Zhuzhou Brownfield Remediation Project|Frank Van Woerden|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Kosovo Health Project|Lorena Kostallari|
|Mongolia Export Development Project|Ulle Lohmus|
|Mongolia Export Development Project|Ulle Lohmus|
|Mongolia Export Development Project|Ulle Lohmus|
`o` The difference is significant at p<.00.

The average share of earnings returned in _between group_ interactions was:

- S-receivers returned 55 percent of earnings to P-initiators

- P-receivers returned 40 percent of earnings to S-initiators

- Difference is significant at p<.02.

Thus P-initiators were equally trusting of participants from their own group and from the state university. There was no “greater solidarity with” or “more trust in” members of their own group. Nor were there effects of privilege. Indeed P- initiators gave more to S-receivers than to their own group P-receivers. P-receivers returned a little more to their own group initiators than to S-initiators. Different group size therefore did not influence behavior.

# _7.2 Earnings and the distribution of gains_

Table 3 shows earnings and the distribution of gains. The total surplus available for distribution is greater for P-initiators – because P-initiators transferred more and in turn received more back. In table 3 the surplus includes the amount transferred by the initiator. Both initiators and receivers earned, on average, positive returns above their initial 10-euro show-up fee.

`o` Average profit for initiators = 4.3 euros `o` Average profit for receivers = 8.3 euros.

P-initiators on average earned substantially more.

Table 3: Earnings and the distribution of gains

|||Total surplus|Distributio|n of surplus|
|---|---|---|---|---|
|||Private university<br>|Initiator<br><br>|Receiver<br>|
|P-initiator|P-receiver|26.4|63%|37%|
|P-initiator|S-receiver|27.1|58%|42%|
|||State university<br>|||
|S-initiator|S-receiver|19.5|59%|41%|
|S-initiator|P-receiver|20.2|61%|39%|

26
In Figure 3.3 we provide a pictorial view of the war victimization based on household responses. We plot the average responses at the department level; darker shades imply a higher average rate of victimization experience for the inhabitants in a department. It is evident that the civil war had an adverse effect on the livelihood of the entire population in Côte d‟Ivoire; however the impact was more prevalent in the Middle and the Northwest of the country. Overall, between 30 to 50 percent of the respondents experienced declines in their income. The incidence of war victimization was more prominent in the departments located near the UN-peace keeping line and to the West where the civil war was more intense. Nearly 30 percent of the respondents had to hide during the war in the Northwestern departments. The conflict in the mid-West of the country is also marked by high levels of internal displacement. The adverse effect of the war on jobs and land is prevalent throughout the country. However, the people in the mid-West reported to have experienced loss of livestock and non-land assets.

Finally, we turn to the education system in Côte d‟Ivoire. It mostly follows the centralized French education system, where the government plays a key role in curriculum development, coordination and allocation of resources and the organization of national examinations through the ministries of Education, Vocational Education and Higher Education. The _Certificat d’etude primaries elementaires_ (CEPE) is awarded after completing six years of primary education, which is followed by seven years of secondary schooling. In the final year of secondary school students earn a baccalaureate degree. Universities, technical and vocational trainings are part of the higher education system in Côte d‟Ivoire (Sany, 2010). As is evident from the ENV-2008 data, in the sub-population consisting of individuals aged 12 and above, about 35 percent earned a CEPE whereas only 10 percent completed the baccalaureate degree. However, almost 40 percent from the same group of people did not complete the CEPE. The average years of education stands a little above 7 years, which is one additional year of education after six years of primary education (CEPE). Based on this, we infer that the age-cohort of primary school goers are likely to be one of the potential victims of war. In this study, we use years of education as the main outcome variable to evaluate the causal effects of war on education in Côte d‟Ivoire.

# **IV. Empirical Outcomes**

10