rafmacalaba/datause-encoder-data huggingface dataset:
- 25 -

Table 5. Dollarization and Solvency Indicators _Panel A_

# _Dependent variable: Natural Lgarithm ofZ-index_

|Variable|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|
|---|---|---|---|---|---|---|---|---|
|Constant|**1.26|7.8|**1.24|6.7|**1.38|6.5|**1.32|5.8|
|Averagedoll|**-0.01|-2.0|**-0.01|-2.1|**-0.01|-2.0|*-0.02|1.9|
|INFV|||-0.03|-0.2|||-0.01|-0.8|
|RERV|||--0.12|-0.4|||0.13|0.4|
|corr(INF,RER)|||0.16|1.0|||0.14|0.9|
|R-squared/NOBS|0.054|58|0.111|49|0.052|56|0.101|66|
|AdjustedR-squared|0.037||0.030||0.035||0.002||
|Method|OLS||OLS||2SLS||2SLS||

# _Panel B_

|_Dependent variabl_|_e:Natural_|_Loga_|_rithmof _|_NPL_|_ratio_||||
|---|---|---|---|---|---|---|---|---|
|Variable|Coeff.t|-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|
|Constant|**1.62|7.8|**1.39|6.7|**1.13|3.8|**1.08|3.5|
|Averagedoll|**0.02|3.4|**0.02|3.7|**0.03|3.9|**0.03|3.1|
|INFV|||**-0.02|-3.2|||**-0.14|-3.1|
|RERV|||**0.35|3.5|||**0.23|2.4|
|corr(INF,RER)|||-14.0|-0.7|||-6.88|-0.4|
|R-squared/NOBS|0.143|63|0.235|54|0.281|62|0.286|53|
|AdjustedR-squared|0.129||0.173||0.269||0.226||
|Method|OLS||OLS||2SLS||2SLS||

Notes: Average doll is the 1990-2001 average foreign deposit to total deposit ratio. INFV is inflation volatility, RERV is real exchange rate volatility, and corr(INFV,RER) is the correlation of inflation and the real exchange rate, computed on quarterly data for the 1995-2001 period. The coefficients of IFV, RERV and corr(INF,RER) are multiplied by 100. Instruments in the 2SLS regressions are: a constant, transition, restrict, Voice, Govteff, Polstab, Regqual, and Corrupt. ** and * indicate significance at the 5% and 10% level respectively.

We then run similar regressions for liquidity risk, based on the correlation between deposit growth volatility (measured as the standard deviation of total deposit growth, computed with yearly data over the period 1990-2001) and deposit dollarization. Deposit dollarization is also found to increase deposit volatility (Table 6).

Table 6. Dollarization and Deposit Volatility _Dependent variable: Natural logarithm of deposit growth volatility_

|Variable|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|
|---|---|---|---|---|---|---|---|---|
|Constant|**2.09|13.4|**1.96|11.9|**1.51|6.4|**1.67|7.3|
|Averagedoll|**0.03|5.1|**0.03|4.7|**0.05|6.4|**0.04|4.8|
|LNFV|||**0.04|2.0|||**0.13|2.3|
|RERV|||*0.04|1.7|||**0.14|6.5|
|corr(INF,RER)|||-11.72|-0.9|||-8.99|-0.6|
|R-squared/NOBS|0.288|96|0.381|77|0.328|84|0.495|66|
|Adjusted R-squared|0.281||0.355||0.320||0.461||
|Method|OLS||OLS||2SLS||2SLS||

Notes: Average doll is the 1990-2001 average foreign deposit to total deposit ratio. INFV is inflation volatility, RERV is real exchange rate volatility, and corr(INFV,RER) is the correlation of inflation and the real exchange rate, computed on quarterly data for the 1995-2001 period. The coefficients of IFV, RERV and corr(INF,RER) are multiplied by 100. Instruments in the 2SLS regressions are: a constant, transition, restrict, Voice, Govteff, Polstab, Regqual, and Corrupt. ** and * indicate significance at the 5% and 10% levels, respectively.
# Table A3: Descriptive statistics: WBES Sample

|**WBES**||||||
|---|---|---|---|---|---|
|Variables|Observation|Mean|Std.Dev|Min|Max|
|Submarine has arrived|15,057|.9443448|.2292622|0|1|
|Proximity to road (KM)|15,028|-2.308359|4.602616|-55.85029|-.0005369|
|Submarine (2 km) * proximity to road (KM)<br>|15,028|-.2960252|1.253145|-15.97813|0|
|Proximity to Hypothetical road (KM)|15,028|-30.82412|30.14145|-146.8549|-.3167718|
|Submarine (2 km) * proximity to hypothetical road (KM)|15,028|-11.3393|23.00974|-104.9434|0|
|lightening|15,028|.4507335|.3021412|.0098068|1.687402|
|Terrain elevation|15,028|417.6429|556.265|1|2098|
|slope|15,028|0.598076|0.743425|0|4.41208|
|Internet connected (0.5 m)|15,028|.1458611|.3529786|0|1|
|Internet connected (1 km)|15,057|.257289|.4371545|0|1|
|Internet connected (1.5 km)<br>|15,028|.318938|.4660805|0|1|
|Internet connected (2 km)|15,028|.783737|.4117093|0|1|
|Submarine Internet (0.5 km)<br>|4,433|0.10413|0.30545|0|1|
|Submarine Internet (1 km)|15,057|.1778014|.4371545|0|1|
|Submarine Internet (1.5 km)<br>|15,028|.257289|.3823582|0|1|
|Submarine Internet (2 km)|15,057|.3645481|.4813192|0|1|
|Establishment’s age|14,531|15.00399|12.77004|0|168|
|Telecommunication is an obstacle to operation|13,929|0.2685046|0.4431974|0|1|
|Losses as percentage of annual sales|12,139|9.22746|13.49414|0|160|
|Percentage owned by the government|15,032|0.8455628|6.968249|0|100|
|Secured a contract with government in last fscal year|7,181|0.2041498|0.4031071|0|1|
|Hours of operation per week last year|15,057|35.48174|26.1427|10|84|
|Number of permanent full time employees 3 fscal years ago|14,411|32.44917|178.1041|0|89|
|Number of full time skilled employees at the end of last fscal year|13,653|15.731|26.506766|0|28|
|Number of full time male employees at the end of last fscal year|6,730|16.171|29.117|0|67|
|Number of full time female employees at the end of last fscal year|6,730|5.8948|20.82994|0|43|
|Number of full time employees last year|8,080|16.435712|40.70892|0|99|
|Number of full time unskilled employee at the end of last fscal years ago|6,613|10.548468|36.523924|0|67|

54
# **D.4 Additional Treatment**

Table D.7 shows the public certification effects of our main labor market outcomes without conditioning on the prespecified covariates. The results are very similar with or without the covariates.

Table D.8 shows public and private certification effects at two points in time: in the text message survey conducted 2-3 days after treatment and the endline phone survey conducted 3-4 months after treatment. This table shows four patterns, which expand on the discussion in footnote 19 of the paper. First, both treatments make candidates more likely to report that their assessment result matches their actual assessment result immediately after treatment. Second, both treatment effects decline over the following 3-4 months, although the different survey methods mean the time comparison should be interpreted cautiously. Third, the public treatment effect on self-beliefs is significantly larger than the private effect after 3-4 months but not after 2-3 days. This suggests that the larger public treatment effect at 3-4 months does not occur because the information it conveys is immediately more credible or easier to understand than the private treatment. Instead, it may be larger because the information is more memorable or the public treatment generates other effects, such as more job interviews or employment that provide more opportunities to learn about skills. Fourth, neither treatment affects self-esteem at either point in time.

Table D.9 shows how treatment effects on employment vary by single index summary measures of candidates’ skills (Panel A) and baseline candidate characteristics that might provide alternative measures of candidates’ skills (Panel B). We discuss these treatment effects in Sections 5.2 and 5.3 of the paper.

Table D.10 reports public and private certification effects on all prespecified workseeker-level job search and labor market outcomes. These are organized into families of conceptually similar outcomes, which we use for multiple testing adjustments. First, we report _q_ -values that control the false discovery rate across outcomes within each family (Benjamini et al., 2006). None of the _q_ -values in this table is substantively different to the corresponding _p_ -values reported in the main paper. Second, we estimate treatment effects on inverse covariance-weighted averages of the outcomes within each family (Anderson, 2008). This provides a single summary test of the information contained across all outcomes in the same family. None of the treatment effects on these averages provides substantively different information to the treatment effects on individual outcomes.

We omit some prespecified outcomes related to beliefs from this paper and analyze them in separate work. The search targeting measure discussed in Section 4 is not prespecified. We did not prespecify an analysis plan for the smaller extension experiments discussed in Section 5.

54
# **III. Implementation**

The primary sampling strategy was very standard. A nominal sample of 2,800 households was to be selected in two stages, with 200 Census sectors -hereafter referred to primary sampling units, or PSUs- in the first stage and 14 households per PSU in the second stage. However, the design departed from the standard in the way it addressed stratification in both phases.

## III.1 First sampling stage

The 200 primary sampling units (PSUs) were stratified into three categories depending on the Venezuelan migrant density , defined as the ratio between the number of Venezuelan cellphones in the PSU (as per Telefonica's prediction) and the total population of the PSU (as per INEC's population census), and allocated into these strata as follows:

- _100 High-density PSUs_ : where the density was more than 0.15.;

- _80 Medium-density PSUs_ : where the density was between 0.05 and 0.15.; and

- _20 Low-density PSUs_ : where the density was less than 0.05.

Within each stratum, the sample was selected with probability proportional to the number of households reported by the 2010 Census.

## III.2 Second sampling stage

All households in each of the selected sectors were listed and stratified into three categories considering nationality and demographic composition. In each stratum, households were selected as follow:

- _Seven_ non-Venezuelan households (i.e. Ecuadoran, Peruvian, Colombian or any other nationality except Venezuelan).

- Seven Venezuelan households without children aged less than 5 years.<sup>13</sup>

- All Venezuelan households with children aged less than 5 years.

Within each stratum, the sample was selected by systematic equal-probability sampling.

The second stage posed some fieldwork management difficulties but is conceptually straightforward. A full listing operation of all households was conducted in the 200 PSUs selected in the first stage. The listing operation consisted of a complete enumeration of all physical structures in the area, with each physical structure being classified as a residential dwelling, commercial building, hospital, church, collective residence, in construction, etc. The listing operation collected information about the number of households occupying each residential dwelling and the number of Venezuelan members living in each household. For each Venezuelan household, the existence of children under five years of age was also gathered. Notice that given the high mobility of migrants, the listing operation was done twice: first a month before fieldwork and then immediately before fieldwork.

To ensure the quality and completeness of the listing operation, enumerators relied on the updated cartography from NSO and high-resolution paper maps identifying all buildings within each segment. The

> 13 In the PSUs with fewer than seven childless Venezuelan households, all of them were selected. Since this was the case in many PSUs, the sample size was reduced from 2,800 to 1,871 households.

7
110

# schooling if the more educated'work fewer hours.

> 22 Becker (1964) used the average earnings (of those with earnings) from tabulations of the 1950 Census and apparently then deflated these benefit streams by participation rates. This approach ignored the sample selection of those who had positive earnings, **as** well as **the** problem of mixing productivity per hour and labor supply responses associated with education. Mincer (1974) examined the 1960 Census public use sample, and fit the individual's logarithm of annual earnings to education and other variables. He could only include individuals with positive earnings and no correction for the potential bias is discussed from his analysis of a selected sample. However, Hincer's and Becker's analysis is focused on males and not females.

A shortcoming of the sample selection correction methodology, as it is commonly applied, is that it can lead one to conclude incorrectly that there is no sample selection bias, because the sample selection rule is not vwell" identified. The only basis for identification can be the functional form assumed for the error in the selection rule equation, e.g. normally distribut,ed (Heckman, 1979). More confidence can be placed in the estimates if theory prescribes conditioning variables that enter significantly in the sample selection rule equation, but ars excluded a priori from the market wage equation. Thus, an insignificant sample selection term (i.e. Heckman's lambda) may become statistically significant when the exclusionary restrictions of the model are selected with more care. _As_ a consequence, the sample selection correction procedure may then change the estimated returns to schooling. Agreement on acceptable criteria for identification would accelerate advancement of empirical research on this topic.
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– indirectly varied somewhat in size, from about 5,500 to 9,000 according to the 2001 Census. Assuming an average household size of 5.5, and allowing for population growth in the interim, the typical catchment area contains about 1,400 households. Deflate this to 1,200 to allow for the fact that as many as one half enjoy only an indirect connection. To arrive at an estimate of the benefit per household, I employ a related estimate from Khandker _et al_ .’s (2009) study of rural roads in Bangladesh, namely, that the ‘commercial’ benefit is 11 percent of per capita household consumption. In 2001, the average household income in the 30-village sample referred to above was Rs 16,600 (van Dillen, 2008). Since average savings rates in these communities are surely low, set average household consumption at Rs 15,000, thereby yielding a corresponding commercial benefit of Rs 1,650 a year, or about Rs 2,250 in 2008 prices. Scaling up by the effective number of households in the catchment, we obtain an aggregate annual benefit in the commercial sphere alone of Rs 2.70 million. At _ρ_ = 5.5 percent, the present value thereof is Rs 38.70 million, which handsomely exceeds the present value of the investment costs in the range of Rs 22.9 – 34.4 million. At _ρ_ = 0 _._ 10 percent, the present value of the ‘commercial’ benefits is Rs 25.30 million, which lies in the middle of the range of _PV_<sup>_∗_</sup> ( _I_ ; _ρ_ = 0 _._ 10), namely, Rs 20.5 – 30.7 million. With the benefits in the spheres of education and health still to come into the reckoning, there is every prospect that all these roads will indeed pass at the test rate of 10 percent, even if the flow of benefits does not grow.

By way of comparison with the first alternative, Bell and van Dillen (2012) estimate that the unit transport costs for goods are reduced by about 5 percent of the net price received for commodities marketed. Khandker _et al_ . (2009) obtain a similar estimate for rural roads in Bangladesh, once an allowance is made for greater volumes marketed. The latter also estimate that the farm-gate price of fertilizers declined by 5 percent. This similarity supports the ‘importation’ of the estimated (proportional) effect on family consumption in Bangladesh to arrive at the same in Orissa.

# **4.2 Education**

The provision of an all-weather road can affect the formation of human capital through formal education in a variety of ways. If there is no school in the village itself, the savings in the time children spend traveling to and fro can be devoted to other pursuits, not least homework, and attendance may be better in adverse weather. The same holds for their teachers wherever the school be located, unless the teachers live very close by. Such a road may also reduce morbidity among children by improving access to health 14
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17
# **TABLE 17** (continued)

||1989|1990|1991|1992|1993|1994|1995|1996|1997|1998|1999|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Asia**|**6.03**|**6.29**|**7.17**|**9.33**|**9.82**|**12.16**|**10.73**|**10.13**|**10.50**|**10A7**|N.A.|
|Korea|0.71|0.64|0.87|0.99|1.01|0.93|1.34|1.32|1.67|1.56|2.09|
|Taiwan|0.56|0.75|0.86|0.87|1.01|1.30|0.99|1.00|1.04|1.22|1.10|
|Hong Kong|0.53|0.55|0.62|0.65|0.09|0.32|0.22|0.14|0.17|0.17|0.18|
|<br>Israel|0.03|0.04|0.05|0.07|0.07|0.11|0.06|0.09|0.10|0.11|0.12|
|Japan|3.77|3.53|3.19|4.89|5.15|6.02|5.45|4.62|3.95|3.62|3.58|
|<br>Singapore|0.14|0.11|0.17|0.17|0.24|0.27|0.40|0.43|0.39|0.39|0.38|
|China|0.00|0.07|0.28|0.68|0.54|0.54|0.72|0.85|1.14|1.29|1.35|
|Other|0.30|0.59|1.13|1.00|1.70|2.68|1.54|1.69|2.05|2.11|N.A.|
|**Africa**|**0.20**|**0.23**|**0.16**|**0.16**|**0.20**|**0.19**|**0.18**|**0.25**|**0.25**|**0.29**|**N.A.**|
|**Oceania**|**0.34**|**0.62**|**0.30**|**0.42**|**_OA1_**|**0.40**|**0.25**|**0.29**|**0.29**|**0.32**|**N.A.**|
|Australia|0.11|0.16|0.16|0.17|0.17|0.21|0.14|0.14|0.15|0.19|N.A.|
|Other|0.23|0.46|0.14|0.25|0.24|0.19|0.11|0.15|0.14|0.12|N.A.|
|**Rest of the World**|**0.12**|**0.03**|**0.00**|**0.00**|**0.02**|**0.13**|**0.07**|**0.12**|**0.17**|**0.21**|**N.A.**|

SOURCE: State of the Nation Report from 1989-98, Secofi for 1999, both with Banxico data.

*Exports includes transportation and insurance expenses.

**Figures may not add up due to rounding off.

48
this concern, a malaria positive worker was approximately 50% more likely to be selected into the study sample. All workers who were allocated an accelerometer received the device in week 1, and wore the same device until the end of the field period, unless it was lost. Workers could not change or transfer trackers through the study period as each tracker was uniquely identified.

Table 1 presents a comparison of worker health and household characteristics for the sample that received an accelerometer and the sample of workers who did not. Workers’ characteristics including age, BMI and hemoglobin score were well balanced between the tracked and non-tracked samples. In the tracked sample, malaria positive cases were significantly higher as intended given the selection strategy. The second panel shows that human capital indicators including literacy, numeracy, school attendance, level of school completed, and plantation experience are all balanced between the tracked and non-tracked sample. The household characteristics of workers are also balanced in terms of household composition including number of spouses, children, and total household size, as well as a household asset index.<sup>17</sup>

# _Work Disruption and Tracker Loss_

The nature of the production process together with the daily working order of processing machinery partially determine harvest activity on any given workday at the plantation. Since sugarcane has an optimal time to be harvested (in order to maximize sugar content), and needs to be processed on the same workday by the sugar factory on the plantation, past planting schedule and current factory activity affect whether sugarcane cutters work on a given day. During our study period, an unexpected mechanical failure at the processing factory resulted in a temporary stoppage of harvest activity during study weeks 5 and 6. Since all workers who had been allocated an accelerometer were already treated by this time, this does not affect our identification strategy,

> 17 The asset index reflects household expenditures which are not measured but rather predicted using the method suggested by Grosh and Baker (1995) and Ahmed and Bouis (2002). The questionnaire included questions on asset ownership drawn from the Nigerian Living Standard Survey 2009, a nationally representative survey, conducted by the National Bureau of Statistics (NBS), which collects detailed data on household consumption and expenditures. We run the weighted regression ���� = ∑����(�<sup>�</sup> ��� + ��) on the NLSS 2010 data to obtain estimates of ��<sup>�</sup> , the coefficient for each asset, which we then use to predict Expi for our own sample. Where ��� represents a dummy variable indicating whether asset _a_ is present in the household. The regression uses population weights as calculated by the NBS. Since the estimates of the coefficients are relatively sensitive to outliers, we exclude the richest 10% of households in our weighted regression on the NLSS 2010 data.

14