rafmacalaba/datause-encoder-data huggingface dataset:
- (1) negative and significant,

- (2) negative or zero and insignificant,

- (3) positive and insignificant, and

- (4) positive and significant.

Across all 44 estimates (from the 19 studies), there are 12 estimates which are negative and significant, 24 which are negative and insignificant, 6 which are positive and insignificant, and 2 which are positive and significant (Table 5 Panel A). In other words, 82% of estimates are negative, and just 5% of estimates are significant and positive (Table 6 and Figure 2). One of those two positive results is an unconditional cash transfer program in Indonesia: in the first disbursement, the impact was slightly negative and highly significant, whereas in the second disbursement, the impact was slightly positive and mildly significant. The size of the coefficient is almost identical to that for expenditures on prepared food. The other positive result, from Peru’s _Juntos_ program, is from a paper that uses two different methods, matching and instrumental variables, and finds opposite results from the two estimates on alcohol consumption: a moderately significant negative impact from the matching estimate and a weakly significant positive impact from the instrumental variables estimate. Estimates on other outcomes are mostly consistent across the two estimation methods. Thus, in both cases of positive significant results, the impacts are weakly significant and are not consistent across estimates within the same study. Furthermore, the effect sizes are very small: one is less than a penny, whereas the other is 21 cents. Even if those estimates accurately reflect changes in expenditures, the changes are trivial.

If we instead consider only the 17 estimates from 8 randomized-control trials, we find 1 estimate which is negative and significant, 13 that are negative and insignificant, 3 that are positive and insignificant, and zero that are positive and significant (Table 5 Panel C). In other words, 82% are negative, and none are positive and significant (Table 6).

Four studies explore the impacts on alternate measures of temptation goods. Two of them, rather than estimating the impact of transfers on expenditure levels or proportions, estimate the impact of the program on the share of individuals who consume any of the temptation good (i.e., who smoke or who drink). They both examine this impact for adolescents in Mexico, in the context of the Oportunidades transfer program. One paper examines the impact using 2004 data and an instrumental variables strategy. It separates the impact of program participation from the impact of total cumulative transfers, with community awareness of the program as an instrument for household participation in the program, and potential transfers (based on household demographics) as an instrument for actual transfers (Galárraga and Gertler 2009). The _net_ effect is reported for smoking for men (a reduction from 30% of men smoking to 16%) and for alcohol consumption for women (a reduction from 22% of women 8
differences between groups is low – irrespective of the way in which we sub-divide the population into groups and irrespective of survey year. In only one instance, corresponding to the 1974/5 survey year and the population breakdown of 8 groups, does the between-group contribution rise above 30%. Even in this case well over two-thirds of all inequality can be attributed to differences between individuals within their caste groups, as opposed to being attributed to differences in average income between groups. Second, when the population is broken down into fewer groups – in an effort to isolate the trajectories of the three main caste groupings in Palanpur as summarized in section 2, the between group component is generally substantially lower still. It is interesting to note that when, for example, the village population is broken down into Scheduled Castes versus all others, the conventional betweengroup contribution is as low as 0.7% in 1957-8 and only as high as 6.4% in 1974-5.

As was noted in the introduction, the conventional between-group decomposition analysis does not appear to attribute a significant role to caste differences in “accounting” for overall inequality, or to the evolution of inequality over time. The general picture for Palanpur looks rather similar to what was observed for India as a whole in studies of inequality at the national level.

Column 4 in Tables 4-7 presents the partitioning index proposed by ELMO (2008). In many instances this between group “contribution” looks quite similar to the conventional statistic. For example, in 1957-8 conventional between group inequality accounted for 23% of total inequality in the case of 8 village caste groupings. In this year the ELMO partitioning statistic suggests that dividing the population into these 8 groups takes one only some 24% of the way towards neatly partitioning the income distribution into 8 non-overlapping groups of the same size as these castes. There is little additional insight into inequality offered by this statistic in this case.

18
<mark>Step 4: The observations of both individuals (the “synthetic” individual and the individual selected in step 1) are part of the new sample of matched individuals. These samples include, for each individual, the wage in brackets and the synthetic wage.</mark> <mark>Repeat steps 1-4 until it exhausts the original sample of individuals in the LFS. We end up with the sample of matched individuals (those who have similar characteristics in both surveys) and another sample of unmatched individuals (those who have different characteristics). For the matched sample, we assign earnings equal to synthetic earnings if their synthetic earnings fall in the corresponding interval. Otherwise, we assign earnings equal to the midpoint of the bracket. For the unmatched sample, we assign earnings equal to the midpoint of the bracket. In total, 65 percent of LFS respondents were successfully matched with HBS respondents; for the other 35 percent, wages were imputed using the average value of the interval reported in LFS.</mark>

**<mark>The results are robust to the imputation technique.</mark>** <mark>Table 6 reports the simulated labor market indicators obtained using wages imputed from the HBS,</mark><sup>36</sup> <mark>and using a uniform imputation technique. The results suggest that there are no significant differences in the effect on unemployment and wages simulated using different wage imputation techniques.</mark>

**Table 6. Ex-ante evaluation of the impact of minimum wages, sensitivity to wage imputation**

||Nopolicy|Imputati<br>|on HBS<br>|Imputatio<br>|n uniform<br>|
|---|---|---|---|---|---|
|||Short-run|Long-run|Short-run|Long-run|
|Employment rate, %|29.75|29.60|29.53|29.56|29.47|
|||[29.57; 29.63]|[29.50; 29.57]|[29.52; 29.60]|[29.43; 29.51]|
|Unemployment rate, %|30.48|30.84|31.00|30.94|31.15|
|||[30.78; 30.91]|[30.92; 31.07]|[30.85; 31.03]|[31.04; 31.25]|
|Youth unemployment rate, %|52.75|53.49|53.82|53.75|54.18|
|||[53.27; 53.77]|[53.54; 54.13]|[53.53; 54.01]|[53.88; 54.45]|
|Share of youth NEETs, %|27.41|27.64|27.70|27.70|27.80|
|||[27.59; 27.69]|[27.64; 27.77]|[27.65; 27.76]|[27.72; 27.86]|
|Change in real earnings, %||0.22|0.03|0.29|0.03|
|_Note: Kosovo HBS and LFS 2017_|_are used. Unifor_|[0.13;0.30]<br>_m wage imputation i_|[-0.07;0.12]<br>_s described in Section_|[0.18;0.41]<br>_2 and HBS wage im_|[-0.11;0.16]<br>_putation is described_|

_<mark>Note: Kosovo HBS and LFS 2017 are used. Uniform wage imputation is described in Section 2 and HBS wage imputation is described</mark> in Section 6.2. Short- (long-) run labor demand elasticity value is set at -0.5 (-0.8) for skilled labor and -0.7 (-1) for unskilled labor. Only employees with the wage between the old and the proposed minimum wage are assumed to be affected. Aged 15-64. 95 percent confidence intervals are in brackets._

> 36 Baseline short- (long-) run labor demand elasticity is used: -0.5 (-0.8) and -0.7 (-1) for skilled and unskilled labor respectively.

30
There is a pressing need to free up financial resources so that Côte d'Ivoire could enhance funding for the health sector and be more prepared for pandemics. By expanding the tax base, enhancing tax collection and compliance, and enacting additional taxes, such as health taxes on alcohol, tobacco, and other unhealthy goods, the government may generate more money for the health sector while earmarking some of these taxes for the PPR. Furthermore, the government could work to increase the efficiency and effectiveness of health spending by making the most of the available resources. This could entail enhancing public financial management in the health sector, particularly the procurement processes and accountability.

# **4. Discussion**

Using a more conservative estimate of the VSL (low values) and a VSL income elasticity ranging from 1 to 1.4, the overall COVID-19 mortality costs in Côte d'Ivoire since the pandemic began range from US$ 100,440,938.78 to US$ 284,342,876.66. The COVID-19 costs range from US$ 5,421,180.27 to US$ 15,347,068.75 when age-related adjustments, a 3% discount rate, and a VSL income elasticity of 1 to 1.4 are taken into account. In the same vein, COVID-19 mortality costs range from US$ 6,804,644.09 to US$ 19,263,580.17 with a 5% discount rate and a VSL income elasticity of 1 to 1.4. More importantly, starting from 2021, the results of the study suggest a decline of the COVID-19 mortality costs. This decrease could be attributed to the government's health policies aimed at reducing COVID-19. As outlined by the World Bank (World Bank., 2021) when the COVID-19 pandemic started, the policy makers in Côte d'Ivoire took forceful action by closing land, air, and marine borders. Sports events, festivals, and meetings with more than 50 people were postponed, and non-essential enterprises, schools (including pre-schools and universities), were closed. All confirmed and probable COVID-19 cases were quarantined in government-run facilities, social distancing, and mask use were actively promoted. Côte d'Ivoire was among the first countries to receive the first wave of COVAX vaccines on February 26, 2021, and carried out its first mass vaccination campaign in December 2021. As of January 22, 2023, Ivoirians have received a cumulative total of 25.26 million COVID-19 vaccine doses. This accounts for 12% of the total population, who have received at least one booster dose of a COVID14
**Table 1: Descriptive Statistics on Education of Adult Children and Their Children**

**Panel 1A: Average years of schooling**

|**Year**|**Daug**<br>**Daughter**|**hters sampl**<br>**Mother**|**e**<br>**Father**|**Son**|**Sons sampl**<br>**Mother**|**e**<br>**Father**|**children**|**All sample**<br>**Mother**|**Father**|
|---|---|---|---|---|---|---|---|---|---|
|**1988**|5.8|2.4|5.2|6.8|2.3|5.0|6.3|2.4|5.1|
|**2002**|9.0|5.3|7.1|9.0|4.9|6.9|9.0|5.0|7.0|

NOTES

1.  children were born in 1967 or later;

2. The samples are: 2057 (1988, daughters), 1884 (1988, sons), 2264 (2002, daughters), 3616 (2002, daughters).

Panel 1B: Correlation of children's education and parents' average education

||**Daughters sample**|**Sons sample**|
|---|---|---|
|1988|0.37|0.24|
|2002|0.32|0.33|
|NOTES|||

1. Children were born in 1967 or later;

2. Children's education is measured by years of schooling; 3. Parents' average education is the average of mother's and father's years of schooling; 4. The samples are: 2057 (1988, daughters), 1884 (1988, sons), 2264 (2002, daughters), 3616 (2002, daughters).

49
Figure 1: ICT Abundance and Income Per Person <!-- Start of picture text --> A. ICT Value out of Non-ICT Value<br>(B.1) 1995 (B.2) 2011<br>-8 -7 -6 -5 -4 -3 -6 -5 -4 -3 -2<br>PWT: Log Real GDP Per Capita PWT: Log Real GDP Per Capita<br>C. Units of ICT per Unit of Non-ICT<br>(C.1) 1995 (C.2) 2011<br>-8 -7 -6 -5 -4 -3 -6 -5 -4 -3 -2<br>PWT: Log Real GDP Per Capita PWT: Log Real GDP Per Capita<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>-5 -5<br>Value of ICT/Value of NICT (PIM, 1995,%) Value of ICT/Value of NICT (PIM, 2011,%)<br>-10 -10<br>20 20<br>16 16<br>12 12<br>8<br>8<br>4<br>4<br>Units of ICT per Unit of Non-ICT (PIM, 1995,%) Units of ICT per Unit of Non-ICT (PIM, 2011,%)<br>0<br>0<br><!-- End of picture text --> _Notes:_ The figures plot measures of ICT abundance across countries in relation to log output per capita. Panel A illustrates ICT values as a fraciton of NICT capital values; panel B shows units of ICT per unit of Non-ICT (ratio of quantity indexes). Column 1 shows this relationship in 1995 and column 2 in 2011.

regressions of our ICT abundance measure on log real GDP per capita and a complete set of time effects. Panel A of Table 2 shows the point estimates for the full sample of 1994-2011 as well as broken down into three sub periods: 1994-2000, 2001-2005, and 2006-2011. These point estimates convey the patterns observed in Figure 1 and suggest three main insights: (a) there is a significantly positive relationship between income per capita and ICT abundance; (b) this relationship is becoming weaker over time, suggesting that developing countries are catching up in terms of ICT accumulation; and (c) the level of ICT/NICT values is relatively stable throughout 1993-2011. We draw this final observation from the estimated time effects. We normalized income per capita 10
financing other than bank loans; and (iii) the quality of the enforcement of bank capital rules.

# Sub-national **Borrowing in Mexico: The Initial Position**

Sub-national debt in Mexico has so far not been a national macroeconomic problem (as it has in Brazil for example), but it could rapidly become one. In 1997, the recorded stock of state and municipal debt was equivalent to about 2 percent of Mexico's GDP (and about 6 percent of total public debt).<sup>3</sup> By comparison, sub-national debt amounts to 5 percent of GDP in Argentina, almost 20 percent in Canada, and somewhat above that level in Brazil. In addition, the Mexican constitution prohibits states from borrowing in foreign exchange or from foreign creditors.

Behind Mexico's relatively comfortable sub-national debt statistics, however, lie three potential destabilizing factors-moral hazard, limited actual servicing capacity, and contingent liabilities. Prior to the 1990s the federal government, through the governing party's infrastructure, had control over the state governments, and thus ultimately over any state borrowing decisions Over the course of the 1990s, rapid democratization ended much of this federal political control, which allowed states to take advantage of the federal government's concern for both the banking system (which, following its 1995 crisis, had

> 3. Most Mexican states have much less disposable income to service debt than usual accounting would suggest, since much of their current expenditures is "tied" to _defacto_ inflexible commitments (notably salaries). The ratio of debt stock to disposable revenue, the latter defined as own taxes plus untied transfers, ranges from a maximum of 1.8 (in Sonora) to a minimum of 0.02 (in Hidalgo). By that measure, the eight most indebted states are Sonora, Nuevo Le6n, Mdxico, Queretaro, Quintana Roo, Baja California Sur, Jalisco, and Sinaloa, all with a ratio of debt stock to disposable revenue greater than 1 (Hemandez and Oliveira, 2000).

4
Table 4: Explaining ENLACE test scores

|_Outcome =_|(1)<br>Math<br>(z-score)|(2)<br>Spanish<br>(z-score)|(3)<br>3rd subject<br>(z-score)|
|---|---|---|---|
|General school|0.523***|0.666***|0.534***|
||(0.121)|(0.143)|(0.100)|
|Male|-0.117*|-0.220***|-0.180***|
||(0.063)|(0.053)|(0.049)|
|Z-score height for age|0.050**|0.049*|0.044**|
||(0.025)|(0.025)|(0.020)|
|Age|-0.059|-0.080*|-0.037|
||(0.048)|(0.046)|(0.042)|
|ln(Monthly per capita total||||
|expenditure)|-0.036|0.008|-0.069|
||(0.065)|(0.069)|(0.061)|
|Age of household head|0.001|0.002|-0.000|
||(0.003)|(0.002)|(0.002)|
|Years of education of household head|0.023**|0.020|0.020**|
||(0.011)|(0.012)|(0.009)|
|Household head is married|-0.018|0.011|0.002|
||(0.123)|(0.140)|(0.095)|
|Running water in the home|0.156**|0.201**|0.168**|
||(0.075)|(0.089)|(0.072)|
|Guerrero|-0.334|-0.657**|-0.445**|
||(0.225)|(0.271)|(0.196)|
|Oaxaca|0.500**|0.365|0.308|
||(0.194)|(0.221)|(0.275)|
|Tabasco|-0.548***|-0.490***|-0.635***|
||(0.135)|(0.146)|(0.126)|
|Veracruz|-0.603***|-0.677***|-0.747***|
||(0.100)|(0.131)|(0.091)|
|Year and grade FE|YES|YES|YES|
|Observations|1,576|1,576|1,573|
|R-squared|0.109|0.150|0.128|

*** p<0.01, ** p<0.05, * p<0.1.

(1) Standard errors are clustered at the village level. (2) The ommited state is Chiapas. (3) Sample includes only individuals from the control group.

33
# **Table A9. Mean Characteristics of the Study Participants vs. General Population of Salvadorians**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|Variable|Study<br>Sample|EHPM<br>Usulután<br>and San<br>Salvador|_P_-val.<br>Diff.|Study<br>Sample<br>Usulután|EHPM<br>Usulután|_P_-val.<br>Diff.|Study<br>Sample<br>San<br>Salvador|EHPM San<br>Salvador|_P_-val.<br>Diff.|
|Age (years)|35.487|35.081|(0.836)|35.658|35.748|(0.976)|35.325|34.457|(0.733)|
|Female (%)|0.549|0.541|(0.916)|0.556|0.536|(0.850)|0.542|0.547|(0.960)|
|High school or higher education|0.285|0.281|(0.951)|0.249|0.221|(0.757)|0.319|0.337|(0.843)|
|Read or write|0.871|0.878|(0.886)|0.846|0.824|(0.791)|0.895|0.928|(0.501)|
|Never married|0.335|0.295|(0.536)|0.330|0.258|(0.451)|0.340|0.329|(0.902)|
|Access to mobile phone|0.983|0.986|(0.856)|0.981|0.977|(0.903)|0.984|0.994|(0.518)|
|Access to internet(Wi-Fi)|0.185|0.156|(0.581)|0.168|0.125|(0.556)|0.202|0.186|(0.828)|
|Observations|2,480|2,212||1,210|804||1,270|1,408||

_Notes:_ This table compares the average characteristics of the individuals in our sample and individuals in El Salvador. The 2022 Household and Multipurpose Survey (EHPM) provided the data for individuals in El Salvador that we compared to data that we measured similarly in our survey. Columns (1) to (3) compare the two full samples and Columns (4) to (9) compare the samples by the Departments of San Salvador and Usulután. We restricted the EHPM sample to working-age household members (aged 15 to 64 years) in rural areas.

46
and Reis (2003) and Tomiura’s (2007) definition of TFP for cross-sectional data.<sup>7</sup> ATFP is defined as Intuitively, this productivity measure starts with average labor productivity, Y/L, and adjusts for capital intensity K/L. Parameter _s_ measures the importance of capital in the production function and can vary between zero and one. In the empirical implementation I follow Hall and Jones (1999) in setting the cost share of capital _s_ = 1/3. This cost share of capital comes from the standard neoclassical approach, which is broadly consistent with national income accounts data for developed countries.

The drawback to ATFP is that it reflects both “true” technical efficiency as well as scale economies.<sup>8</sup> ATFP will be a good measure of technical efficiency if there are constant returns to scale and _s_ = 1/3 is a reasonable measure of the cost share of capital. Even though the assumption of constant returns to scale seems to be a valid one, setting the cost share of capital of developing countries to be the same as for developed countries is worrisome.<sup>9</sup> Therefore, I will also use a lower cost share of capital in order to test the robustness of the results.

The second group of variables of interest from the Enterprise Survey includes firm characteristics that might influence a firm’s output and productivity. In this category,

> 7 I also estimate total factor productivity (TFP) using a constant returns to scale Cobb–Douglas specification of total output on capital, labor, and materials separately for each sector. I then calculate the firm-level residual using these sector coefficients. The residual is my firm-level measure of TFP. I use this measure of TFP on every regression that follows. Those results have been omitted as those measures of TFP are not robust. The results are not considerably different that the ones presented here. Results are available upon request.

> 8 See Head and Reis (2003) for a more detailed explanation.

> 9 I estimate a Cobb Douglas production function without restricting the coefficients to sum to one. The data reveals that constant returns to scale is not a bad assumption since the sum of the parameters for all three inputs range between 0.97 and 1.01 across sectors and the average of the sum equals 0.99. Results are available upon request.

10
# **Table 3. Determinants of Informality**

_Method of estimation: Ordinary Least Squares with Robust Standard Errors_

|**_Dependent variable: Four types of in_**<br>|**_formality mea_**<br>|**_sures, country aver_**<br> <br>Informality|**_age_**<br><br>measures||
|---|---|---|---|---|
||Schneider Shadow|Heritage Foundation|Self|Non-contributor to|
||Economy index|<br>Informal Market index|Employment|Pension Scheme|
|_Explanatory variables:_|(% of GDP)|(1-5: higher, more)<br>(%|of total employment)|(% of labor force)|
|_Average of 2000-2005 by country_|[1]|[2]|[3]|[4]|
|Law and Order|-3.2360**|-0.0969*|-1.6925*|-2.9764*|
|(ICRG, index ranging 0-6: higher, better)|-2.57|-1.76|-1.84|-1.67|
|Business Regulatory Freedom|-2.0074*|-0.5333***|-2.5196**|-5.8675**|
|(The Fraser Institute, index ranging 0-10: higher,<br>less regulated)|-1.80|-9.95|-2.17|-2.28|
|Average Years of Secondary Schooling|-1.9684*|-0.1152**|-2.1527**|-5.8114***|
|(Barro and Lee 2001)|-1.70|-2.00|-2.25|-3.27|
|Sociodemographic Factors|3.8438**|0.5027***|5.9743***|21.6130***|
|(average of share of youth population, share of<br>rural population, and share of agriculture in GDP)|2.00<br>|4.99|3.77|7.31|
|Constant|60.3429***|6.6326***|54.7254***|113.3110***|
||10.48|31.72|14.06|11.40|
|No. of observations|84|86|57|70|
|Adjusted R-squared|0.57|0.89|0.78|0.88|

## Notes:

1. t-statistics are presented below the corresponding coefficients.

2. *, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively.

3. See Appendix 1 for countries included in each regression and Appendix 2 for definitions and sources of variables and periods used to compute country averages of informality measures.

<u>Source: Authors’ estimation</u> 21