rafmacalaba/datause-encoder-data huggingface dataset:
the oil shock. The negative slope of the curve denotes the progressive impact of the shock. However, the poorest percentiles still lose significantly so that the equalizing effect of this result should not be overstated.

_Figure 1: Growth incidence curve due to the oil shock_ <!-- Start of picture text --> ‐4<br>‐5<br>Overall Change<br>‐6<br>‐7<br>‐8<br>‐9<br>0 10 20 30 40 50 60 70 80 90<br>Population percentiles<br>consumption)<br>Change in welfare (% change in per capita<br><!-- End of picture text --> Source: HBS data and micro‐simulation results. Note: the points represent the difference in per capita consumption measured with and without the oil shock; the line is obtained as a polynomial fit of the points.

What explains this heterogeneous impact of the shock? Table 1 offers some clues. The skill and urban premia are decreasing and one may expect skilled workers and (among the unskilled) urban ones to be towards the richer tail of the distribution. This table also highlights that among the workers displaced by the shock, about 100,000 unskilled ones will have to move from better paid urban occupations to rural lower paid jobs. However, the aggregate results of this table do not inform from which part of the distribution these displaced unskilled workers are more likely to come. Finally, the lower reduction of food prices vis‐à‐vis prices of other consumption items suggests a potential regressive impact given that poorer households spend a larger share on food than richer households.<sup>13</sup> Other channels of transmission of the shock can also contribute to its uneven incidence. For example, rents in oil producing and closely related sectors (such as refineries) will be directly hit. Given the large capital stocks of the oil sector, capital returns may also suffer larger losses vis‐à‐vis labor returns. This shift

> 13 Note that if rich consumers purchase a larger share of imported goods, they may be impacted more severely than poorer ones by the increase in import prices. Some authors [Broda and Romalis 2008] have studied this issue for the case of the US. They found that lower import prices of goods originating in China have actually helped the poorer consumers in the US. However, the current household data for Russia do not allow to investigate this channel.

15
Table F.7: Productivity and Technology Sophistication Controlling for ln(K/L) and ln(Labor Cost/L), GBF and SSBF MOST

|VARIABLES|(1)<br>ln(VAPW)|(2)<br>ln(VAPW)|
|---|---|---|
|ln(K/L)|0.14***<br>(0.03)|0.14***<br>(0.03)|
|ln(Labor Cost/L)|0.67***|0.67***|
||(0.04)|(0.04)|
|GBF EXT<br>|0.66**<br>(0.29)||
|GBF EXT<sup>2</sup>|-0.08*||
||(0.04)||
|GBF EXT*AGRI||0.92**<br>(0.45)|
|GBF EXT*MANF||-0.16*<br>(0.09)|
|GBF EXT*SVC||0.14<br>(0.09)|
|SSBF MOST*Agriculture|-0.22|-0.20|
||(0.21)|(0.21)|
|SSBF MOST*Food Processing|0.35**<br>(0.17)|0.35**<br>(0.17)|
|SSBF MOST*Apparel|0.26**|0.23**|
||(0.12)|(0.12)|
|SSBF MOST*Retail and Wholesale|0.09|0.06|
||(0.14)|(0.14)|
|SSBF MOST*Other Manufacturing|0.10|0.09|
||(0.08)|(0.08)|
|SSBF MOST*Other Services|0.08<br>(0.13)|0.08<br>(0.14)|
|Var(ABF EXT)|0.28***|0.30***|
||(0.10)|(0.09)|
|Firm Characteristics|_~~√~~_|_~~√~~_|
|Observations|2,496|2,496|
|R-squared|0.64|0.64|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

100
|||**Provinc**<br>**Mass**|**e level inter**<br>**media treat**|**vention**<br>**ment**|||**Commun**|**ity treatmen**|**t sample**|**District level**|**intervention**|<br>**School**|**component**|**sample**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Contr|ol mean|Treatm|ent mean||Contro|l mean|Treatm|ent mean||Contr|ol mean|Treatm|ent mean||
||N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|
|**Material of dwelling's**||||||||||||||||
|**walls**||||||||||||||||
|Esteras|699|0.029|715|0.042|-0.013|699|0.029|755|0.032|-0.003|679|0.034|700|0.024|0.01|
|Brick|699|0.094|715|0.062|0.032|699|0.094|755|0.127|-0.033|679|0.09|700|0.123|-0.033|
|Concrete|699|0.054|715|0.014|**0.040****|699|0.054|755|0.057|-0.003|679|0.019|700|0.07|**-0.051****|
|Unbaked brick, adobe|699|0.584|715|0.664|-0.08|699|0.584|755|0.576|0.008|679|0.58|700|0.543|0.037|
|Wood, logs|699|0.103|715|0.088|0.015|699|0.103|755|0.044|0.059|679|0.138|700|0.051|0.087|
|Other|699|0.136|715|0.13|0.006|699|0.136|755|0.164|-0.028|679|0.138|700|0.189|-0.051|
|**Material of dwelling's**||||||||||||||||
|**roof**||||||||||||||||
|Esteras|699|0.04|715|0.056|-0.016|699|0.04|755|0.045|-0.005|679|0.05|700|0.054|-0.004|
|Brick|699|0.023|715|0.038|-0.015|699|0.023|755|0.019|0.004|679|0.029|700|0.024|0.005|
|Concrete|699|0.052|715|0.017|**0.037***|699|0.052|755|0.038|0.014|679|0.024|700|0.053|**-0.029***|
|Wood, logs|699|0.019|715|0.007|0.012|699|0.019|755|0.009|0.01|679|0.012|700|0.02|-0.008|
|Tin, zinc sheeting|699|0.534|715|0.47|0.102|699|0.534|755|0.668|**-0.134***|679|0.571|700|0.636|-0.065|
|Bamboo|699|0.006|715|0.007|-0.001|699|0.006|755|0.028|-0.022|679|0.01|700|0.023|-0.013|
|Other|699|0.328|715|0.406|-0.078|699|0.328|755|0.193|**0.135****|679|0.303|700|0.19|**0.113***|
|**Material of dwelling's**||||||||||||||||
|**floor**||||||||||||||||
|Painted wood|699|0.009|713|0.004|0.005|699|0.009|753|0.009|0|679|0.013|700|0.017|-0.004|
|Concrete|699|0.156|713|0.111|0.045|699|0.156|753|0.159|-0.003|679|0.138|700|0.217|**0.055**|
|Clay, earthen floor|699|0.701|713|0.749|-0.048|699|0.701|753|0.699|0.002|679|0.698|700|0.636|0.062|
|Non polished concrete|699|0.076|713|0.093|-0.017|699|0.076|753|0.098|-0.022|679|0.082|700|0.091|-0.009|
|Other|699|0.059|713|0.043|0.016|699|0.059|753|0.035|0.024|679|0.068|700|0.039|0.029|
|**HH has food not covered**|685|0.225|651|0.257|-0.032|685|0.225|704|0.207|0.018|663|0.27|658|0.24|0.03|
|**HH is clean**|682|0.543|687|0.518|0.025|682|0.543|715|0.593|-0.05|663|0.508|669|0.538|-0.03|
|**HH with garbage in**||||||||||||||||
|**kitchen or house**|686|0.541|677|0.589|-0.048|686|0.541|717|0.488|0.053|668|0.581|666|0.568|0.013|

43
natural level of competition in markets and that each market moves toward this natural level. In both cases, the progression is potentially slower than some market participants facing change may expect. Radical change is unusual. More gradual and organic change is the norm particularly with respect to consolidation cases. After an initial two year period following liberalization, progress is also more organic than dramatic (see Figure 2 for several individual country trend examples).

**<u>Figure 2: Progress of Country Data Series where longer trends are available</u>**

<!-- Start of picture text --> 1,210<br>1,010<br>810<br>610<br>410<br>210 Life Insurance<br>Non Life Insurance<br>10<br>1991 1996 2001 2006<br>Year<br>In Argentina, progressive increases from very low<br>levels are evident albeit that the non life sector has<br>seen the index reduce more recently.<br>1,810<br>1,610<br>1,410<br>1,210<br>1,010<br>810<br>610<br>410<br>Life Insurance<br>210<br>Non Life Insurance<br>10<br>1991 1996 2001 2006<br>Year<br>Herfindahl Value<br>Herfindahl Value<br><!-- End of picture text --> In Argentina, progressive increases from very low levels are evident albeit that the non life sector has seen the index reduce more recently.

In Chile, both indexes are lower than average and show a gradual increase over time.

<!-- Start of picture text --> 610<br>510<br>410<br>310<br>210<br>110 Life Insurance<br>Non Life Insurance<br>10<br>1991 1996 2001 2006<br>Year<br>Herfindahl Value<br><!-- End of picture text --> In Brazil, the non life sector has remained persistently diverse whereas stepped change in the life sector is evident as it moves toward levels closer to data set averages.

6
**Table 3:  Lagged effects of typhoons on total PCE and different components of PCE (using wind damage index)**

||PCE<br>Total<br>(1)|PCE<br>on Food<br>(2)|PCE<br>Non-<br>Food<br>(3)|PCE<br>Protein<br>(4)|PCE<br>Fruit<br>(5)|PCE<br>Cereal<br>(6)|PCE<br>Educati<br>on<br>(7)|PCE<br>Medical<br>(8)|
|---|---|---|---|---|---|---|---|---|
|f203_hy|-0.166***|-0.246***|-0.0710|-0.494***|-0.114|-0.0485|-0.135|-0.119|
||(-3.65)|(-5.44)|(-1.21)|(-6.82)|(-1.52)|(-1.31)|(-0.92)|(-0.84)|
|f203_bhy|-0.116|-0.132|-0.115|-0.150|0.0443|-0.0412|-0.305|-0.257|
||(-1.44)|(-1.78)|(-1.03)|(-1.17)|(0.34)|(-0.59)|(-1.12)|(-0.91)|
|adj. R2|0.632|0.504|0.635|0.384|0.305|0.171|0.298|0.227|
|N|412,287|412,269|412,287|410,243|410,150|411,119|255,586|355,403|

Notes: Additional explanatory variables included but not reported here: family size, head is female, age of head, marital status of head, education level of head, head has a job, building type (single house vs, apartment etc.), type of material for roof, type of material for wall, floor area, type of toilet, water source, and separate binary variables for semester, year, and region, along with a complete set of interactions among these binary variables. Standard errors are corrected for clustering at the barangay level.

# (ii) Changing the shock measure

Tables 4 and 5 check the robustness of the findings in Tables 1 and 2, by using a binary variable (W119_hy), taking the value of 1 if the barangay experienced any (at least once) windspeeds over 119 km/hr during the last 6 months (or during the second half of the 12-month period) immediately preceding the month of interview of the household.

Overall the estimates in Tables 4 and 5 confirm that results reported in tables 1 and 2 are very robust. The estimate of 2 in col. 1 of Table 4 reveals that exposure of a barangay to a typhoon in the 6 months before the month of interview, is associated with a statistically significant decline in PCE by 6.7 percent. This estimate is very comparable to the impact estimates of ββ typhoons on PCE in the literature. For example, for the Philippines, Anttila-Hughes and Hsiang (2013) using provincial panel data, estimate a reduction between 5.9% and 7.1% in consumption, whereas Ishizawa and Miranda (2016), estimate a decline ranging between 2% and 4% for Central American countries. Baez et al. (2015) using panel data also show a fall in the consumption of Guatemalan households by 12.6%.

Along similar lines, PCE on food decline by 5.1% (see col. 2) and PCE on non-food decline by 7.8%. Also, PCE on protein seem to be negatively affected (decline by 10.1 pp) whereas PCE on fruits and vegetables, cereals, education and medical services appear to be unaffected.<sup>21</sup>

> 21 We have also confirmed that changing the reference period of the shock measure based on the binary variable W119_bhy, does not result in any significant lagged effects of typhoons on total household PCE or any of its components (as is the case with the _f_ index used in Table 3).

18
# _Transport vouchers in Brazil_

Box 3 presents a summary of the ‘Vale Transporte’ scheme used in Brazil. This demand side subsidy is considered somewhat less targeted than the means tested transfer in Chile, since the potential beneficiaries are not means tested. However, because of the way the system operates, it does generate a self selection of beneficiaries (see Box 3) that probably keeps higher income formal workers from benefiting from the scheme. Accordingly this system can be classified as a mixture of a categorical subsidy (because it only benefits formal workers) and a self-selected mechanism.

## **Box 3: The ‘Vale Transporte Scheme in Brazil’**

This demand side subsidy mechanism was introduced in 1985 and works as follows. Employers retain 6% of formal workers earnings. In return, these workers receive from their employer transport vouchers for the home-to-work and return trips required during a month. Employers buy these vouchers at the beginning of the month from a selling agency (can be a Bank, a union of transport operators or a municipal or state government agency). Transport operators accept these vouchers and trade them in with the issuing agency for money.

Resources spent by firms on Vale Trasporte are tax deductible, thus around 35% of the scheme is funded by general tax revenues. The rest is funded by consumers (higher prices), workers (lower wages or higher unemployment), or owners (lower profits). The incidence of this labor tax needs to be studied further.

One striking characteristic of the Vale Transporte is that it provides an interesting and probably effective self-selection targeting mechanism. Workers can opt out of the system, and higher income earners have the incentives to do so since 6% of their salaries will generally be higher than what they spend on commuting. From 3 minimum wages upwards, public transport costs are below 6% of salaries for workers that take one mode of transport to get to and from work. Thus, workers with higher incomes will usually opt out of the system, helping to reduce leakages to higher income groups.

A substantial number of vouchers are sold by workers in the black market at a discount and these workers then walk, cycle or car-pool to work. Although this does not hurt formal workers, who still benefit from the scheme, it does point to the potential welfare enhancing properties of a direct monetary transfer instead of using voucher that workers later sell at a discount in the secondary market.

Gomide, A., S. Leite and J. Rebelo (2004) argue for the direct transfer of benefits to low income workers in the informal sector or the unemployed, groups that do not benefit from the Vale Transporte scheme. They note that about 50% of workers in Metropolitan Regions of Brazil belong to the informal sector and do not benefit from the scheme, a point also raised by Carruthers, Dick and Saurkar (2005).

_Source:_ The World Bank, Internal Urban Transport Mission Report, Annex IV and Gomide, A., S. Leite and J. Rebelo (2004) 17
Box 1: The Growth of Soybean Production–a Blessing and for Some a Curse <mark>Steady growth in soybean production to service expanding export markets is placing greater pressure on fragile ecosystems and their inhabitants in Argentina and elsewhere in South America (specifically Brazil). Argentina is the world’s third largest soybean producer, accounting for 17 percent of global output (after the U.S. and Brazil with 35 and 27 percent, respectively) and also the third largest exporter with 28 percent of the market. At least 98 percent of Argentina’s soybean production is genetically modified (GM) and exports are directed primarily to the growing</mark> Asian market.

<mark>While soybean cultivation delivers economic benefits, there is increasing evidence that the expansion of this crop is having negative social impacts. Social impacts include loss of livelihood security (especially for local populations dependent on natural forest and aquatic resources) and</mark> limited employment opportunities.<sup>7</sup> <mark>Soybeans were introduced in the 1980s and now occupy over 14 million hectares, more than all other crops combined. Soybeans were concentrated until recently in the provinces of Buenos Aires, Córdoba, and Santa Fe, employing mechanized GM technology and replacing other crops. Initial impacts of the conversion of the Pampas to arable farmland took the form of soil erosion and degradation, causing river flooding. Since the late 1990s, some 10 percent of production has spread to the provinces of Entre Ríos, Chaco, Santiago del Estero, Salta, and Tucumán, at the expense of Chaco’s bush savannahs and the Yungas subtropical forests. In Chaco, 2.4 million hectares have been cleared to make way for soybeans. Soil erosion, sedimentation, and increased risk of flooding have accompanied soybean expansion. Deforestation caused by soybean expansion will compromise this stock of natural capital including a forest loss rate of 10,000 hectares per year. Moreover, soybeans have overtaken sugar and tobacco, two key crops of small</mark> farmers, and plantation forests as the main driver of deforestation.

<mark>The loss of land and livelihood experienced by small farmers squeezed out through land speculation and concentration is not easily quantified. A further consideration is that large-scale mechanized soybean farming predominates in Argentina, generating only one job per 200 hectares, compared with one job per eight hectares for typical smallholder operations. This induces a process of rural out-migration and a destabilization of livelihoods, which have much wider impacts</mark> including loss of food security and urban overpopulation (see also Section 6).

_Source_ : Oxford Analytica; http://www.oxweb.com

> 7 In addition to the social consequences of soybean production, ecological consequences include deforestation, soil erosion, river sedimentation, agrochemical pollution, and loss of natural habitats and biodiversity.

12
# **Appendix: Table A1**

# Matching Statistics

||(1)<br>|(2)<br>|(3)|
|---|---|---|---|
||Mothers matched<br>in the sample<br>(n=1437)|Mothers unmatched<br>in the sample<br>(n=260)|Mean Diff|
|**Mother**||||
|Age|37.87|37.34|0.53|
||(0.20)|(0.52)|(0.52)|
|Education (years)|1.34|1.38|0.04|
||(0.07)|(0.19)|(0.19)|
|Educated (Y/N?)|0.24|0.23|0.01|
||(0.011)|(0.026)|(0.02)|
|Time Spent on Child Needs<br>|94.47|88.50|5.97|
|(minutes/day)|(3.73)|(8.41)|(9.46)|
|Time Spent on Child School|5.05|3.46|1.59|
|Work (minutes/day)|(0.60)|(1.14)|(1.49)|
|**Child**||||
|Age (Years)|9.97|9.74|0.23|
||(0.04)|(0.10)|(0.11)|
|Female|0.47|0.49|-0.02|
||(0.01)|(0.02)|(0.02)|
|Enrolled|0.76|0.76|-0.00|
||(0.01)|(0.02)|(0.02)|
|Public|0.71|0.69|0.02|
||(0.01)|(0.02)|(0.02)|

_Notes_ : Means and standard error of the mean in parentheses. The table compares attributes of children and mothers for the sample of mothers whose reported birth village could be matched to a village in the Pakistani census with the mothers whose villages we were unable to find. The reasons for the “missing mothers” are detailed in the text.

39
<!-- Start of picture text --> Table 3: Expenditure Patterns by Income Quintile<br>1 () 11UQ t 2d dst 3rd Hllghest 11l<br>7 N h M! t:' Ht?,w~545'5~Z<br>Tables 3-5 provide background information on the three household samples.<br>l'able 3 reports the expenditure shares by income quintile. Across inconle groups within<br>countries, food expenditure shares are declining in income while education and transport<br>shares are strongly rising. Clothing and health expenditure shares showv a more muted<br>positive relation with income shares. The same pattern holds across counatries: Bulgarian<br>households, on average the poorest in the sample, spend about ninety percent of their<br>income on housing and food, compared to around seventy percent fDr Hungary and<br>Poland. Table 4 reports the matching expenditures shares across age groups. Food,<br>housing and health expenditure shares strongly increase with age, wvhile education,<br>clothing and transportation shares are not unexpectedly higher for households headed by<br>.Table 4: Expenditure Patterns by Age Group<br>.s-29349 560-64)9 I5+<br>heepniuesaebyicmqunieAcosicegrpswtn<br>Ift~d.'~.:' ' 7i37 5 8.13 10.70 8.22<br>Tables 3-5 provide background information on the threehouseholdsamples.<br><!-- End of picture text --> Tables 3-5 provide background information on the three household samples. l'able 3 reports the expenditure shares by income quintile. Across inconle groups within countries, food expenditure shares are declining in income while education and transport shares are strongly rising. Clothing and health expenditure shares showv a more muted positive relation with income shares. The same pattern holds across counatries: Bulgarian households, on average the poorest in the sample, spend about ninety percent of their income on housing and food, compared to around seventy percent fDr Hungary and Poland. Table 4 reports the matching expenditures shares across age groups. Food, housing and health expenditure shares strongly increase with age, wvhile education, clothing and transportation shares are not unexpectedly higher for households headed by younger individuals.

Tal eot
# 1. Introduction

Child work is a cause and a consequence of poverty and lack of opportunity. Children<sup>1</sup> in poor households in low- and middle-income countries may contribute to their families’ welfare by doing household chores or paid work outside the home. Despite the long-term returns to education, the short-term high marginal returns to labor provide a rationale for child work to exist and illustrate the fundamental difficulty in reducing it (Beegle, Dehejia and Gatti 2009). Moreover, although child work can negatively affect education and schooling outcomes, the reverse is true as well: child work can help children be more productive, raising their prospective earnings and providing incentive to join the labor force (Phoumin and Fukui 2006).

There is an important difference between child labor and child work. According to the International Labour Organization (ILO), children’s participation in work that does not affect their health and personal development or interfere with their schooling is generally considered as being something positive: it equips them with skills and experience and helps prepare them to be productive members of society in adulthood. By contrast, child labor is usually defined as work that deprives children of their childhood, their potential and their dignity. This refers to work that (1) severely interferes with their ability to attend school, which ultimately harms income-earning potential in adulthood, and (2) is mentally and physically dangerous for children. Child labor depends on the child’s age, the type and hours of work performed, the conditions under which it is performed, and the objectives pursued by individual countries.<sup>2</sup> Child labor has long been a feature of Myanmar’s economy, and has translated into a large and unprotected child labor market countrywide. Data from ILO’s 2015 Labor Force Survey (LFS) indicates that 9.3 percent of Myanmar’s total child population (12.14 million children aged 5-17 years) were engaged in child labor at the time of the survey. Working for pay or profit, child workers fall into one of four main categories: (1) children aged 5-17 engaged in hazardous work<sup>3</sup> for at least one hour per week; (2) children

> 1 In this study, a child is defined as anybody between the ages of 5 and 17.

> 2 What is child labor? https://www.ilo.org/ipec/facts/lang--en/index.htm.

> 3 The definition of hazardous work includes the following: (1) work which exposes children to dust and fumes; fire, gas and flames, loud noise or vibration; extreme cold or heat; (2) work underground, under water, at dangerous heights or in confined spaces; (3) work with dangerous machinery, equipment and tools, or which involves the manual handling or transport of heavy loads; (4) work in an unhealthy environment which may, for example, expose children to hazardous substances, agents or processes, or to temperatures, noise levels, or vibrations damaging to their health; and (5) work under particularly difficult conditions such as working long hours or during the night or work where the child is unreasonably confined to the employer’s premises (Department of Labour 2016).

2