rafmacalaba/datause-encoder-data huggingface dataset:
# **2. Child Labor: Definition and Theoretical Framework**

In this section, we discuss three key conceptual issues. First, we discuss the key empirical challenges to define child labor in the empirical literature. Second, we present a simple theoretical framework to help us understand the pathways through which the different programs may affect children’s allocation of time within the household. Finally, we discuss the challenges to identify the impacts of policies on child labor and the selection of studies summarized in this paper.

# **_2.1 What Is Child Labor and How Is It Measured?_**

Several widely adopted international conventions set the legal boundaries that define child labor, including the ILO Convention No 138 on the legal minimum age, the ILO Convention No 182 on the worst forms of child labor, and the UN Convention on the Rights of the Child. The conventions, however, contain a few flexibility clauses left to the discretion of the competent national authority. Thus, there is no single legal definition of child labor used across countries and there is no single statistical measure of child labor.

The International Conference of Labour Statisticians (ICLS) is the authoritative body to set global standards in labor statistics. The Resolution concerning statistics on child labor, adopted by the 18<sup>th</sup> ICLS in 2008, structures the measurement of child labor around two elements: age of the child (includes all children in the 5 to 17 age group) and the type of activities performed by the child. The resolution distinguishes between “children in productive activities” and “child labor” as follows:

- “ _Children in productive activities_ ” are comprised by

- i) “ _Children in employment_ ” includes all types of paid and non-paid productive activities performed by the child.

- ii) “ _Children in other productive activities_ ” includes children who perform unpaid household services, that is, the production of domestic and personal services by a household member for consumption within their own household or household chores.

- “ _Child labor_ ” is more restrictive and excludes all children working legally in accordance with the ILO Conventions. A child laborer is defined as:

- i) Any economically active child under the age of 12

- ii) Children in the 12-14 age category engaged in productive activities that do not fall under permissible light work<sup>4</sup>

> 4 Light work is defined as not likely to be harmful to the child’s health or development; and not affecting school attendance, participation in vocational orientation or training program.

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# **<mark>Table A4 – Impact of FD Law on Bank Lending to Firms (Across Sectors) Given State Public Debt</mark>**

This table reports OLS estimates of the impact of the FD Law on bank lending to private firms operating in the primary, secondary, and tertiary sectors, in states of varying ex ante public indebtedness. Primary sector includes the sectors of agriculture and mining, secondary sector includes sectors such as construction and manufacturing, and tertiary sector includes sectors such as retail, finance, education, and health. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided <u>in Table A1 in the Appendix.</u>

|||Volumef,b,m<br>|||Investmentf,b,<br>|m<br>|W<br>|orking Capita<br>|lf,b,m<br>|
|---|---|---|---|---|---|---|---|---|---|
||Primary<br>(1)|Secondary<br>(2)|Tertiary<br>(3)|Primary<br>(4)|Secondary<br>(5)|Tertiary<br>(6)|Primary<br>(7)|Secondary<br>(8)|Tertiary<br>(9)|
|Postm*DebtStates,16Q1|-0.003|0.042***|0.006|-0.063*|0.095***|0.040**|-0.002|0.042***|0.010|
||(0.015)|(0.008)|(0.007)|(0.036)|(0.020)|(0.017)|(0.016)|(0.008)|(0.007)|
|Observations|54,027|348,390|849,588|10,355|65,068|131,104|47,983|311,905|771,476|
|R-squared|0.869|0.809|0.790|0.905|0.877|0.858|0.871|0.805|0.788|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

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and final goods as follows: intermediate goods are primary goods, semi-finished goods, parts and components; and final goods are consumption goods and capital goods.

We use two disaggregations. In the first, trade flows are divided into capital goods, intermediate inputs and consumption. In the second, we divide trade flows into intermediate inputs, final goods and primary goods. We construct index for trade proximity (TP) as follows. For given aggregate bilateral trade flows (total, intermediate input or final goods) between a country _i_ and a country _j_ , we define: where _Tj→j_ is the export value from country _j_ to country _i_ .

**Measure of GVC participation based on Value Added.** One of the main purposes in this paper is to identify production linkages between countries. Over the past decade, several accounting and theoretical framework have been developed to measure GVC participation. In this paper, we use the recent GVC indicators from Borin and Mancini (2019). They offer a new toolkit for value-added accounting of trade flows at the aggregate, bilateral, and sectoral levels that can be used to investigate a broad set of empirical questions—including in this case an assessment of the global inflation synchronization.

**Product Stickiness.** Another related feature of GVCs is their relational aspect. Some products are associated with a higher degree of rigidity in the buyer–seller relationships, which is sometimes associated with a high degree of product customization to integrate within the same production process. Martin et al. (2018) propose a measure of business relationships’ stickiness based on the duration of firm-to-firm trade and use firm level data in France to measure a “stickiness” index for more than 4,000 HS6 products. We use their classification to separate trade flow into their “sticky” and “non-sticky” parts, based on the products that are traded.

**Financial Proximity.** Financial interconnection could impact GDP comovement in a way that is not related to international trade. We use data consolidated banking statistics from the Bank for International Settlement to construct an index of financial proximity.<sup>12</sup> We use the total bilateral cross-border claims (including bank and non-bank sectors for all maturities) between

12 The dataset is available here: `https://stats.bis.org/` .

6
Financial Regulation and Government Revenue

# **Appendix C: Branch Cost and Annual Reports Calculation**

In this section, we provide an estimate of the cost to the CBE of opening a new branch, based on interviews about the variables affecting branch cost and on historical data available available through the CBE’s annual reports from the years 2006 and 2012.

The annual reports provide an informative breakdown of the non-interest expenses of CBE, as well as the exact number of branches operated by the bank. Thus, based on the data provided by the annual reports, we can calculate an approximation of the cost of opening a new branch. We find this cost by using the difference in those yearly expenses attributable to branches, net of depreciation of existing branches, and by dividing this number by the count of new branches opened over the years in consideration. This should provide us with an approximation of the mean cost of opening a new branch.

Nonetheless, we cannot simply use the total bank expenses in a given year, because not all bank non-interest expenses are required for opening a new branch. Therefore, we have to use only those expenses that bank executives indicate as core in calculating branch opening costs, such as salaries and employee benefits, depreciation, postage and telecommunication, occupancy expenses, stationery and office supplies, travel costs, service charge, motor vehicle running costs, legal and consultancy fees, repairs and maintenance, insurance, water and electricity, board fees, land and building tax, and electronic data processing.

The calculation of a branch cost is rather essential. The sum of the previous selected expenses is produced for each year (net of depreciation) and we attribute to the average cost of a branch the difference between the cost of two years divided by the number of newly installed branches, as follows: In order to render the nominal average cost comparable across years, we divide this figure by a price index. Clearly, this calculation presents a few large assumptions: 1) there are no branch cost externalities; 2) geographic disparities are omitted; 3) with the exception of inflation and depreciation, we are assigning all costs to branches.

25
According to the Enterprise Survey of 2012, around 25 percent of firms in Russia point to an inadequately educated workforce as a major constraint for business growth. Skills acquired before the transition might be obsolete in the new business environment, and firms might prefer to hire young workers than re-train older ones. This can also be confirmed by the flat wage-age profiles computed taking advantage of the longitudinal data (Atencio and Posadas, 2013). Thirdly, most of the increase in the share of college graduates is due to the higher educational attainment of female workers, who do not manage to obtain the full payoff of their education as the gender gap in pay has not shrunk during the last 25 years (Atencio and Posadas, 2013).

Geographical location (urban versus rural) became an important source of wage dispersion after the transition, with a large rural-urban wage gap. This gap narrowed throughout the period, such that, even though urban wages were still higher than rural ones in 2012, rural-urban location was a less important factor to explain wage differentials compared with 2002, particularly at the low end of the wage distribution.

Regional convergence of wages was also a relevant factor to explain wage compression. Guriev and Vakulenko (2015) suggest that the reduction of the barriers to mobility—which became possible due to the generalized economic growth of the 2000s—fostered convergence on incomes and earnings between regions. In line with this, Gimpelson (2015) suggest that regional convergence of earnings explains 2/3 of the decline in wage inequality between 2005 and 2013.

Convergence in productivity among different types of firms is a key factor behind the compression of the wage distribution, captured with type of ownership and size of firms. In 2002, large, foreign, private firms were paying higher wages, especially to workers at the top of the wage distribution. By 2012, these payoffs considerably decreased. On the one hand, there has been convergence in the wages paid by firms, with larger firms offering wages closer to that of smaller firms. On the other hand, the payoff of working for private and foreign firm has been fading away. All this process could be part of a one single phenomenon related to the continuation of the productivity change associated to the privatization of state owned enterprises (SOEs). Many SOEs were sold into smaller firms, which in turn gave birth to other smaller firms through backward and forward linkages. These firms could have needed more time to catch up in productivity. Consistent with this hypothesis is the shift of workers from large public firms to 26
tradeoff between taxes on gasoline versus the rest of the tax system, aggregated as a tax on labor. In this general-equilibrium context, they estimate the second-best optimal gasoline tax taking into account the corresponding externalities (adjusted Pigovian tax), the balance between commodity taxation and labor taxation (Ramsey tax), and congestion feedback.

Thus, a comprehensive analysis must be carried out when considering policy options to ensure that the effects on the goods that display higher degrees of complementary and substitutability are included in the analysis.

# 4.1 ENERGY SUBSIDIES<sup>13</sup>

Many governments provide subsidies for energy, either explicitly or implicitly, to producers and consumers. The International Energy Agency (IEA) estimated that fossil fuel subsidies to consumers in 37 countries, representing 95 percent of the global fossil fuel consumption that is subsidized, reached $557 billion in 2008. The Global Subsidies Initiative estimated in 2009 that global subsidies to fossil fuel producers amounted to approximately $100 billion (GSI 2009). Coady and others (2010) estimated that globally consumer subsidies for petroleum products had been $57 billion in 2003, $519 billion in 2008, and $136 billion in 2009, while projecting a rebound to reach $240 billion in 2010. Including the tax subsidy—the effect of taxes set below optimal levels—the amount of petroleum product subsidies could reach $740 billion in 2010 according to the study. The year-to-year variation in these totals is largely due to changes in the international oil price.

Energy sector subsidies are large and widespread in both developed and developing countries. Consumer subsidies are particularly prominent in many developing countries. Petroleum product subsidies are known to be substantial in certain cases, especially in some major oil-exporting countries. Power sector subsidies that result from under-pricing, excessive losses, and bill collection failure are common in those developing countries for which detailed investigations have been conducted. However, more systematic information is needed to give a comprehensive picture.

While subsidies can conceivably have benefits in terms of support for the poor, job creation, industry protection, or energy security, they also carry costs. These can include fiscal costs and effects on the balance of payments, growth, and global externalities. In particular, in economies with large energy consumption, the extra demand for energy induced by the lower consumer prices can work against energy security and have global effects by possibly raising prices on the world market by increasing demand (in the case of widely traded fuels) and through higher GHG emissions resulting from consumption of more fossil fuels.

Victor (2009) offers some general lessons with respect to reforming petroleum product subsidies and that can be applied also to power sector subsidies.

> 13 This section draws from Bacon and others (2010) “Subsidies in the Energy Sector: An Overview” Background Paper for the World Bank Group Energy Strategy.

20
**Steady state analysis.** The steady state variables associated with the permanent enforceability shock are given by the third column of table 2. In both economies, steady state capital is equal to its autarkic steady state level. Compared to the no-shock trajectory, consumption is lower in both economies. In the emerging economy, this reflects higher liquidity rents. In the developed economy this reflects lower steady state capital and output. The steady state interest rate is substantially lower compared to the noshock scenario, at -31%. With a higher liquidity premium, steady-state trade balances become slightly more extreme: the emerging economy exports an additional 0.2% of its GDP, and the ratio of imports to GDP in the developed economy increases by 0.6%.

**Transitional dynamics.** Figures 5 and 6 present the transitional dynamics associated with the enforceability shock, compared to the uninterrupted equilibrium path. The timing of the shock was chosen as _t_<sup> ̄</sup> = 4, to match an 8% growth rate in the emerging market at _t_ = 3 (in 2006, the average growth rate of the BRIC economies was 8.55%<sup>14</sup> ).

The dynamics of the model can qualitatively account for three interesting features of the recent crisis. First, in the model, the shock is associated with a sharp and permanent decline in interest rates and a surge in liquidity premiums. This reflects a shock to liquidity supply, as collateral-backed claims cease to be liquid. This is qualitatively consistent with the decline in the treasury rate following the 2007-2008 crisis.

Second, the model generates a decline in investment in the developed economy, and an increase in investment in the emerging economy. The decline in investment in the developed economy reflects the lower private return to capital. The increase in investment in the emerging economy is a result of the developed economy running down its capital stock: in part, this is achieved through saving in emerging economies, to smooth the declining consumption

> 14Source: World Development Indicators.

32
In the pass-through estimation, all product groups are aggregated into two main categories: agriculture and manufacturing. Within these two broad categories, all products are assumed to have the same domestic price pass-through coefficient. This model allows changes in prices to be different across the 32 Mexican states, which are further differentiated by urban and rural areas. To assure compatibility with the CGE estimates, the changes in regional prices consequent to movement in the international prices are rescaled, while still keeping the change faced by the average household equal to the one estimated by the GTAP model.<sup>9</sup> The model utilized to estimate domestic price pass-through is based on the effect of tariff liberalization on domestic prices as they vary with distance from the US border. In this model, the effect of a change in tariff is perceived in local markets in the same way as a movement in the world price, therefore the extent to which domestic prices move in function of movement of the tariff can be interpreted as the degree of correlation between border prices and retail prices. In summary, the model tracks the effect of a change in price at the U.S. border (produced by the change in tariff) to changes in the price at the regional level so as to capture how much of the movement in the border prices is reflected in each of the retail prices in different geographic areas.<sup>10</sup> To capture differences in pass-through across states, the pass-through coefficient is interacted with the distance variable.<sup>11</sup> Moreover, this interaction term is further interacted with a rural and an urban dummy so as to investigate possible differences in pass-through between

> 9 The GTAP Armington specification produces average market prices already adjusted for imperfect price pass-through.

> 10 Prices are corrected for quality issues following the standard methodology by Prais and Houthakker (1955).

> 11 The variable used in the model to capture differences in the movement in the retail prices is the driving distance from the US border. Distance is measured in 1000 km.

10
**Figure 6: Banks’ risks and sovereign stress in EMDEs**

<!-- Start of picture text --> A. Sovereign EMBI spreads annual median 2017 (bps) vs. Probability of default of financial institutions (%)<br>2017<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>0 0.5 1 1.5 2 2.5 3 3.5 4<br>Probability of default of financial institutions (%)<br>Sovereign EMBI spreads annual median (bps)<br><!-- End of picture text --> Sources: University of Singapore – Credit Research Initiative; Bloomberg, Fitch Connect. Own calculations.

- B. Haircut in Government Securities to wipe out Common Equity Tier 1 capital (% holdings of Government securities), by region 2016

<!-- Start of picture text --> Haircut (% Holding Gov. 25 pctl median<br>Sec)<br>75 pctl<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>AFR EAP ECA MNA SAR<br><!-- End of picture text --> Data as of 2016, except for Georgia, Jordan, Eswatini (2015), and Cambodia, Kazakhstan, Kenya, Senegal, Tanzania (2014). Sample coverage: 474 commercial banks in 33 EMDEs. Source: Fitch Connect. Own calculations.

48
# **<mark>Table 1: Characteristics of Job Applicants at Baseline, by Treatment Status</mark>**

|Dependent variab|les|Obs.|Mean (treatment<br>group, baseline)|Mean (control<br>group, baseline)|Mean<br>difference|Normalized<br>difference|
|---|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|(5)|
|**Demographics**|Age (in years)|827|23.6|24.2|-.588*|-.098|
||||[4.02]|[4.43]|(.319)||
||Married (yes=1)|827|.245|.297|-.051|-.082|
||||[.431]|[.458]|(.034)||
||Has any child(ren) (yes=1)|827|.201|.254|-.053*|-.089|
||||[.401]|[.436]|(.032)||
||Migrated to Addis Ababa (yes=1)|827|.689|.695|-.006|-.010|
||||[.463]|[.461]|(.036)||
|**Education**|Educational attainment (in completed years of<br>|827|9.66|9.57|.095|.041|
||schooling)||[1.65]|[1.63]|(.127)||
||Currently enrolled in any form of formal<br>|827|.096|.114|-.018|-.041|
||education program (yes=1)||[.295]|[.319]|(.023)||
|**Economic**<br>|Ever employed (yes=1)|803|.865|.867|-.002|-.003|
|**Activity**|||[.342]|[.341]|(.027)||
||Ever employed in factory work (yes=1)|803|.202|.231|-.029|-.049|
||||[.402]|[.422]|(.032)||
||Had any cash earnings in the past 4 weeks<br>|827|.315|.297|.018|.028|
||(yes=1)||[.465]|[.458]|(.036)||
||Pays any rent (yes=1)|827|.315|.343|-.028|-.043|
|Notes: The table|reports mean values of key covariates and differenc|e in mean|[.465]<br>s between the treatm|[.476]<br>ent and control grou|(.036)<br>p (balancing te|st of these|

Notes: The table reports mean values of key covariates and difference in means between the treatment and control group (balancing test of these variables). Testing joint hypothesis that each coefficient obtained from a regression of treatment on all variables listed above is equal to 0 yields an F- stat (p value of F-test) of 0.59 (0.83). Standard errors in parentheses and standard deviations in brackets. *** denotes significance at 1%, ** at 5%, and * at 10%.

13
<!-- Start of picture text --> Utility Function Agriculture Sectors<br>utility<br>output bagasse (if i = sugarcane industry)<br>Savings<br>leisure<br>materials<br>goods and services<br>(non-energy consumption) CO2 emissions<br>electricity land i<br>capital labor<br>other fuels & CO2<br>emissions<br>electricity<br> nsk sk other fuels & CO2<br>emissions<br>CO2 emissions fuel 1 CO2 emissions fuel 1<br><!-- End of picture text -->

**Figure 1.** Utility and Production Functions

<!-- Start of picture text --> Non-agriculture and non-electricity Sectors Electricity Sectors<br>output output<br>materials materials<br>labor<br>CO2 emissions<br>capital labor CO2 emissions<br> nsk sk<br>electricity electricity<br> nsk sk<br>other fuels & CO2 other fuels & CO2<br>emissions emissions<br>CO2 emissions fuel 1 CO2 emissions fuel 1<br><!-- End of picture text --> 12