rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10331

# **Abstract**

This paper studies the impact of child labor standards in regional trade agreements on a variety of child labor market outcomes, including employment, education, and household inequality. It develops a stylized general equilibrium model of child labor in an economy open to international trade and considers the impact of regional trade agreements with and without child labor bans. The paper empirically investigates the effects of these clauses in trade agreements in a broad international panel of 101 developing countries, using harmonized survey microdata. Exploiting quasi-experimental methods to obtain plausibly causal estimates, the analysis finds that regional trade agreements without child-labor bans lead to reductions in child employment and increases in school enrollment, particularly for older children aged 14–17 years. Child labor bans in regional trade agreements perversely increase employment of children aged 14–17 years and decrease school enrollment for both young and older children. These effects appear to decrease inter-household income inequality - through increased child earnings. The findings are consis tent with the theoretical predictions from the model and the literature on child labor bans.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rabman@sdsu.edu, clundberg@sdsu.edu, jem6x@virginia.edu, and mruta@imf.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely
income countries. The semi-elasticity is 0.10 and 0.03 for lower- and upper-middle-income countries, respectively. We assume that under-5 mortality is not impacted by income shocks in high-income countries. Unlike the results from low- and middle-income countries, studies analyzing data from the United Stated find mortality to be pro-cyclical (Ruhm, 2000; Dehejia and Lleras-Muney, 2004).

To map the estimated semi-elasticities into our calibration, we define _s_ 0 _−_ 5 to be the share of children under five years old in the total number of children of ages 0 to 15. The semi-elasticity of child mortality with respect to consumption is given by where _βg_ represents the regression coefficients for low-, lower-middle- and upper-middleincome countries. _ν_ (1) equals zero in high income countries.

It is important to note that we convert the annual semi-elasticity estimate into weekly frequency in the quantification, to match the period definition in the model. Unfortunately, we are unable to estimate the mortality semi-elasticity at a shorter frequency due to data – limitations the national accounts data for many developing countries are only available annually. However, we believe the underlying relationship operates at a shorter frequency than annual for several reasons. Baird et al. (2011) find that only contemporaneous GDP deviations are correlated with mortality likelihood even though a large share of the infants in the estimating dataset experienced the majority of the in-utero period in the lagged year and an equal share of infants experienced the majority of their first year of life in the leading year. Moreover, the authors find that the coefficients on economic conditions in utero and after the first month of life are both small and insignificant. By contrast, the coefficient on per capita GDP in the first month is large, significant, and very close in magnitude to the main effect reported in the paper. These results underscore that it is the economic conditions around birth (say the last months of pregnancy and the first months of life) that matter most for infant survival during economic contractions. As 47 percent
Discussion Papers_ 5094, Institute for the Study of Labor (IZA).

# <u>OECD (2020).</u> _OECD Employment Outlook,_ OECD.

- <u>OECD (2021).</u> _Temporary employment (indicator)_ . doi: 10.1787/75589b8a-en (Accessed on 30 August 2021).

- <u>Oreopoulos, P., Wachter, T. and A. Heisz (2006). “The Permanent and Transitory Effects of</u> Graduating in a Recession: An Analysis of Earnings and Job Mobility Using Matched Employer-Employee Data.” _NBER Working Paper_ no. 12159. Cambridge, Mass.: National Bureau of Economic Research.

- <u>Origo, F. and L. Pagani (2009). “Flexicurity and job satisfaction in Europe: The importance of</u> perceived and actual job stability for wellbeing at work.” _Labour Economics_ , 16: 547-555.

- <u>Ortiz, L. P., Díez, Á. S., and A. Apaolaza (2020). “Employment quality and gender equality. An</u> analysis for the European Union.” _Regional and Sectoral Economic Studies_ , _20_ (2): 5-18.

- <u>Parsons, D. (1986). “The employment relationship: job attachment, work effort, and the nature of</u> contracts.” In Ashenfelter, O. and R. Layard (eds.), _Handbook of Labour Economics_ , Amsterdam: North-Holland.

- <u>Passaretta, G. and M. Wolbers (2019).</u> “Temporary employment at labour market entry in Europe: Labour market dualism, transitions to secure employment and upward mobility.” _Economic and Industrial Democracy_ , _40_ (2): 382-408.

- <u><mark>Pinelli, D., Torre, R., Pace, L., Cassio, L., and
pay cash. It is attractive for the wholesalers to buy oil from the DOTs for two reasons: ( _i_ ) a significant proportion of sales by DOTs to the wholesalers are on credit, and ( _ii_ ) DOTs buy in bulk and get price discounts from the refiners, part of which they can share with the wholesalers. Estimates from a survey conducted by the authors in 2013 shows that about 32 percent of quantity transacted between the DOTs and wholesalers are on credit without collateral; relational credit is thus an important aspect of the marketing network.

The DOTs buy the DOs for oil deliverable by the refiner after a stipulated period of time (usually 2 weeks). It is important to appreciate that the DOTs do not own any storage, and do not take delivery of the oil. The DO layer of the market interacts vertically with the refiners upstream and the wholesalers downstream, and also horizontally with other DOTs (relatively smaller DOTs buy from the large DOTs). The horizontal transactions among the DOTs have evolved into something like an embryonic commodity exchange in Moulovibazar in Dhaka and Khatunganj in Chittagong where speculators operate with the help of brokers, primarily during the upswing in the market.<sup>8</sup> However, note that the DO layer is not a futures market, because there is no “settlement” at the end of the day (it is not marked to market). Also, unlike a futures contract, the payment is made at the time of the contract, not at the delivery date. The DO is also not a standard forward contract, because the stipulated delivery date is almost never enforced. Thus the DO contract resembles something like an American call option. In some cases, a DO may not be executed for many months. This implies that the DOTs bear less price risk compared to a standard forward contract.

# **(3.2) The Policy Reform**

The policy reform focused on the DO layer of the market. The law banning DO (Delivery Order
. W., Sorichetta, A., Steele, J., ... & Tatem, A. J. (2019). Exploring the use of mobile phone data for national migration statistics.</mark> _<mark>Palgrave communications</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(1), 1-10.</mark> **<mark>[</mark> 2]** <mark>Letta, M., Montalbano, P., & Tol, R. S. (2018). Temperature shocks, short-term growth and poverty thresholds: Evidence from rural Tanzania.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>112</mark>_ <mark>, 13-32.</mark> **[4]** <mark>Lilleør, H. B., & Van den Broeck, K. (2011). Economic drivers of migration and climate change in LDCs.</mark> _<mark>Global Environmental Change</mark>_ <mark>, 21, S70-S81.</mark> **<mark>[</mark> 1]** <mark>Liu, M. Y., Shamdasani, Y., & Taraz, V. (2022). Climate change and labor reallocation: Evidence from six decades of the Indian Census.</mark> _<mark>American Economic Journal: Economic Policy</mark>_ <mark>, forthcoming.</mark> **<mark>[</mark> 1]** <mark>Lu, X., Wrathall, D. J., Sundsøy, P. R., Nadiruzzaman, M., Wetter, E., Iqbal, A., ... &
village.|
|Attended meeting where eligible households were<br>identified in 2010/11|Follow-up|Equals to 1 if household head reports having attended the village or hamlet<br>meeting where eligible households were identified in 2010/11.|
|Asked about ability to pay in 2010/11|Follow-up|Equals to 1 if household head reports being asked by the VVC or village hamlet<br>official if he or she could afford the voucher top-up in 2010/11.|
|Household received vouchers in 2010/11|Follow-up|Equals to 1 if household reports having received vouchers in 2010/11.|
|Satisfaction with selection of beneficiaries in 2010/11|Follow-up|Equals to 1 if household head reports being very satisfied with the selection of<br>beneficiaries in 2010/11.|
|Household received vouchers in 2011/12|Follow-up|Equals to 1 if household reports having received vouchers in 2011/12.|
|Used improved seeds in 2009/10|Baseline|Equals to 1 if household head reports using any improved seeds on any parcel in<br>the long rainy season of 2009/10.|
|Used inorganic fertilizer in 2009/10|Baseline|Equals to 1 if household head reports using any inorganic fertilizer on any parcel<br>in the long rainy season of 2009/10.|
|Used agricultural inputs in 2009/10|Baseline|Equals to 1 if household head reports using any inorganic fertilizer or any<br>improved seeds on any parcel in the long rainy season of 2009/10.|
|Welfare standardized index|Follow-up|Standardized sum of 2 welfare outcome variables, also standardized: (i) percent of<br>output sold and (ii) PC food security index.|
.2. The variable _NTMpdt_ is the count of the number NTM imposed by the importing country _d_ , on product _p_ , in year _t_ (as discussed in subsection 3.1). The variable _Tari f fcphs_ 4 _dt_ is the specific bilateral tariff imposed by the destination country _d_ at the HS4-digit product level, on imports from the exporting country _c_ . To reduce the number of missing values, we access the tariff data at the HS4 digit level.

Equation 4.1 also includes a host of fixed effects. _ωipd_ is the firm-origin-HS6 product fixed effect, which accounts for all time-invariant firm unobservable characteristics. _λipt_ is the firmHS6 product-year fixed effect, which controls for all firm-level time-varying characteristics, 11
_The Cultural Trade Index_ Page | **4** of **14**

interpretation, and practical usability.<sup>7</sup> If you dig deeper you can find numbers (on cultural data) here and there — and one huge step forward would be to simply update the Standard Industrial Classification and the Standard Occupational Classification to truly account for services and culture. While the CTI will not solve the major challenges in cultural calculus — especially since it would mostly be concerned with curating and ranking information — it could become a useful research portal, one that compliments other resources concerned with patterns of global cultural trade. Ultimately, the CTI could play an important role as the ‘to go place’ for cultural trade analysis, thereby inducing the need to allocate research funds for cultural data. It could even fuel the impetus for countries to develop their cultural statistical accounts.

- 4) To fuel competition: To return to the cultural tourism example above, country Y might be inclined to improve its rank in cultural tourism as it competes to beat country X for the first spot.  But let us consider another example: arts education.  If country A is the leading destination for enrolling international students in dance, this might make countries B and C ask what they can do to attract more dance students.  This kind of competition is also not new, and it can be a good thing, even as Adam Smith noted.<sup>8</sup>

- 5) To help provide information that could be useful to advocate for fair pay for artists:  Except for some exceptions, ordinary artists rarely make the money; it is the superstars and the companies such as those in today’s digital space.<sup>9</sup> The CTI could help advocates for artists’ fair pay gather trade information and how this relates to artists’ compensation.  Let us say country A is leading in music exports.  The information on why country A is doing well in music exports
changing gender-biased laws, shifting gender norms, and protecting women from gender-based violence. Engaging men to create a more supportive environment may also be crucial.

# 8. Conclusions

This paper reviews evidence on interventions that can address the constraints faced by growth-oriented, women-led micro, small, and medium-sized enterprises (WMSMEs). Three cross-cutting recommendations emerge from the reviewed interventions. First, evidence of modest average treatment effects and treatment effect heterogeneity across various interventions suggests the need for better targeting and segmentation. Second, women-led firms face multiple constraints, and addressing them requires a package of multiple interventions. Evaluating the components of the optimal package represents a promising avenue for future research. Third, the successful implementation and uptake of the proposed solutions require consideration of contextual constraints that differentially affect WMSMEs and the appropriate adaptation of program implementation.

<mark>The modest average treatment effects observed in various interventions, along with the evidence of heterogeneity, indicate that not all strategies are equally effective for every WMSME. This variability in effectiveness underscores the importance of developing a nuanced understanding of which interventions work best for specific types of businesses or entrepreneurs. Better targeting and segmentation can be achieved by analyzing characteristics such as the industry sector, geography (e.g., rural vs. urban areas), business size, and the entrepreneur's background, including her motivations and reported constraints. For instance, WSMEs in the technology sector might benefit from training in digital skills, while those in agriculture might prioritize access to land that can serve as collateral. Start-ups might need help with business planning and seed funding, the allocation of which could be improved through psychometrics, while more established businesses might benefit from blended finance and export assistance. Improved targeting will require surveys, focus groups and data analysis to identify distinct segments with the WSME population and tailor interventions accordingly. As more sex-disaggregated data become available, machine learning techniques may offer better predictions and be used in combination with expert</mark> analysis, peers’ and trainers’ evaluations to conduct this segmentation.
# **_2. Data_**

We use new exporter-level data from customs for 11 countries in the LAC region, including Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Mexico, Nicaragua, and Peru covering the 2000s. These data are part of the new _Exporter Dynamics Database_ presented in Cebeci, Fernandes, Freund, and Pierola (2012). The starting point for the construction of the database is raw exporter-level customs datasets generally obtained from national customs agencies, and the data cover the universe of transactions in each country. Each country dataset provides information at the exporter-productdestination-year level based on six variables: country of origin, exporting firm identifier, country of destination, product, export value, and year.<sup>7</sup> The raw exporter-level datasets were subjected to uniform reformatting and to a series of cleaning procedures. A few key aspects are discussed below by highlighting the specificities of LAC countries, but further details are provided in Cebeci, Fernandes, Freund, and Pierola (2012).

Firms were identified by their actual names, their tax identification number, or artificial unique codes randomly created by the local customs agency. These codes allow us to follow firms over time and create a panel for each country.<sup>8</sup> Regarding the destination country, Colombia, Costa Rica, Ecuador, and Peru use a special trade regime whereby firms’ sales to their free zones/customs warehouses are recorded by customs as exports, but we exclude this type of transaction from those countries’ datasets.<sup>9</sup> Regarding product nomenclatures, we use a timeconsistent ‘consolidated’ Harmonized System (HS) classification at the 6-digit level that concords and harmonizes product codes across the HS 1996, 2002, and 2007 versions (used in the raw exporter-level datasets).<sup>10</sup> Export values are measured in US dollars (USD) converted from local currency when necessary using official exchange rates
review above, the scope and severity of shocks faced by households, together with the available resources at home and in communities, determine the extent of each household’s success in responding to such shocks.

Table 2 also includes descriptive statistics for the main explanatory variables employed in the analyses. Both the IHS3 and IHPS rounds contain information on household socio‐demographic characteristics that can be used to proxy the consumption smoothing capacity of households, including the size and life‐cycle of the family according to the age and gender composition of its members; as well as the age, gender, civil status and ethnicity of the household head. We have also created two indices of wealth, as detailed in footnotes a and b, Table 2. The first index includes measures of household wealth and education levels, and the second index includes measures of agricultural specific assets and landholdings. Higher levels of wealth, education, agricultural assets and landholdings are all expected to be associated with higher welfare outcomes per capita. The impact on variance measures, however, is ambiguous. Greater wealth levels may enable households to pursue production and income strategies that are more lucrative but also more risky. On the other hand, wealthier households may be more able to finance adoption of risk‐ reducing activities, such as pursuing income diversification strategies or adopting relatively costly sustainable land management activities (Arslan et al., 2015; Asfaw et al., 2015).

We also control for the availability of socioeconomic infrastructure that exists in the community and the degree of accessibility and slope as proxies for the structures of opportunity that rural households face at the village level. These variables are included to capture a measure of the community conditions relevant to households’ economic and social decisions. Higher infrastructure and accessibility scores are expected to correlate with greater ease of conducting economic and social activities, and thus lead to higher, and potentially more stable, welfare outcomes. Higher slope levels are included to capture agricultural land that is more susceptible to erosion and flood damage, and which may
therefore continue to benefit from subsidies even after the 2013 reforms. However, since the total subsidy provided to households with moderate consumption (between around 150 and 300 kWh/month) is greater than that for households below 150 kWh/month, the bulk of subsidies still goes to better‐off households.</mark> One potential shortcoming of the above analysis is that the survey asks about total electricity consumption over the preceding month. Self‐ reports may be inaccurate, or may include expenditure on other forms of electrical power such as batteries or uninterrupted power supplies. In order to corroborate these findings, we now examine actual billing data.

Data on monthly electricity consumption, along with other household details, were obtained for 325,926 households from three electricity distribution companies (DISCOs): Gujranwala Electric Power Company (GEPCO), Faisalabad Electric Supply Company (FESCO) and Islamabad Electric Supply Company (IESCO).<sup>4</sup> These DISCOs together serve almost 38 percent of Pakistan’s residential electricity consumers.<sup>5</sup> The three DISCOs also cover a significant share of northern Pakistan and include a mix of semi‐urban, rural and urban areas. For this analysis IESCO, FESCO and GEPCO each provided a 10‐15 percent random

> 4 Other DISCOs were approached for inclusion in the analysis, but data could not be sourced with the right specifications in time for this study.

> 5 For a breakdown of billing data please refer to the Appendix.

6
in the distribution of regional intersectoral productivity gaps in the nonfarm sector. To allow for a comparison of statistics between 2003 and 2014, we redefine some key variables. Specifically, an informal enterprise is redefined as an enterprise that is not registered with RGD, labor productivity is redefined as revenue per worker (given the absence of value-added data for enterprises in the NIC 2003 data), and the intersectoral productivity gap is redefined as the log of the ratio in labor productivity between the informal and formal sectors (given the absence of sector- and enterprise- specific price deflators over time).

We do not see any clear pattern in the change in the distribution of intersectoral productivity gaps between the richest and the poorest regions (Figure 13). Except in Northern,

> 21 See GSS (2006) for further information on the design and implementation of the National Industrial Census 2003.

23
would affect prices in a more functional market, such as flood risk, not being internalized.

# **5 Conclusions**

This paper investigates the relationship between flood exposure and rents in Accra, Addis Ababa, Antananarivo, and Dar es Salaam, using a hedonic property price approach with four cross-sectional household surveys complemented with data on job density. Flood exposure is measured using self-reported information from households on whether they have been affected by flooding. Flood risk is measured with self-reported information on the perception of the risk of future flood exposure.

The results suggest that flood exposure/risk is associated with lower rents in three of the four cities studied. The willingness to pay to avoid flood exposure is between 14 percent (in Accra and Antananarivo) and 56 percent (in Addis Ababa) of average rents. In contrast, in Dar es Salaam, flood exposure/risk is associated with higher rents. The relationship between flood exposure and rents is made more complex by the close correlation between tenure and flood exposure, the lack of public awareness on flood risk, high transaction costs of housing and the importance of omitted variables, such as cultural and historical importance, presence of community networks, access to green spaces and other amenities which potentially could encourage people to move to areas with high flood risk.

The paper uses a novel way to measure access to jobs, based on data on the spatial distribution of amenities and economic activities. In Accra and Addis Ababa, rents are negatively correlated with job density, which is an unexpected finding for an efficient housing market. In Antananarivo, a more monocentric city compared with the other three cities, areas with high job density are more expensive, on average. No relationship between job density and rent is found for Dar es Salaam. When interacting job density with flood exposure/risk for each city, the negative effect of job density on rents is higher when flood exposure/risk is high in Accra and Addis Ababa, and the positive effect of job density on rents becomes negative when