rafmacalaba/datause-encoder-data huggingface dataset:
other cause (Patton, et al. 2009). The majority of these adolescent pregnancies take place within the context of early marriage (UNICEF 2001). Delaying pregnancies to a more healthy age is not only desirable from a general reproductive health perspective, but it may also lead to more economic and social empowerment of young women who can continue their education or secure a more lucrative job. To the best of our knowledge, there has not been systematic research to measure the opportunity cost of teenage motherhood to economic growth.

Recent work has begun to quantify the potential that human development investments in girls have on GDP growth rates. Hanushek and Woessmann (2007), using cross-sectional regressions for 50 countries, estimate that each additional year of schooling boosts long-run growth by 0.58 percentage points per year. A World Bank study in 1999 demonstrates through data simulation for a selection of 100 countries, that increasing the secondary education of girls by 1% results in annual income increase of 0.3% per capita. Such an increase is substantial for many developing countries. The study concludes that ―societies that have a preference for not investing in girls pay a price for it in terms of slower growth and reduced income‖ (Dollar and Gatti 1999).

Recent empirical work conducted by the World Bank on Latin American and Caribbean countries has broadened this work to consider the importance of a range of youth behaviors on GDP growth. These studies use non-parametric methodologies to quantify the individual and social costs related to risky youth behavior, which includes among others unemployment, early school leaving and adolescent pregnancy (Cunningham et al. 2008, World Bank 2003, 2007). Evidence from Latin America estimates the social cost due to these youth problems equal to 2 percent of GDP annually (Cunningham et al. 2008).

In this paper, we aim to expand the above findings on the linkages between investing in girls and potential increases in national income by broadening the scope of the analysis to include several aspects of adolescent girls’ lives in countries across the world. These include early school dropout, teenage pregnancy, and joblessness, defined as the ratio of the number of girls between the age of 15 and 24 who are not in school or in the labor force as a share of the female working age population aged 15-24. Although these issues have been extensively discussed by social development specialists (Levine et 3
# **Table 3a: Relative Performance of CIF and LASSO vis-à-vis OLS**

|Test Set|Relative Mean Squared Error<br>Math<br>Kiswahili|
|---|---|
|MSE<sup>OLS</sup>/MSE<sup>CIF</sup>|1.21<br>[0.99,1.53]<br>1.14<br>[0.85,1.61]|
|MSE<sup>OLS</sup>/MSE<sup>LASSO</sup>|1.23<br>[0.96,1.63]<br>1.22<br>[0.97,1.46]|

Note: (i) Figures in parenthesis show 95% C.I. (ii) Relative MSE is defined as MSE<sup>OLS</sup> /MSE<sup>ML Model</sup>

**Table 3b: Absolute Performance of CIF and LASSO vis-à-vis OLS**

|Machine Learning Algorithm|Absolute Mean Squ<br>Math|ared Error(Test Set)<br>Kiswahili|
|---|---|---|
|Conditional Inference Forest|85.30<br>[54.74,113.14]|175.82<br>[103.78,292.90]|
|LASSO|83.38<br>[55.25,115.43]|161.07<br>[105.28,224.07]|
|OLS|102.38<br>[70.14,140.05]|194.69<br>[121.98,283.49]|

Note: Figures in parenthesis show 95% C.I.

30
workers in the eighteen to twenty-five age group, and it declines to about 1.9 for the workers of pre-retirement age. Middle-aged women both in urban and rural areas tend to have a higher wage advantage as compared to their younger and older counterparts. Patterns of wage differential with respect to education level are similar to the ones estimated by the OLS. The wage differential is higher among illiterate urban male workers as compared to urban men with higher educational attainments. Among rural men, the wage differential ranges from 110% for the illiterate to 122% for workers with university or higher education. The ratio of public-to-private wages is highest among illiterate urban women, followed by urban women with complete primary and abovesecondary education.

# _Propensity Score Method (PSM) estimates_

Wage differentials presented in Table 11 measure the ratio of wages for a sample of public sector workers for whom we were able to find appropriate matches, i.e., workers with identical observable characteristic, but employed in the formal private sector.

The magnitude of the PSM-estimated wage differential falls between the OLS and selection bias-corrected wage differentials discussed above. As Table 11 shows, on average, the wage advantage of public sector workers over similar workers in the private sector appears to be 68%. While patterns of wage differentials with respect to occupation, age, and education are quite similar across OLS and SBC methods, PSM estimates are somewhat different. For example, workers in administrative occupations (high-skilled workers) seem to enjoy the highest wage advantage of all occupational categories; wage differentials seems to increase with age; and the overall differential in rural areas seems to be lower than in urban areas. Also, the divergence in patterns of wage differentials between the OLS and SBC estimates on one hand, and the PSM estimates on the other, is greater for women than for men. Notwithstanding these differences, that could possibly be explained by the fact that PSM uses a different sample of working individuals, the magnitudes of the wage differentials are quite similar across all three methods.

22
<!-- Start of picture text --> 250 31 35<br>28 30<br>200 24 177<br>25<br>20 150<br>150 124 20<br>15 98<br>100 is<br>68<br>10<br>50<br>5<br>(0) 0<br>2011 2012 2013 2014 2015<br>ll Installed capacity (GW) ealllGeneration CbillionkWh)<br>eb Subsidy (billion yuan)<br><!-- End of picture text -->
Standard LTGM for middle‐income countries, slightly lower for LI countries, and higher for HI countries. The latter is because HI countries tend to have the lowest share of total capital owned by the public sector. The effect of public investment on growth is naturally much smaller in the LTGM‐PC when φ =0.10 (dashed gray line), but also much smaller than the comparable effect in the Standard LTGM (except in HI countries). Greater consistency with the Standard LTGM is one reason we prefer the φ�0.17 calibration over the φ =0.10 calibration.<sup>34</sup>

# _6. 3 The effect of private investment on growth_

Graph 4 reports the effect of a 1ppt of GDP expansion in _private_ investment on growth, which is largest for LI countries. In the first period following the shock, growth increases by the MPK<sup>�</sup> (Equation 16B) which as argued above is twice as large for LI countries as HI countries (25ppts versus 12ppts). As before, the increase in growth falls over time as private capital accumulates, raising K<sup>�</sup> ⁄Y and lowering the marginal product of additional private capital. Nonetheless the effect of higher private investment on growth is quite persistent, verifying our claim above that developing countries are particularly short of private capital.

**<u>Graph 4: Incremental Output Growth from a 1 ppt. Increase in Private Investment</u>**

**in the LTGM‐PC (Congestion** , ζ�1 **)**

<!-- Start of picture text --> HI UMI<br>% %<br>0.35 0.35<br>0.30 0.30<br>0.25 0.25<br>0.20 0.20<br>0.15 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>Standard LTGM Congestion, φ = 0.17 Standard LTGM Congestion, φ = 0.17<br>Congestion, φ = 0.10 Congestion, φ = 0.10<br>LMI LI<br>% %<br>0.35 0.35<br>0.30 0.30<br>0.25 0.25<br>0.20 0.20<br>0.15 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>Standard LTGM Congestion, φ = 0.17 Standard LTGM Congestion, φ = 0.17<br>Congestion, φ = 0.10 Congestion, φ = 0.10<br>2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br>2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br><!-- End of picture text --> 34 With the pure public good setting (Appendix 3, Graph A3.1), the immediate effect of higher public investment is broadly similar to that of the congestion setting, but is more persistent and higher than the congestion setting (and hence the Standard LTGM).

25
Policy Research Working Paper 9410

# **Abstract**

Agricultural labor accounts for the largest share of child labor worldwide. Yet, measurement of farm labor statistics is challenging due to its inherent seasonality, variable and irregular work schedules, and the varying saliences of individuals’ work activities. The problem is further complicated by the presence of widespread gender stratification of work and social lives. This study reports the findings of three randomized survey design interventions over the agricultural coffee calendar in rural Ethiopia to address whether response by proxy rather than self-report has effects on the measurement of child labor statistics within and across seasons. While the estimates do not report differences for boys across all seasons, the analysis shows sizable self/proxy discrepancies in child labor statistics for girls. Overall, the results highlight concerns on the use of survey proxy respondents in agricultural labor, particularly for girls. The main findings have important implications for policymakers about data collection in rural areas in developing countries.

This paper is a product of the Knowledge and Strategy Team, Development Economics. 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 galdo@carleton.ca, ana_dammert@carleton.ca, and degnet06@yahoo.com.

_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 those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
-6-

provisions for losses in the OECD banking system). Similarly, policy changes and market behaviour have, over the last few years, set in motion forces that are now reducing the imbalances in the industrialized countries. Thus, the 40 per cent fall in the effective rate of the dollar against other OECD currencies from its early 1985 peak is beginning to affect trade flows, while the US Federal budget deficit has been sharply cut in the last year both in absolute terms and, even more, relative to CUP.

Though these changes are welcome, their progress is both slow and uncertain. As was seen above, the debt situation of the developing countries may have been stabilized, but remains very fragile, while progress on reducing the US Federal deficit is likely to be interrupted, at least in the short run, if only because of impending electoral deadlines and the timing vagaries of tax reform. OFCD external imbalances are expected to diminish in thg cnurse of 1988, aed probably beyond, but, on present trends and policies, the US current deficit will remain very large for a number of years, thus implying continuing and rapid additions to the country's external indebtedness.

The slowness of these -5 cments, in turn, constrains the growth of the world economy. In the Un Wed States, the need for a reduction in the Federal budget deficit implies a need for a relatively restrictive fiscal policy over the medium term. At the same time, the sluggishness of the current Lalance's response to exchange rate changes creates the danger of further falls in the value of the dollar. Be it to prevent these, or to offset the inflationary effects of past depreciations on the domestic ecovomy, monetary policy may also find itself constrained into a restrictive posture. Though growth would, no doubt, be stimulated by the
# WPS3608

## **APPLICATION OF SAFEGUARDS AND ANTI-DUMPING DUTIES IN COLOMBIA**

Mauricio Reina and Sandra Zuluaga<sup>1</sup>

## **Abstract**

Colombia's experience in the use of safeguards and anti-dumping duties differs from international trends. On the one hand, the number of investigations conducted is substantially lower than that recorded in most of the hemisphere's large and medium-size countries. On the other, while there is a growing international trend of more frequent use of anti-dumping as opposed to safeguards, in Colombia the safeguard process has been the more used policy instrument. Although several large and medium-size firms are familiar with the application of safeguards and anti-dumping duties, there is still a relative unfamiliarity regarding the instruments in most of the private sector. The institutional arrangements related to the investigations and the decision-making processes have proven to be stable and sound. The trade liberalization process in the country has created awareness of the importance of preserving the competitiveness of production chains to strengthen their insertion in international markets, which has restrained the authorities from restricting access to intermediate goods and raw materials. The evaluation of the Colombian experience also raises concerns about the potential discretional use of these instruments. The relatively intense use of safeguards and anti-dumping measures in some specific periods and sectors, especially in the agricultural sector, shows that the institutional framework itself is not always enough to guarantee a disciplined use of the instruments.

### World Bank Policy Research Working Paper 3608, May 2005

_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 those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the_

> 1 This document has taken into account some points of view of a number of Colombian Government Officials and private sector representatives. We are grateful for these opinions.

1
<mark>e0118571.</mark> **<mark>[</mark> 1]** <mark>Neumann, K., Sietz, D., Hilderink, H., Janssen, P., Kok, M., & van Dijk, H. (2015b). Environmental drivers of human migration in drylands–A spatial picture.</mark> _<mark>Applied Geography</mark>_ <mark>,</mark> _<mark>56</mark>_ <mark>, 116-126.</mark> **<mark>[</mark> 1]** <mark>Niva, V., Kallio, M., Muttarak, R., Taka, M., Varis, O., & Kummu, M. (2021). Global migration is driven by the complex interplay between environmental and social factors.</mark> _<mark>Environmental Research Letters</mark>_ <mark>,</mark> _<mark>16</mark>_ <mark>(11), 114019.</mark> **<mark>[</mark> 1]** <mark>Nordhaus, W. D., & Moffat, A. (2017).</mark> _<mark>A survey of global impacts of climate change: replication, survey methods, and a statistical analysis</mark>_ <mark>(No. w23646). National Bureau of Economic Research.</mark> **<mark>[</mark> 3]** <mark>Ocello, C., Petrucci, A., Testa, M. R., & Vignoli, D. (2015). Environmental aspects of internal migration in Tanzania.</mark> _<mark>Population and Environment</mark>_ <mark>,</mark> _<mark>37</mark>_ <mark>(1), 99-108.</mark> **[1]** <mark>Ortiz-Bobea, A. (2013). Is weather really additive in agricultural production.</mark> _<mark>Implications for climate change impacts Resources For the Future Discussion Papers</mark>_ <mark>.</mark> **<mark>[</mark> 3]** <mark>Overeem, A., R. Robinson, J. C., Leijnse, H., Steeneveld, G. J., P. Horn, B. K., & Uijlenhoet, R. (2013). Crowdsourcing urban air temperatures from smartphone battery temperatures.</mark> _<mark>Geophysical Research Letters</mark>_ <mark>,</mark> _<mark>40</mark>_ <mark>(15), 4081-4085.</mark> **<mark>[</mark> 3]** <mark>Pace, N., Sebastian, A., Daidone, S., Prifti, E., & Davis, B. (2022). Mediation analysis of the impact of the Zimbabwe Harmonized Social Cash Transfer Programme on food security and nutrition.</mark> _<mark>Food Policy</mark>_ <mark>,</mark> _<mark>106</mark>_ <mark>, 102190.</mark> **<mark>[</mark> 4]** <mark>Parnell, S., & Ruwani, W. (2011). Sub-Saharan African urbanisation and global environmental change.</mark> _<mark>Global Environmental Change</mark>_ <mark>, 21, S12-S20.</mark> **<mark>[</mark> 1]** <mark>Peri, G., & Sasahara, A. (2019).</mark> _<mark>The impact of global warming on rural-urban migrations: evidence from global big data</mark>_ <mark>(No. w25728). National Bureau of Economic Research.</mark> **[1]** <mark>Piguet, E., Pécoud, A., & De Guchteneire, P. (2011). Migration and climate change: An overview.</mark> _<mark>Refugee Survey Quarterly</mark>_ <mark>,</mark> _<mark>30</mark>_ <mark>(3), 1-23.</mark> **<mark>[</mark> 1]** <mark>Prifti, E., Daidone, S., & Davis, B. (2019). Causal pathways of the productive impacts of cash transfers: Experimental evidence from Lesotho.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>115</mark>_ <mark>, 258-268.</mark> **[4]** 48
first-order dominance, such that no matter what poverty line or poverty measure is used the eligible participants are poorer than the ineligible participants.<sup>6</sup> Most of the ineligibility stems from not complying with the dependency criteria (having dependents of the head that are 18 years of age or handicapped). Tighter enforcement of this criterion would improve the program’s performance in reaching the poor, albeit slightly. Turning to the pre-crisis baseline survey for October 2001, we find that 43% of _Jefes_ participants in October 2002 were employed a year earlier, while 38% were inactive and only 19% were unemployed (Table 3). The unemployed participants were more likely to be represented in the bottom decile of the income distribution.<sup>7</sup> _Jefes_ participants and applicants have similar baseline characteristics in the panel.

One possible source of bias in our use of applicants as a comparison group is that the participants may have received larger latent income shocks in the crisis than did the applicants that have not yet joined the program. This could happen if those households who received the largest shock were first to apply and be accepted. Then the measured income losses for the applicants during the crisis will underestimate the counterfactual income losses for participants.

We can assess the likely extent of mismatch in terms of shocks by comparing the income changes under alternative assumptions about the share of foregone income in the construction of the counterfactual income of _Jefes_ participants. Then we can calculate the corresponding income shock using the panel data. The comparison in the distribution of shocks between _Jefes_ participants and the applicants not yet receiving the program gives us an idea of the extent of the bias under alternative hypotheses on the net gains from the program. If our identifying

> 6 This holds for a broad class of additive poverty measures (Atkinson, 1987).

> 7 More precisely, 26% of previously unemployed participants are in the bottom decile of the income per capita distribution in 2001, as compared to 16% of the previously inactives and 11% of the previously employed participants.

12
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Leerkes, A., Os, R., & Boersema, E. (2017). What drives ‘soft deportation’? Understanding the rise in Assisted Voluntary Return among rejected asylum seekers in the Netherlands. _Population, Space and Place_ .

Legrain, P. (2016). Refugees Work: A humanitarian investment that yields economic dividends. _Tent Foundation and Open Network_ .

Liebig, T., & Mo, J. (2013). The fiscal impact of immigration in OECD countries. _International Migration Outlook 2013_ , 125‐189.

Lucas, R. E. (2005). _International migration and economic development: Lessons from low‐ income countries_ . Edward Elgar Publishing.

Morrison, J. (2000). External evaluation of the voluntary return project for refugees in the United Kingdom. _London: Refugee Action_ .

Murgasova, Z., Ilahi, N., Miniane, J., Scott, A., & Hollar, I. V. (2015). _The Western Balkans: 15 years of economic transition_ . International Monetary Fund.

Nadler, Kovacs et al "Return Migration and Regional Development in Europe” 2016 OECD (2013) International Migration Outlook 2003, Chapter 3, The Fiscal Impact of Immigration in OECD Countries, <u>http://www.globalmigrationgroup.org/system/files/Liebig_and_Mo_2013.pdf</u> OECD, How will the refugee surge affect the European economy? (2015). Retrieved June 28, 2017, from <u>https://www.oecd.org/migration/How‐will‐the‐refugee‐surge‐affect‐the‐</u> ‐ <u>European economy.pdf</u>
Figure 5: 68 % bands of the GIRF distributions for different trade weights <!-- Start of picture text --> china usa euro japan<br>0.0 .00 .00 .00<br>-0.4 -.04 -.04 -.04<br>-0.8 -.08<br>-.08 -.08<br>-1.2 -.12<br>-.12 -.12<br>-1.6 -.16<br>-2.0 -.16 -.16 -.20<br>-2.4 -.20 -.20 -.24<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>austlia india indns kor<br>.050 .02 .05 .08<br>.025 .01 .00 .04<br>.000 .00 -.05 .00<br>-.025 -.01 -.10 -.04<br>-.050 -.02 -.15 -.08<br>-.03<br>-.075 -.04 -.20 -.12<br>-.100 -.05 -.25 -.16<br>-.125 -.06 -.30 -.20<br>-.150 -.07 -.35 -.24<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>mal nzld phlp sing<br>.05 .08 .08 .10<br>.00 .06 .05<br>-.05-.10-.15 .04.02 .04.00 -.05.00<br>-.20-.25-.30-.35 -.02-.04.00 -.04-.08 -.10-.15-.20-.25<br>-.40 -.06 -.12 -.30<br>5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20<br>thai<br>.04<br>.00<br>-.04<br>-.08<br>-.12<br>-.16<br>-.20<br>-.24<br>-.28<br>-.32<br>5 10 15 20<br><!-- End of picture text --> Note: Please refer to Table A1 in the Appendix for a glossary of acronyms. The lines correspond to 16th and 84th percentiles of the distribution, calculated by the trade weights of 1985 ( _red dash_ ), and that of 2013 ( _blue line_ ).

# **5.2 Further investigations**

## **5.2.1 The impact of a potential US GDP shock**

For comparison with the impact of a potential Chinese real GDP shock, analyzed in the previous section, we will now investigate the impact of a one percent drop in US real GDP on other countries. First, we review the GIRFs of China, the Eurozone, and Japan in Figure 8. Although the response of China with the trade weights of 2013 is found to be entirely positive, which is quite counter-intuitive, the effect of the US on China becomes weaker over the sample period.

On the other hand, for the Eurozone and Japan, we note that, although the impact is 28