rafmacalaba/datause-encoder-data huggingface dataset:
of its exchange rate, abolition of import licensing with the important exceptions that the restrictions on imports of manufactured consumer goods and on foreign trade in agriculture remained in place, convertibility (with some notable exceptions) of the rupee on the current account; reduction in the number of tariff lines as well as tariff rates; reduction in excise duties on a number of commodities; some limited reforms of direct taxes; abolition of industrial licensing except for investment in a few industries for locational reasons or for environmental considerations, relaxation of restrictions on large industrial houses under the Monopolies and Restrictive Trade Practices (MRTP) Act; easing of entry requirements (including equity participation) for direct foreign investment; and allowing private investment in some industries hitherto reserved for public sector investment.

In general, India has gotten good results from its reform program, with per capita income growth above 4% per annum in the 1990s. Growth and poverty reduction have been particularly strong in states that have made the most progress liberalizing the regulatory framework and providing a good environment for delivery of infrastructure services (Goswami et al. 2002).

# _Vietnam_

The same collection that contains Eckaus’s study of China also has a case study of Vietnam (Dollar and Ljunggren, 1997): Vietnam has made a remarkable turnaround during the past decade. In the mid-1980s the country suffered from hyperinflation and economic stagnation; it was not able to feed its population; and hundreds of thousands of people were signaling their dissatisfaction by fleeing in unsafe boats. A decade later, the government had restored macroeconomic stability; growth had accelerated to the 8-9 percent range; the country had become the second largest rice exporter in the world; and overseas Vietnamese were returning with their capital to take advantage of expanding investment opportunities. During this period there has also been a total transformation of Vietnam’s foreign trade and investment, with the economy now far more open than ten years ago.

That Vietnam was able to grow throughout its adjustment period can be attributed to the fact that the economy was being increasingly opened to the international market. As part of its overall effort to stabilize the economy, the government unified its various controlled exchange rates in 1989 and devalued the unified rate to the level prevailing in the parallel market. This was tantamount to a 73 percent _real_ 33
Loaiza Quintero, O. L., Muñetón Santa, G., and Gabriel Vanegas, J. (2018). Forced Displacement and Multidimensional Poverty in Antioquia, Colombia: An Assessment by Means of a Seemingly Unrelated Regression. _Journal of Regional Research,_ 41: p167-190.

<mark>Milazzo, A., & Van De Walle, D. (2017). Women left behind? Poverty and headship in Africa.</mark> _<mark>Demography</mark>_ <mark>,</mark> _<mark>54</mark>_ <mark>(3), 1119-1145.</mark> <mark>Munoz-Boudet, A.M., Buitrago, P., De La Briere, B., Newhouse, D., Matulevich, E.R., Scott, K., & Suarez-Becerra, P. (2018). “Gender Differences in Poverty and Household Composition through the Life-Cycle: A Global Perspective.” Policy Research Working Paper No. 8360. World Bank, Washington, DC.</mark> <mark>https://openknowledge.worldbank.org/handle/10986/29426</mark> <mark>Nagar, S. E., Bamkar, S., & Tønnessen, L. (2017). Girls, Child Marriage, and Education in Red Sea State, Sudan: Perspectives on Girls’ Freedom to Choose.</mark> _<mark>Sudan Report</mark>_ <mark>.</mark> Nguyen, K. (2019) ‘As drought compounds security woes, Somalis flee to Ethiopia’, UNHRC UK. 20 September 2019. Retrieved from <u>https://www.unhcr.org/news/stories/2019/9/5d8495984/drought-compounds-security-woessomalis-flee-ethiopia.html.</u> Overseas Development Institute. (2019a). The Impact of Displacement on Gender Roles and Relations: The Case of IDPs from FATA, Pakistan. London: Overseas Development Institute. Retrieved from: https://www.odi.org/sites/odi.org.uk/files/resource-documents/12610.pdf.

Overseas Development Institute. (2019b). Gender in Displacement: The state of play. London: Overseas Development Institute. Retrieved from: https://www.odi.org/sites/odi.org.uk/files/resourcedocuments/201911_gender_in_displacement_hpg_working_paper_web.pdf.

OECD. (2019), "Gender differences in immigrant integration", in _Settling In 2018: Indicators of Immigrant Integration_ , OECD Publishing, Paris, https://doi.org/10.1787/978926430721610-en.

Oruc, N. (2015). Urban IDPs and Poverty: Analysis of the Effect of Mass Forced Displacement on Urban Poverty in Bosnia and Herzegovina. _Croatian Economic Survey,_ 17(1): p. 47-40.

<mark>Quisumbing, A. R., Haddad, L., & Peña, C. (2001). Are women overrepresented among the poor? An analysis of poverty in 10 developing countries.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>66</mark>_ <mark>(1), 225-269.</mark> <mark>Rodríguez, L. (2016). Intrahousehold inequalities in child rights and well-being. A barrier to progress?</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>83</mark>_ <mark>, 111-134.</mark> <mark>Rubiano-Matulevich, E. (2021). Do Gender Norms Relax with Conflict-Induced Displacement? The Case of Colombia. W</mark> orld Bank and Policy Research Working Paper, forthcoming . <mark>World Bank Group.</mark> 32
55% of female-single IDP earners are considered multidimensionally poor, the equivalent figure is 16 percentage points lower (39%) in IDP households dependent on multiple male earners. Similarly, in Somalia, 70% of female-single earner households are poor, which is the highest poverty rate among IDP and non-IDP households.

Households that depend mostly on women’s earnings seem more likely to fall into poverty. For example, in Ethiopia, poverty rates among female-single earners and multiple-female earners in refugee households are the highest (52% and 57%, respectively). These rates are strikingly high and pass even the poverty rate of households without earners. In contrast, 23% of refugee households whose income depend on a single male earner and 16% of those that depend on multiple male earners are poor. However, it is worth noting that regardless of the earnings category, the odds of falling into poverty are larger among refugee or IDP households with at least one earner compared to their host communities.

_Table 8. Poverty headcount ratios by earnings profile and displacement status_

||Ethio|pia|N.E Nige|ria|Soma|lia|Suda|n|
|---|---|---|---|---|---|---|---|---|
||Host<br>community|Refugees|Host<br>community|IDP|Host<br>community|<br>IDP|Host<br>community|IDP|
|No earners|40%|51%|62%|50%|32%|64%|7%|45%|
|Remittance recipients only|7%|8%|76%|69%|16%|37%|-|-|
|Female single earner|12%|52%|23%|37%|35%|70%|24%|55%|
|Male single earner|8%|23%|4%|14%|24%|50%|8%|39%|
|Majority female earners|3%|57%|9%|28%|9%|28%|15%|45%|
|Equal contribution|10%|24%|17%|16%|23%|63%|8%|41%|
|Majoritymale earners|5%|16%|15%|19%|20%|52%|9%|39%|

Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).

Overall, the results show that besides gender, displacement status and the number of household contributors plays a key role in the identification and level of poverty. In comparison with female-headed non-displaced households, more female-headed displaced households are classified as multidimensionally poor. According to the number and gender of the household income contributors, we find that refugee and IDP households are the most vulnerable regardless of the family classification. Among displaced populations, households dependent on a unique earner or on female earners are disproportionally more likely to fall into poverty, compared to other households with at least one earner. These findings indicate that differentiation by gender of the head, displacement status and subgroups of headship have important implications for policy and targeting.

# **7. MPI and monetary poverty**

We now compare the results using monetary and multidimensional poverty measures, as both can provide us with different insights into the short-term and long-term hardships that households face in displacement situations. Monetary poverty is calculated using the international poverty line of US$1.90 per capita per day and an aggregate of consumption per capita (Pape et al 2017) from the sum of expenditures on food items, non-food items and the 25
Steckel, Jan C., Ira I. Dorband, Lorenzo Montrone, Hauke Ward, Leonard Missbach, Fabian Hafner, Michael Jakob and Sebastian Renner (2021). Distributional impacts of carbon pricing in developing Asia. Nature Sustainability 4:1005–1014.

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- Timilsina, Govinda R.; Sebsibie,Samuel. Distributional Effects of Carbon Tax in Ethiopia : A Computable General Equilibrium Analysis (English). Policy Research working paper ; no. WPS10476 Washington,D.C.:World Bank Group.

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- Timilsina, Govinda and Sheoli Pargal (2020). Economics of Energy Subsidy Reforms in Bangladesh.  Energy Policy 142, https://doi.org/10.1016/j.enpol.2020.111539.

- Timilsina, Govinda, Jun Pang and Yuanhua Chang (2022). Economic Impacts of Meeting China’s NDC through Carbon Taxes with Alternative Schemes for Recycling Tax Revenues. World Bank Policy Research Working Paper #10087, World Bank, Washington, DC.

- Timilsina, Govinda, Yazid Dissou, Mike Toman and Dirk Heine (2021). Carbon Tax in an Economy with Informality: A Computable General Equilibrium Analysis for Côte d’Ivoire”. World Bank Policy Research Working Paper, WPS 9710. World Bank Washington, DC.

- Williams, Roberton C., III, Hal Gordon, Dallas Burtraw, Jared C. Carbone, and Richard D. Morgenstern (2015). The Initial Incidence of a Carbon Tax across Income Groups. National Tax Journal 68 (1):195-213.

- World Bank (2022a). World Development Data Indicator Database. <u>WWW.worldbank. World</u> Bank, Washington, DC.

- World Bank (2022b). Egypt: Country Climate and Development Report. World Bank. Washington, DC.

World Bank (2023). State and Trends of Carbon Pricing 2023, World Bank, Washington, DC.
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|Ln city Income|0.529***<br>(0.0458)|-0.181<br>(0.181)|0.517***<br>(0.104)|0.687***<br>(0.0590)|-1.115***<br>(0.223)|
|---|---|---|---|---|---|
|Ln Population|1.132***|1.427***|1.765***|0.675**|-0.915*|
||(0.143)|(0.388)|(0.248)|(0.216)|(0.390)|
|year=2015|-1.235***<br>(0.0293)|-1.945***<br>(0.124)|-1.306***<br>(0.0545)|-1.280***<br>(0.0467)|-0.655***<br>(0.0502)|
|FE|City + Year|City + Year|City + Year|City + Year|City + Year|
|Controls|No|No|No|No|No|
|R-squared|0.250|0.324|0.200|0.289|0.469|
|N. of observations|18787|1423|7233|7377|2754|

Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 The evolution of built-up area also has implications for population density. We estimate Income and population elasticities of naïve density measured as population over built-up area. Using the GHSL panel data, different specifications are estimated including nonlinear effects for the largest cities based on the following regression: 1 2 2 3 4 2 (3) ln FFaaPPsseettDD As shown previously, as cities become richer, they expand responding to a higher demand for space and cc,tt = αα+ ββ ln IIPPeecc,tt + ββ ln IIPPeecc,tt + ββ ln FFooppcc,tt + ββ ln FFooppcc,tt + λλtt + γγcc + εεcc,tt As shown previously, as cities become richer, they expand responding to a higher demand for space and supply for built-up, and therefore become less densely populated. The income elasticity of density is -0.10 using the between and within estimator; introducing instruments for incomes increases it to -0.15. Table 7 provides estimates of the income elasticity of density using various specifications. Figure 9plots out densities over incomes levels using the nonlinear specification for cities larger than 500,000 people (column 5, Table 7). The chart reveals that density declines rapidly between low and middle incomes, and it stabilizes as cities reach $20,000 per capita. As population keeps growing, some of these large cities will provide space through higher buildings, increasing the livability of its inhabitants while allowing for larger agglomeration gains.

**Table 7: Determinants of naïve population density (population over built-up)**

|**Dependent variable:**<br>**Population density**<br>**over**<br>**built-up**|**Between +**<br>**Within**<br>**Estimator**|**Between +**<br>**Within**<br>**Estimator**<br>**with lag**|**Between +**<br>**Within**<br>**Estimator**<br>**with NTLs**|**Between +**<br>**Within**<br>**Estimator**<br>**with IV for**<br>**income**|**For Cities >**<br>**500k only**|**Nonlinear**<br>**specification**<br>**for Cities >**<br>**500k**|
|---|---|---|---|---|---|---|
|Ln Income<br>Ln Population<br>Ln lagged Income|-0.105***<br>(0.00484)<br>0.649***<br>(0.0177)|-0.181***<br>(0.00650)|0.692***<br>(0.0180)|-0.149***<br>(0.00439)<br>0.627***<br>(0.00856)|-0.0762***<br>(0.0103)<br>0.784***<br>(0.0277)|-0.397***<br>(0.0946)<br>-0.446<br>(0.380)|
|Ln lagged population||0.478***<br>(0.0347)|||||
|Square Ln Income||||||0.0197***<br>(0.00584)|

19
Average residential electricity tariffs in OECD countries currently stand at US$0.12 per kilowatt-hour, so that more than 80% of OECD countries have residential tariffs high enough to make a significant contribution towards capital costs. As in the case of water, Latin America has the strongest record on cost recovery of any of the developing regions, with average residential tariffs at US$0.09 per kilowatt-hour and just over half of countries practicing residential tariffs that make a significant contribution to the recovery of capital costs. These tariffs are about 50% higher than those observed in other developing regions that typically lie in the US$0.04-0.06 per kilowatt-hour range.

As before, there is a strong relationship between cost recovery and the income level of the country. Average tariffs rise from around US$0.05 per kilowatt-hour in low-income countries, to US$0.06-0.07 per kilowatt-hour in middle-income countries and US$0.12 per kilowatt-hour in high-income countries. The percentage of utilities whose tariffs make some significant contribution to covering capital costs rises correspondingly from around 25% in low-income countries to around 30% in middle-income countries, and 80% in high-income countries. Once again, it is striking to note that Latin America’s performance on cost recovery is above the average for upper-middle-income countries.

**<u>Table 6: Overview of average residential electricity tariffs</u>**

||Mean<br>(US$/m<sup>3</sup>)|Median<br>(US$/m<sup>3</sup>)|Standard<br>deviation<br>|Range<br>(US$/|<br>m<sup>3</sup>)|IQ Ra<br>(US$/|nge<br>m<sup>3</sup>)|Degr<br>(%)|ee of cost r|ecovery|
|---|---|---|---|---|---|---|---|---|---|---|
||||(US$/m<sup>3</sup>)|Min|Max|Min|Max|Nil|Partial<br>O&M|Partial<br>capital|
|**Global**|0.08|0.07|0.04|0.01|0.21|0.05|0.10|15|44|41|
|**By income**|||||||||||
|HIC|0.12|0.11|0.04|0.06|0.21|0.09|0.13|0|17|83|
|UMIC|0.07|0.06|0.03|0.04|0.14|0.05|0.09|0|71|29|
|LMIC|0.06|0.05|0.03|0.03|0.14|0.04|0.08|27|50|23|
|LIC|0.05|0.05|0.03|0.01|0.13|0.04|0.06|31|44|25|
|**By region**|||||||||||
|OECD|0.12|0.11|0.04|0.06|0.21|0.09|0.13|0|17|83|
|LAC|0.09|0.09|0.03|0.05|0.14|0.06|0.10|0|47|53|
|ECA|0.06|0.04|0.03|0.02|0.14|0.04|0.08|31|38|31|
|EAP|0.05|0.05|0.02|0.01|0.08|0.04|0.06|29|65|6|
|SSA|0.05|0.06|0.02|0.03|0.08|0.04|0.06|29|71|0|
|SAS|0.04|0.04|0.01|0.04|0.05|0.04|0.05|33|67|0|

Sources: OECD 2004, ERRA 2004, EIA 2004, OLADE 2004, Estache and Gassner 2004, UN-ESCAP 2004.

Notes: Data drawn from 84 countries worldwide, broken down as follows OECD (23), LAC (19), ECA(18), SSA(13), EAP (8), SAS (3) Comparing the results presented in Table 6 for electricity, with those presented in Table 3 for water, it is evident that ⎯ while the same general patterns hold ⎯ the absolute extent of cost recovery is far higher for electricity services across the income spectrum. Thus, while 85% of countries (and 69% of low-income countries) achieve some degree of cost recovery for electricity, only 60% of utilities (and 12% of low-income utilities) achieve some degree of cost recovery for water. Moreover, average tariffs for electricity in high13
employment, which was and remains to be the single most important source of livelihood in urban Zimbabwe.

# **Parameter Stability Tests**

This section investigates if changes in parameter space following the shocks that we observed on tables 7 through 9 are indeed statistically significant and their implications for policy. The Chow test is the most common one used for testing structural changes. But the assumption of equal variance for error terms in both periods is crucial for its validity. Such an assumption fails for the Zimbabwe ICES and thus variance correction steps are needed before implementing the Chow test.<sup>14</sup> Table 10 presents the results of parameter stability test using the Chow test. Structural parameter stability test indicates significant shift in coefficients following the economic shocks. Specifically we observe significant declines in returns to human capital and physical assets in rural areas. On the other hand, returns to income diversification were significantly higher following the shocks. The role of boys and girls in welfare generation increased following the shocks, particularly in rural areas. This may suggest that more children are involved in income generating activities during economic crisis. In addition to highlighting the importance of income diversification as a risk coping strategy, this finding also has implications, among other things, on the empirical validity of poverty targeting and mappings techniques that combine survey and census data collected at different points in time (see, for instance, Hentschel, et al., 2000).

# **Decomposition of Household Welfare Changes**

The rural and urban results discussed above show that income diversification had significant impact in weathering away some of the negative effects of the economic shocks that hit Zimbabwe in the early 1990s. Given that there were changes in other structural variables and the Chow test on Table 10 also showed significant changes in the parameter estimates, it would be useful to decompose the impacts of explanatory variables on the changes in household welfare.

Denote the means of the dependent variable (log of real per capita consumption) and the explanatory variables for time t as<sup>_y_</sup> _t_<sup>and</sup> _xt_ , respectively. Denoting bt as a corresponding vector of parameter estimates, one can obtain:

> 14 A simple variance adjustment procedure was used before implementing the Chow test. The procedure is not reported to save space and is available upon request.

12
**Table 3: Return International Migrants and Change in Gender Attitudes**

||**_Employme_**|**_nt Index_**|**_General_**|**_Index_**|**_Decision-ma_**|**_king Index_**|**_GBV In_**|**_dex_**|**_Extreme_**|**_Index_**|
|---|---|---|---|---|---|---|---|---|---|---|
||_R(k=GULF)_|_R(k=SA)_|_R(k=GULF)_|_R(k=SA)_|_R(k=GULF)_|_R(k=SA)_|_R(k=GULF)_|_R(k=SA)_|_R(k=GULF)_|_R(k=SA)_|
|Difference|-3.975***|-7.809***|2.346***|5.795***|-0.0544|2.289***|0.468|2.954***|-0.689|2.553***|
||[0.995]|[1.024]|[0.733]|[1.015]|[0.703]|[0.581]|[0.650]|[0.631]|[0.698]|[0.627]|
|LM-stats for<br>under-<br>identification|23.71|66.87|23.71|66.87|23.71|66.87|23.71|66.87|23.71|66.87|
|F-stats for<br>weak<br>identification|6.258|17.49|6.258|17.49|6.258|17.49|6.258|17.49|6.258|17.49|
|Hansen J-stats|90.70|52.03|50.88|52.03|26.80|43.69|112.9|117.9|14.55|30.80|
|Obs.|8946|8870|8946|8870|8946|8870|8946|8870|8946|8870|

Notes: (1) All specifications with GMM standard errors. * p<0.10, ** p<0.05, *** p<0.01; (2) Data source: KMS 2013; (3) Full estimation results with corateis are provided in Appendix B.

25
presents more details on these variables.

Despite the relevance of participant characterization in terms of violence before the intervention, the dangerous public school context in El Salvador prevented us from directly asking students about violence during the registration phase. Specifically, it was impossible for the NGO or schools to ensure that this personal information would be kept confidential during the study, an impediment that could endanger both children and instructors.

School behavior reports for each student were the only available administrative data that could have been used as a proxy for violence. Although these reports describe delinquent and violent actions committed within school facilities, they are not a comprehensive measure of violence because they do not include actions committed by children in other relevant domains such as at home and in their communities.

To address this constraint and to estimate a comprehensive proxy for violence, we rely on Dinarte (2018)’s prediction of violence for each enrolled child. Using available baseline data, Dinarte followed Chandler et al. (2011) and estimated a predictive model of violence and crime from existing data (FUSADES, 2015) using a two-sample least-squares strategy. First, the author estimated the likelihood of having committed a violent act _Vf_ as a function of a wide range of covariates _Xf_ , such as student characteristics (e.g., age, gender, time spent alone at home, and education level); children’s household variables (e.g., residence area, mother’s education, household composition); and school-level controls (e.g., school location and commuting time to school). Then, exploiting the availability of these variables in the registration forms of enrolled students, she predicted the measure of propensity for violence (IVV) for each child, using the vector of estimated coefficients.

In Dinarte (2018) a full section explains and discusses this estimation, including some robustness checks. Since the IVV may not be a perfect measure of violence, she provides some evidence that it is clearly the best proxy of propensity for violence given this particular context. For example, the author finds a positive and statistically significant correlation between the IVV estimation and misbehavior reports.

# **B. Follow-Up**

The contents and structure of the intervention are oriented to directly affect noncognitive outcomes such as children’s violence and misbehaviors. The program may also have some indirect effects on academic outcomes, since changes in children’s noncognitive skills could affect the learning process and thereby improve academic performance (Cunha and Heckman, 2008; Heckman and Kautz, 2012). Considering this, we collected data on these two categories of outcomes.

Follow-up data on noncognitive outcomes were collected only from enrolled participants in school 13