rafmacalaba/datause-encoder-data huggingface dataset:
in finding safe havens and psychological treatment. Such guarantee of a safe outside of marriage option may discourage the use of violence within a marriage, regardless of whether it was used as a bargaining tool or direct utility was derived from it.

Improvements in women’s outside options have been shown to trigger reduction in domestic violence. For example, Aizer (2010) demonstrates how better economic opportunities (relative to men) are associated with a reduction in hospitalizations due to domestic abuse in California. Stevenson and Wolfers (2006) find that adoption of unilateral divorce law in the US resulted into a drop in female homicide and domestic violence. In a more recent study, Brassiolo (2012) finds a decline in spousal conflict and in extreme partner violence in response to introducing less stringent divorce legislation in Spain. Heath (2012) finds an apparently perverse result in Bangladesh, where participation in the labor force is associated with increased risk of domestic violence, but only among women with low education or young age at marriage. The author suggests two alternative explanations for this heterogeneous effect. On the one hand, entering the labor force may not effectively change outside options of women with initially low bargaining power, but only marginally increases their bargaining power. In this case, husbands may use domestic violence instrumentally to counteract the increase in bargaining power, but only in marriages where women cannot flee. Alternatively, women facing domestic violence may selfselect to participate in the labor force in order to be able to break the cycle of violence.

In addition to a potential to lower domestic violence through providing safer outside options for women, DEAMs may be discouraging violence through other channels. Specifically, perpetrating violence becomes more risky, when DEAM provides a convenient way to report it for any witness.

As in any developing country, implementation is a challenge. On average, each DEAM has 2.6 officers, often below the recommended number of officers based on the size of the city (SENASP 2010). Other limitations include limited hours of operation, or lack of specific services. A 2010 report (Sardenberg and Queiroz de C. Gomes 2010), which consisted of surveys & interviews in 40 DEAMs<sup>3</sup> located in capital cities of all but two states, reports that 65 percent were only open during commercial hours, and only 58% investigated homicides. Over half offered psychological orientation to victims, but less than a fourth offered legal assistance. However, in another report

> 3 The specific stations were chosen “for their relevant role in National Policy Confronting Violence against Women” (page 13).

6
Furuta, Marie and Sarah Salway. 2006. “Women’s position within the household as a determinant of maternal health care use in Nepal.” _International Family Planning Perspectives_ 32(1): 17-27.

Gammage, Sarah, Naila Kabeer & Yana van der Meulen Rodgers. 2015. “Voice and agency: where are we now?” _Feminist Economics_ .

Ghuman, Sharon, Hedy Lee, and Herbert L. Smith. 2006. "Measurement of women's autonomy according to women and their husbands: Results from five Asian countries." _Social Science Research_ , 35(1): 1-28.

Golla, Anne Marie, Anju Malhotra, Priya Nanda, and Rekha Mehra. 2011. “Understanding and measuring women’s economic empowerment: definition, framework and indicators.” ICRW Report, Washington, DC.

<mark>Haggard, P. and Eitam, B. eds., 2015.</mark> _<mark>The Sense of Agency</mark>_ <mark>. Oxford University Press.</mark> Heath, Rachel. 2014. “Women’s access to labor market opportunities, control of household resources, and domestic violence.” _World Development_ 57: 32-46.

<mark>Heckman, J.J., Stixrud, J. and Urzua, S., 2006. The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior.</mark> _<mark>Journal of Labor Economics</mark>_ <mark>,</mark> _<mark>24</mark>_ <mark>(3), pp.411-482.</mark> <mark>Heckman, J.J. and Kautz, T., 2012. Hard evidence on soft skills.</mark> _<mark>Labour economics</mark>_ <mark>,</mark> _<mark>19</mark>_ <mark>(4), pp.451-464.</mark> Humphreys, Macartan, Raul Sanchez de la Sierra, Peter van der Windt, 2012. “Social and Economic Impacts of Tuungane: Final Report on the Effects of a Community Driven Reconstruction Program in Eastern Democratic Republic of Congo.” Columbia University, June 2012.

Ibrahim, Solava, and Sabina Alkire. 2007. “Empowerment and agency: a proposal for internationallycomparable indicators.” _Oxford Development Studies_ 35:4: 379-403.

Jejeebhoy, Shireen J. 2002. “Convergence and Divergence in Spouses’ Perspectives on Women’s Autonomy in Rural India.” _Studies in Family Planning_ 33(4): 299-308.

<mark>Johnson, S., 2015. Capacities to Aspire and Capacities to Save: A Gendered Analysis of Motivations for Liquidity Management.</mark> Kabeer, Naila. 1999. “Resources, agency, achievements: reflections on the measurement of women’s empowerment.” _Development and Change_ 30(3): 435-464.

Kabeer, Naila. 2001. “Reflections on the measurement of women’s empowerment.” In _Discussing Women’s Empowerment—Theory and Practice_ . Sida Studies No. 3. Stockholm: Novum Grafiska AB.

<mark>Kelley, T.M. and Stack, S.A., 2000. Thought recognition, locus of control, and adolescent wellbeing.</mark> _<mark>Adolescence</mark>_ <mark>,</mark> _<mark>35</mark>_ <mark>(139), pp.531-550.</mark> Khwaja, Asim I. 2005. “Measuring empowerment at the community level: an economist’s perspective,” in: Deepa Narayan (ed.) _Measuring Empowerment: Cross-disciplinary Perspectives_ , pp. 267–284 (Washington, DC: The World Bank).

Kishor, Sunita. 2000. “Empowerment of women in Egypt and links to the survival and health of their infants.” in H.B. Presser, G. Sen (Eds.), _Women’s Empowerment and Demographic Processes_ , Oxford University Press, Oxford (2000), pp. 119–156.

62
error, _μit_ + _εit − βεi,t−_ 1 .

Dropping all the inputs to focus solely on the persistence coefficient, the expected bias due to both of these sources is The coefficient is biased upward by learning heterogeneity and downward by measurement error. These effects only cancel exactly when Cov( _ηi, yi,t_<sup>_∗_</sup> _−_ 1<sup>) =</sup><sup>_σ_</sup> _ε_<sup>2</sup><sup>_β_(Arellano,2003).</sup> Furthermore, bias in the persistence coefficient leads to bias in the input coefficients, _α_ . To see this, consider imposing a biased _β_<sup>ˆ</sup> and estimating the resulting model The error term now includes ( _β − β_<sup>ˆ</sup> ) _yi,t−_ 1. Since inputs and lagged achievement are generally positively correlated, the input coefficient will, in general, by biased downward if _β_<sup>ˆ</sup> _> β_ . The precise bias, however, depends on the degree of serial correlation of inputs and on the potential correlation between inputs and learning heterogeneity that remains in _μit_ .

This is more clearly illustrated in the case of the restricted value-added model (assuming that _β_ = 1) where: Therefore, if indeed there is perfect persistence as assumed and if inputs are uncorrelated with _ηi_ , OLS yields consistent estimates of the parameters _α_ . However, if _β <_ 1, OLS estimation of _α_ now results in two competing biases. By assuming an incorrect persistence coefficient we leave a portion of past achievement in the error term. This misspecification biases the input coefficient downward by the first term in (4). The second term captures possible correlation between current inputs and omitted learning heterogeneity. If there is none, then the second term is zero, and the bias will be unambiguously negative.

6
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE of cheaper hydro power reduced underpricing (figure 6). These hidden costs drained around 1.8 percent of Sierra Leone‘s GDP. NPA‘s hidden costs are particularly burdensome considering that access rates are under 5 percent of the population. While Sierra Leone‘s overall burden of hidden costs is average for West Africa, its resources forgone due to power losses are the highest in the region (figure 7). And in comparison to South Africa, Africa‘s best performer in terms of hidden costs, Sierra Leone‘s hidden-costs burden is high.

**Table 4. Large hidden costs corrode revenues from NPA**

||Power billings<br>(GWh/year)|System<br>losses<br>(%)<br>Collection<br>ratio (%)<br>Cost<br>recovery<br>benchmark<br>(US$/kWh)|Average<br>revenue<br>(US$/kWh)|Average<br>effective<br>tariff<br>(US$/kWh)<br>Total hidden costs<br>(US millions/<br>year)<br>(% revenues)|
|---|---|---|---|---|
|2003|69|33<br>93<br>0.30|0.24|0.22<br>12<br>74|
|2004|53|33<br>99<br>0.30|0.23|0.22<br>9<br>69|
|2005|33|33<br>90<br>0.34|0.29|0.22<br>8<br>81|
|2006|29|38<br>89<br>0.47|0.23|0.29<br>11<br>161|
|2007|28|29<br>89<br>0.48|0.18|0.29<br>9<br>178|
|2008*|—|42<br>50<br>—|—|0.41<br>—<br>—|
|2009*|73|45<br>88<br>0.44|0.21|0.36<br>21<br>141|
|2010*|226|45<br>88<br>0.28|0.21|0.31<br>33<br>69|
|_Source_: World B<br>_Note_: — = data<br>**Figure 6. Und**<br>0<br>50<br>100<br>150<br>200<br>**% of the revenues**|ank 2005; World<br>not available. * du<br>**erpricing and n**<br>2003|Bank 2007; NPA data; and World Bank staff e<br>e to conflicting data sources estimates must b<br>**etwork losses have been major challe**<br>2004<br>2005<br>2006|stimates<br>e interpreted wit<br>**nges at differe**<br>2007|h caution.<br>**nt periods for NPA**<br>2009<br>2010|
|||Losses<br>Underpricing<br>C|ollection Inef|ficiencies|

18
> <sup>14</sup>

studies of programmatic parties (e.g., Jones 2005 in Latin America and Kitschelt, et al. 1999 in Eastern Europe) and finds a high correlation. Moreover, in this same study, he presents evidence that the extent to which countries exhibit programmatic parties has a similar impact on government policy choices as the number of years of continuing competitive elections.

All of these regime characteristics are new to the conflict literature, which generally uses the degree of democratization to characterize polities, as measured broadly by the 20 point Polity democracy-autocracy measure (Jaggers and Gurr 1995). Although Polity has much greater year coverage than any other political dataset, it does not have data on political parties that are an essential element of the analysis here. In addition, the Polity democracyautocracy measure are more difficult to interpret than the DPI variables. The Polity democracy measure is a composite of other Polity variables, each assigned a weight and then added together to give the final democracy score. By construction, similar democracy measures over time can be associated with significantly different configurations of the component indicators, as long as they add up to the same final number. Similarly, quite different models for constraining executives or electing leaders could drive similar values of the component variables that comprise the democracy measure.

Finally, the qualitative nature of the Polity variable also makes it more susceptible to bias. This is particularly important in civil war research. Hegre, et al. (2001) and others, for example, find that countries with intermediate values of the Polity measure are most vulnerable to civil war. Their operationalization of the Polity measure is common in the literature. However, countries can fall into the intermediate category for several reasons. One is the violent overthrow of the regime, which leads Polity evaluators to downgrade the country with respect to the institutionalization of political competition (PARREG), one of the elements of the Polity democracy index. To the extent that this is the case, the Polity measure is, by construction, endogenous to insurgency.

# **Data and Specification**

While the political measures used in the analysis below are new to the conflict literature, the measure of insurgency, control variables and the econometric specifications used follow the literature closely. Specifically, the core specification uses the Correlates of War conflict data (Doyle and Sambanis) and the control variables employed by Fearon and Laitin (2003), where further details on these variables can be found. Among the criteria to
**Table 3: Children 0-5 years old Baseline (2006) Characteristics**

||**Tre**|**atment**|**Com**|**parison**|**Difference**|**P-Value***|
|---|---|---|---|---|---|---|
|**Variable**|**Mean**|**Std Dev**|**Mean**|**Std Dev**|||
|**Child characteristics**|||||||
|Age (in months)|30.45|(0.58)|29.95|(0.58)|0.50|0.51|
|Female (=1)|0.50|(0.01)|0.49|(0.01)|0.01|0.59|
|**Parental characteristics**|||||||
|Mother present (=1)|0.97|(0.01)|0.96|(0.01)|0.01|0.13|
|Mother's age|31.62|(0.33)|32.08|(0.32)|-0.45|0.38|
|Mother's years of education|5.57|(0.19)|5.61|(0.20)|-0.03|0.99|
|Father present (=1)|0.87|(0.02)|0.90|(0.02)|-0.03|0.30|
|Father's age|36.10|(0.39)|36.58|(0.40)|-0.48|0.33|
|Father's years of education|6.06|(0.31)|6.27|(0.34)|-0.20|0.65|
|**Household characteristics**|||||||
|Number of household members|5.06|(0.09)|5.21|(0.09)|-0.15|0.26|
|Number of members 0-5 years old|2.18|(0.03)|2.21|(0.03)|-0.03|0.44|
|Own land (=1)|0.87|(0.02)|0.92|(0.02)|-0.05|0.15|
|Value of Assets (household, animals, farm and ent<br>|erprise e<br>|quipment)<br>|||||
|_Quartile 1_(=1)|0.22|(0.03)|0.23|(0.02)|-0.01|0.77|
|_Quartile 2_(=1)|0.28|(0.03)|0.25|(0.02)|0.03|0.27|
|_Quartile 3_(=1)|0.26|(0.04)|0.28|(0.03)|-0.02|0.71|
|_Quartile 4_(=1)|0.23|(0.02)|0.24|(0.02)|-0.01|0.71|
|**Child preventive medical care**|||||||
|Visit by child 0-23 months in last 4 weeks (=1)|0.21|(0.02)|0.24|(0.03)|-0.03|0.56|
|Visit by child 24-59 months in last 4 weeks (=1)|0.08|(0.02)|0.14|(0.02)|-0.05|0.11|
|12-23 month old is fully immunized (=1)<br>* P-values are for cluster-adjusted t-test or chi-square|0.62<br>d tests o|(0.06)<br>f difference.|0.67<br>|(0.06)|-0.05|0.52|

22
# **Spatial Heterogeneity of COVID-19 Impacts on Urban Household Incomes: Between- and Within-City Evidence from Two African Countries**

Yele Maweki Batana, World Bank Shohei Nakamura, World Bank<sup>1</sup> Anirudh Rajashekar, World Bank Mervy Ever Viboudoulou Vilpoux, World Bank Christina Wieser, World Bank Keywords: Accessibility; Mobility; Urban labor markets; Poverty; COVID-19 JEL Codes: R14, R21, R41, O18

> 1 Corresponding Author: snakamura2@worldbank.org. We would like to thank Ambar Narayan, Nistha Sinha, Tom Bundervoet, David Newhouse, Megha Mukim, and Judy Baker for their useful comments. We would also like to appreciate the support for data access from Alemayehu Ambel, David Newhouse, Siwei Tian, and Feraud Seuyong. 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.
**Table 2: Poverty Transitions from Pre- to Post-fiscal Policy, Percent A: Men**

|||**Post-fiscal**|**APL**|**Post-fiscal**|**RPL**|
|---|---|---|---|---|---|
||**Poverty Status**|**Poor**|**Non-poor**|**Poor**|**Non-poor**|
|Pre-fiscal|Poor|15.92|4.84|20.84|5.84|
||Nonpoor|1.14|78.1|1.37|71.95|
|**B: Women **||||||
|||**Post-fiscal**|**APL**|**Post-fiscal**|**RPL**|
||**Poverty Status**|**Poor**|**Non-poor**|**Poor**|**Non-poor**|
|Pre-fiscal|Poor|19.93|6.37|26.57|6.28|
||Nonpoor|1.56|72.14|1.86|65.29|

_Source_ : Authors’ calculations; ESS 2018/19 data. _Note_ : APL = absolute poverty line; RPL = relative poverty line.

As Figure 9 showed, the fiscal system shifts income distribution to the right. The mobility matrix in Table 3 also shows the share of individuals who transited from one income group to another as they moved from (1) market income to (2) disposable income to (3) final income.

**Table** **<u>3: Mobility Matrices from Market to Disposable and Final Income, Percent</u>**

|**A: Men**||||||||||
|---|---|---|---|---|---|---|---|---|---|
|Y<sup>d</sup><5050|96.3|3.3|0.2|0|76.5|17.9|4.5|0.9|20.8|
|B<sup>f</sup>|3.6|91.4|4.8|0.1|5.3|63.4|27.7|3.6|15.4|
|C<sup>g</sup>|0.2|2.8|95.5|1.5|0.6|5.7|74.7|19|21|
|Y>11053|0.2|0.3|2.1|97.4|0.5|0.3|5.3|93.9|42.9|
|**B: Women**||||||||||
|||**Di**|**sposab**|**le Income**<sup>**b**</sup>|||**Fin**|**al Income**<sup>**c**</sup>|**Percent of**|
|**Market Income**<sup>**a**</sup>|**Y<5050**|**B**|**C**|**Y>11053**|**Y<5050**|**B**|**C**|**Y>11053**|**Population**|
|Y<sup>d</sup><5050<sup>e</sup>|96.9|2.9|0.2|0.1|75.8|20.2|2.9|1.2|26.3|
|B<sup>f</sup>|3.7|92|4.2|0.1|6.6|64.6|26|2.8|17.4|
|C<sup>g</sup>|0.2|3.9|94.4|1.5|1.1|7|73.6|18.2|21.6|
|Y>11053<sup>h</sup>|0.2|0.1|3.2|96.5|0.5|0.5|6.4|92.5|34.7|

_Source_ : Authors’ calculations; ESS 2018/19 data. _Note_ : a. Market income refers to pretax income and private transfers.

b. Disposable income = Market income – direct tax + direct transfers.

c. Final income = Consumable income + in-kind transfers for education and health.

d. Y = Individual income for the corresponding income concept.

e. Income group below birr 5,050: a calipered absolute poverty line that could produce the same number of poor people as the Household Consumption Expenditure Survey using “disposable income.” f. Income group between the absolute poverty line and 7,287, the 40<sup>th</sup> percentile of market income.

g. Income group between 7,290 and the official poverty line at June 2019 prices.

h. Income group above the official poverty line.

There appears to be less transition or movement across income groups from market to disposable income—the diagonal values are above 92 percent for both women and men. In movement from market to final income, however, there is some change in income groups. Although it appears that the majority do not change income groups along the transition from market income to disposable and then to final income, net fiscal policy allows a higher share of both women and men to make 18
# **References**

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35
|log exp-dest<br>(7)|1.125***<br>[0.008]<br>155,002<br>0.387<br>Y<br>Y<br>Y|n columns (3)-(7)|ent variable is the|of export revenue||
|---|---|---|---|---|---|
|rs<br>log exp-dest<br>(6)|0.468***<br>[0.012]<br>155,002<br>0.244<br>Y<br>Y<br>Y|od 2005-2009. I|n (5) the depend|riable is the log|*10% level.|
|exporte<br>log exp<br>(5)|1.593***<br>[0.013]<br>29,798<br>0.479<br>Y<br>Y|ms in the peri|09. In colum|dependent va|l, **5% level,|
|log sales<br>(4)|0.613***<br>[0.020]<br>29,798<br>0.254<br>Y<br>Y|ufacturing fr|eriod 2005-20|) and (7) the|r. ***1% leve|
|log empl<br>(3)|0.571***<br>[0.011]<br>29,798<br>0.264<br>Y<br>Y|d of all man|rters in the p|columns (6|by frm-yea|
|rms<br>log sales<br>(2)|0.577***<br>[0.004]<br>178,674<br>0.216<br>Y<br>Y|le is compose|acturing expo|stinations. In|kets, clustered|
|all f<br>log empl<br>(1)|0.454***<br>[0.003]<br>178,674<br>0.225<br>Y<br>Y|imation samp|d of all manuf|frm in all de|errors in brac|
|le:|-age<br>-destination-age<br>ear efects<br>cts<br>n-year efects<br>fects|umns (1)-(2) the est|n sample is compose|port revenue of the|n. Robust standard|
|iab|rt<br>rt<br><br>ed<br>-y<br>fe<br>io<br> e|col|io|ex|io|
|ar|e<br>po<br>po<br>s.)<br>ar<br>try<br> e<br>at<br>try|n|mat|tal|nat|
|Dep.v|log ag<br>log ex<br>log ex<br>N (ob<br>R-squ<br>indus<br>region<br>destin<br>indus|Notes: I|the esti|log of to|by desti|

26
|Index<br>Type|Standardized<br>Across<br>Countries|Time<br>Period|Description|Pre-COVID-19 Ke|rnel Density Graph|
|---|---|---|---|---|---|
|Fraction|Yes|Pre-<br>COVID-<br>19|For this index we collapse pre-COVID variables into<br>income categories that are consistent across countries.<br>Because the index only draws from the rich pre-COVID<br>data, we are able to include 12 income categories<br>available across all four countries: remittances; in-kind<br>assistance from friends and family; investments and<br>savings; income from properties; pension; non-farm<br>enterprise; crop sales and consumption; livestock sales;<br>livestock products sales and consumption; wages;<br>government and NGO assistance; and other.|0<br>1<br>2<br>3<br>4<br>Density<br>.2<br>.4<br>.6<br>.8<br>1<br>Ethiopia<br>0<br>1<br>2<br>3<br>4<br>Density<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>Specialization Index<br>Nigeria|0<br>1<br>2<br>3<br>4<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>Malawi<br>0<br>1<br>2<br>3<br>4<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>Specialization Index<br>Uganda|
|||||4<br>Ethiopia|4<br>Malawi|
||||For this index we collapse pre-COVID variables into<br>income categories that vary across country. As a|0<br>1<br>2<br>3<br>Density|0<br>1<br>2<br>3|
|Fraction|No|Pre-<br>COVID-<br>19|result, this index allow us to observe income sources at<br>the most granular level available in the pre-COVID<br>data. We generate this index using 19 income sources<br>in Ethiopia, 21 in Malawi, 15 in Nigeria, and 13 in<br>Uganda.|0<br>.2<br>.4<br>.6<br>.8<br>1<br>0<br>1<br>2<br>3<br>4<br>Density<br>Nigeria|.2<br>.4<br>.6<br>.8<br>1<br>0<br>1<br>2<br>3<br>4<br>Uganda|
|||||0<br>.2<br>.4<br>.6<br>.8<br>1<br>Specialization Index|0<br>.2<br>.4<br>.6<br>.8<br>1<br>Specialization Index|

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