rafmacalaba/datause-encoder-data huggingface dataset:
Female employment rates in the Northern and Eastern provinces have made limited progress in the last 4 years. Figure 6a indicates that the disparity between female employment in the North and East and the Sri Lankan average has fallen roughly from 15 to 12 percentage points, with more rapid progress in Northern province. Meanwhile, for youth, there is a smaller geographic disparity in employment rates. In the North, the youth employment rate is similar to the national average, while the gap has largely remained constant in Eastern province (Figure 6b).

**Figure 6: Employment rate for women and youth in the Northern and Eastern provinces vs. other parts of Sri Lanka (Source: LFS 2015)**

<!-- Start of picture text --> a. Employment rate of women b. Employment rate of youth<br>p y p y y<br>35% 35%<br>30% 30%<br>25% 25%<br>20% 20%<br>15% 15%<br>2011 2012 2013 2014 201 2011 2012 2013 2014 2015<br>Northern Province Eastern Province Sri Lanka Northern Province Eastern Province Sri Lanka<br>Employment rate Employment rate<br><!-- End of picture text --> To sum up, the most striking feature of labor markets in the Northern and East provinces is low employment rates, which mainly results from disproportionately low employment rates for women. In contrast, there is no evidence that these provinces have an unfavorable structure of employment. Agricultural jobs are less prevalent in these regions and a higher share of jobs are in the service sector. Workers in these provinces are more likely to be in wage employment and their wage earnings are no lower than in provinces other than Western. Given the importance of the gender employment gap, the next section probes in greater detail which types of women are not working in the North and East, compared to other regions, and which types of jobs are underrepresented among female workers.

# 3 Which workers and jobs are missing in the North and the East?

Here we further explore two demographic groups that appear to drive the employment gap between the Northern and Eastern provinces and the rest of the country: women and youth. Here we attempt to identify characteristics that may help us understand why these two demographic groups have lower employment rates. In Section 4, we will examine whether the level of these characteristics or their effect on employment rates is driving down employment rates for these groups.

8
**Trade and Civil Conflict: Revisiting the Cross-Country Evidence**<sup>*****</sup>

Massimiliano Calì<sup>†</sup> (World Bank) and Alen Mulabdic (Graduate Institute)

**Key words** : conflict, fragile countries, trade policy, commodity prices. **JEL Classification** : D74, F10, F13, F14, Q02, Q34.

> * The authors thank Nicolas Berman, Amir Fouad, Elena Ianchovichina, Philip Keefer, Nicola Pontara, Raju Singh, Quy Toan-Do and William Shaw for useful comments. All remaining errors are the authors’ alone.

> † Corresponding author: mcali@worldbank.org.
hourly wages and full-time/part-time status for those currently employed.<sup>18</sup> We also differentiate between families with and without children. Table A3 of the appendix presents the sample size for each country.

**In the time usage section of module 4, a randomly selected individual reported time spent in activities during the previous day.**<sup>19</sup> For this paper, we clustered the section’s 40 detailed activities into three groups—leisure, work, and home production—and we report the time allocation between these three categories. Because the time usage section was administered to a randomly selected person inside the dwelling, the final number of husbands and wives who responded is less than the overall sample used in the labor characterization.

**Figure 1: Distribution of time usage by country**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>ALB MNE MKD SRB BIH KOS<br>Home production Leisure Work<br><!-- End of picture text --> _Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey. _Note:_ Each bar represents the average time allocation between home production, leisure, and work of husbands and wives on each labor market state.

**Figure 1 shows the time allocation profile for the marginalized Roma population, without differentiating by gender.** In Albania, husbands and wives spent more time working outside of household than in other RRS countries, about 6.6 percent of total activity time. In Bosnia and Herzegovina and Kosovo, husbands and wives spent less time working, about 3.2 percent and 2.6 percent of the total time, respectively. In terms of time spent in home production, Bosnia and Herzegovina, Montenegro, and North Macedonia reported 33.8 percent, 32.5 percent, and 31.8 percent of total activity time, respectively; while in Albania husband and wives dedicated only 24.2 percent of total time to home production.

> (b) were seeking work in the past four weeks; and/or (c) are currently available for work, i.e. were available for paid employment or self-employment for the next two weeks.

> 18 The definition for the euro zone is used: part-time work is equal to 30 hours or fewer per week.

> 19 Activities are listed in table A1 of the appendix.

11
|**_Depend_**|**_ent variable:_**<br>(1)|**_Standardiz_**<br>(2)|**_ed PPVT_**<br>(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|_Panel A. OLS and IV second-stage estimations_|OLS w<br>variables|ith PDS-se<br>and full reg|lected<br>ressor set|IV with P<br>and f|DS-selected<br>ull regress|variables<br>or set|
||MHI-1|MHI-2|MHI-3|MHI-1|MHI-2|MHI-3|
|Maternal Mental Health (-)|0.006|0.006|0.008|-0.065|-0.032|-0.034|
||(0.0471)|-0.0233|(0.0253)|(0.501)|(0.245)|(0.265)|
|Observations|1,095|1,095|1,095|1095|1095|1095|
|Mother controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Child controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|
|_Panel B. First-stage estimation_||||MHI-1|MHI-2|MHI-3|
|Shock: Crop or livestock loss||||0.338***|0.689***|0.639***|
|||||(0.091)|(0.184)|(0.172)|
|Observations||||1095|1095|1095|
|Mother controls||||Yes|Yes|Yes|
|Child controls||||Yes|Yes|Yes|
|Household controls||||Yes|Yes|Yes|
|Weak identification F-Stats (Full IV set)||||13.61|13.65|11.75|

Table IV presents the effects of maternal mental health on children vocabulary at the age of 8 years. Dependent variable was measured using the standardized value of the PPVT test. Mental health indexes are standardized values of the SRQ-20 items using three different estimation approaches. Columns (1)-(3) present estimated coefficients using OLS and columns (4)-(6) show coefficients using IV. Both approaches were implemented using the option PDS-selected variables and full regressor available in the LASSO command. Selected instrument was suffering crop or livestock loss during pregnancy or within the first year of index child. Selected controls are mother's age, mother is indigenous, mother literacy, wealth index, living in urban area, consumption of durable goods index, and Height for age Z-score. * and ** indicate statistical significance at 10% and 5%, respectively. Robust standard errors in parentheses 29
has shown, for example, that most studies find a positive or non-significant effect of FD on hosts’ employment, wages and household well-being, a finding that disputes much of the popular perceptions on this question (Verme and Schuettler, 2021).

One of the important factors that has limited research on FDPs in the past was the chronic shortage of quality microdata, something that is quickly changing. Today, microdata on these populations can be found in two main publicly available repositories: The World Bank microdata library and the UNHCR microdata library recently established in collaboration with the World Bank. An analysis of these data repositories as of October 2022 shows that the WB microdata library has 576 data sets on refugees (454 dated after 2011) and 206 on Internally Displaced (142 dated after 2011), whereas the UNHCR microdata library has 274 data sets related to refugees (263 dated after 2011) and 34 data sets on IDPs (all dated after 2010).<sup>5</sup> This new research area is also generating significant innovations with the potential to expand research methods in the poverty measurement field. In the area of targeting based on means-tests, for example, a study has shown that Receiving Operations Characteristics (ROC) curves can be an effective decision making tool for humanitarian assistance programs (Verme and Gigliarano, 2019) while another study found that poverty differences in prediction methods for targeting purposes among refugees are attributable to few data fields suggesting that refugee homogeneity can make poverty predictions and targeting easier as compared to regular populations (Altindag et Al., 2021, Beltramo et al., 2019). The existence of the UNHCR refugee registration system, which can be regarded as a live census of refugees, has encouraged others to use cross-survey imputation techniques to estimate poverty among refugees even in the absence of income or consumption data (Dang and Verme, 2021, Beltramo et al., 2021). The mobile nature of refugees and IDPs also lends itself to experimenting with new methodologies to measure poverty with alternative methods such as mobile phones (Blumenstock et al., 2015; Pape et al. 2020, Wieser et al., 2021), or satellite imagery and remote sensing data (Abelson et al., 2014; Neal et al., 2016). For example, night lights or types of infrastructures captured by satellite imagery can provide gross estimates of poverty. In essence, poverty measurement among FD populations benefits from decades of developments in the poverty measurement field, but also provides new opportunities to expand the field because of the atypical characteristics of these populations.

> 5 Note that many microdata sets are present in both the WB and the UNHCR microdata libraries.

6
<mark>Fiegenbaum, A. and Karnani, A., 1991. Output flexibility—a competitive advantage for small firms.</mark> _<mark>Strategic management journal</mark>_ <mark>,</mark> _<mark>12</mark>_ <mark>(2), pp.101-114.</mark> <mark>Grant, M., Human, B. and Le Pelley, B., 1997. Seasonality ‘in insights—Tourism intelligencepapers.</mark> _<mark>British tourist authority. London: English Tourist BoardA5–A9</mark>_ <mark>.</mark> <mark>Hagspiel, V., Huisman, K.J. and Kort, P.M., 2016. Volume flexibility and capacity investment under demand uncertainty.</mark> _<mark>International Journal of Production Economics</mark>_ <mark>,</mark> _<mark>178</mark>_ <mark>, pp.95-108.</mark> <mark>Hernández-Guedes, C., Pérez-Rodríguez, J.V. and Manrique-de-Lara-Peñate, C., 2024. Input inefficiencies in the hotel industry. A non-radial directional performance measurement.</mark> _<mark>Tourism Economics</mark>_ <mark>, p.13548166241229603.</mark> <mark>Higham, J. and Hinch, T., 2002. Tourism, sport and seasons: the challenges and potential of overcoming seasonality in the sport and tourism sectors.</mark> _<mark>Tourism management</mark>_ <mark>,</mark> _<mark>23</mark>_ <mark>(2), pp.175-185.</mark> <mark>Hirsch, S., Mishra, A., Möhring, N. and Finger, R., 2020. Revisiting firm flexibility and efficiency: evidence from the EU dairy processing industry.</mark> _<mark>European Review of Agricultural Economics</mark>_ <mark>,</mark> _<mark>47</mark>_ <mark>(3), pp.971-1008.</mark> <mark>Jang, S.S., 2004. Mitigating tourism seasonality: A quantitative approach.</mark> _<mark>Annals of tourism research</mark>_ <mark>,</mark> _<mark>31</mark>_ <mark>(4), pp.819-836.</mark> <mark>Jones, P. and Siag, A., 2009. A re-examination of the factors that influence productivity in hotels: A study of the housekeeping function.</mark> _<mark>Tourism and Hospitality Research</mark>_ <mark>,</mark> _<mark>9</mark>_ <mark>(3), pp.224-234.</mark> <mark>Kleinknecht, A., Oostendorp, R.M., Pradhan, M.P. and Naastepad, C.W.M., 2006. Flexible labour, firm performance and the Dutch job creation miracle.</mark> _<mark>International Review of Applied Economics</mark>_ <mark>,</mark> _<mark>20</mark>_ <mark>(2), pp.171-187.</mark> <mark>Lado-Sestayo, R. and Fernández-Castro, Á.S., 2019. The impact of tourist destination on hotel efficiency: A data envelopment analysis approach.</mark> _<mark>European Journal of Operational Research</mark>_ <mark>,</mark> _<mark>272</mark>_ <mark>(2), pp.674-686.</mark> <mark>Lundtorp, S., 2001. Measuring tourism seasonality.</mark> _<mark>Seasonality in tourism</mark>_ <mark>,</mark> _<mark>3</mark>_ <mark>(3), pp.23-50.</mark> <mark>Manasakis, C., Apostolakis, A. and Datseris, G., 2013. Using data envelopment analysis to measure hotel efficiency in Crete.</mark> _<mark>International Journal of Contemporary Hospitality Management</mark>_ <mark>,</mark> _<mark>25</mark>_ <mark>(4), pp.510-535.</mark> <mark>Mera, A.J.M., Pozo, A.G. and Ollero, J.L.S., 2017. Labour flexibility and productivity in the andalusian lodging sector.</mark> _<mark>Revista de Estudios Regionales</mark>_ <mark>, (108), pp.17-41.</mark> 31
O.A.M.D.G.

Act per million inhabitants. This is related to both the incidence of drug crime and police efficiency. Previous studies, e.g. Martinez, Medina and Steiner (2001) and Martinez and Medina (2003) use arrests under the Drug Act as a proxy of local law and order enforcement. Regarding welfare and wealth, the database provides the official Indicator for Quality of Life. This indicator combines different variables about access to services. The dataset further covers the sum of assets held by banks in the municipality, as recorded by Financial Regulation. Finally, we can approximate inequality through the percentage of people in the two lowest income strata, in line with Bourguignon (2003).

# **Measuring violence**

As the introduction showed, the country is suffering from various kinds of violence. These can roughly be divided into first, violence that explicitly challenges the state, such as that proceeding from the guerrillas and paramilitaries, and second, common delinquency, proceeding from organized small gangs and unorganized crime. (A third force, drug cartels, in their original form belong largely to the past, since the Cali and Medellin cartels have been dismantled and much of the drug business has been picked up by both guerrillas and paramilitaries, and smaller fractionalized gangs.) As mentioned before, the perpetrators of the two kinds of violence use different means. Guerrillas and paramilitaries rely on displacement threats, kidnappings, extortion, massacres, and combat, the common delinquents prefer muggings, carjacking, and vendettas to adjust accounts, mostly among themselves. Not all of this violence is officially, let alone exhaustively, measured.<sup>13</sup> Regarding violence challenging the state,

> 13 Because of this there have been efforts to measure the actions of the armed groups more precisely. Vargas, Restrepo and Spagat (2004) have recently put together a dataset of guerrilla, paramilitary and other militia actions and their victims, carefully handpicking from NGO and church databases and local press articles. However, their database relies on the judgment of the authors, and for example consciously leaves out armed encounters that cannot be attributed to actors in the armed conflict. We therefore prefer to rely on Medina et al. (2001).

12
comparative purposes, we also show the English 1801-3 data where the top 1 percent share, as well as the steepness of the line after the top percentile, are similar to those of Rome. It seems that the main difference among the very rich in Rome 14 and England 1801-3 was that the people just below the very top of the income pyramid were, relative to the mean, somewhat less rich in England than in Rome. Finally, notice that in all three cases, the top 5 percent of income recipients received between 30 and just over 40 percent of total income. In contrast, the top 5 percent received about a quarter of total income in modern United States and United Kingdom, while the share is 27 percent in modern Chile and a third in Brazil.

Table 3 also reports several modern comparators. In all cases but one (Mexico), their top 1 percent share is less, and for most cases, much less, than that estimated for our sample of ancient societies. The low top 1 percent share combined with a low cut-off point (characteristic of advanced societies) betokens a distribution where, first, the richest 1 percent are not extravagantly rich (in contrast with the American Bill Gates or the Roman Marcus Licinus Crassus), and where, second, they are not very different from the rest of the population. Since we have already noted that Gini coefficients between the ancient and contemporary poor societies are not very different, this difference in the average top 1 percent shares between the ancient and modern implies that the link between top income share and overall inequality is not very strong among ancient societies.

**Life at the Bottom: The Unskilled Rural Wage Relative to Average Income**

22
Table 6 Wage Functions Women in Private Wage and in Nonvage Employment

|Independent|Wage Wo|rkers|Nonwage|Workers|
|---|---|---|---|---|
|variables|OLS<br> <br>(1)|Sel.Adj.<br>(2)|**OLS**<br>(3)|Sel.Adj.<br>(4)|
|Constant|-0.247|-0.260|-1.109*|-1.095*|
||(0.67)|(0.70)|(2.78)|(2.74)|
|Age|0.022<br>(1.19)|0.023<br>(1.14)|0.064*<br>(3.21)|<br>0.064*<br>(3.18)|
|Age squared|-0.0001<br>(0.54)|-0.0001<br>(0.56)|-0.0007*<br>(3.06)|-0.0008*<br>(3.03)|
|Tenure in|0.053*|0.054*|0.022*|<br>0.022*|
|current job|(3.41)|(3.38)|(1.77)|(1.88)|
|Tenure in current|-0.001*|-0.001*|-0.0004|-0.0004|
|**job**squared|(2.59)|(2.56)|(1.20)|(1.24)|
|Spline primary<br>education|0.046<br>(1.05)|0.045<br>(1.06)|0.099*<br>(3.15)|<br>0.098*<br>(3.14)|
|Spline secondary|0.056*|0.056*|0.032|0.034|
|education|(2.63)|(2.64)|(1.34)|(1.40)|
|Spline higher|0.014|0.017|0.021|0.017|
|education|(0.69)|(0.74)|(0.56)|(0.44)|
|School diploma|-0.015<br>(0.23)|0.025<br>(0.26)|a/|a/|
|Last school attended was|-0.230*|-0.229*|-0.038|-0.032|
|public|(2.97)|(2.94)|(0.35)|(0.29)|
|Job-training|-0.004|-0.006|0.099|0.096|
||(0.06)|(0.09)|(1.05)|(1.10)|
|Father's years of schooling|0.006<br>(0.67)|0.006<br>(0.65)|0.027*<br>(1.97)|<br>0.027*<br>(1.88)|
|Mother's years of schooling|0.020*<br>(1.69)|0.020*<br>(1.68)|0.014<br>(0.84)|0.013<br>(0.75)|
|Married or living together|0.175*<br>(2.43)|0.171*<br>(2.41)|0.006<br>(0.08)|0.005<br>(0.006)|
|Residence in Lima|0.150*|0.147*|0.002|0.013|
||(2.21)|(2.17)|(0.03)|(0.01)|

|**-34-**|
|---|
¶able 3: E dmated Yeaira<sup>of co</sup> btnfdolmo lby Age, Ede1ulom momd Gemier A. ChITei

|MlRes<br>Age<br>|||_<br>_<br>__<br>||_<br>__<br>5i|
|---|---|---|---|---|---|
|i<br>p<br>|meompRele<br>rimU<br> <br>|imtompRiete<br>secomdairy<br>|ConepRete<br>secoidaMry<br>|unpto4poet<br>seco_md_r_<br>|+yeanpost<br>_eco____<br>|
|16- 20<br>|3.37<br>|3.02<br>|1.65<br>|1.28<br>|0.00<br>|
|21- 25<br>|3.65<br>|3.89<br>|4.19<br>|4.07<br>|2.79<br>|
|26- 30|3.63<br>|4.13<br>|4.49<br>|4.32<br>|4.46<br>|
|31- 35<br>|3.84<br>|4.29<br>|4.70<br>|4.70<br>|4.87<br>|
|36- 40|3.97<br>|4.30<br>|4.69<br>|4.62<br>|4.90<br>|
|41- _45_<br>|4.40<br>|4.35<br>|4.53<br>|4.47<br>|4.75<br>|
|46- 50|3.89<br>|4.24<br>|4.12<br>|4.23<br>|4.71<br>|
|_51_-_55_<br>|3.74<br>|4.20<br>|3.97<br>|4.01<br>|4.77<br>|
|56-60|3.03<br>|3.32<br>|3.70<br>|3.83<br>|4.66<br>|
|61- 65<br>|2.46<br>|2.31<br>|2.25<br>|3.34<br>|3.06<br>|
|Total16-65|35.98|38.05|38.29|38.97|38.97|
|YenMnaRes<br>A||||||
|ge||||||
|16- 20|3.64<br>|2.85<br>|1.39<br>|1.21<br>|0.00<br>|
|21-25|3.20<br>|3.66<br>|3.92<br>|3.91<br>|3.26<br>|
|26- 30|2.96<br>|3.52<br>|3.33<br>|3.78<br>|4.61<br>|
|31- 35|1.64<br>|1.92<br>|2.94<br>|3.74<br>|4.23<br>|
|36- 40<br>|2.35<br>|2.83<br>|3.37<br>|3.71<br>|4.82<br>41|
|_41-_ 45|2.44|2.91<br>|3.37<br>|4.37<br>|.6<br>|
|46- s0<br>|2.09<br>|2.21<br>|3.38<br>|3.36<br>|4.65<br>|
|52_-55_|2.54|2.331<br>|2.47<br>|3.44<br>|4.63<br>|
|56- 60<br>|1.63<br>|1.85<br>|2.38<br>|4.16<br>|3.96<br>128|
|61- 65|0.93|0.11<br>|0.25<br>|0.24<br>|.<br>|
|Totall16.65|23.42|24.17|26.80|32.92|36.05|

# Notes:

1. Based on labor force experience<sup>of a cross-section of adults in urban areaswho</sup> worked at some points and<sup>are affiliatedto the socialsecurity system.</sup>

We assume that the "typical"<sup>manandwoman withineach schooling category</sup> accumulates contributions<sup>asasinglepersonfirst,getsmarried,and continuesto</sup> accumulate contributions<sup>asamarriedpersonafterwards. Themarriageageforthe</sup> "typical" man and woman<sup>istheageatwhich50%ofthe correspondingcategoryis</sup> married. See Appendix B<sup>for more details on data sources.</sup> 45
pact on the time spent on domestic and care responsibilities of female entrepreneurs, relative to male entrepreneurs. That is, women entrepreneurs took on a greater share of the increase in domestic and care work than men in response to a school closure policy. As a result of this extra domestic work burden, female-led businesses were 2 percentage points more likely to close their business in response to a school closure policy than male-led businesses and spent less time working in their business.<sup>8</sup> Married women with children are also more likely to explicitly report that caring for children, household chores and homeschooling affected their ability to focus on work than married men with children throughout the pandemic.

Third, we show how the impacts of school closure vary as a function of pre-existing beliefs and norms around gender equality in a country. We test how gender norms, measured in 2019 before the onset of the COVID-19 crisis, may have mitigated or exacerbated the gender-differentiated economic impacts of a school closure policy.<sup>9</sup> Heterogeneity analysis by beliefs and norms suggests that the school closure impacts were greater in societies with more conservative views towards gender equality relative to those with more liberal views. Specifically, women entrepreneurs with children in relatively more conservative societies were more likely to close their business and spend a greater amount of time on domestic and care work than women in more liberal societies, in response to a school closure policy.

– Norms around care differ across societies but are highly gendered within all societies women often shoulder a larger share of domestic and care work than men. Among parents, mothers are traditionally the main providers of childcare and accumulate a disproportionate amount of housework (Gimenez-Nadal and Sevilla, 2012). In this paper we find that a childcare shock and increased responsibilities within the home disproportionately impacted women entrepreneurs with children. Our results are directly relevant to the ongoing policy debate on advocating for care-related policies, as well as programs addressing gender norms in unpaid domestic and care work, and engaging men in fatherhood care duties (Devercelli

> 8The average closure rate over the six survey waves in 2020 was 17% for male-led businesses.

> 9Norms and personal beliefs towards gender equality were collected prior to the start of the pandemic from entrepreneurs in the FoB survey in December 2019 from 108 countries.

5