rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 450<br>E15 *<br>8,<br>2 100 o<br>2<br>co83: 550<br>G3 2<br>=<br>0<br>BRA MEX ARG VEN GTM COL CUB BOL CHL ECU NIC DOM PER HND<br><!-- End of picture text --> <!-- Start of picture text --> 20000<br>:<br>2 15000 ~<br>N, ‘<br>2<br>:32 | 10000 Fa<br>a© 5000<br>0<br>BRA MEX ARG VEN GTM COL CUB BOL CHL ECU NIC DOM PER HND<br><!-- End of picture text -->

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<!-- Start of picture text --> 15<br>a<br>——2<br>—<br>— 4<br>10<br>os<br>00<br>05<br>“1.0<br>rxchange_20105 rPerimeter_2010 Fulines_2010 Greuity_avg_2010 Intersection_density_2010 Srreet_density_2010 Sprawi_2010<br><!-- End of picture text -->
Lee, L. F., Rosenzweig, M. R., & Pitt, M. M. (1997). The effects of improved nutrition, sanitation, and water quality on child health in high-mortality populations. _Journal of Econometrics_ , _77_ (1), 209-235.

Luby SP, Agboatwalla M, Painter J, Altaf A, Billhimer W, Keswick B, Hoekstra RM (2006) Combining drinking water treatment and hand washing for diarrhoea prevention, a cluster randomised controlled trial. Trop Med Int Health. 2006 Apr; 11(4):479-89.

Murray _et al_ . (2012). Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380: 2197–223 Ngure, F. M., Humphrey, J. H., Mbuya, M. N., Majo, F., Mutasa, K., Govha, M., ... & Stoltzfus, R. J. (2013). Formative research on hygiene behaviors and geophagy among infants and young children and implications of exposure to fecal bacteria. _The American journal of tropical medicine and hygiene_ , _89_ (4), 709-716.

Patil, S. R., Arnold, B. F., Salvatore, A. L., Briceno, B., Ganguly, S., Colford Jr, J. M., & Gertler, P. J. (2014). The Effect of India's Total Sanitation Campaign on Defecation Behaviors and Child Health in Rural Madhya Pradesh: A Cluster Randomized Controlled Trial. PLoS medicine, 11(8), e1001709.

Perez, E, Cardosi, J, Coombes, Y, Devine, J, Grossman, A, Kullmann, C, Kumar, CA, Mukherjee, N, Prakash, M, Robiarto, A, Setiawan, D, Singh, U, and Wartono D (2012). What does it take to scale up rural sanitation, WSP working paper, June 2012.

Prüss-Ustün, A., Bartram, J., Clasen, T., Colford, J. M., Cumming, O., Curtis, V., Bonjour, S., Dangour, A. D., De France, J., Fewtrell, L., Freeman, M. C., Gordon, B., Hunter, P. R., Johnston, R. B., Mathers, C., Mäusezahl, D., Medlicott, K., Neira, M., Stocks, M., Wolf, J. and Cairncross, S. (2014), Burden of disease from inadequate water, sanitation and hygiene in low- and middleincome settings: a retrospective analysis of data from 145 countries. Tropical Medicine & International Health. doi: 10.1111/tmi.12329 Rabie, T. and Curtis, V. (2006), Handwashing and risk of respiratory infections: a quantitative systematic review. Tropical Medicine & International Health, 11: 258–267. doi: 10.1111/j.13653156.2006.01568.x Schmidt, W. P. (2014). The elusive effect of water and sanitation on the global burden of disease. _Tropical Medicine & International Health_ , _19_ (5), 522-527.

Victora CG, Adair L, Fall C, _et al_ . Maternal and child undernutrition: consequences for adult health and human capital. Lancet 2008;371:340–57 Waddington H, Snilstveit B, White H and Fewtrell L (2009) Water, sanitation and hygiene interventions to combat childhood diarrhoea in developing countries. Synthetic review 1. New Delhi: 3ie.

WHO (2011) Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Vitamin and Mineral Nutrition Information System. Geneva, World Health Organization.

WHO-Unicef (2013a) Levels and Trends in Child Mortality. Report 2013. World Health Organization 34
developed and less-developed episodes by restricting the sample to the OECD, and to the 1980s and 1990s. Following the suggestiorn of Goldfajn and Gupta (1999) that interest rate defenses are only successful when the banking system is strong, I restrict the sample to those episodes that were not preceded by a banking crisis in any of the previous five years in the rows labelled "No Banking Crisis". Since one might expect that tightening monetary policy will only be effective if the exchange rate is not too overvalued, I construct a crude indicator of real exchange rate overvaluation as the trend growth rate of the real CPI-weighted exchange rate versus the US in the previous twelve months. In the rows labelled "No Real Cvervaluation", Irestrict the sample to those episodes where this growth rate is below the median for the entire sample. To capture the notion that a given defense may be more credible if the monetary authority can back up its commitmen **t** o a fixed exchange rate with a large stock of foreign currency reserves, I also divide the sample in half according to non-gold reserves relative to imports, and consider only the high-reserves subsample in the rows labelled "High Reserves". I also proxy for the overall weakness of the country's external payments position using the average over the previous twelve months of that country's borrowing from the International Monetary Fund, expressed as a share of its quota in the organization. In the rows labelled "Low Quota Drawings", I consider only those episodes where the country has no obligations to the IMF according to this measure. Finally, I consider the argument that it is easier to defend against a speculative attack during a booming economy than during a recession, presumably because the domestic economy is better able to withstand any of the adverse effects of high interest rates during the high point in the business cycle. I measure this as the deviation of real per capita GDP growth in a country from its average, in the five preceding years, and then I divide countries in two at the median value of this deviation and consider only the booming economies in the rows labelled 'High FPoint in Cycle".

The results in Tables 5 and 6 are not very supportive of the conventional view that tightening monetary policy lowers the probability that a speculative attack ends in a devaluation of the currency. In the vast majority of cases, the mean change in monetary policy is not significantly different during failed and successful attacks, and changes in the stance of monetary policy are not statistically significant predictors of the outcome of the speculative attack. Only in five cases are the estimated effects statistically significant at the 95% level, notably in the OECD subsample. However, when one 14
This empirical approach allows us to compare districts that are similar in observable characteristics with the only dierence being the exposure to violence while dealing with non-observable variables with the use of panel data.

We run several models where the dependent variable is always FLFP rates expressed as a number between "zero" and "one". The variable of interest at the right hand side switches between dierent proxies of violence measured as ows at the district level for each year. In the Robustness section we provide alternative specications with the aim of shedding light on the non-linearity of the relation by using (i) a single dummy variable equal to "one" if there was an attack in a year in a given area; (ii) a set of dummy variables for dierent ranges of attacks number; (iii) number of attacks lagged one time period.

# 4.2 Quantile Regressions

Violence may have dierent eects depending on the initial level of FLFP in each district. For instance, the occurrence of a terrorist attack could reduce participation relatively more in areas with higher initial participation or, alternatively, have more impact in areas with lower rates. Or there might be no dierence in the eect along the distribution of rates. To formally test this possibility, we estimate a set of quantile regressions which basically look for estimates of parameters by minimizing the dierence between the explanatory variables and a given quantile of the dependent variable (as opposed to OLS that minimizes with respect to the mean). We run the estimations using ve points of the FLFP rates distributions: quantiles 10, 25, 50, 75 and 90; and use our three measures of violence (attacks, people wounded, or people killed).

# 4.3 Tobit Estimation

An empirical approximation to equation 6 consists of estimating a Tobit model augmented by the exposure to violence in each district. Due to the widely missing data on wages for each worker, we use "education" as a proxy. However, we include for control the average district wage, calculated using available data in each country (relative to the mean) to proxy for the market value of working (or outside option of remaining in domestic work). Finally, we control for the number of children under age seven in the household, as well as country, and time xed eects.

Under this setting, the interaction term _β_ 5 identies the average dierence in hours supplied between a woman whose spouse is employed compared to a woman whose spouse is unemployed when an attack occurs, measured as when one extra person dies or is wounded. We estimate equation (8) using only females who are not head of households, and we disregard presence or absence of other working household members. We remove India from this estimation, as mentioned, because we lack information on number of working hours, and we use the individual-level data (that is, we 10
_COTE D’IVOIRE: Volatility, Shocks and Growth_

# **ANNEX II: ADDITIONAL DATA AND INFORMATION**

## **Time Series and Trends**

Figure 14 shows data similar to that of Figure 1. Figure 14 shows the natural logarithms of GDP per worker in constant local currency units, that is logged 1995 CFA. We present this to make a few simple points. The time profile of GDP per capita and per worker are almost identical, as the labor force participation rate is very stable over time, so GDP per capita (an income measure) is (roughly) the same as output per worker (a productivity measure). The profiles over time of the logged data are the same as the un-logged profile, although the scale on the x-axis changes. And, data in constant CFA is very similar to data in constant dollars in this instance.

**Figure 14 Output per Worker Versus Trend, in Logs, Cote d’Ivoire, 1960-2002**

<!-- Start of picture text --> Log of Real Output (GDP) Per Worker<br>14.6<br>14.4<br>14.2<br>14<br>13.8<br>GDP per worker<br>HP Trend<br>13.6<br>13.4<br>ln(GDP per Worker)<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000<br><!-- End of picture text --> Figure 15 show cocoa yields per hectare in Cote d’Ivoire over time, a measure of the productivity of land. Note that the trend in yields increased over time. By this measure of productivity or efficiency, Cote d’Ivoire does pretty well. The pattern of cycles, that is oscillations of annual yields across trend, at first glance seems different from the more general cycle observed in the macroeconomic, trade, a commodity price variables. While cycles in cocoa prices and output seem to be in harmony with the trade and output
**-18-**

Table 6: Socio-economic Characteristics of Bulgarian Households, **1992**

|||Chacterict<br>:|DocileI<br>: Dil|e2<br>Do|cile3<br>Do|cile4<br>Dci|le 5<br>De|cile 6<br>Dci|le 7<br>Dec|ile.8<br>Doc|le, 9<br>Dile|<br>10<br>Nat|iosl<br>L209e|20|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|I.Hoselowd Size (persos)|3.3|2.7|3.0|3.2|3.3|3.2|3.1|3.2|2*f|2.3|3||
|Distnbution ofsize|||||||||||||
|1. Average number of childrn|1.0|0.7|0.9|1.0|1.0|1.0|0.9|0.8|0.7|0.4|0.8|0.9|
|2.Average Number of**sposue**|0.7|0.6|0.8|0.8|0.8|0.8|0.8|0.S|0.8|0.8|0.8|0.7|
|3.0thers(partont, relatives, etc)|0.6|0.4|0.3|0.4|0.5|0.4|0.4|0.6|0.3|0.1|0.4|0.4|
|.I.S&xof head of housebold (%)|||||||||||||
|Male|71|67|81|81|81|85|81|86|86|84|80|69|
|Female|29|33|19|19|19|15|19|14|14|16|20|31|
|1Ll. Ageofhea4 of household (y3s)|55.7|59.6|56.3|s7.3|52.2|53.3|53.4|_54.5_|54.6|55.3|54.8|57.6|
|Dtributionof age|||||||||||||
|under 30 year|2.7|1.8|2.7|3.2|3.2|2.7|2.7|0.5|3.2|0.5|2.3|2.3|
|30 - 39|14.9|10.5|10.9|18.6|_19.4_|12.3|11.4|14.5|10.0|6.8|13|12.7|
|40- 49|18.6|_15.4_|21.9|19.1|21.7|22.7|25.0|18.6|22.7|19.1|20.4|17.0|
|50_-_ 59|19.5|12.3|16.3|22.7|24.0|28.7|26.4|31.4|25.0|40.9|24.7|15.8|
|60 - 64|12.6|15.5|13.7|10.9|10.0|14.1|14.0|10.0|14.6|11.3|12.6|14.1|
|65 +|31.7|44.5|34.5|25.5|21.7|19.5|20.5|25.0|24.5|21.4|27.0|38.1|
|IV. Educatiom of head ofoehoid<br>()|||||||||||||
|No educatxio|14.0|15.9|11.8|11.4|12.2|6.4|11.8|15.9|10.0|10.0|11.9|15.0|
|Prinmaylevel|35.3|31.8|33.2|37.7|29.4|42.3|38.6|36.8|44.1|50.5|37.5|33.6|
|Sewmodary level|39.4|43.6|38.2|42.3|47.5|35.9|32.3|35.5|30.9|27.7|37.3|41.5|
|Semi high andhigh level|11.3|8.6|16.8|13.6|10.9|15.5|17.3|11.8|15.0|11.8|13.3|10.0|
|V. Economicactivity of headof h<br>alshod (%|||||||||||||
|Economically active|29.4|24.1|41.4|44.5|54.3|50.9|55.9|56.4|56.8|55.5|46.9|26.8|
|Unemployed|10.9|3.2|2.2|4.1|0.9|0.9|0.0|1.4|0.9|2.7|2.7|7.0|
|Economicaly initive (pensiners,<br>etc)|57.0|69.5|55.5|50.5|42.5|47.7|43.6|41.8|40.9|41,4|49|63.3|
|Others|2.8|3.2|0.9|0.9|2.3|0.5|0.5|0.5|1.4|0.5|1.3|2.9|
|Vl. Profesion ofheadofbasehold(%)|||||||||||||
|Legislatives, high level ofriclal|1.3|0.0|5.0|3.8|3.9|4.3|4.8|6.1|5.2|7.9<br>|4.6<br>|0.7<br>|
|Experts|9.0|10.9|15.0|19.1|11.9|14.4|18.1|13.0|18.5|12.7|14.6|9.8|
|Technicmnu|10.2|6.3|9.0|5.7|11.2|11.8|8.6|9.91|9.6|6.4|9|8.5|
|Ofriceworkern|6.4|_1.5_|6.0|0.9|3.1|0.9|5.6|5.3|6.7|1.6|3.9|4.2|
|Maintemancestaffintade|10.3|4.7|_15.0_|12.4|10.3|_8.5_|4.7|_5.3_|3.0|6.3|8.1|7.7|
|Skilled Labor (agriculum)|1.3|4.7|3.0|1.9|2.4|1.7|3.1|7.7|2.2|7.2|3.1|2.9|
|SkilLd hbor(industry)|19.2|21.9|19.0|24.8|28.6|27.1|22.9|3.0|25.9|19.0|23.5|20.4|
|_OperAtors_of machine and_Ines_|18.0|29.7|12.0|17.1|16.7|18.6|18.9|23.7|14.1|25.4|18.5|23.2|
|Unlkilled labor|17.9|6.2|10.0|8.6|7.9|8.5|10.2|18.3|7.4|12.7|9.6|12.7|
|Others|6.4|14.1|6.0|5.7|4.0|4.2|3.1|4.6|7.4|0.8|5.1|9.9|

Source: World Bank staff estirnats from 1992 Individual Budget of Howeholds, NSI, baed en montbly<sup>data for June, 1992.</sup>
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE As a result, SENELEC covered only 72 percent of its costs, leaving capital investment largely unfunded. The associated financial burden was approximately 1.5 percent of GDP.

In the water sector, it is estimated that the average total cost of producing utility water is $1.25 per cubic meters (m<sup>3</sup> ), while the average effective tariff was only $1.13 in 2008, which meant that the main utilities in the sector covered 91 percent of their cost through the tariff (actual numbers will depend on billing and collection rates). The associated financial burden was approximately 0.8 percent of GDP (figure 23).

**Figure 23. Underpricing of water a more serious issue in Senegal than elsewhere**

Financial burden of underpricing in 2007, as percentage of GDP <!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power Water<br>Senegal Nonfragile<br>Source : Derived from Briceño-Garmendia, Smits, and Foster (2009).<br>Note: GDP = gross domestic product; LIC = low-income countries<br>% GDP<br><!-- End of picture text --> Access to utility services in Senegal is relatively high by African standards. Nevertheless, access patterns remain regressive, though in the case of power a significant minority of lower-income households do have access (figures 24).

**Figure 24. Consumption of infrastructure services in Senegal varies by income quintile**

<!-- Start of picture text --> a. Mode of water supply, by income quintile b. Prevalence of connection to power grid among population, by<br>income quintile<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>Piped water Stand posts Power<br>Wells/boreholes Surface water<br>Percenage of population Percenatage of population<br><!-- End of picture text --> _Source_ : Banerjee and others 2008b _Note_ : Q1—first budget quintile, Q2—second budget quintile, and so on.

Because of inequitable access to power services in Senegal, subsidized tariffs are necessarily quite regressive. This has been confirmed by recent empirical analysis showing that poor households capture far less than their fair share of power and water subsidies in Senegal. In the case of power, poor households capture less than half of the subsidy that should reach them based solely on their share of the overall population; in the case of water, however, poor households capture close to 80 percent of the 39
<!-- Start of picture text -->
1.6<br>1.5<br>1.4<br>1.3<br>1.2<br>dak<br>g4<br>= 0.9<br>= 0.7<br>= 0.6<br>0.5<br>6.4<br>0.3<br>0.2<br>0.1<br>0<br>i) 5 10 15<br>OlConventional Occurrences<br>MENSO-scaled<br><!-- End of picture text -->
13 in the questionnaire.<sup>6</sup> Accuracy tests on the sibling mortality data collected in the RDHS are discussed in more detail elsewhere.<sup>7</sup> Moreover, we disaggregate sibling deaths by gender, differentiating between women who lost a brother (BROTHERDEATH) and a sister (SISTERDEATH) in 1994. As discussed below in more detail, these variables are likely to capture the direct effects of genocide at the household most accurately. Women still living with their parents who recorded a sibling death during the genocide have a very high probability of having experienced the sibling death very closely. This may shape fertility negatively, for example through the stigma attached to having been part of a conflict victim household.

# **b) Marital status and sex ratios**

The third proxy is a specific form of marital status, namely whether or not a woman is a widow (WIDOW). This proxy is available in both the pre-genocide and post-genocide RDHS. However, the RDHS questionnaire does not record the husband’s cause of death or date of death. It is hence impossible to distinguish conflict widows from HIV/AIDS widows or other widows. Still, the majority of widows in the 2000 and 2005 waves are very likely to be genocide widows (Brück and Schindler 2009) – that is, formerly wives of Tutsi husbands or moderate Hutu husbands. Given the gender-unbalanced mortality during the genocide, widowhood can be assumed to be exogenous. This point is underlined in Fig. 4, which depicts the distribution of current marital status (single, married, divorced, widowed) for women of various age groups in 1992, 2000, and 2005. The proportion of widows relative to married and unmarried women increased considerably after the genocide, particularly for younger women. For instance, the proportion of widows among women of age 25-34 years doubled between 1992 and 2000. Moreover, the proportion of widows rises steadily with older birth cohorts; a similar pattern is apparent for divorced women. This may indicate that once a

> 6 The original question was ‘How many years ago did [name of sibling] die?’ The 2005 RDHS was collected between February and July 2005, while the genocide occurred between April and July 1994. Hence, a woman interviewed in February 2005 whose sibling died in May 1994 lost her sibling ten (discrete) years before the interview, which translates into 1995. In contrast, the 2000 RDHS was collected between June and November 2000, so most respondents who lost a sibling during the genocide period would have reported the death occurring six years ago, which translates into 1994.

> 7 Verwimp and de Walque (2010) use the same RDHS data from 2000 to analyze excess mortality patterns related to the 1994 genocide. They find that the mean and median of both the siblings’ sex and date of birth are similar and conclude that there is no evidence for a systematic bias in the reporting of sibling deaths.
GVC participants – which is proxied throughout this paper by firms that both import and export – varies across countries. Around one in four manufacturers in Morocco, Tunisia and Jordan can be classified as GVC participants, a share that is significantly higher than the averages of lower-middle-income and upper-middle income economies (Figure 2). In Egypt, this share is particularly low, at 5% and remains below the average of lower-middle income countries. Tunisia and Morocco have opted for an economic model oriented toward exports and industrialization supported by a pro-active policy of attracting FDI (Saliola and Zanfei, 2009). This has enabled transfer of technology and know-how. For example, Morocco has become an important supplier of global value chains in the clothing, automobile parts and aerospace industries (AfDB, OECD and UNDP, 2014; Amraoui et al., 2019).

Fig.1 Imports and exports of goods in 2019 Fig. 2 Trading profiles in 2019 (% of firms) (% of GDP) <!-- Start of picture text --> 100<br>EGY<br>JOR<br>80<br>LBN<br>MAR<br>60<br>PSE<br>TUN<br>40<br>MENA<br>20<br>LMI<br>UMI<br>0<br>0 10 20 30 40 50 60<br>Percentage of GDP<br>Nontrader Importer Exporter GVC participant<br>Import Export<br><!-- End of picture text --> _Source:_ the authors’ calculations based on World Bank World _Source:_ the authors’ calculations based on EBRD-EIB-WB Development Indicators. _Note:_ regional share calculated as Enterprise Survey. simple average of the countries.

While the level of trade participation of firms in the MENA region is comparable to other lower-middle income (LMI) and upper-middle-income (UMI) economies, we find that innovation rates are particularly low (Figure 3). This lack of innovation, which has even been deteriorating in recent years, is also reflected in the low level of investment in intangible assets, except Morocco (Figure 4). According to the Global Innovation Index, an annual global ranking based on countries’ characteristics and performance in innovation, innovative activity in MENA economies lag behind Southeast Asia and East Asia (Morrar, 2019). The low and deteriorating investment levels, both in fixed and intangible assets, may be explained by worsening external financing conditions of private firms and crowding out of private sector investment (Akbas et al., 2021). This is worrisome as this could have negative consequences for the medium- to long-term economic prospects in MENA. Our findings are in line with previous literature documenting that developing countries do not invest enough in innovation despite its central role in closing the gap between developed and developing countries (Cirera and Maloney, 2017). During the COVID-19 crisis, innovation proved to be key for firms to be more resilient and to adapt better to the economic shock.

5