rafmacalaba/datause-encoder-data huggingface dataset:
<mark>male nonparticipation. In that experiment, wives’ home production times do not change, but, interestingly, males increase their time in all labor markets. This reflects how household optimization processes interact with policies, making spouses reallocate the portfolio activities. In this experiment, as wives go more to the labor market, husbands increase home production time, and some decide to not participate in the labor market.</mark>

**Table 9: Observed and simulated mean accepted wages**

||Benchmark|Arrival|Laid-off|Wages|Pt|Home_P|Fertility|Search|
|---|---|---|---|---|---|---|---|---|
|pt_m|1.3|1.3|1.3|1.5|1.3|1.3|1.3|1.3|
|ft_m|0.7|0.7|0.7|0.7|0.7|0.7|0.7|0.7|
|pt_w|1.2|1.2|1.2|1.5|1.1|1.2|1.2|1.2|
|ft_w|0.6|0.6|0.6|0.7|0.6|0.6|0.6|0.6|

_Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey.

_Note:_ Abbreviations: FT= employed full-time; PT= employed part-time. This table presents the converted values in 2011 USD PPP dollars.

**Figure 6: Mean time in home production by labor market state (%)** Males without children Males with children

<!-- Start of picture text --> 60 60<br>50 50<br>40 40<br>NP NP<br>30 30<br>UE UE<br>20 PT 20 PT<br>10 FT 10 FT<br>0 0<br>Females without children Females with children<br>60 60<br>50 50<br>40 40<br>NP NP<br>30 30<br>UE UE<br>20 PT 20 PT<br>10 FT 10 FT<br>0 0<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 in home production of husbands and wives on each labor market state.

# **7. Gender Gap Decomposition**

**The gender gap decomposition equalizes female parameters to male values; this describes a Roma labor market and time use without gender differences.**<sup>34</sup> Table 10 <mark>presents the definitions for the six</mark>

> 34 As males’ search cost is higher than that for females, the search costs implemented in this experiment are those of the females.

28
bands derived from the block bootstrap procedure.<sup>33</sup> For the sample as a whole, shown in the top graphs, average exposure rose steadily until the onset of the global crisis in 2007, from just under 3 percent of trend GDP in the initial window to over 6 percent at the peak, and stagnated – or even declined slightly – post-crisis. In the final window, exposure remained above 5 percent of trend GDP, pointing to a substantial (and statistically significant, according to the 95 percent confidence bands) rise in financial globalization since the beginning of the period of analysis.

The middle graphs show that the rise was especially marked among advanced countries – from 3 percent of trend GDP initially, to over 8 percent at the pre-crisis peak and a subsequent plateau at around 8 percent. While the initial rise is statistically strongly significant, the slight post-crisis decline that the figures suggest is not, given the overlap between the 95 percent confidence regions at the peak and at the end of the sample.

The rise was also noticeable, albeit of smaller magnitude, among emerging countries (bottom graphs) – from 2-3 percent of trend GDP to just under 5 percent at the peak. After the post-crisis decline, the estimated exposure of flows to common shocks in the final window remains above the initial-window level, but the poor precision of the estimates does not allow firm conclusions regarding the extent, or even the presence, of the rise in globalization between the beginning and the end of the sample. Overall, these results do suggest a cycle of financial globalization ascent and subsequent stagnation, especially pronounced among advanced countries, with the global financial crisis separating the two phases.<sup>34</sup> 33We exclude Ireland from these figures because of its extremely high exposure to common shocks (as shown in Table 6). Including Ireland exaggerates the rising time profile shown in the figure.

> 34Appendix B shows that the results for advanced and emerging countries are little changed if developing countries are added to the analysis. In turn, developing-country outflows exhibit also increasing average exposure to common shocks in their outflows prior to the crisis. Inflows, however, show a somewhat irregular pattern, with declining exposure at first, followed by a sustained rise after 2005. One possible reason for this differential behavior vis-a-vis the other country groups is the larger role of official flows (including development assistance) in developing-country inflows.

28
|Year 1980|-0.102<sup>***</sup>|-0.110<sup>***</sup>|-0.070<sup>**</sup>|-0.082<sup>**</sup>|-0.046<sup>*</sup>|-0.403<sup>***</sup>|-0.024|-0.006|-0.113<sup>**</sup>|0.049|0.068|-0.259<sup>***</sup>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(0.035)|(0.037)|(0.028)|(0.037)|(0.024)|(0.095)|(0.050)|(0.040)|(0.051)|(0.044)|(0.051)|(0.079)|
|Year 1981|-0.152<sup>***</sup>|-0.157<sup>***</sup>|-0.063<sup>**</sup>|-0.109<sup>**</sup>|-0.056<sup>**</sup>|-0.523<sup>***</sup>|0.021|0.040|-0.054|0.078|0.040|-0.370<sup>***</sup>|
||(0.034)|(0.038)|(0.028)|(0.038)|(0.020)|(0.115)|(0.038)|(0.029)|(0.045)|(0.046)|(0.024)|(0.098)|
|_N_|8823|8823|8823|8823|8823|8577|1715|1715|1715|1715|1715|1661|
|adj._R_<sup>2</sup>|0.035|0.038|0.083|0.032|0.020|0.047|0.011|0.029|0.179|0.028|0.066|0.033|

Notes: All regressions add birth governorate fixed-effects, household size and gender. Standard errors are clustered at the birth governorate level. *** p<0.01, ** p<0.05, * p<0.1.

15
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49
these two studies. We also highlight an economic loss about 3.1 higher than found in Kešeljević and Spruk (2023) (10) although the authors also identify dramatic human development and infant mortality costs of the Syrian civil war. To the best of our knowledge, the estimated cumulative loss of GDP _per capita_ during the CAR civil war is among the highest in the academic literature, just below the Libyan case between 2011-14 (12)– which was a mix of revolts following the _Arab_ – _Spring_ , a civil war and an international war and comparable in magnitude with the local cumulative loss of GDP found in the Donetsk and Luhansk _Oblasts_ of Ukraine between 2013-16 (13).

It is important to note that we do not discuss here any potential solution for the CAR, including the potential role of official development aid, structural reforms and financing programs (1,30– 34). The remainder of the paper is structured as follows: The first section presents the methodology used to estimate the socio-economic impacts of the 2013 civil war in CAR as well as our identification strategy. The second section elaborates on the key variables used and data. The third section presents the empirical results. The last section provides some concluding remarks.

# **Methodology and identification strategy**

To assess the socio-economic impacts of the 2013 civil war in CAR, we consider country-year level data, with a post-`treatment’ period starting in 2013. We distinguish two phases in the civil war: the period 2013-14, when the Séléka rebel coalition took over Bangui and a subsequent period (2015-17) when the conflict has fragmented into several localized skirmishes concentrated mostly in regions (called locally _Préfectures_ ) close to the northern and western borders and in the East of the country. We do not go beyond 2017 to avoid noisy effects possibly coming from more recent events (e.g., Wagner group starting to operate in 2018, leading to the cut of budget support from France and other traditional partners; the COVID-19 lockdowns, the crypto-asset law of 2022). As the main macro-socio indicators, we focused on GDP _per capita_ , industrial value added and manufacturing value added (over GDP) from the World Development Indicators (35), satellite NTL from the DMSP data (22), and a reduced version of the (harmonized) human asset index.

We use a SCM to estimate the expected levels of our key outcomes in the absence of war and compare theses to the observed actual series to quantify the war’s impact on CAR’s socioeconomic development during the period 2013-17. This method provides causal estimates and incorporates pre-intervention dynamics, especially with small-N settings. It has previously been used to estimate the effects of local conflicts and massive revolts (8,9) although these papers focused only on the evolution (and demand decomposition) of GDP _per capita_ series after the events. More recently, the SCM has also been used to assess the social and institutional impacts of the Syrian civil war (10). We used permutation (or placebo) tests to assess whether our results could be due to chance.

5
Page 6 The second difference is the importance of the savings bank. This indicated the segmentation between a group of banks that were net borrowers and lent to the enterprise sector (e.g., the commercial arnd specialized banks) and a group of banks that were net lenders and captured most of the deposits. This is also an indicator of bank competition, since net borrower banks depended on funding by net lender banks.

Table 2. Structure of the Banking Systom

|**Ratio of all**qecialized banks'**asets**to<br>**total assets.**<br>,I<br>V_|47 7<br>_<br>_<br>_<br>_<br>_|79.1<br>_<br>_<br>_<br>_<br>_<br>_<br>_|32.2<br> <br>_<br>_<br>_<br>_<br>__<br>_<br>_|54.0<br>_<br>_<br>_<br>_<br>_<br>_|52.3<br>_<br>_<br>_<br>_<br>_<br>_<br>_|
|---|---|---|---|---|---|
|**Ratio ofcommercial**banks'**asset**to<br>**totalasset**_1,2/_|35 0|8**_5_**|67.8|**25.5**|18.2|
|Ratio of**totl savings**bank deposits to<br>total deposits.**1/**|**'.5**|12.1|**52.3**<br>___|46.2<br>I<br>__|80.8<br>_|
|Savings Bank's houaeh&ds' deposits**as**<br>ratio of total household deposits.1/|81.3|**70.5**|100.0|100.0<br>_|**100.0**|
|Ratio of Ssvings Bank loans to deposits.|100.0|61.3|16.9|33.5|4.9|

Source: World Bank (1989,1990 1991b and 1991c) and Thome (1992).

i/ Estimnated at the date of the breakup of the mono-bank. Bec4.us in Hungary the centrl bank held a large portion of the banking systens' total Issets the sum of the ratios of conunercial and specialized banks' assets to total assets is low relative to the other countries.

2/ In the ease of CSFR, these ratios are calculated using total loans instead of total asuts, as assets by group of banks were not available.

In Hungary and Poland this segmentation was less sharp. The high ratio of savings bank loans to deposits indicates that they were not a source of funds for commercial and specialized banks. Instead, savings banks used their resources to grant mortgage and other types of loans to households.

In the CSFR, Bulgaria and Romania, the savings banks were net lenders to the specialized and commercial banks. When the Bulgarian authorities broke up the mono-bank in 1990 the Bulgarian Savings Bank was using nearly 70 percent ot its deposits to fund the commercial and specialized banks. In the CSFR and Romania, it was 83 and 95 percent respectively. Savings bank deposits in these countries accounted for larger proportions of total and households deposits.
(narrower) credibility of traditional executive and patrimonial regimes begins to wane. A key challenge will be averting increasingly polarizing and short term claims over rents, and ensure that public expenditure management acts as a glue rather than solvent for peaceful political transitions. Resource rents on the one hand afford governments with the ability to underwrite distribution programs to buttress political stability, but also may reduce incentives to tax the non-resource economy (Brautigam et al. 2008; Knack 2008). Low levels of taxation of a productive society will risk dampening accountability, while resource dependence may furthermore be associated with an anemic private sector. The autocratic and patrimonial state-society relationships found in much of the region have also manifested themselves in centralized rent control and distribution, in addition to recourse to repression of groups or regions.

**Table 8. Medium Term Fiscal Management in MENA**

|||<br>**Wealth of N**|<br>**ations(2008)**||"|**Subsidy**|**Medium Term**|**Expenditure F**|**rameworks**|
|---|---|---|---|---|---|---|---|---|---|
|Sub-||Savings (%|Extractives||Genuine|||||
|Region|Country|GNI)|Depletion|Education|Savings"|Fuel(USD)|MTFF(%)<br>|MTBF(%)|MTPF(%)|
|||(i)|(ii)|(iii)|(iv)|(v)|(vi)|(vii)|(viii)|
|N. Africa|**Algeria***|58.8|30.1|4.5|21.4|0.34|1|1|0|
||Dijbuti|...|0|3.6|...|1.62|0|0|0|
||Egypt|23.5|15|4.4|2.1|0.33|0|0|0|
||**Libya***|66.8|38.8|...|...|0.14|...|...|...|
||Morocco|31.4|6.1|5.2|19.8|1.29|1|0|0|
||**Sudan***|15.9|19.2|0.9|-13.1|0.56|...|...|...|
||Tunisia|22.6|10.5|6.7|7|0.96|1|0|0|
|Other|**Bahrain***|45.4|26.4|4.4|15.6|0.21|1|0|0|
||**Iran***|...|...|4.2|...|0.10|0|0|0|
||**Iraq***|...|...|....|...|_0.33_|1|0|0|
||Israel|19.8|0.5|5.9|11.3|1.50|1|0|0|
||Jordan|13.7|4.7|5.6|3.6|0.61|1|1|0|
||**Kuwait***|58.7|38|3|9.7|0.24|1|0|0|
||Lebanon|10.1|0|1.8|14.8|0.76|1|0|0|
||**Oman***|...|...|3.9|...|0.31|0|0|0|
||Palestinian Territory|...|...|...|...|1.34|1|0|0|
||**Qatar***|...|...|...|...|0.22|0|0|0|
||**Saudi Arabia***|48.3|43.5|7.2|-1.8|0.16|0|0|0|
||**Syria***|12.6|43.5|2.6|-15.2|0.85|0|0|0|
||**UAE***|...|...|...|...|0.45|0|0|0|
||**Yemen***|...|43.5|...|...|0.30|1|0|0|
|**Average**|**/Total**|**32.9**|**21.3**|**4.3**|**6.3**|**0.60**|**11**|**2**|**0**|
|_Source:_W|ealth of Nations(2011:|195),PRMPS M|TEF Databas|e,IMF(viii),W|DI|||||
|_Notes:_* C|ountries denotes net oi|l &gas exporte|rs(2008) (see T|able 1)||||||

20
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27
||results, balancing benefits and<br>harms, and considering other<br>relevant evidence|**Discussion – Paragraphs 2 to 7**<br>**Conclusion**|
|---|---|---|
|**Other information**|||
|**Registration**<br>23|Registration number and name of trial<br>registry|**Abstract**<br>**Methods – Paragraph 6**|
|**Protocol**<br>24|Where the full trial protocol can be<br>accessed, if available|**Methods – Paragraph 1**<br>**Text S1**|
|**Funding**<br>25|Sources of funding and other support<br>(such as supplyof drugs),role of funders|**Funding statement**|

53
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**Figure 3: Lorenz Curve and Inequality Coefficients by Region using HBS**

<!-- Start of picture text --> Lorenz Curve by Area Lorenz Curve by Area<br>Tanzania 2007 Household Budget Survey Tanzania 2011-2012 Household Budget Survey<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumulative population proportion Cumulative population proportion<br>Rural Other Urban Rural Other Urban<br>Dar Essalam Line of perfect equality Dar Essalam Line of perfect equality<br>2007 2011/12<br>Gini p90/p10 Income shares Gini p90/p10 Income shares<br>Low Top Low Top<br>quintile quintile quintile quintile<br>Mainland 38.5 5.2 6.6 45.7 35.8 4.4 7.7 44.1<br>Rural 35.5 4.7 7.3 43.3 29.9 3.5 9.0 39.1<br>Other<br>40.0 6.0 6.0 46.6 38.1 4.9 7.0 45.7<br>urban<br>Dar es<br>40.1 5.6 6.4 47.3 36.0 4.4 7.7 44.4<br>Salaam<br>Source: HBS 2007 and 2011/12.<br>1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br><!-- End of picture text --> In contrast, NPS data suggest a deterioration of the distribution of welfare particularly in the rural areas where inequality seems to have significantly increased between 2008 and 2013. As shown in Figure 4, the increase in inequality seems to have been driven by a deterioration of the consumption share accruing to the poorest population groups, which declined by around 12 percent at the national level and by 11 percent in rural areas. The consumption share of the poor seems however to have improved in the urban sector, particularly in Dar es Salaam where it increased by over 20 percent.

6