rafmacalaba/datause-encoder-data huggingface dataset:
We test the theories by regressing measures of loan risk, bank stability, and bank equity capital on several measures of market power, using bank-level data for 23 industrial nations. We take account of the endogeneity of market power by employing activity restrictions, banking freedom, and the percent of foreign- and government-owned banks as instruments. Our results suggest that, consistent with the traditional “competition-fragility” view, banks with a higher degree of market power also have less overall risk exposure. However, the data also provide some support for one element of the “competition-stability” view – that market power does increase loan risk in these nations. This risk may be offset in part by higher equity capital ratios.

16
**Table A10:** Parameter Estimates: Presence of Children

||Househ<br>with Ch|(1)<br>olds<br>ildren|Ho<br>witho|(2)<br>useholds<br>ut Children|p-value<br>(1) = (2)|
|---|---|---|---|---|---|
|_Satisfed with overall h_|_ouse quality (_|_1=Yes)_||||
|Permanent Effect|_βP_+_βPK_|0.115<br>(0.213)|_βP_|-0.235**<br>(0.091)|0.234|
|Transitory Effect|_βT_+_βTK_|0.258<br>(0.213)|_βT_|0.698***<br>(0.163)|0.170|
|Delay Effect|_βD_+_βDK_|0.248**<br>(0.105)|_βD_|0.283***<br>(0.089)|0.834|
|_Satisfed with quality o_|_f family life (1_|_=Yes)_||||
|Permanent Effect|_βP_+_βPK_|0.096<br>(0.072)|_βP_|0.006<br>(0.048)|0.280|
|Transitory Effect|_βT_+_βTK_|0.260*<br>(0.145)|_βT_|0.316***<br>(0.109)|0.886|
|Delay Effect|_βD_+_βDK_|0.585**<br>(0.257)|_βD_|0.455**<br>(0.203)|0.471|

Notes: Of the total of 1,658 panel observations, 614 correspond to households with children up to age 10. The specifications include baseline values as covariates when available. Column 1 (2) reports the structural parameter estimates of the permanent, transitory and delay effect of the following NLS regression for households with (without) children: _Yict_ = _β_ 0 + **T** _ic ×_ � _βP_ + _βPK_ **1** _icK_ + ( _βT_ + _βTK_ **1** _icK_ ) _e_<sup>_−δT_(</sup><sup>_t−Tic_)</sup> + ( _βD_ + _βDK_ **1** _icK_ )( _e_<sup>_−δD_(</sup><sup>_Tic−Te_)</sup> _−_ 1)� + _Xic_<sup>_′θ_+</sup> _εict_ . The last column reports the p-value of a test that the coefficient _βjK_ = 0, for _j_ = _P_ , _T_ or _D_ . Coefficients with *, ** and *** indicate that they are statistically significant at the 10, 5 and 1%, respectively.

31
These results indicate that although men and women are equally engaged in the labor market in Russia, the jobs they do are very different—and they are rewarded very differently, too. Women are in a flat career path compared to men. This is usually referred to in the literature as women having jobs, not careers (Goldin, 2006; Bertrand, 2011). The two main labor market skills—education and experience— show larger payoffs for men than for women, especially at the bottom of the earnings distribution. This can be corroborated when we look at the age–wage profiles for men and women (see Figure A2).

To shed more light on the possible reasons for women’s flat earnings, we have estimated an augmented human capital model that includes occupation, industry, and other covariates related to job productivity. By looking at the RIF estimates of the dummy variables for the occupations, it can be concluded that professional women at the top of the earnings distribution have lower returns than men. Conversely, women at the median of the earnings distribution have higher returns than men in service jobs.

All the results so far suggest that women—either by their own choice or by lack of access—occupy jobs that have lower returns to labor skills. Moreover, productivity (and thus wages) can also depend on firm characteristics such as type of ownership or size. Ideally, firm effects are quantified using employer–employee data (Vieira, Cardoso, and Portela, 2005). Fortunately, the richness of the RLMS data allows us to explore these effects by adding covariates to describe firm characteristics. There is evidence that publically owned firms are less productive than private firms since they face less market competition. With the transition to a market economy, private firms as well as privatized firms, had gone through important increases in productivity, and the wedge between the public and the private sector increased (Calvo et al., 2015). For women, and to a lesser extent men, working for a public or semipublic firm has a negative impact on earnings, and the size of the impact is larger at the top of the earnings distribution. In particular, at the 90th percentile, women who work for a public firm earn 34 percent less than women who work for a private firm. Larger firms are also often thought to have higher productivity since they make higher investments in capital. The effect of firm size is highly nonmonotonic along the earnings distribution for women, while it shows very little variation for men. For women the impact of working in a large firm is always positive and it increases along the earnings distribution.

Finally, the RLMS allows us to explore the importance of promotion and job-to-job transitions in earnings with a reduced-form approach. There are two strings of the labor economics field that further explain wage determination, and in each of them gender differences were found. First, job-matching theory predicts that job changes result in wage increases. Employed workers spend time searching for a better match if the chances of finding one are larger than the cost of the on-the-job search. Empirical 14
**Figure 17. Which industries offer higher salaries to college graduates?**

_Source_ : Authors.

_Note_ : Shown are regression coefficients and their 95% confidence intervals. The sample used is the job postings that require undergraduate degrees and 0–2 years of experience. The same regression specifications reported in **Error! Reference source not found.** are applied, that the education dummies and experience dummies are not controlled for.

Figure 18 looks at the return to experience. The ICT sector (which advertises the highest entrylevel salaries, as shown in Figure 17) has the higest salary growth trajectory, so the ICT sector may be a good career choice, in terms of salaries, for postsecondary school graduates. By contrast, the education and health sector (where the initial salaries are the lowest) has a relatively low salary growth trajectory.

**Figure 18. Returns to experience by industries**

_Source_ : Authors.

_Note_ : Shown are salary differences, within a given industry, between job postings that require different levels of professional experience. These differences are estimated by regressions separately run for a subsample of job postings in each industry that require an undergraduate education. The controls are dummies for preferred genders, firm size dummies, employment type dummies, time shift dummies, the number of vacancies, and year-month dummies.

21
Heggelund, G.M. (2004) _Environment and Resettlement Politics in China._ Ashgate Publishing Ltd., London.

Hill, R., Diener, K., Miller, S., White, T. (2006) IDP livelihoods and personal security: case studies from Colombia and Sudan. _Refugee Survey Quarterly_ 25(2): 40-57.

Holling, C.S. (1973) Resilience and stability of ecological systems. _Annual Review of Ecology and Systematics_ 4: 1-23.

Horgan, J. (1999) The Itaparica Dam Project in north-eastern Brazil: models and reality. _Forced Migration Review_ 4: 25-28.

Horst, C. (2006) Refugee livelihoods: continuity and transformations. _Refugee Survey Quarterly_ 25(2): 6-22.

Howard, P. and Homer-Dixon, T. (1998) The case of Chiapas, Mexico. In T. Homer-Dixon and J. Blitt (eds.), _Ecoviolence: Links Among Environment, Population, and Security_ . Rowman and Littlefield, Lanham: 19-66.

Hugo, G. (1996) Environmental Concerns and International Migration. _International Migration Review_ 30(1): 105-131.

Hugo, G. (2008) _Migration, Development and Environment_ , draft paper for research workshop on ‘Migration and Environment: Developing a Research Agenda’, Munich, Germany, viewed 8 December 2008, <http://www.populationenvironmentresearch.org/seminars082008.jsp> Hulme, M., Doherty, R., Ngara, T., New, M. and Lister, D. (2001) African climate change: 19002100. _Climate Research_ 17: 145-168.

Hutton, D. and E. Haque (2004) Human Vulnerability, dislocation and resettlement: adaptation processes of river-bank erosion- induced displaces in Bangladesh.

_Disasters_ 28(1): 41-62.

IPCC (2007) _Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change_ [Core Writing Team, Pachauri, R.K and Reisinger, A.(eds.)]. IPCC, Geneva, Switzerland.

Jacobsen, K. (1997) Refugees' environmental impact: the effect of patterns of settlement. _Journal of Refugee Studies_ 10(1): 19-36.

Jacobsen, K. (2002) Livelihoods in conflict: the pursuit of livelihoods by refugees and the impact on the human security of host communities. _International Migration_ 40(5): 95-122.

55
_Table 9- Theil index of inequality between and within components and their ratio. For better illustration, standardized beta coefficients are reported. Standard errors are in the parenthesis. Shock 1 is measured as the year-on-year difference in log(USD to IRR rate) each month. Shock 2 is calculated as the year-on-year difference in the cyclical component of log(USD to IRR rate) in each month. The cyclical component is computed using HodrickPrescott filter._

**Panel A: Summary statistics of Theil index**

||**Theil in Iran**|**Theil in Afghan**|**Between**|**Within**|**Ratio**|
|---|---|---|---|---|---|
|**Mean**|9.85E-04|4.98E-04|1.84E-06|9.81E-04|1.87E-03|
|**SD**|6.82E-05|3.73E-04|1.69E-06|6.73E-05|1.71E-03|

**Panel B: Regressions (standardized coefficients are reported)**

|**Dep variable:**|**Between**|**component**|**Within c**|**omponent**|**Ratio (Betw**|**een/within)**|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Lag of dep variable**|0.121|0.115|0.515***|0.551***|0.127|0.119|
||(0.099)|(0.099)|(0.083)|(0.081)|(0.099)|(0.099)|
|**Shock 1: Xrate**|0.189*||0.207**||0.171*||
||(0.000)||(0.000)||(0.000)||
|**Shock 2: Cyclical Xrate**||0.200**||0.152*||0.187*|
|||(0.000)||(0.000)||(0.001)|
|**Observations**|107|107|107|107|107|107|
|**R-squared**|0.063|0.067|0.394|0.380|0.056|0.061|

31
<!-- Start of picture text --> 170,000<br>100,000<br>80,000<br>60,000<br>40,000<br>20,000<br>0<br>2001 2002 2003 2004 2005 2000 2007 2008 2009 2010 2011 2012 2013 2014<br>8 Credit guarantees and guarantees to Multilateral Development Banks B§ On-lending<br><!-- End of picture text -->
31 Table 2. Estimated costs of<sup>Egypt's explicit and implicit subsidies, 1990/91 to 1995/96 (in LE millions) (nominal terms)</sup>

||1990/91|1991/92|1992/93|<sup>1993/94</sup>|<sup>1994/95</sup>|1995/96|
|---|---|---|---|---|---|---|
|Explicit Subsidies|||||||
|Food|2,400|2,482|2,450|2,486|2,492<br>|3,099<br>|
|Transportation'|---||---|---<br>|326<br>|301<br>|
|Total2|6,000|7,600|4,000|3,500|3,700|4,200|
|Implicit Subsidies<br>|||||||
|Water<sup>3</sup><br>|---|700<br>|800<br>|950<br>|||
|Electricity<br>4<br>|---|3,100|3,200|3,000|||
|Oil products|---|---|---|---<br>|||
|Total<sup>5</sup>||3,800|4,000|3,950|||

Notes and sources:

- 1/ Transportation subsidies<sup>are incomplete and are based only on subsidies to Cairo and Alexandria Transportation Authorities.</sup> Value of other transportation<sup>subsidies is unknown. All estimates by International Monetary Fund.</sup>

- 2/ Value of food and transportation<sup>subsidies does not sum to total explicit subsidies because of inGcompieiedaia. Toiai expli,ct subusiudies</sup> estimated by International Monetary<sup>Fund.</sup>

- 3/ Water subsidies, based on<sup>operational and investment subsidies for piped water in Cairo and Alexandria, estimated by vvorld Bank,</sup> Arab Republic of Egypt: Social<sup>Welfare Study (Strenathening the Social Safety Net) (Washington, DC: World Bank, 1995), p.17.</sup> Estimates do not include value<sup>of water subsidies to rural farmers.</sup>

- 4/ Electricity subsidies, measured<sup>as the difference between current yield and long-run marginal cost, estimated by World Bank, Arab</sup> Republic of Egypt: Social Welfare<sup>Study (Strengthening the Social Safety Net) (Washington, DC: World Bank, 1995), p.18.</sup>

- 5/ Total implicit subsidies calculated<sup>as sum of all implicit subsidies.</sup>
Table 1: Timeline of FO Operationalization

||||||**% **|**channe**|**ls in c**|**ircle-y**|**ear**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Circle**|**No. channels (FOs)**|**Authority**|**2006**|**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|
|**LCC East**|229 (84)|PID (pre)|4|4|4|4|4|4|0|0|0|
|||FO (1st)|96|96|96|0|0|0|4|4|0|
|||PID (post)|0|0|0|96|96|24|12|12|32|
|||FO (2nd)|0|0|0|0|0|73|84|85|68|
|**LCC West**|195 (73)|PID (pre)|100|100|3|3|2|2|2|2|0|
|||FO (1st)|0|0|97|97|98|1|1|0|2|
|||PID (post)|0|0|0|0|0|98|98|98|24|
|||FO (2nd)|0|0|0|0|0|0|0|0|74|
|**Bahawalnagar**|140 (67)|PID (pre)|100|100|100|100|100|0|0|0|0|
|||FO (1st)|0|0|0|0|0|100|100|100|59|
|||PID (post)|0|0|0|0|0|0|0|0|41|
|||FO (2nd)|0|0|0|0|0|0|0|0|0|
|**LBDC**|221 (52)|PID (pre)|100|100|100|100|100|100|2|2|0|
|||FO (1st)|0|0|0|0|0|0|98|98|100|
|||PID (post)|0|0|0|0|0|0|0|0|0|
|||FO (2nd)|0|0|0|0|0|0|0|0|0|
|**Derajat**|222 (120)|PID (pre)|100|100|100|100|100|100|100|0|0|
|||FO (1st)|0|0|0|0|0|0|0|100|100|
|||PID (post)|0|0|0|0|0|0|0|0|0|
|||FO (2nd)|0|0|0|0|0|0|0|0|0|

_Notes:_ Under the column heading “Authority” are PID (pre) = Punjab Irrigation Department pre-reform; FO (1st) = First tenure of Farmer Organization; PID (post) = Punjab Irrigation Department post-1st FO tenure; FO (2nd) = Second tenure of Farmer Organization. Thus, for example, in 2011, 167 (73% of 229) channels in LCC East were in their second FO tenure.

We construct a version of the “delivery performance ratio” or DPR (see, e.g., Waijjen et al. 1997) for the economically most important _kharif_ (summer) season, which runs from mid-April to mid-October. During _rabi_ season, from November to March, 42% of channels in Punjab are dry. Letting _d_ index days and _t_ index year, define for _j_ = _H_ ( _ead_ ) _, T_ ( _ail_ ), where _Q_<sup>_j_</sup> _id_<sup>isdailydischargeatposition</sup><sup>_j_ofchannel</sup><sup>_i_and</sup><sup>_Q_ ̄</sup><sup>_j_</sup> _id_<sup>isthe</sup> corresponding authorized daily discharge.

Figure 3 shows how head and tail DPRs vary across years for all channels in the 5 FO circles and 12 non-FO circles.<sup>8</sup> Two key facts emerge: First, the Indus Basin irrigation

> 8Even though a channel is in an FO circle, it may not actually come to be managed by an FO until as late as 2013 (see Table 1).

11
**<u>Table 5: Rice Income in Mali for different household groups, 2006</u>**

||Percentage o|f households r<br>income|eceiving|Average i|ncome for all hou|seholds|Average income|for households wit|hpositive income|
|---|---|---|---|---|---|---|---|---|---|
||Auto|||Auto|||Auto|||
||consumption|Sales|Total|consumption|Sales|Total|consumption|Sales|Total|
|**All**|38.50%|12.49%|39.49%|39,639.63|41,835.37|81,475.00|102,881.81|334,831.00|206,293.50|
|**Residence area**||||||||||
|Urban|19.60%|3.83%|20.06%|14,430.79|14,013.19|28,443.98|73,606.23|366,354.90|141,808.60|
|Rural|49.80%|17.67%|51.10%|54,685.70|58,441.21|113,126.90|109,757.41|330,757.80|221,402.30|
|**Region**||||||||||
|Kayes|24.50%|2.49%|24.90%|9,957.25|1,676.04|11,633.29|40,570.47|67,219.67|46,722.14|
|Koulikoro|31.10%|2.65%|31.81%|17,629.43|2,513.26|20,142.69|56,680.58|95,001.69|63,327.95|
|Sikasso|32.00%|5.87%|32.66%|17,606.31|7,820.18|25,426.48|54,998.14|133,207.00|77,849.48|
|Ségou|52.60%|29.53%|53.52%|48,060.66|174,259.90|222,320.60|91,372.99|590,135.80|415,367.50|
|Mopti|47.50%|11.38%|49.51%|69,796.45|31,723.79|101,520.20|147,071.86|278,774.60|205,055.40|
|Tombouctou|70.80%|38.94%|73.44%|122,662.75|34,265.89|156,928.60|173,185.98|87,992.68|213,676.90|
|Gao|51.70%|22.02%|52.92%|85,852.87|18,117.87|103,970.70|166,030.19|82,296.15|196,451.50|
|Kidal|10.40%|0.00%|10.39%|4,498.24|0.00|4,498.24|43,310.52|0.00|43,310.52|
|Bamako|15.10%|0.23%|15.13%|9,575.45|1,045.17|10,620.62|63,301.80|450,000.00|70,211.27|
|**Quintile**||||||||||
|Q1|41.00%|12.44%|42.44%|21,468.60|23,810.04|45,278.64|52,307.88|191,352.50|106,684.60|
|Q2|43.20%|13.38%|44.14%|40,056.87|38,529.16|78,586.02|92,808.03|288,066.60|178,025.20|
|Q3|48.10%|18.62%|49.83%|52,960.09|64,117.68|117,077.80|110,215.71|344,413.80|234,936.30|
|Q4|41.00%|14.92%|41.58%|48,836.13|60,887.89|109,724.00|119,088.93|408,077.90|263,888.00|
|Q5|25.70%|5.74%|26.18%|31,995.06|22,178.38|54,173.43|124,531.98|386,168.30|206,901.40|

Source: Authors’ estimation using ELIM 2006.
31

# **2.3. Health sector approaches to GBV**

## **2.3.1  Overview of GBV and the health sector**

As more epidemiological evidence has emerged highlighting the links between GBV and a wide variety of serious health problems, international attention is increasingly focused on the role of the health sector in addressing violence. In 1996 the World Health Assembly declared violence to be a public health priority, and followed this up in 2002 with the publication of the World Report on Violence and Health, with in-depth discussions of intimate partner and sexual violence (World Health Organization 2002). The World Health Organization has carried out a multi-country study with detailed information on the prevalence and health effects of violence in ten countries. This report, which will be published in late 2004, will provide the first in-depth overview of violence using standardized methods.

Health services, and particularly reproductive health programs, provide a unique window of opportunity to address the needs of abused women, since most women come into contact with the health system at some point in their lives. Because violence presents serious risks to women’s health, routine screening for violence among all women who attend health services, regardless of the reason for the visit is increasingly considered the standard of care in the United States and many industrialized countries (American Medical Association, 1992; Buel, 2001).

In the United States, despite extensive training of providers, progress in implementing family violence screening policies has been slow. Research indicates that although battered women use primary and secondary health services more than non-abused women, a very small percentage of them are identified by health workers (Cohen, De Vos et al., 1997; Sagot, 2000) In developing countries violence is even less likely to be detected within health services. A Demographic and Health Survey in Nicaragua found that over one-third of women who had experienced partner abuse had never told anyone about their situation, Although 57% of the women had suffered one or more injuries as a result of violence, only 13% had ever received medical attention for injuries. Even then, most women still did not disclose the cause of their injuries. Only 7% of women reported having ever sought help for violence at a health center or hospital (Rosales, Loaiza et al., 1999).

Most research indicates that if women are asked about violence in private, in a nonjudgmental and empathetic way, they will answer truthfully. In fact most women, regardless of whether they have been abused themselves, feel that physicians should ask their patients routinely about violence (Friedman, Samet et al., 1992; Velzeboer, Ellsberg et al., 2003) However, health providers are typically reluctant to ask women about experiences of violence, even when women show clear signs of abuse, for fear of offending women, or of opening up “a Pandora’s box” of issues that they will not know how to address (Sugg and Inui, 1992). Many providers worry that screening for abuse will add yet another burden to
top of that, school closures had a direct effect on children’s learning ( _e_ . _g_ ., see the reviews of the evidence by Hammerstein _et al_ ., 2021, and Werner and Woessmann, 2021). Particularly in Latin America, the impact of the pandemic on these three dimensions has been among the strongest worldwide. Indeed, considering all these dimensions in their simulations—health crisis, economic downturn, and educational losses—Neidhöfer _et al_ . (2021) predict a large drop in the likelihood of completing secondary education for current cohorts aged 15 to 19 in Latin America. Subsequent studies based on real-time information from surveys or administrative data on learning losses, disconnection from school, and drop-out rates in 2020 confirm that the pandemic had a significant negative short-term effect on education in basically all countries in the region (see the review of the real-time evidence included in Lustig _et al_ ., 2021).

While these estimates refer to the year 2020, the situation did not improve substantially in 2021. Although Latin American economies recovered slightly with respect to the previous year, a high number of infections still limited school openings and in person learning in most countries.<sup>2</sup> Table 1 shows the number of weeks that schools were fully or partially closed during the period 2020-2021 in each country, the regular weeks of school that children would have had without the pandemic in these two years, as well as the number of cumulative COVID-19 cases and deaths at the end of 2021. In most countries, the share of weeks with closed schools exceeds 90% of the instructional time in the two academic years. The average across all countries is 85%. Interestingly, although surely the epidemiological situation was the main driver of the decision to close schools at the national level, at the cross-country level there seems to be no clear pattern of association between the relative number of cases and deaths and the number of weeks with closed schools in a country.

The closure of schools was accompanied by the efforts of national education systems to provide remote learning options and tools to hamper the learning

> 2 The debate on the efficacy of school closures as a measure to mitigate the spread of SARS-CoV2 in the first and subsequent waves is still ongoing, although most of the evidence highlights that children play an important role in the community transmission of the virus due to a higher exposure in schools (see Pierce _et al_ ., 2022).

4
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