rafmacalaba/datause-encoder-data huggingface dataset:
Meuwissen LE, Gorter AC, Segura Z, Kester AD, Knottnerus JA. (2006a) Uncovering and responding to needs for sexual and reproductive health care among poor urban female adolescents in Nicaragua. _Tropical Medicine & International Health_ , Vol. 11(12): pp.1858-67 Meuwissen LE, Gorter AC, Kester AD, Knottnerus JA. (2006 b) Does a competitive voucher program for adolescents improve the quality of reproductive health care? A simulated patient study in Nicaragua. _BMC Public Health_ . Vol. 7; 6: p. 204 Meuwissen, L.E., Anna C. Gorter, Andre J.A. Knottnerus (2006c) Impact of accessible sexual and reproductive health care on poor and underserved adolescents in Managua, Nicaragua: a quasiexperimental intervention study, _Journal of Adolescent Health_ , Volume 38, Issue 1, January 2006, p. 56.e1-56.e9 Meuwissen, L. E., Gorter, A. C., Kester, A. D. M. and Knottnerus, J. A. (2006d) Can a comprehensive voucher programme prompt changes in doctors’ knowledge, attitudes and practices related to sexual and reproductive health care for adolescents? A case study from Latin America. _Tropical Medicine & International Health_ , vol. 11: pp. 889–898 Meuwissen, L.E., Anna C. Gorter, and J. A. Knottnerus (2006e) Perceived quality of reproductive care for girls in a competitive voucher programme. A quasi-experimental intervention study, Managua, Nicaragua _Int J Qual Health Care_ (February 2006) vol. 18(1): p. 35-42 Morris, S., Rafael Flores, Pedro Olinto, Juan Manuel Medina (2004) Monetary incentives in primary health care and effects on use and coverage of preventive health care interventions in rural Honduras: cluster randomised trial _, The Lancet_ , Volume 364, Issue 9450, p. 2030-2037 Ngabo, F and J Humuza (201) Taking It to the Streets: Performance-Based Financing for Community Health in Rwanda; RBF brief; The World Bank; www.rbfhealth.org PAHO/WHO (2007) _Social protection in health schemes for mothers, newborn & child population. Lessons learned from the Latin American regions._ , PAHO/WHO, Washingon D.C.

Paul, V. (2010) India: conditional cash transfers for in-facility deliveries, _The Lancet_ , Volume 375, Issue 9730, 5 June 2010-11 June 2010, Pages 1943-1944 Por, I., Horeman, D., Narin, S., & Van Damme, W. (2008) "Improving access to safe delivery for poor pregnant women: a case study of vouchers plus health equity funds in three health districts in Cambodia," in _Reducing financial barriers to obstetric care_ , F. Richard, S. Witter, & V. De Brouwere, eds., ITG Press, Antwerp Por, I. Horemans D, Souk N, Van Damme W. (2010) Using targeted vouchers and health equity funds to improve access to skilled birth attendants for poor women: a case study in three rural health districts in Cambodia. _BMC Pregnancy Childbirth_ . 2010 Jan 7; 10:1 Powell-Jackson, T., Neupane, B., Tiwari, S., Morrison, J., & Costello, A. (2008) _Final report of the evaluation of the Safe Delivery Incentive Programme_ , DFID, London.

20
Table C4: Robustness of Workers’ Adjustment to GDP Decline in Firms’ Destinations using Alternative Shocks

||Average mo|nths worked|Average r|eal wages|
|---|---|---|---|---|
||Shock GDP growth<br>export destinations<br>fxing weights to<br>2004-2007|Shock GDP growth<br>export destinations<br>fxing weights<br>to 2006-2007|Shock GDP growth<br>export destinations<br>fxing weights to<br>2004-2007|Shock GDP growth<br>export destinations<br>fxing weights<br>to 2006-2007|
||(1)|(2)|(3)|(4)|
|GFC frm shock*2009|-0.0534***<br>(0.0099)|-0.0565***<br>(0.0103)|-0.0067**<br>(0.0029)|-0.0075***<br>(0.0029)|
|GFC frm shock*2010|-0.0514***<br>(0.0084)|-0.0504***<br>(0.0085)|-0.0101***<br>(0.0027)|-0.0106***<br>(0.0027)|
|GFC frm shock*2011|-0.0431***<br>(0.0078)|-0.0413***<br>(0.0079)|-0.0106***<br>(0.0027)|-0.0110***<br>(0.0028)|
|GFC frm shock*2012|-0.0390***<br>(0.0073)|-0.0373***<br>(0.0074)|-0.0113***<br>(0.0027)|-0.0120***<br>(0.0029)|
|GFC frm shock*2013|-0.0340***<br>(0.0070)|-0.0329***<br>(0.0071)|-0.0125***<br>(0.0028)|-0.0132***<br>(0.0030)|
|GFC frm shock*2014|-0.0310***<br>(0.0068)|-0.0309***<br>(0.0069)|-0.0132***<br>(0.0029)|-0.0138***<br>(0.0031)|
|GFC frm shock*2015|-0.0287***<br>(0.0067)|-0.0285***<br>(0.0068)|-0.0131***<br>(0.0030)|-0.0137***<br>(0.0031)|
|GFC frm shock*2016|-0.0267***<br>(0.0067)|-0.0261***<br>(0.0067)|-0.0127***<br>(0.0030)|-0.0130***<br>(0.0032)|
|GFC frm shock*2017|-0.0252***<br>(0.0066)|-0.0249***<br>(0.0067)|-0.0122***<br>(0.0031)|-0.0126***<br>(0.0032)|
|Observations|3,349,256|3,163,251|3,332,057|3,147,149|
|R-squared|0.1254|0.1272|0.2784|0.2765|
|2-digit industry X Year FE|Yes|Yes|Yes|Yes|
|State X Year FE|Yes|Yes|Yes|Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. All specifications control for vectors of worker and firm characteristics as of 2007 described in Section III. FE stands for 48
# **Not Your Average Job: Measuring Farm Labor in Tanzania**<sup>1</sup>

Vellore Arthi University of Oxford Kathleen Beegle World Bank Joachim De Weerdt Universities of Antwerp and Leuven Amparo Palacios-López World Bank JEL Codes: C8, O12, Q12 Keywords : Recall error, Measurement error, Farm labor, Agricultural productivity 1 This study is an output of the “Minding the (Data) Gap: Improving Measurements of Agricultural Productivity through Methodological Validation and Research” project led by the Living Standards Measurement Study team of the World Bank and funded by the U.K. Department for International Development. Additional funding was received from the IZA/DFID Growth and Labour Markets in Low Income Countries Program (GLM-LIC) under grant agreement GA-C3-RA1-360 and from the World Bank Research Committee. The authors gratefully acknowledge the comments of participants at the Centre for the Study of African Economies’ Research Workshop, the European Survey Research Association Conference, the Growth and Labour Markets in Low-Income Countries Programme Conferences, the Northeast Universities Development Consortium, the Structural Transformation of African Agriculture and Rural Spaces Conference, and seminar series at the University of Antwerp, the University of Namur, the University of Washington, and the World Bank. The data have been collected on _surveybe_ and the fieldwork has been expertly implemented by Economic Development Initiatives.
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10 (James 1992).

Despite thee arguments favoring prefunding of benefit promises, public sector pension plans have typically accumulated fewer assets relative to benefits, than have their private sector counterparts (Table 5). In the 1980's, many public employers contributed less to their public employees' pension accounts than they were required to according to actuarial computations. This is in part due to shortfalls in tax revenue collected by American states and cities during the last recession, leading some public employers to fall behind on their pension contribution schedules. Many public pensions are therefore underfunded, meaning that plan assets are insufficient to cover benefits promised to retirees. A study of 1989 data showed that state and local employer pension conaibutions were about 10-15% below target, and the pattern of shortfall was most persistent for systems where unemployment was higher than it had been for some time. This effect persisted even when controlling for habit persistence, holding constant past cumulative funding levels (Mitchell and Smith 1992 a and b). Therefore a full analysis of funding must take into account the possibility that fiscal stress undermines PERS plans' fiaiancial stability.

Previous research suggests several reasons that the structure of PERS Boards might have an effect on pension fundi..g. One consideration is that a Board composed of political appointees may feel differently about funding requirements than would a Board heavily weighted with pension-covered elected members. As an example of this, consider the court case recently filed by Califomia state retirees against Govemor Pete Wilson. State budget deficits at the end of the 1980s drove the Govemor to propose raising the Olivia S. Mitchell - Public Pension Governance and Performance
reported in the SINAN data are thus considered less severe than the cases reported in the hospitalization data. In an additional exploration of the SINAN data, the clinical reports and the hospital emergency room reports were separated during the study to determine if the analysis might differ according to whether the facilities treat less or more severe cases.

_The limitations of violence data_ . The use of the variables as proxies for IPV is grounded on a conceptualization of the relationship of IPV to the overall universe of incidents of violence against women (UNODC 2019). The concept is expressed by using concentric circles to indicate that IPV is at the core of violence against women, which then broadens to include violence by other family members, femicide by nonfamily members, and then any incident that has resulted in female homicide. The variables are thus meant to be proxies for IPV, the center of the model. Because these are proxy variables, the limitations of the data must be considered. In most cases, it is not possible to determine where within the concentric circle model the incident lies. One may not know if the perpetrators are intimate partners or strangers, although one may control for the general level of violence relative to the incidence of violence against men. However, the characteristics of the victims (age, education) and the events (time of day, in the home or not) are similar in reports in which the perpetrator is identified as a partner or ex-partner and in the case of the hospitalizations and homicides on which this information is not available (Reynolds 2020).

The issue of whether these data exclude some incidents, as is so frequently a concern in surveys, also arises. Homicide data are generally considered the most accurate because there are few reporting issues. Data on hospitalizations because of violence rely mainly on the reporting of physicians on injuries they believe result from aggression. Because these are typically serious injuries involving overnight hospitalization, the likelihood of misclassification is smaller than in the case of more minor injuries that are the subject of such reports. In the latter situation, patients are more likely to be able to hide the cause of the lesser injuries. For example, a broken arm may be explained as the outcome of a fall, while several broken bones at once might raise more questions. Thus, the less severe reports have the potential for additional reporting bias among patients and physicians.

With the exception of homicides, it may also not be clear if particular individuals have been treated only once or on several separate occasions. Likewise, findings about the medical costs of violence are more straightforward than findings about the mental health impacts of violence or the long-term effects on children who are witnesses or victims.

10
Malaysian students declined substantially. The discrepancies in mathematics and science test scores between the counterfactual sample and Malaysian students grew over time.

The red vertical line in Figure 1 indicates the older PPSMI-affected cohort of eighth graders (TIMSS 2007 participants). The estimates show that with the implementation of PPSMI, the average test scores the older PPSMI-affected cohort of Malaysian students fell by 8.6% in mathematics and 8.4% in science when compared to the counterfactual sample. This negative outcome is attributed to the switch in the language of instruction from a native language to a nonnative language between primary and secondary education. The effect contains: (i) the adjustment cost associated with learning and teaching from the switch in the language of instruction between primary and secondary education; (ii) the adverse effect on a student’s ability to learn in their nonnative language during secondary education; and (iii) the adverse effect on a secondary school teacher’s ability to teach in a language they are not well trained to use.

The blue vertical line in Figure 1 indicates the younger PPSMI-affected cohort of eighth graders (TIMSS 2011 participants). The estimates show that with the implementation of PPSMI, the average test scores of the younger PPSMI-affected cohort of Malaysian students fell by 18.6% in mathematics and 20.6% in science when compared to the counterfactual sample. Since this younger cohort of students always learned mathematics and science in a non-native language, the results demonstrate that using a non-native language of instruction is detrimental to a student’s learning in mathematics and science. The larger negative impact on the younger cohort of PPSMaffected students than on the older cohort of PPSMI-affected students indicates that the cumulative negative effects of learning in a non-native language outweigh the negative effects of switching from using a native language into a non-native language between primary and secondary education. However, we are unable to differentiate the negative effect on a teacher’s ability to 17
# **Table A2: Mapping from the GTAP Regions to the Regions of our Model**

|**GTAP cod**|**e**<br>**GTAP 8 Region**|**Region in our Model**|
|---|---|---|
|102|Egypt|COMESA|
|113|South Central Africa<br>- Angola<br>- Congo the Democratic<br>Republic of the|all SADC<br>|
|114|Ethiopia|COMESA|
|115|Kenya|Kenya|
|116|Madagascar|SADC|
|117|Malawi|SADC|
|118|Mauritius||
|||SADC|
|119|Mozambique|SADC|
|120|Tanzania United Republic<br>of|Tanzania|
|121|Uganda|Uganda|
|122|Zambia||
|||SADC|
|123|Zimbabwe|SADC|
|124|Rest of Eastern Africa<br>||
||- Burundi|COMESA|
||- Comoros|COMESA|
||- Djibouti|COMESA|
||- Eritrea|COMESA|
||- Mayotte<br>|COMESA|
||- Rwanda|Rwanda|
||- Seychelles|COMESA|
||- Somalia|COMESA|
||- Sudan|COMESA|
|125|Botswana|SADC|
|126|Namibia|SADC|
|127|South Africa|SADC|
|128|Rest of South African<br>Customs Union<br>- Lesotho|all SADC|
||<br>- Swaziland||

86
estimate in the main sample was 4 percentage points). The penalty for an children aged 4 to 6 years old is 3 percentage points (instead of 2 percentage points), and that of children aged 7 to 12 years is less than 1.3 percentage points (instead of less than 1 percentage point).

# **6. Conclusion**

During most of the 2000s, Guatemala grew at a relatively high rate, fertility rates declined, and female educational attainment rose without precedent. Despite these promising trends, Guatemala remained the country with the lowest FLFP rate in the LCR. This under-utilization poses a large cost to this upper-middle-income country where most women are not engaged in paid jobs. This paper explored a wide set of micro data sets between 2002 and 2018 to investigate the role of different factors in influencing the women’s decision to participate in paid employment to shed new light on the reasons behind the trend over the 2000s and the drivers of its large within-country dispersion. We assessed the role of: (i) individual and household socioeconomic characteristics (such as the level of education or the number of children), (ii) local social norms and attitudes towards women (at home and in the community), (iii) social public policies (such as investments in pre-schools), and (iv) local labor market characteristics (such as the ratio of male to female employment or the composition of employment across sectors of activity). Traditionally, a small state, weak governance, and chronically low tax revenues lead to limited public investment in infrastructure and human capital, largely explaining the lack of developmental progress and large gaps in social outcomes. We conjecture that regional differences in these factors influence women’s willingness and ability to participate in paid employment.

Even though our sample only covers data up to 2018, the impact of COVID-19 raises an even gloomier scenario for FLFP in Guatemala. Globally, women suffer larger work stoppage rates than men (Alon et al., 2021; Kugler et al., 2021). Estimates for Guatemala indicate that women were 20 percentage points more likely than men to lose their jobs in the early stages of the pandemic (Cucagna and Romero, 2021). The larger impact of the COVID-19 pandemic recession on female employment and the unbalanced burden of childcare activities across genders are very likely to further impact women’s LFP. Moreover, women that were initially forced to leave their job due to the pandemic are likely to become permanently affected by facing long unemployment spells and eventually dropping out of the labor force.

We showed that the increases in FLFP of 5.7 percentage points between 2002 to 2018 (from 26% to 32%) was partly explained by the drastic increases in the educational attainment of women, the reduction in fertility and the country’s structural transformation towards services. However, a large component remained unexplained by the proposed Oaxaca-Blinder decomposition.

17
# **Table 19: Promoting access to finance in rural areas**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|-0.363***|-0.357***|-0.351***|-0.279***|-0.254***|-0.218***|-0.218***|-0.201***|-0.251***|
||(0.119)|(0.112)|(0.114)|(0.065)|(0.040)|(0.051)|(0.053)|(0.036)|(0.045)|
|Population density (log)|-0.081|-0.079|-0.082|-0.070|-0.050|-0.043|-0.065|-0.028||
||(0.055)|(0.056)|(0.055)|(0.043)|(0.031)|(0.029)|(0.055)|(0.031)||
|Expected GDP (log)|0.147***|0.147***|0.140***|0.101***|0.092***|0.078***|0.078***|0.083***|0.085***|
||(0.047)|(0.048)|(0.048)|(0.033)|(0.027)|(0.026)|(0.026)|(0.028)|(0.029)|
|# deposit accounts/1,000 adults (log,<br>lagged)|0.098|0.091|0.090|0.061||||||
|(commercial banks)|(0.082)|(0.080)|(0.080)|(0.060)||||||
|# loans/1,000 adults (log, lagged)|0.061|0.050|0.039|||||||
|(commercial banks)|(0.081)|(0.071)|(0.069)|||||||
|Deposit insurance|-0.068<br>(0.138)|-0.064<br>(0.140)||||||||
|Credit rank|0.001<br>(0.001)|||||||||
|# branches/100,000 adults (log, lagged)<br>(commercial banks)||||||0.025<br>(0.044)||||
|# branches/km<sup>2</sup>(log, lagged)|||||||0.022|||
|(commercial banks)|||||||(0.043)|||
|# commercial banks||||||||-0.000<br>(0.000)||
|Number of Observations|58|58|58|79|126|108|108|97|126|
|Log-likelihood|-23.29|-23.38|-23.47|-31.92|-46.74|-38.90|-38.93|-35.41|-47.94|
|Pseudo R<sup>2</sup>|0.318|0.316|0.313|0.302|0.380|0.334|0.334|0.318|0.364|
|Robust standard errors in parentheses, *** p<br>Dependent variable: FI Rural (effective) - eq<br>reports marginal effects ofprobit estimation.|<0.01, ** p<0<br>uals 1 if the fi<br>|.05, * p<0.1<br>nancial regulat|or oversees pro|moting access to|finance in rura|l areas and has|a dedicated tea|m, equals 0 oth|erwise. Table|

45
<!-- Start of picture text --> 40 Copper a” 8, —HistoricalentproductionNickel ______,L<br>30} / | === Not Zero Scenario Fa<br>=Eom*Foe) = 6 | |-ases=+Stated Policy Scenario ’7<br>520} | 54 - weet<br>° | oo<br>Ps1970 1980| 1990| 2000 2010| 2020 20301 2040 71970 1980! 1990 20001 2010 2020| 2030 2040<br>1 . . ‘Cobalt" - - 40 . . Lithiumt ” .<br>0.8 | ral4 /‘<br>Eos /’v2 | 30 /:<br>Ss7E04=o // at)| 20 4:a<br>o2| i a | 10} / |<br>1970 1980 1990 2000 2010 2020 2030 2040 1970 1980 1990 2000 2010 2020 2030 2040<br><!-- End of picture text -->

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12-a Table 2 Auto-Reressive of Inflation in Apeetina, Bre4il, **and** Eolivis

|||Independent<br>Vari|ableo|||
|---|---|---|---|---|---|
|Period<br>Equ|ation<br>**Conotont**<br>|.PINF(t-1)|Trend|Adjustod<br>R-S|O.W.|
|||||qwars||
|1. Argentina: ftf|ore**and**After**the**Au-r.|el||||
|**1961.01-1995.06**|<br>**(1)**<br>**-0.7404**<br>(-.'9)||**0.3770**<br>(12.56)<br>|**0.7606**<br>(157.66)<br>|1.21<br>|
||(2)<br>-0.3363<br>(-.29)<br>(3)<br>2.3429<br>(2.10)|0.3963<br>(3.04)<br>0.5683<br>(11.59)|0.2292<br>(4.09)|0.7864<br>(96.16)<br>0.7193<br>(134.20)|1.31<br>2.10|
|19U4.06**1916.06**|(1)<br>-44.0694<br>||0.6103<br>|0.6453<br>|**0.83**|
||(-4.66)||(5.07)|(32.13)||
||<br>(2)<br>-21.3654<br>1|0.6291<br>334|0.2843<br>(213)|0.6769<br>(2923)|1.12|
||(-.97)<br>(3)<br>1.6260<br>(1.06)|(.)<br>0.9231<br>(6.74)|.|.<br>0.6309<br>(46.46)|2.04|
|196.06-1919.09|(1)<br>-141.554<br>||1.6797<br>376|0.2566<br>1406|0.97|
||(-3.24)||(.)|(.)||
||(2)<br>-70.3082<br>|0.5137<br>|0.6380<br>|0.4362<br>|1.75|
||(-1.64)<br> <br>|(3.61)<br>|(1.86)|(15.82)<br>||
||(3)<br>6.4197<br>|0.6246<br>||0.3732<br>|1.70|
||(1.52)|(4.66)||(23.63)||
|2.BRAZIL:Before|**and**After<br>theCruzado|||||
|1.90.02-1965.12|(1)<br>4.5829<br>||0.0832<br>751|0.438<br>FS33|1.24|
||(9.84)||(,)|(.)||
||(2)<br>2.8617<br>|0.3755<br> <br>|0.0532<br>|0.4968<br>|2.04|
||(414)|<br>(329)|(3.79)|(35.83)||
||.<br>(3)<br>2.7086|<br>.<br>0.6588||0.4019|2.27|
||<br> <br>(3.59j|<br>(6.93)||(48.05)||
|1967.01 1939.12|(1)<br>9.7948||0.6622<br>|0.3379<br>|0.55|
||'3.03)||(4.34)<br>|(18.86)<br>||
||(2)<br>2.0479<br>|0.7681<br>|0.2187<br>|0.8836<br>|1.31|
||(.77)|(5.52)|(1.66)|(35.51)||
||(3)<br>3.5029|0.8928||0.6466|1.35|
||(1-38)|(8.06;,||(5S.02)||
|3. SOLIVIA:Hyper-|Inmlation|||||
||<br> <br>917774||21944|03121|184|
|1993.01-1986.06|<br>(1)<br>-.||.<br>|.<br>|.|
||(-2.71)||(3.88)|(1S.G5.)||
||(2)<br>-75.3915<br>|0.0791<br>|2.0252<br>|0.2328<br>|1.96|
||(-2.22)<br> <br>|(.43)<br>|(2.91)|(7.42)<br>||
||(3)<br>21.2929|1.3842||0.1171|2.01|
||<br>(2.67)|(2.26)||(5.11)||

Figures in parentheses **wnder** tho **ootimated coOff;cients are t-statistics.** Figures in **parentheseo** under the **adjusted** R-oquare **are**<sup>**F-statistics.**</sup> Notes: Equation I CPINF(t) **s** C * TREND Equation 2 CPINF(t) **a** C . CPINF(t-1) . TREND Equation 3 CPINF(t) * C . CPSNF(t-1) Notations CPZNP a CPT Inflation CPINFT a Trend of CPI Inflation (CPI(t)/CPI(t-12)) - I