rafmacalaba/datause-encoder-data huggingface dataset:
- Dupas, P. and Robinson, J. (2013a). Saving Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya. _American Economic Journal: Aplied Economics_ , 5(1):163–192.

- Dupas, P. and Robinson, J. (2013b). Why Don’t the Poor Save More? Evidence from Health Savings Experiments. _American Economic Review_ , 103(4):1138–71.

- Durevall, D. and Lindskog, A. (2012). Economic inequality and HIV in Malawi. _World Development_ , 40(7):1435–1451.

- Fortson, J. G. (2011). Mortality Risk and Human Capital Investment: The Impact of HIV/AIDS in Sub-Saharan Africa. _The Review of Economics and Statistics_ , 93(1):1–15.

- Gibson, J., Beegle, K., De Weerdt, J., and Friedman, J. (2014). What Does Variation in Survey Design Reveal About the Nature of Measurement Errors in Household Consumption? _Oxford Bulletin of Economics and Statistics_ .

- Gneezy, U., Leonard, K. L., and List, J. A. (2009). Gender Difference in Competitions: Evidence from a Matrilineal and a Patriarchal Society. _Econometrica_ , 77(5):1637–1664.

- Gollin, D., Parente, S. L., and Rogerson, R. (2002). The Role of Agriculture in Development. _American Economic Review_ , 92(2):160–164.

- Gollin, D., Parente, S. L., and Rogerson, R. (2004). Farm Work, Home Work, and International Productivity Differences. _Review of Economic Dynamics_ , 7(4):827–850.

- Greenwood, J., Kircher, P., Santos, C., and Tertilt, M. (2013). An Equilibrium Model of the African HIV/AIDS Epidemic. NBER Working Paper 18953.

- Heathcote, J., Storesletten, K., and Violante, G. L. (2009). Quantitative Macroeconomics with Heterogeneous Households. NBER Working Paper No. 14678.

- Iorio, D. and Santaeul`alia-Llopis, R. (2015). Education, HIV Status, and Risky Sexual Behavior: How Much Does the Stage of the HIV Epidemic Matter? Working Paper, University of Bologna and Washington University in St. Louis.

- Jacoby, H. G. and Skoufias, E. (1997). Risk, Financial Markets, and Human Capital in a Developing Country. _Review of Economic Studies_ , 64(3):311–335.

- Kaminski, J., Christiaensen, L., and Gilbert, C. L. (2014). The End of Seasonality? New Insights from Sub-Saharan Africa. Policy Research Working Paper, World Bank.

- Karlan, D., Ratan, A. L., and Zinman, J. (2014). Savings by and for the Poor: A Research Review and Agenda. _Review of Income and Wealth_ , 60(1):36–78.

- Krueger, D. and Perri, F. (2006). Does Income Inequality Lead to Consumption Inequality? Evidence and Theory. _Review of Economic Studies_ , 73:163–193.

- Krueger, D., Perri, F., Pistaferri, L., and Violante, G. L. (2010). Cross Sectional Facts for Macroeconomists. _Review of Economic Dynamics_ , 13(1). Special Issue, January 2010.

43
# _2. Lending performance_

According to WEDP MIS reports, 2,538 new clients who have received WEDP loan between July 2017 to July 2018, a 48% increase from the number of new clients the year before. So far, a total of 9,338 loan data has been uploaded in MIS, which can track WEDP loan by city and by disbursing MFI respectively.

Table 4: Number and Size of Loans by City (as of June 2018)

|**City**|**Number of Loans**|**Ave. Loa**|**n Size**|
|---|---|---|---|
|Addis Ababa|5085|ETB|274,057|
|Bahir Dar|1064|ETB|389,659|
|Hawassa|312|ETB|256,170|
|Adama|1269|ETB|192,032|
|Mekelle|1180|ETB|172,883|
|Dire Dawa|232|ETB|118,095|
|Assela|13|ETB|253,077|
|**Total**|**9155**|**ETB**|**236,568**|

Table 5: Number and Size of Loans by MFIs (as of July 2018)

|**MFI**|**Number of Loan**|**Ave. Loan Size**|
|---|---|---|
|WASASA|358|ETB 356,022|
|SFPI|308|ETB 418,498|
|OCSSCO|1,380|ETB 283,019|
|ADCSI|2,957|ETB 224,826|
|ACSI|1,079|ETB 628,908|
|DECSI|1,195|ETB 290,835|
|HARBU|249|ETB 291,332|
|OMO|204|ETB 413,501|
|MEKLIT|80|ETB 200,018|
|METEMAMEN|206|ETB 283,226|
|AGAR|715|ETB 304,663|
|VISION|606|ETB 231,654|
|**Total**|**9,337**|**ETB 327,208**|

# _3. Discrepancy between registration and training of new members_

Chart 3: Trends of WEDP Client Registration and Training from April 2018 to September 2018 <!-- Start of picture text --> 1500<br>1159<br>1021<br>1000 731<br>1087<br>475<br>383<br>500 207 230<br>156<br>157 321 53<br>0<br>Apr May Jun Jul Aug Sep<br>Newly Registered Newly Trained<br><!-- End of picture text --> 19
about efficiency-enhancing transfers is constrained by potential tenants' endowment of assets and other means of production (bullocks).

Thus, while land rental markets improve the allocation of resources in the presence of factor market distortions by bringing land to imperfectly or non-tradable factors of production (experience, family labor, animal power), the gains are constrained by endowments of potential transactors. In addition, there is evidence of fixed transaction costs which preclude some poor households that would desire only relatively minor adjustments, from entering the tenancy market. Similarly, data from India suggest the prevalence of imperfect adjustment whereby, on average, farmers realize only about 75% of the desired level of land transactions (Skoufias 1995). The latter study also indicates that the adjustment effected by the land rental market is asymmetric for net in-renters and out-renters; consistent with the view that market power depends on relative scarcity of factors; in this environment of land scarcity, it is easier to rent out than to rent in.

What, then, are the magnitudes of the productivity effects that are brought about by the operation of land rental markets? A comparison can be performed between sharecropped and owned (or cash-rented) plots for the same household (in order to control for unobservable farmer attributes) allows to estimate the loss due to the second-best nature of sharecropping. Bell (1976) was the first to conduct such an analysis, finding that farmers indeed exert less effort on tenanted plots. Applying the same methodology, Shaban (1987) found that, on average, tenancy was associated with a 32 percent lower output; but the difference was only 16 percent once adjustments were made for differences in land quality. Inputs of family labor and draft animals were significantly lower on sharecropped plots than on owned parcels. No statistically significant differences in productivity were found between owned plots and plots rented on a fixed-rent basis, confirming that fixed-rent contracts induce higher productivity. Other studies yield results that point in the same direction (Sen 1981).

The productivity loss entailed in sharecropping can be reduced through close social relationships, as confirmed by Sadoulet et al. (1997). Their study compared the attributes of contracts with kin and nonkin, finding that non-kin sharecroppers use significantly less inputs and obtain less output. However, there is neither a disincentive effect nor a reduction in output for sharecropping among close kin. This suggests that embedding contractual arrangements in a long-term relationship offers considerable potential to attenuate the disincentives and productivity losses that are otherwise associated with sharecropping contracts. This evidence is in line with the comprehensive review of the literature by Otsuka et al. (1992) who found a large number of studies about equally split between efficiency and inefficiency of sharecropping contracts. Studies that did not find a disincentive effect of sharecropping 25
6

- Internal management/organizational requirements

- Liquidity and diversification requirements

- Depositor (savings) protection schemes

- Provisioning requirements

- Accounting/information disclosure requirements

- Discipline/problem institutions/exit, and

- Supervision.

Also, as is evident in the survey, the majority of questions are structured to be in a yes/no format, or otherwise require a precise, often quantitative, response. Experience suggests that simple and precise questions increase the response rate and reduce the potential for mis-interpretation. With the fourth survey, we now have data spanning about 12-13 years.

We have noted in past work that the individual responses in the survey likely are of interest in their own right, especially for authorities who want to compare particular features of their own banking systems with those in other countries. However, notwithstanding interest in individual responses, it is difficult to extract lessons from so many responses. Yet policy makers want to know the general direction in which to proceed with reforms (e.g., whether to emphasize bank activity restrictions, capital requirements, bank supervision, or private monitoring) to improve banking systems. Consequently, this group will appreciate a greater degree of grouping and aggregation (and thus quantification) of the responses, as will empirical researchers bound by degrees of freedom (and a need for quantifiable variables). So we follow our earlier practice (BCL 2006, 2004, and 2001) and aggregate the data into broader indices, the principal ones being: Overall Restrictions (on bank activities), Entry Requirements, Official Supervisory Powers, Private Monitoring, and Capital Regulation. As in the past, we stress that there is no unique grouping or aggregation (or even quantification), and we still encourage researchers to experiment with their own groupings.<sup>5</sup>

> 5 See BCL (2006) for the description of the indices, and the caveat on their arbitrary nature. For example, we include the Certified Audit Required variable, which measures whether an external audit by a licensed or certified auditor is required of banks, in the index of Private Monitoring. Yet, in the countries in which this is a requirement imposed by
discrimination. First, it could be legal and justified on the basis of the state’s paternalistic attitude towards women and their families, such as in China or in respect to migration policies. Second, it could be illegal and difficult to “prove” that it occurs systematically, such as in the case of Lahey’s study. Third, it could be illegal but justified as a business case (“women are unable to cope physically with the demands of the job”) such as in the Air India case.

Yet an important marker of status is family background. As noted earlier, employers unconsciously may select candidates that are from their own background, but in other cases, employers may have a clear vision of the entity they wish to create. Here, social background of their employees, as opposed to only their human capital endowments, plays an important part in building that vision. That names can be identifiers for social status is well known. Caste names in India and Nepal are often dead giveaways (unless they are deliberately changed). Deshpande and Newman (2007) found that prospective hiring managers almost universally ask questions about family backgrounds during employment interviews. Upper caste students can offer biographies that are much closer to the uppermiddle-class professional ideal. While Scheduled Caste students perceive a hidden agenda in questions on family background, the same questions appear to upper caste students to be innocuous and sensible inquiries from a human resources perspective.

Finally, names of applicants can be another giveaway about their origin and socioeconomic status. Experimental studies in the US and elsewhere show that first names (that conform to racial or national stereotypes) can also mar individuals’ chances in the labor market (see for instance Bertrand and Mulianathan 2003). In another instance, while Bravo et al’s (2008) experiment in Chile showed no statistically significant difference in callbacks to applicants by employers based on social class, gender or neighborhood, yet it is instructive that their proxy for social class was surname. Finally not merely lower class, but upper class lineage can also be identified by names. Ying Pan’s (2011) analysis is illustrative here. It finds that Chinese husbands and wives of the largest local lineage (identified by name) are more likely to become village administrators, and young men of the largest local lineage are more likely to have local non-agricultural jobs and higher wages.

22
3

# 1. Introduction

This paper uses household survey data from Peru to estimate the differences between males and females in participa.ion in the labor market, productivity (measured by wages) and economic recurns to schooling. The purpose is to (a) identify those characteristics that enable some women, though not many, to participate in this sector, (b) determine whether the private economic returns to education vary by gender and influence school enrollment, and (c) evaluate the extent to which the male-female wage gap is caused by differences in human capital. Identifying the constraints to the labor market participation and productivity of women is particularly important in countries like Peru that have an underutilized female labor force.' Results suggest that male-female differences in human capital (for instance, education) account for some observed differences in labor market participation and productivity. The results on returns to education show that the private rate of return is generally higher for women than for men, which appears inconsistent with lower school enrollment for girls than for boys, especially at the secondary level. When unobserved family characteristics that influence wage and return estimates are controlled, however, the results indicate that parents may have reasons, at least in rural areas, for investing less in daughters than sons. More research is necessary on the social and private benefits and costs of schooling to quantify the factors that influence this decision.

The paper uses a human capital model to analyze wages and labor market participation in the formal sector that was developed by Becker (1964) 'See appendix table Al. Note that women's average labor force participation rate is lower i. Latin Americe. than in Asia and Africa (IDB 1987).
<!-- Start of picture text --> zo<br>i, 21-60<br>Ls a 61-50<br>~ A _ MMMM 21-150<br>fs al HMM i71- 150<br>hs | a<br><!-- End of picture text -->

<!-- Start of picture text --> 5, <O70<br>*, 2 O71 80<br>a r Hg 051 -1.00<br>ea He 111-120<br>‘A 1.<br>I<br><!-- End of picture text -->
<!-- Start of picture text --> Average of post-treatment ATT(e)'s: 0.46 (0.197<br>E 1<br>5<br>Ox Ofennfnnnnnnng : ceeceteeeettetlccestenstnnestafecsessesetsinfeseesseestensssesseeseetefeenee<br>-3 -2 -1 0 1 2 3 4 5 6<br>Number of years since DSM<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>Average of post-treatment ATT(e)'s:|0.55 (0.551)<br>o 2<br>£<br>ep pdt gal<br>:<br><_9<br>-3 -2 -1 0 1 2 3 4 5<br>Number of years since DSM<br><!-- End of picture text -->
In this chapter, we review the methodological challenges that accompany attempts to measure the impact of conflict on mental health. First, we review typical survey instruments used to measure mental health status, and discuss the potential problems related to the use of mental health scales in cross-sectional analyses. The benefit of mental health measures, compared to alternative measures of well-being such as happiness or life satisfaction surveys, is that their ability to predict anxiety or clinical depression can be assessed. The current scarcity of data on this topic can be mitigated by the systematic inclusion of mental health modules in multi-purpose household surveys such as Living Measurement Standards Study (LSMS) surveys, together with sustained efforts to validate the mental health scales.

Second, we summarize the measures of conflict typically used in the literature. We emphasize the limitations of using subjective assessments of conflict intensity elicited in a survey context because of potential recall biases; these biases are likely to be greater in the study of mental health, since mental health status might itself affect the respondents’ ability to recall events accurately.

Third, we discuss the special problems of causal inference in this context. Conflicts are associated with large population displacements, which can lead to systematic biases in both the composition of the survey respondents, as well as in their reported mental health. In fact, the links between migration and mental health can be a fruitful area of research. Further, conflicts often occur in places that are subject to other risk factors for mental health distress (e.g. in socially polarized places), implying that a positive association between conflict intensity and mental health status does not necessarily establish a causal impact of the former on the latter.

Fourth, we discuss the potential mechanisms through which conflict might affect mental health. Conflict might affect individuals’ expectations about the future, memories of past traumatic events can hamper people’s ability to recover from the conflict, or the hardship of postconflict reconstruction might itself be a source of stress. While the medical literature almost exclusively emphasizes the second channel, a good understanding of the obstacles to mental health recovery is instrumental to the proper design of post-conflict reconstruction policies. We review the current empirical literature in terms of how well it is able to address these conceptual and empirical concerns, and emphasize that these concerns also apply to impact evaluations of post-conflict reconstruction policies.

Finally, we illustrate the findings from our study of mental health in a specific post3
|P117496|2009|2011||0 Closed|Africa|
|---|---|---|---|---|---|
|P117510|2011|2011||0 Closed|Burundi|
|P117511|2011|2011||0 Closed|Iraq|
|P117511|2011|2012||0 Closed|Iraq|
|P117511|2011|2013||0 Closed|Iraq|
|P117511|2011|2020|7/6/2019|1 Closed|Iraq|
|P117558|2010|2011||0 Closed|Congo,Democratic Re|
|P117558|2010|2012||0 Closed|Congo,Democratic Re|
|P117558|2010|2013||0 Closed|Congo,Democratic Re|
|P117582|2010|2010||0 Closed|Liberia|
|P117582|2010|2011||0 Closed|Liberia|
|P117590|2011|2011|10/11/2010|1 Closed|Colombia|
|P117590|2011|2011|5/1/2011|2 Closed|Colombia|
|P117590|2011|2012|1/29/2012|3 Closed|Colombia|
|P117590|2011|2013|8/24/2012|4 Closed|Colombia|
|P117590|2011|2013|5/16/2013|5 Closed|Colombia|
|P117590|2011|2014|12/9/2013|6 Closed|Colombia|
|P117590|2011|2015|7/6/2014|7 Closed|Colombia|
|P117590|2011|2015|1/15/2015|8 Closed|Colombia|
|P117590|2011|2016|8/10/2015|9 Closed|Colombia|
|P117590|2011|2016|12/22/2015|10 Closed|Colombia|
|P117593|2010|2010|5/28/2010|1 Closed|Africa|
|P117596|2013|2013||0 Active|China|
|P117596|2013|2014|10/16/2013|1 Active|China|
|P117596|2013|2014|11/15/2013|2 Active|China|
|P117596|2013|2014|6/9/2014|3 Active|China|
|P117596|2013|2015|12/8/2014|4 Active|China|
|P117596|2013|2015|6/2/2015|5 Active|China|
|P117596|2013|2016|12/2/2015|6 Active|China|
|P117596|2013|2016|2/3/2016|7 Active|China|
|P117596|2013|2017|9/1/2016|8 Active|China|
|P117596|2013|2017|2/10/2017|9 Active|China|
|P117596|2013|2018|8/3/2017|10 Active|China|
|P117596|2013|2018|2/25/2018|11 Active|China|
|P117596|2013|2019|9/19/2018|12 Active|China|
|P117596|2013|2019|5/15/2019|13 Active|China|
|P117596|2013|2020|1/8/2020|14 Active|China|
|P117596|2013|2021|7/23/2020|15 Active|China|
|P117604|2010|2010||0 Closed|Belize|
|P117604|2010|2011||0 Closed|Belize|
|P117604|2010|2012||0 Closed|Belize|
|P117608|2010|2010||0 Closed|Yemen,Republic of|
|P117608|2010|2011|10/31/2010|1 Closed|Yemen,Republic of|
|P117608|2010|2012|7/13/2011|2 Closed|Yemen,Republic of|
|P117608|2010|2012|1/1/2012|3 Closed|Yemen,Republic of|
|P117608|2010|2013|12/5/2012|4 Closed|Yemen,Republic of|
|P117608|2010|2014|7/4/2013|5 Closed|Yemen,Republic of|
even when young individuals in big cities are of relatively low ability, their labor market earnings can exceed those of high-ability individuals in small cities. These individuals in small cities can raise their future earnings by moving to bigger cities for college, although this career path may not be accessible to everyone. Policies expanding college access, particularly for students from low income families, might hence mitigate spatial inequality in Colombia.

# **References**

- Ahlin, Lina, Martin Andersson, and Per Thulin. 2017. Human capital sorting: The “when” and “who” of the sorting of educated workers to urban regions. _Journal of Regional Science_ 58(3): 581–610.

- Borjas, George J., Stephen G. Bronars, and Stephen J. Trejo. 1992. Self-selection and internal migration in the United States. _Journal of Urban Economics_ 32(2): 159–185.

- Bosquet, Clément and Henry G. Overman. 2019. Why does birthplace matter so much? _Journal of Urban Economics_ 110: 26–34.

- Brunello, Giorgio and Lorenzo Cappellari. 2008. The labour market effects of _Alma Mater_ : Evidence from Italy. _Economics of Education Review_ 27(5): 564–574.

- Carlsen, Fredrik, Jørn Rattsø, and Hildegunn E. Stokke. 2016. Education, experience, and urban wage premium. _Regional Science and Urban Economics_ 60: 39–49.

- Carranza, Juan Esteban and María Marta Ferreyra. 2019. Increasing higher education access: Supply, sorting, and outcomes in Colombia. _Journal of Human Capital_ 13(1): 95–136.

- Combes, Pierre-Philippe, Gilles Duranton, and Laurent Gobillon. 2008. Spatial wage disparities: Sorting matters! _Journal of Urban Economics_ 63(2): 723–742.

- Combes, Pierre-Philippe, Gilles Duranton, Laurent Gobillon, and Sébastien Roux. 2012. Sorting and local wage and skill distributions in France. _Regional Science and Urban Economics_ 42(6): 913–930.

- D’Costa, Sabine and Henry G. Overman. 2014. The urban wage growth premium: Sorting or learning? _Regional Science and Urban Economics_ 48: 168–179.

- De la Roca, Jorge. 2017. Selection in initial and return migration: Evidence from moves across Spanish cities. _Journal of Urban Economics_ 100: 33–53.

- De la Roca, Jorge and Diego Puga. 2017. Learning by working in big cities. _Review of Economic Studies_ 84(1): 106–142.

- Duranton, Gilles. 2015. A proposal to delineate metropolitan areas in Colombia. _Revista Desarrollo y Sociedad_ 75: 223–264.

- Duranton, Gilles. 2016. Agglomeration effects in Colombia. _Journal of Regional Science_ 56(2): 210– 238.

- Duranton, Gilles and Diego Puga. 2004. Micro-foundations of urban agglomeration economies. In J. Vernon Henderson and Jacques-François Thisse (eds.) _Handbook of Regional and Urban Economics_ , volume 4. Amsterdam: Elsevier, 2063–2117.

30