rafmacalaba/datause-encoder-data huggingface dataset:
In both cases, it is possible that even if a woman’s sexual activity is unchanged her likelihood of an STI increases. We thus present corroborating evidence using the selfreported sexual behaviors. We find that women experiencing shocks are 12% more likely to have unprotected sex. In addition, for unmarried women, where the relationship between shocks and STIs is strongest, we find that shocks lead to almost a three-fold increase in paid sex (sex in exchange for cash or gifts). Finally, using reported income data, we estimate a negative income elasticity with respect to STIs. For unmarried women, we find a negative income elasticity with respect to paid sex as well.

Our work is consistent with previous studies in economics documenting links between negatives shocks and transactional sex (Robinson and Yeh, 2011,2012; Dupas and Robinson, 2012). There are two important distinctions with our study. First, these previous studies show increases in transactional sex for commercial sex workers, in other words, they see changes on the intensive margin. We present evidence suggesting that women are entering the market for transactional sex as a response to shocks, or that shocks are leading to changes on the extensive margin. Second, while transactional sex is considered risky, previous studies did not observe adverse health outcomes due to this behavior. Our study provides evidence that transactional sex is resulting in higher rates of STIs, which can result in infertility, premature births, and death due to complications. As such, this work also contributes to the large body of literature documenting the effects of negative shocks on health outcomes (Maccini and Yang 2009; Alderman _et al._ 2006) and the relationships between income and disease (Oster 2012).

Our study also provides additional evidence that transactional sex is not limited to sex workers, but maybe seen as a common risk-coping mechanism for a much larger population (Luke 2006; Swidler and Watkins 2007). In addition, this study contributes to understanding how economic conditions affect sexual behavior and HIV. Dinkelman _et al._ (2008) find that household level shocks increase risky sexual activity for young women in South Africa. Burke et al. (2013), using climate data and the Demographic Health Surveys, find that across Sub-Saharan Africa, individuals exposed to droughts are much more likely to be infected with HIV; their results are consistent with women 4
by priming farmers to think about the attainable harvest in the absence of shock. Testing these different options should be the subject of a future survey experiment.

# **For the attribution of losses to disasters**

In our study region, rainfall-related shocks present the most common reasons for crop losses. While these shocks are covariant, they lack a clear-cut benchmark that would make relying on farmers’ self-reports alone a dependable strategy to attribute losses to disasters. While for largescale drought, it may be feasible to rely on self-reports, more fine-grained measurement in years with less severe drought would benefit from a high density of weather stations or the distribution of micro-sensors to accurately capture differences in drought exposure.

Lastly, there is ample scope for future research. So far, we can mostly only study the internal consistency of different methods of measuring crop loss in survey data with no benchmark that would allow for an appraisal of their accuracy. An important element to ascertain the suitability of different options to estimate crop losses through survey data will thus be establishing a credible “gold standard” against which different methods can be compared. Furthermore, farmers’ expectation-building still leaves a large (behavioral) black box. Better understanding the factors that underlie this mental model would provide crucial insights into the feasibility of using farmers’ expectations to proxy for the attainable harvest. Similarly, future research may look into the possibility of drawing on a large set of possible predictors, including farmers’ expectations, planted area and plant productivity loss proxies, inputs, and plot characteristics to predict yields. Finally, one pivotal question this paper leaves for future research is the representativeness of losses elicited from a sample. In order to build accurate national and sub-national aggregates of losses and deliver evidence for disaster risk reduction policies in general and SDG 1.5.2 and Sendai indicator C-2 in particular, it will be central to develop a sound methodology to representatively aggregate losses measured through surveys. An important validation exercise will then be the comparison between post-disaster needs assessments (PDNAs) and existing aggregate estimates of disaster crop losses and the estimates formed from micro survey data. In this realm, it will also be essential to keep investing in the collection of accurate ground data on disaster incidences and impacts at different scales, explore synergies between different data sources, and link this information to preventive disaster risk reduction interventions.

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31

- Fry, M.J., 1978, "Money and Capital or Financial Deepening in Economic Development" Journal of Money, Credit and Banking 10, No. 4, November.

- Gelb, A.H., 1988, Oil Windfallso Blessing or Curse? Oxford University Press.

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- Hanson J. A. And Neal, C.R., 1987, "The Demand for Liquid Assets," Industry Department, World Bank, January 3.

- Hinds, M., 1988, "Economic Effects of Financial Crisis," PPR Working Paper No. 104, World Bank, October.

- International Monetary Fund, 1983, Interest Rate Policies in Developing EconomicA, Occasional Paper No. 22, (Washington, D.C., October).

- Keynes, J.M., 1930, A Treatise on Money, Vol. II. MacMillan.

- Khatkhate, D.R., 1988, "Assessing the Impact of Interest Rates in Less Developed Countries," World Develogment, Vol. 16, No. 5, pp. 577-588.

- McKinnon, R.I., 1988, "Financial Liberalization and Economic Development: A Reassessment of Interest Rate Policies in Asia and Latin

- America," International Centre for Economic Growth, Occasional Paper, November 6.

- McKinnon, R.I., 1973, Money and Capital in Economic Development, Washington, D.C. Brookings Institution.

- Neal C. R., 1989, "The Demand for Financial Assets in 117 Developing Countries," World Bank, ITF, Background Paper for 1989 WDR.
# _Core Results_

Table 2 reports regressions of changes in the $1.25/day and $2/day poverty headcounts against percentage changes in food prices using OLS and the IV GMM. For both regressors the standard errors are clustered at the country level, and, though not reported, both models include time dummies to control for generic international economic trends.

The most striking feature of regressions 1 through 4 in table 2 is that all the coefficients are negative and significant at the 5% level or higher, implying that increases in real food prices tend to reduce poverty rates at both the $1.25/day and $2/day poverty lines. The magnitude of the estimated coefficients is similar across the two poverty lines but very different across estimators. Specifically, the IV GMM estimates are several times larger than the robust regression estimates, suggesting that the OLS estimates might be biased upwards, perhaps because increases in domestic food prices are often driven by negative domestic shocks such as droughts or broader economic crises.<sup>8</sup> Even so, the IV GMM point estimates also need to be interpreted cautiously. While the exclusion assumption is supported by the hypothesis tests in the bottom of table 2, the KleibergenPaap rk Wald F statistic suggests that our instrument (international food prices) is relatively weak, which is to be expected since international price transmission into domestic markets is quite variable and since the basket of nine international prices is not always relevant to domestic consumption and production patterns.<sup>9</sup> Consistent with a weak instrument problem, the standard errors on the IV GMM coefficient estimates are relatively large. This suggests we should be cautious in attaching too much importance to an economic interpretation of the magnitudes of these coefficients. Bearing that caveat in mind, the six percent food price increase in the median country from 2005 to 2008 would predict a 1.76 percentage point decline in $1.25 poverty in the average country based on the least squares regression result and a 4.9 point decline based on the IV GMM result.

> 8. OLS estimates reported in appendix S2 are also significant and negative but somewhat smaller than the robust regression estimates.

> 9. In appendix table S2.4, we explore whether the instrument is stronger in a sample of more recent poverty observations in which there was more variability in international food prices, restricting the sample to 200.

9
In our baseline specifications, we estimated the surprise component of FOMC announcements by _percentage changes_ in 2-year Treasury yields as done by Fischer (2016). An alternative approach would be to use _basis points changes_ in the yields instead. Percentage changes and basis points changes of 2-year Treasury yields around FOMC announcements exhibit a high correlation of 0.85. However, during the period of exceptionally low yields between August 2011 and January 2014, when 2-year Treasuries were generally below 0.4%, FOMC announcements resulted in relatively small basis point changes, but due to the low denominator, these reflected in sizeable changes in percentage terms. Whereas three FOMC announcements during that period, the “Operation Twist” in September 2011 and the “Taper Tantrum” events in May and September 2013, resulted in percentage changes of Treasury yields exceeding their mean by at least two standard deviations, no announcement during that period saw basis point changes in yields exceed their sample mean by that same margin. This observation leads to the conjecture that the surprise component of FOMC announcements is not fully captured by basis point changes alone, but also depends on the prevailing yield level. To formally test this hypothesis, we regress changes in emerging economies’ asset prices on basis point changes in 2-year Treasury yields and the interaction between those yield changes and a dummy that takes the value 1 if the level of 2-year Treasuries is below 0.4%.

**Table 11: Changes and Level Effects**

||<br>k=1|Exchange rate<br>k=2|s<br>k=3|<br>k=1|Equity prices<br>k=2|<br>k=3|k=1|Bond yields<br>k=2|k=3|
|---|---|---|---|---|---|---|---|---|---|
|2-year Treasury yield (bps)|0.071***|0.085***|0.071***|-0.128***|-0.194***|-0.189***|0.075**|0.120***|0.115***|
||[6.38]|[6.54]|[6.67]|[-9.51]|[-10.25]|[-8.23]|[2.45]|[3.79]|[3.42]|
|2-year T-yield*Dummy||||||||||
|Low Yield|0.074***|0.062***|0.071***|-0.058|-0.137**|-0.193**|0.284***|0.295***|0.352***|
||[5.11]|[4.73]|[4.28]|[-0.99]|[-2.28]|[-2.84]|[6.66]|[5.02]|[5.93]|
|Observations|1,300|1,300|1,300|1,194|1,200|1,197|1,049|1,052|1,053|
|R-squared|0.112|0.107|0.072|0.089|0.131|0.117|0.055|0.070|0.067|
|# of countries|20|20|20|19|19|19|17|17|17|

_Notes:_ Dependent variables are percent change in exchange rate, equity prices, or bond yields, between the day of FOMC announcements and k days after that, as per Equation 1. The independent variable is the basis point (bps) change in 2- year Treasury yield between t and t-1, t being the day of the FOMC announcements. Low Yield is a dummy variable that takes the value 1 if the level of 2-year Treasury yields is below 0.4% and 0 otherwise. Regressions include country fixed effects. Robust t statistics are in parentheses. *, **, *** indicate that the coefficients are significant at 10, 5, and 1 percent level.

The results in Table 11 clearly indicate that the impact of a given basis point change in Treasury yields on emerging economies assets is not independent of the level of the yield. It was significantly higher in times when yields were very low. Given the importance of both, changes and levels of US 27
In addition, we use information on past shocks as reported in the MxFLS household and community questionnaires to check the robustness of our results to locality-specific events.

A related, though different, concern has to do with a possible bias arising from location-specific time trends. The regression equation controls for state-time fixed effects, and all effects are estimated relative to control communities. However, if there is permanent divergence or convergence in growth rates between our treatment community groups and the control community group, time effects are not completely accounted for. Also, one might worry that the relatively small number of control communities in our sample might affect our results. To address this concern, we also report results following an alternative identification strategy that does not use children in control communities. We estimate equation (1) for each locality group separately, which means that now impact effects are directly identified out of differences across the four cohort-time intercepts.<sup>22</sup> The cost to following this alternative is that now we need somewhat stronger assumption to control for time trends within locality groups. For community groups 1 and 3, we need the difference between a given cohort and the younger (older) group to be a good counterfactual for the difference between that cohort and the older (younger) group. Furthermore, there is no way to control for time effects when we evaluate the second group of communities. One additional feature of this alternative is that now the analysis can be restricted to the sample of households who report being program participants.<sup>23</sup> In addition, Mexico experienced a financial crisis in 1994, known as the Tequila crises, which generated a widespread fall in income, consumption and wages. This is important for us because the group of children we labeled “older cohort” includes those children born between 1994 and

> 22 Due to sample size constraints, covariates are no longer cohort specific. 23 This is not possible under the main identification strategy, because by construction, there are no program participants in control communities.

22
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We build on Benjamin (1992) and allow farmers to face three crucial constraints that cause deviations from expected profit maximization. First, access to insurance may be limited, so farmers may reduce labor and input use to avoid basis risk. Second, credit or access constraints may limit input use. Third, farmers’ off farm labor allocations may be constrained from above, resulting in overutilization of labor on the household farm.<sup>33,34</sup> We model access to irrigation on the sample plot (the “sample plot shock”) as a labor- and input-complementing increase in the productivity of the sample plot. This offers predictions consistent with our results in Section 3: the sample plot shock increases production, labor allocations, and input allocations on the sample plot.

Next, we consider the impacts of the sample plot shock on production decisions on the most important plot in this framework.

**Proposition 1.** _If no constraint binds, separation holds and input and labor use on the most important plot does not respond to the sample plot shock._

In the absence of the three constraints listed above, households maximize expected profits. As access to irrigation on the sample plot does not impact marginal products or prices on the most important plot, labor and input allocations on the most important plot do not respond to the sample plot shock.

**Proposition 2.** _If input, labor, or insurance constraints bind, then input and labor use are reduced on the most important plot in response to the sample plot shock._

The logic case-by-case is as follows. First, if input constraints bind, then the increase in inputs on the sample plot caused by access to irrigation must be associated with a reduction in inputs on the most important plot. Second, if labor constraints bind, then the increase in labor on the sample plot caused by access to irrigation must be associated with a reduction in the sum of labor on the most important plot

> 33These constraints correspond with those most commonly cited by farmers in focus groups as driving crop choice. In particular, farmers frequently cite _imbaraga_ , or strength, of the household head (corresponding to labor market constraints), _igishoro_ , or access to capital (corresponding to credit or input market constraints), and _isoko_ , or access to markets (corresponding to price risk, or insurance market constraints).

> 34We model labor constraints as a constraint on off farm labor allocations from above because this generates a shadow wage that is below the market wage, and we note this generalizes a model without an off farm labor market. Agness et al. (2020) provide evidence in Kenya that households’ shadow wages are almost always below the market wage. In addition, as we discuss in Section 3.2.2, our results on profits are consistent with a shadow wage that is below the market wage.

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## Ortain

The discussion of Britsh labor market policy in Section D described the transformation In the late 1980s from industry-specMc to locally-based training. As noted, the earlier industry-speMific training system operated through Industrial Training Boards (ITBs) authorized to Impose taxes on employers to support extensive apprenticeship programs. These programs Included nationally recognized exams that allowed Individuals near or at the end of their training to obtain formal qualifications beyond the apprenticeship.

Blanchf1ower and Lynch (1991) compare the post-school training experience of young non-college graduates in Brtain and the U.S. using two large longitudinal data sets that provide detailed micro data for youth during the 1970s and early 1980s. The authors' principal finding is that non-college bound youth in Britain received much more post-school training than similarAmerican youth. The primary source of this training in Britain, especially for males, was apprenticeships. Apprenticeship training In both countries is also found to have a large poskive effect on eamings. In Britain, this positive effect is substantially boosted when apprenticeship training Is associated with formal qualifications obtained during or at the completion of the apprenticeshi **p** rogram.

Blanchflower and Lynch sugges **t** hat these resuits have an interesting policy implication relating to the Thatcher government's policy of replacing ITB apprenticeship programs by locally-based programs under the Youth Training program. The implication is that If Youth Training is to deliver high quality training that meets the skill requirements of local employers, attention should be paid to certifying the skills acquired through training programs.

# F. Lessons for Developina and Middle-Income Countries

Many developing and middle-income ceuntries have recently taken steps to integrate themselves more completely into the world economy. These steps typically include the removal of policies which protected Inefficient domestic industries from international competition. The industrial restructuring that resuits often leads to layoffs and plant closures and large-scale worker displacement.
**How Do Borrowers Respond to a Debt Moratorium? Experimental Evidence from Consumer Loans in India**<sup>*</sup>

Stefano Fiorin<sup>†</sup> Joseph Hall<sup>‡</sup> Martin Kanz<sup>§</sup>

**JEL:** G2, G5, O12 **Keywords:** Debt forbearance, moral hazard, relational contracting

> *This version: March 9, 2023. We are grateful for discussions with Viral Acharya, Deniz Aydin, Iwan Barankay, Daniel Björkegren, Emily Breza, Martin Brown, Greg Buchak, Leonardo Burstzyn, Anusha Chari, Shawn Cole, Stefano DellaVigna, Mert Demirer, Xavier Giné, Luigi Guiso, Jonas Hjort, Tullio Jappelli, Gianmarco León-Ciliotta, Nicola Limodio, John List, Gregor Matvos, David McKenzie, Muhammad Meki, Robert Metcalfe, Tarun Ramadorai, Rodney Ramcharan, Imran Rasul, Gautam Rao, Claudia Robles-Garcia, Chris Roth, Farzad Saidi, Amit Seru, Janis Skrastins, Jonathan Zinman, and Jeffrey Zwiebel as well as comments and suggestions from seminar and conference participants. We are especially grateful to Shivashish Chatterjee and the staff of DMI Finance for making this research possible. This project was reviewed in advance and approved by the Institutional Review Board at Stanford University (Protocol #IRB-55386). The experiments are registered with the American Economic Association registry for randomized control trials (AEARCTR-0006109). The opinions expressed in this paper do not necessarily represent the views of the World Bank, its executive directors, or the countries they represent.

> †Bocconi University, IGIER, LEAP, and CEPR, Email: `stefano.fiorin@unibocconi.it` .

> ‡Stanford GSB, Email: `jphall@stanford.edu` .

> §World Bank and CEPR, Email: `mkanz@worldbank.org` .