rafmacalaba/data-use-mentions-extended huggingface dataset:
for the period 1966-94.</sup> The study includes 33 crops, 8 livestock products,<sup>six crop by-products, and 17 input categories.</sup> Inputs and outputs were valued at farmgate harvest<sup>prices, also collected or estimated at the</sup> district level.

Input categories for the crop sector included land,<sup>labor, water, machinery (separately for tractor,</sup> thresher, and harvester), draught animals, fertilizer<sup>(separately for nitrogen, phosphorus, and</sup> potash), and pesticide costs (separately for aerial<sup>and ground spray), and for the livestock sector,</sup> labor, fodder and feed, and interest and maintenance<sup>costs (shed, veterinary supplies, and other</sup> cost).<sup>10</sup> The annual use of these inputs for 19 districts<sup>over a period of 1966-94 was estimated.</sup> All stock values were converted to flow values.<sup>Land was evaluated at its rental value, and</sup> family labor at its opportunity cost of unskilled<sup>labor. Labor stocks were multiplied by a year-</sup> and gender-specific participation rate (number<sup>of days labor used in agriculture in a year), based</sup> on household survey data collected annually by<sup>the Punjab Economic Research Institute (PERI).</sup> Similarly, machinery service price include interest<sup>and depreciation on capital, and operation</sup> cost which includes labor, fuel, shed and maintenance<sup>costs based on annual utilization rates.</sup> Changes in the quality of conventional inputs are<sup>often confounded with changes in TFP (Alston,</sup> Norton, and Pardey, 1995). To avoid this problem<sup>i
because they can directly affect both the outcomes of interest (such as employment, income, and poverty status) and the probability of electricity being present in the village. While the survey was carried out in both urban and rural areas, community characteristics are available only for the rural sample. In the analysis, we therefore use only the rural sample, consisting of more than 24,000 original households.

The 2005 sample had 24,191 households. Because many of these split into multiple households over time, the number of households increased to 28,446 in 2012. Table 1 shows the distribution of sample households among six geographic regions as well as union territories as a group. North India accounts for the largest share of households in the sample (more than 30 percent), followed by South India.

Data on the rural electrification rate among sample households in 2005 and 2012 show large variation by region and year (table 2). The union territories and the rural vicinity of the national capital, New

> 4 For a detailed description of the IHDS, see the IHDS website at http://www.ihds.umd.edu.

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/sup> +2 ) - (∆ _i, jSt_<sup>_card_</sup> +1<sup>_−_∆</sup><sup>_i,jS_</sup> _t_<sup>_NewClient card_</sup> +1 ) _>_ 0 there is a sharp substitution between card and cash technologies that can be exclusively attributed the the second shock. The increase of the contactless payment limit may substitute contact payment to contactless payment. Moreover, it is possible to estimate the substitution between card payment methods. If for instance _Si_<sup>_contact_</sup> _,t_ +2<sup>_−Scontact_</sup> _j,t_ +2<sup>_<_0 and</sup><sup>_S_</sup> _i_<sup>_card_</sup> _,t_ +2<sup>_−Scard_</sup> _j,t_ +2<sup>_>_0,</sup> then a negative externality occurs, and the merchants gain lower sales from contact card payments while still gaining from the shock via a cannibalization between contact and contactless payments. Finally, note that if ∆ _i, jSt_<sup>_card_</sup> +3<sup>_−_∆</sup><sup>_i,jS_</sup> _t_<sup>_card_</sup> +2<sup>_>_0, after removing the effects of previous shocks that affect</sup> the whole market and one of means of payment, the second shock as a whole increases the sales of merchants contactless payment relative to those without. This highlights the ability of merchants with the latest technology to mitigate the effect of a second shock that impacts the whole economy.

Using card-sales transactions data, score matching and difference-in-differences methods, I propose in the next section to study the resilience of merchant with contactless payment to three shocks that
Sumatra, Lampung, Bali, West Nusa Tenggara, South Kalimantan, South Sulawesi, and all provinces of Java. The baseline sample, comprising more than 7,000 households and over 30,000 individuals, accounted for 83 percent of the Indonesian population (Peterson, 1995).<sup>8</sup> Since 1993, four full follow-up waves in 1997, 2000, 2007, and 2014 have been conducted. In addition, a smaller survey comprising 25 percent of the baseline sample was conducted in 1998 to better understand the impact of the Asian Financial Crisis (Thomas, Frankenberg, & Smith, 2001). The IFLS remains as one of the longest-running high-quality longitudinal surveys among low- and middle-income countries.

Relative to SAKERNAS, the IFLS is a much more comprehensive survey comprised of various modules on different topics, such as household income, expenditure, wealth, health and wellbeing, migration, and employment. The employment module gives the IFLS a distinct advantage over SAKERNAS due to the rich set of information it collects from respondents. In addition to basic labor market information also collected in SAKERNAS, the 2007 and 2014 waves of the IFLS also ask respondents about their overall job satisfaction and other characteristics of their job (e.g., physical effort, job stress). The longitudinal nature of the IFLS, combined with its extensive time coverage spanning 21 years, makes it a key resource for analyzing household and individual-level changes as well as long-term trajectories. This feature is especially important in the context of informality, where labor market transitions and longer-term employment outcomes remain an active area of research. Importantly, the longitudinal dimension of the survey allows panel methods to be deployed to address – or at the very least, minimize – the endogeneity of informal employment.

While the IFLS has clear advantages over SAKERNAS, it also has several limitations. First, it contains limited measures of informal employment that do not fully capture the ICLS-17 definition. Unlike SAKERNAS, which integrated more direct questions to capture informality in 2016,
During 1993-94, the rural CPR for the age group of 15 to 24 years was 52.7 percent, a figure which increased to 55.5 percent during 2011-12 (Table 8). Moreover, the FLFP rate for this age group declined by 19.3 percent (from 42 percent to 22.7 percent) by 2011-12, and during this period the decline was more than compensated by a gain of 22.1 percent in educational participation among working age females. Similarly, in urban areas, the CPR increased by 11 percent (from 49.2 to 60.2 percent), despite a decline in the FLFP rate by only 3.8 percent (from 18.3 to 14.5 percent) during 1993-94 to 2011-12. These figures support the argument that the decline in the FLFP rate for females between 15 to 24 years of age was to a large extent (or may be completely) due to an increase in female enrollment in education.

While evidence from the latest NSSO data corroborates this fact for the age group of 15-24 years (Panel I in Table 8), it falls short to explain the overall drop which occurred for the entire working age female population (Panel II). In terms of change, during 1993-94 to 2011-12, the rural CPR declined by 7.9 percentage points and the urban CPR declined by 0.8 percentage points (Table 8). In rural areas, the decline in CPR was caused by a 13.2 percentage point decline in the FLFP rate, which outweighed a 5.2 percentage point rise in educational attendance. This suggests that the decline in the total FLFP rate during 1993-94 to 2011-12 across all age groups was not in fact due to a rise in educational attendance. To be specific, 62 percent of the total drop in the FLFP rate which occurred in rural areas during 1993-94 to 2011-12 was due to reasons other than increased attendance in educational institutions. In urban areas, only 28 percent of the total drop in FLFP was
Our field experiments involve the 50 villages in Gujarat where rainfall insurance was offered in 2007. Twenty of these villages had not previously been exposed to the product, while in the remaining 30 villages SEWA had marketed insurance to households in 2006. We use different field experiments for these two groups of villages. For villages with no prior exposure to insurance, SEWA used portable _video players_ to deliver a 90-second marketing message directly to household-decision makers.<sup>14</sup> Each treated household was randomly assigned one of eight different videos. For villages where insurance had been offered in 2006, SEWA instead distributed _flyers_ to households, containing one of six randomly assigned messages.

These treatments were delivered to a cross-section of households in each village, including all the households which participated in the 2006 survey. Each treated household received a non-transferable coupon bearing their name and address, to be presented for a discount when insurance was purchased. The coupon serial number indicated which marketing message the household received. The size of this discount was randomized in the 20 villages receiving video treatments: 40% of households receive Rs. 5, 40% receive Rs. 15, and 20% receive Rs. 30. This randomization allows us to estimate the price elasticity of rainfall insurance demand. In the 30 villages receiving flyer treatments, the discount was always fixed at Rs. 5.

_Gujarat: Details of video and flyer messages. –_ In the video experiments, we randomize the message viewed by the household along four dimensions. One experiment tests the sensitivity of demand to the prominence of the trusted SEWA brand. The other three treatments test the sensitivity of demand to framing effects. A full description of the combinations of treatments used is presented in the Online Appendix B.<sup>15</sup> Basic features are as follows:

- SEWA Brand (Yes or No): SEWA has worked for many years in the study villages, while IFFCO-TOKIO is almost unknown. In the “Strong SEWA brand” treatment, videos clearly indicate
This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in Prova Brasil. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample. All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights.
significant ways. Studies vary across many dimensions, such as whether the analysis is carried out for representative households or actual households (i.e. micro-simulation), whether it is static or dynamic, single- or multi-region, partial- or general-equilibrium, and so forth.

Out of these possibilities, four broad categories of study are identified based upon the principal methodology employed. These categories are loosely defined and unlikely to satisfy every reader, but they provide a starting point for getting a grip on what is an extraordinarily wide range of approaches. The first category encompasses a broad array of _partial-equilibrium_ and/or _cost-of-living_ approaches. These studies are typically based on household expenditure data, and generally emphasize commodity markets and their role in determining poverty impacts, or at least as a measure of poverty across time. Factor earnings are frequently ignored.

In contrast, studies in the second category all involve some form of _general equilibrium_ model that accounts for commodity, terms of trade, and factor market effects. These studies are usually based on disaggregated economy-wide Social Accounting Matrices for individual countries.<sup>2</sup> They are also closely related to the third category of study, which represents a relatively recent approach – general equilibrium simulation coupled with incidence analysis based on household survey data. These studies may be thought of as involving a _micro-macro synthesis_ . While the term “micro-macro” has been used differently in other contexts, in this paper it is meant to refer to the linking of a model based on household survey (micro) data with a model based primarily on macro-level data.

The fourth category of study attempts to estimate the long run potential for poverty reduction through the effect of trade liberalization on economic growth. This is typically undertaken in a series of steps, whereby the effect of trade liberalization on capital accumulation, productivity and economic growth is first estimated, thereupon using some sort of reduced form relationship to estimate the poverty reduction that might occur as a result of this growth. Whereas the first three sets of studies tend
literature does, but also between domestic and international markets. Cross-border issuances account for a sizeable share of worldwide borrowing through corporate bonds and syndicated loans (56 percent during 1991-2014). Then, we analyze how changes in debt issuance composition during crises impacted the total amount borrowed and the new terms of financing. We focus on maturity and currency denomination at the firm and aggregate (country) levels. Our analysis of a more complete set of debt markets across advanced and emerging economies yields different patterns on the dynamics of debt than those obtained by studies based on individual markets, individual economies, or balance sheet data. The differences in results point to the importance of analyzing jointly the various markets in which corporations borrow.

To conduct the analysis, we use debt issuance data at the transaction level for 50 different economies and four types of debt markets around the world: domestic and international (cross-border) corporate bonds and syndicated loans. Our sample comprises 56,826 (listed and unlisted) firms and 183,732 (domestic and cross-border) issuances during 1991-2014. We also expand existing analyses by covering the universe of non-financial firms (not just publicly listed corporations) issuing debt in these four debt markets. Unlisted firms are in fact significant in our sample of issuers: they account for 45 percent and 71 percent of the total number of firms issuing corporate bonds and syndicated loans, respectively. Lastly, we also consider different types of financial shocks to evaluate changes in debt issuance activity. We focus on the 2008-2009 Global Financial Crisis (GFC) as an international banking crisis and on domestic banking crises during 1991-2014.

Our main findings can be summarized as follows. First, firms moved away from crisis-hit markets during financial crises, changing their debt issuance composition. Firms switched both between bonds and syndicated loans and between domestic and international debt. The direction of the market switches varied with the crisis origin. When the GFC hit banks in major advanced economies, firms worldwide increased their propensity to issue (i) bonds relative to syndicated loans 2
at protecting the communities in these countries and increasing resilience, particularly of those in the lowest socioeconomic strata.

Results for the GDP per capita analysis show robust, statistically significant effects in the short term with the three hurricane damage indexes modelling different specifications. The household analysis, on the other hand, despite data limitations, gives an insight into the household characteristics that contribute to dealing with hurricane windstorm risk better. The data for the household analysis, however, has important drawbacks, particularly in terms of time coverage, and is more difficult to handle for comparability purposes.

An interesting area for future research has to do with the long‐term cumulative effects of hurricanes. In the literature there is no consensus on whether there are long‐term impacts in the national economy as well than at the household level. A more detailed analysis on long‐term impact of hurricanes for Central America on the determinants of economic growth and social progress in the region could be valuable. Moreover, the challenge is also in quantifying the indirect effects of disasters. Estimating both direct and indirect economic effects of disasters is central to designing effective DRM policies to support the most vulnerable population in a more efficient way. The effects of adverse natural events impact upon a variety of human welfare indicators and these effects add up. Furthermore, a discussion regarding coping mechanisms would also be relevant. Understanding how households exposed to natural events make decisions, the context in which these decisions are made, and other behavioral elements, could help identify and better characterize their coping mechanisms, resilience levels, and mitigation strategies. Understanding this is paramount in designing effective risk managing strategies. Qualitative research plays an important role in this particular set of questions and could complement quantitative analysis, enriching the lessons learned along the way.

# 6. REFERENCES

Anttila‐Hughes, J. & Hsiang, S. (2014). Destruction, Disinvestment, and Death: Economic and Human Losses Following Environmental Disaster. Available at SSRN: <u>http://ssrn.com/
010<sup>[B]</sup>||0.046<sup>[B]</sup>||0.021<sup>[B]</sup>||0.068<sup>[B]</sup>||0.324||
|Arellano-Bond AR(1) test (p-value)|0.000||0.000||0.000||0.000||0.000||
|Arellano-Bond AR(2)test(p-value)|0.968||0.962||0.970||0.975||0.981||

Notes:<sup>[A]</sup> GDP data are adjusted for international differences in purchasing power of the dollar.<sup>[B]</sup> When the _p-_ value of the Hansen test is a borderline case, the instruments are further validated by the supplementary evidence from the Sargan test of the over-identifying restrictions. Robust standard errors in parentheses; asterisks indicate statistical significance at the ***1, **5, and *10 percent level. Data for the transition economies refer only from 1997 onwards. All equations include time period and group-specific dummies.

The results are remarkably similar when _de facto_ or quantity-based measures of the international financial openness are used (Table 4, columns 5 to 8). In this case, a country’s international financial openness is defined as the sum of its gross stocks of foreign assets and liabilities (scaled by GDP) as measured by Lane and Milesi-Ferretti (2007); referred to as FINOPEN in the table. The results for most country groups are in line with the prevailing view in the literature: the role of international financial openness is indirect, with collateral benefits, such as the development of the domestic financial sector, the discipline on macroeconomic policies, and the promotion of specialization (e.g., Kose et
Feldman 1992, Bargh 2006). Impacts from priming can help keep a mental idea (temporarily) within reach (Hopkins 2011). If framing affects how people internalize their response to various experiences, this is also relevant as a potential policy tool for designing interventions.

In social pyschology and other fields, research has linked priming to attitudes, perception, and preferences, as well as to measurement error and reporting (particularly of sensitive data) (Epley and Gilovich 2001, Rasinski, Visser et al. 2005, Bargh 2006, Steele and Ambady 2006, Mazar, Amir et al. 2008, Van de Walle and Van Ryzin 2011). Priming has also shown impacts on political opinions and candidate preferences, and has been used as a tool in media, speeches, and elsewhere (Druckman and Holmes 2004, Althaus and Kim 2006, Cassino and Erisen 2010, Hopkins 2011, Naoi and Kume 2011).

In an early lab experiment, Lerner and Keltner (2001) concluded that fearful recollections lead to an increase in risk-averse behavior and angry recollections to an increase in risk-seeking behavior. In our setting, one can imagine that recall of conflict could lead to either fear or anger, depending on the individual and the context. Meanwhile, Miranda and Persons (1988) suggest that cognitive vulnerability to depression can be triggered by experimental priming. Most studies report that prior experience modulates this connection, with depressed individuals being more influenced by negative primes (Scher, Ingram et al. 2005). However, the evidence is equivocal (Brockmeyer et al. 2012; LeMoult et al. 2012) and, thus requires further testing.

# 3. Data and research design

In 2015 and 2016, the Nigerian Bureau of Statistics, with the support of the World Bank Living Standards Measurement Study (LSMS) team, conducted the third wave of the General Household Survey Panel (GHS-Panel) with over 4,500 nationally-representative households. Data were collected on consumption, behavioral preferences, depressive symptoms, history of conflicts (5 years)
’s WTO accession in 2002, demand for migrant workers in manufacturing, construction, and labor-intensive services increased greatly. Many provinces, especially coastal provinces reduced regulatory barriers for migrant workers in 2003. The number of migrant workers grew more rapidly during 2003-2008. Richer provinces in coastal regions tend to have higher migrant worker shares than poorer interior provinces (Tombe and Zhu 2019). As the population census data only tracks migrant workers who have lived in the current address for at least half a year, this paper could not exclude short-term migrants in the sample. Internal migration could have a bigger effect on our estimation.

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(ralmeida@worldbank.org). We thank the Brazilian Ministry of Labor for providing us the data on enforcement of labor regulation that we use in our paper and important information about the process of enforcement, especially Edgar Brandao, Sandra Brandao and Marcelo Campos. We thank George Clarke, Saul Estrin, Ana Fernandes, Mary Hallward-Driemeier, John Haltiwanger, Jennifer Hunt, Sara Lemos, Adriana Kugler, Stefano Scarpetta, Luis Serven, Rodrigo Soares and participants in several seminars for valuable suggestions. Carneiro thanks the hospitality of the Poverty Unit of the World Bank Research Group.
>60<br>50<br>40<br>30<br>20<br>10<br>0<br>AFR EAP ECA LAC MNA SAR<br><!-- End of picture text -->

- D. Private credit (% GDP) 2017 regions

<!-- Start of picture text --> 100<br>25 pctl<br>90<br>median<br>80 75 pctl<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>AFR EAP ECA LAC MNA SAR<br><!-- End of picture text -->

- F. Regulatory capital capital (% Risk weighted assets) 2017 regions

<!-- Start of picture text --> 35<br>25 pctl<br>30 median<br>75 pctl<br>25<br>20<br>15<br>10<br>5<br>0<br>AFR EAP ECA LAC MNA SAR<br><!-- End of picture text --> Note: pctl means percentile. Sources: Bank assets, Fitch Connect; Private credit, World Development Indicators, WB; Regulatory capital / Risk weighted assets, Financial Soundness Indicators IMF.

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