rafmacalaba/datause-encoder-data huggingface dataset:
using domestic and border prices for those key food items in 103 countries that together account for more than 90 percent of the world market for each of rice, wheat, maize and edible oils. Most of those are obtained from the 82-country sample in the Distortions to Agricultural Incentives (DAI) database that was recently updated by Anderson and Nelgen (2013).<sup>4</sup> Because the DAI database does not include data for some of the smaller developing countries for which we have household data, we supplement the DAI data with estimates from other sources, particularly the FAO’s domestic price and trade series. Details are provided in the Appendix. The results in column 1 of Table 3 suggest that the aggregate effect of all countries’ price-insulating behavior during 2006–08 was to raise the price in the international marketplace by 52 percent for rice, by 18 percent for both wheat and maize, and by 31 percent for edible oils.<sup>5</sup> The extent of the contribution by each country to the changes in international rice, wheat, maize and oilseed prices can be seen by the size of each country’s shaded rectangle in Figure 1. In this diagram, the countries whose protection (measured by the proportional change in T) fell by more than the increase in the international price—also shown in Figure 1 as the ‘world price impact’ line—were effective in sheltering themselves from at least part of the increase in that price. Those countries where protection fell by less than the increase in the international price experienced a domestic price rise that was greater than would have occurred in the absence of production and consumption of these food items in most countries, making each country’s share of world output very similar to its consumption share.

4 For this study, a particularly important change from the version of the DAI used in earlier versions of this study was a move from using unit values to international indicator prices when updating changes in international prices. Of greatest importance for this study was the case of rice in India where the estimates of export prices in Pursell, Gulati and Gupta (2009) were originally updated using export unit values and are now updated as an index to parallel the proportional increase in the Thai 5% broken rice price. For 2006-2008, the unit value of India’s exports was strongly affected by a large increase in the share of much-more-expensive basmati rice exports.

> 5 These are slightly higher from the comparable estimates for the three cereals in Martin and Anderson (2012), because a larger sample of countries is used here.

9
Given the positive effect on short-term loan use and the negative effect on the Equifax credit score in the case of thick file applicants, we now ask how the EFL tool affected mediumrun loan use. Table 5 shows that the RD analysis finds no effect of the EFL tool on loan use 24 to 31 months after the initial loan application. Some of the estimated coefficients are positive, while others are negative, but for the most part, they are all close to zero and none of them is statistically significant. Table A7 in the appendix shows the corresponding results for our two other measures of loan use. We obtained similar results for the subsamples of thin and thick file applicants (results are not shown).

# **6. Conclusions**

We study the use of a psychometric credit application to better assess credit risk and extend credit to SMEs. The psychometric credit application was developed by EFL with the goal of identifying traits that characterize the credit risk posed by loan recipients, traits that make it possible to select loan applicants who can generate enough cash flow to service their debt and who are willing to repay their debt.

In the context of a pilot exercise conducted by one of the five largest banks in Peru, we find that the EFL tool can increase SME loan use in the short-run. The increase in SME loan use is particularly large for applicants with thin credit bureau files, usually shunned by the formal financial sector. For this group, we find that the increase in access is not accompanied by a statistically significant reduction in the Equifax credit score (our measure of repayment behavior) suggesting the EFL tool is a viable screening method for this group.

For applicants with thick credit bureau files, the EFL tool does not lead to increased access other than from our partner bank and it does lead to a statistically significant reduction in the Equifax credit score, which may in part be due to worse repayment behavior. Potential reasons for this negative effect are that (i) the EFL tool was applied to all applicants regardless of their credit history and applicants with bad credit histories were offered a loan if their EFL score was above the threshold and (ii) our partner bank may have exerted less effort when monitoring loans and collecting payments during the pilot project due to the credit guarantee that covered the pool of eligible loans―after exhausting the first loss amount to be assumed by our partner bank. Our findings here highlight the importance of credit history information for assessing credit risk and serving the SME market.

17
both hypotheses have roughly the same explanatory power in accounting for the dynamics of the gender gap in tertiary enrollment.

Column 3 of Table VI reports the estimation results when the two hypotheses’ parameters are jointly estimated using equation 9. Both the tail and mean hypotheses’ parameter estimates are individually highly significant for tertiary education. Accounting for both the tail and mean hypotheses’ parameters increases the explanatory of the model: the R-squared increases to 0.52 compared to the specification with the tail hypothesis parameters only (0.47) or with the mean hypothesis parameters only (0.46). The model explanatory power therefore gains from jointly accounting for both hypotheses.

Columns 4 to 6 of Table VI display the estimation results from the same regressions as in column 1 to 3, but only for countries where GDP per capita data is available for all years covered by the Barro-Lee data. The results reported in column 4 to 6 are consistent with the baseline estimates reported in column 1 to 3. For tertiary enrollment, both the mean and tail hypothesis parameters are highly statistically significant when estimated in separate regressions (column 4 and 5) as well as jointly (column 6). As in the baseline sample, the model goodness of fit increases in column 6 when both hypotheses’ parameters are included (0.56), compared to R-squared of the specifications in column 4 (0.50) and column 5 (0.48), where only one of the two hypotheses is accounted for.

In Column 7, GDP per capita is added to equation 9 as an additional expalnatory variable. The rationale behind this approach is to check whether our main results are not driven by changes in economic development over time that could be associated with both enrollment rates in education and gender ratios. The explanatory power of both the tail and mean hypotheses’ parameters remain statistically significant at the one percent level, and the R-squared is only marginally improved by the inclusion of GDP per capita.

Figure VI displays the of our model when the tail and mean hypotheses are separately and jointly estimated. This visual evidence brings additional insights on the extent to which each of the hypotheses accurately predicts the gender gap reversal in educational attainment. In the upper panel, the tail hypothesis fitted for tertiary education predicts a relatively late reversal of the gender gap with respect to the total enrollment rate in tertiary education, while the mean hypothesis 24
Europe have been set at a constant proportion of average income (strictly the median).<sup>8</sup> RD effects in poor countries as strong as those claimed for rich ones would cast serious doubt on the justification for many current development policies, notably the emphasis currently placed on promoting economic growth in poor countries. Negative externalities in consumption from RD would also suggest that poor people face inefficiently high incentives to escape poverty, because they do not take account of the negative spillover effects of their income gains on social comparators. By this view, promoting poverty reduction would entail welfare efficiency costs— pointing to a potentially important trade-off for development policy.

However, while RD postulates a negative external effect on welfare of the poor from an economic gain to the relevant reference group, other theories and evidence point instead to <u>positive external effects. The uninsured risks facing poor people, and the scope for falling into</u> permanent destitution, are known to foster various arrangements for mutual support and risk sharing.<sup>9</sup> Local communities have been seen to play an important role in these and other sources of positive external effects, such as through employment and local public goods.<sup>10</sup> The pace of urbanization in a developing country could also be expected to play an important role in the balance of positive and negative external effects. . It can be conjectured that community-based risk sharing and collective action erodes with urbanization; the repeated interactions amongst essentially the same group of people that sustain informal risk-sharing arrangements are clearly more common in village-based societies than urban areas with more mobile populations.<sup>11</sup> Perceptions of RD, on the other hand, may well be stronger in urban areas.

If the positive externalities are the dominant social effect, with RD playing at best a secondary role, then welfare-consistent relative-poverty measurement would take a radically different form to (say) the standard methods found in the developed-country literature: the income poverty line would fall as mean income of the reference group rose.

> 8 This has been a common practice in Western Europe. Although the US government has traditionally used absolute poverty lines, there have been proposals to introduce relative poverty lines; see Citro and Michael (1995) and Betson et al. (2000).

> 9 There is a large literature, including Ravallion and Dearden (1988), Murgai et al. (2002), Ligon et al., (2002), Fafchamps and Lund (2003), Maitra and Ray (2003) and Cox et al. (2004).

> 10 For an interesting perspective on the role of communities see Bowles and Gintis (2002).  In a development context, see Mansuri and Rao (2004). Jalan and Ravallion (2002) find evidence of positive externalities for poor rural households living in areas with better-off neighbors (in southwest China).

> 11 For supportive evidence see Ravallion and Dearden (1988) (using data for Java, Indonesia).

5
Policy Research Working Paper 10960

# **Abstract**

Although deaths and economic losses due to extreme heat are rising globally, heatwaves remain a “hidden hazard” whose impacts are underrecognized due to measurement and valuation challenges. Cities in India are developing Heat Action Plans that combine physical cooling measures (such as urban greening and reflective roofs) with public health measures (such as heat-health early warning systems). However, there is a key knowledge gap on the relative efficacy of these actions. To inform debate on how scarce public funds could most efficiently be allocated to reduce deaths and productivity loss due to extreme heat, this paper develops spatially explicit heat risk maps for Lucknow, Chennai, and Surat under climate scenarios; models future health and economic losses under a “no intervention” scenario; and estimates the costs and benefits of alternative sets of heat mitigation actions. The modeling suggests that by 2050, the number of heat-related deaths could rise by one-third for the case study cities, while labor productivity losses could affect between 2 and 4 percent of their economic output. Among the interventions typically considered in city Heat Action Plans, benefit-to-cost ratios are favorable but vary significantly. Urban greening investments more than cover their costs based on the health and labor productivity benefits of the heat stress reduction they yield (benefit-cost ratio of 3:1). However, heat-health early warning systems offer the greatest harm reduction per dollar invested (benefit-cost ratios exceeding 50:1), suggesting that they are “low-hanging fruit” whose wider implementation across Indian and global cities should be prioritized.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at njones@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table A.3: Productivity Decompositions and Policy Changes: OLS Productivity

||Aggregate|Average|Reallocation|
|---|---|---|---|
||(1)|(2)|(3)|
|Final Goods Tariff|-.063|-.058|-.006|
||(.024)<sup>_∗∗∗_</sup>|(.026)<sup>_∗∗_</sup>|(.012)|
|Input Tariff|-.473|-.413|-.059|
||(.095)<sup>_∗∗∗_</sup>|(.102)<sup>_∗∗∗_</sup>|(.053)|
|FDI Reform|-.001|.011|-.013|
||(.010)|(.009)|(.007)<sup>_∗_</sup>|
|Delicensed|-.009|.011|-.020|
||(.014)|(.015)|(.010)<sup>_∗∗_</sup>|
|Obs.|17074|17074|17074|
|_R_<sup>2</sup>|.133|.166|.021|

Each observation is a state-industry. Dependent variable names are given at the top of each column. “Aggregate” indicates market-share-weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. All specifications are fixed-effects analyses at the state-industry level, and include year dummies. Each observation is weighted by the total number of firms in the state-industry across all years, and standard errors are clustered at the state-industry level. TFP is estimated using OLS.

55
-19

-

Table 1: Comodity-Linked Bond Values for Different Comodity Bundle Prices, Firm Volue and Correlations Using the Binomial Pricing Nodel (E¢F-l00, Ro0.12, T-5.0, a 04., a .0.3. Number of Iterotions N-10)

||Fire Value,<br>V&200||||
|---|---|---|---|---|
|||P.0.0|0.35|0.70|
|P-100|Binomial|86.22|93.59|103.19|
||Schwsrtz|65.45|93.34|102.54|
||Difference()|0.90|0.27|0.63|
|PO0|Binomial|77.46|83,46|89.46|
||Schwartz|77.34|63.20|89.26|
||Difference(S)|0.18|0.31|0.22|
|P=50|Binrmial|65.30|67.83|69.76|
||Schwartz|65.01|67.67|69.62|
||Difference(S)|0.57|0.24|0.20|
||Fire Value, V-40||||
|||P.0.0|0.35|0.70|
|P.100|BInomial|99.89|105.00|109.16|
||Schwartz|99.00|104.66|100.70|
||Difference<br>()|0.90|0.32|O.42|
|Pas0|Binomial|86.88|90.53|92.30|
||Schwartz|86.57|90.02|92.35|
||Difference_(S)_|0.36|0.57|-0.05|
|P-50|Binomial|69.30|70.22|70.59|
||Schwartz|68.89|70.14|70.58|
||Difference<br>(5)|0.60|0.11|0.01|

Fire Value, v-1000

|||P0.0|0.35|0.70|Dsf. Free|
|---|---|---|---|---|---|
|P-100|Binomial|107.95|<br>109.06|109.58|<br>109.41|
||Schwartz|107.15|100.92|109.40|109.41|
||Difference<br>(1)|0.75|<br>0.15|0.16|<br>0.00|
|P.o0|Blnomial|91.59|<br>92.50|<br>92.42|<br>92.42|
||Schwartz|91.45|92.41|92.60|92.60|
||Difference**()**|0.15|<br>0.10|0.20|<br>-0.19|
|P-50|Binomial|70.64|<br>70.58|<br>70.61|<br>70.61|
||Schwartz|70.39|<br>70.61|<br>70.64|<br>70.64|
||Difference(S)|0.36|-0.04|-0.04|-0.04|
instruments simultaneously at the same time each day, crossing the equator around 1:30 PM local time. NASA’s EOS-Aura OMI science team at Goddard Space Flight Center retrieves, filters, and publishes the data in easily useable format, at daily frequency up through just a few days earlier, with informative illustrations, videos, and context. At the time of writing, easily accessible city-level OMI NO2 VCDs with visualizations were available below.

### **Data and Visualizations:**<sup>5</sup> `https://so2.gsfc.nasa.gov/no2/no2_index.html`

Finally, we note that while alternative NO2 VCDs from other satellites are available back to the mid-1990s, accumulating all into one analysis leads to technical inconsistencies due to differences in instruments. Thus, the fact that OMI has at this point consistently made readings using the same instrumentation for over 15 years is the crux of why a systematic global macroeconomic study like ours is just now possible. We provide additional information, and other NO2 satellite data past, present, and future, in Online Appendix A.1.

# **3 Data**

## **3.1 Basic Characteristics**

**Summary Statistics.** We begin with some simple summary statistics for the OMI NO2 data we utilize. Table 1 Panel A compares the data set, which is comprised of country average VCDs per area, to GDP as reported in v. 10.0 of the Penn World Tables. Also included is night lights per area, formed from data joining older DMSP-OLS sensors with newer VIIRS sensors.<sup>6</sup> It is apparent that the primary differences between NO2 and lights are that NO2 has an overall lesser average growth rate, and is more coarsely gridded. With respect to the latter point, as Donaldson and Storeygard (2016) note (p. 192), though there may be a tendency to equate resolution with information value, this may not always be the case; as we will show, this difference in grid size does not diminish the explanatory power of NO2 versus lights at the country-level. Moreover, other NO2 sensors present and future have higher resolution if a more

> 5(Lamsal et al., 2021). Alternative ways to access the complete raw data set, such as to compute averages over all country area used later in this paper, are available through NASA’s Earth Science Data Systems (ESDS) at `https://earthdata.nasa. gov/` and GES DISC tool at `https://disc.gsfc.nasa.gov/` . Visualization tools are available at `https://worldview. earthdata.nasa.gov/` and `https://giovanni.gsfc.nasa.gov/giovanni/` .

> 6We discuss the construction of the night lights data set we use, along with all technical definitions, in Online Appendix B.1. In short, we use inter-calibrated DMSP-OLS data until the end of 2013, when the improved stray light-corrected, stable-lights VIIRS product becomes available (see Mills et al. (2013) for this data product and Gibson et al. (2020) for why VIIRS is preferable to DMSP-OLS when available). With respect to GDP, the most recent vintage of the Penn World Tables v. 10.0 ends in 2019. To compute GDP in 2020, we used World Bank data on 2020 growth for all 81 countries with data available at the time of writing. Thus, we consider an unbalanced panel.

6
that ESS and EWCS have been coordinated by different research teams, and the fieldwork providers for the two studies have been also different in all but three countries. For few countries, we will also provide comparisons with other studies which have information on work without contract.

# **2.1 Measuring informal employment**

Although international guidelines for a statistical definition of informal employment have been developed by ILO (see ILO, 2002; ILO, 2003; Hussmanns, 2004), the literature suggests a variety of approaches to identifying informal working relationships using, ̳legalistic‘, ̳ _de facto_ ‘, or ̳productive‘ definitions (see e.g. Henley et al., 2006; Perry et al., 2007; Bernabè, 2008; OECD, 2009; Pfau-Effinger, 2009). Legalistic definitions refer (in the simplest cases) to social security contributions or to employment status (self-employment vs. dependent employment) and, in the latter case, to employment contract. _De facto_ ( ̳in law or in practice‘) definitions take into account various situations when labor regulations are not applied, not enforced, or not complied with for any reason. Productive definitions rely on characteristics of the employer and/or the employed, e.g. size of establishment or occupation of a self-employed person. Combinations of these approaches are common; the ILO guidelines distinguish nine categories (cells) within informal employment (see ILO, 2003 or Hussmanns, 2004 for details).

One can further distinguish informal employment at the main or secondary job. Moreover, the concept of informal employment overlaps with the concept of under-declared work (also known as ̳envelope wages‘ or ̳quasi-formal employment‘, see Riedmann and Fischer, 2008; Williams and Renooy, 2008; Williams, 2009)<sup>8</sup> . Arguably, such quasi-formal employment falls into ILO (2003) definition of ―informal employment outside the informal sector‖; however, ILO (2003) asserts that ―for purposes of analysis and policy-making, it may be useful to disaggregate the different types of informal jobs‖, and work without a contract is clearly a category which deserves to be analyzed separately. _Hereafter, as far as employees are concerned, we apply the term ‘informal employment’ only to work without a contract_ .

To identify informal employees, different surveys use either direct questions about employment contract/ ̳labor card‘/ ̳tax book‘ or indirect questions (e.g. about social security contributions, paid annual leave or sick leave), see e.g. Hussmanns (2004), Henley et al. (2006), Perry et al. (2007), Bernabè (2008). Although, as shown by Henley et al. (2006),

> 8 There are also broader, activity-based, concepts of undeclared work and shadow economy activities (Pedersen, 2003; Djankov et al., 2003; Hanousek and Palda, 2003; Schneider, 2005; Williams and  Renooy, 2008; Williams, 2009; Feld and Schneider, 2010; Schneider et al. 2010) which are not considered in this paper.

7