rafmacalaba/data-use-mentions-extended huggingface dataset:
reduction in the number of hours worked, which suggests that employers adapted by cutting back on their workers’ schedules.

An adverse effect on employment has been seen in the agricultural sector (Bhorat, Kanbur and Stanwix, 2011). According to estimates by Bhorat et al. (2011), the probability of finding a job in agriculture fell by 13 percentage points following the introduction of minimum wage legislation for this sector. On the other hand, there were no changes in the number of hours worked, suggesting that the only adjustment channel was laying off personnel and the schedule of those who remained employed was unaffected.

A more recent study by Garbers (2015), also on South Africa, supports the findings just mentioned for workers in agriculture. Based on panel data for 1997‐2007, this study found job losses among the less qualified workers in the commercial agricultural sector. Landowners made various adjustments in response to higher labor costs resulting from introduction of the minimum wage and subsequent increases. While on the one hand there was an uptick in the demand for more qualified workers and those with more years of experience, on the other hand the owners of larger farms decided to intensify agricultural production by buying tractors and adopting other new technologies.

Dinkelman and Ranchhod (2012) studied the impact of introducing a minimum wage for domestic workers in South Africa in 2002. Using the Difference‐in‐Differences estimator, this 11
34 Figure 9: Households living in electrified neighborhoods that have household connection <!-- Start of picture text --> 100<br>Bottom quintile Quintile 2<br>90<br>Quintile 3 Quintile 4<br>80<br>Top quintile<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Urban Rural National Urban Rural National Urban Rural National<br>40% 13% 26% 49% 19% 39% 92% 80% 87%<br>Malawi Niger Nigeria<br>Source : World Bank staff calculations using household survey data.<br>Figure 10: Households without grid connection living in electrified neighborhoods<br>100<br>Bottom quintile Quintile 2 Quintile 3<br>90<br>Quintile 4 Top quintile<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Urban Rural National Urban Rural National Urban Rural National<br>85% 19% 27% 94% 8% 17% 62% 17% 22%<br>Malawi Niger Nigeria<br>% of households in community<br>% of households without connection<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data.

Figure 10: Households without grid connection living in electrified neighborhoods _Source_ : World Bank staff calculations using household survey data.

For households living in communities connected to the grid, in Malawi and Nigeria the survey asked households without grid electricity why they had not connected (Table 13). The reasons were specified in the questionnaire and households selected from a range of options. Not surprisingly, unaffordability of the connection charge was the most common reason. In Malawi, the payment for the initial connection to
Yang (2009) present empirical evidence using Indonesian data that for females, local rainfall variation around the time of birth significantly affects schooling, health, and socioeconomic status measured in adulthood, inconsistent with the notion that households are diversified against rainfall risk. Duflo and Udry (2004) reject the null of perfect risk sharing with respect to rainfall shocks even _within_ households. They show that rainfall-induced relative income shocks to female-tended crops cause changes in the relative expenditure share of goods favored by women, such as child education. Dercon and Outes (2009) present evidence that rainfall shocks generate plausibly exogenous variation in income that can lead to poverty traps.

Furthermore, Rosenzweig and Binswanger (1993) present evidence that households do engage in costly “income smoothing” in response to rainfall risk, activities which they estimate significantly reduce average income. Using Indian data, these authors estimate that a onestandard-deviation increase in average rainfall volatility is associated with a statistically significant reduction in risk taking and profits, equivalent to 15% of average profits for the median farmer and 35% of average profits for the bottom wealth quartile. Less directly focused on rainfall shocks, Morduch (1995) and Townsend (1994) present evidence that poor households are further from the full risk-sharing benchmark than wealthy households.

Household responses to qualitative surveys conducted by us in Andhra Pradesh are also consistent with the proposition that households are not fully insured against rainfall shocks. In a 2004 survey, we asked households to list the three most important sources of risk they face. Notably, 89% of farmers cited drought as the most significant source of risk. (See Giné et al. [2008] for a table summarizing results of this question.) In addition, in surveys conducted in Andhra Pradesh and Gujarat, households that chose not to purchase insurance against rainfall risk were asked why. Only a very small fraction of these households (between 2% and 25%,
stage of pregnancy, women are also more likely to be aware that they are pregnant and can recognize miscarriage as such.

ENADID data virtually replicates the epidemiologically predicted proportion of naturally occurring clinical miscarriages. Of all pregnancies reported in 2005, 7.05 percent ended in clinical miscarriages (Table 3). This number just about accounts for biologically inevitable miscarriages – being too low to also include abortions reported as miscarriages. We conclude that women who resort to clandestine abortion do not report a pregnancy at all, as opposed to reporting such a pregnancy as having terminated in miscarriage.

# **3.3. How to account for women who chose to abort, but had a miscarriage prior to executing this option?**

Another challenge to the identification strategy is that, among women who miscarried, there is a fraction who would have aborted had miscarriage not occurred before they acted on their decision. These women may be different from women who chose to carry the pregnancy to term, and therefore will not constitute a valid comparison group. To derive an unbiased estimate of the effect of early childbearing, we need to compare the outcomes of women who had children to the outcomes of women who miscarried, but who intended to carry the pregnancy to term, or (using earlier terminology) whose latent status is _B*_ . Hotz et al. (2005) use data on reported abortions to correct their estimates for the possibility that among women who miscarried, some had latent status _A*_ . However, as shown above, ENADID 2006 does not contain reliable information on abortions.

To account for the possibility that women who miscarried may include women with latent status _A*_ , we will take advantage of the fact that ENADID includes the number of months of pregnancy when a miscarriage occurred. If we define latent status as a permanent characteristic that does not vary with time, women with latent status _A*_ know that they want to abort soon after they discover that they are pregnant. They have many incentives to act
biodiversity management, particularly for migratory marine species. Cooperative governance in these joint regime areas is critical for ensuring that marine resources are used sustainably while safeguarding the biodiversity that exists in shared marine ecosystems.

By overlaying these critical geopolitical boundaries onto our species occurrence maps, we aim to identify areas where conservation efforts may be more difficult due to political, legal, or social challenges. These areas are often at the intersection of biodiversity hotspots and governance weaknesses, making them crucial for targeted conservation actions and policy interventions.

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# **Stability and Evolution of Preferences for Improved Cookstoves - A Differencein-Difference Analysis of a Choice Experiment from Ethiopia**

# **1. Introduction**

Non-market valuation methods are used to determine the value, preferences and potential consumer demand for non-market environmental goods and policies. Valuation studies, in particular statedpreference methods that involve surveys, are often costly, and therefore conducted infrequently (Bliem et al. 2012, Brouwer and Bateman 2005, Mørkbak and Olsen 2014, Price et al. 2016). If the values obtained from these studies are stable over time, meaning there is no significant change in preferences among the given survey sample over time, they can be used in future analysis (Bliem et al. 2012, Brouwer and Bateman 2005, Morten and Olsen 2014, Price et al. 2016). Therefore, rather than acquiring funding for data collection, historical data that exhibit stability can be used for benefit transfer and cost-benefit analysis in the future.

However, due to the hypothetical nature of these valuation techniques, it is often difficult to determine the credibility or validity of the results as time progresses. Credibility is a measure of temporal validity, or as Mørkbak and Olsen (2014) state, “Does an individual respondent provide identical answers to identical questions over time, assuming that preferences are stable?” If preferences are not found to be stable, the use of results from stated preference studies conducted in the past for benefit transfer, cost-benefit analysis and calculation of welfare over time becomes problematic.

Further, the few studies on measuring stability of preferences elicited from choice experiment surveys have been conducted in developed country settings. At the same time there is a growing use of non-market valuation surveys, especially choice experiments, in developing country contexts to guide environmental policy and regulations. Understanding if preferences elicited in developing country settings, especially in rural communities, are stable is important with regard to using results from non-market valuation studies over time.

Therefore, the first focus of this work is to determine if preferences elicited using
functions. The exogenous interest rate schedules for local (unconstrained and constrained) and central debt are defined à la Schmitt-Grohe and Uribe (2003):

and satisfy Assumption 2. Grande et al. (2013) estimate that a 1-percentage-point increase in public debt-to-GDP ratio corresponds to a 3-basis-point increase in interest rates among OECD countries during 1995–2011. Based on this estimate, we set the elasticity parameter for central debt _ψd_ = 0.03. There is no consensus on the interest rate elasticity of local debt, and so in the baseline, we set _ψb_ equal to _ψd_ . In Section 4.3.2, we use alternative values of _ψb_ and _ψd_ for sensitivity analysis.

**Revenue (** _e_ , _f_ **) and transfer (** _T_<sup> ̄</sup> **).** Data on government revenue, inter-governmental transfer, and central and local (regional) government debt come from Eyraud and Lusinyan (2013) and IMF’s Government Finance Statistics (GFS), and are normalized by trend national GDP. The lower bound _T_  ̄ on uniform transfer is not directly observable in the data. Sorribas-Navarro (2011) documents that over 1987–1996, about 85% of central-to-regional transfers (grants) in Spain were non-discretionary (formula-based), and this ratio fell to 81% during 2002–2006. This non-discretionary transfer corresponds to the uniform transfer _T_<sup>_u_</sup> in our model. Because _T_<sup>_u_</sup> = _T_<sup> ̄</sup> (Proposition 1), _T_<sup> ̄</sup> is then calculated as the product of the total central-to-regional transfer from the data and the share of nondiscretionary transfers reported by Sorribas-Navarro (2011).<sup>37</sup>

> 37In the data, a government’s fiscal balances (flow) do not necessarily match the change in debt (stock). For example, the central government
proxy respondents may not classify them as work activities when reporting information for someone else.

# **8 Cost Implications of the Survey Design**

As highlighted in Bardasi et al. (2011), different survey methods may have different implementation costs. We use the cost data related to this randomized survey experiment to estimate the cost implications of each treatment arm. The households’ members assigned to LOA underwent a marginally longer interview since they participated in the additional LOA module. It took participants an average of 3.1 minutes to complete this module, and each household assigned to LOA had on average, 2.5 members. Therefore, the additional interview duration per household is 7.8 minutes. Since the average total cost per interview in C was US$135.4 and the average duration was 111.6 minutes, the cost for the additional time required to complete the LOA module is US$9.44 (US$1.21/minute x 7.8 minutes), which corresponds to 6.9% of the total cost per interview for group C. In light of these calculations, the cost implications of adding this module are negligible.

The cost implications of ESR are not small compared to C. Although the average duration of the survey was similar between enforced self-reporting and proxy reporting, the additional costs of ESR are due to the fact that interviewers had to pay more visits to ESR households to ensure that they located all of the respondents. Households in C and LOA needed an average of 1.50 and 1.49 visits, respectively, to collect the necessary information from all respondents. However, households assigned to ESR required an average of 1.94 visits to complete the interviews. Since the average cost per visit per household was US$90.27, it costs 29.6% more to complete an interview in the ESR group compared to C. We do note, however, that these costs are lower than estimates for other settings. For example
. The authors use a panel of 13 upstream/downstream product pairs, e.g., methanol and formaldehyde, for 1977-1992 to identify the effects of antidumping duties in the upstream industry on production and sales in the downstream industry. Antidumping duties upstream are confirmed to have a negative effect on downstream production. However, the authors do not find a significant negative effect on downstream value, perhaps because the direction of the effect on total revenue of lower quantity supplied depends on the elasticity of demand for the downstream product. A challenge for this type of research is finding upstream products tied significantly to specific downstream activities. Results are likely weaker to the extent that downstream producers have access to good substitutes for the input affected by antidumping.

> 24 Gawande, Hoekman, and Cui (2012) find evidence for seven large emerging market economies that participation in global supply chains helped  to keep protectionism in check during the 2008-9 global crisis.

22
errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 The evolution of built-up area also has implications for population density. We estimate Income and population elasticities of naïve density measured as population over built-up area. Using the GHSL panel data, different specifications are estimated including nonlinear effects for the largest cities based on the following regression: 1 2 2 3 4 2 (3) ln FFaaPPsseettDD As shown previously, as cities become richer, they expand responding to a higher demand for space and cc,tt = αα+ ββ ln IIPPeecc,tt + ββ ln IIPPeecc,tt + ββ ln FFooppcc,tt + ββ ln FFooppcc,tt + λλtt + γγcc + εεcc,tt As shown previously, as cities become richer, they expand responding to a higher demand for space and supply for built-up, and therefore become less densely populated. The income elasticity of density is -0.10 using the between and within estimator; introducing instruments for incomes increases it to -0.15. Table 7 provides estimates of the income elasticity of density using various specifications. Figure 9plots out densities over incomes levels using the nonlinear specification for cities larger than 500,000 people (column 5, Table 7). The chart reveals that density declines rapidly between low and middle incomes, and it stabilizes as cities reach $20,000 per capita. As population keeps growing, some of these large cities will provide space through higher buildings, increasing the livability of its inhabitants while allowing for larger agglomeration gains.

**Table 7: Determinants of naïve population density (population over built-up)**

|**Dependent variable:**<br>**Population density**<br>**over**<br>**built-up**|**Between +**<br>**Within**<br>**Estimator**|**Between +**<br>*
5 Yet not only are subjectively reported shock modules often not included in standard household surveys, they also raise issues of endogeneity, especially when incorporated in consumption regressions. These can typically only be overcome through the use of panel data, which are rarely available at the national level. The location, time, and household specificity of the subjective shock data also limits generalizations across time and space. In contrast, knowledge of the historical distribution of weather patterns allows the construction of probability distributions of weather events, which is useful to explore patterns of adaptation over time. It also helps in controlling for potential endogeneity of the extreme weather event measures in the absence of panel data. Interpolation of the extreme weather event data across space further permits the construction of disaggregated natural hazard maps, a useful tool in the spatial allocation of interventions and the simulation of the welfare effects of changing climate patterns.

Several global meteorological databases are available. But with the exception of the cyclone databases (UNEP/GRID‐Europe, 2007a,b) they do not have a high resolution typically. This paper exploits access to Vietnam’s detailed historical daily weather station data from 166 stations across the country to generate geographically disaggregated rainfall maps at the 0.1‐degree resolution (corresponding nominally to 11 km at the equator).<sup>4</sup> These data form the basis to derive geo‐ referenced local measures of drought and localized floods/heavy rainfall. To also capture riverine floods, which may occur downstream following heavy rainfall in the mountains or as a result of storm surges in coastal areas, riverine flood indicators will be constructed based on the satellite based flood data from the Dartmouth Flood Observatory (DFO) (2008). The 1980‐2006 geo‐ referenced UNEP/GRID‐Europe storm track dataset is used to separately explore the effects from high winds and gusts from cyclones. As the damage from heavy rainfall associated with cyclones will already be captured by the localized and riverine flood indicators, these measures will capture the
avoid double-counting, exposures should be examined individually and cannot be aggregated across different ecosystem services. As noted by van Toor et al. (2020), these estimates should be considered as a lower bound as it only accounts for the first-order dependencies of an economic sector on ecosystem services.

• _Method 2 – Ecosystem services with high or very high dependencies._ This method gauges the total share of the portfolio with business processes that are highly or very highly dependent on at least one ecosystem service. Following the illustration in Figure 2, this is equivalent to assuming that the dollar borrowed by a firm in NACE division “Crop and animal production, hunting and related service activities” is equally distributed across six business processes associated to this economic sector. Some of these six processes will by highly (H) or very highly (VH) dependent on ecosystem services. For example, “Largescale rainfed arable crops” depends VH on three ecosystems and H on another nine, and so the process is flagged as highly dependent. In the unlikely case that the other five business processes exhibited no H/VH dependency on any ecosystem service, then one-sixth of each dollar borrowed by a firm in this NACE division would count as dependent. This approximation is then replicated for all economic sectors, and the exposures can be added for the entire portfolio. A caveat is that this metric is related to a diverse range of ecosystem services and could underestimate the exposure of firms dependent on ecosystem services with lower materiality ratings. Figure 6 addresses this limitation by integrating all ecosystem service materiality ratings.

# **3. Estimating financial physical risks of biodiversity loss**

This section presents the findings on countries’ bank exposure to physical risks by comparing their dependency on ecosystem services.

Banks in our sample of emerging markets allocate around half of their total credit portfolio to firms whose business processes are highly or very highly dependent on one or more ecosystem services.<sup>11</sup> This metric appears to be negatively correlated with the countries’
frm shock*2013|-0.0041**|0.0001|
||(0.0021)|(0.0010)|
|GFC frm shock*2014|-0.0039*|0.0001|
||(0.0020)|(0.0013)|
|GFC frm shock*2015|-0.0037*|-0.0008|
||(0.0020)|(0.0018)|
|GFC frm shock*2016|-0.0032*|0.0001|
||(0.0020)|(0.0015)|
|GFC frm shock*2017|-0.0031|0.0005|
||(0.0019)|(0.0019)|
|Observations|3,046,065|42,807|
|R-squared|0.3213|0.0371|
|2-digit industry X Year FE|Yes|Yes|
|State X Year FE|Yes|Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, a d * 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 fixed effects. The estimating sample in column (2) includes only workers with contracts.

36
**1.** Population density greater than 1 person / 100m<sup>2</sup> in the WorldPop data set

**2.** Travel times to any medical facility greater than 24 hours in the preliminary monsoon model

We employed the medical facility layer as it has the greatest nationwide coverage of all the data sets. The 24‐ hour cutoff was chosen after a desk review of freely available satellite imagery in these areas; we determined it struck a good balance between highlighting extreme cases of error and appropriately narrowing the focus for a potentially unwieldy assignment.

# Land cover and river network

Land cover determines travel times for the off‐road travel surface. Our analysis employs the International Centre for Integrated Mountain Development (ICIMOD)’s “Land cover of Nepal 2010” layer as it offered the strongest combination of recentness, completeness and resolution. Additional river network data was extracted from OSM in a vector polyline format, converted to a raster and merged with the ICIMOD data set. Cells were reclassified from eight to seven categories based on expected speeds across each land surface when flat (see Table 6).
,057 km and the unclassified network of 15,104 km. Most of the traffic is concentrated in the area surrounding Luanda (figure 10), but overall traffic levels are comparatively low (table 8). The inadequate condition of the roads caused by years of destruction and undermaintenance is one of the factors contributing to the low traffic levels. As much as 58 percent of Angola’s classified network and 40 percent of its rural network is in poor condition, which is among the worst road condition statistics in Africa (figures 10 and 11). Only 17 percent of classified and urban roads are paved. Poor road quality in combination with very low road density and a lack of bridges—as many were destroyed or imbedded with mines during the war—makes some of the provincial capitals impossible to access by road. In addition, feeder roads are nonexistent in many parts of the country.

18