rafmacalaba/data-use-mentions-extended huggingface dataset:
supply. In the next section, we use a calibrated version of the model to quantify the effects of inelastic demand on both a government’s bond supply and its default policy.

# **5 Quantitative Analysis**

## **5.1 Calibration**

We calibrate the model at a quarterly frequency using data on Argentina, a benchmark case commonly studied in the quantitative sovereign debt literature. The calibration follows a two-step procedure. We first fix a subset of parameters to standard values in the literature or based on historical Argentine data. We then internally calibrate the remaining parameters to match relevant moments for Argentine spreads and other business cycle statistics.

In terms of functional forms and stochastic processes, we assume that the government has CRRA preferences: _u_ ( _c_ ) =<sup>_<u>c</u>_</sup> 1<sup>1</sup> _−_<sup>_−_</sup> _γ_<sup>_γ_,where</sup><sup>_γ_denotestheriskaversion.Outputfollowsan</sup> AR(1) process given by log ( _y_<sup>_′_</sup> ) = _ρy_ log ( _y_ ) + _ε_<sup>_′_</sup> _y_<sup>,with</sup><sup>_ε′_</sup> _y_<sup>_∼N_(0</sup><sup>_, σy_).Ifthegovernment</sup> defaults, output costs are governed by a quadratic loss function _φ_ ( _y_ ) = max � _d_ 0 _y_ + _d_ 1 _y_<sup>2</sup> _,_ 0�. For _d_ 0 _<_ 0 and _d_ 1 _>_ 0, the output cost is zero whenever 0 _≤ y ≤−_<sup>_<u>d</u>_</sup> _d_<sup><u>
2022b. “Households: Real disposable Income, per head: Quarter on Quarter-1 growth %: Chained volume measure £: Seasonally adjusted.” https://www.ons.gov.uk/economy/grossdomesticproductgdp/timeseries/crxz/ukea Palomino, Juan C., Juan G. Rodríguez, and Raquel Sebastian. 2020. "Wage Inequality and Poverty Effects of Lockdown and Social Distancing in Europe."</mark> _<mark>European Economic Review</mark>_ <mark>129: 103564.</mark> <mark>Piketty, T., Saez, E., & Zucman, G. 2018. “Distributional National Accounts: methods and estimates for the United States.”</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>, 133(2), pp. 553-609</mark> <mark>Prydz, Espen Beer, Dean Jolliffe, Daniel Gerszon Mahler, Christoph Lakner, and Prem Sangraula.</mark> <mark>2019. “National Accounts Data used in Global Poverty Measurement.” Global Poverty Monitoring Technical Note 8.</mark> <mark>Prydz, Espen Beer, Dean Jolliffe, and Umar Serajuddin. Forthcoming. "Mind the gap: disparities in assessments of living standards using national accounts and surveys."</mark> _<mark>Review of Income and Wealth</mark>_ <mark>.</mark> <mark>Rastrigina, O., Leventi, C., Vujackov S. and Sutherland, H. 2016. “Nowcasting: estimating developments in median household income and risk of poverty in 2014 and 2015.” Research Note 1/2015, Social Situation Monitor, European Commission</mark> <mark>Ravallion, Martin. 2003. "Measuring Aggregate Welfare in Developing Countries: How well do national accounts and surveys agree?"</mark> _<mark>The Review
# _Constraints to Tajikistan’s Market Accessibility_

The fact that transport cost between Dushanbe and Khujand are higher than the cost of exporting a container of the same weight from Moldova to the United States (Table 4) illustrates how challenging domestic connectivity is in Tajikistan. We can use Geographical Information System (GIS) data to assess the market accessibility of Tajik settlements (Map 1).<sup>7</sup> Map 1 confirms the poor domestic market accessibility of Tajikistan, with a clear geographical segmentation of the country between the North and the South. Accessibility seems to be limited by small settlements and high travel time, particularly in GBAO. Dushanbe, Kurgan Tyube, Kulyab and their surroundings have a relatively high access to domestic market, but they are quite isolated from Khujand and its surroundings in the Northern part of the country better connected via rail and road to Kyrgyzstan, Uzbekistan and all the way to Kazakhstan and Russia.

**Table 4. Domestic Freight Rates, Tajikistan, 2009**

||**Distance (km)**|**USD per 10 tons of freight**|
|---|---|---|
|Within Dushanbe|12|14–112|
|Dushanbe–Tursunzade|56|242|
|Dushanbe–Kurgan-tyube|99|546|
|Dushanbe–Kulyab|204|909|
|Dushanbe–Nijny Panj|206|1,091|
|Dushanbe–Jirgatal|287|1,697|
|Moldova to United States<sup>a</sup>|7,668|1,895|
|Dushanbe–Khujand|341|1,940|
|Dushanbe–Kanibadan|421|2,424|
|Dushanbe–Isfara|448|2,546|
|Dushanbe–Khorog|524|2,606|

_Source:_ International Finance Corporation and World Bank 2009; Ministry of Transport, Tajikistan. aData from _Doing Business_ 2009.

The Tajik government is considering upgrading or rehabilitating a
data in that case is model-based EBP estimates derived from the census, which are also subject to bias due to informative sampling when predicting out of sample.

_Table 2: Predictions using geospatial data are much less accurate in non-sampled areas_

|Country|Indicator|Source of survey<br>data|Source of<br>validation data|R<sup>2</sup>for<br>sampled<br>areas|R<sup>2</sup>for non-<br>sampled areas|
|---|---|---|---|---|---|
|Burkina Faso|Poverty|2018 EHCVM|Census-based<br>EBP estimates|0.76|0.21|
|Madagascar|Wealth|2018 census<br>(simulated samples)|Census|0.83|0.62|
|Mexico|Poverty|2014 MCS-ENIGH|EBP estimates<br>using<br>Intercensus|0.74|0.49|
|Mexico|Poverty|2015 Intracensus|Design-based<br>simulation<br>using<br>Intercensus|0.88|0.64|
|Malawi|Wealth|2018 census<br>(simulated samples)|Census|0.79|0.53|
|Malawi|Poverty|2018 census<br>(simulated samples)|Derived from<br>census-based<br>predictions|0.76|0.64|
|Mozambique|Wealth|2019 census<br>(simulated samples)|Census|0.85|0.71|
|Sri Lanka|Wealth|2012 census<br>(simulated samples)|Census|0.90|0.80|

Results from household-level EBP models using geospatial auxiliary data. Sources: Edochie et al. (forthcoming), Newhouse et al. (2022), Merfeld and Newhouse (2023) Pfefferman and Sverchkov (2007) proposed a
sup>|-0.018<sup>_∗∗_</sup>|-3.432<sup>_∗∗∗_</sup>|
|(0.010)|(0.011)|(1.240)|(0.009)|(0.009)|(1.166)|
|Control Mean (wt)<br>0.030|0.030|4.898|0.030|0.030|4.639|
|Observations<br>2,653|2,653|2,653|2,653|2,653|2,653|
|Equality b/w Exp/Non-exp (p)<br>0.047|0.044|0.324|0.067|0.094|0.481|

This table shows correlational and causal effects of female agent on sales outcomes from administrative records. Panel A shows correlational estimates using the non-experimental sample, while Panel B shows causal estimates from the experimental sample. Any represents any sale, Total represents number of sales, and Total price is the cumulative price of all sales in EUR. All outcomes based on customer purchases only. Sales are measured within 24 or 48 hours of the start of the chat. Female indicator in Panel B determined in customer’s first chat of the day. Controls include month, customer location, customer purchase history, and customer chat history fixed effects. The experimental estimates additionally include agent fixed effects. The control group means are reweighted to match the OLS weights. For the correlational estimates, the control mean is reweighted by month fixed effects, while in the experimental estimates it is reweighted by agentmonth fixed effects. Standard errors two-way clustered at the agent-day and customer-day level.<sup>_∗_</sup> _p <_ 0.10, _∗∗ p <_ 0.05, _∗∗∗ p
# **6. Conclusions**

Despite the growing policy relevance of climate migration and the acceleration of climate change impacts, our understanding of the causal relationship between climate shocks and human mobility outcomes remains limited. While this is in part due to the existence of severe data gaps, there is much that can be done to inform policy making, even with the current data. In this paper, we carried out an empirical test which shows that coupling rich household survey data and theoretical insights with recently developed data-driven techniques is a promising route that can shed new light on the key drivers underlying heterogeneous mobility responses in the face of climate risk and help target groups most at risk of resource-constrained immobility.

We find that pre-shock wealth, _in situ_ adaptive capacity, and repeated exposure are the most important drivers able to determine not only the magnitude but even the sign of the effects of weather anomalies on the (im)mobility outcomes of Nigerian farming households. While local adaptive capacity acts as a substitute for migration, the importance of climate-induced immobility is consistent with recent global evidence that many people will be trapped in impoverished and troubled regions and that climate-induced poverty is likely to be a real threat <mark>(Burzyński et al., 2022). T</mark> he inability to migrate in reaction to climate change is an important and neglected policy concern (Castells-Quintana et al., 2018) and stresses the need for policies that facilitate both local adaptation as well as more internal migration to lift vulnerable people out of poverty traps (Kraay & McKenzie, 2014). If there is an inflection point at which migration starts to decrease as households get substantially wealthier and, in turn, become able to adapt _in situ—_ i.e., a kind of 'climate migration Kuznets curve'—we do not observe it, most likely because even relatively richer Nigerian farming households are still poor.

In terms of policy prescriptions, our findings suggest that, in this and similar contexts, any onesize-fits-all approach for the
# **Can We Rely on VIIRS Nightlights to Estimate the Short‐Term Impacts of Natural Disasters?**

**Evidence from Five Southeast Asian Countries**

## **Emmanuel Skoufias***

**Eric Strobl** Thomas Tveit****

### **JEL Classification:** Q54, C63, R11, R5,O18

**Keywords:** Remotely Sensed Data, Natural Disasters, Damage Index, Floods, Earthquakes, Typhoons

*The World Bank Group, Washington, DC USA

** University of Bern, Switzerland
*13.33**|**1.49**|**2.1**|**100**|

_Source_ : Computation based on the 2019 GIC Survey.

**Table 3. Respondents by income classification of foreign owner country**

|**Country**||**Decline to answer**|**High income**|**Low or middle income**|**Total**|
|---|---|---|---|---|---|
|Brazil|n|0|228|22|250|
||%|0|91.2|8.8|100|
|China|n|6|229|15|250|
||%|2.4|91.6|6|100|
|India|n|2|232|16|250|
||%|0.8|92.8|6.4|100|
|Indonesia|n|0|209|49|258|
||%|0|81.01|18.99|100|
|Malaysia|n|0|217|33|250|
||%|0|86.8|13.2|100|
|Mexico|n|1|222|26|249|
||%|0.4|89.16|10.44|100|
|Nigeria|n|0|83|81|164|
||%|0|50.61|49.39|100|
|Thailand|n|0|229|21|250|
||%|0|91.6|8.4|100|
|Turkey|n|0|222|28|250|
||%|0|88.8|11.2|100|
|Vietnam
ity (2009, 2). World Bank data also show that the general poverty rate for ethnic minority groups in Vietnam in 2006 was 52 percent, more than five times that of ethnic majority groups (Dang 2010). The country made some reforms in recent years to address the problem, but the economic divide between the majority Kinh and the ethnic minorities— as highlighted by the United Nations Committee on the Elimination of Racial Discrimination (UNCERD 2012)—is still profound, even when those groups live in the same mountainous area.

“Institutionalized discrimination is bad for people and for societies. Widespread discrimination is also bad for economies. There is clear evidence that when societies enact laws that prevent productive people from fully participating in the workforce, economies suffer.” Jim Yong Kim, president, World Bank Group (Kim 2014) In 2013, the United Nations independent expert (now special rapporteur on minority issues) warned that hundreds of minority groups in Africa were in need of attention and protection because minorities remain among the poorest people, trapping them in a cycle of discrimination, exclusion, poverty, and underdevelopment.<sup>13</sup> As reported by Minority Rights Group International, the Batwa people, a pastoralist minority in the Great Lakes Region in Africa, are among the poorest of the poor. A combination of lack of access to cultivable lands, poor health, and poor education places them as one of the most marginalized and poorest minorities on the continent.<sup>14</sup> Similarly, indigenous peoples (who include about 5 percent of the world’s population) are estimated to represent 15 percent of the world’s poor (Hall and Patrinos 2012). In Mexico, as in most Latin American countries, they consistently rank lower than nonindigenous peoples on poverty indicators (Garcia-Moreno and Patrinos 2011). As noted by the World Bank Latin America and the Caribbean [LAC] Equity lab, “Even if the Latin American and Caribbean region has seen significant progress in
**Figure A.2. Scale Factors and Headcount Poverty Rate, RLMS 1994-2017**

<!-- Start of picture text --> 80<br>Per capita income<br>70 Equivalent income<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Headcount poverty (%)<br>1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

**Note:** Population weights are applied. Standard errors are adjusted for complex survey design. Poverty line is defined as a minimum regional subsistence level per person for each year provided by the Rosstat. Household incomes are adjusted with equivalence scale weights from Table A.7 using BUC model (where baseline elasticity equals 0.407 and every child has a weight 0.048). Both the poverty thresholds and household incomes are converted to constant December prices of 2011 using regional CPI indices provided by the Rosstat. Real values of household incomes are also adjusted for regional differences in the cost-ofliving.

51
credit agencies offered them working capital financing.<sup>6</sup> ' Many foreign traders and input suppliers had indicated that they would be willing to cover the commercial risk of supplying inputs on credits if the Government could credibly guarantee that it would not change regulations and rules retro-actively, regulations and rules which may adversely affect the repayment capacity of their borrowers. Against this background, the PEGF had a developmental objective and was established to provide a credible guarantee' against country and political risk to enable viable local firms to attract foreign private financing for pre-export financing.

85. _Sectors Targeted/lInstrument/Risk Targeted._ The guarantee was targeted at import supply companies, commercial lenders, and trading companies and provided coverage against political risk. It was to cover damages caused by government actions or inaction 58 The development of the instrument was also not based on a thorough capital market and demand analysis that could have provided some guidance for the design of the instrument and the actual demand. See Implementation Completion Report (1998).

> 59 A survey carried of potential clients carried out by Salomon Brothers found a "poor perception of banks that do participate". Implementation Completion Report.

60 See for more detail: Onno Ruhl, Alfred Watkins, "A Pre-Export Guarantee Facility in Moldova - Mitigating Political Risk in Transition", The World Bank, Public Policy fir the Private Sector, Note No.64, December 1995. The World Bank, Report No. P-6502 MD, 1995.

**61** If export credit agencies provide cover they require a full govemment counterguarantee covering both commercial and political risk.
thirds of the country’s districts. In parallel, more than 100 major violent events are identified and geocoded based on the Global Terrorism Database (GTD).

The study builds its understanding of the formal sector response to conflict by analyzing the dynamic of mobile phone traffic and casualties during the 2015 Taliban attack on Kunduz, Afghanistan’s fifth largest city. It then estimates a dynamic specification linking the number of firms active in district and month to the number of major violent events in that district, over a 12-month period spanning a quarter YY<sup>FF</sup> ii before the event and three quarters afterward: tt CC The specification includes fixed effects for districts ( ) and months ( ) as well as a quadratic time trend at the district level. It does not include other control variables. ii tt δδ ττ The results show no impact in the three months preceding the violent event. The number of active firms falls by 4.9 percent in the month of the event, with the decline growing to 6 percent in the following month and persisting for up to five months. Six months after the event the impact becomes statistically insignificant. A more disaggregated analysis shows that larger firms are more responsive to violence.

As for illicit activities, Lind et al. (2014) explore how the intensity of conflict in Afghanistan changes the incentives faced by farmers and spurs opium production. Poppies are more drought-resistant than wheat, the main alternative crop. They also take little space relative to their value and can easily be transported off roads. The destruction or disabling of irrigation and transportation networks resulting from conflict therefore makes poppies relatively more profitable than wheat.

Data are from 329 Afghan districts from 2001 to 2007. The study notes that there are no available data on the amount of fighting during this period. One possible alternative was to rely on the number of combat activities by year, but information on such events was scarce and mostly based on media coverage. For this reason, the study bases its measure of
constraints, uncertainty, and firm-level decisions, similar to previous studies like Apedo-Amah et al. (2020), Torres et al. (2023), Cirera et al. (2021), Avalos et al. (2022), Avalos et al. (2023), and Constantinescu et al. (2022), we rely on three rounds of data from the World Bank Business Pulse Survey (BPS).<sup>1</sup> In addition to the large set of variables analyzing firm performance, supply chain and international trade-related disruptions, financial fragility, and the different margins

> 1The Business Pulse Survey, which finished collecting data in 2022, includes data for around 150,000 firms in 87 countries.

4
signicant estimate on _INVij,t_ , which suggests that, on average, the PTAs with investment provisions in our sample have led to a 34.33% (std.err 14.535) increase in FDI between their members. This result complements the ndings from Laget et al. (2021), who use rm level data for the period 2003-2015 and obtain positive estimates of the eects of provisions related to `intellectual property rights' and `visa and asylum', which vary between 32 and 50 percent, but do not nd signicant eects of investment provisions on FDI.

Finally, in column (4) of Table 2 we use the DTA database to construct a continuous variable ( _INV_ DEPTHij,t_ ), which counts the number of investment provisions within each of the DTAs in our sample. The number of investment provisions across the DTAs in our sample varies between 7 and 41. The estimates from column (4) do not reveal a signicant impact of the increase in the depth (number of provisions) on FDI. In fact, and pushing inference to the limit, our estimates suggest that the impact of additional provisions is actually negative. A possible interpretation of this result is that more investment provisions make the agreements more dicult to comply with. Despite the fact that our estimate on _INV_ DEPTHij,t_ is insignicant, we use it in combination with the positive estimates on _INVij,t_ to construct a continuous FDI response to the impact of DTAs. The resulting eects are all positive and vary between 16.4% (std.err. 19.06) and 66.41% (std.err. 32.99), depending on the number of investment provisions.<sup>19</sup> We conclude the econometric analysis with two additional sets of experiments that further capitalize on the richness of the DCRTA data set to shed light on the links between DTA provisions and FDI.<sup>20</
rather than the income effects associated with the coffee mills. This is important because mass construction of schools could, for instance, not only expand access to schooling opportunities but also have a positive impact on the quality of teaching and learning by reducing overcrowding.

To address this issue, we combine data on primary school construction in Rwanda with the data on coffee mills and explore the association between the rollout of coffee mills and changes in school access. Specifically, using data geolocation and the year of construction of the universe of primary schools in the country, we compute the number of (new) primary schools as well as the cumulative number of primary schools operational per year and correlate it with the rollout of coffee mills at the municipality level.<sup>17</sup> The results as shown in Table A2 in the (online) Appendix, reveal no association between the rollout of the coffee mills and expansion in primary schools. Thus, we can rule out the possibility of a contemporaneous expansion in school access contaminating our results.

# **6 Mechanisms**

In this section, we explore the potential pathways through which exposure to coffee mills influences student performance. We hypothesize and test three key channels: income, health, and school attendance. To test these channels, we combine data from the Rwanda DHS with data on the location and operational status of the coffee mills. However, unlike the main analysis where we define the catchment area using a 2.5km radius from a coffee mill, we are unable to use the same distance threshold with the DHS data, due to the inherent displacements of the coordinates in the DHS survey. Due to privacy concerns, the geo-coordinates of the DHS are displaced by up to 2km in urban centers and 5km in rural areas.<sup>18</sup> As a result, defining treatment and control locations based on distance thresholds below 5km, especially in the context of our study where 88% of the sample are rural, is likely to induce significant
, making up less than 4 percent of terrorist cases in all three countries. Lastly, military targets are important only in Sri Lanka (25 percent, versus 1 percent in Bangladesh and 7 percent in India).

As noted earlier, our identication strategy exploits the geographic variation in terrorist attacks. Figure 3 illustrates the argument using as example Bangladesh and Sri Lanka. In the former, the cumulative number of attacks since 1972 has been scattered throughout the dierent zilas while in the later, terrorist groups have targeted the Sinhalese-populated cities of Colombo and Batticaloa. Therefore, when estimating the eect of the incidence of terrorism on FLFP we compare the administrative units that are not attacked to those attacked after controlling for other relevant explanatory variables.

# 3.2 Labor Force Surveys

FLFP rates and distinct individuals' characteristics are measured using labor force surveys (LFS) that, due to our empirical strategy, we later collapse at dierent administrative divisions depending on the country. Importantly, labor force participation is not measured identically in the dierent country surveys and even within countries through years 7
natural resources, skilled female labor, unskilled female labor, skilled male labor and unskilled male labor, all substituting against each other.

We modified this standard production function as follows, because this standard formulation implicitly assumes gender equality and does not directly pose male and female labor as being substitutes on a one-on-one .

We let unskilled and skilled labor substitute each other in every sector as it would do in the standard model, but in separate nests for male and female labor. The elasticities here are taken from the standard model as well. In other words, male unskilled labor substitutes imperfectly with male skilled labor, with elasticities in the GTAP model that exist for all the factor endowments (ESUBVA).

- We let male and female composite labor act as perfect substitutes at a higher level of nesting than the above.

Another major change that we have introduced to the standard model is the estimation and inclusion of a labor supply equation. From the literature, we found that this elasticity of labor supply with respect to real wages ranges between 0.6 and 1 for the developed countries, such as Germany and the United States.<sup>26</sup> For South Asian countries, the literature on this area has been quite dated; therefore, we employed the long time-series

> 26 See for example (last accessed on Jan 15, 2020): <u>https://voxeu.org/article/aggregating-labour-supplyelasticities http://conference.iza.org/conference_files/ESSLE2013/merz_m878.pdf.</u>

26