rafmacalaba/data-use-mentions-extended huggingface dataset:
be able to set their own discount rate to reflect local financing conditions or connect the discount rate to interest rates in a macroeconomic model (see the Discussion section).

In summary, the country specific data required to run FTT-FLEX are:

- Historical data for the production shares of each technology

- Total electricity demand projections

- Production maxima for physically limited technologies like hydroelectricity, wind, and solar

Since the future share dynamics depend on the shares today (this is an important feature of the model dynamics), the historical production shares by technology are the main country specific initial condition of setting up the model.

For some technologies geography places a hard physical limit on the potential for domestic capacity. This is most clearly the case for hydroelectric power, for which potential sites are easily enumerated, but it is also a factor for wind and solar, for which the scope for adding capacity is not boundless. If such data can be obtained, the model implements a cost penalty to the technology as its specific capacity limit is approached.

In the absence of more detailed data, the dynamics of the underlying diffusion theory does most of the heavy lifting. The analysis and conclusions that can be drawn are thus strongly contingent on how appropriate the theory is to the domestic setting (see Limitations section in this paper). The robust empirical support that has been found for these dynamics in Mercure et al., 2021, Mercure et al., 2018b and Semieniuk et al., 2022 in the context of the FTT model suggests that incorporating these dynamics even in low-data contexts is likely preferable to the alternative of assuming static costs.

8
4. Repeat steps 1 to 3 a large number of times _M_ . If _yc_<sup>_∗_(</sup><sup>_m_)</sup> denotes the _mth_ replicate of the census vector _yc_<sup>_∗_= (</sup><sup>_y_</sup> _c,s_<sup>_T, y_</sup> _c,r_<sup>_∗T_)</sup><sup>_T_,then</sup><sup>_τ ∗_</sup> _c_<sup>(</sup><sup>_m_)</sup> = _f_ ( _yc_<sup>_∗_(</sup><sup>_m_)</sup> ) is the corresponding indicator, _m_ = 1 _, . . . , M_ . The final MC approximation to the EB estimator of _τc_ is just the average across the _M_ simulations of these indicators Molina (2019) comments that the effect of adding the survey data is negligible when the sample is small relative to the census population of the area. Thus, a slight variation of the EB estimator, called Census EB (see e.g. Molina 2019), avoids appending the survey data in step 2 by generating all the census welfares (including the sample ones), similarly as the non-sampled ones are generated. Thus, the welfare for each household _h_ in the census is generated from the model with _ηc_<sup>_∗_and</sup><sup>_e∗_</sup> _ch_<sup>obtained exactly the same as in step 2.The result is the full census vector for area</sup><sup>_c_given</sup> by _yc_<sup>_∗_= (</sup><sup>_y_</sup> _c_<sup>_∗_</sup> 1<sup>_,
Because of these complexities, this article takes a different route by providing the first ex post analysis of survey data collected before, during and shortly after the 2008 food crisis across a large number of countries. Specifically, we examine the results from an indicator of self-assessed problems affording sufficient amounts of food, which was collected as part of the Gallup World Poll (GWP). Although subjective data certainly have shortcomings (an issue we discuss in detail below), their advantage in this context is that they are substantially cheaper to collect relative to the more objective monetary or anthropometric indicators found in standard household welfare surveys. Hence, the country and time coverage of the GWP surveys is their primary advantage. Specifically, the GWP surveys allow us to examine self-assessed food insecurity trends in 69 low- and middle-income countries, of which China is the most prominent exclusion. This substantial cross-country coverage also allows us to test whether changes in this indicator are explained by variations in food inflation and economic growth.

The basic conclusion from the Gallup data is that at the peak of the crisis (2008), global food insecurity was either not higher or even substantially lower than it was before the crisis. The raw results for the 69 countries for which we have precrisis (2005–06) and mid-crisis (2008) data suggest that 132 million people became more food secure. If 2007 is used as the “precrisis” benchmark, the picture is more neutral because self-assessed food insecurity was essentially unchanged between 2007 and 2008. However, these surprisingly optimistic global trends mask large regional variations. Global trends are clearly driven by declining food insecurity in India and several other large developing countries. However, on average, self-assessed food insecurity increased in many African countries and most Latin American countries. It decreased somewhat in Eastern Europe and Central Asia, but it probably rose in the Middle East (for which the GWP 4
***|0.017***|
||(0.003)|(0.004)|(0.005)|(0.005)|
|Secondary Education|0.007|0.008|0.011***|0.008**|
||(0.004)|(0.005)|(0.003)|(0.003)|
|**Marital status (single omit.)**|||||
|Ever married|0.003|0.001|0.003|-0.011|
||(0.004)|(0.007)|(0.005)|(0.006)|
|**Baseline probability (reference**<br>**group)**|0.000|0.001|0.001*|0.003**|
||(0.000)|(0.001)|(0.000)|(0.001)|
|**N (Obs.)**|4903|4860|17910|17912|

Source: Authors’ calculations based on TLMPS 2014, ELMPS 2018 Notes: *p<0.05; **p<0.01; ***p<0.001. Standard errors in parentheses. “Market” denotes an outcome of being reclassified from not 3-month market employed to added market employed (compared to either remaining non-employed or already being detected as 3-month market employed). “Extended” denotes an outcome of being reclassified from not 3-month extended work to added extended work (compared to either remaining non-working or already being detected as 3-month extended working).

# **6 Discussion and conclusions**

Women’s employment in LMICs is often under-detected, and disproportionately so, compared to measures for men (Anker 1983; Anker and Anker 1989; Desiere
**Figure 14: District Share of HHs with Migrants by District Per Capita Consumption Quintile**

<!-- Start of picture text --> 25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Bottom Quint 2 3 4 Top Quint<br>Share of HHs w/Migrants<br><!-- End of picture text --> _Source: Author’s calculations on the basis of the baseline survey for Listening to the Citizens of Uzbekistan (2018), the baseline survey for Listening to Tajikistan (2015), the household budget survey of Kazakhstan (2017), and the household budget survey for Kyrgyzstan (2016)._ These results complement other analyses that focus on migration; providing useful regional context to this analysis. Where household-level determinants of migration have been analyzed in more detail, there is a strong relationship between migration and local economic challenges, including low labor force participation, household welfare shocks, and living in an area with greater dependence on social protection benefits. Absent financial support from migrants, poverty rates and unemployment rates in struggling regions would be significantly higher, while average incomes would be much lower.

Another example is country specific: in Tajikistan, the government and World Bank are in advanced discussions of expanding the number of kindergartens and early childhood learning centers. But what areas need more assistance? The following figures (15-16) overlay the poverty maps for the country with details on the number of educational facilities per 1,000 children, highlighting areas with significant co-incidence of high poverty rates and low coverage.

29
"...drought [in "rice countries"] is, perhaps, scarce ever so universal as necessarily to occasion a famine, if the government would allow a free trade."–Adam Smith (1776, IV.5.45)

# 1 Introduction

Random ‡uctuations in weather may have severe adverse e¤ects on agricultural producers and consumers. How can policy makers mitigate these impacts? Recent empirical research suggests that greater openness to trade alleviates the adverse e¤ects of weather shocks on domestic consumers. Using district-level panel data for colonial India in 1875-1919, and exploiting the construction of the railroad network to identify the e¤ects of increased openness, Burgess and Donaldson (2011) document that the arrival of railroads dramatically constrained the ability of rainfall shortages to cause famine.<sup>1</sup> But while this evidence points to the existence of a causal link between openness and weather-related famine, import tari¤s on agricultural goods remain high in many nations, and relatively little is known about the extent to which countries use trade policy strategically to mitigate the impacts of weather shocks on domestic welfare.<sup>2</sup> In this paper, we examine whether and how rainfall shocks a¤ect tari¤ setting in the agricultural sector. To identify key mechanisms at play, we ...rst set up a model of international oligopoly in which domestic and foreign agricultural producers compete in the home market. International trade is potentially costly due to import tari¤s optimally set by the domestic policy maker. A rainfall shortage increases the marginal costs of domestic producers, thus generating a shortfall in agricultural output that foreign producers have an incentive to meet. Consequently, the shock a¤ects the marginal e¤ects of import tari¤s on tari¤ revenue, domestic pro...ts and the consumer surplus, and thereby the optimal policy response.

We ...nd that the impact of a rainfall shock on optimal import tari¤s is not
Lithuania, Estonia, and Slovenia. Leverage levels among export-orientated firms and the widespread contraction of export income in the region, suggests, however, that debt overhang in the tradable sector may be an issue for a broader set of countries: Latvia, Lithuania Estonia, Slovenia, Macedonia, Bosnia, Turkey, the Slovak Republic, Serbia, the Czech Republic and Croatia. Finally, our firm-level analysis suggests that despite sharp depreciations in many countries during the crisis, unhedged foreign currency borrowing by firms does not seem to be a source of debt overhang. The countries with a high incidence of unhedged FX loans experienced only minor depreciations during the crisis.

We next examine the potential for **debt overhang in the household sector** by country using data from the 2010 EBRD Life in Transition Survey (LITS). We find that mortgage debt seems to have had a significant adverse impact on household consumption and investment during the crisis. However, due to the low incidence of mortgage debt in the region hardly any country, except Estonia, seems to face a threat of debt overhang. The use of credit cards is much more widespread in the region than mortgage debt. However, credit card use has had a negligible effect on household income and investment during the crisis. It is unlikely therefore that credit card debt may jeopardize future economic activity in the region. Our household-level analysis further suggests that FX mortgages hardly pose a threat to future household consumption and investment. Only a minor fraction of households in the region are exposed to foreign currency mortgages. Moreover those countries in which FX mortgages are most prevalent did not experience major exchange rate shocks during the crisis.

We also assess the potential for **debt overhang in the banking sector** due to the significant loan losses incurred in many countries of the region during the crisis. The strong increase in non-performing loans (NPL) compared to pre-crisis bank profitability suggests that debt overhang in the banking sector is a threat in Ukraine, Latvia, Lithuania, Hungary as well as in Georgia
inequality; thus, examining the extent to which income inequality is driven by productivity vs. preference heterogeneity is a _positive_ step towards understanding the welfare benefits of redistribution.<sup>2</sup> This paper shows that we can use information encoded in labor supply elasticities to learn about the extent to which labor income inequality is driven by heterogeneity in productivities vs. heterogeneity in preferences for consumption relative to leisure. To see why labor supply elasticities contain information about income inequality, consider a population of individuals in a static world who choose how many hours to work conditional on heterogeneous productivities and preferences over the consumption/leisure trade-off. To simplify ideas, assume productivity is equivalent to the hourly wage rate. In this canonical world, labor income inequality can first be decomposed into differences in hourly

> 1We will focus solely on labor income inequality throughout this paper.

> 2However, decomposing income inequality into productivities and preferences is not necessarily complete; if individuals have redistributive preferences which depend on the determinants of productivities and preferences, then a more complete decomposition of income inequality will be needed to assess the welfare benefits of redistribution. For example, individuals may have different tastes for redistribution if preference heterogeneity is mostly due to innate disutility of labor vs. disutility of labor due to poor health. Nonetheless, even if this is the case, our work is a useful step towards better understanding the sources of income inequality.

1
eligible for survey inclusion if: (a) they had been included in the PSNP; or (b) if they were not included in the PSNP but were considered poor.<sup>3</sup> From this list, we randomly selected five PSNP and five non-PSNP households. Our March 2019 sample included 2,626 households with a young child aged 0-24 months, of which 2,551<sup>4</sup> were successfully re-interviewed in August 2019 (Berhane et al., 2020).<sup>5</sup> In June 2020, we conducted a phone survey of these households. As part of the 2019 surveys, we collected information on households’ ownership of mobile phones, their phone numbers and permission to contact them by phone if needed. About 54 percent (1387/2551) of the August 2019 sample had access to a phone. In addition, members of our 2019 survey teams were able to help us locate additional households who now have mobile phones. Using this information, we interviewed 1,497 (about 59 percent) of the 2,551 households who took part in the August 2019 survey. As was the case in the 2019 surveys, the primary respondent was the mother of the preschool child. Several methods were deployed by the survey team to minimize non-response, including using built-in out-of-network reminders, allowing extended appointments, and making several call attempts at different hours of the day. On average, each interview required about two call attempts to succeed, about 53 percent were reached at first attempt, and about 3 percent of the interviews needed a minimum of 7 (and a maximum of 17) call attempts to succeed.<sup>6</sup> An obvious concern with this approach is that the phone sample will differ in systematic ways from the original 2019 sample, not least because ownership of mobile phones is correlated with wealth in this population (see Table 1). We account for this in the following way. Our
blackboards, and toilets. The school-level outcomes of student-teacher and student-classroom ratios are constructed by us using the data on total number of students, teachers, and classrooms. These databases also serve as the source of baseline data on school-level covariates, namely location, gender type, level, and registration status.

The SLQAT serves as the source of data on the school’s student pass rate, our treatment assignment variable _z_ . Separate electronic databases have been constructed by PEF for phase-3 and phase-4 SLQAT takers. These databases contain the total score for each student that took the test. In the databases, student test scores are organized by school and, within school, by grade. The school identification information provided in the databases comprise of the school’s name (at times, with some address elements) and location (tehsil<sup>22</sup> and district). Although PEF constructed the SLQAT pass rate for each school, we used the student test score data in order to construct our own measure of school SLQAT pass rates. The match rate between their calculation of school SLQAT pass rates and ours’ in both phases is nearly perfect at 99.5%. While it makes virtually no difference, we use our measure of school SLQAT pass rates as the treatment assignment variable Constructing a single electronic database for our analysis required that the application data are linked with the SLQAT data at the school level. Unfortunately, the same unique school identification variable was not used across databases. Consequently, we linked the application databases to the SLQAT databases using an iterative visual-matching process. First, schools were matched across databases using the district name and school name variables.<sup>23</sup> Exact matching failed in a number of cases as PEF did not maintain consistency in the spelling, word ordering,

> 22 Tehsil is the spatial unit of government administration one tier below district. There are 127 tehsils in Punjab. 23 Although information on the school’s tehsil was also available in both the application
model via increases in import tariffs based on the detailed tariff line data from the CARD database (Li 2018). We assume that these trade policy shocks stay in the model throughout the entire simulated timeframe, i.e. until 2030. We also include all existing and ongoing trade agreements with a pre-defined set of tariff changes based on the published tariff schedules reported by the International Trade Center (ITC 2020). We refer to this scenario as the globalized world, as it represents the continuation of past trends.

# Policy scenarios

## _Introducing two scenarios that are “hostile” to GVCs and one that is “friendly.”_

Our scenarios examine the impact of changes in trade and climate policies, including reshoring and carbon taxes, on the pace of economic recovery from the COVID-19 pandemic (Figure 2). We further explore how developed and developing countries are impacted by such polices and what actions developing countries can take to cushion the impact of policy shocks. In this section, we discuss the construction of the corresponding policy shocks. A summary of the scenarios is provided in Annex C.

> 3 U.S. retail sales data https://www.census.gov/retail/index.html

> 4 See OECD (2020a).

> 5 <mark>World Bank's Food Price Index increased by 3 percent in 2020 according the October 2020 edition of World Bank's Commodity Markets Outlook.</mark>

7
Table 2 shows a summary of basic demographic and labor market characteristics of Russians and migrant (defined as non-Russians) workers. On average, migrants are not very different from Russian workers in age and gender composition. However, they are significantly less schooled, less likely to reside in urban areas, more likely to work in market services, and work longer hours. While 15.7 percent of Russians report to be informally employed, for migrant workers that share is significantly higher at 26.2 percent. They also get paid less per hour and have a 29 percent likelihood of receiving part or all of their wages unofficially, compared to 19 percent among Russian workers. These characteristics indicate that migrant workers are most likely clustered at the lower end of the income distribution.

We find that being a migrant (defined as non-Russian) worker is associated with being an informal worker, but not any type of informal worker. The results in Table 3 indicate that migrant workers are more likely to be working informally (column 1), and this association remains significant even after controlling for occupation characteristics (column 2). However, if dropping from the sample self-employed informal workers, being informal loses statistical significance (column 3). In other words, the Russian / non-Russian status does not involve a higher likelihood of being salaried informal (i.e., salaried but without a contract in their primary job).

Again, the important limitation of this analysis is that traditional surveys do not capture either across regions or illegal migrants. Hence, our results do not definitively conclude that migrant workers do not impact informality in Russia. We find evidence with the data at hand that nonRussians are more likely to work as self-employed informals, but not salaried informals. The analysis involving non-Russians, because of not capturing other forms of migration, should be viewed with the caveat that the share of migrants is likely underestimated in the survey and that a more fine-tuned definition of migrants through better surveys may render rather different results.

# **III
, the budget would be allocated across a 24-month period that includes a 6-month design phase, a 12-month implementation phase and a 6-month data processing, editing, analysis and dissemination phase. Table 3 provides a template for a budget of approximately USD 540,000 to finance technical assistance activities for a multi-topic household survey with field-based data entry and a resident advisor stationed in the country. The budget covers staff time, consultant expenditures, and travel and miscellaneous costs spread across a 24month period and the three distinct phases of implementation as described above.

As suggested by the World Bank Household Survey Strategy, in low-capacity, data deprived settings, the chief part of the technical assistance budget of the budget would be allocated towards

> 17 This section details the types of activities that could be financed as part of the technical assistance provided to the NSOs in data-deprived in support of the design, implementation and analysis of the household surveys of interest. The choice of the elements of the technical assistance package is anchored in the recommendations of the World Bank Household Survey Strategy. The financing arrangement for the technical assistance provision is one factor that would mediate the way in which these services are provided to the countries.

10
An example of a regression equation is shown in Table 1; this one covers the entire sample of 176,141 households. The estimated coefficients are sensible: they show, for instance, that expenditure per capita is higher in Bangkok, and for better-educated households, and for households that include professionals, managers, or government employees.

<!-- Start of picture text --> 12000<br>Bangkok<br>Center<br>10000 South<br>North<br>Northeast<br>8000<br>6000<br>4000<br>2000<br>0<br>2007m1 2007m7 2008m1 2008m7 2009m1 2009m7 2010m1<br>Real expenditure/capita, baht/month<br><!-- End of picture text -->

**Figure 6.1.  Real per capita expenditure by region, Thailand, 2007:M1 – 2010:M6** Source:  Thailand Socio-Economic Surveys of 2007, 2008, 2009, and 2010.  Shaded area marks period of recession.

<!-- Start of picture text --> 16000<br>14000<br>12000<br>10000<br>8000<br>Top decile<br>6000<br>Decile 5<br>Bottom decile<br>4000<br>2000<br>0<br>2007m1 2007m7 2008m1 2008m7 2009m1 2009m7 2010m1<br>Real expenditure/capita, baht/month<br><!-- End of picture text --> Losers and Winners in Recession: Thailand 2008-09 13
**|**2SLS**|
|Violent Shock|-0.0009|-0.0952**|-0.0032|-0.0014|0.0040|0.0418**|-0.0072|0.0127|0.0074|0.0421*|
||(0.0106)|(0.0392)|(0.0057)|(0.0216)|(0.0049)|(0.0193)|(0.0081)|(0.0247)|(0.0057)|(0.0234)|
|Rural District Price Index|0.0045|0.0034|0.0075***|0.0076***|-0.0015|-0.0010|-0.0057**|-0.0055**|-0.0048***|-0.0044**|
||(0.0029)|(0.0045)|(0.0016)|(0.0016)|(0.0012)|(0.0018)|(0.0023)|(0.0025)|(0.0015)|(0.0018)|
|Livestock Income as Share of Total Agricultural<br>|-0.0183*|-0.0242**|0.0093|0.0094|0.0013|0.0037|-0.0117|-0.0104|0.0194***|0.0215***|
|Income|(0.0099)|(0.0118)|(0.0064)|(0.0066)|(0.0053)|(0.0055)|(0.0073)|(0.0076)|(0.0065)|(0.0068)|
|_Dummy_Participation