rafmacalaba/data-use-mentions-extended huggingface dataset:
- Dijkstra, L. and Poelman, H. (2014). A harmonised definition of cities and rural areas: the new degree of urbanisation. Regional Working Paper. European Comission Directorate-General for Regional and Urban Policy.

- Donaldson, D. and Storeygard, A (2016). The view from above: Applications of satellite data in economics. _Journal of Economic Perspectives_ , **30,** number 4, 171–198.

- Ebener, S., Murray, C., Tandon, A. and Elvidge, C. C. (2005). From wealth to health: modelling the distribution of income per capita at the sub-national level using nighttime light imagery. _International Journal of Health Geographics_ , **4,** number 1.

- Elbers, C., Fujii, T., Lanjouw, P., Ozler, B. and Yin, W. (2007). Poverty alleviation through geographic targeting: How much does disaggregation help? _Journal of Development Economics_ , **83,** number 1, 198–213.

- Elbers, C., Lanjouw, J. and Lanjouw, P. (2003). Micro–level estimation of poverty and inequality. _Econometrica_ , **71,** number 1, 355–364.

- Elbers, C., Lanjouw, J. and Lanjouw, P. (2005). Crime and local inequality in south africa. _Journal of Economic Geography_ , **5,** 101–118.

- Elbers, C., Lanjouw, P., and Leite, P. (2008). Brazil within brazil: Testing the poverty mapping methodology in minas gerais. Policy Research Working Paper 4513. The World Bank.

- Elbers, C. and van der Weide, R. (2014). Estimation of normal mixtures in a nested error model with an application to small area estimation of poverty and inequality. Policy Research Working Paper 6962. The World Bank.

- Elvidge,
a hard task. Furthermore, even if these data are available, there are still endogeneity issues related to the non-random nature of the migration decision that should be addressed through sound identification strategies or, ideally, experimental approaches (McKenzie and Yang, 2010; McKenzie et al., 2010).

In the context of climate migration, to understand the relationship of sequential interactions between the decision to migrate and other _in situ_ adaptation strategies, data is needed on coping strategies, agricultural practices, climate risk perception, and risk aversion and preferences. If the aim is to shed light on whether, in a given setting, the prevailing response will be mobility or immobility, it is necessary to combine data on migrants with information on potential migrants, the intention to migrate in response to shocks and changes, climate-induced liquidity constraints, stated preferences, and other similar information.

Further complicating matters are complexities tied to the nature of the climatic events themselves. In the case of fast-onset shocks, standard data sources are unlikely to capture the migratory responses of the affected population, and _ad hoc,_ swiftly implemented post-disaster surveys are needed. On the other hand, slow-onset

> 17 One well-known exception is the Mexican Family Life Survey (MxFLS) managed by the Iberoamerican University and the Center for Economic Research and Teaching, which has been collecting information on a wide range of socioeconomic and demographic indicators for more than 7,500 households over a 10-years period as well as on the individuals or households that grew out from the - original sample, including those who migrated within Mexico or to the US. For details about the MxFLS, see http://www.ennvih <u>mxfls.org/english/index.html.</u>

10
varies markedly by type or work. About 10 percent of formal workers are paid part or all their earnings off the record. In contrast, among salaried informal workers (i.e., in a firm but without a contract), 91.5 percent report to have received all or part of their wages unofficially. Self-employed workers have a 66 percent chance of being paid off the record (Table 7).

Finally, we find that a statistically significant wage gap between formal and informal workers exists since the mid 2000s and has widened since. We use information on net wages received and hours worked to back out an hourly wage rate for all workers. Nominal wages are spatially and temporally deflated.<sup>21</sup> Differences in hourly wages of informal and formal were not statistically significant at the beginning of the past decade, but a clear divergence is observed since 2005 (Figure 10). Moreover, while real wages declined with the onset of the global (2009) and oil-andsanctions (2015) crises for both groups, it appears that the drop in real wages was larger for informal workers that formal workers. The difference is partly due to the fact that, among informals, the decline is more acute among the self-employed than among salaried without a

> 21 For spatial deflation, we use the cost of a fixed basket of goods in 2014 for all Russian regions. For temporal deflation, we use regional CPI series. All wages <u>are expressed in 2014 Moscow prices.</u> Informal Employment and Worker’s Wellbeing in Russia 20/67
of household income changes, due to lack of information across all the countries under study here and thus to maintain comparability.</mark><sup>4</sup> <mark>As such, it predicts total income changes, poverty rates, and shares of vulnerable and middle-class populations that</mark> can be attributed to job losses, labor income changes, and changes in remittances.

<mark>We proceed by, first, presenting an in-depth analysis of employment elasticity projections for 15 developing countries selected based on the availability of post-crisis employment data from Labor Force Surveys or other official sources. The sample includes 5 countries from the Europe and Central Asia region, 6 from Latin America and the Caribbean, 3 from East Asia and Pacific, and one from Sub-Saharan Africa. When no employment data is available, employment elasticities are a crucial input to project employment changes and simulate the distributional impact of a shock. However, these estimates may provide biased inputs during a crisis period and, especially, during the recent pandemic when large drops in GDP were observed. Employment projections based on elasticities may overestimate the employment loss, for instance, because most countries have implemented crisis-relief measures such as cash transfers and wage subsidies that may have protected employment but not been reflected in GDP growth. We therefore use pre-COVID-19 data on GDP and employment levels to perform a sensitivity analysis using different model specifications. The projected employment levels are then compared with observed 2020 values. The differences or biases between actual and projected employment levels are correlated with the</mark>

> 3 For example, some studies do not distinguish the job loss channel by sector of activity (Brum and De Rosa, 2021) or assign the probability of losing a job exogenously (ECLAC, 2020) . The former, however, consider amenability of working from home when predicting job loss.

> 4 Brum and Da Rosa (2021), World Bank (2021), and ECLAC (2020) include government transfers in the simulation of household income changes
Finally, it is worth noting that the model provides a good fit predicting the observed transitions. It predicts 88 percent of truly observed transitions. It predicts a transition incidence of 26 percent of working age individuals in the sample, while the truly observed incidence is 29 percent.

# **6. Conclusions**

This paper quantifies labor transitions in Colombia between 2008 and 2009, taking advantage of panel data collected throughout the crisis. Results confirm that in the context of the financial crisis, labor transitions remain large in Colombia and asymmetric in nature: a worker is more likely to move from formality to informality than _vice versa_ , while an inactive individual is more likely to remain so than change his or her condition. This is consistent with previous evidence in Colombia. What the current analysis adds is that this asymmetry may mask different patterns across occupations. Salaried formal workers may first try to become salaried informal workers, rather than moving into unemployment or out of the labor force, while a self-employed informal worker may directly move into unemployment and out of the labor force.

Workers from low socioeconomic status transition less than workers with higher socioeconomic backgrounds, which may pick up the endogeneity of the decision to move—that is, moving in order to improve welfare conditions. The evidence also confirms that transitions may respond not only to an individual motivation to improve his or her condition, but may be part of a household strategy to maximize its welfare conditions. When looking at the profiles of those who transition and those who do not, the substantive differences in individual earnings between transitioning and non-transitioning workers vanish when comparing their total household per capita incomes.

23
Economies included in the Global Findex survey and database

|**Economy**|**Region**<sup>**a**</sup>|**Income group**|**Data collection**<br>**period**|**Interviews**|**Design**<br>**effect**<sup>**b**</sup>|**Margin**<br>**of error**<sup>**c**</sup>|**Mode of**<br>**interviewing**|**Languages**|**Over-**<br>**sample**<sup>**d**</sup>|**Exclusions and other sampling details**|
|---|---|---|---|---|---|---|---|---|---|---|
|India|SAR|Lower middle|Apr 11–Jun 16|3,518|1.47|2.0|Face to face|Assamese,<br>Bengali,<br>Gujarati, Hindi,<br>Kannada,<br>Malayalam,<br>Marathi, Oriya,<br>Punjabi, Tamil,<br>Telugu||The sample excludes the Northeast states<br>and remote islands. The excluded area<br>represents approximately 10% of the total<br>adult population.|
|Indonesia|EAP|Lower middle|May 18–May 31|1,000|1.48|3.8|Face to face|Bahasa<br>Indonesia|||
|Iran, Islamic<br>Rep.<sup>e</sup>|MENA|Upper
which is consistent with Deaton’s (2007) observation that income does not explain crosscountry height differences.<sup>4</sup> This is reflected in panel (a) of figure 2, which plots the residuals after regressing height of children under 3 years old on the log of GDP against the residuals after regressing open defecation on the log of GDP. The association remains, and the _R_<sup>2</sup> is similar: sanitation linearly explains 54.2 percent of variation in child height, and the sanitation residual explains 53.9 percent of the variation in the height residual.

Panel (b) of figure 2 adds average height and exposure to open defecation in wealth subsets of India’s 2005 DHS to the basic plot of country mean height and sanitation. Included in published DHS data is a classification of households into wealth quintiles based on asset ownership. Average height of children within these groups is plotted against the rate of open defecation among all households in the primary sampling unit where they live, that is, the local open defecation to which they are exposed. Additionally, I follow Tarozzi (2008) in identifying an elite top 2.5 percent of the Indian population: children who live in urban homes with flush toilets that they do not share with other households; whose mothers are literate and have been to secondary school; and whose families have electricity, a radio, a refrigerator, and a motorcycle or car. Even these relatively rich children are shorter than healthy norms; this is expected, because 7 percent of the households living near even these rich children defecate openly. Indeed, the graph shows that their stunted height is approximately what would be predicted given the open defecation in their environment. More broadly, the association between height and sanitation among these wealth groups is close to the the international trend computed from country means. Exposure to nearby open defecation linearly explains 99.5 percent of the variation in child height across the five asset

> 4GDP per capita
# **I. Introduction**

Understanding firms’ needs and constraints in terms of workforce and related skills is key to designing better education and labor market policies. In Kosovo, a perception of skill shortages among firms and an oversupply of labor, especially youth entering the labor market, coexist. Aligning policies, training programs and curricula to the current and future needs of the labor market is challenging in the absence of relevant and real-time data. Data on labor supply and the results of job matches are available from standard surveys such as the Labor Force Survey, while detailed data on job creation and skill needs are often scarce. This paper exploits online vacancy data from the four largest job portals in Kosovo in 2018 to better understand its potential for addressing important information gaps between labor market demand and skills.

The key contribution of this paper is to provide an analysis of skill demand with a high level of granularity and precision, such that our results can directly inform policy making. We conduct a textual analysis of the job descriptions and job titles to identify the incidence of skills, education, and experience requirements across industries. For this purpose, we construct search dictionaries of specific keywords, key phrases and text patterns to approximate demand for skills, education, and work experience with the occurrence of these in job portal ads. For the skill analysis, we develop a skill taxonomy that consists of three layers, providing detailed information on skills in demand. First, we employ a general taxonomy that is in line with the literature on skills to show how demand varies across socio-emotional, cognitive, and technical skills.<sup>2</sup> Second, we disaggregate these three broad types of skills into 15 categories (one cognitive skill, five socio-emotional skills, and nine technical skills) to investigate how demand for specific skills varies across industries and occupations. Third, each of the 15 categories is defined by 152 skill requirements, which offers the highest level of granularity in the analysis of skills in demand. This layer
# **References**

- Abadie, A., Chingos, M. M., and West, M. R. (2018). Endogenous stratification in randomized experiments. _Review of Economics and Statistics_ , 100(4):567–580.

- Ahmed, S., McIntosh, C., and Sarris, A. (2020). The impact of commercial rainfall index insurance: Experimental evidence from Ethiopia. _American Journal of Agricultural Economics_ , 102(4):1154–1176.

- Ali, W., Abdulai, A., and Mishra, A. K. (2020). Recent advances in the analyses of demand for agricultural insurance in developing and emerging countries. _Annual Review of Resource Economics_ , 12:411–430.

- Andrews, I. and Kasy, M. (2019). Identification of and correction for publication bias. _American Economic Review_ , 109(8):2766–94.

- Andrews, I. and Oster, E. (2019). A simple approximation for evaluating external validity bias. _Economics Letters_ , 178:58–62.

- Athey, S., Chetty, R., and Imbens, G. (2020). Combining experimental and observational data to estimate treatment effects on long term outcomes. Mimeo.

- Bandiera, O., Fischer, G., Prat, A., and Ytsma, E. (2021). Do women respond less to performance pay? Building evidence from multiple experiments. _American Economic Review: Insights_ , 3(4):435–54.

- Bédécarrats, F., Guérin, I., and Roubaud, F. (2020). _Randomized control trials in the field of development: A critical perspective_ . Oxford University Press.

- Belissa, T., Bulte, E., Cecchi, F., Gangopadhyay, S., and Lensink, R. (2019). Liquidity constraints, informal institutions, and the adoption of weather
with informality. Directly affected households have more insecure tenure arrangements than non-affected ones. Regression analysis shows that they are less likely to hold proof of tenure for the dwellings they rent. Female headed households are more likely to be directly affected than male headed households. Since women often have the double responsibility of provider and caretaker, they may face particular challenges when coping with floods. This will be discussed in more detail in the following sections. Directly affected households are less likely to pay for garbage services – a potential proxy for garbage collection quality . Finally, being directly impacted by floods is not related to housing quality or access to public services. This is in contrast with results found in Accra, Ghana (Erman et al, 2018), where a similar study was carried out.

Indirectly impacted households tend to be located in areas without access to reliable infrastructure. These households are less likely to have drains outside their dwelling and drains in the area tend to be overall rare and of poor quality as measured by data collected by Ramani Huria 2.0.<sup>23</sup> There is also evidence that other services such as water and sanitation are of lower quality among the indirectly affected households compared to the non-affected population. This seems to suggest that indirectly affected households live in areas where heavy rain can more easily affect infrastructure due to poor quality and bad drains. Therefore, improving the quality of infrastructure could mitigate the indirect effects of floods. In addition, indirectly affected households also tend to have a higher number of household members than other households. This is intuitive since having more household members increases the risk of having one of them miss work, school or fall ill due to floods.

Households residing in flood risk areas tend to live closer to work. The travel time of breadwinners of directly affected households is comparatively shorter than non-affected households.<sup>24</sup> They live 13 minutes closer to work. This suggests that households potentially face a trade-off between living close to work and
data collected in the country (e.g., IPC 2018; WFP 2019; IPC 2020: IPC 2022). This leaves significant gaps as to how households are able to cope with the humanitarian disaster, how the private sector has adapted to the decline in capacity and the many conflict-related macroeconomic shocks, all the ways in which the conflict intersects with

> 7Although the exact conditions placed on data collection vary from survey to survey and organization to organization, the Social Fund for Development tried to conduct a survey on the humanitarian situation in DFA-controlled regions, but was forced to abandon the survey due to a large number of restrictions. In these restrictions, the DFA would not permit any questions to be asked about the conflict itself or anything that might traditionally be deemed sensitive, there were a large number of restrictions that interfere with the sampling and could bias the results (intentionally or not), and the DFA-appointed individuals needed to accompany the survey teams. See Aghajanian and Ghorpade (2022) for details.

9
-29-

Table **6: Comparing resource availability and education outcomes in countries at** $400 **and $200** in **per capita** GNP, **1993**

|a/<br>Indicator|Richer country's<br>gb<br>advantage|Percentage<br>contribution to richer<br>country's advantage|
|---|---|---|
|Budget allocations|||
|Overall govermnent budget as % of GNP|1.00|0.0|
|Public spending on education as % of government budget|1.13|33.0|
|Primnary education as % of public spending on education|0.95|-13.2|
|Primary education spending as % of GNP|1.07|19.8|
|School-aged children's share of population|1.06|17.3|
|Teacher pay relative to per capita GNP|1.26|62.9|
|_Public spending per primary school-aged child_|1.43|100.0|
|Enolmenit roe|I 21.|.<br>_52.7_|
|NPupil-teachf**r**atio|1'<br>!<br>.:.||
|_Publicsphid_<br>.4<br>_e3_||1-0.0|

Source: authors' calculation based on data in previous table.

a! The variables that are not italicized are defined in the previous table. The first two italicized variables refer to the net impact of the preceding three variables; while the last italicized variable refers to the net impact of the preceding two variables.

b/ Following equation (3), the richer country's advantage for the indicators in rows 1,2,3 and 8 are computed as the ratio between its data and that of the poorer country; its advantage in the indicators in rows 5
work could investigate the ability of real-time indicators observed in the past to produce _exante_ predictions of the economic losses implied by the actual values of predictors obtained during the COVID-19 pandemic. The accuracy of such predictions can be validated by comparing them against the actual GDP figures that usually come with a one-year lag. Also, the results point out potential caveats associated with the use of the “digital” real-time indicators, as a non-trivial proportion of people in low-income countries face limited internet access. With the availability of data at a higher geographic resolution, it would be possible to assess the models’ performance in settings with comparable rates of internet penetration—e.g., at the subnational level in a given country—and examine how the prediction accuracy depends upon the internet coverage in a more detailed way.

Despite these potential extensions, the analysis above can help shed light on how best to monitor the economic impact of a crisis like COVID-19 when survey data is scarce. Real-time indicators have several advantages in monitoring the impact of crises. They are often readily available, low cost, and do not involve face-to-face interactions, which is particularly important in a pandemic. Some indicators, particularly those relating to mobility, may be particularly valuable during the COVID-19 crisis when mobility was restricted. Nonetheless, the analysis suggests that Google search terms and food prices are promising indicators for tracking the trajectory of economic impacts during crises, particularly when combined in a parsimonious model that predicts postcrisis outcomes.

# References

- <mark>Abay, K. A., Tafere, K., & Woldemichael, A. (2020). Winners and losers from COVID-19: Global evidence from Google Search.</mark> Policy Research Working Pape <mark>r No. 9268. World Bank, Washington, DC.</mark>

- <mark>Beyer, R. C., Chhabra, E., Galdo, V., and Rama, M. (2018).</mark> _<mark>M<
# **5. Conclusions**

Do high interest rates help to defend exchange rates that come under speculative attack? The evidence considered in this paper suggests that the answer is no. Although proponents of the view that high interest rates can support a currency under attack can point to episodes such as Sweden in the summer of 1992, while proponents of the contrarian view that high interest rates weaken currencies can point to Korea in the fall of 1997 as supportive anecdotes, a systematic examination of interest rates around a large number of historical speculative attack episodes indicates a striking lack of evidence that the stance of monetary policy is correlated with the outcome of speculative attacks. In particular, I find no evidence that interest rates systematically increase or decrease during failed speculative attacks, nor that raising interest rates lowers or raises the probability that a speculative attack fails. This basic finding is robust to alternative measures of the stance of monetary policy, to interactions which control for differences in fundamentals across speculative attack episodes, and to controlling for the endogeneity of the policy response to a speculative attack.

Nevertheless, several shortcomings of this paper suggest that it may be premature to conclude that monetary policy is entirely ineffective in during speculative attacks. In the interests of covering a sample of spieculative attacks large enough to include interesting variation in the outcome of speculative attacks, the policy response to the speculative attack, and the fundamentals that are likely to determine both the outcome of the attack and the efficacy of the policy response, I have made several compromises with regards to data and methodology. Three such compromises, and possible strategies to avoid them in future research, deserve mention.

First, I have relied on readily-available but relatively low-frequency monthly data to identify speculative attacks and the response of policy. This is unfortunate given that much of the economically interesting variation during speculative attack episodes is likely to occur at much higher daily, or even hourly, frequencies. The use of monthly data also precludes modeling the likely path-dependence in the effects of interest rates on speculative pressures,