rafmacalaba/datause-encoder-data huggingface dataset:
**Table 10. Correlates of Survey Attrition**

Dependent Variable: In Panel (1=yes, 0= no)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||<br>OLS|<br>OLS|<br>OLS|<br>Probit|<br>OLS|<br>Probit|
|Treated|0.047*<br>(0.025)|0.084**<br>(0.011)|0.081**<br>(0.013)|0.087**<br>(0.014)|-0.039<br>(0.067)|-0.035<br>(0.066)|
|Aged 16 to 19||-0.082***|-0.069***|-0.074***|-0.082***|-0.082***|
|||(0.023)|(0.023)|(0.026)|(0.011)|(0.015)|
|Aged 20 to 24||-0.018|-0.017|-0.018|-0.017|-0.018|
|||(0.012)|(0.013)|(0.015)|(0.014)|(0.016)|
|In School||0.058***|0.054***|0.054***|0.070***|0.062***|
|||(0.017)|(0.018)|(0.015)|(0.002)|(0.004)|
|Completed at least||-0.019|-0.011|-0.013|-0.057***|-0.049***|
|<br>some primary school||(0.048)|(0.050)|(0.048)|(0.007)|(0.007)|
|Married or||0.006|0.010|0.008|-0.007***|-0.007***|
|cohabitating||(0.012)|(0.011)|(0.012)|(0.002)|(0.002)|
|Has Child||-0.017|-0.018|-0.015|-0.051***|-0.045***|
|||(0.028)|(0.027)|(0.025)|(0.002)|(0.002)|
|Is Pregnant||0.033|0.028|0.024|0.016**|0.013**|
|||(0.023)|(0.022)|(0.021)|(0.006)|(0.005)|
|Any IGA||-0.046***|-0.041**|-0.039**|-0.052***|-0.046***|
|||(0.017)|(0.018)|(0.017)|(0.003)|(0.004)|
|Program Dropout||-0.203***|-0.198***|-0.219***|-0.197***|-0.215***|
|||(0.070)|(0.068)|(0.074)|(0.069)|(0.076)|
|Treated x Age 16-19|||||0.021|0.012|
||||||(0.031)|(0.027)|
|Treated x In School|||||-0.028|-0.019|
||||||(0.023)|(0.029)|
|Treated x Completed|||||0.068|0.068|
|at least some primary|||||(0.060)|(0.065)|
|school|||||||
|Treated x Married or|||||0.028|0.026|
|Cohabitating|||||(0.019)|(0.020)|
|Treated x Has Child|||||0.055*|0.054*|
||||||(0.030)|(0.029)|
|Treated x Pregnant|||||0.018|0.018|
||||||(0.050)|(0.050)|
|Treated x Any IGA|||||0.018|0.012|
||||||(0.023)|(0.024)|
|Observations|1895|1895|1895|1895|1895|1895|
|CommunityDummies|No|No|Yes|Yes|Yes|Yes|

Note: Probit models in columns (4) and (6) report marginal effects.

45
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Across the region, there has been a tendency for aircraft fleets to be renewed and scaled down in size to facilitate consolidation of routes towards a hub and spoke system. The aircraft fleet serving Mali has renewed rapidly in recent years, with the share of the fleet comprising aircraft of recent manufacture rising from 87 percent in 2001 to 96 percent in 2007, putting Mali ahead of many of its neighbors. Moreover, the share of medium or smaller aircraft serving Mali increased from 27 percent in 2001 to about 54 percent in 2007, in support of the emerging hub and spoke system.

# **Challenges**

Mali has a declining air market and has been progressively losing international connectivity (figure 8). This is partially a result of the collapse of Air Afrique (2004) and Air Senegal (2009), which left the regional market very fragmented. Moreover, between 2004 and 2008, the number of flights per week in Mali fell from 77 to 41. This represents a contraction of the market of 47 percent, the worst market contraction in West Africa during the period if Mauritania is excluded. Nevertheless the airline market is fairly competitive (Herfindhal of 12 percent) perhaps as a direct consequence of the implementation of the Yamoussoukro Decision of opening Africa airs and reflecting the high traffic and competition developed along the route Bamako–Dakar, operated by more than six carriers. This is an important achievement as until 2007, three airlines—Air France, Royal Air Maroc, and Air Senegal— controlled more than half of the market.

**Figure 8. Mali’s air transport market is thin and declining**

a. Seats b. City pairs <!-- Start of picture text --> 900,000 45<br>800,000 40<br>700,000 35<br>600,000 30<br>500,000 25<br>400,000 20<br>300,000 15<br>200,000 10<br>100,000 5<br>0 0<br>2001 2004 2007 2001 2004 2007<br>Total<br>International<br>Total International Intercontinental Domestic<br>Intercontinental excluding flights between NA and SSA<br>Number of seats<br>Number of city pairs-<br><!-- End of picture text -->

## Source: Bofinger 2009.

Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data Note: As reported to international reservation systems Like many other African countries, Mali continues to face significant safety and security issues in air transport. Mali failed the FAA/IASA Audit, and none of its carriers have passed the IATA/IOSA audit.

18
Banks that belong to multinational corporations may have better access to credit lines from their parent companies and, thus, be less affected by liquidity contractions. In fact, Arena et al. (2006) find some evidence that foreign banks are less affected by monetary conditions than their peers. State owned banks may also be less affected by crisis, because they count with an implicit (or explicit) government guarantee. Thus, if bank ownership is an important determinant of the response to the current crisis, and correlates with a bank’s wholesale dependence, the differential returns documented above may not indicate the existence of liquidity related transmission. The results reported in Figure 10 control for this possibility by adding a dummy variable that indicates if a bank has some foreign or state ownership, using information from Micco and Panizza (2006), at the cost of importantly reducing the sample to 586 banks. The results show that foreign owned banks do relatively better and state owned banks relatively worse during the Lehman episode, although the CADR of state banks after the episode are not statistically significant, and those of foreign owned banks only marginally two days after the event. Also, controlling for ownership does not change the relative decline in returns for banks more dependent on wholesale funds, but it reduces the significance of the CADR because of the large decline in sample size.

The failure of Lehman Brothers not only resulted in a large liquidity crunch with a sudden dry up of interbank loan markets, but also resulted in potential losses for banks that were directly exposed to this institution. The results reported in Panel C add a dummy that takes the value of one for banks that declared exposure to Lehman in the weeks following its collapse to the baseline specification to control for the possibility that the measure of wholesale dependence may be capturing direct exposure to Lehman, and, therefore, counterparty risk rather than wholesale liquidity dependence. The data for the construction of the dummy variable come from the _Daily List of Companies Reporting Lehman Bros Exposure_ published by Dow Jones News services between September 15 and October 15, 2008. I use a dummy variable as a control because the data on the size of the exposure is not comprehensive. The results show indeed that banks that were directly exposed to Lehman suffered a large relative decline in their returns, of about one percentage point in a single day. Nonetheless, the CADR for banks with high liquidity dependence are still negative, significant, and of similar magnitude as in the baseline.

between wholesale dependence and the measures of leverage, size, and asset composition to the baseline specification. The results (not reported) only show a significant differential effect of wholesale dependence among larger banks, which probably reflects the higher reliance of these banks on international and more volatile sources of wholesale funds.

21
<!-- Start of picture text --> Figure 19: Prospects of upward mobility, by gender and economic status<br>(a) Gender (b) Economic status<br><!-- End of picture text --> _Source_ : Tiwari et al (2017) Upward mobility patterns across gender groups reveal no clear difference between males and females in their upward mobility prospects. In all three countries, the point estimates for upward mobility are higher for individuals in the top 60 percent of the income distribution in comparison to the bottom 40 percent, but the difference is only statistically significant in Azerbaijan.

# Conclusion

In this paper we carry out a comprehensive analysis of economic and social mobility in countries in the South Caucasus. Country heterogeneity in terms of the nature of the data available for analysis has dictated the methodologies used but on the whole, the effort has been to complement available household survey data in each of the countries in the sub-region with other sources of data to shed light on this important topic and deepen the available analysis on poverty and inequality. In particular, we have taken advantage of the most recent round of the Life in Transition Survey (2016) to fill key data gaps in Azerbaijan, which is included in this analysis. The availability of panel data in the case of Georgia has enabled us to delve deeper than we are able to do for the other two countries to not only identify patterns specific to Georgia, but also to elucidate the kind of analysis that could be done in the other countries if similar data were available. Finally, we also carry out some original work on social mobility and benchmark the progress in South Caucasus against the performance of other transition countries in the region.

The key findings that emerge from this analysis are as follows. _First_ , despite the progress that has been made in reducing poverty in both Armenia and Georgia over the last decade, there appears to be a significant amount of churning around the poverty line. In Georgia, for example, more than half of the households identified as poor in 2011, 2013 and 2015 were not poor in the previous 37
Fourth, we contribute to the literature on scarring effects of recessions, mass layoffs, and plant closures: Jacobson et al. (1993); Kletzer (1998); Davis & von Wachter (2011); Krolikowski (2017); Flaaen et al. (2019); Lachowska et al. (2020); Bertheau et al. (2023), and Schmieder et al. (2023). Instead of focusing on the variation of shocks across US regions, we directly link firm foreign shocks to their workers’ outcomes. Also, we show, for a developing country, long-lasting effects of negative foreign shocks even for non-displaced workers.

Fifth, we add to the thin US-focused literature on the role that income support programs yield in mitigating worker adjustment to shocks: Autor et al. (2013, 2014) and Yagan (2019). We show only limited replacement for lost earnings for Brazil from unemployment insurance, complementing similar evidence obtained for the US by Yagan (2019), also as a result of the GFC. The income replacement of Bolsa Familia we estimate is lower than that of welfare programs in the US found by Autor et al. (2013, 2014).

The paper proceeds as follows. Section II describes the data and provides summary statistics. Section III presents the empirical design to study the long-run impacts of GFC foreign shocks on workers and the baseline results as well as results for heterogeneity across worker characteristics. Section IV identifies the driving mechanisms for the persistent effects on workers, studying the reallocation of workers across firms and sectors and estimating various margins of adjustment by firms in response to foreign shocks. Section V examines the mediating role of informality and lack of competition for the impacts of GFC foreign shocks. Section VI evaluates the ability of assistance programs to buffer workers’ crisisinduced earnings losses. Section VII examines responses in a different labor market, using data from Ecuador. Section VIII concludes.

# **II. Data and Summary Statistics**

The analysis in this paper draws on rich administrative longitudinal worker-firm data for Brazil over the 2004-2017 period from several main sources. The first source is a longitudinal linked employer-employee database covering the universe of formal workers and firms. The second is a longitudinal manufacturing firm census. The third is firm customs data on exports and imports by firm-partner country. Importantly, we are able to link the employer-employee database with the worker registry of unemployment insurance, the national registry of lowincome households providing data on payments from the cash transfer program Bolsa Familia, and the beneficiary records of training activity from Brazil’s main training provider. Overall, our data cover the universe of formal workers in Brazil along with their firms’ exposure to the GFC through their export market participation and also all the benefits disbursed to mitigate the worker-level impacts, including support received from income assistance and job training. We supplement these data with information from the Brazilian census. Next, we 6
# **<u>WEB THEORY APPENDIX</u>**

# **Urban Natural Increase and the Speed of Urbanization**

**<u>Summary:</u>** _This Web Theory Appendix shows in a calibration exercise that faster changes in urbanization are indeed mechanically associated with higher urban rates of natural increase ceteris paribus. Rural natural increase then mechanically lowers urbanization rates._

The urbanization rate at the start of year _t_ , _Ut_ , is the ratio of the urban population _U popt_ to the total population _Popt_ . The change in urbanization in year _t_ can be expressed as: Substituting equations (3) and (4) into equation (8), and noting that _△Popt_ = _Nnit ∗ Popt_ with _Nnit_ the national rate of natural increase in year _t_ , and _Popt_ the total population at the start of year _t_ , we obtain: Three insights emerge. First, the change in urbanization is a relative concept and depends on both urban and rural natural increase ( _Unit −Rnit_ ), with the latter mitigating the positive effect of the former. Consequently, rapid urban natural increase can coexist with a relatively slow change in urbanization. Put differently, countries with similar changes in urbanization may be experiencing very different rates of urban growth, if higher rates of urban natural increase are offset to a similar extent by higher rates of rural natural increase. As countries with higher urban natural increase also tend to experience higher rural natural increase ( _Unit_ and _Rnit_ tend to be highly correlated) this is not so far-fetched. This contrasts with urban growth, where urban natural increase translates one to one in urban growth (at least in an accounting sense and contemporaneously - see further below). Urban congestion is thus likely also more directly linked to urban growth than to changes in the rate of urbanization, a point we will revisit below.

Second, the effect of migration on changes in urbanization tends to be larger than the effect of urban natural increase. Not only is the effect of the latter mitigated by rural natural increase (typically even overturned if _Rnit_ exceeds _Unit_ ), it is further conditioned by the A. 2
**Table 3. Output, Factor Inputs, and Population Projections, 2005-2020 (annual, average growth rates, in percent)**

|Trading Partner|GDP|Unskilled<br>Labor|Skilled<br>Labor|Physical<br>Capital|Population|
|---|---|---|---|---|---|
|Australia and New Zealand<br>|3.5|1.6|0.6|3.8|0.7|
|China|6.6|0.8|3.9|8.5|0.6|
|Japan|1.6|0.2|-0.7|2.5|-0.2|
|Korea|4.7|2.0|5.8|4.9|0.3|
|Hong Kong and Taiwan, China|4.3|0.6|3.0|4.9|0.4|
|Indonesia|5.2|2.7|6.5|4.7|1.1|
|Malaysia|5.6|-1.4|3.9|5.8|1.4|
|Philippines|3.5|1.8|4.6|3.5|1.5|
|Singapore|4.9|0.6|1.1|5.3|0.8|
|Thailand|4.6|0.1|3.2|3.9|0.5|
|Vietnam|5.4|1.4|1.9|6.0|1.1|
|Rest of South East Asia|3.1|1.3|3.6|3.6|1.0|
|India|5.5|1.6|4.0|6.1|1.1|
|Rest of South Asia|5.0|2.1|3.6|5.1|1.7|
|Canada|2.6|1.6|0.9|3.2|0.4|
|USA|3.2|1.5|0.8|3.9|0.7|
|Mexico|3.8|2.7|4.6|3.3|1.4|
|Argentina and Brazil|3.6|0.9|3.7|3.1|1.0|
|Rest of Latin America|3.3|1.6|3.8|3.6|1.3|
|European Union 25 and EFTA|2.3|0.3|0.0|2.6|-0.1|
|Former Soviet Union|3.2|0.3|0.8|3.6|-0.1|
|Middle East and North Africa|4.1|1.7|3.3|4.1|1.6|
|Sub-Saharan Africa|3.5|2.6|3.3|3.2|1.9|
|Rest of the World|3.7|0.7|1.2|2.6|0.5|
|_Low income countries_|4.7|1.7|3.1|4.2|1.5|
|_Middle income countries_|4.5|1.0|3.1|3.9|0.8|
|_High income countries_|2.7|0.9|0.4|3.0|0.2|
|World|3.1|0.9|0.8|3.2|0.9|

Source: World Bank projections to 2015 extrapolated to 2020 We then assess the impact of strong growth on the quality and variety of exports from China and India. Quality improvements in exports have recently been identified as a key influence on the performance of rapidly growing exporters such as China and India (Hummels and Klenow 2005). We follow Hummels and Klenow who observe that larger economies export more in absolute terms than smaller economies and analyze the extent to which larger economies export higher volumes of each good (intensive margin growth), a wider set of goods (the extensive margin), and improved-quality goods. Their estimates imply that rising quality in existing product lines accounts for <u>increases</u> of approximately 0.09 percent in export prices for each one percent increase in income levels, despite increases of 0.34 percent in the 14
following the April nadir. There is an uptick in visits to residences in all regions and a drop in visits to retail/recreation locations, again with the biggest changes in April. Interestingly, Latin America and the Caribbean show the largest changes in all four categories, with the biggest drops in visits to workplace, grocery/pharmacy, and retail/recreation locations and the biggest increase in visits to residential locations.

The indicators may even track variability in the severity of the crisis, not just the initial drop. For example, there is an increase in visits to retail and recreation in Europe and Central Asia in July and August, and it coincides with summer in these areas, as well as with a lull in new coronavirus cases.

Across all regions, the pattern is clear: there is a sudden, large decrease in visits to non-residential locations in March and April. Moreover, visits to these locations – especially workplaces and retail/recreation – remain far below their February levels throughout the entirety of 2020.

Figure 8 presents the final set of mobility data, which disaggregates the data by country income. While we do see decreases in visits to workplace, grocery/pharmacy, and retail/recreation locations and an increase in visits to residential locations, one clear pattern is that visits to workplaces decreased the least in low-income countries. Whether this is due to differences in the underlying data – e.g. differences in the people who are contributing to the data – or actual differences in behavior is an open question. One possible explanation is that the classification of residences and workplaces is more difficult in developing economies, where a large proportion of the population is self-employed. In Sub-Saharan Africa as a whole, for example, more than 80 percent of workers may be self-employed (Cho et al., 2016), which means that “residence” and “workplace” may be one and the same.

In all the mobility data, April shows the biggest change from baseline. While visits to many locations increased somewhat rapidly after April, overall visits remained significantly below the baseline month well into the year. This finding appears especially true for both workplaces and retail/recreation.

# b. Search data

We present trends for six different search terms – food bank, assistance, online education, prayer, bible, and quran – in Figure 9 and Figure 10. We begin with trends by region in Figure 9. The topleft panel presents results for how often people searched for the term “food bank.” There is a large increase in just one region: North America. This could be due to the availability of food banks in that region relative to the others, rather than differences in actual needs. The top-middle panel presents results for “assistance.” Here, we see the most pronounced spike in two regions: South Asia and East Asia and the Pacific.

The top-right panel shows trends in searches for “online education.” East Asia and the Pacific and Europe and Central Asia show marked increases in searches for the term in April. Sub-Saharan Africa also shows an increase, though the magnitude of the change is much smaller than for the other two regions.

9
15 Table 2: Estimates of Elasticities of Exports to World GDP and Own GDP to Exports

||Adva|nced Econ|omies|Emergin|g Market E|conomies|
|---|---|---|---|---|---|---|
||1986‐2000|2001‐2014|_# countries_|1986‐2000|2001‐2014|_# countries_|
|Elasticities of Export Volume to Real World|||||||
|GDP (excl. own GDP):|||||||
|Gross exports of goods and services|2.5|1.9|_28_|2.7|1.9|_43_|
|Value‐added exports of goods and services|2|1.8|_22_|3.2|3.2|_6_|
|Elasticities of Real GDP to Export Volume:|||||||
|Gross exports of goods and services|0.5|0.6|_18_|0.7|1|_48_|
|Value‐added exports ofgoods and services|0.5|0.6|_18_|0.5|0.7|_7_|

_Sources:_ IMF World Economic Outlook, IMF Information Notice System, World Input‐Output Database, IMF Information Notice System. _Notes:_ Countries included in the averages passed the following filters: data to start no later than 1992; elasticities to be neither negative nor outliers. Estimations to derive elasticiy of exports to GDP include Real Effective Exchange Rate as an explanatory factor.

The results for the sensitivity of domestic growth to export growth (eg) are presented in the last two rows of Table 2. We find that for the average high-income economy, the elasticity of domestic GDP to exports is very close across the two periods (0.5 and 0.6, respectively), both in gross terms and in value added terms. For the average developing economy, the change over time in the estimated elasticity is slightly larger: from 0.7 to 1.0 in gross terms, and from 0.5 to 0.7 in value added terms. T-tests confirmed that the differences in elasticities between 1990s and 2000s were statistically significant for estimates based on gross export data, but not for estimates based on value-added trade data, possibly due to the reduced sample size.<sup>6</sup>

> 6 A set of alternative methodologies validated the results reported in Table 2. First, averages were recalculated after removing elasticities from estimations with non-significant coefficients of adjustment. Second, ratios of 10-year (for gross) and 5-year (for value-added) moving averages of export and GDP percentage changes were computed and averaged by country group. Differences in the 2000s relative to 1990s were assessed by inspecting the monotonicity of the country-specific ratios. Finally, estimates of elasticities were derived using panel estimations with country fixed effects. Regressions were run only on the gross export data set – due to limitations in the value added exports data– and separately for high-income and emerging market economies. Elasticities for the 1990s and 2000s were found to be statistically different from each other.