rafmacalaba/datause-encoder-data huggingface dataset:
A large share of foreign currency deposits can also be found in countries which have long been bastions of financial stability. One variant of this is the international, regional or offshore banking center. Many of the countries which offer tax, secrecy and other advantages to offshore depositors end up with a high fraction of foreign currency in the liabilities of their banking systems. Five such countries are among the top three dozen countries in the world for deposit dollarization.<sup>6</sup> (For some of these countries it is likely that a sizable fraction of even what are treated in the statistics as foreign currency deposits of residents is beneficially owned by non-residents. Such countries need to be treated separately.) The other variant relates to the growing euro zone (which acquires a 13<sup>th</sup> member, Slovenia, on January 1, 2007) and its fringe of aspirants which have adopted currency board or similar arrangements based on the euro. Their banking and business communities are already adjusting their portfolio behavior on the expectation that these links will be transformed into full euro membership before too long. Five such countries<sup>7</sup> are among the top three dozen in the world for deposit dollarization (or euroization, as it might better be called in this context).

All of these offshore, regional financial center or euro-convergent countries are maintaining tight pegs in practice, though it is not quite obvious why there should be a necessary or logical link between an exchange rate peg and building up an offshore center.

Of the remaining score of high dollarization countries, only four hold a tight US dollar relationship in practice (we used a standard deviation of annual change in the US dollar exchange rate of not more than 2.25 percent per annum 2000-2005 as the criterion for a tight relationship). So, despite much discussion in the literature (cf. Calvo and Reinhart, 2002) “fear of floating” is not the dominant exchange rate policy of high dollarization countries (other than regional or offshore financial centers); a clear majority choose more flexible regimes.

Thus we have two classes of highly dollarized economies: (a) a mixed bag of countries with a history of high or hyperinflation, many of them now reforming or reformed; and (b) island rocks of financial stability. Lebanon is an interesting hybrid case, combining as it does aspects of both types, given its high inflation during wartime—the Lebanese pound price of US dollars rose 200 fold in the 8 years to 1992—and its resumed role as a major regional banking center with exceptional monetary depth (M2 is 250 percent of GDP) and a rigid exchange rate for the past decade.

> 6 Costa Rica, Djibouti, Lebanon, Maldives, Uruguay. The case of Hong Kong—an international financial center—is somewhat similar from this point of view.

> 7 Bosnia IH, Bulgaria, Croatia, Lithuania and Macedonia. The data on dollarization in Guinea-Bissau, a euro-linked country since 1997, relates to previous years.

8
**Table 1. Multinomial Logit Estimation with 2013 SILC (Marginal Effects at Average): Aggregated Social Assistance**

||**FE**|**IE**|**U**|**OLF**|**Avg. X**|
|---|---|---|---|---|---|
|Avg. P.|0.2467|0.2288|0.0458|.4785||
|Household (HH) Income (log)|0.1594***|-0.0188***|-0.0216***|-0.1189***|10.1361|
|Aggregate Social Assistance|-0.0971***|0.0869***|0.0206***|-0.0104|0.1399|
|Number of Obs.|47,828|Design Df|47,827|Population Size|50,604,002|
|F-Stat.|288.19|P>F|0.0000|||

_Notes_ : FE = formal employed, IE = informal employed, U = unemployed, and OLF = out of the labor force. Control variables: age, gender, marital status, education, number of children, home ownership, family status (extended family in household), and spatial difference (living in urban or rural area). Coefficients are significant at 1 percent (***), 5 percent (**), 10 percent (*). Avg. X: the average values of explanatory variables. Since the effects of explanatory variables on dependent variables vary along the explanatory variable vector X resulting from the nonlinear nature of the multinomial logit model, we estimated the effect coefficients around the sample average of X.

In order to overcome this issue, we ignored the panel weights of the SILCs and tried to maximize the sample variation by combining all the accessible observations on the annual transitions contained within the different SILC panels. In order to construct our final data set, we began by extracting all the transition observations that occurred within the available SILC panels. For example, we got observations on the 2009–10, 2010–11, and 2011–12 transitions from the 2012 panel, and the 2008– 09, 2009–10, and 2010–11 transitions from the 2011 panel. We repeated this process for all the available panels, from the 2008 to the 2012 panels. As can be seen, transition observations of a particular year can overlap for different SILC panels. Therefore, for a particular year, we picked transitions from the SILC panel that included the highest number of observations for that transition period. Combining these, we ended up with a pooled sample of pseudo-cross sections that contain yearly transitions of 2006–07, 2007–08, 2008–09, 2009–10, 2010-2011, and 2011–12, respectively. The resulting data set contained 97,418 observations.

# **5 Estimation Results**

The effect of social assistance on the status of individuals in the Turkish labor market was investigated through various multinomial regressions. First, we estimated the probability that an individual would remain in one of the four labor market statuses, which are defined as formal-employed, informalemployed, unemployed, and out of the labor force, by using the 2013 SILC data set. Also using this data set, we were able to disaggregate social assistance into three categories: social assistance received for children, for housing, and for other social services. Only social assistance provided by the 13
income as a result of the expansion in CCTs among the poorest. In the rest of the subperiods, the coefficients are statistically indistinguishable from zero.

To investigate these results further, the analysis tested the previous augmented model by adding extra controls for concepts of either public spending or revenues and with and without CCT data. Invariably, the story holds under different specifications: the positive and significant effects of initial food poverty headcount ratios on income growth are found over 2000–14, in particular during the expansion of CCT coverage in 2000–05.<sup>17</sup> One such specification is shown in table 7, panel c, in which the point estimates for the annualized growth rate in the number of beneficiary families exhibit positive and significant effects in the first years of the program’s expansion, consistent with the findings in the conditional income _β_ -convergence model above.

The second channel, that is, the growth elasticity of poverty reduction, can be analyzed through a variation of equation (5) by regressing _gi_ ( _Pit_ ) on the growth rate in mean per capita income interacted with the initial poverty headcount ratios. This adjusted rate is given by the growth rate in municipality’s mean per capita income, multiplied by 1 minus the municipality’s initial poverty headcount ratio ( _Pit−τ_ ), which penalizes more the sensitivity of food poverty to subsequent growth rates in municipalities starting out relatively poorer. The poverty-adjusted growth elasticity of poverty reduction is then defined as follows: The estimates for the whole sample of municipalities are shown in table 8 (panel a). Notice that they increased in absolute value in all periods relative to the ordinary elasticities in table 5. To illustrate the implications of the poverty-adjusted elasticity, consider, for instance, the value of –1 _._ 983 in 1992–2014. If a municipality’s initial food poverty rate is 10 percent and the municipality experiences a 4 percent annual growth rate in mean per capita income, then that municipality would expect an annual poverty reduction of 7.1 percent. If, instead, initial poverty stands at 70.0 percent, and the annual income growth rate is again 4 percent, then the municipality would expect a poverty reduction of only 2.4 percent annually. Then, as in the previous section, poverty tends to be less responsive to growth, or the elasticity declines in absolute value, the higher the initial poverty rate.

However, the estimates reveal that poverty-adjusted elasticities are consistently higher in absolute value in poorer municipalities than in richer counterparts. For instance, at

> 17The various specifications of this augmented model are shown in tables 37–44 of the ancillary

28
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Missing from this discussion has been information on how COVID-19 has affected the ability and intent of African men to migrate. Such data are rare, since another consequence of the pandemic was to shut down data collection efforts in most countries. We use a large-scale panel survey of Gambian households fielded in 2019 (pre-pandemic) and again in September and October 2020 (during the COVID-19 global pandemic) to examine how intentions to migrate of males aged 18 to 33 have changed due to COVID-19, directly asking about the effect of COVID-19. We take a comparative perspective, examining changes in intentions to migrate to Europe, and changes in intentions to migrate to neighboring Senegal. We provide a simple model to illustrate the main channels through which migration intentions will be affected, noting that the reduction in home income is likely to increase the desire to migrate, while lower income earning opportunities and worsening health conditions abroad will act to reduce this desire, and higher costs and lower wealth may make liquidity constraints more binding. Our first empirical result is that the net effect has been to reduce the intention to migrate to Europe for 31 percent of youth, and to Senegal for 34 percent of youth; with fewer than 4 percent increasing this likelihood.

We then use our survey questions and model to examine the channels through which this reduction in migration intentions occurs, and to examine which youth have had their migration intentions most affected by the pandemic. We find the largest reduction in the intention to migrate for individuals who were uncertain about their migration intentions pre-COVID-19. For these youth, the expected utility of different destinations was not that different from one another, so that the reductions in earnings opportunities abroad and increase in migration costs would be enough to change their migration decision. In contrast, those who had little intention to migrate to begin with are less affected, while a small number of those who definitely wanted to migrate appear to have doubled-down on their decision and become even more likely to move. Our results also highlight the importance of liquidity constraints, as we find youth from poorer households and those who stop receiving remittances are more likely to have reduced their intention to migrate. The overall reduction in migration intentions is slightly larger for intentions to migrate to Senegal than for Europe, although liquidity constraints seem more important in explaining changes in intentions to migrate to Europe. These results suggest that the first-order effect on migration intentions is not something specific to irregular migration to Europe, but more general impacts of the pandemic on the ability and desire to move to any destination.

3
Table 6: Impacts on children’s educational inputs: school attendance <u>(OLS)</u>

|Dependent variable:||One if|attend||
|---|---|---|---|---|
|Timing of visit in 2014:|Feb|April|August|Any|
||(a)|(b)|(c)|(d)|
|**With no baseline cont**|**rols**||||
|All products|0.110***|0.097**|0.043|0.084***|
||(0.040)|(0.036)|(0.031)|(0.025)|
|One product|0.043|0.089**|0.005|0.045*|
||(0.038)|(0.041)|(0.032)|(0.024)|
|Visit in April|-|-|-|-0.083**<br>(0.033)|
|Visit in August|-|-|-|0.002<br>(0.057)|
|All = one (p-val.)|0.133|0.827|0.216|0.108|
|All + one = 0 (p-val.)|0.022|0.008|0.387|0.004|
|R-squared|0.180|0.188|0.208|0.112|
|No. of obs.|882|882|882|2646|
||(e)|(f)|(g)|(h)|
|**With baseline control**|**s**||||
|All products|0.104**|0.093**|0.035|0.077***|
||(0.039)|(0.037)|(0.031)|(0.025)|
|One product|0.043|0.087**|0.002|0.044*|
||(0.037)|(0.042)|(0.032)|(0.024)|
|Visit in April|-|-|-|-0.083**<br>(0.033)|
|Visit in August|-|-|-|0.002<br>(0.057)|
|All = one (p-val.)|0.176|0.887|0.295|0.153|
|All + one = 0 (p-val.)|0.024|0.012|0.496|0.007|
|R-squared|0.241|0.228|0.253|0.155|
|No. of obs.|882|882|882|2646|
|School FE|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|
|Mean _y_ (control)|0.561|0.481|0.589|0.544|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

30
question was administered to couples, which means our data set covers the years 2010-2016.<sup>1</sup> Restricting to non-missing observations results in a sample of 71,274 couples.<sup>2</sup>

# **3.2 Empirical Strategy**

The empirical strategy developed throughout this paper consists of two main parts. In the first part, to understand the relationship between the directionality of intra-household contention over power and common women’s empowerment proxies, we estimate the following OLS specification: Where i is the outcome reflecting women’s participation in decision-making in country _c_ ; i 1 comprises a set of variables reflecting women’s status and empowerment proxies,yy<sup>3</sup> i 2 comprises XX a set of individual and household characteristics,<sup>4</sup> specifies country fixed effects and XX ii i indicates the error term. θθ εε 1 Our sample includes Benin (2011), Burkina Faso (2010), Burundi (2016), Cameroon (2011), the Comoros (2012), Côte d'Ivoire (2011), the Democratic Republic of Congo (2013), Ethiopia (2016), The Gambia (2013), Ghana (2014), Kenya (2014), Lesotho (2014), Liberia (2013), Madagascar (2009), Mali (2012-13), Mozambique (2011), Namibia (2013), Nigeria (2013), Rwanda (20142015), Senegal (2014), Sierra Leone (2013), Uganda (2016), Zambia (2013), Zimbabwe (2015).

> 2 It should be noted that when looking at relationships with outcomes, our sample changes due to data availability and conditionally applicable questions. We re-run our analysis for the most restrictive sample to ensure that our results are not driven by sample selection issues (results available upon request).

> 3 The set of women’s status and empowerment proxies includes the following variables: difference in years of schooling between husband and wife, a dichotomous variable for wife not working, for wife works off farm, for wife says she earns more than her husband, for wife says she owns land, for woman condones a husband beating his wife, for wife is aged 15-19, wife is aged 20-34, difference in age of husband and wife, wife was married before age 20, years of marriage, total number of children that the woman has, total number of children who died and a dichotomous variable for couple in a polygamous marriage.

> 4 The set of individual and household level characteristics includes a dichotomous variable for husband works in agriculture, for rural area, a variable that indicates the time in minutes to the nearest water source (log). A dummy for household has electricity and wealth quantile dummies.

10
and would not qualify for the tari¤ preference under EBA. According to Annex II of the GSP (Generalized System of Preferences) guidebook, which details ROOs of all products, for an apparel product to be considered as having originated from a country, it must start its local manufacturing process from yarn,<sup>14</sup> i.e., the use of imported fabrics would result in the item failing to meet ROOs for the purpose of tari¤ and quota preferences under GSP or EBA for the case of LDCs. It would, thus, be subject to MFN tari¤s of about 12% to 15% on price.

Table 1a: Trade policy environment

||US||EU|
|---|---|---|---|
|ROOs|ROOs (local assembly)|Meeting ROOs (yarn|forward rule) is optional|
||are required|Option 1: no ROOs met|Option 2: ROOs are met|
|Quotas|Yes|No|No|
|Tari¤s|t20% (MFN tari¤)|t12% (MFN tari¤)|0% (Preferences are given)|

Firms making garments from woven material (woven ...rms) mostly assemble cut fabrics into garments. THus, domestic woven cloth was essential to meeting the ROOs. Given its limited supply,<sup>15</sup> such cloth commanded a corresponding price premium. The 20% higher price for domestic cloth translates into a signi...cantly higher cost of production, as cloth is the lion’s share of the input cost. The cost of cloth to FOB (free on board) price is roughly 70-75% for shirts, dresses, and trousers,<sup>16</sup> resulting in about a 15% cost disadvantage from meeting ROOs.<sup>17</sup> Thus, the costs and bene...ts of invoking preferences under the EBA were similar. For this reason, not all woven ...rms chose to invoke these preferences when exporting to the EU. This feature allows us to estimate the ...xed documentation costs of invoking

> 14For the details on the EBA user guide and Annex II on GSP, please see the online Appendix available at the authors’web-sites.

> 15Of 1320 million meters of total demand in 2001, only 190 was supplied locally in wovens according to a study by Development Initiative in 2005.

> 16See Table 33 in Development Initiative (2005).

> 17In contrast, India has the ability to meet its woven cloth needs domestically at competitive prices so that its ...rms can avail themselves of GSP preferences in the EU. As a result, Bangladeshi ...rms ...nd themselves at a disadvantage in woven garments.

10
found elsewhere.<sup>17</sup> In Senegal, this may reflect the negative correlation between income and life expectancy which has been well-documented worldwide (see, for example, Feinstein 1993), or the lower quality of the sanitary environment and access to health care of poor households. Furthermore, we also observe a greater spousal age gap in poorer households, mechanically increasing the probability of widowhood for poorer women. Nevertheless, there is also a surprising negative correlation between rural residence at the time of dissolution and widowhood.

With respect to selection into divorce, the story is somewhat different. This is expected as, unlike with widowhood, divorce results from the choice of at least one spouse. A husband’s positive characteristics are associated with a lower probability of divorce, while a wife’s positive characteristics (education) are positively correlated with divorce. Furthermore, urban areas see more divorces. This suggests that women who have the means (social as well as economic) to exercise their independence are more likely to be divorced, probably often at their own initiative. That said, women appear to be less willing to divorce men with desirable economic situations.

# **5.2 Selection into remarriage**

Table 5 presents the correlates of remarriage following a dissolution, while Table A3 presents the rural urban decomposition of those estimates. Age at dissolution is the strongest correlate of remarriage for both divorcees and widows. Indeed, if the dissolution happens before the age of 25, the probability of remarriage is 1.8 times higher for widows and increases by more than 60% for divorcees, relative to the situation in which the dissolution happens after age 40. Women with more traditional backgrounds are more likely to remarry (in particular after widowhood): this holds for rural dwellers, daughters of polygamous men, and women who were fostered in childhood. There may be different reasons for the latter two correlations, from a larger kin network to a better acceptance of polygamy. Irrespective of the age at dissolution, a polygamous husband awaits three-quarters of women who remarry following widowhood, and half of those who do so after divorce. Conversely, educated women, who are more likely to be divorced, are also more likely not to remarry following divorce (this is driven by urban divorcees). Finally, having a son from the previous union is correlated with lower

> 17 For example, on the basis of longitudinal data on health and retirement for the U.S., it has been shown that, allowing for the fact that poorer men are more likely than other men to die young, widows, and particularly young ones, are more likely to come from poorer households (Hurd and Wise 1989, Sevak et al. 2003).

13
# **Impact of the Russian Invasion on Ukrainian Farmers’ Productivity,**

# **Rural Welfare, and Food Security**

Klaus Deininger<sup>1</sup> Daniel Ayalew Ali<sup>1</sup> Ming Fang<sup>2</sup> JEL Codes: Q10, O13, H56, R14 Keywords: Ukraine, land market, credit markets, conflict/war, agricultural production 1 The World Bank, 1818 H Street NW, Washington, DC 2 Michigan State University The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Funding support from the European Union (ENI/2017/387-093 and ENI/2020/418-654) is gratefully acknowledged. We particularly thank Victoria Zakhozha and the KIIS team for data collection, Nataliia Kussul and Andrii Shelestov for support with data processing, and Denis Bashlyk, Markiyan Dmytrasevych, Ben Hell, Thea Hilhorst, Roman Hrab, Sergyi Kubakh, Vasyl Kvartiuk, Andrii Martin, Roman Neyter, Oleg Nivievskyi, Taras Vysotskyi and Sergyi Zorya for helpful discussions and insightful comments and Daria Manzhura as well as Tania Khorzovskaya for outstanding coordination and administrative support.