rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 4: 10-year monthly bond yields for A- and B-rated issuers**

We present the end-of-month bond yield data. Although all bonds studied are investment grade, we can discern a clear co-movement in the yields of A-rated issuers, which is much less pronounced in B-rated issuers.

<!-- Start of picture text --> A-rated issuers (AAA to A-)<br>15.0<br>12.5<br>10.0<br>7.5<br>5.0<br>2.5<br>0.0<br>B-rated issuers (BBB+ to BB-)<br>2010 2012 2014 2016 2018 2020 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> explain the original sin. Instead, only the total size of the economy matters. Further, Claessens et al. (2007) find that the depth of the domestic financial system, exchange rate regimes and the composition of the investor base matter for how much a country decides to issue in foreign currencies. Regarding the sovereign credit risk, Amstad et al. (2020) do not find evidence for differences in credit ratings due to higher inflation risks. They also find that the gap in credit ratings between currencies has narrowed within the past two decades due to favorable global reserve conditions.

We use Bloomberg’s end-of-month generic government bond indices for the 10-year and 2-year tenors (Figure 4, Appendix). We treat the former as the long-term yields in this study and proxy the slope of the yield curve through the difference between the 10- and 2-year rates. Missing data points are imputed through cubic spline interpolation (Waggoner, 1997). See Figure 10, Appendix, for a visual example of this procedure.

# **2.3**

To control for effects due to short-term growth variables, we include monthly GDP growth and consumer price index (CPI) growth, to capture the most important macroeconomic factors Ludvigson et al. (2009) identified as determinants of bond returns. We use quarterly GDP data and monthly CPI data from the IMF International Financial Statistics database. To obtain monthly GDP figures, we first calculate quarterly growth rates, Δ _퐺퐷푃푡_<sup>_푄_/</sup><sup>_퐺퐷푃_</sup> _푡_<sup>_푄_</sup> −1<sup>and then use the bench-</sup> marking method of Denton (1971), as advocated by the IMF (Di Fonzo et al., 2012; Marini, 2016). See Figure 12, Appendix for an example of this benchmarking method. For inflation figures, we calculate the monthly percentage change as Δ _퐶푃퐼푡_<sup>_푀_/</sup><sup>_퐶푃퐼_</sup> _푡_<sup>_푀_</sup> −1<sup>.We restrict our attention to these two</sup> variables due to limited data availability of other country-specific indicators.

Further, we include debt-to-GDP ratios to account for a country’s fiscal deficit and reserves-toGDP ratios to account for its liquidity in foreign reserves. Both indicators are obtained from the 9
25 Table 8. Permanent Movement, Australia: Financial Years 1975/76 to 1986/87

||||Depar|tures||
|---|---|---|---|---|---|
|Financial||Non-Aust|ralian Born|||
|Year|Arrivals|**No.**|Percent of<br>Departures|Total<br>|<br>Departures<br>as Percent<br>of Arrivals|
|1975/76|52,750|18,280|<br>66.6|27,430|<br>52.0|
|1976/77|70,920|16,450|<br>66.9|24,590|<br>34.7|
|1977/78|73,170|15,680|<br>67.9|23,100|<br>31.6|
|1978/79|67,190|17,640|<br>69.4|25,430|<br>37.8|
|1979/80|80,750|15,030|<br>68.3|22,020|<br>27.3|
|1980/81|110,690|13,550|<br>69.5|19,500|<br>17.6|
|1981/82|118,030|15,080|<br>72.2|20,890|<br>17.7|
|1982/83|93,010|18,850|<br>75.9|24,830|<br>26.7|
|1983/84|68,810|17,810|<br>73.3|24,300|<br>35.3|
|1984/85|77,510|14,330|<br>70.3|20,380|<br>26.3|
|1985/86|92,590|12,500|<br>69.1|18,100|<br>19.5|
|1986/87|113,540|13,830|<br>69.4|19,930|<br>17.6|

# N.B.: Figures have been rounded to the nearest 10.

Source: Hugo, 1988:Table 10.
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# ⮚ **_Modeling approach_**

The nutritional status has been modeled from DHS data using a multinomial logit regression for each type of individual (children and adults). In order to capture the heterogeneity that may be present between zones - standard of living, local agricultural cultures, accessibility of markets, etc. - a model for each of the six zones of Nigeria was compiled. The following variables were identified as factors influencing the nutritional status of individuals:

- The asset index, which is supposed to represent the household's standard of living and includes characteristics of the dwelling, the movable and immovable assets owned by the household

- Socio-demographic characteristics of the household’s head such as age, gender, education level, marital status

- Socio-demographic characteristics of the individual when applicable

- Location (urban/rural) and household size

# ⮚ **_Inference approach_**

Nutritional status has been attributed in NLSS data using, on the one hand inferred probabilities from the model built in DHS data and on the other hand, estimated thresholds such that NLSS data could reproduce DHS individuals’ nutritional status per zone.

Especially, thresholds have been chosen such that shares of adult women categories (undernourished, normal, obese) and shares of children (normal, moderately stunted, extremely stunted) are similar to shares in DHS data per zone. However, due to lack of information in DHS data on other demographic categories of population (children between 6 and 14, adult men between 15 and 49 and elders over 50), some assumptions have been made on shares of undernourished/obese among those people, in a conservative approach, on the basis of available third source information:

- Half as many obese men as obese women in the 15-49 age group, in each of the zones of Nigeria, a conservative assumption in light of Chukwuonye et al (2013).

- No undernourished men in the 15-49 age group (conservative approach)

- All individuals in the 6-14 and over-50 age groups are in a normal nutritional condition (conservative approach)

# ⮚ **_Choice of classification thresholds_**

The process of classifying individuals into one or the other of the categories, once a binary logit probability model has been estimated, generally consists of assigning an individual to a group if its probability of belonging is greater than a certain level, and further, examining the distribution table between observed values and the predicted values. The choice of the classification threshold is therefore decisive since the quality of the predictions obtained depends on it.

Several methods are generally accepted for the choice of the binary categorization threshold, some of them relying on the choice of a 50% threshold assuming equi-probability between the events, or the 44
<!-- Start of picture text --> 500 1000 2000 5000 10,000 20,000 50,000 100,000<br>GDP per capita (PPP, constant 2011)<br>1.9$ poverty line<br>3.2$ poverty line<br>5.5$ poverty line<br>21.7$ poverty line<br>100,000<br>10,000<br>1000<br>100<br>10<br>1<br>0.1<br><!-- End of picture text --> Figure 3: Break-even _α_ ˆ in all countries as of early June 2020 (baseline, distributionneutral contraction) compute a second estimate of poverty-years that is based on a 3.6% increase in Gini coecients of all countries in 2020.<sup>21</sup> We also consider the poverty impact in case the pandemic requires more stringent and sustained lockdown measures. To this end, we utilize the fact that the GEP also provides a downside growth scenario, which precisely assumes a worse and longer recovery phase (see Figure A.8).<sup>22</sup> Analogues of Figure 3 for each of these alternative poverty scenarios are shown in Figure A.10 in the Appendix.

Overall, Figures 3 and A.10 conrm that, if the epidemic suddenly ended at the beginning of June 2020, its poverty costs would in most countries be very large relative to its mortality costs, and especially so in developing countries. Importantly, however, this nding does not mean that lockdowns were misguided, excessively long, or too strongly enforced. Our ndings so far do not allow any conclusions on the optimality of actual policy responses. They merely allow us to quantify dierent sources of the welfare impact of the crisis (the epidemic taken together with actual policy responses), as it unfolded up to early June.

There are two reasons why these results cannot be used to assess the policy response.

> 21One challenge with modeling the impact of changes in Gini coecients is that there are innitely many possible distributional changes resulting in the same change in the Gini. Using the framework of Lakner et al. (2020), we assume that inequality increases in a manner consistent with a linearly increasing growth incidence curve, approximating what was found in Argentina.

> 22For most high-income countries, for which we are using WEO forecasts rather than GEP, there is only one GDP scenario, so the baseline and downside scenarios coincide.

14
Figure H.3. Labor Outcomes – Fuzzy RD Plots with Local-linear Fitted Polynomial <!-- Start of picture text --> Labor Outcomes (Index) Formal Job<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 1 Sample average within bin Polynomial fit of order 1<br>Hours Worked (log) Job Satisfaction<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 1 Sample average within bin Polynomial fit of order 1<br>Reservation Wage (log) Self-Employed<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 1 Sample average within bin Polynomial fit of order 1<br>1.5<br>.15<br>1<br>.1<br>.5 .05<br>Formal Job<br>Labor Outcomes (Index) 0 0<br>-.5 -.05<br>4.2 .55<br>4.1 .5<br>4 .45<br>.4<br>3.9<br>.35<br>3.8 Job Satisfaction<br>Hours Worked (log) .3<br>3.7 .25<br>3.6 .2<br>.64 .5<br>.62<br>.45<br>.6<br>.58 .4<br>.56 .35<br>Self-Employed<br>.54<br>Reservation Wage (log) .3<br>.52<br>.5 .25<br><!-- End of picture text --> _Notes:_ Each graph depicts the bias-corrected estimator 200 days around June 8, 2018, when the RAMV census closed, and the mean squared error (MSERD) optimal bandwidths. Confidence intervals are at the 95% significance level.

70
migration costs (broadly defined) and migration deterrent has a long history in the migration literature (Ravenstein, 1885; Stewart, 1941; Schwarz, 1973; Lucas, 2001; Lucas 2015).<sup>3</sup> Migration networks (such as the presence of a family member at destination) could help overcome migration costs, even over large distances (Munshi, 2003; Bao et al. 2007; Stuart and Taylor, 2019), though the presence of a migration network is often itself inversely related with distance.

The results confirm that destinations that are closer and have higher local living standards are more likely to be chosen, but distance is, by quite some margin, the most important determinant. Expected wages would need to go up by 5.7 standard deviations to offset a 1 standard deviation increase in distance. Education increases the attractiveness of higher living standards at destination. Nonetheless, simulations show that towns still trump cities in our sample over most of the education gradient. Only after completing upper secondary education do cities become more attractive. The poor, on the other hand, are even more deterred by the effects of distance, while being less inclined to respond to the benefits from a higher (expected) living standard at destination. With the larger share of rural populations being poorer, less educated and living closer to towns than to large urban centers, it does not surprise that a smaller than expected share of rural migrants eventually ends up in the larger, more distant cities.

The paper proceeds as follows. We first present the data and characterize the study area of Kagera and its migrants in more detail (section 2). Section 3 motivates the regression specification and identification strategy analyzing the effects of distance and destination attractiveness on destination choice, as well as how they change given key migrant characteristics. Section 4 presents the empirical results and interprets them with respect to the observed migration patterns of Kagera’s population (more to towns than to cities). Section 5 concludes.

> 3Ravenstein’s first law of migration, formulated in 1885 and derived from observing place of birth in the British 1871 and 1881 censuses, states that most migrants move only a short distance (Lucas, 2001). The importance of distance for migration has further been generalized into a gravity model of migration, in which the number of people migrating is inversely proportional to the square of the distance between origin and destination and proportional to the product of their populations (Stewart, 1941).

3
subsequent labor force surveys. Therefore, we cannot extend the validity of the results to more recent years, in which the impact of drought events became more frequent and intense. In this respect, our estimates should be interpreted as an early evaluation of the effects that severe droughts may have in the mid-run. Second, the individual data employed in this study do not allow for tracking individuals over time. This hinders the possibility to directly observe individual out-migration as a response to shock; nevertheless, our outcome variables are normalized to the local labor force and therefore represent the net of migration effects. Despite these limitations, our results deliver important implications for policy makers as, to the best or our knowledge, this is the first quasi-experimental study that explores the labor market effects of severe drought shocks.

In Morocco, where structural transformation of the economy is slow, and low growth of industry prevents the labor force from being efficiently reallocated, recurrent droughts may accelerate the detrimental interaction between a changing climate and the local socioeconomic context. Our study offers new evidence to help policy makers properly tackle and assess the expected climate impacts by planning and developing safety-net measures and reforms. The latter should be targeted towards the most vulnerable population groups, i.e., women and informal workers, in order to create the conditions for a more even distribution of the impacts and costs of climate change.

21
-22-

education proxy, respectively). The OLS estimate (-0.0301) is next, showing that in this case the country-specific bias is stronger than the errors-in-variables bias. The highest (and only positive) estimate for -y is obtained using the fl-matrix procedure assuming no countryspecific effects (0.0078), estimator which isolates the upward bias due to country-specific factors.

Our consistent estimates for -y imply the following values for ,B, the speed of convergence: .0626 (not using the education variable) and .0494 (using it). These values are about two and a half and three times as high as those obtained in previous empirical papers (see in particular Barro and Sta-i-Martin (1992), and Mankiw, Romer, and Weil (1992)). A figure commonly provided in studies of convergence is the "half life," which is the time it tkes for an economy to move halfway to its own steady state. From equation (1), we find that the half life, T, can be calculated from an estimate of ,B as follows

|_T =_log2|
|---|

Therefore, the fl-matrix method controlling for country-specific effects and using the education proxy as a regressor predicts a half life of about 14 years, while previous studies, suffering from errors-in-variables and specific effects biases, predict one of about 34.7 years. This could be interpreted as "good news' for poor countries. However, such interpretation would be inappropriate since the convergence occurs with respect to the country's own steady state level. As we will say later on, a higher rate of convergence is related to a low share of physical capital in the production function, which implies that decreasing returns set
access to partner countries and for the impact on the terms-of-trade,<sup>9</sup> small open economy models cannot assess them endogenously. Our framework allows us to explicitly evaluate the importance to Kenya, Tanzania and the EACU members of improved market access or reduced trade costs, as well as losses EACU members may suffer as partner countries may raise export prices to each other.

There are 18 sectors in the model. The mapping from the 57 sectors in the GTAP 8.1 data set to sectors of our model is shown in appendix A. There are three categories of firms: (1) four perfectly competitive goods and services sectors: (2) seven imperfectly competitive goods sectors; and (3) seven services sectors in which there is foreign direct investment. The cost, production and pricing structures in the three categories differ widely.

Primary factors are skilled labor, unskilled labor, capital (including land) and natural resources. Regarding capital, there is mobile capital and sector-specific capital in imperfectly competitive goods sectors and services sectors with FDI; and primary inputs imported by multinational service providers, reflecting specialized management expertise or technology of the firm. There is some sector specific capital for each imperfectly competitive firm (and for firms in services sectors with FDI) for each region of the model. In the sectors where there is sector specific capital, there are decreasing returns to scale in the use of the mobile factors and supply curves in these sectors slope up. We calibrate the elasticity of substitution between sector specific capital and other inputs in each sector so that the elasticity of supply of the firms is consistent with econometric evidence that indicates that the supply response depends on the level of development and the technological complexity of the product. We also conduct sensitivity analysis with respect to the sector elasticities of supply.

# **3.1 Perfectly Competitive Goods and Services Sectors**

Regardless of sector, all firms minimize the cost of production. In the competitive goods and services sectors, goods or services are produced under constant returns to scale and where price equals marginal costs with zero profits. This includes agriculture, utilities, trade and “other services.”<sup>10</sup> In these sectors, products are differentiated by country of origin, i.e., we employ the Armington assumption. All goods producing firms (including imperfectly competitive firms) can sell on the domestic market or export. Firms optimize their output decision between exports and

> 9  See Wonnacott and Wonnacott (1981) and Harrison, Rutherford and Tarr (2002).

> 10 Economies of scale are typically estimated to be small in agriculture, trade and other services.

7
**Table 7: Probit on wrong answers in the ES. Marginal effects**

<u>Dependent variable: Wrong answer provided in the short questionnaires</u>

|<br>VARIABLES|<br>(1)|<br>(2)|(3)|(4)|
|---|---|---|---|---|
|Firm is potential innovator Y:1 N:0|0.294***|0.317***|0.280*|0.230|
||(0.084)|(0.089)|(0.152)|(0.160)|
|Manager's years of experience|0.003||‐0.004||
||(0.005)||(0.008)||
|Respondent experience with firm||‐0.002||0.006|
|||(0.006)||(0.012)|
|Respondent is woman Y:1 N:0|‐0.175|‐0.226*|‐0.333*|‐0.391*|
||(0.117)|(0.128)|(0.185)|(0.203)|
|Use of show cards Y:1 N:0|||0.109|0.045|
||||(0.136)|(0.148)|
|Nr of interviews per supervisor|‐0.006|‐0.003|0.005|0.008|
||(0.004)|(0.005)|(0.014)|(0.015)|
|Enumerator's perception of truthful interv Y:1 N:0|‐0.086|‐0.062|‐0.125|‐0.088|
||(0.074)|(0.083)|(0.144)|(0.158)|
|Length ES interview (minutes)|‐0.000|‐0.000|0.000|‐0.000|
||(0.001)|(0.001)|(0.002)|(0.002)|
|Average number of meetings with tax officials|0.004|0.006|0.003|0.016|
||(0.009)|(0.009)|(0.033)|(0.035)|
|Firm expected to pay informal payment Y:1 N:0|‐0.102|‐0.042|‐0.218|‐0.248|
||(0.082)|(0.089)|(0.171)|(0.184)|
|Employment growth (%)|0.003|0.004|0.000|0.001|
||(0.002)|(0.003)|(0.004)|(0.004)|
|Size (log)|‐0.119***|‐0.151***|‐0.235***|‐0.234***|
||(0.032)|(0.034)|(0.074)|(0.080)|
|Age (log)|0.034|0.087|0.007|‐0.061|
||(0.063)|(0.078)|(0.108)|(0.137)|
|Foreign ownership (25+%)|‐0.049|‐0.059|0.276|0.261|
||(0.156)|(0.167)|(0.245)|(0.258)|
|Export (directly or indirectly)|0.122|0.162|0.204|0.226|
||(0.098)|(0.107)|(0.175)|(0.184)|
|Constant|‐1.050***|‐1.114***|‐0.775**|‐0.857**|
||(0.249)|(0.264)|(0.385)|(0.397)|
|Observations|7,876|7,005|1,562|1,405|

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 Country & ISIC 2‐digit FE not shown 32