rafmacalaba/datause-encoder-data huggingface dataset:
public consumption is unlikely to fall further going forward, implying that Ethiopia cannot rely on this variable as a growth driver like in the past.

**_<u>Table 2: Assumed Annual Growth Rates of Policy Variables by Scenario (percent)</u>_**

||**Late 2**<br>**Base**|**000s**<br>**value**|**Scen**<br>**Busines**|**ario A:**<br>**s as Usual**|**Early **<br>**Scena**<br>**Private**<br>**Ref**|**2020s**<br>**rio B:**<br>**Sector**<br>**orm**|**Scen**<br>**Accelera**<br>**Inves**|**ario C:**<br>**ted Public**<br>**tment**|
|---|---|---|---|---|---|---|---|---|
||**2000s**<br>**Δ%**|**Level**|**Δ%**|**Level**|**Δ%**|**Level**|**Δ%**|**Level**|
|_Structural:_|||||||||
|Δln(schooling)|6.4|32.8|3.3|45.4|4.5|51.1|2.0|40.0|
|Δln(credit/GDP)|-1.3|20.1|-1.3|17.7|1.3|22.8|-2.5|15.6|
|Δln(trade open)|2.0|0.2|0.0|0.2|0.7|0.21|0.0|0.2|
|Δln(govt C)|-4.0|9.1|1.0|10.1|0.0|9.1|3.0|12.2|
|Δln(tele lines)|7.2|1.1|7.2|2.2|3.6|1.6|14.4|4.2|
|_Stabilization:_|||||||||
|Δln(inflation)|5.2|18.4|0.0|18.4|-10.0|6.4|4.0|27.2|
|Δln(exch rate)|1.6|39.7|1.6|46.5|0.0|39.7|3.2|54.4|

Note: The assumed percent changes do not imply that underlying percentage values change by the depicted percentage points, but by the percentage. To illustrate, a 4 percent increase of an inflation rate of 10 percent leads to an inflation rate of 10.4 percent, not 14 percent.

All scenarios, if realized, would nevertheless represent a remarkable growth performance for Ethiopia by historical and international standards. A per capita growth rate of 4 percent is clearly not as high as the 6.5 percent rate Ethiopia achieved in the Late 2000s. On the other hand, it is still one standard deviation higher than the global historical average since 1950 of 2 percent calculated by Summers and Pritchett (2014). It is also higher than any of the growth rates observed in Ethiopia in the post-WW2 period. The work by Summers and Pritchett is instructive for being more cautious in extrapolating recent historical growth trends into the future, particularly during periods of growth acceleration. For instance, the authors find that episodes of per capita growth of above 6 percent tend to be extremely short-lived with a median duration of nine years. In fact, according to the authors, the single most robust and striking fact about cross-national growth rates is regression to the global mean of 2 percent per capita growth. Thus, in an international context, a slowdown in Ethiopia’s per capita growth rate to 4 percent still represents a better-than-average long term growth performance.

26
rates remained at the 1985 levels -.ntil the year 2080 (i.e. for 95 years) rather that what the population _did_ look like in 1963. This is of particular importance when the model is used for cross-sectional analysis, although it should be borne in mind that even for lifetime analysis the cohort experience does not reflect a 'real' lifetime.

It is also important to point out that the micro unit which LIFEMOD simulates is the individual rather than the family or th household, and therefore it is only the characteristics of the cohort individuals _themselves_ that are modelled. For example we have no comprehensive information on the children of each cohort memb .;t simply their age, parity, and whether or not they are participating in fu: -. education (that is, whether or not they are classified as dependent chi;.. .. LIFEMOD does not contain detailed information about the wider househo'u composition of cohort members, unlike other micro-simulation models such as that developed at Tilburg University (Nelissen, 1987).

Figure 2 <!-- Start of picture text --> Distribution of Earnings by Age, 1985<br>Full-time males: New Earnings Survey and LIFEMOD Data<br>250<br>200_<br>2CO~~~ -<br>-V 150 _<br>o a) tOO _ _0<br>Ur4er 18 18-20 20-24 25-29 30-39 40-49 50-59 60-64<br>E NES 1985 L IFEMCD<br><!-- End of picture text --> 12
Figure 1: 5-to-10 year forward interest rates of government debt securities denominated in local currency <!-- Start of picture text --> Advanced economies excl. the euro area Emerging markets<br>10 10<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>06 07 08 09 10 11 12 13 14 15 16 17 18 06 07 08 09 10 11 12 13 14 15 16 17 18<br>(%) (%)<br><!-- End of picture text --> Figure 2: Two-year-ahead expected public debt to GDP ratio <!-- Start of picture text --> Advanced economies excl. the euro area Emerging markets<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>06 07 08 09 10 11 12 13 14 15 16 17 18 06 07 08 09 10 11 12 13 14 15 16 17 18<br>Figure 3: The share of government debt securities denominated in local currency<br>Advanced economies excl. the euro area Emerging markets<br>100 100<br>90 90<br>80 80<br>70 70<br>Foreign private<br>60 60 Foreign official<br>Foreign private<br>50 Foreign official 50 Domestic private<br>Domestic official<br>40 Domestic private 40<br>Domestic official<br>30 30<br>20 20<br>10 10<br>0 0<br>06 07 08 09 10 11 12 13 14 15 16 17 18 06 07 08 09 10 11 12 13 14 15 16 17 18<br>(%) (%)<br>(%) (%)<br><!-- End of picture text --> Notes: Advanced economies excluding the euro area(Australia, Canada, the Czech Republic, Denmark, Japan, Korea, Norway, Sweden, Switzerland , the United Kingdom and United States) and emerging markets(Bulgaria, Columbia, China, Hungary, India, Indonesia, Malaysia, Mexico, Peru, the Philippines, Poland, Thailand, Turkey and South Africa). Lines and shadow areas show the median and interquartile range within each country group, respectively. Calculations of share of governmet debt based on data from Arslanalp and Tsuda (2014a), Arslanalp and Tsuda (2014b) and BIS Debt Securities Statistics (See 2.4 Data for details).

27
**Table 2: Real Non - Oil GDP Growth, 2003 - 2009 (percent per annum)**

||**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|
|---|---|---|---|---|---|---|---|
|Nigeria|5.8|13.2|8.6|9.4|9.5|9|8.3|
|**Oilproducers**||||||||
|Angola|10.3|9|14.1|27.5|20.1|14.7|8.1|
|Cameroon|4.9|4.9|3.2|2.9|4.1|3.2|3|
|Gabon|0.8|2.3|4.3|4.9|6.2|3|2.3|
|Chad|6|-0.5|11|4.7|3.1|3.2|-0.5|
|Congo,Rep.|5.4|5|5.4|5.9|6.6|5.4|3.9|
|Equatorial Guinea|3.7|15.4|25.8|29.8|47.2|18.1|27.6|
|**Non-oilproducers**||||||||
|Ghana|5.2|5.6|5.9|6.4|6.3|7.3|3.5|
|Kenya|2.9|5.1|5.7|6.1|6.9|2.1|3.8|
|Tanzania|5.7|6.7|7.4|6.7|7.1|7.4|6|
|South Africa|3.1|4.8|5.1|5|4.8|3.7|-1.8|

_Source: WDI/Various IMF reports._ 8. An analysis of the sources of growth shows that, while TFP seems to have improved significantly since 2000, relative to the US it has been declining and has only recently improved (Figures 1 and 2).

5
**Table 3b. Summary Results: Poverty reduction among the elderly with means-tested minimum pensions** Poverty line = $2.5 a day

|**Country**|**Current**<br>**poverty**<br>**headcount**<br>**ratio**|**Absolute**<br>**reduction**<br>**in poverty**<br>**headcount**<br>**ratio**|**Relative**<br>**reduction**<br>**in poverty**<br>**headcount**<br>**ratio**|**Relative**<br>**cost of**<br>**program,**<br>**elderly**<br>**>=60**<br>**years**|**Relative**<br>**cost of**<br>**program,**<br>**elderly**<br>**>=65**<br>**years**|
|---|---|---|---|---|---|
|**Argentina**|0.03|0.02|0.796|0.006|0.004|
|**Bolivia**|0.17|0.09|0.523|0.006|0.003|
|**Brazil**|0.01|0.01|0.526|0|0|
|**Chile**|0|0|0.348|0|0|
|**Colombia**|0.22|0.11|0.504|0.015|0.011|
|**Costa Rica**|0.07|0.05|0.72|0.004|0.003|
|**Domin Rep**|0.02|0.01|0.514|0.005|0.004|
|**Ecuador**|0.13|0.06|0.423|0.003|0.003|
|**El Salvador**|0.1|0.07|0.658|0.017|0.013|
|**Guatemala**|0.08|0.03|0.365|0.002|0.002|
|**Honduras**|0.21|0.11|0.5|0.011|0.008|
|**Mexico**|0.15|0.08|0.536|0.003|0.003|
|**Nicaragua**|0.12|0.02|0.16|0.003|0.002|
|**Panama**|0.06|0.05|0.722|0.005|0.003|
|**Paraguay**|0.1|0.07|0.661|0.01|0.008|
|**Peru**|0.12|0.08|0.648|0.011|0.008|
|**Uruguay**|0|0|0.679|0.003|0.002|
|**Venezuela**|0.1|0.05|0.51|0.011|0.008|

Source: Authors’ calculations using data from the Socio-Economic Database for Latin America and the Caribbean (CEDLAS and World Bank) OECD adjusted HH sizes are used in calculating the poverty headcount OECD adjusted HH sizes are used in calculating the poverty headcount ratios.

26
Krasell, P. and A. Smith (1998). Income and Wealth Heterogeneity in the Macroeconomy, Journal of Political Economy, vol. 106(5), pp. 867896.

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32
People’s Unequal Exposure to Air Pollution: Evidence for the World’s Coal-Fired Power Plants Xinming Du, Jun Rentschler, Jason Russ<sup>∗</sup>

**Keywords:** Air pollution, coal power, environmental justice **JEL codes:** Q50, Q56, O44, D63

**Topics:** Air pollution, Power sector economics, Environmental Policies and Institutions

**Acknowledgements:** The authors thank Esteban Balseca, Richard Damania, Kichan Kim, and Christoph Klaiber for valuable feedback on an earlier draft of this study. This study is a technical background paper to the World Bank flagship report "Detox Development: Repurposing environmentally harmful subsidies" (2023).

> ∗Du: Columbia University, xd2197@columbia.edu; Rentschler: The World Bank, jrentschler@worldbank.org; Russ: The World Bank, jruss@worldbank.org
due to depreciation-induced improvement in trade balance. A simultaneous equation model would have captured this indirect<sup>effect eas1ly.</sup> In its absence, we try to capture this<sup>in an ad hoc manner. Real</sup> depreciation affects both export<sup>supply and import demand, the magnitude of</sup> which depend on their price-sensitivity.<sup>This influences trade balance and</sup> thus foreign exchange reserves.<sup>3</sup> We use the estimated export supply and import demand equations shown in Table<sup>10 in conjunction with the estimated</sup> private investment function to simulate<sup>the effects of real depreciation.</sup> Figure 5 compares the behavior of private<sup>investment under one-shot and</sup> gradual real depreciation. The no depreciation<sup>(baseline) scenario captures</sup> essentially the effect of lagged reserves.<sup>The one-shot 15% real depreciation</sup> in year T and the gradual<sup>24</sup> 15% real depreciation<sup>equally phased over three</sup> years (T, T+1 and T+2) are shown (see<sup>Figure 5).</sup> 3Host of the positive effects of real depreciation<sup>on reserves comes out</sup> of decreases in total non-fuel imports. This<sup>is because price elasticity of</sup> exports is much lower than imports. Given<sup>Kenya's past policy regime and the</sup> resulting predominance of relatively inelastic<sup>exports (e.g., coffee, tea,</sup> sisal) in Kenyan exports, this is not surprising.<sup>The neeffect of</sup> depreciation on imports is adjusted for the<sup>increase in imports that results</sup> from higher export earnings and reserves.

24Since real depreciation is a function of nominal depreciation and demestic fiscal monetary policy stance, gradual<sup>real depreciation could also</sup> be viewed as slow "pass through" of<sup>a large nominal devaluation.</sup> 33
the incentives for competition—there is evidence that the relationship between competition and innovation is non-linear (Aghion, Bloom, Blundell, Griffith and Howitt, 2005), and most economists remain positive about the role of competition for improving firm performance based on empirical evidence (Li, 1997; Xu, 2000; Li and Xu, 2004). There is also evidence that firms facing more fierce competition tend to experience greater financing obstacles and corruption (Beck, Demirgüç-Kunt, Maksimovic, 2005). We thus include a dummy variable ( _Compete_ ) that equals 1 if the firm answered ‘Yes’ to the question: “Does this establishment compete against unregistered or informal firms?”<sup>12</sup> About 55 percent of our sample firms believe that they compete against informal firms (see Panel A of Table 1).

We now turn to measures of country-level financial and institutional development, which play important roles in how firm-level financial information is disseminated and used by various economic agents. As a control for country-level financial/credit market development, we use the ratio of domestic banking credit to the private sector over GDP (“ _Priv_ ”). There are very wide variations in this variable in our sample countries, ranging from a low of 0.9 to a high of 121.5, with a mean of 33.9 (see Panel A of Table 1).

To control for country level corruption, we use corruption control ( _CorruptionControl_ ) from the World Bank’s _World Governance Indicators_ (WGI) database. This measure ranges from -2.5 (weak control) to +2.5 (strong control). In our robustness checks, we use alternative measures of corruption by controlling for a country’s government effectiveness ( _GovernmentEffectiveness_ ), which measures the quality of a country’s public services, civil services, and policy formulation and implementation. _GovernmentEffectiveness_ also ranges from -2.5 (weak) to +2.5 (strong). Both _CorruptionControl_ and _GovernmentEffectiveness_ vary widely across countries in our sample (see Panel A of Table 1).

> 12 Arguably this is not the best measure of competition since it does not capture competition from the formal sector. However, informal sector accounts for a large share of the economy in developing countries (La Porta and Shleifer, 2014); moreover, the WBES does not contain better measures.

10
inflation to be in order, comparability is a problem. We shall later return to the question of the applicability of the U.S. model in the Turkish context.

The starting point must be the only national survey, published so conducted in 1985. Like the urban places surveys it was conducted in October-November which is a slack season in agriculture. Unemployed are divided into "active" and "inactive" unemployed, the latter apparently so-called discouraged workers (ibid.p.ix)'I have included the "inactive" unemployed and operate also with a wider concept of unemployed, including in addition "unemployed not seeking employment" and "seasonal workers not seeking a job". Information about such unemployed has been collected by all the Turkish LFSs. It is not clear howkunemployed not seeking employmenO is defined. Indeed, strictly speaking this is nothing but the definition of labor not in the labor force. We may also here be encountering something corresponding to "discouraged workers". It may make sense to include both this category and seasonal workers not employed at the time of the survey even though they do not seek work just now.The measured national become unemployment rates for 1985 / then 12.3 and 12.7 percent, (see Table 1.3) respectively/. Comparing with the measured unemployment rates for urban places (see below) for 1985 we find, computing the difference between national and urban LFSs, implicit unemployment rates for rural areas of 10.3 and 10.7 percent, respectively, a surprisingly high rural unemployment rate, even considering the

-4-
Table 3: Dependent Variables: Estimated Trade Elasticities

||(1)|
|---|---|
|UK|34.42***|
||(0.57)|
|Constant|-48.21***|
||(0.01)|
|HS 6-digit product FE|Yes|
|Observation|417,580|

Table 4: Potential Short-Run Impact of Brexit

||Overall|Tarif-|Heterogeneity-|
|---|---|---|---|
|||Induced|Induced|
|EU’s Bilateral-TRI wrt the UK (%)|4.41|||
|Welfare change of the EU (%)|-0.34|-0.37|0.03|
|Welfare change of the EU ($B)|-0.55|-0.60|0.05|
|EU’s Bilateral-OTRI wrt the UK (%)|2.29|||
|Trade impact of the UK (%)|-6.38|-7.81|1.43|
|Trade impact of the UK ($B)|-10.2|-12.5|2.28|

Table 5: Robustness Check Using Homogeneous Elasticities Across All Partner Countries

||Overall|Tarif-|Heterogeneity-|
|---|---|---|---|
|||Induced|Induced|
|EU’s Bilateral-TRI wrt the UK (%)|5.20|||
|Welfare change of the EU (%)|-5.52|-4.36|-1.17|
|Welfare change of the EU ($B)|-8.83|-6.97|-1.86|
|EU’s Bilateral-OTRI wrt the UK (%)|3.26|||
|Trade impact of the UK (%)|-139.38|-120.76|-18.62|
|Trade impact of the UK ($B)|-223|-193|-29.8|

<u>Table 6: Robustness Checks with Diferent Elasticities</u>

||Ho|mogene|ous|Elasticities fr|om|Reshufed|
|---|---|---|---|---|---|---|
||E|lasticit|y|Caliendo-Parro|Ossa|Elasticities|
||-3.85|-4|-8|(2015)|(2015)|w/ no Correlation|
||(1)|(2)|(3)|(4)|(5)|(6)|
|EU’s Bilateral-TRI wrt the UK (%)|4.60|4.60|4.60|2.91|4.01|4.48|
|Welfare change of the EU (%)|-0.37|-0.39|-0.77|-0.15|-0.20|-1.22|
|Welfare change of the EU ($B)|-0.60|-0.62|-1.23|-0.26|-0.31|-1.95|
|EU’s Bilateral-OTRI wrt the UK (%)|2.81|2.81|2.81|2.35|2.23|2.77|
|Trade impact of the UK (%)|-7.81|-8.20|-19.1|-8.13|-5.68|-33.0|
|Trade impact of the UK ($B)|-12.5|-13.1|-30.6|-14.48|-8.69|-52.7|

29
# **How Has COVID-19 Affected the Intention to Migrate via the Backway to Europe**

# **and to a Neighboring African Country?**

# **Survey Evidence and a Salience Experiment in The Gambia**<sup>**#**</sup>

Tijan L. Bah, _University of Navarra, ICS, Navarra Center for International Development_ Catia Batista, _Nova School of Business and Economics and Novafrica_ Flore Gubert, _IRD, UMR LEDa-DIAL, PSL, U. Paris-Dauphine, CNRS, and Paris School of Economics_ David McKenzie, _Development Research Group, the World Bank_

**_Keywords:_** Migration intentions; COVID-19 Pandemic; Priming and Salience Experiments; Backway migration; The Gambia.

**_JEL Classification codes: F22, O15, J61, C93_**

> # This work was supported by the European Union grant number AMIF-2017-AG-INFO 824432. Ethical approval was granted by the Nova School of Business and Economics on March 1, 2019. We thank Moritz Meyer for helpful comments, and Vitor Cavalcante and Mam Jarra Njie for excellent research assistance.
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are computed using White's robust procedure.

Regression 1.1 show that there is a significant negative correlation between inflation and growth, after controlling for the traditional variables. Since the dependent variable is the log of inflation, the parameter implies that reducing inflation by 10 percent (not percentage points) would increase growth by 0.06 percent. That is, reducing inflation to half of its value (the average inflation rate is 15 percent) would increase growth by 0.4 [-0.057xlog(O.5)] percentage points.<sup>2 3</sup> If the regression were run with linear inflation the effect of inflation would still be significant, but weaker, since a reduction of inflation by 10 percent would increase growth by 0.2 percentage points. Regression 1.2 separates inflation in high and low inflation, using 20 percent as a cutoff. The results show a similar coefficient and they indicate that is high inflation what is harmful for growth.

In contrast to the regressions that separate the sample between high- and lowinflation countries, when the equation is constrained for a sample of industrialized countries (regression 1.5) the negative effect of inflation and growth remains strong, and the coefficient declines only slightly. This apparent contradiction may be explained by the interactions of inflation with the other explanatory variables. But mainly, this is due to the fact that low-inflation combines low-inflation and low-growth African countries with moderate-growth and low-inflation industrialized countries. Finally, the effect of inflation on growth in developing countries appears to be stronger (regression 1.6).

Regressions 1.3 and 1.4 reproduce the first ones with the addition of the rate of investment as dependent variable. The coefficient on investment is significant, but the inclusion of this variable does not change significantly the coefficients on inflation. According to the results, at least three quarters of the effects of inflation on growth is through the efficiency channel.

In table 2 I check the robustness of the results. First, I do not use White's correction, despite the evidence of heteroscedasticity, since I found that the t-statistics tend in general to experience a slight increase, and thus, I use the approach most negative for the robustness of inflation. Second, I add the index of number of revolutions and coups per year from Barro (1991), since it appears also to reduce the 2 3 1n De **Gregorio** (1993) the coefficient is -0.008 and in Sarel (1995) -0.025 when the break is considered and -0.008 **otherwise.** 25
<!-- Start of picture text --> 8<br>na<br>2<br>ey<br>_-fo}goWO 50 54 ia 53<br>s sv<br>30 30<br>2 24 25<br>15 17<br>9<br>1 1 2 1<br>oO<br><=5km 5-10km 10-15km >15km<br>(HG Pre-school/Primary [0 Secondary<br>(University (2) Other<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>x<br>2<br>S<br>_-Bo° wo 54 48<br>s& 30<br>29<br>g 22<br>12<br>1 1<br>oO<br>Precarious Non-precarious<br>(MG Pre-school/Primary (EN Secondary<br>(University (2) Other<br><!-- End of picture text -->