rafmacalaba/datause-encoder-data huggingface dataset:
Compared to the previous scenario, the supply of skilled labor to the tradables sectors increases by less and that of unskilled labor remains the same. As a consequence, the output of sector 1 will increase by less than previously, while that of sector 2 will fall by more (or increase by less). Hence, in the current setup the expansion of the health sector in the short-term by recruiting immigrant skilled workers also has favorable implications for the outputs of the two tradables sectors compared to an expansion in the long-term using domestic workers only.

Abandoning the simplifying assumption that the skill-intensity of the health sector is identical to the skilled-unskilled national endowment ratio complicates the analysis, but reference to the standard R results gives us some insight. For example, if the health sector is more skill-intensive than that assumed, then an expansion of that sector will reduce the skilled-unskilled ratio of the workers available to the tradables sectors. This will reduce the size of the skill-intensive sector 1 and increase the size of the other sector relative to that shown in Figure 4.

The diagrammatic analysis is useful in identifying the varied effects of different ways of expanding of the health sector on the effective endowments of labor and the outputs of the other production sectors. However, it is limited in that there are a number of possible cases, the consideration of which would require multiple diagrams. To remedy this, we use the standard proportional change analysis to derive the changes in sectoral outputs in a more general setting.

_The Heckscher-Ohlin-Samuelson Model_ The full employment conditions for the two factors are
Figure **1.** bTurkey: Deficits, Inflation and Elections, 1981 - 1991 .9 <!-- Start of picture text --> Deficit (PSBR. % of GNP)<br>Inflation (CPI)<br>10-<br>10<br>8<br>~~~~~~~~~~~~~~80<br>6<br>60<br>4<br>4-0<br>Refeand Refera dum<br>R O ttr duml Retcrandt Rcmorn du m Local elsc. 20<br>A 0<br>Gen.Elec. By-ec e n.Else<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991<br><!-- End of picture text -->

**Oatet** Sourcese 1. W.B. Country Study,1990 2. C.E.M., Oct.1991
PRE **Workino** Paoer **Series**

|LIQAuthor||_Dat_<br>e|Contact<br>for<br>pagper|
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|WPS416 Improving Data on Poverty;nthe<br>Third World: 1 he World Bank's<br>Living Standards Measurement Study|Paul Glewwe<br>|May 1990|A. Murphy<br>33750|
|WPS417 Modeling the Macroeconomic<br>Requirements of Policy Reform|William Easterly<br>E. C. Hwa<br>Piyabha Kongsamut<br>Jan Zizek|May 1990|R. Luz<br>34303|
|WPS418 Does Devaluation Hurt Private<br>Investment? The Indonesian Case|Ajay Chhibber<br>Nemat Shafik|May 1990|M. Colinet<br>33490|
|WPS419 The Design and Sequencing of<br>Trade and Investment Policy Reform:<br>An Institutional Analysis|Brian Levy<br>|May 1990|B. Levy<br>37488|
|WPS420 Making Bank Irrigation Investments<br>More Sustainable|Gerald T. O'Mara|May 1990|C. Spooner<br>30464|
|WPS421 Taxation of Financial Intermediation:<br>Measurement Principles and<br>Application to Five African Countries|Christophe Chamley<br>Patrick Honohan<br>|May 1990|W. Pitayato-<br>nakarn<br>37666|
|WPS422 Civil Service Reform and the World<br>Bank|Barbara Nunberg<br>John Nellis|May 1990|R. Malcolm<br>37495|
|WPS423 Reiat;ve Price Changes and the<br>Growth of the Public Sector|M. Shahbaz Khan|May 1990|WDR Office<br>31393|
|WPS424 Mexico's External Debt<br>Restructuring in 1989-1990: An<br>Economic Analysis|Sweder van Wijnbergen|||
|WPS425 The Earmarking of Government<br>Revenues in Colombia|William A. McCleary<br>Evamaria Uribe Tobon|||
|WPS426 Growth-Oriented Adjustment<br>Programs: A Statistical Analysis|Riccardo Faini<br>Jaime de Melo<br>Abdel Senhadji-Semiali<br>Julie Stanton|||
|WPS427 Exchange Reform, Parallel Markets<br>and Inflation in Africa: The Case<br>of Ghana|Ajay Chhibber<br>Nemat Shafik|May 1990|M. Colinet<br>33490|
|WPS428 Perestroyka and Its Irmplications<br>for European Socialist Countries|Bela Balassa|May 1990|N. Campbell<br>33769|
<!-- Start of picture text --> Rent - OLD, Metropolitan Rent - OLD, Lower Urban Rent - OLD, Upper Urban<br>ciel<br>eof<br><!-- End of picture text --> <!-- Start of picture text --> Q 24<br>ip -|<br>= Sey I i T i T T T T 1 i 1 1 T T<br>2 4 6 8 10 2 4 6 8 10 2 4 6 8 10<br>Log of self reported rent<br><!-- End of picture text -->
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- **Fairhead, James** , “Paths of Authority: Roads, the State and the Market in Eastern Zaire,” _The European Journal of Development Research_ , 1992, _4_ (2), 17–35.

- **Getmanski, Anne, Guy Grossman, and Austin L. Wright** , “Border walls and smuggling spillovers,” _Quarterly Journal of Political Science_ , 2019, _14_ (3), 329–347.

- **Gonz ́alez, Felipe, Josepa Miquel-Florensa, Mounu Prem, and St ́ephane Straub** , “The Dark Side of Infrastructure: Roads, Repression, and Land in Authoritarian Paraguay,” _SSRN Working Paper_ , 2023.

- **Harari, Mariaflavia and Eliana La Ferrara** , “Conflict, Climate, and Cells: A Disaggregated Analysis,” _The Review of Economics and Statistics_ , October 2018, _100_ (4), 594–608.

- **Hendrix, Cullen S.** , “Head for the Hills? Rough Terrain, State Capacity, and Civil War Onset,” _Civil Wars_ , 2011, _13_ (4), 345–370.

- **Herbst, Jeffrey** , _States and Power in Africa: Comparative Lessons in Authority and Control_ , Vol. 149, Princeton University Press, 2014.

- **Idler, A.** , “The Logic of Illicit Flows in Armed Conflict,” _World Politics_ , 2020, _72_ (3), 335–376.

- **Jimenez-Ayora, Pablo and Mehmet A. Uluba ̧so ̆glu** , “What underlies weak states? The role of terrain ruggedness,” _European Journal of Political Economy_ , 2015, _39_ , 167– 183.

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31
16 Finally, there is a big difference between the cross section and first difference income elasticities of the vehicle-road ratio at the urban level. The vehicle-road ratio is increasing over time (first difference results) nearly in step with income, whereas the cross

**Figure 5. Vehicle-Road ratio and Average Road Speeds in Urban Areas**

<!-- Start of picture text --> 60 -<br>Hou s ton d Actual<br>50 - Melbourne. Brisbane SF - Equation<br>40 Phoenix * Detroit Amsterdam<br>cv Copenhagen * Singapore * Munich<br>" 30 London * * Frankfunt<br>9 ~~~~~Chicago*<br>o Zu i h ~~~~~~~~~~~~Berlin<br>E20 *HK<br>>z Tokyo<br>10<br>0<br>0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0<br>Number of vehicles per km of road<br><!-- End of picture text --> Source: generated from data provided in Newman and Kenworthy (1989).

section income elasticity is much smaller. The increase in the urban vehicle-road ratio, and associated urban congestion, is emerging as the key issue in urban transportation.

Finally, it is interesting to see whether a saturation level for the vehicle-road ratio exists at the urban level, where the road network is often congested. The saturation level for the vehicle-road ratio is defined as the possible maximum vehicle-road ratio among any combinations of income level, population, population density, gasoline prices, etc.. It can be estimated by searching across alternative assumed saturation levels in regressions specified in the form of an S-shaped logistic function. The regression with the highest R- square gives the saturation level under a "business as usual" scenario.<sup>4</sup> These logistic regressions have the following functional form:

> V/R=S/(l +e bO+bl **Y± b**<sup>**2**</sup> **P+b3 D+b**<sup>**4**</sup> **G),**

where V/R is the vehicle-road ratio; S, the saturation level; Y, per capita GNP; P, population; D, population density; G, gasoline prices; and b0, ... b4, parameters.

The saturation level for the vehicle-road ratio at the urban level is estimated to be 4 Searching over a single parameter value to maximize R<sup>2</sup> produces a maximum likelihood estimate of the parameter value.
General Assembly, and debt service (as share of GNI). Controlling for these additional influences considerably reduces any remaining risk of omitted variable bias. As our final test for robustness, we employ Clemens et al.’s (2012) permutations of Rajan and Subramanian (2008) instead of those of Burnside and Dollar (2000).<sup>27</sup> TABLE 3. Tests for Robustness

|||Coefficient of<br>interaction|Observations|
|---|---|---|---|
|(1)|UNSC dummy|‐0.626***|393|
|(2)|UNSC in first‐differences|‐0.384**|393|
|(3)|Control variables lagged|‐1.012**|359|
|(4)|Early impact aid|‐1.470*|354|
|(5)|Including all ICRG institutional<br>measures|‐0.975**|214|
|(6)|Including Imports/GDP|‐1.254***|378|
|(7)|Including Trade/GDP|‐1.261***|378|
|(8)|Including FDI/GDP|‐1.125***|317|
|(9)|Including voting with U.S. in UNGA|‐1.260***|385|
|(10)|Including Debt/GNI|‐1.397***|367|
|(11)|Rajan/Subramanian|‐1.365**|351|

**Notes:** The dependent variable is growth of real GDP per capita and covers the 1974–2009 period. All regressions use averages over four years, include variables in first differences (except for the UNSC variable, which is estimated in first

27. In a previous version of this paper we estimated all main models in this framework and also show regressions using those of Bueno de Mesquita and Smith (2010). See Dreher et al. (2014a) for details.

30
more detailed estimated probabilities offer analysts the possibility of drilling down to the country pairsector level to conduct country-based policy analysis.

The only other papers of which we are aware that seek to quantify the tradability of services are Jensen and Kletzer (2005), Gervais and Jensen (2013), and Borchsenius et al. (2010). Gervais and Jensen (2013) is the most developed contribution, and adopts a methodology that is generally representative of the other two. The authors use detailed micro-data for the USA to build indices of tradability for services sectors based on the observed level of geographical dispersion: an industry must be tradable within the USA if it is highly geographically concentrated, on the assumption that a few locations thereby provide services for the rest of the country. Conversely, if an activity is highly geographically dispersed, it must be relatively non-tradable because the supplier and the consumer presumably need to be in the same location.

In addition to providing a descriptive analysis of the tradability of different services sectors compared with manufacturing, Gervais and Jensen (2013) also use their data on the geographical distribution of demand and supply to back out estimated trade costs from a model in which trade is determined by the interplay between agglomeration forces (scale economies), and dispersion forces (trade costs). Trade within the USA is relatively “free” compared with trade between countries, so the authors interpret their trade cost estimates as “technological constraints that cannot be reduced by trade policies” (Gervais and Jensen, 2013, p. 31). Even on this restricted reading of their finding, domestic trade costs are very high in services compared with manufacturing: 80% ad valorem in wholesale and retail trade, 87% in business services, and 102% in personal services; for manufacturing, the comparable figure is 33%. Their findings therefore align well with recent work on international trade costs in services, which show that ad valorem equivalents are up to twice or three times higher in services than in manufacturing (Miroudot et al., Forthcoming).

Gervais and Jensen (2013) give their paper an international scope by using their model and estimates of tariff equivalents for services drawn from other sources to estimate the welfare and employment effects 4
26

# **Table 1.  Summary Data on Non-Remittance and Remittance-Receiving Households (contd)**

Notes: N = 3,884 households. All values are weighted; standard deviations in parentheses with the exception of columns (4) and (5) that show T-tests. In 2006, US$ 1.00 = 9,000 Ghanaian cedis.

Source: 2005/06 Ghana GLSS 5 Survey (sub-sample).

- Significant at the 0.10 level.

- ** Significant at the 0.05 level.

- *** Significant at the 0.01
# **5. The long–run impact of COVID-19 on incomes, poverty, and inequality**

In this section we carry out microsimulations to provide rough estimates of the potential impact of the COVID-19 pandemic on future incomes and indicators of poverty and inequality through the educational channels discussed in previous sections. The exercises are necessarily based on several restricting assumptions and hence should be taken only as informed back-of-the-envelope calculations, aimed at informing the public debate on this very relevant issue.

## **5.1. Methodology**

We start from an income distribution in baseline year _to_ . We choose 2019, the most recent year with available microdata from a national household survey for most countries. Data for 2021 is not yet available for most countries, and 2020 was a very unusual year to use it as baseline.

For simplicity, and to focus only on the impact of the pandemic through the educational channel, we assume that without the COVID-19 pandemic the educational structure and income distribution _T_ years after _to_ ( _tT_ ) would be identical to those in the baseline year. For simplicity in the explanation, we focus on year 2045, _i_ . _e_ . 25 years after the COVID-19 shock. In section 5.3 we extend the analysis to the dynamics of the impact over time. Again, for simplicity, we initially assume that the pandemic in 2020 affected human capital accumulation of children and youths aged 5 to 20. Consequently, in year 2045 the workers who were affected by the pandemic back in 2020 are those aged 30-45. This will be our “treatment group” _G_ .<sup>13</sup> The key step of the methodology consists in changing the years of education of each person _i_ in country _c_ belonging to group _G_ and simulating her labor income _xic_<sup>_V_</sup> . In particular, we subtract _kic_ years (or fraction of a year) of education

> 13 This approach is similar to one that assesses the counterfactual impact on the current generation of adults of a hypothetical COVID-19 shock occurred 25 years ago.

16
intensity for each of the Rwandan provinces, and we compare schooling outcomes in provinces that experienced more destructive fighting with outcomes in provinces that experienced lower war intensity. We rely on the fact that the genocide’s intensity varied across provinces in Rwanda, with some areas experiencing more intense fighting and significantly more killings.

We interact a given war intensity measures with the difference-in-differences variable (young cohort times 2000 DHS data) to estimate a difference-in-difference-in-differences regression. Identification comes from comparing the schooling achievement in high and low intensity war-impacted provinces for similarly aged individuals in 1992 (prior to the genocide) and 2000 (after the genocide). The change in schooling attainment for the older cohort of children in the high war intensity provinces relative to the lower war intensity provinces acts as a control group for what the change in the younger cohort’s education rates should have looked like in the absence of the conflict. By taking advantage of geographical variation in the intensity of the genocide as well as having information from before and after the conflict, it makes our identification strategy significantly more robust than if we only relied on one source of variation. As a second robustness check, we recognize that the intensity of the genocide might be endogenous and could be correlated to levels or trends in educational achievement of the population, so we estimate a series of specifications in which we instrument for the genocide intensity measures, using the distance to the Ugandan border as an instrument. Provinces close to the Ugandan border were reached faster by the RPF who stopped the killings perpetrated by the Interhamwe militia, and therefore these provinces are more likely to be low war intensity provinces. This instrumental variable strategy assumes that the distance to the Ugandan border has no impact, except through the genocide, on children’s educational achievements.

10
55 THE HOUSING MARKET OF KUMASI Table **3.5:** Summary Descriptive Statistics

|||Tenure<br>(percent)|Median<br>length<br>of stay<br>(years)|Median<br>monthly<br>consumption<br>(CedLs)|Median<br>nmonthly<br>rent<br>(Cedis)|Medlan<br>Rent to<br>consumptlon<br>(percent)|
|---|---|---|---|---|---|---|
||Owners|10|16<br>(8,<br>26)|16300<br>(11100,<br>24100)|||
||Family<br>housers|25|16<br>8,<br>28)|11700<br>(8000,<br>16300)|||
||Renters|62|8<br>(4,<br>16)|12100<br>(9400.<br>16900)|300<br>(200,<br>300)|2.3<br>(1.7,3,4)|
||Sub-renters|3|6<br>(2,<br>18)|11600<br>(7780,<br>20150)|300<br>(300,<br>300)|3.9<br>(2.7,5.7)|
|Notes:|Numbers<br>inparenth<br>family<br>housers<br>|eses<br>under<br>arehouseholds|<br>medians<br> <br>living|are<br>the<br>first<br>an<br>rent<br>free<br>in<br>a|d third<br>quarti<br>house<br>belonging|les<br>of the<br>dlstrlbutionst<br> <br>to<br>family<br>members.|
average treatment effect on the treated (ATET) and is in fact equal to the structural or price effect.

Note that the conventional approach to impact evaluation also relies on _ceteris paribus_ variation of treatment in order to identify its average effect on the treated. Within that logic, the composition effect is equivalent to _selection bias_ that must be driven to zero by the use of randomization, propensity score matching or similar methods. Randomization ensures that the distribution of observed and unobserved characteristics is the same for both the treated and the control group. By balancing observed and unobserved characteristics between the groups prior to the administration of treatment, randomization guarantees that the average difference in outcome between the two groups is due to treatment alone, hence the causal interpretation given to this parameter under those circumstances. In other words, the first term on the right hand side of equation (2), that is the endowment effect or selection bias, is equal to zero under random assignment to treatment and full compliance<sup>7</sup> . It is clear that randomization is designed to implement a ceteris paribus variation in treatment.

In the context of observational studies where the investigator does not have control over the assignment of subjects to treatment, the determination of the causal effect of treatment hinges critically on the understanding of the underlying _treatment assignment or selection mechanism_ which must explain how people end up in alternative treatment states. The assumption of selection on observables (also known as _ignorability_ ) is often invoked to implement _ceteris paribus_ identification of the average treatment effect through conditioning by stratification. Basically, conditioning by stratification entails comparing only those subjects with the same value of covariates x across the two groups (treated and untreated). This type of selection of individuals from the two groups is known as _matching_ .

> 7 Heckman and Smith (1995) explain that the mean outcome of the control group provides an acceptable estimate of the counterfactual mean if randomization does not alter the pool of participants or their behavior, and if no close substitutes for the experimental program are readily available.  These authors further note that randomization does not eliminate selection bias, but rather balances it between the two samples (participants and nonparticipants) so that it cancels out when computing mean impact.  There would be randomization bias if those who participate in an experiment differ from those who would have participated in the absence of randomization.  Furthermore, substitution bias would occur if members of the control group can easily obtain elsewhere close substitutes for the treatment.

11
9 TaAle 1: Value and Share of Merchandise World Trade Under Minilateral Arrangements

||Sh|are of 1988 Trad|e_(%)_|Value|of 1988 Trade|(Smnillion)|
|---|---|---|---|---|---|---|
||All Items|All Non-Oil<br>Goods|All<br>Manufactures|All Items<br>|All Non-oil<br>Goods|All<br>Manufactures|
|TOTAL WORLD EXPORTS|100.00|100.00|100.00|2,829,098|2,562,252|1,980,066|
|OECD RELATED ARRANGEMENTS|38.66|40.62|42.06|1,093,644|1,040,692|832,759|
|Intra/trade of Dev. Europe|30.52|32.30|34.02|863,405|827,715|673,702|
|United States and Canada|5.32|5.49|5.20|150,391|140,562|103,028|
|EEC Regional Arrangements|2.05|1.98|1.96|57,976|50,853|38,803|
|EFTA and Turkey (a)|0.46|0.51|0.54|12,985|12,952|10,625|
|United States and Israel|0.19|0.21|0.24|5,507|5,449|4,730|
|Australia and New Zealand|0.10|0.10|0.08|2,795|2,646|1,612|
|Australia and Papua New Guinea|0.02|0.02|0.01|585|515|259|
|DEVELOPING COUNTRY ARRANGEMENTS|2.74|3.13|3.13|77,619|80,320|61,918|
|Hong Kong-China|1.27|1.40|1.64|36,012|35,995|32,427|
|<br>ASEAN|0.80|1.06|0.95|22,648|27,191|18,783|
|LAIA|0.37|0.40|0.32|10,562|10,149|6,376|
|Gulf Cooperation Council|0.16|0.16|0.13|4,650|4,170|2,560|
|Economic Community West Africa|0.05|0.04|0.03|1,513|953|650|
|Central American Common Market|0.02|0.02|0.02|570|567|422|
|SADCC|0.02|0.01|0.01|537|375|210|
|Mahgreb|0.02|0.02|0.01|517|440|200|
|CARICOM|0.02|0.01|0.01|426|320|170|
|UDEAC|0.01|0.01|0.01|184|160|120|
|TOTAL TRADE FLOWS UNDER|||||||
|MINILATERAL ARRANGEMENTS|41.40|43.75|45.18|1,171,263|1,121,012|894,677|
|POTENTIAL NEW ARRANGEMENTS|4.42|4.11|4.07|124,911|105,338|80,666|
|United States and Mexico|1.54|1.54|1.56|43,460|39,449|30,934|
|Canada and Mexico|0.05|0.05|0.05|<br>1,439|<br>1,388|1,002|
|Eastern Europe and Dev. Europe|2.83|2.52|2.46|80,012|64,501|48,730|
|TRADE FLOWS UNDER|||||||
|POTENTLkL AND EXISTING ARRANGEMENTS|<br>45.82|47.86|49.26|1,296,174|1,226,350|975,343|
|Memo Item: Collapsed Arrangements|4.87|4.16|3.67|137,879|106,644|72,749|
|Eastem Europe Intra-Trade|||||||
|<br>Eastem Europe and Cuba|4.47|3.78|3.45|126,459|96,775|68,357|
||0.40|0.39|0.22|11,420|9,869|4,392|

Turkey and EFTA signed a free trade agreement in October 1991. This arrangement was expected to come into force in .pril 1992. Notes: Data compiled from UNCTAD (1989 and 1990) and Inotai (1991) with some statistics drawn directly form the United Nations COMTRADE data base. Developed Europe and Cuba were reporters in the COMTRADE base and are the sources for Eastem European data on these trade flows. For some of the<sup>developing</sup> country arrangements (SADCC, UJDEA, etc.) manufacturs and non-oil trade values were estimated by applying the share of the- goods in a previous year to the 1988 trade totals. The 1976 Bangkok agreement (Bangladesh, India, Republic of Korea, Sri Lanka and Laos) was not included because at present only 3 percent of the members' intra-trade is exchanged under tariffpreferences. The above tabulation consider the exchange of goods between EEC member states to constitute international trade. If the EEC were assume to be asngle unit, the global share of merchandise trade under existing and potential FrAs would be about 31 percent.