rafmacalaba/datause-encoder-data huggingface dataset:
the inputs. Confirmation percentage is defined as the proportion of the reviewed studies showing positive and significant relationship between the specific input and test scores. At the primary level, it is clear that class instructional time, school library, textbooks, and class frequency of homework have the highest confirmation rates at 73.1 - 88.9 percent. On the other hand, teacher's salary level and school teacher/pupil ratio have the lowest confirmation rates at 36.4 and 34.6 percent. More recent studies also tend to stress the effectiveness of improving of physical facilities. Relating the cost of these inputs to their marginal effects on test scores, available estimates further show that in fact textbooks and other educational materials along with improvement of physical facilities have much higher cost-effectiveness than increased teacher salary, years of experience and teacher/pupil ratio.

|**Table 1. Confirmation Percentages of Various Educational Inputs Sorted by Direct**|
|---|
|**Importance to Teacher Utility**|

||Number of<br>studies|Positive and<br>sianificant relation|Confirmation<br>Percentaae|
|---|---|---|---|
|**Primary**Schools:||||
|Teacher's salary level|11|4|36.4|
|School teacher pupil ratio|26|9|34.6|
|Teacher's years of schooling|18|9|50.0|
|Teachees experence|23|13|56.5|
|Class instructional time|17|15|88.2|
|Class frequency of homework|<br>11|9|81.8|
|School library|18|16|88.9|
|School textbooks|26|19|73.1|
|**Secondary Schools:**||||
|Teachers salary level|11|2|18.2|
|School teacher pupil ratio|22|2|9.1|
|Teacher's expenence|12|1|8.3|
|Class instructional time|16|12|75.0|
|School textbooks|13|7|53.8|

Source: Fuller and Clarke (1994).

Several lessons might be drawn from these studies. First, given the abovementioned differential effects, it is not surprising that differences in aggregate education' budget does not appear to have a tight association with learning outcomes. It all depends on how budgets are allocated and used. Second, in the absence of local information about the relative effectiveness of inputs, improving availability of text books, workbooks, educational materials, school library, and physical facilities would be a prudent choice over other inputs such as increasing teacher/student ratio, teacher salary, and experience especially if schools have a shortage of the previous type of inputs. Nevertheless, in view of the findings that the relative impact and cost of particular inputs depends on the local conditions of schools and their student, it is important to collect local information about the issue.

3
# **Figure A.3 Size of Informal Firms** **_(%)_ , by Gender**

<!-- Start of picture text --> a. Women b. Men<br>1.84<br>0.5<br>4.35 10.53<br>7.73<br>15.57<br>72.06<br>87.42<br>Self-employed 2 3 - 5 6 and up Self-employed 2 3 - 5 6 and up<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data.

**Table A.2. Descriptive Statistics, Capital Distribution, by Sector and Province**

||All<br>B|ot. decile|1st q.|2nd q.|3rd q.|4th q.|Top decile|
|---|---|---|---|---|---|---|---|
|**Sectors**||||||||
|Food/Agribusiness|0.3840|0.1431|0.3646|0.2826|0.2259|0.1268|0.0556|
|Clothing and apparel|0.0839|0.0475|0.0955|0.1663|0.2775|0.4607|0.1158|
|Industry|0.1082|0.0478|0.1556|0.2627|0.3462|0.2355|0.0928|
|Construction|0.0154|0.0000|0.0570|0.3706|0.2665|0.3059|0.1793|
|Transport|0.0305|0.0641|0.0837|0.1155|0.1192|0.6816|0.4243|
|Hotels and restaurants|0.0269|0.0329|0.1083|0.0458|0.3068|0.5391|0.1372|
|Wholesale|0.0153|0.1730|0.2136|0.2352|0.1053|0.4458|0.1059|
|Primary and mining|0.0195|0.0558|0.1352|0.1209|0.1958|0.5481|0.2489|
|Retail shops and repair services|0.2601|0.1092|0.2733|0.2406|0.2854|0.2007|0.0649|
|Other activities|0.0562|0.1440|0.2153|0.2303|0.1248|0.4296|0.2602|
|**Regions/Provinces**||||||||
|Equateur|0.0753|0.0677|0.2009|0.2853|0.3553|0.1585|0.0332|
|Bandundu|0.1026|0.1751|0.3569|0.3245|0.1995|0.1192|0.0408|
|Bas-Congo|0.0488|0.1036|0.2136|0.2845|0.2001|0.3018|0.1550|
|Katanga|0.1129|0.0625|0.1579|0.2027|0.2296|0.4098|0.1999|
|Kasaï Oriental|0.1013|0.1331|0.3403|0.2358|0.1782|0.2456|0.0512|
|Kasaï Occidental|0.1080|0.1203|0.3514|0.2657|0.2284|0.1545|0.0325|
|Kinshasa|0.2347|0.0639|0.1936|0.2107|0.3089|0.2868|0.1325|
|Nord-Kivu|0.0427|0.2353|0.4230|0.2303|0.2282|0.1185|0.0094|
|Sud-Kivu|0.0489|0.2161|0.3539|0.3271|0.2024|0.1166|0.0649|
|Maniema|0.0221|0.0423|0.2734|0.1812|0.2453|0.3000|0.1535|
|Province Orientale<br>Note: Means are weighted using sam|0.1027<br>pling weights.|0.0914|0.1750|0.1705|0.2827|0.3718|0.1644|

29
amenable to home-based-work by a factor of 2.9, compared to a factor of 1.1 in high income countries. Failing to correct for this bias would result in an overestimation of the resilience of developing countries, lagging regions, and poor people during the pandemic.

Jobs amenable to working from home are also less prevalent in lagging regions within countries. They are less likely to be held by young, poorly educated, and poorly paid workers as well as those with temporary contracts. Workers’ skills, as proxied by their education levels, are the strongest predictor of their ability to work from home. The labor market burden of the COVID-19 pandemic is thus more likely to be shouldered by the poor, who are more vulnerable to start with. Absent remedial action, COVID-19 is likely to exacerbate income inequality and pre-existing socioeconomic disparities, especially in high-income countries where more jobs amenable to telework are available but are disproportionately held by high-income workers. The overall labor market burden of COVID-19, by contrast, is likely to be larger in developing countries, where fewer workers will be able to continue their employment as usual, and where social protection systems are typically less generous, or lacking altogether.

This paper contributes to the rapidly growing body of literature on the amenability of jobs to working from home<sup>1</sup> by considering infrastructure constraints and by offering global estimates of the prevalence of jobs amenable to working from home. We conduct a cross-country comparative analysis using occupation-level data for 107 countries from the ILO and complement it with an indepth analysis of a range of countries (EU countries, Brazil, India, Mexico and Turkey) using individual-level data from labor force surveys. This allows us to validate the use of occupation (as opposed to individual) level data.

The already influential work of Dingel and Neiman (2020)—DN2020 from now on—uses the Occupational Information Network (O*NET) surveys containing information about whether the occupation requires working outdoors, using specialized equipment, contact with the public and so forth, to assess if an occupation can be done at home. They assume that if the occupation requires at least one of such tasks, then it cannot be done at home.<sup>2</sup> These occupation-level

> 1 A related strand of literature examines  labor market vulnerability to COVID-19 by examining which jobs are deemed essential across countries (see e.g. Tomer and Kane, 2020, del Rio-Chanona et al., 2020, Fassani and Massa, 2020), and which ones require extensive face-to-face interaction (Avdiu and Nayar, 2020). Kahn et al. (2020) use data from unemployment insurance claims and vacancies to assess the impacts of COVID-19 in the United States. 2 There are other studies that also use O*NET to estimate the jobs that can be done from home. For example, Leibovici, Santacreu, and Famiglietti (2020) focus on whether the occupation requires physical proximity to other people and

3
# **Table 7. Victimization and mental health by event type and perpetrator**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|||||||||||||
|Conflict events<br>|0.046**<br>(0.023)|0.046**<br>(0.019)|||||||||||
|Property|||0.017<br>(0.063)|0.039<br>(0.055)|||||||||
|Violence|||||0.169***<br>(0.045)|0.180***<br>(0.039)|||||||
|Insurgents|||||||0.083***<br>(0.022)|0.077***<br>(0.026)|||||
|Bandits/criminals<br>|||||||||0.196**<br>(0.081)|0.283***<br>(0.068)|||
|Pastoralists/nomads|||||||||||-0.036<br>(0.033)|-0.012<br>(0.030)|
|Observations|717|643|717|643|717|643|717|643|717|643|717|643|
|R-squared|0.010|0.185|0.000|0.178|0.010|0.188|0.016|0.186|0.007|0.190|0.002|0.177|
|HH controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Geo controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|ZoneFE|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|

_Note:_ Dependent variable is the log CES-D index collected in wave 3 of the GHS panel. Conflict variables are cumulative for the years 2010–16. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Household variables include controls for waves 1, 2, and 3, and geographical controls are for wave 3. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 39
# **Table 17a.  Impact of 20 Percent CO2 Reduction through Energy Intensity Standards on Output by Sector and Regional Market. (Results are Percentage Change from Base Year)**

|Good|**`Central`**|**`Siberia`**|**`South`**|**`North`**|**`Urals`**|**`Far East`**|**`Moscow `**|**`St. Pete.`**|**`Tumen`**|**`Northwest`**|
|---|---|---|---|---|---|---|---|---|---|---|
|**`NFM`**|10.0|4.2|7.0|4.0|11.4|8.3|19.1|22.5||81.1|
|**`SCI`**|1.1|0.7|0.3|0.5|0.6|0.8|1.0|1.0|-1.8|1.2|
|**`FIN`**|-0.6|-0.7|-0.9|-0.7|-0.7|-0.8|-0.9|-0.7|-1.0|-0.6|
|**`TMS`**|-1.5|-2.0|-1.8|-1.7|-1.3|-2.0|-2.2|-1.9|-0.7|-1.8|
|**HEA**|-1.6|-1.5|-2.0|-1.8|-1.6|-2.0|-2.1|-1.8|-1.7|-1.6|
|**`CON`**|-1.2|-1.9|-1.9|-2.2|-1.9|-0.8|-1.3|-1.1|-5.8|-0.5|
|**`GAS`**||-1.2|-1.0|-1.5|0.3|0.9|||-15.5|2.8|
|**`CRU`**||-2.7|-2.4|-2.5|-2.3|-2.4|||-3.3|-2.3|
|**`COL`**|-2.3|-2.8|-2.9|-2.8|-2.4|-2.4|||||
|**`FOO`**|-1.6|-3.0|-2.8|-4.3|-2.9|-1.8|-1.2|-2.5|-12.7|-1.0|
|**`AGR`**|-1.7|-3.9|-4.0|-4.6|-3.5|-2.8|-2.6|-2.8|-13.1|-1.3|
|**PST**|-4.3|-4.2|-3.9|-4.3|-4.0|-4.1|-3.7|-4.4|-4.0|-4.3|
|**`OTI`**|-3.3|-5.0|-3.9|-5.2|-4.0|-3.8|-3.4|-3.8|-9.7|-3.1|
|**`RLW`**|-5.4|-5.4|-5.5|-5.7|-5.9|-4.6|-5.0|-5.0|-4.8|-5.3|
|**`OTH`**|-4.7|-5.2|-4.6|-6.1|-5.0|-4.5|-6.3|-6.6|-10.8|-3.7|
|**`MWO`**|-4.5|-8.8|-6.1|-5.9|-5.8|-5.0|-4.9|-4.5|-10.3|-2.6|
|**HOU**|-5.5|-5.7|-6.2|-6.5|-5.9|-6.3|-6.0|-5.9|-8.2|-5.6|
|**`TRO`**|-6.4|-6.9|-6.0|-6.6|-6.3|-6.7|-6.3|-6.5|-5.6|-6.7|
|**TRD**|-9.0|-6.9|-5.6|-5.6|-6.2|-6.6|-6.8|-7.8|-1.5|-8.4|
|**`TPP`**|-4.7|-9.5|-6.6|-9.1|-5.1|-11.7|-6.7|-6.4|-7.4|-2.6|
|**CLI**|-5.7|-6.1|-6.5|-7.8|-6.2|-6.8|-5.8|-6.7|-13.5|-5.5|
|**`TRK`**|-8.3|-8.0|-7.0|-7.6|-7.5|-8.1|-8.2|-8.3|-4.0|-8.5|
|**`MAR`**|-7.8|-8.0|-7.8|-8.5|-6.9|-8.5|-7.9|-9.6|-5.3|-6.4|
|**`CNM`**|-6.2|-9.6|-6.3|-8.5|-6.5|-7.7|-10.1|-9.0|-12.0|-8.2|
|**`FME`**|-8.4|-11.9|-13.2|-12.5|-10.3|-7.9|-11.2|-18.6||-7.6|
|**`PIP`**|-17.0|-15.6|-9.0|-5.2|-10.5|-12.4|-17.8|-14.8|1.0|-18.9|
|**`CHM`**|-13.9|-22.5|-16.0|-15.0|-17.0|-13.3|-15.2|-16.8|-31.7|-16.4|
|**`OIL`**|-17.9|-20.5|-19.0|-19.6|-18.7|-17.9|-18.8|-16.0|-28.0||
|**ELE**|-25.3|-23.0|-23.4|-22.8|-24.8|-22.0|-21.0|-23.8|-19.0|-25.4|
|**`AIR`**|-25.4|-23.9|-25.0|-31.2|-21.4|-31.2|-21.9|-31.3|-29.8|-30.4|

See table 1 for sector codes. **Source: Authors’ estimates.** 58
84

|COUNTRY|PROJECT|LEGAL COMPONENT|FINANCIERS|
|---|---|---|---|
|Venezuela|ludiciary Infrastructure Project<br>(Loan No. 3514 VE), December 30,<br>1993|This project constitutes the first Bank supported operation<br>with the exclusive purpose of judicial reform. The first<br>component includes a program for the institutional<br>strengthening of the Consejo de la Judicatura (Conseiol, the<br>constitutional body in charge of the administration of the<br>judiciary in Venezuela including (a) planning, financial<br>management and program budgeting; (b) studies in subjects<br>such as alternative dispute resolution methods, computerized<br>database on statutory and case law and matters of procedural<br>law; and (c) training of the staff of the Consejo in areas of<br>financial management and policy analysis, analysis of court<br>statistics, auditing procedures, court record keeping and court<br>management and supervision.|IBRD $30 m.|
|||The second component involves a program to modernize the<br>system of courtroom administration, in two phases: (a) a pilot<br>phase where alternative models of court organization and<br>automation will be implemented fourteen courts (including<br>public defender's offices); and (b) a second phase consisting<br>of the extension of the pilot models, such extension to be<br>based on the evaluation of the pilot phase.||
|___________|_<br>_____________________________|Training program for judges and other judiciary personnel as<br>well as technical assistance to strengthen the institutional<br>capabilities of the Judicial School. The last component<br>involves the construction, rehabilitation and upgrading of<br>court buildings.||
> - 12 -

the Oslo Toll Ring was the openng date of a newly constructed tunnel in Oslo, which underscored to the public the government's commitment to earmark toll revenues. Thus actual and proposed road use charging schemes in Europe and elsewhere attest to the theoretical finding reported in HKau (1992).s Adjustments to the current road financing instruments of first registration taxes, annual license fees and g&oline taxes should be allowed for. This is because the perceived fairness of the road charging scheme -- aside - from efficiency arguments may require some of the toll revenues to be plowed back to motorists, public transport users and the transport system. If redistribution is found to be necessary for the political acceptance of the scheme, it is desirable that the road pricing system be technologically capable of revern certin current vehicle charges.

# IL TIAmc **RESnRA** MEASU

10. Traffic restraint can be achieved via either the regulatory approach on quantity with socalled command and control measures or the market-based approach (i.e., pricing approach). Examples of the regulatory approach to roadway usage are to control the physical number of vehicles, for instance, by an *odds and evens' system and by restricting the amount and hours of parking spaces (May, 1991). The odd/even number plate system prohibits the use of private vehicle one day a week, as has been done in Mexico City since November 1989 mainly because of its air pollution problem (Carbajo, 1992). Such Draconian measures have been found to be ineffective when applied permanently (Eskeland, 1992). Aside from public transport, government and residents' vehicles, private vehicles are forbidden to enter a cordon surrounding central Milan, Italy, during the day between 7:30 am. and 4:30 p.m. Sometmes thought of as a cordon toll (Orski, 1992), Milan's cordon is actually a nonprice one. Other historical cities such as Florence, Rome and Bologna, Italy; Strasbourg, France; (36teborg, Sweden; and Tunis,

**in IHau (1992),** it is SW (di) t road pncig hit most people and dht road uers og to be oompetd dircly or indiraly, by the ch _n of X tolU revamus.
# Appendix 3. Interactions between Country Characteristics and Global Factors

# Table 1. <u>Country-Industry Level Logit Regressions (Equation (1))</u>

|Dependent|Variable:<br>Ph|Issuance Co|untry-ind<br>It|ustry mont<br>Ph|hly issuanc|e above 20<br>It|00-07 avera<br>Ph|ge (1=Yes,|0=No)|Ph||It|Ph|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||us<br>Factor|_GDPPC_|ner-<br>action|us<br>Factor|_GROWTH_|ner-<br>action|us<br>Factor|_EXT_|Interaction|us<br>Factor|_CAD_|ner-<br>action|us<br>Factor|_PCRED_|Interaction|
|_VIX_|-1.710|||-0.710***|||-0.735***|||-0.713***|||-0.351*|||
||(1.269)|||(0.135)|||(0.154)|||(0.103)|||(0.184)|||
|Pull Factor||0.504|||0.0126|||0.00351|||-0.0334|||0.0197**||
|||(0.464)|||(0.0631)|||(0.00719)|||(0.0381)|||(0.00918)||
|Interaction|||0.119|||-0.000720|||0.000504|||0.000276|||-0.00724***|
||||(0.150)|||(0.0200)|||(0.00231)|||(0.0132)|||(0.00270)|
||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||
||70,644|61<br>427||70,644|61<br>427||70,644|61<br>427||70,644|61<br>427||70,644|61<br>427||
|_RISK_|-0.535|||-0.642***|||-0.575***|||-0.628***|||-0.462***|||
||(0.873)|||(0.120)|||(0.128)|||(0.0912)|||(0.154)|||
|Pull Factor||1.034***|||-0.000531|||0.00636*|||-0.0322***|||0.00104||
|||(0.159)|||(0.0201)|||(0.00329)|||(0.0118)|||(0.00511)||
|Interaction|||-0.0111|||0.00310|||-0.00126|||-0.000528|||-0.00343*|
||||(0.105)|||(0.0154)|||(0.00173)|||(0.00939)|||(0.00208)|
||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||
||70,644|61<br>427||70,644|61<br>427||70,644|61<br>427||70,644|61<br>427||70,644|61<br>427||
|_FED_|2.540***|||0.353***|||0.894***|||0.569***|||0.178|||
||(0.939)|||(0.101)|||(0.101)|||(0.0812)|||(0.142)|||
|Pull Factor||3.183**|||-0.704**|||0.112***|||-0.212|||-0.111***||
|||(1.376)|||(0.328)|||(0.0274)|||(0.182)|||(0.0368)||
|Interaction|||-0.229**|||0.0540**|||-0.00789***|||0.0135|||0.00774***|
||||(0.106)|||(0.0236)|||(0.00194)|||(0.0129)|||(0.00255)|
||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||
||53,928|59<br>413||53,928|59<br>413||53,928|59<br>413||53,928|59<br>413||53,928|59<br>413||
|_LIBOR_|-1.018**|||-0.473***|||-0.452***|||-0.462***|||-0.408***|||
||(0.442)|||(0.0631)|||(0.0684)|||(0.0495)|||(0.0871)|||
|Pull Factor||0.921***|||-0.000968|||0.00742*|||-0.0288|||0.00225||
|||(0.190)|||(0.0294)|||(0.00392)|||(0.0178)|||(0.00584)||
|i||||||||||||||||
|Interacton|||0.0654|||0.00266|||-0.000242|||8.13e-05|||-0.00105|
||||(0.0510)|||(0.00923)|||(0.00115)|||(0.00579)|||(0.00117)|
||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||_Obs._|_#C_<br>_#Id_||
||60,732|61<br>427||60,732|61<br>427||60,732|61<br>427||60,732|61<br>427||60,732|61<br>427||

Robust standard errors clustered on the country-industry level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Note: Obs. = Observations, #C = Number of Countries, #Id = Number of Industries 40
113 and financial assistance. This can mean either assisting the private sector expand the supply of family planning service delivery through assistance and removal of impediments, or through efforts to raise the demand for private care. The former implies access to technical assistance and capital for family planning-related investments. The latter suggests encouraging alternative financers of health care, that is, promoting insurance coverage, the development of HMOs, and other third-party payers through tax incentives or other regulatory means.

Private Sector Options and Their Applicability Across Countries A number of different kinds of interventions can be undertaken to -romote private sector activity. Table V.1 summarize these options, explaining the possible initiatives that can affect the demand for private sources or the supply of private contraceptive services, and the necessary steps for initiating each.

Because of the complexity of the effects of the multiple objectives to be achieved by specific interventions, the folloving discussion is organized around major options.<sup>1 9</sup> The major kinds of interventions that government can make to foster greater private sector activity cover a broad range. Not all of these are applicable in all cases, and in some countries all of these can be difficult to implement. The degree to vhich public and private entities coexist, the 19. For example, establishing a reimbursement system to replace or complement direct public provision of family planning relies on private providers to deliver care vhile the public sector pays for it. This kind of system obviously expands the demand for private family planning services, stimulates private investment in family planning, and reduces the role of public entity; hovever, it can be a complex and involved proposition vhen it is part of an overall shift in government's financing of health and family planning.

**mlItp:?auily Planning Paper** (Wole.
Table 4. The Influence of Altemative Defmitions on Country Classifications of Manufachues Exporters

||UNCTA<br>1987 value of<br>exports ($milion)|D/GATT definit<br>Rank in 72<br>countries|ion of manufac<br>Compound g<br>1982-87|tures<br>rowth rate /o)<br>1977-87|UN<br>1987 value of<br>exports ($million)|IDO definitio<br>Rank in 72<br>countries|n of manufatures<br>Compound gr<br>1982-87|<br>wthate ('/o)<br>1977487|
|---|---|---|---|---|---|---|---|---|
|UNC'AD's Major Exporters of|||||||||
|Manufactrs<br>|||||||||
|Argentina|1,528|16|3.6|7.7|3,523|16|40.1|3.8|
|Brazil|10,255|6|13.4|17.6|16,645|5|9.8|13.3|
|Hong Kong|23,101|4|11.2|13.0|22,890|4|11.9|13.0|
|Korea, Rep.|36,998|2|23.0|18.8|38,293|2|22.9|1&6|
|Singapore|11,803|7|20.8|20.5|15,197|7|_IIA_|16.5|
|Taiwan, China|48,048|1|22.4|21.5|50,483|1|22.1|20.8|
|Other Coatiies<br>|||||||||
|China|27,488|3|25.3|28.0|31,529|3|21.1|25.7~|
|India|6,462|10|13.6|10.7|5,720|11|12.7|8.3|
|Indonesia<br>|2,970|13|39.1|37.2|5,533|12|17.5|15.3|
|sraed<sup>2</sup>|5,761|11|14.6|13.8|4,099|14|12.0|15.1|
|Malaysia<br>|7,197|8|16.4|19.8|10,164|8|12.6|13.2|
|Mexico<br>2|14,531|5|22.7|19.2|16,358|6|21.6|17.0|
|Pakistan|2,125|14|18.9|17.5|2,721|18|17.2|17.2|
|Philppines|3,566|12|9.7|17.5|4,870|13|6.2|10.6|
|Saudi Arabia|1,885|_15_|23.4|28.1|6,033|10|17A|15.8|
|Thailand|5,034|9|25.8|24.0|6,522|9|19.5|183|
|Venezuela|579|17|14.4|19.1|4,019|_15_|-2.4|4.4|

1 Yugoslavia is claTified by UNCrAD as a developing country and is also included in the major exporters of manufactures group. 2 Irael is class by UNCrAD as a developed country.

3 Iladudes products manufactured im offshore assembly plants located m Mexico.
17

# TABLE 4

EFFECTS OF A 4 PERCENT, ONE-TIME INCREASE IN THE U.S. MONEY SUPPLY IN THE TAYLOR MULTI-COUNTRY RATIONAL EXPECTATIONS HODEL (Percent Change from Baseline Unless Otherwise Noted)

|Y<br>E||United|States|||Jap|an|||RO|ECD||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|R|GNP|PGNP|<br>RSa|Eb|GNP|PGNP||RSa|Eb|GNP||PGNP|<br>R|Eb|
||||||Incre|ase Una|nticipated||||||
|1985|0.83|0.23|-0.61|-4.1|0.01|-0.01|-0.01|4.4|0.04|0.02|0.02|4.2|
|1986|0.50|0.85|-0.50|-3.4|-0.08|-0.08|-0.02|3.7|-0.06|-0.10|.0.10|3.6|
|1987|0.45|1.42|-0.34|-3.1|*0.21|0.22|-0.05|3.3|-0.11|-0.23|-0.23|3.2|
|1988|0.33|1.83|-0.31|-2.9|-0.31|0.41|*0.07|3.1|-0.13|-0.36|-0.36|3.0|
|1989|0.19|2.11|-0.30|-2.7|-0.31|-0.61|-0.08|2.8|-0.12|-0.46|-0.46|2.8|
|1990|0.13|2.27|-0.29|*2.5|-0.24|0.76|-0.08|2.6|-0.07|-0.52|-0.52|2.6|
|||||Increa|se Antici|pated<br>T|hree Yea|rs in Ad|vance||||
|1985|0.32|0.12|0.12|-2.5|-0.01|-0.01|-0.01|2.7|0.00|-0.02|-0.02|2.5|
|1986|0.37|0.50|0.24|-2.7|-0.07|-0.07|-0.02|2.8|-0.04|-0.08|-0.08|2.7|
|1987|0.46|0.99|0.36|-3.0|-0.16|-0.18|-0.03|3.2|-0.08|-0.17|-0.17|3.1|
|1988|0.45|1.48|-0.44|-3.2|-0.23|-0.34|-0.05|3.4|-0.10|-0.29|-0.29|3.3|
|1989|0.26|1.85|-0.39|-2.8|-0.27|-0.51|-0.07|2.9|-0.12|-0.40|-0.40|2.9|
|1990|0.18|2.08|-0.31|-2.6|-0.25|-0.67|-0.07|2.7|-0.09|-0.48|-0.48|2.6|

Source: John B. Taylor in R. C. Bryant et al (1988).

aPercentage-point change from baseline short-term interest rntez bThe exchange rate, measured as weighted units of foreign exchange per unit of domestic currency.