rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 4: Commitment and Projections of Fiscal and Economic Outcomes**

# Albania

<!-- Start of picture text --> Debt under different levels of commitment Budget balance under different levels of commitment<br>% GDP Zero 0.1 0.2 % GDP Zero 0.1 0.2<br>90 0.3 0.4 0.5 10.0 0.3 0.4 0.5<br>0.6 0.7 0.8 0.6 0.7 0.8<br>80 0.9 7.5 0.9<br>5.0<br>70<br>2.5<br>60<br>0.0<br>50<br>-2.5<br>40<br>-5.0<br>30 -7.5<br>20 -10.0<br>08 10 12 14 16 18 20 22 24 26 28 30 08 10 12 14 16 18 20 22 24 26 28 30<br>GDP growth under different levels of commitment Inflation under different levels of commitment<br>% pts Zero 0.1 0.2 % pts Zero 0.1 0.2<br>7 0.3 0.4 0.5 6 0.3 0.4 0.5<br>6 0.6 0.7 0.8 0.6 0.7 0.8<br>0.9 5 0.9<br>5<br>4<br>4<br>3 3<br>2 2<br>1<br>1<br>0<br>0<br>-1<br>-2 -1<br>08 10 12 14 16 18 20 22 24 26 28 30 08 10 12 14 16 18 20 22 24 26 28 30<br>Montenegro<br>Debt under different levels of commitment Budget balance under different levels of commitment<br>% GDP Zero 0.1 0.2 % GDP Zero 0.1 0.2<br>140 0.3 0.4 0.5 4 0.3 0.4 0.5<br>0.6 0.7 0.8 0.6 0.7 0.8<br>120 0.9 2 0.9<br>0<br>100<br>-2<br>80<br>-4<br>60<br>-6<br>40 -8<br>20 -10<br>08 10 12 14 16 18 20 22 24 26 28 30 08 10 12 14 16 18 20 22 24 26 28 30<br>GDP growth under different levels of commitment Inflation under different levels of commitment<br>% pts Zero 0.1 0.2 % pts Zero 0.1 0.2<br>8 0.3 0.4 0.5 5 0.3 0.4 0.5<br>0.6 0.7 0.8 0.6 0.7 0.8<br>6 0.9 4 0.9<br>4 3<br>2 2<br>0 1<br>-2 0<br>-4 -1<br>-6 -2<br>08 10 12 14 16 18 20 22 24 26 28 30 08 10 12 14 16 18 20 22 24 26 28 30<br><!-- End of picture text --> Source: Jooste, Burns, and Song (2019).

24
# **4.5 Tariff and Trade Data**

Trade data and tariff rates are taken from the GTAP 9.1 database. For tariff rates, GTAP uses the MAcMap-HS6 database, described in Guimbard, Jean and Mimouni (2011). The ASEAN members have committed themselves to a maximum tariff of five percent within ASEAN. The five percent maximum tariff has reportedly been achieved among the original six members of ASEAN (The Philippines, Brunei, Indonesia, Malaysia, Singapore and Thailand). The remaining four members, Cambodia, Myanmar, Laos and Vietnam, have committed to implement the maximum five percent tariff, but have been allowed a longer adjustment period. Consequently, we adjust all tariffs above five percent among ASEAN members to five percent in our benchmark. The tariff rates and the ad valorem equivalents of the barriers on FDI are listed in table 3.

# **4.6 Social Accounting Matrices**

The core structural data of the model comes from the GTAP 9.1 dataset.<sup>**27**</sup> Exports and imports and by trading partner of The Philippines from this source are available in tables 2.

# **4.7 Share of Market Captured by Foreign Direct Investors in Services**

In the business services sectors of our model, it was necessary to calculate their market share in The Philippines of each region of our model. Except for telecommunications and insurance, for the shares of the sector captured by the aggregate of foreign firms, we used data from the final Census of Philippine Business and Industry for 2012.<sup>28</sup> To allocate the shares among foreign firms, we used data from the Central Bank of the Philippines in their report “Net Foreign Direct Investment Flows by Country of Origin, 20142016, in millions of dollars.”<sup>29</sup> This report provides data on aggregate foreign direct investment (FDI) in The Philippines by source country for the years 2014-2016. We assumed that the foreign ownership shares sectors correspond to the shares of aggregate FDI by source country. We took the average of FDI flows for 2015 and 2016 to calculate the shares.

In the case of telecommunications, we obtained shares of the market by company from the Annual Report of the Philippine National Telecommunications Commission, 2014. Company shares were calculated based on the number of subscribers.<sup>30</sup> There are two large telephone

> 27 See https://www.gtap.agecon.purdue.edu/databases/default.asp.

> 28 <u>https://psa.gov.ph/content/2012-census-philippine-business-and-industry-economy-wide-all-establishments-finalresults. We thank Daniel Reyes and his research assistant for these data and calculations.</u>

> 29 <u>http://www.bsp.gov.ph/statistics/spei_new/tab10_fdc.htm. Calculations based on equity flows as data on</u> reinvested earnings and debt are not available by country.

> 30 http://ntc.gov.ph/wp-content/uploads/2015/10/reports/Annual_Report_2014.pdf

25
new data set of national poverty lines gives an estimate of the log poverty line of 3.52 ($33.75 per month) for the expected poverty line in the poorest country in the sample, with a White standard error of 0.105. Figure 2 also gives a more flexible non-parametric regression of the national poverty lines against log mean consumption; the predicted values give the estimated mean poverty line at any given level of mean consumption.<sup>15</sup> The lowest predicted value is $37.14 per month and the mean of the predicted values in the poorest 15 countries is $37.98 per month. Since this method does not impose any parametric functional form on the data, it can be considered a more robust estimate of the expected value of the poverty line in the poorest country. Based on these observations, Ravallion et al. (2008) propose an international poverty line of $1.25 a day for 2005 ($38 per month).<sup>16</sup> It should be noted that the poverty line of $44.25 for 2005 that one obtains if one updates the old (1993) international line of $32.74 for inflation in the US is clearly well above the lines found in the poorest countries in Figure 2 (echoing the finding of Chen and Ravallion, 2001, comparing the 1993 and 1985 PPPs).

# **New poverty estimates for China**

Our primary focus in this paper is assessing the extent of poverty in China based on an international poverty line. Of course, this need not agree with the national poverty line in any one country. Naturally, the PPP for China has no bearing on the poverty counts using national poverty lines (such as reported for China in Ravallion and Chen, 2007). Both the official poverty line for China and the (higher) line used by Ravallion and Chen (2007) are lower than the international line used here. China’s official poverty line for rural areas is about $20/month, making it one of the lowest lines in the developing world. The line used by Ravallion and Chen is closer to the international line, though still lower (at about $30 per month). (China’s official poverty line has not been updated in real terms since the middle 1980s, even though mean household consumption has increased by a factor of about four. At the time of writing, proposals

> 15 We use a Locally Weighted Scatter Plot Smoothing, also known as LOWESS.  Figure 2 gives the default bandwidth in the STATA program for LOWESS. Ravallion et al. (2008) discuss sensitivity to the choice of smoothing parameter.

> 16 Note that Ravallion et al. (2008) also argue in favor of a slightly higher line, $1.25 a day, which is the mean of a reference group of the poorest 15 countries. This raises the level of poverty in China slightly, but does not change our main conclusions.

9
# **_Measurement of attitudes_**

Our main outcome is the response to the question “is the city or area where you live a good place to live for immigrants from other countries?”. We use this question because it is the only question on immigrants in most GWP (and therefore global) country-years. However, it is not a direct question about perceptions towards immigrants, and the interpretation is potentially ambiguous. We therefore assess our measure with extensive supplementary data.

First, we examine the individual-level correlations between our main outcome and other questions that are asked in a subset of GWP country-years which capture views about immigrants more directly. Table 1 follows the measurement methodology in Asher et al. (2021). It reports coefficients from regressions of the outcome listed in the left column as the dependent variable, our main outcome as the independent variable, and additional fixed effects in some specifications. The results show that respondents who say their area is a good place to live for immigrants are significantly less likely to agree with statements expressing negative views of immigrants. This is consistent across all nine measure and within region-years.

Second, we compare the region-year averages of our main outcome with questions on immigrant perceptions asked in other surveys. We compile 12 additional cross-country surveys repeated over time by reputable organizations.<sup>19</sup> We merge these with GWP and with each other at the lowest possible sub-national level. We then synthesize common questions across different surveys to further populate country-year and region-year measures on attitudes toward immigrants.<sup>20</sup> The synthesized questions and their sources are listed in Table A2.

Table A3 follows the same methodology as Table 1 but uses region-year level averages instead of individual level correlations. The results tell the same story; our main outcome, whether the respondent believes the current area is a good place for

> 19The datasets are the Afrobarometer, Arab Barometer, Asian Barometer, European Election Study, European Social Survey, Eurobarometer, International Social Survey, Latinobarometer, Pew Global Attitudes, Transatlantic Trends, World Bank Country Opinion Survey, and the World Values Survey.

> 20The most common question asked in the different surveys is whether the respondent would be “ok with an immigrant neighbor”, for instance.

11
|_Panel C: Employment of Ill Worker_||||
|---|---|---|---|
|Other worker ill|0.068|0.454|5.080|
||[0.061]|[0.381]|[3.430]|
|Other worker ill X That worker is self‐employed|0.029|‐0.072|‐0.434|
||[0.065]|[0.553]|[5.552]|
|Female X Other worker ill|‐0.136|‐0.801|‐7.966|
||[0.098]|[0.510]|[5.235]|
|Female X Other worker ill X That worker is self‐employed|0.012|0.476|3.450|
||[0.107]|[0.729]|[7.449]|
|P‐value for equal responses: Males|0.747|0.534|0.502|
|P‐value for equal responses: Females|0.606|0.413|0.526|
|Mean dependent variable|0.807|4.279|37.660|
|Observations|3,951|3,948|3,948|
|R‐squared|0.507|||

_Notes: Other worker ill = 1 if another household member reported missing work for an entire week in which s/he was planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. All specifications include individual fixed effects. Column 1 is estimated by OLS and columns 2 and 3 are estimated using least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Regressions include week‐of‐interview dummies interacted with gender, week‐of interview dummies interacted with city, and controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
Graph **1** <!-- Start of picture text --> Share of Public Sector in the<br>Economy, 1980-90<br>percent of GNP<br>25<br>In etme t<br>79 ... ..........<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989E 1990 T<br>E: Estimate T: Target<br><!-- End of picture text --> SOURCE: State Planning Organization.

# D. Dissatisfaction with SEE Performance

20. By 1980 the poor performance of SEEs had become all too visible. Of a PSBR of 10.3 percent of GNP in that year, SEEs accounted for 6.4 percentage points, that is nearly two thirds. Part of the poor performance stemmed from internal inefficiency, but a large part was also caused by government-imposed constraints, such as frozen prices, overstaffing and politically motivated managerial appointments. Perceiving SEEs more as a drag than a dynamo for the 11
# **Figure 6. Regional average time required to obtain a TWP (in calendar weeks)**

<!-- Start of picture text --> MENA<br>OECD<br>LAC<br>Global average<br>SSA<br>SAR<br>ECA<br>EAP<br>0 2 4 6 8 10 12<br><!-- End of picture text --> Source: FDI Regulations Database Note: Regional abbreviations, and the number of economies covered by the ESE indicators per region, are as follows: EAP = East Asia and the Pacific (10 economies); ECA = Eastern Europe and Central Asia (21 economies); LAC = Latin America and the Caribbean (14 economies); MENA = Middle East and North Africa (8 economies); OECD = High Income economies in the Organization for Economic Cooperation and Development (17 economies); SAR = South Asia Region (5 economies); SSA = SubSaharan Africa (18 economies).

12
<!-- Start of picture text --> oTM os<br>Enon |\ }H \ 4/s aa<br>c) Pia TM~ fr is i wo!<br>2 Va jd an H<br>é 1 ee i<br>aeof/ Hli 4svy<br>325“ '<br>o ;<br>2011m1 2012m1 2013m1 2014m1<br>month<br>——— IHC fim — -~-~-~~ Synthetic firm<br><!-- End of picture text -->
**Table 5: Directional impacts of public works programs on women’s empowerment and psychological well-being**

|||Survey|Women’s|Psychological|
|---|---|---|---|---|
|||Timing|Empowerment|Well-being|
|||(1)|(2)|(3)|
|Comoros||-4m|0|0|
|Côte d’Ivoire||-3m|NA|+|
|Lao PDR||-2m|+|NA|
|Djibouti||0m|NA|0|
|Sierra Leone||1m|NA|NA|
|Egypt,<br>Arab|Rep.,<br>|1m|+|0|
|Community<br>|||||
|Egypt,<br>Arab|Rep.,<br>|4m|NA|+|
|Infrastructure|||||
|Djibouti||9m|0|0|
|Tunisia||12m|+|+|
|Rural DRC||12m|0|0|
|Ethiopia||12m|NA|NA|
|Côte d’Ivoire||12m|NA|+|
|Urban DRC||25m|0|0|
|Egypt,<br>Arab|Rep.,<br>|27m|0|0|
|Community|||||
|Tunisia||66m|0|0|

Notes: These results are shown to reflect the sign of estimates instead of coefficient estimates or effect sizes because of differences in measurement. Point estimates that are not statistically significant are coded as 0. Countries are ordered by survey timing with respect to program completion, i.e., we first present countries that measure impacts during the program, followed by countries that measure 1 to 66 months post-program impacts. The Egypt Community study refers to psychological well-being as subjective well-being. See Appendix Table A3 for country specific variable definitions on women’s empowerment and psychological wellbeing.

# **7. Conclusion: Lessons for policy and directions for future research**

Public works programs in low-and middle-income countries are designed to offer shortrun employment and earnings opportunities to poor households and are often introduced to facilitate recovery from shocks (e.g., drought, conflict). These programs frequently also aim to sustainably raise living standards via, for example, improvements in assets, selfemployment, savings, and community infrastructure. This paper reviews experimental evidence from 11 public works programs that together offer both internal and external validity across diverse settings. Some important conclusions and policy lessons emerge from the review.

29
# IL A RETROSPECTIVE OF ESTIMATING CHINESE REAL INCOME

4. Because national income figures published by China since the 1950's were computed by the Material Product System, many economists have been interested in estimating China's real income by Western statistical definitions and pracdces (Liu, 1968; Swamy, 1973; Liu and Yeh, 1965, 1973; Eckstein, 1973; Perkins, 1975, 1980, 1988). The emphasis of these efforts was on developing esfimates of Clina's real income in Chinese currency (uan), rather than in U.S. dollars.

5. Since the early 1980's, China's statistical authorities have begun to calculate gross national product (GNP) accordng to standard inteational methodology. In 1992, a new national income account **s** ytem conformed with the basic definition and methodology of the _UnitedNaivon System of National Accounts_ (SNA) (United Nations, 1968) was implemented to estimate the size of Chinas economy in Chinese anency.

6. Though more accuate and detaled data for China have been issued, the debate over U.S. dollar esfimates of China's GDP has confinued. The range of e)dsting dollar measures of GDP by different methds reflects the difficulty of esfimating the size of a trnitional economy. The most oftenred methods include the World Bank Atlas approach (World Bank, 1991c), the reduced information method (Ahmad, 1980), ICP technique (Kravis,et al., 1975, 1978, 1982) and productionbased PPP approach (Taylor, 1991). Efforts to calculate a US. dolar value of China's GDP by different methods have produced estimates that vary by a factor of '0, as shown in Table 2: 3
# **Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS1033 Latin Americ**a**nd the Caribbean<br>Region (and Northern<br>America)<br>Popuiation Projections,<br>1992-93<br>Edition|My T. Vu<br>Eduard Bos<br>Ann Levin|November<br>1992|0. Nadora<br>31091|
|WPS1034 Revising Financial Sector Policy<br>in Transitional<br>Socialist Economies:<br>Will Universal Banks Prove Viable?|David H. Scott<br>|November<br>1992|K. Waeiti<br>37664|
|WPS1035 How Import Protection Affects the<br>Philippines'<br>Motor Vehicle Industry|Wendy E. Takacs|November<br>1992|D. Ballantyne<br>37947|
|WPS1 036 Output Decline in Hungaty<br>and Poland in 1990-91: Structural<br>Change and Aggregate<br>Shocks|Simon Commander<br>Fabrizio Coricell|November<br>1992|0. del Cid<br>35195|
|WPS1037 Vocational Secondary<br>Schooling,<br>Occupational<br>Choice, and Earnings<br>in Brazil|Ana-Maria<br>Arriagada<br>Adrian Ziderman|November<br>1992|C. Cristobal<br>33640|
|WPS1038 Determinants<br>ofExpatriate Workers'<br>Remittances<br>inNorth Africa and<br>Europe|Ibrahim A. Elbadawi<br>Robert de Rezende<br>Rocha|November<br>1992<br>|A. Marahon<br>31450|
|WPS1 039 Education, Externalities,<br>Fertility,<br>and Economic<br>Growth|Martin Weale|November<br>1992|PHREE<br>33680|
|WPS1040 Lessons of Trade Liberalizatio**n**<br>I<br>Latin America for Economies<br>in<br>Transition|Jaime de Melo<br>Sumana<br>Dhar|November<br>1992|D. Ballantyne<br>37947|
|WPS1041 Family Planning Success Stories in<br>Bangladesh<br>and India|Moni Nag|November<br>1992|0. Nadora<br>31091|
|WPS1042 Family Planning<br>Success in Two<br>Cities in Zaire|Jane T. Bertrand<br>Judith E. Brown|November 1992|0. Nadora<br>31091|
|WPS1043 Deriving Developing<br>Country<br>Repayment<br>Capacity<br>from the Marke<br>Prices of Sovereign<br>Debt|Stijn Claessens<br>t George Pennacchi|November<br>1992|R. Vo<br>33722|
|WPS1044 Hospital Cost Functions<br>for<br>Developing<br>Countries|Adam Wagstaff<br>Howard<br>Barnum|November<br>1992|0. Nadora<br>31091|
|WPS1045 Social Gains from Female Education<br>A Cross-National<br>Study|: Kalanidhi<br>Subbarao<br>Laura Raney|November<br>1992|M. Abundo<br>36820|
|WPS1046 World Bank Project-Financed<br>Research<br>on Population,<br>Health,<br>and Nutrition|J. Price Gittinger<br>Carol Bradford|November<br>1992|0. Nadora<br>31091|
than seven times the value of foreign aid and almost fivefold the amount of foreign direct investment in 2005.

In view of the extremely dynamic nature of the Albanian migratory phenomenon, the main objective of this paper is to model migration trends and update our knowledge and understanding of migration in Albania, based on the 2005 Albanian Living Standards Measurement Survey (ALSMS) carried out by the Albanian Institute of Statistics (INSTAT) in collaboration with the World Bank. The 2005 ALSMS contains a number of innovations, particularly in relation to the migration module, which enable us to fully characterize the evolution of migration since its onset in 1990. Specifically, the paper will analyze the determinants of the decision to migrate, and to return, using a conditional hazard model, allowing us to assess the influence of changing conditions, e.g. household demographics and other time-dependent variables such as shocks, networks and migration policies. The goal is to answer some of lingering questions in the current policy dialogue on migration in Albania. Are migration and the remittance flow tapering off? Is the demographic and socio-economic make-up of migrants changing over time? Is the flow of returnees increasing, and what is its composition?

# **Characterizing international migration flows**

As widely documented in previous studies (for instance Carletto _et al._ 2004; INSTAT 2004; King _et al._ 2005; King and Vullnetari 2003), Albania is a country on the move, with massive levels of both internal and international migration. In this section of our paper, based on full migration histories reconstructed in the ALSMS 2005 using recall methods, we reconstruct international migration from Albania since 1990. ALSMS 2005 contains an unusually rich module on international migration for both current and former household members. In addition, the migration module collected extensive information on migrant networks.

Specifically, we will describe two different migration patterns, current and past migrants, and later model them separately. This latter group is also analyzed based on the year of last return to better characterize the flow of returnees. Current migrants are defined as all former household members who no longer live in the household and are currently living abroad.<sup>1</sup> Conversely, past migrants are all current household members who have been abroad for at least one month since 1990, and have now returned to live in 3
island states are somewhat connected, as well as the southern islands, but that connectivity between the northern and southern islands as a set is rather poor.

**Table 12: Travel Time Matrix for Travel Between Small Island States, for December 5, 2012 (hours and fractions**

**thereof)**

||||||**Destination**<br>|<br>|||
|---|---|---|---|---|---|---|---|---|
|||**Antigua**|**Dominica**|**Grenada**|**Nevis**|**St. Kitts**|**St. Lucia**|**St. Vincent**|
||**Antigua**||0.7|6.5|0.5|0.5|1.1|4.0|
||**Dominica**|0.7||5.5|2.0|5.3|2.7|3.2|
|**in**<br>|**Grenada**|3.7|2.7||5.0|5.3|3.1|2.1|
|**rig**<br>|**Nevis**|0.5|**2.2**|**6.0**||overnight|overnight|5.1|
|**O**<br>|**St. Kitts**|0.5|**7.1**|overnight|overnight||overnight|**9.2**|
||**St. Lucia**|3.5|2.8|4.2|9.6|**4.3**||overnight|
||**St. Vincent**|3.1|2.5|0.6|overnight|**9.3**|0.5||

_Source: Authors’ calculation on data collected through the reservation system at www. LIAT.com._ _Note: Bolded underlined connections required another travel day, in some cases as late as December 14, 2012. Some connections require an overnight stay. Shaded connections are direct. For further details, see Appendix I._

# <u>Hub Countries</u>

As a previous section on airport pairs showed, the regional air transport hubs are Antigua, Barbados, and Trinidad. Hubs are usually determined by the airlines serving them, and both Antigua and Barbados are shareholders in LIAT, whereas Trinidad’s Piarco International Airport is the hub for Caribbean Airlines. Though both the Dominican Republic and Jamaica have very high volumes, their extra-regional traffic is for their own tourism destinations, and there is no transfer hub activity to other Caribbean countries.

Barbados acts as the southern hub for LIAT’s service in the small island states, and Antigua as the northern hub. Trinidad also acts as a transfer point to LIAT, but is also the only inter-regional gateway for Guyana and Suriname.

One important element of the hub system among the smaller islands is that it is only effective in transferring passengers _within the same airline_ . There are only two main airlines serving the smaller islands in the Caribbean, Caribbean Air and LIAT, with, as mentioned earlier, LIAT having _no interlining capability_ , making it impossible for a passenger to check luggage with LIAT to the final destination when switching to another airline in a transfer hub.<sup>16</sup> This sharply reduces the availability of airport pairs realistically served by the hubs (Table 13).

> 16 In order to switch airlines, the passenger would first have to go through immigration, then pick up their luggage, go through customs, and then go to the airline counter in the check-in area to re-check their luggage for the next flight.

44