rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Pet, n_#-kan tnm li<br><!-- End of picture text --> <!-- Start of picture text --> ilulml<br><!-- End of picture text --> WPS 596 Ihis paper- a product of the Intemational Trade Division, Intemational Economics Department- is part of a larger effort in PRE to understand the implications for world commodity markets of changes in developing countries' trade policies and to assist developing countries in designing good trade and industry policies. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Pauline Kokila, room S7-040, extension 33716 (60 pages).

The Mexican sugar industry has been subject to extensive government controls over land ownership, cultivation, harvesting, milling, marketing, distribution, and pricing. The many objectives of these intervendons include protecting producers and consumers from world price variability, ensuring self-sufficiency, guaranteeing employment and social welfare, providing cheap milling services, and protecting domestic soft drink manufacturers.

Borrell's calculations show that although the interventions have helped stabilize industry rcturns to some degree, the estimated effective rate of assistance points to a high degree of resource distortion. In years when world prices were low, such as in 1985-88, the effective rate of assistance is estimated in the range of 70-390 percent.

econometric model of the Mexican sugar industry. This was linked to a global model of the world sugar industry. Stochastic simulations projecdng the Mexican sugar industry under these policies show consumption increasing faster than production and Mexico increasing its sugar imports. It appears unlikely that under such policies Mexico would return to being an exporter of sugar.

in simulations of a sugar industry operating under essentially free trade conditions, Mexico becomes a significant sugar exporter. Production, trade, and stocks are more variable, and consumption growth is curtailed. But welfare in the economy ac a whole would be increased substantially. The main beneficiaries would be sugar producers, and the losers consumers - but the loss to consumers would average less than US$3.20 per person per year.

To analyze the impact of changes in Mexican sugar policies, Borrell constructed an The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and External Affairs Complex. Anobjective of the series is to getthese findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necess . ly represent official Bank policy.

Produced by the PRE Dissemination Centcr
and Malaysia due to reach this level by 2012), and an estimated global market of USD 20 to 40 billion (to reach over 100 billion by 2012) (Drager and Smith, 2009).

But is it all trade? As illustrated in Figure 2, McKinsey adopted a narrow definition of medical travel, to exclude expatriates seeking care in their country of residence (25 to 30 percent of all patients treated abroad) and travelers seeking emergency cares (30 to 35 percent); in total, medical travelers would represent only 35 to 45 percent of all patients treated abroad.

**Figure 2: A definition of medical travel**

<!-- Start of picture text --> All international inpatients* receiving care<br>100%<br>Subtract expatriates<br>seeking care in their -25 to -30%<br>country of current<br>residence<br>Subtract emergency cases -30 to 35%<br>35-45%<br>Medical travelers<br><!-- End of picture text -->

* Outpatients are excluded from analysis because providers don’t collect detailed outpatient data; a few providers, however, have substantial numbers of international outpatients. **Source:** Ehrbert et al. (2008).

An important question, beyond statistics, is the rationale of medical tourism. Why do patients seek treatment abroad? According to the same study by McKinsey, the main drivers of medical travel are as follows (Figure 3):

- most advanced technology;

- better quality care for medically necessary procedures;

- quicker access for medically necessary procedures;

- lower cost care for medically necessary procedures; and

- lower cost care for discretionary procedures.

- 14 -
Table 3. Coefficients of Variation and Trends for World Commodity Prices and Volumes, 1961-82.'

||1961-|1973|1974-|1987|1961|-1987<br>J|
|---|---|---|---|---|---|---|
|_~=|Volume|Price|Volume|Price<br>=|Volume<br>_|<br>_Price_|
|Beef|6<br>(6.3)|6<br>(49)|_5_<br>(4.6)|14<br>(-1.6)|7<br>_(5.1)_|21<br>-1.2|
|Coffee<br>.__________<br>.(2.4)|4|9<br>_(-0.5_|6<br>_)_<br>(2.2)|37<br>(-2.6)|6<br>(1.6)|31<br>(0.7)|
|Cotton|_5_<br>(1.5)|13<br>(-0.2)|7<br>(1.7)|_15_<br> <br>(-4.8)|7<br>(1.0)|17<br> <br>(-1.3)|
|Groundnuts|14<br>f_-3.9)|11<br>(1.4)|11<br>(-4.8)|36<br>(4.6)|_15_<br>(-3.0)|30<br>(-0.4)|
|Maize|14<br>(7.5)|12<br>(-0.2)|14<br>(1.3)|19<br>(-6.4)|19<br>(5.3)|19<br>(-2.2)|
|Sisal|_5_<br>-(-2.1)|35<br>(4.2)|17<br>(-7.6)|32<br>(-7.8)|13<br>(-5.8)|40<br>(-1.5)|
|Sugar|6<br>(1.7)|58<br>(1. 1)|7<br>(1.7)|62<br> _(-15.5_|7<br>_)_<br>(2.1)|78<br>(-1.0)|
|Tea|3<br>(2.8)|_5_<br>(-5.6)|3<br>_(2.5)_|27<br>(-2.5)|4<br>(2.6)|19<br>(-3.4)|
|Tobacco|6<br>_(2.5)_|12<br>(0. 1)|4<br>(0.2)|10<br>(-2.0)|7<br>(2.0)|11<br>(-1.3)|

wlends were derived from a linear regression of price on time, converted into own average annual rate of growth.

18
# **3. Three Sub-Saharan African Economies**

This section describes the data used in the empirical analysis and provides a sketch of the economies of Cote d'IvoiTe, Ghana, and Zimbabwe. The data used in the analysis are the (log) real GDP in the industry, agriculture, and service sectors from the World Bank African Regional Database for 1965-97 (figure 1).6 The plots reveal an overall positive trend in all output series.<sup>7</sup> First-order differences of the series (not shown) appear stationary, indicating that the series are integrated of order one, I(1). This is further supported by results from augmented Dickey-Fuller tests (see the appendix). Therefore, the series may possibly co-integrate.

**Figure 1. Sectoral GDP for C6te d'Ivoire, Zimbabwe and Ghana**

<!-- Start of picture text --> ind_civ=_~_ ~ agr_Piv=._.s.p.ciu=._.<br>27.6 - 28.5 -<br>i n&..gl,a=__ ag!_gha=,.... ser_sha=___<br>26.4 27._ 27<br>25.8 25. H m- 26.4 , 26 26.7 75 ' _<br>264 -26. 25.<br>2in _____wr _-we= ___ . _<br>25.2 - {-,2 26.4 5<br>1985 1985 1985<br>22 21*.6**-222.5-<br>28.4 ~~~~26. 21<br>21.6 2 * 22<br>21.8 ~ ~ V-I2. 2<br>1985 1995 1995<br><!-- End of picture text --> _Note:_ The series are log(real GDP, factor prices) ind: industry, agr: agriculture, ser: service, civ: Cote d'Ivoire, zwe: Zimbabwe, gha: Ghana. _Source:_ World Bank African Regional Database.

6 For Ghana, however, the series end in 1996.

7 Ghanaian industrial output experiences a major collapse in the early 1980s, which may make possible subsectoral relationships disappear. This proves to be the case in the co-integration analysis in the next section; accordingly, we propose disaggregating Ghanaian industrial output into the four subsectors manufacturing, construction, gas and water, and mining.

6
that average program impact between 2003 and 2005 was an increase of 6 female students per school in terms of absolute change and an increase of 9 percent female enrollment in terms of relative change. A triangulation effort of our findings using school census data is also corroborated with evidence of the impact of the stipend using two rounds of a nationally representative household survey before and after the intervention.

# **3. Methodology**

## _Outcome of interest and unit of analysis_

Frequently used indicators of access to education such as gross or net enrollment rates at a particular level of schooling (e.g. middle school grade 6-8) or school participation rate for a particular age-group (e.g. 10-14 year olds) by geographic regions (e.g. districts) are best estimated using representative household surveys or population censuses. The last Population Census in Pakistan was conducted in 1998. In terms of large-scale household surveys, the two relevant ones are: Pakistan Integrated Household Survey 2001-2002 (PIHS 2001-02) for the preintervention period, and Pakistan Social and Living Standard Measurement survey (PSLM 200405) for the post-intervention period. Unfortunately the sample size and statistical representation for sub-provincial level analysis limited in these surveys – hence, we can only draw upon them to supplement our impact analysis.

The primary data for this study available to us come from the PMIU’s government school census for 2003 to 2005. These school censuses provide school-level information on enrollments by grade for both pre-program and post-program periods. Since public schools in Pakistan are either boys only or girls only, schools can conveniently form the units of our analysis<sup>10</sup> . This implies that our impact estimate on school enrollments can be measured in terms of average growth in grade 6-8 enrollments in program schools relative to non-program schools. Program effect can be interpreted in the following manner: when eligible schools offered stipends to girls, did these schools record an _extra increase_ in enrollments in grade 6-8 relative to non-program schools? We will measure changes in enrollments in terms of both _absolute_ and _percent growth_ . While absolute growth estimates change in actual size of student enrollment, percent growth would estimate change relative to some initial figure. When there is a wide variation in initial middle

> 10 We also estimate program impacts using districts as the unit of observation in one of our sensitivity checks (see section 5).

7
2016/17 aims to produce poverty and welfare statistics with similar accuracy across all districts. In addition, we expect that achieving low RSEs and SEs in the estimation of districtlevel poverty and welfare will be more difficult than at the division level, as well as at the quarterly level. Therefore, allocating an equal number of PSUs across districts ensures that the final sample is evenly distributed across all districts.

In the second stage, we randomly select 20 households from each PSU, resulting in 720 households allocated to each of the districts (36 PSUs*20 households). This brings the total targeted sample size to 46,080 households (2,304 PSUs*20 households). Assigning 36 PSUs to each district was determined as follows. First, this is the minimum sample size needed to achieve an RSE of less than 10 percent for the mean nominal per capita expenditure for each district and division (urban/rural), and for each quarter at the national level with 95 percent confidence (our selected accuracy statistics to determine sample size). To arrive at this number (36), we use a formula (derived in Annex 3) that is a simple extension of the original formula proposed by Cochran (1977) to determine sample size for continuous target variables under simple random sampling when controlling for a desired RSE.<sup>13</sup> This simple extension, which introduces survey design effects to the original formula, results in the following expression: where _n_ is the minimum sample size required for allocation to each district in order to achieve a certain level in the accuracy statistic ���<sup>�</sup> � associated with the targeted variable ��; ��������� is the coefficient of variation of the targeted variable estimated under the assumption of simple random sampling; _DEFF_ is the design effect of the target variable; and ��/� is the critical value of a standard normal distribution with α% confidence level.<sup>14</sup>

> 13 Derivation of the formula to determine optimal sample size for proportions when one is interested in controlling for SE instead is provided in Annex 3.

14 This formula is derived under the assumption that the targeted variable �� is normally distributed. 14
outpatient. Deficiencies in hospital statistics mean that it is not possible to calculate all these unit costs for all districts.

The distribution of recurrent costs in Table 5 gives an accurate picture of the resources absorbed by rural services as opposed to the hospital, namely 27% to 39%. While the hospital absorbs 61% to 73% of district recurrent costs, part of this is spent on primary health care services for the hospital', local population. The secondary care services of the hospital absorb 40 to 58% of district recurrent costs.

The relative importance of the different wards varies considerably between the hospitals. In Salima and Kasungu maternity has the largest share, in Chiradzulu and Rumphi the children's ward, and in Mulanje and Nkhata Bay the male ward. However, none of these differences is very large, except the occasional one which reflects major differences in bed *numbers between the wards (for instance, the small maternity in Mulanje).

The unit recurrent costs show considerable variation between the hospitals. The most expensive hospital is consistently Mulanje, and the cheapest, Rumphi. In terms of cost per bed, Mulanje is 7S% more expensive than Rumphi, in terms of cost per inpatient, 83% more expensive, and in terms of cost per inpatient day, 238% more expensive.

These differences should, however, be interpreted with great caution since they are highly sensitive to the accuracy of the activity statistics used. Mulanje reports a bed occupancy rate of 76%, 10
Table 20. Indonesian Exports, ICT use, and Log of Skills Measures. Male workers. 2011-2015

||||||N|on-routine<br>|skill measur<br>|es|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||An|alytical|||Interp|ersonal|||M|anual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.000605||0.000599|0.000472|0.000567||0.000563|0.000447|-0.000529||-0.000526|-0.000419|
||(0.000204)***||(0.000204)**|*(0.000209)**|(0.000185)***||(0.000186)**|*(0.000187)**|(0.000176)***||(0.000176)**|*(0.000177)**|
|Share of hhlds with computer||-0.0966|-0.0960|-0.111||-0.0664|-0.0658|-0.0797||0.0435|0.0428|0.0557|
|||(0.0690)|(0.0703)|(0.0710)||(0.0623)|(0.0634)|(0.0635)||(0.0584)|(0.0591)|(0.0587)|
|IDN exports per worker *||||0.00301||||0.00275||||-0.00256|
|Share of hhlds with computer||||(0.00108)***||||(0.00105)***||||(0.00105)**|
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|
|R-squared|0.728|0.728|0.728|0.729|0.307|0.310|0.308|0.309|0.824|0.824|0.824|0.824|
|||M|anual|Routine skil|l measures|Co|gnitive||||||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|||||
|IDN exports per worker|-0.000341||-0.000335|-0.000227|-0.000390||-0.000388|-0.000297|||||
||(0.000361)||(0.000360)|(0.000366)|(0.000169)**||(0.000169)**|(0.000169)*|||||
|Share of hhlds with computer||0.0901|0.0894|0.102||0.0418|0.0413|0.0523|||||
|||(0.0658)|(0.0662)|(0.0658)||(0.0609)|(0.0613)|(0.0606)|||||
|IDN exports per worker *||||-0.00257||||-0.00216|||||
|Share of hhlds with computer||||(0.00123)**||||(0.00107)**|||||
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|||||
|R-squared|0.970|0.970|0.970|0.970|0.817|0.818|0.817|0.817|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and province linear time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

39
## **Table 6: Reversals of South-South migration could lead to massively lower welfare for natives in developing countries that never migrated**

<u>Welfare impacts on natives, new Migrants and incumbent migrants, difference from baseline in 2030 (US$ Billion)</u>

||**PP**|**P-Adjusted Real**<br>**Non-**|**Income Change**||
|---|---|---|---|---|
||**Return**|**Mirants in**|**Remainin**||
||<br>**Migrants**|**g**<br>**Origin**|**g**<br>**Migrants**|**Total**|
|Bangladesh|0.05|-9.60|0.12|-9.43|
|Brazil|0.04|-0.80|0.07|-0.69|
|Central Asia|-0.70|-12.12|0.34|-12.48|
|China|2.31|-1.50|0.19|0.99|
|India|5.82|-8.52|0.02|-2.68|
|Indonesia|1.64|-11.62|0.04|-9.94|
|Low-Income Sub-Saharan Africa|0.39|-9.03|0.25|-8.39|
|Mexico|-0.04|-0.65|0.02|-0.67|
|Middle-Income Middle East and North Africa|1.92|-7.34|0.10|-5.32|
|Other Former Soviet Union and Other Eastern Europe|-2.14|-11.27|0.50|-12.92|
|Other Latin America and Caribbean|2.44|-26.36|0.96|-22.96|
|Other Low-Income South and East Asia|-0.66|-5.24|0.09|-5.81|
|Other Middle-Income Asia|-0.25|-11.15|0.11|-11.29|
|Other Middle-Income Sub-Saharan Africa|0.22|-3.13|0.09|-2.82|
|Pakistan|0.28|-2.96|0.05|-2.63|
|South Africa|-3.36|-23.80|0.65|-26.51|
|Turkey|-0.02|-0.60|0.01|-0.61|
|**Total LMI**|7.93|-145.68|3.59|-134.16|

Source: Authors’ simulations

# **5. Conclusions**

The literature on the gains of international migration tends to revisit three conclusions. The first conclusion is that a relaxation in the barriers to international labor mobility leads to more workers from developing countries moving to developed countries – South-North migration – and can be expected to increase global welfare. The second conclusion is that this greater migration will accelerate the real GDP growth of the host countries although the wages of native workers will be depressed and may even fall. The third conclusion is that increased South-North migration is often to the benefit of the developing countries, brain drain arguments aside, due to factors like upward pressures on wages of those remaining and due to higher remittance flows that increase investment and consumption.

This paper uses a global dynamic economic simulation approach to nuance the discussion of international migration and development beyond these robust conclusions. Scenario analyses are conducted with the model to present two sets of findings. The first set is from a scenario where existing binding restrictions on South-North migration are relaxed such that the total labor force in each of the developed host regions rises eventually by 3 percent relative to a business-as-usual 24
Figure 2. Percentage distribution of countries by average annual TFR decline, and of declines in first and second decades after transition started <!-- Start of picture text --> Percent<br>40<br>I Country estimates<br>-4- First decade<br>30 - - Second aecade<br>2 0-<br>10 '<br>0 038 0 063 0.088 0.113 0 138 0 163 0.188 0.212 0.238<br>Annual decline (groun midpoints)<br><!-- End of picture text --> The estimates from the three data sets are summarized in Table 2, which provides medians and 'ercentiles, as well as the means across data sets. The typical annual decline in TFR might be said to he roughly .12 points. Using the tenth iad ninetieth percentiles, one could assign half this raue as roughly indicating slow fertility decline and double this rate as roughly indicating rapid fertility decline. A rapid decline estimate of .24 is slightly above the annual rapid decline of .2164 estimated in previous work (Bulatao and Elwan 1985), but appears appropriate in comparison to recent declines in Africa not considered in that work: .20 points a year in 1984-89 in Keniya, .25 points a year in 1980-86 ie Figure 3. Estima.ed annual TFR change from two data sets: 32 countries <!-- Start of picture text -->
-0 02<br>-o  04  -<br>-0.06  0<br>-0.08  0<br>-0 10  0  1<br>§ 0 12  -0  w  °<br>8 _  - O 19 O  w  0  0  a  0<br>-0120  _  o<br>-0.22  -<br>-0.24  -<br>-0 26-<br>-o.2as<br>-2  -0 2S -A  -0 21 -0 19 -o 17 -0 15 -0 13 -0 11 -o  09  -0 07 -0 05 -0 03<br>SLrvey, cernus.  cr  regtsatia7..-  c.?tO<br><!-- End of picture text -->

9
labor and capital income are not necessarily subject to the same tax rate. Starting in the 1990s, a number of economies have adopted dual-income tax systems, whereby labor and capital incomes are subject to different schedules, with labor income typically subject to a progressive tax schedules and (some) capital income subject to flat tax rates.

To account for these facts, we proceed as follows. We start with the baseline assumption, consistent with US data, that 15% of PIT revenues derive from capital and 85% from labor. We then use data from Jensen (2022), which documents the location of the PIT threshold across countries, to adjust for the share of capital income in the PIT at the country-year level. Since richer taxpayers derive a larger share of their income from capital, countries with a high PIT exemption threshold—and thus more exempted taxpayers—have a larger capital share of PIT revenue. Finally, we use data from the OECD to account for dual income tax systems. Specifically, when dividends face a lower tax rate than ordinary income, we compute the ratio of the statutory rate on dividends to that of the top rate on labor income, and adjust the capital share of PIT revenue down when this ratio is below one. Appendix B.2 discusses these adjustments in further detail.

The resulting share of PIT revenue allocated to capital varies between 7% and 35%, depending on countries and years. Over time, this share falls from a global average of 19% in 1965 to 14% in 2018, due to both a reduction in PIT exemption thresholds and to the adoption of lower tax rates on dividends in some countries.

**Effective tax rates.** The total tax revenue assigned to labor and capital is:

where _λic_ is the allocation to labor of each type of tax _τi_ in country _c_ (see Table A1).

The effective tax rates (ETR) on labor and on capital, _ETRL_ and _ETRK_ , are computed by dividing tax revenue collected by the size of the respective labor and capital income flows: 10
**Table 9: Impacts on Use of Finance**

||Z score on<br>use of<br>finance|Amount<br>that<br>business<br>can borrow<br>in 2 weeks<br>(U$S)|Amount<br>borrowed<br>(U$S)|Bank<br>contacted<br>the firm|Has<br>insurance<br>for business|Amount in<br>bank<br>savings<br>(U$S)|High<br>relative<br>savings|Does not<br>take<br>business<br>money for<br>the<br>household|Business<br>keeps<br>financial<br>records|
|---|---|---|---|---|---|---|---|---|---|
|Treatment 1: BRC|0.014|-10.174|13.380|0.002|0.004|-17.560|-0.002|-0.010|0.032*|
||(0.020)|(34.061)|(14.912)|(0.013)|(0.003)|(23.705)|(0.017)|(0.014)|(0.017)|
||0.487|0.765|0.370|0.888|0.202|0.459|0.918|0.491|0.067|
||_0.912_|_0.771_|_0.370_|_0.888_|_0.273_|_0.516_|_0.918_|_0.553_|_0.092_|
|Treatment 2: BRC + TPIN|0.006|-31.505|18.367|0.014|0.008|15.738|0.003|-0.007|-0.026|
||(0.030)|(46.437)|(19.850)|(0.018)|(0.005)|(34.799)|(0.023)|(0.019)|(0.022)|
||0.835|0.498|0.355|0.431|0.129|0.651|0.914|0.719|0.243|
||_0.912_|_0.697_|_0.370_|_0.539_|_0.273_|_0.651_|_0.918_|_0.719_|_0.274_|
|Treatment 3: BRCE + IS + BBA|0.203***|91.969***|19.137*|0.090***|0.079***|44.334|0.030*|0.057***|0.081***|
||(0.021)|(33.942)|(11.392)|(0.013)|(0.005)|(41.621)|(0.016)|(0.013)|(0.015)|
||0.000|0.007|0.093|0.000|0.000|0.287|0.056|0.000|0.000|
||_0.000_|_0.016_|_0.140_|_0.000_|_0.000_|_0.369_|_0.072_|_0.000_|_0.000_|
|Control group mean|-0.000|570.947|79.003|0.100|0.009|178.966|0.527|0.287|0.457|
|Sample size|10900|10900|10900|5350|10900|10900|10900|10900|10900|
|p-value: Treatment 1 = Treatment 2|0.797|0.634|0.822|0.497|0.482|0.326|0.851|0.867|0.010|
|p-value: Treatment 1 = Treatment 3|0.000|0.001|0.705|0.000|0.000|0.116|0.039|0.000|0.002|
|p-value: Treatment 2 = Treatment 3|0.000|0.006|0.969|0.000|0.000|0.544|0.207|0.000|0.000|
|p-value test of<br>equality|0.000|0.002|0.372|0.000|0.000|0.386|0.113|0.000|0.000|
|Treatment 1|0.769|0.189|0.742|0.940|0.869|0.794|0.377|0.750|0.435|
|Treatment 2|0.501|0.322|0.574|0.173|0.740|0.047|0.988|0.777|0.166|
|Treatment 3|0.867|0.691|0.160|0.738|0.471|0.207|0.954|0.581|0.278|

41