rafmacalaba/datause-encoder-data huggingface dataset:
The migration patterns also vary within PRD across cities (table 1) with a sharp difference between Guangzhou and the other cities, and between PRD and the rest of the economy. In Guangzhou, an international metropolis and traditional economic center, about 50 percent of the inflow of total migrants (4.28 million) come from other parts of Guangdong Province, 50 percent come from other provinces. Shenzhen, followed by Dongguan, Foshan and Zhongshan, the dynamic export-oriented economy and emerging economic centers, the inflow of migrants are mostly from other provinces. For example, of the total inflow of migrants to Shenzhen (6.07 million) and that to Foshan (2.4 million), about one-third come from other parts of Guangdong, two-thirds from other provinces; for Dongguan and Zhongshan, respectively, out of the 5.01 million and 1.1 million total inflow of migrants, nearly 80 percent come from other provinces. While for the less developed cities, such as Zhaoqing, out of the 370,000 total inflow of migrants, some two-thirds come from other parts of the province. Non-PRD cities in Guangdong share the similar migration patterns as Zhaoqing. The ratios of intra- vs. interprovincial migration in most cities remained similar in 2000 and 2010.

The majority of the migrant workers from Guangdong chose to move to large cities, while those from other provinces largely concentrated in the PRD, particularly Shenzhen and Dongguan. As shown in figure 5, 75 % of the intra-provincial migrant population concentrates in the PRD, of which 20 % in Guangzhou and Shenzhen each, while 95 % of the intra-provincial migrant population concentrated in the PRD, of which some 28 % concentrates in Dongguan, 27 % in Shenzhen, 14 % in Guangzhou, and 10 % in Foshan, and employed in the labor-intensive industries. The pattern is similar in 2010 as in 2000.<sup>4</sup>

**Figure 5: Distribution of intra- and inter-provincial migration inflows in Guangdong (2010)**

<!-- Start of picture text --> 2010 Intra-provincial migration 2010 Inter-provincial migration<br>Other cities<br>Zhaoqing<br>Other cities26 % Guangzhou Jiangmen 1 % 6 % Guangzhou<br>21 % 2 % 14 %<br>Zhongshan<br>Zhaoqing 6 %<br>3 %<br>Shenzhen<br>27 %<br>Jiangmen<br>4 % Dongguan<br>Shenzhen 28 %<br>Zhongshan 20 %<br>2 %<br>Zhuhai<br>Dongguan Huizhou Foshan 3 % Huizhou 4 % Foshan Zhuhai 2 %<br>8 % 5 % 8 % 10 %<br><!-- End of picture text --> Data source: China Bureau of Statistics, 2005; Guangdong Bureau of Statistics, 2012.

4 Migration data in 2000 available upon request.

8
have heavily invested to convert their crippled export industry from unprepared to prepared meat. The consequence of this is reflected in the large increase in their exports between 2003 and 2005. Table 6 reports overall trade and exports originating from the four major suppliers of prepared poultry meat.

Table 6 - Exports of prepared poultry meat (million US$)

|Year|Thailand|China|Brazil|USA|Others|Total<br>Trade|
|---|---|---|---|---|---|---|
|1995|164|43|7|219|99|531|
|1996|190|150|12|221|91|665|
|1997|229|150|10|261|99|748|
|1998|284|156|17|230|88|776|
|1999|326|214|22|190|105|857|
|2000|365|333|28|200|149|1075|
|2001|407|421|48|242|174|1291|
|2002|452|512|65|208|170|1407|
|2003|566|492|97|197|207|1558|
|2004|798|490|110|186|258|1841|
|2005|1164|639|256|198|242|2499|
|2006|1185|709|419|222|271|2806|

Source: COMTRADE Database Note: Excludes intra-EU trade.

As of 2006, the export market for prepared poultry meat is dominated by the two countries where the poultry industry was worse hit. Thailand’s total exports in 2006 were worth almost 1.2 billion US$, while China accounted for about 700 million US$. Brazil’s exports have also grown but at a smaller pace.

As in the case of unprepared poultry products, the market for prepared poultry is highly segmented with each importer relying on a limited number of suppliers. Table 7 illustrates the market shares in each import market as a three year average before and after the onset of avian influenza.

19
Policy Research Working Paper 10981

# Behaviorally Informed Messages Increase COVID-19 Vaccination Intentions

Insights from a Global Meta-Analysis _Daniel Alejandro Pinzon Hernandez JungKyu Rhys Lim Michelle Dugas Ellen Moscoe Mohamad Chatila Corey Cameron Renos Vakis Zeina Afif Victor Hugo Orozco Olvera_ Development Economics Development Impact Group November 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
**Figure 5.  Group comparisons: employment to population ratio**

<!-- Start of picture text --> Employment Ratio<br>Men Youth Least educated Urban<br>(vs. Women) (vs. Adults) (vs. More educated) (vs. Rural)<br>LT<br>ID<br>LV<br>MK<br>MK<br>BGSA BR<br>SA TRPH<br>JO EG BGAR JO RO MEAR EGPL JO PL<br>ME CH BRPH RO CHTRPH EGPL CH JO BG PH EGID<br>RO PL MK<br>LV ME<br>BGSACR ME BR ID LT<br>TR AR LV<br>ID CR CR<br>CR<br>LT<br>LV<br>LT<br>-20 -15 -10 -5 0 -20 -15 -10 -5 0 -20 -15 -10 -5 0 -20 -15 -10 -5 0<br>GDP slowdown<br>Lines indicate mean value and running line smooth<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br><!-- End of picture text --> Note: The figure shows the disparity between groups, controlling for other characteristics, in the slowdown in the employment to population ratio. The plotted number is the coefficient from regression for each country (equation (3) in Appendix B). The horizontal axis represents the slowdown in real GDP growth rate as defined in Table 1. Two letter codes represent countries.

35
A set of structural reforms introduced in the early-middle 2000s managed to curb inflation. Turkey implemented stronger monetary and fiscal policy frameworks, adopted a flexible exchange regime, advanced reforms for the banking sector, deepened trade and financial integration and consolidated the independence of the Central Bank circumscribing its mission to establishing and maintaining price stability (World Bank 2014). The prospects of the EU accession process, as well as accompanying IMF and World Bank programs, helped to anchor these economic reforms while Turkey regained the confidence of international markets. By 2004, the inflation rate had fallen to single digits for the first time in 34 years.

Figure 1. Inflation in Turkey, Advanced Economies, EMDEs, and Global Inflation (annual %, 1970-2019)<sup>5</sup> <!-- Start of picture text --> 120<br>Advanced economies EMDEs Turkey Global Inflation<br>105<br>94<br>100<br>85<br>80<br>69<br>62<br>54<br>60<br>48<br>40<br>24<br>15<br>20<br>9<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2019<br><!-- End of picture text --> Notes: Regional-Global aggregations are median inflation rates. Global, Advanced Economy and EMDE inflation rates between 1970 and 2017 are from Ha, Kose, Ohnsorge (2019). Inflation rates for Turkey and global inflation after 2017 are from World Bank Open Data which uses the International Monetary Fund, International Financial Statistics and data files. _Source_ : Ha, Kose, Ohnsorge (2019) and IMF Data Except for a mild uptick to 10.4% in the midst of the Global Financial Crisis in 2008, inflation remained stable until 2017, when it began to pick up. Inflation accelerated in the following year compounded by a substantial weakening of the Turkish lira and macro instability associated to the 2018-19 economic crises. At the peak of the exchange rate crisis (August-September 2018), the Turkish lira had lost over 70% of its value against the U.S. dollar relative the level of the exchange rate in January 2018 (World Bank 2019). While inflation subsided in the second half of 2019, the

> 5 Regional-Global aggregations are median inflation rates. The Global, Advanced Economy and EMDE inflation rates between 1970 and 2017 are from Ha, Kose, Ohnsorge (2019).  The inflation rates for Turkey and global inflation after 2017 are from World Bank Open Data which uses the International Monetary Fund, International Financial Statistics and data files.

7
person) are distribution neutral; more plausibly the main reasons why surveys underestimate consumption or income would also lead to an underestimation of inequality.<sup>11</sup> In almost all cases, the PA poverty lines were constructed using some version of the Cost-of-Basic-Needs method.<sup>12</sup> This aims to approximate a COL index that reflects the differences in prices faced between urban and rural areas, weighted by the consumption patterns of people living in a neighborhood of the country-specific poverty line. This is consistent with the use of an absolute poverty standard across countries.

While our method appears to be the best option that is currently feasible, internal consistency is questionable if the urban-rural COL differential varies by income. This may stem from differences in the prices faced between the poor and others, or differences in consumption patterns. Then the differential from the PA may not be right for the international poverty lines. If the COL differential tends to rise with income then we will tend to overestimate urban poverty by the $1 a day line in middle-income countries relative to low-income countries, given that the PA poverty line will tend to be above the international line for most middle-income countries. To help assess robustness, we also estimate poverty measures for a “$2 a day” line that is more typical of the poverty lines used in middle-income countries.

A data constraint that can also create internal inconsistencies is that in setting poverty lines, location-specific prices are typically only available for food goods. Also, while nutritional requirements for good health provide a defensible anchor in setting a reference food bundle, it is less obvious in practice what normative criteria should be applied in defining “non-food basic needs.” The problem is compounded by the fact that poor rural infrastructure (such as

> 11 For example, Banerjee and Piketty (2005) attribute up to 40 percent of the difference between the (higher) growth of GDP per capita and (lower) growth of mean household per capita consumption from household surveys in India to unreported increase in the incomes of the rich. Selective compliance with random samples could well be an equally important source of bias, although the sign is theoretically ambiguous; Korinek et al. (2006) provide evidence on the impact of selective non-response for the US. On the problems of selective non-response in surveys more generally see Groves and Couper (1998). 12

The precise method used varies from country-to-country, depending on the data available. On the methods sued in setting poverty lines see Ravallion (1994, 1998).

9
<!-- Start of picture text --> 90%<br>75%<br>~ e—@.<br>2C)e<br>©<br>60% e@ Aa<br>2 e e— @<br>5<br>d<br>®<br>& 45% "~e—e<br>3S<br>fo)<br>ip)<br>a=}<br>a<br>® 30%<br>ro)<br>is)<br><x<br>15% 2 ee °—e<br>e e—_*—e \ / © \<br>0%<br>2007 2009 2011 2013 2015 2017 2019 2021<br>Year<br>-@- Employer -@-+ Medium and Large Firms Micro and Small Firms Self-employed<br><!-- End of picture text -->
11

## Tabl- A

|Pension**and**Wage**R**|**ates**in|**Ukraine**||||
|---|---|---|---|---|---|
||1991|1992|1993|1994|1995|
|Average pension/average wage|0.44|0.49|0.42|0.36|0.32|
|Maximum pension/average wage|1.07|0.69|0.45|0.42|0.31|
|Average pension/Average social pension|1.63|2.58|2.85|2.53|2.62|
|Average wage (000 kbv/month)||6.37|155.1|1375.5|7347.91|
|Wage bill (as % of GDP)<br>||_<br>||<br>33%|23%|271;|<br>33%|
|Real wage (1990=100)||81.3|36.8|32.9|36.9|

Source: Data provided by the Ukrainian authorities, Tacis, and World Bank estimates.

|**Pension Fund B**|**alance a**|**s% of GDP**||||
|---|---|---|---|---|---|
||1992|1993<br>_<br>_<br>_<br>_<br>_<br>_ __|1994<br>______<br>__|1995<br>_____|1996<br>(pro.)|
|Pension Fund balance|2.8|0.7|0.8|0.1|0.0|
|Revenue|11.6|9.0|8.8|8.0|8.1|
|Payroll Contributions|.|6.3|8.2|7.2|7.6|
|Budget Transfers|..|2.6|0.5|0.6|0.5|
|Other|.|0.1|0.1|0.2|0.0|
|Expenditure|8.8|8.3|8.0|7.9|8.1|
|Old-age pensions|..|6.4|5.2|4.2|4.6|
|Military pensions|..|0.2|0.3|0.4|0.4|
|Childallowances|..|0.2|0.2|0.2|0.2|
|Other pensions|..|1.5|2.3|3.1|2.9|
|Memo:||||||
|Consolidated government budget balance|-17.8|-10.3|-8.2|-5.2|-3.4|
|Revenue|41.6|44.0|45.5|42.5|42.8|
|Expenditure|59.4|54.4|53.7|47.7|46.2|
|GDP (trillions of Kbv)|5.03|148.3|1138|5293|8686|

Source: Data provided by the Ukrainian authorities and World Bank estimates.

# IV. **Non-Reform Scenarios: Medium Term Sustainability Prospects**

Before the sharp economic downturn, the accounts of the Ukraine Pension Fund were balanced. Indeed, between 1990 and 1992, the Fund was able to run a small surplus and pensioners enjoyed the relatively generous provisions outlined in Section II. The current cutbacks were forced by the stark economic reality and are viewed by many decision makers as only temporary. This explains why the most fundamental characteristics of the Soviet-era
resulting business cycles by the duration of expansions and contractions and their amplitude. Amplitude measures the cumulative growth of GDP during an expansion and contraction. Duration of an expansion is defined as the length (in quarters) between a trough and a peak; while duration of a contraction is the length between a peak and a trough.

Next we compare the properties of expansions and contractions in small and large countries. As noted above, we define a country to be small if its population is less than 4.151 million people. Large countries have population greater or equal to 4.151 million people. Under this classification, our quarterly data set contains 22 small countries and 47 large countries.<sup>13</sup> Table **1** shows the mean and standard deviation for durations and amplitudes of expansions and contractions, separately for small and large countries in our data set.

**Table 1. Characteristics of expansions and contractions, by** **<u>country size</u>**

||<br>|<br>**Du**<br>**(in q**<br>|<br>**ration**<br>**uarters)**<br>|**Am**<br>|**plitude**<br>|
|---|---|---|---|---|---|
|**Small**<br>**(pop<4.151mil)**|Obs|Mean|Std. Dev.|Mean|Std. Dev.|
|expansion|22|17.62|10.00|0.29|0.21|
|contraction|22|4.30|2.26|-0.07|0.06|
|**Large**<br>**(pop>=4.151mil)**||||||
|expansion|47|23.87|11.63|0.29|0.16|
|contraction|46|4.38|2.71|-0.05|0.05|

Two key features differentiate business cycles in small and large countries. First, the average duration of expansions is shorter in small countries (at 17.6 quarters) relative to large countries (at 23.9 quarters) and the difference is statistically significant at 5% significance level. The average duration of contractions is comparable in small and large countries, equal to about 4 quarters. Second, contractions are more pronounced in small countries, with the average cumulative drop in GDP equal to 7% in small countries, as opposed to 5% in large countries. In contrast, the amplitude of the expansions is the same in the two groups of countries, equal to 29%.

We interpret these results as providing mixed evidence on the differences in business cycles between small and large countries – by some measures, the business cycles are similar in the two groups, by others -- they are distinct. These results for small versus large countries echo the findings in the literature comparing developed and developing countries business cycles. For instance, using Bry and Boschan (1971) procedure to determine the turning points in GDP series for 15 developing countries, Rand and Tarp (2002) argued that the average length of the business cycle for these countries is only between 7 and 18 quarters, or 4.5 years. Other studies, using

> 13 We do not use the 1.5 million population threshold to define small states as this gives a very small sub-sample of small countries in the quarterly data set.

15
<!-- Start of picture text --> MI-Demand at Cycle<br>and Seasonal Frequencies<br>Log Inflation as Measure of Opportunity Cost<br>VECM, STSM and Merged Approaches*<br><!-- End of picture text --> <!-- Start of picture text --> 0.5<br>0.4-<br>0.3-<br>0.2-<br>0.1-<br>0.0<br>-0.2<br>lr t- 00 X a, -<br>--- VECM - STSM - Merged VECM<br><!-- End of picture text -->

*For VECM, chart shows fitted values of short-run modeL For STSM and merged models, chart shows sums of cycles plus seasonal components.

In particular, the similarity of the short-run dynamics based on the STSM and merged approaches, together with the distinctiveness of the VECM-based short-run dynamics, is a characteristic that has been robust to changes in the definition of the opportunity cost variable. Moreover, notice that whereas VECMs attributed the sharp fall in money demand in 1996 to the zero frequency (i.e. a shock in the level of the trend component), <!-- Start of picture text --> Ml-Demand at Cycle<br>and Seasonal Frequencies<br>Depreciation as Measure of Opportunity Cost<br>VECM, STSM and Merged Approaches *<br>0.4 -<br>0.3 -4<br>0.2-<br>0.1<br>0.0 -V<br>-0.1I<br>-0.2<br>-0.3<br>-0.4<br>05 c~~ OS . ~~ ~ O: Os ~ OS 00 0: 05 05 0 0 0_<br>-- - VECM - STSM Merged VECM<br><!-- End of picture text -->

*For VECM, chart shows fitted values of short-run modeL For STSM and merged models, chart shows sums of cycles plus seasonal components.

Money Demand in Venezuela, Mario A. Cuevas 29
Policy ReseaRch WoRking PaPeR 4517

# **Abstract**

This paper examines the financing of tertiary education in Brazil, Colombia, Mexico and Peru, comparing the affordability and accessibility of tertiary education with that in high-income countries. To measure affordability, the authors estimate education costs, living costs, grants, and loans. Further, they compute the participation rate, attainment rate, and socio-economic equity index in education and the gender equity index as indicators of accessibility. This is the first study attempting to estimate affordability of tertiary education in Latin America within a global context. The analysis combines information from household surveys, expenditure surveys, and administrative and institutional databases.

The findings show that families in Latin America have to pay 60 percent of per-capita income for tertiary education per student per year compared with 19 percent in high-income countries. Living costs are significant, at 29 percent of gross domestic product per capita in Latin America (19 percent in high-income countries). Student assistance through grants and loans plays a marginal role in improving affordability. Moreover, the paper confirms previous findings of low access to tertiary education in the region. One policy implication of the findings is that Latin American governments could take steps to make tertiary education more affordable through student assistance.

This paper—a product of the Education Sector Unit in the Human Development Sector at the Latin America and Caribbean Region—is part of a larger effort in the department to is part of a larger effort in the region to provide analysis and policy options for education reform. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ablom@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 8179

# **Abstract**

This note describes a new measure of multidimensional poverty developed for Armenia. In 2013, the National Statistical Service of the Republic of Armenia and the World Bank began work on a national measure of multidimensional poverty to supplement the consumption poverty indicator. This measure, which was identified through consultations with many stakeholders in Armenia, reflects deprivations specific to Armenia in the areas of education, health, labor, housing conditions, and basic needs. The approach offers insights into the complexity, depth, and persistence of poverty in the country; tailoring it specifically to the country context enhances its relevance for policy. This note uses the new measure to describe national trends and regional patterns.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mmeyer3@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> oO<br>ive)<br>*<br>S.<br>*<br>* *<br>*<br>= * 2° *<br>es<br>Oo * - of, * *<br>A ** *<br>*J*<br>7<br>3) eat 3 **+3 of** e o%e*<br>oO<br>2<br>0 20 40 60 80 100<br>cpi<br>——— Fitted value Bribery incidence (percent of firms experiencing at least one pbrib<br><!-- End of picture text -->