rafmacalaba/datause-encoder-data huggingface dataset:
Researchers have exploited smaller lottery programs. The first such set of studies considered the Pacific Access Category program for Tonga, which provides an opportunity for 250 individuals each year to move to New Zealand, with a random ballot used to select among all eligible applications received. McKenzie et al. (2010) collaborated with the New Zealand Department of Labour to draw a sample of individuals who had their names selected in this ballot, as well as a sample of those who applied but whose names were not drawn. They then surveyed the ballot winners in New Zealand, their remaining family members in Tonga, and the ballot losers in Tonga. As with the U.S. diversity visa, not all those whose names were chosen in the lottery migrated, so the authors also had to survey in Tonga the ballot winners who did not migrate.

If everyone who applied for the migration lottery ended up migrating, and no one who lost the lottery migrated, then linear regression of equation 1 (with or without the X controls) for the sample of lottery applicants, would give a consistent estimate of the causal impact of migration for people who enter the migration lottery program. However, in practice, some of those who win the lottery may not move (they may change their mind, or fail an entry requirement), whilst a few of those who lose the lottery may find other ways of migrating. In such cases, the outcome of the lottery can be used as an instrumental variable for migration, and still be used to identify the impact of migration on the outcome of interest. If the impact of migration varies across individuals (is heterogeneous), then what will be identified is the local average treatment effect (LATE). Recently there has been debate as to whether the LATE is a parameter of interest in many experiments (e.g. Deaton, 2010 and Imbens, 2010). However, in the case of a migration lottery, it is easy to argue that the parameter is giving an effect of policy interest. The LATE tells us the impact of migrating for someone who would migrate if they won the lottery, and not migrate otherwise. This is precisely what we would be interested in when assessing the impacts of such policies on development outcomes.

McKenzie et al. (2010) use this migration lottery to estimate that Tongans moving to New Zealand have a 263 percent increase in income, within the first year of moving. The migrants also benefit in terms of improved mental health (Stillman et al, 2009). The authors then use a sample of non-applicants to the lottery and a large sample of the overall population and compare the experimental estimate to what one would obtain using non-experimental methods.

4
Figure 3: Aggregate Headcount Poverty Rates – GHS (USD 1.90$ International Poverty Line) a) <!-- Start of picture text --> Aggregate Poverty Rates -GHS (1.90$ International Poverty Line)<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>child 0-4 child 5-9 child 10-14 child 15-17 adult 18-59 adult 60above adult child<br>Wave 1 Wave 2 Wave 3<br><!-- End of picture text --> b)

# **A G G R E G ATE P OV E R TY R A TE S - G HS (1.90$ INTE R NA TIONA L P OV E R TY LINE )**

<!-- Start of picture text --> adult child child north child south child rural child urban<br>W A V E 1 W A VE 2 W A V E 3<br>69%<br>57% 56% 59% 60% 63%<br>45% 48% 50%<br>33% 28% 24% 35% 31% 24% 36% 24%<br>18%<br><!-- End of picture text --> Source: Authors’ estimations based on GHS Wave 1-3. Note: Individuals with missing age were dropped from the analysis.

Figure 4 a): Rural- Urban Headcount Poverty Rates (USD 1.90$ International Poverty Line) Figure 4 b): North-South Headcount Poverty Rates (USD 1.90$ International Poverty Line) <!-- Start of picture text --> Rural-Urban Poverty Rates - GHS North-South Poverty Rates - GHS<br>(1.90$ International Poverty Line) (1.90$ International Poverty Line)<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0%<br>0%<br>Wave 1_R Wave 2_R Wave 3_R Wave 1_N Wave 2_N Wave 3_N<br>Wave 1_U Wave 2_U Wave 3_U Wave 1_S Wave 2_S Wave 3_S<br><!-- End of picture text --> Source: Authors’ estimations based on GHS Wave 1-3. Note: Individuals with missing age were dropped from the analysis.

Looking beyond national aggregates, Figure 5a presents a breakdown by region and child age group throughout the waves. Figures 5 b and c further differentiate between rural and urban sector in each 15
- Clausen, B., A. Kraay, and Z. Nyiri (2011). Corruption and Confidence in Public Institutions: Evidence from a Global Survey. _The World Bank Economic Review 25(2)_ , 212–249.

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as evolution of spatial disparity. This paper constructs a measure of spatial disparity which can be tracked overtime. More importantly, we introduce a decomposition analysis which can discern the relative in‡uences of access to infrastructure and services and household attributes on spatial disparity. The spatial disparity index estimated and analyzed in this paper is comparable to "neighborhood correlations" widely used to study impact of childhood environment on adult outcomes (Solon, Page and Duncan (2000), Page and Solon (2003), Raaum, Salvanes and Sorensen (2006)). To the best of our knowledge, no study has yet used this measure to study spatial inequality in welfare.

This method is applied to measure and decompose spatial disparity in welfare in Bangladesh which made great strides in reducing poverty during the last decades. Empirical estimates based on two rounds of Household Income and Expenditure Surveys 2000 and 2010 suggest signi...cant spatial convergence in living standards in urban areas and in the country as a whole. The extent of spatial disparity is lower in rural areas compared with urban areas and there has been no signi...cant change in rural areas during the decade. The sustained di¤erences in the magnitudes of spatial disparity between rural and urban areas is indicative of slower mobility of workers across rural and urban activities due to skill constraint. Our results also show that welfare di¤erences across communities in both survey years can be explained mostly by three community level factors: average years of education, percentage of household with electricity, and with phone. In rural areas, agro-climatic conditions are also important for explaining welfare variations across communities. These results show that a large part of variations in welfare in Bangladesh is indeed due to unequal distribution of basic opportunities (e.g. electricity).

Between 2000 and 2010, households in both urban and rural areas experienced signi...cant improvements in access to basic infrastructure and services such as electricity, phone and sanitation. The decline in spatial disparity in urban areas is due mainly to improved infrastructure access with sorting of educated workers playing a smaller role. We ...nd the opposite in the case of rural 28
Asian countries – it was only slightly higher than Taiwan (58 percent) but lower than most other comparator countries. The employment rate as percent of working age population of the Philippines (57 percent) was also relatively low, compared with countries such as China (79 percent) and Thailand (74 percent). The relative high unemployment rate in the Philippines (6.4 percent in 2007) may to some extent be related to its definition.<sup>2</sup>

**Table 2: Participation Rate to Total Working Age Population in Selected East Asian**

**<u>Economies</u>**

|Year|**Philippines**|China|Indonesia|Korea|Malaysia|Singapore|Taiwan|Thailand|Vietnam|
|---|---|---|---|---|---|---|---|---|---|
|2003|**63.0**|77.1|67.2|60.9|66.9|65.5|55.8|73.0|73.7|
|2004|**62.2**|77.8|67.4|61.5|66.9|65.5|56.1|73.4|73.2|
|2005|**63.2**|78.4|67.6|61.4|66.9|65.4|56.2|73.7|73.0|
|2006|**62.2**|78.9|66.7|61.3|66.8|65.3|57.9|72.9|72.9|
|2007|**61.2**|79.4|67.7|60.9|67.0|65.0|58.2|73.6|72.6|

_Source:_ LABORSTAT, ILO _Note:_ Working Age Population is defined as the population over 15 years old except in Malaysia where it is defined as the population over 15 years old and under 65 years old.

**Table 3: Employment Rate to Total Working Age Population in Selected East Asian**

**<u>Economies</u>**

|Year|**Philippines**|China|Indonesia|Korea|Malaysia|Singapore|Taiwan|Thailand|Vietnam|
|---|---|---|---|---|---|---|---|---|---|
|2003|**59.2**|74.5|59.4|58.0|62.7|48.0|53.0|71.7|72.4|
|2004|**58.0**|74.1|58.9|58.5|62.0|47.8|53.6|72.2|72.5|
|2005|**58.5**|73.7|58.0|58.7|60.9|N/A|53.9|72.6|N/A|
|2006|**57.7**|73.3|57.9|58.8|60.9|50.5|55.7|71.9|N/A|
|2007|**57.4**|73.0|59.7|58.9|61.0|50.6|56.0|72.6|N/A|

_Source:_ LABORSTAT, ILO

**<u>Table 4: Unemployment Rate to Total Labor Force in Selected East Asian Economies</u>**

|Year|**Philippines**|China|Indonesia|Korea|Malaysia|Singapore|Taiwan|Thailand|Vietnam|
|---|---|---|---|---|---|---|---|---|---|
|2003|**6.2**|4.3|9.7|3.6|3.6|5.9|5.0|1.5|2.3|
|2004|**6.8**|4.2|9.9|3.7|3.5|5.8|4.4|1.5|2.1|
|2005|**7.4**|4.2|11.2|3.7|3.5|N/A|4.1|1.4|N/A|
|2006|**7.3**|4.1|10.3|3.5|3.3|4.5|5.1|1.2|N/A|
|2007|**6.3**|4.0|9.1|3.2|3.2|4.0|5.1|1.2|N/A|

_Source_ : LABORSTAT, ILO _Note_ : The definition of unemployed is slightly different across countries. See LABORSTAT for details.

# **2.  Disparities in labor market performance across regions**

Labor market performance varies widely across regions in the Philippines, especially between the National Capital Region (NCR) and the other three island groups (Luzon, Visayas, and Mindanao). This is related to their significant difference in industrial structure and sources of employment (Table 5).<sup>3</sup> In NCR, the share of agriculture is negligible, and the industrial and

> 2 According to the NSO definition, the unemployed in the Philippines includes discouraged worker, the temporary laid-offs, temporary disabled and etc. See Annex 1 for details.

> 3 Following the official administrative division of Philippines, Manila National Capital and 16 subregions are

3
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 4. Benchmarking the performance of Sudan’s roads**

|Indicator|Unit|Sudan|Low-<br>income<br>countries|East Africa|Middle-<br>income<br>countries|Resource<br>rich|
|---|---|---|---|---|---|---|
|Classified road density|km/1,000 km<sup>2</sup>of arable land area|37|88|101|278|57|
|Paving ratio|% of primary network paved|47|71.6||32|82.1|
|Paved road traffic|AADT (vehicles per day)|369|1,341|1,549|3,798|1,408|
|Unpaved road traffic|AADT (vehicles per day)|13|39|47|75|54|
|Classified network<br>condition|Percentage in good or fair condition|26|86|59||80|
|Paved network condition|% of paved roads in good or fair condition|24|86|79|82|68|
|Unpaved network<br>condition|% of unpaved roads in good or fair condition|0|56||58|61|

_Source:_ Derived from AICD calculations.

_Note:_ The paving ratio for Sudan is calculated based on the classified road network that is paved. The paved network condition in Sudan is based on regional and national roads in Sudan. The paved road network for Sudan does not include roads of fair quality because the length of roads with fair quality is unknown. AADT = average annual daily traffic.

# **Table 5a. Land area in Sudan with high agricultural suitability**

|Percent contribution to aggregate agriculture value|South Sudan|Sudan|All parts of<br>Sudan|
|---|---|---|---|
|< 10% but high suitability|4,381|1,438|5,819|
|10%–50%|2,279|4,793|7,072|
|> 50%|54|265|319|
|Total area of high agricultural suitability|6,714|6,496|13,210|

# **Table 5b. Distribution of agricultural value within Sudan**

|Percent contribution to aggregate agriculture value|South Sudan|Sudan|All parts of<br>Sudan|
|---|---|---|---|
|< 10% but high suitability|65|22|44|
|10%–50%|34|74|54|
|> 50%|1|4|2|
|Total area of high agricultural suitability|100|100|100|
|_Source:_AICD calculations.||||

> <sup>15</sup>
Financial Regulation and Development

# **Quarterly Variation Confidence Intervals**

Figure F2: Bank Aggregates and Policy Change: 95% Confidence Intervals <!-- Start of picture text --> 0 5 10 15<br>Quarter<br>NBE Bill Difference Difference − 5pc<br>Difference − 95pc<br>0 5 10 15<br>Time<br>Safe Asset Difference Difference − 5pc<br>Difference − 95pc<br>1500<br>1000<br>500<br>Million Birr − Real 2010 0<br>−500<br>1000<br>500<br>0<br>Million Birr − Real 2010<br>−500<br>−1000<br><!-- End of picture text --> 52
<!-- Start of picture text --> GDP compared to baseline: adaptation<br>5<br>ty)<br>5<br>-10<br>-15<br>-20<br>-25<br>10 20 30 40 50 60 70 80<br><!-- End of picture text --> <!-- Start of picture text --> GDP compared to baseline: contingency fund<br>fo}<br>4<br>-8<br>-12<br>-16<br>-20<br>24<br>-28<br>10 20 30 40 50 60 70 80<br><!-- End of picture text --> <!-- Start of picture text --> GDP compared to baseline: insurance<br>0<br>5<br>-10<br>-15<br>-20<br>-25<br>-30<br>-35<br>10 20 30 40 50 60 70 80<br><!-- End of picture text --> <!-- Start of picture text --> GDP compared to baseline: debt<br>2<br>0<br>-2<br>4<br>-6<br>8<br>-10<br>-12<br>10 20 30 40 50 60 70 80<br><!-- End of picture text -->
10 share price (expressed in points per share) of a fund's portfolio of shares acquired in the 12 voucher auction, the lower the book value of those shares . If one takes the total book value of shares privatized in the first wave, the average book value per voucher book invested amounts to CSK32,400. In the case of IPFs, the average book value was only CSK29,000. This indicates that funds typically invested in more expensive shares compared to individuals (see Table 5).

**Table 5: Investment mode**

||Vouch<br>inve|er points<br>sted|Number o<br>acqu|f shares<br>ired|Average book value<br>per voucher book|
|---|---|---|---|---|---|
|by|million|in %|CSK<br>million|in %|CSK|
|IPFs|6,112|71.4|176.0|63.4|29,000|
|Individuals|2,454|28.6|101.7|36.6|41,400|
|TOTAL|8,566|100.0|277.7|100.0|32,400|

Source: Mejstrik et al. (1994) and author's calc lations.

Investments by individual investors had two features (see Table 22 in the appendix). First, they were neutral to enterprise size as far as their potential influence in the enterprise was concerned, since as individuals they were too small to exert any influence over even a small company. Second, individual investors seemed to prefer large (and therefore well known) enterprises<sup>3</sup> . This is very much in contrast to funds, especially small investment funds that preferred to invest in small companies (71% of the companies in which small funds invested were small companies). It should be mentioned that share prices for smaller companies were on average considerably higher'<sup>4</sup> than share prices for bigger companies.

12 In the Czech and Slovak Republics, it is customary to divide the total book value of the fund's shares by the number of voucher books transferred to the fund to derive a "book value per voucher book". A similar calculation can be made for all the shares bought by individuals who did not transfer their vouchers to a fund.

**13** Individual investors spent 57% of their voucher points to bid for large enterprises.

**14** The average price is defined here as the unweighted mean of share prices of all companies.
the lion-share of the “rich” that are missing or whose incomes are understated in the HIECS arguably reside in either Cairo or Alexandria, and (ii) the real estate markets are most developed in Cairo and Alexandria such that the coverage and the quality of the house price data are highest for these two cities (henceforward we will refer to these as districts).

Table 2 provides some basic statistics on the number of observations available to us. For the house price databases we only counted observations above the median house price value (which practically coincides with the mode of the house price density). Since we are interested in the top tail behavior of the house price distribution, we do not use the lower house price values.

|sub-group|Database<br>Betak-online<br>Bezaat|HIECS|
|---|---|---|
|Cairo|5772<br>8475|1289|
|Alexandria|1293<br>2012|767|
|Urban Egypt||6935|

Table 2: Number of observations used The following sections proceed with the empirical application that combines the household expenditure survey and the house price data. A validation of our methodology in a controlled setting where only the survey data are used can be found in the Annex.

# **4.1 Pareto tail index estimated on income survey data**

This subsection presents first estimates of the Pareto tail index of Cairo’s and Alexandria’s income distributions by using household survey data only. These estimates will serve as a reference point. Under the assumption of Pareto distributed top tails we have that: 1 _− F_ 2( _y_ ) = � _τy_ � _−θ_ . Rearranging terms yields: If this assumption holds true, a plot of log ( _y_ ) against _−_ log (1 _− F_ 2( _y_ )) should reveal a linear relationship with a slope parameter equal to<sup><u>1</u></sup> _θ_<sup>.Figure1provides</sup> this plot using the top 10 percent of the household expenditure data from the HIECS. For the majority of data points a linear relationship seems to provide a reasonable fit. A deviation from linearity can be observed however toward the far end of the income spectrum, where the slope appears to fall. Consequently, we should expect estimates of _θ_ to come out higher if we were to increase the income threshold above which observations are included.

17
A. Sanghi and D. Johnson <!-- Start of picture text --> 2000<br>1000<br>0<br>BELGIUM CHINA FRANCE GERMANY JAPAN<br>Country<br>Debt (USD Millions)<br><!-- End of picture text --> Figure 34: Kenya owes most debt to China (2015) Source: The National Treasury of Kenya (2015) <!-- Start of picture text --> 2500<br>2000<br>China<br>Japan<br>1500<br>France<br>1000 Germany<br>Belgium<br>500<br>0<br>2013Q3 2014Q1 2014Q3 2015Q1<br>Year<br>Debt (USD Millions)<br><!-- End of picture text --> Figure 35: Kenya’s debt to China outpaces the rest (2015) Source: The National Treasury of Kenya (2015)

# **5.4 Supply-side Shortages: Reducing labor costs**

Chinese firms may see Kenya’s lower labor cost as an advantage, but the unit labor cost is still quite high. The unit cost of labor in Kenya is 25 percent of the value added, but is only 15 percent of value added in China. The combination of low wages and high productivity results in a low unit cost of production for Chinese workers; low productivity, however, raises the unit labor cost of Kenyan labor. Reducing the unit cost of labor —wages increases should at most match productivity increases —through employment subsidies or reduction of formal sector wages can attract more Chinese firms aiming to take advantage of the low cost Kenyan labor force, especially firms that are developing export businesses to the United States after the renewal of AGOA. It may also help export competitiveness generally and help to reduce the overall trade deficit.

38
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14