rafmacalaba/datause-encoder-data huggingface dataset:
# **5. POST-EARTHQUAKE CHARACTERIZATION OF ASSET-WEALTH**<sup>**29**</sup>

# **5.1.Data Sources and Methodology**

A post-earthquake food security-oriented survey was conducted in June 2010 by the CNSA in collaboration with its main partners (ACF, FEWS-Net, Oxfam GB, FAO, UNICEF and WFP). The sampling used for the household survey is a probabilistic cluster method, using two stages: (i) enumeration sections (geographical areas) and camps and (ii) households. 2003 census data is used to select the enumeration sections, with a probability proportional to population size. Eight households are then selected randomly in each section. Camps are selected using the International Organization for Migration (IOM) data; the number of camps selected was proportional to the size of the communes. The sampling method yielded 1901 interviewed households, located in the disaster areas (camp and noncamp sites) as well as in some non-directly affected areas. Geographic strata covered by the EFSA II survey are presented in Figure 2.

To randomly select households, different methods were used for the urban households, the rural households and the camps. For urban households, survey investigators observe and mark the location of households on a street map that does not contain socioeconomic infrastructure, and the households are randomly selected. For rural households, previously mapped buildings are randomly selected using enumeration section maps, and households living in those buildings are interviewed; if there are no households inside, then the closest household is selected. For camps, survey investigators start from the centre of the camp and walk towards the outside in a different randomly selected paths. They number each household encountered in the way, and randomly select two households to interview. For all three types of sampling, when multiple households are found living in the same building or tent, a single household is randomly selected.

# **5.2. Assets**

Based on the June survey, an asset index is calculated using a wider set of preearthquake dichotomous variables, namely some durable goods not declared in the February survey and access to basic utilities. Table 14 reports both weights and contributions to inertia. Weights have signs consistent with interpretation of the first component as an asset-poverty index. In directly affected areas, contribution to inertia of lighting appears to be particularly high (26.7%). Water source also contributes in a large extent to inertia (18.9%). Having tools or material for fishery, agricultural production and handicraft contributes to 12.2% of the inertia explained by the first component of the analysis. Other items contribute to less than 10% of inertia each.

> 29 This section is an excerpt from Echevin (2011).

37
One criticism of the current income classification is that the thresholds are dated and somewhat arbitrary. Some feel that it is a less relevant classification now than in the past since the majority of the world’s extreme poor now live in countries classified as middle income (e.g., Kanbur and Sumner, 2012; Ravallion, 2012). Indeed, over 70 percent of the world’s total population—some 5 billion people—lived in countries classified as middle income in FY16; less than 10 percent lived in low income countries (see Figure 2). However, Table 2 shows that the estimated incidence of extreme poverty is considerably higher among low income countries as a whole (47.2%), compared with lower middle countries (18.7%) or upper middle income countries (5.4%).

**Table 2. Extremely poor population in each income group, 2012**

||Extreme poverty<br>headcount<br>(% living below US$ 1.90 a<br>day at 2011 PPP)|Share of population<br>(%)|Share of extremely poor<br>population (%)|
|---|---|---|---|
|Low|47.2|8.3|30.1|
|Lower middle|18.7|39.4|56.3|
|Upper middle|5.4|32.8|13.6|
|High|0.0|19.5|0.0|
|World|12.7|100.0|100.0|

Source: World Development Indicators and PovcalNet, accessed on December 8, 2015.

The use of market exchange rates for converting GNI to a common currency is also felt to be sub-optimal. The common suggestion is to use purchasing power parities (PPP); some argued that, at least for a period of time, there is a need for both a PPP and US dollar based GNI per capita classification.

Users also voiced concerns about the volatility of the classification—in other words, an undesirable characteristic of a country classification system is that reclassifications occur too frequently. Suggestions to address this were put forward, including restricting classification changes until a country has been in the new category for a fixed time period, or above a set percentage of the threshold, or the use of moving averages of the GNI per capita estimates. There were also proposals for the publication of aggregates with a much closer link to the Bank’s operational activities. For example, measures could be based on fragile and conflict-affected 12
# **2. The program, interventions, and evaluation design**

# **_2.1 The BOS Program_**

Indonesia has been implementing school-based management reforms over the last decade through the School Operational Assistance programme (BOS). The program, initiated by the Ministry of Education and Culture<sup>5</sup> in 2005, provides all Primary and Junior Secondary schools (public and private) with block grants, which are meant to cover all operational costs of running a school, except salaries of staff who are civil servants. The program currently provides grants to 228,000 schools benefiting an estimated 43 million students. In 2012, Rp.23.5 trillion<sup>6</sup> or 8.1% of the total government education budget, was spent on BOS and there are plans to extend BOS to senior secondary education.

Along with the school grant, the BOS program also provides schools with greater autonomy and flexibility in managing their resources and in turn, schools are expected to use these resources in a transparent and accountable way. The program aimed to strengthen school planning and budgeting processes, as well as to strengthen parental and community oversight. In particular, School Committees were given formal roles in developing school development plans and in programming the BOS resources. These reforms, coupled with the decentralization of the responsibility of the oversight of basic education from the central government to district governments, have fundamentally transformed how the education system is managed in Indonesia.<sup>7</sup> Despite these efforts, parental participation in school decision making was still low at the time of this experiment. In a nationally representative survey on school-based management carried out in 2010-11 (World Bank 2012), two-thirds of schools reported making decisions without the participation of the School Committee or parents. Most schools reported making decisions “by consensus,” usually led by the principal. In 22 percent of schools, principals alone reported making all decisions. School Committees were largely ineffective at playing the mediator role

> 5 Then called Ministry of National Education (MoNE)

> 6 The exchange rate at the time was roughly IDR9400 to US$1.

> 7 School-based management is a form of decentralization in education service delivery, devolving a degree of authority from the government to the school level. Education Law 20/2003 and Minister of Education and Culture Decree 044/2002 on School Committee and District Education Council have laid the ground for Indonesia’s schoolbased management. World Bank (2012) provides a succinct account of the status of SBM implementation in Indonesia.

6
# 5 Conclusion

Due to the poor quality of international data, assessing the economic impact of the brain drain is a challenging issue. Building on the seminal work of Carrington and Detragiache (1998), this paper provides a new dataset on skilled emigration rates describing the loss of skilled workers in both developing and developed countries. By increasing the number of observations and improving their degree of reliability, our method provides useful material for econometric analysis of the causes and consequences of the brain drain.

In absolute terms, we show that the largest stocks of educated emigrants are from Europe, Southern and Eastern Asia and, to a lesser extent, Central America. Nevertheless, as a proportion of the local educated labor force, the highest rates are observed in Central America and Africa (on the Western and Eastern coasts). Between 1990 and 2000, these regions also experienced the strongest increase in brain drain.

Our dataset is obviously evolutionary. It can be improved in several ways:

- 2 This release uses survey data when census data are not available yet. By incorporating new OECD censuses, we will improve the quality of our estimates;

- 2 Adding immigration data from non-OECD countries (Gulf states, South Africa, Eastern Asia) would also strongly improve the quality of our estimates for the major countries involved (Egypt, Jordan, Indonesia, India, Pakistan, Zimbabwe, Namibia.);

- 2 Repeating the exercise for 1980 would also ease the econometric exploitation of the database.

Despite these possible developments, we believe that the current version of our database delivers information that is rich enough to assess the changes in the international distribution of migration rates, to test for the (push and pull) determinants per skill group or to evaluate the macroeconomic consequences of migration on source and destination countries.

# References

- [1] Adams, R. (2003): International migration, remittances and the brain drain: a study of 24 labor-exporting countries, World Bank Policy Research Working Paper, n. 2972.

- [2] Antecol, H., D. Cobb-Clark and S. Trejo (2003): Immigration policy and the skills of immigrants to Australia, Canada and the United States, Journal of Human Resources, 38, 1: 198-218.

34
22 Many processing mills are thought to survive only due to state subsidies such as value-added tax rebates (Changjin et al., 2008; TFT, 2007). With the exception of some large foreign enterprises or joint ventures, wood processing companies largely follow a low-cost/low-price competitive strategy with a focus on quantity rather than quality. For example, furniture and other wood products are usually of low-tomedium quality (Castaño, 2002; Changjin et al., 2008).

There are few signs that Chinese civil society is exerting the sorts of pressures evident in northern countries where NGOs actively lobby to ensure high environmental standards in the timber value chain. There is compelling evidence that a significant (but unknown<sup>8</sup> ) share of Chinese log imports are “illegally sourced”,<sup>9</sup> including from Gabon (EIA, 2005; ITTO, 2005; Stark and Cheung, 2006). It is widely argued that China is at the center of illegal log trade and processing (Global Timber, 2009; Katsigris et al., 2002; White et al., 2006).<sup>10</sup> Insofar as the Chinese market shows any distinctive preferences for wood products, consumers appear to like dark wood characteristics, as do Indian consumers. Chinese producers favor Okoumé (a variety with which Gabon is particularly well-endowed and whose fiber characteristics make it ideal for peeling), and darker hardwoods. This contrasts with European preferences for lighter-colored woods.

# <u>EU and China Comparison</u>

This contrast between the drivers of consumption and the determinants of market access in the EU and China surfaces in the preferences of global buyers operating in Gabon. Buyers from China tend to place a premium on low price and large volumes. They are generally less concerned with specific varieties than are the EU buyers, and also show particularly low preferences for environmental compliance and the quality of the logs, which they are purchasing (Figure 14). Specifically with respect to environmental standards, Chinese buyers make very few demands from Gabonese suppliers, particularly in comparison to EU buyers (Figure 15).

**Figure 14: European and Chinese Buyers’ Requirements – Wood Logs (1=not important; 5=very important)**

<!-- Start of picture text --> EU Price<br>5<br>CN<br>4<br>3<br>Environmental 2<br>Variety tree species<br>compliance 1<br>0<br>Volume Quality<br><!-- End of picture text --> _Source:_ Terheggen’s fieldwork interviews

> 8 The share of illegally sourced logs of total log trade volumes is estimated to be around ten percent (EIA, 2005).

> 9 Illegal logging occurs when timber is harvested, transported, bought or sold in contravention of national laws (EIA, 2005; Greenpeace, 2009)

> 10  “China’s sources for hardwood log imports reads like a who’s who of countries with problems with illegal logging” (EIA, 2005:3)
# **4. Descriptive Statistics**

In this section, we report some key descriptive statistics from the Domah survey.

## **4.1 Countries with Autonomous Electricity Regulators**

Table 1 shows that by 1998 just under half the countries in our sample had an autonomous electricity regulator<sup>22</sup> – mainly in Latin America. However, in the following three years 3 African and 1 Caribbean country joined the set. Asia provides an exception to the spread of autonomous regulators with only 1 country (Philippines) having an autonomous regulator before 2001.

By 2001, a majority of countries had regulators classified (at least in legal terms) as autonomous.

**Table 1:  The Trend towards Autonomous Regulators (by Continent)**

||||**1998**||**2001**|
|---|---|---|---|---|---|
||**Total**|**Ministry**|**Autonomous**|**Ministry**|**Autonomous**|
|**Africa**|5|5|0|3|2|
|**Asia**|4|3|1|3|1|
|**Carrib**|6|3|3|2|4|
|**Latin America**|13|4|9|4|9|
|**Total**|28|15|13|11|17|

Source: Domah 2001 survey, supplemented and updated by authors

## **4.2 Countries with Electricity Regulatory Laws**

Even where there was no autonomous regulator, laws for the reform of the ESI Including regulatory reform were being passed. Table 2 shows the regional distribution of electricity reform laws for those states without autonomous regulation. According to the Domah data, all the countries with autonomous regulators had enacted an electricity regulatory law. By the end of our sample period only two countries in the sample (Barbados and Indonesia) did not have any electricity regulatory law in place.

These laws sometimes provided for IPPs or other elements of market reform, for commercialisation and sometimes for unbundling and competition in generation and supply<sup>23</sup> . If the laws covered regulation, they typically specified the powers and duties of the Ministry (or designated Ministry agency/department) in carrying out regulatory functions.

> 22 The Domah questionnaire used the term “autonomous” rather than “independent”, not least because it is more neutral.  We treat the two terms as synonymous.

> 23 However, the actual introduction of competition and/or privatisation took place at some later date, typically with several events at different times.  This is why we cannot within this data set obtain good indicators for privatisation and competition.

19
# **Rental Regulation and Its Consequences on Measures of Well‐Being in the Arab Republic of Egypt**

_Gabriel Lara Ibarra_<sup>_1_</sup> _Vibhuti Mendiratta Tara Vishwanath_

## **JEL: I32, D63, G18**

## **Keywords**

Welfare measurement, rental regulation, inequality, Egypt

> 1 We are very grateful to the Central Agency for Public Mobilization and Statistics (CAPMAS) for their kind support, comments and suggestions, and for providing access to the necessary data to produce this study. We also thank participants in the World Bank Poverty SAR/MENA Seminar, Dean Joliffe, Sergio Olivieri, and Benu Bidani for very helpful comments and suggestions.

1
pattern of indirect tax-incidence.<sup>6</sup> Next, we consider the welfare effects of a uniform increase in the VAT rates to increase government revenue. This scenario indicates the marginal cost of funds (MCF) and is a measure of how the VAT operates in the context of other model distortions. Finally, we describe alternative tax structures that are revenue neutral and that shift the tax burden away from low-income households: (1) we uniformly reduce VAT tax rates and increase income taxes on high-income households; (2) we remove the VAT on agriculture and food and replace the lost government revenue with either an increase in the income tax rates on high-income households or an increase in the other VAT rates.

We will use a variety of measures to describe the effect of a VAT on welfare. We report the regressiveness of the VAT, the progressiveness of the complete tax system, and the equivalent variation derived from the changes in household consumption. We also show the effects of changes in the tax system on total government revenue, the components of government revenue, and the structure of the economy.

We begin with a brief description of the CGE model, focusing on the tax structure and the changes necessary to represent a VAT with rebates. We then simulate alternative tax structures to describe the welfare effects of a VAT on various household groups. Our conclusions follow.

# **2. The CGE Model**

CGE models incorporate consumer and producer behavior as well as the interaction between other economic agents and therefore incorporate all effects on the distribution of income and economic welfare. We use the CGE model by Löfgren et al (2001).<sup>7</sup> The CGE model equations are solved simultaneously with the computer software package GAMS and the solver PATH.

# **2.1 The Tax Structure of South Africa**

Typical of a middle-income or more advanced developing country, the tax base of South Africa is derived more from manufacturing activities and formal services, which now account for more than two-thirds of the economy. The production structure of South Africa is reflected in Table 1, which is derived from a recent social accounting matrix (SAM). While the mining sector remains a significant source of export revenue, its share in the economy is less than 7 percent and, as an export sector, it does not contribute to VAT revenue.

> 6 Devarajan and Hossain (1995) analyze the combined incidence of taxes and public expenditure in the Philippines. They simulated the price effect of a particular tax, by eliminating the tax from the Philippines tax system and simultaneously replacing it with an increase in proportional income tax (Devarajan and Hossain,1995:10).

> 7 A detailed discussion on the mathematical structure of the standard model can be found in Löfgren et al (2001).

3
in the use of short panels. In fact, 20.7% of the total number of country-sample years were years with an autonomous regulator and 31% with an electricity or energy regulatory law.

The key data sources used are:

- US Energy Information Agency – for data on generation capacity by country (GW) 1980-2001 (Noted that the EIA series does not distinguish between publicly and privately owned generation capacity.)

- World Bank Development Indicators - for Per capita GDP in $US1995; electric power transmission and distribution losses and other control variables

- The Preetum Domah 2001 survey of electricity regulators for data on electricity regulatory governance, privatisation and competition (supplemented by the authors’ own research).<sup>18</sup>

The Domah survey data (covering 50 developed, transition and developing countries) are the best data currently available to estimate the impact electricity regulators, not least because it allows the _dating_ of regulatory reforms, primarily because it records the year in which key regulatory legislation was enacted.

The Domah data set is very suitable for a preliminary investigation of the impact of regulation but is far from ideal. In particular, it suffers from the following:

- 1) The data on electricity market structure is relatively weak and the data on privatisation very limited;

- 2) There is no data on the informal, practical aspects of regulation (e.g. security of tenure of regulatory agency heads or commissioners, etc);

- 3) The data on regulatory governance, competition and privatisation has no time dimension beyond a simple 0/1 dichotomy set at the year in which key regulatory legislation was enacted;

- 4) The data on the formal aspects of regulation only allows for a 4-element index rather than a larger index.  These data weaknesses should be born in mind when considering the econometric results.

# **3.2.2. Econometric Issues**

Panel data generally allow major opportunities for carrying out investigations that are not possible with single-year cross sections or single-country time series, but these give rise to a number of issues which need taking into account for estimation. In our case, with data on 28 countries for 21 years, we have a large and long panel. Because of missing observations, it is an unbalanced panel.

> 18 See Domah, Pollitt, and Stern (2002) for full details.  We are very grateful to Preetum Domah for permission to use the information from his survey in this paper.

16
**Table 5.   Distribution of MFN tariff rates in Russia in 2001-2005**

||||**_200_**|**_1_**|**_200_**|**_2_**|**_200_**|**_3_**|**_200_**|**_4_**|**_200_**|**_5_**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_Actua_**|**_l rate ran_**|**_ge, %_**|_Frequency_<sup>_a_</sup>|_Percent_<sup>_b_</sup>|_Frequency_|_Percent_|_Frequency_|_Percent_|_Frequency_|_Percent_|_Frequency_|_Percent_|
|_0_|_to <_|_5_|88|0.79|109|0.98|113|1.01|115|1.03|234|2.06|
|_5_|_to <_|_10_|4,324|39.04|4,414|39.59|4,379|39.24|4,414|39.35|4,410|38.8|
|_10_|_to <_|_15_|1,927|17.4|1,912|17.15|1,900|17.03|1,943|17.32|1,950|17.16|
|_15_|_to <_|_20_|2,901|26.19|2,810|25.21|2,816|25.23|2,765|24.65|2,823|24.84|
|_20_|_to <_|_25_|1,456|13.15|1,398|12.54|1,333|11.94|1,435|12.79|1,447|12.73|
|_25_|_to <_|_30_|183|1.65|212|1.9|208|1.86|200|1.78|189|1.66|
|_30_|_to <_|_35_|61|0.55|91|0.82|68|0.61|89|0.79|95|0.84|
|_35_|_to <_|_40_|23|0.21|44|0.39|89|0.8|60|0.53|50|0.44|
|_40_|_to <_|_45_|34|0.31|39|0.35|56|0.5|37|0.33|30|0.26|
|_45_|_to <_|_50_|17|0.15|25|0.22|30|0.27|16|0.14|24|0.21|
|_50_|_to <_|_55_|7|0.06|11|0.1|23|0.21|18|0.16|12|0.11|
|_55_|_to <_|_60_|7|0.06|10|0.09|21|0.19|14|0.12|7|0.06|
|_60_|_to <_|_65_|3|0.03|8|0.07|25|0.22|26|0.23|20|0.18|
|_65_|_to <_|_70_|5|0.05|10|0.09|10|0.09|4|0.04|9|0.08|
|_70_|_to <_|_75_|5|0.05|1|0.01|12|0.11|5|0.04|2|0.02|
|_75_|_to <_|_80_|5|0.05|7|0.06|4|0.04|7|0.06|2|0.02|
|_80_|_to <_|_85_|4|0.04|8|0.07|15|0.13|23|0.21|21|0.18|
|_85_|_to <_|_90_|4|0.04|4|0.04|6|0.05|4|0.04|6|0.05|
|_90_|_to <_|_95_|2|0.02|2|0.02|6|0.05|2|0.02|4|0.04|
|_95_|_to <_|_100_|1|0.01|2|0.02|4|0.04|1|0.01|3|0.03|
|_100_|_to <_|_150_|13|0.12|14|0.13|25|0.22|16|0.14|13|0.11|
|_150_|_to <_|_200_|4|0.04|7|0.06|5|0.04|15|0.13|6|0.05|
|_200_|_to <_|_250_|0|0|1|0.01|3|0.03|4|0.04|3|0.03|
|_250_|_to <_|_300_|0|0|4|0.04|3|0.03|5|0.04|3|0.03|
|_300_|_to <_|_350_|0|0|3|0.03|5|0.04|0|0|0|0|
|_350_|_to <_|_400_|0|0|1|0.01|1|0.01|0|0|1|0.01|
|_400_|_to <_|_450_|1|0.01|0|0|0|0|0|0|0|0|
|_450_|_to <_|_500_|0|0|1|0.01|0|0|0|0|1|0.01|
||_>_|_500_|1|0.01|0|0|1|0.01|0|0|0|0|
|||**_Total_**|11,076|100|11,148|100|11,160|100|11,218|100|11,365|100|

_Notes:_ _a Number of lines at 10-digit level b Percentage out of total number of product lines_ 20