rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix D Imputing the Missing Values for Predictions**

# **_Physical Investment_**

# _Greenfield electricity projects_

We imputed the missing physical investment amounts in greenfield infrastructure for small hydro projects in Indonesia; wind and solar PV projects in Romania and Ukraine; solar PV projects in South Africa; and small hydro, biomass, and solar PV projects in Brazil by estimating the amount of investment needed per megawatt for the energy projects using the same technology in the same country. The imputations are obtained from the following regression: I . ii ii ii We used the following number of observations to input missing values: PPhEEssiiccP PPeessttmmeeIItt = ββ× CCPPCCPPcciittEE + PP

- Indonesian small hydro projects: 7 observations were used to impute 1 missing value.

- Romanian wind projects: 18 observations were used to impute 2 missing values.

- Romanian solar PV projects: 4 observations were used to impute 2 missing values.

- Ukrainian wind projects: 6 observations were used to impute 1 missing value.

- Ukrainian solar PV projects: 25 observations were used to impute 3 missing values.

- South African solar PV projects: 37 observations were used to impute 1 missing value.

- Brazilian small hydro projects: 73 observations were used to impute 1 missing value.

- Brazilian biomass projects: 36 observations were used to impute 2 missing values.

- Brazilian solar PV projects: 17 observations were used to impute 1 missing value.

# _Brownfield roads projects in Indonesia_

We imputed the missing physical investment amounts for build, rehabilitate, operate, and transfer projects in Indonesia by estimating the physical investment in roads projects with the same type of PPP implementation method in the East Asia and Pacific region based on the contract period and road length, using the following regression: I . ii ii ii ii We used 29 observations to impute two missing values. PPhEEssiiccP PPeessttmmeeIItt = RRffPPdd EEeeIILLtth + CCffIIttP cctt PPeePPiiffdd + PP We used 29 observations to impute two missing values.

# _Projects implemented by equity sales in Argentina_

We imputed the missing physical investment amounts for Argentinian projects awarded through equity sales (all of which are electricity projects) by estimating the yearly investments in other projects awarded in the same manner using the initial size of the project. Initial size is the initial number of connections for any project involving electricity distribution (generation capacity in megawatt hours for generation projects and transmission line length for transmission projects): 42
# **Table 4: Panel B – Nested Logit Regressions**

Log Odds ratio between Owning with and without a Mortgage

|**Country**|**parttime**|**unemp**|**retired**|**urban**|**suburban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|
|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_||||||||||
|Czech Republic|0.36|0.38|0.60 ***|-0.31 ***|||-0.54||-0.26|
|Estonia|0.34|0.32|1.22 *|-0.11|||0.08||-0.36|
|Hungary|-0.36|0.22|0.36 **|-0.24 ***|0.03|0.35|0.29|0.18|0.07|
|Latvia|0.11|0.09|-0.33|-1.20 ***||-1.29|-0.42|0.21|0.49|
|Lithuania||-5.85||-0.26|||0.10||-0.39|
|Poland|-1.28 ***|-0.07|-0.41|-0.45 ***|0.01|0.10|0.19|-0.79 ***|-0.38 *|
|Slovak Republic|0.11|0.55|0.16|-0.98 ***|-0.14|-1.28|-1.47|0.84|0.70|
|_EU-15 and Cyprus_||||||||||
|Austria|-0.62 *|-0.30|0.05|-0.03|-0.37 ***|-0.04|-0.30|0.52 *|0.45|
|Belgium|-0.39|0.90 **|1.08 ***|-0.07|0.20|0.67|0.68|0.72 **|1.12 ***|
|Cyprus|0.16|0.35|0.60|-0.47 **|-0.10|-0.96|-1.96 ***|1.24 ***|1.11 ***|
|Denmark|-0.23|0.05|-0.23|0.14|0.03|3.29|3.55|0.98|1.02|
|Finland|0.26|0.11|0.46 ***|0.01|-0.12|0.11|0.77 *|0.18|0.60 ***|
|France|-0.33|-0.38|-0.12|-0.17 *|-0.20 **|1.16 ***|1.50 ***|-0.08|0.30|
|Greece|-1.06 *|-0.46|-0.47|-0.20|-0.37 **|-1.15|-1.63 ***|-0.28|-0.12|
|Ireland|-0.62 *|-0.64|0.88 **|-1.00 ***|-0.81 ***|-0.32|1.29 **|0.10|0.24|
|Italy|-0.78 ***|-0.46|-0.33 *|-0.56 ***|-0.42 ***|-0.41|-0.43|0.11|0.44 ***|
|Luxembourg|-0.69|0.70|0.16|-0.25|-0.13|0.88|1.50|0.96 **|1.39 ***|
|Netherlands|0.00|-0.05|0.03|||1.29 ***|1.03 **|1.18 ***|1.06 ***|
|Portugal|-0.57|-0.25|0.01|-0.32 **|0.03|-0.48|-0.15|0.02|0.41|
|Spain|-0.45|-0.01|0.18|-0.27 ***|-0.41 ***|-0.27|0.20|0.52 ***|0.60 ***|
|Sweden|0.24|-0.06|-0.16|-0.17|-0.09|-0.36|-1.21|0.66|0.85 *|
|United Kingdom|-0.09|-1.05 **|1.30 ***|-0.10|||1.81 ***||0.56 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2007 10
- 77 -

Figure 8-B

# Real Wage and Real Exchange Rate

<!-- Start of picture text --> AI0I0A TINA<br>em. em<br>sJo<br>910 s0 ~ .''.. ~ NEl , . _ _ . ."''''''"' . ~<br>70<br>;0 ': :15 SIAl SCANE Ui i7 r 61 9 70 7 t 7i<br><!-- End of picture text --> 600 <!-- Start of picture text --> ito<br>90 ~ . ~~~~~ . ifl"2*T<br>710 Sol- -<br>AOUJiF N(1 10T11&Ail ISE'4 A PUNI 40 TA(ILC UE'IHANN AP1MI<br>tlii A7.It.<br>205<br>&. 611 64 65 66 67 6 fi9 70 71 7. 1 71<br>1Ul'rl- 0l71| "I"I. IN *|*11t 1 IM,6 ew- # 18*"1wi.<br><!-- End of picture text -->

<!-- Start of picture text --> :1o 1"' .":<br>600<br>62 6 6 657 W o 70 71 7.Z 73<br>IIIAYSIISIS VI4'N 14 IFtAISA r, 11TAPIPMUT1O'AI o *_<br><!-- End of picture text -->
**Table 3. Weighted ranking of host countries in SSA (top 20 in SSA)**

|Number of refugees,|2013|Refugees to 1,000 inh|abitants|Refugees to 1,000 km<sup>2</sup>||
|---|---|---|---|---|---|
|Kenya|534,938|Chad|33.88|Rwanda|2,897.11|
|Chad|434,479|Djibouti|22.93|Burundi|1,680.58|
|Ethiopia|433,936|South Sudan|20.32|Djibouti|919.76|
|South Sudan|229,587|Liberia|12.40|Kenya|914.79|
|Uganda|220,555|Kenya|12.06|Uganda|910.04|
|Sudan|159,857|Congo, Rep. of|11.47|Gambia|882.36|
|Cameroon<br>Dem. Rep. of the|114,753|Rwanda|6.23|Liberia|553.04|
|Congo<br>United Rep. of|113,362|Uganda|5.87|Ethiopia|383.53|
|Tanzania|102,099|Gambia|5.17|Togo|359.88|
|Rwanda|73,349|Cameroon|5.16|South Sudan|355.23|
|South Africa|65,881|Guinea‐Bissau|5.01|Chad|340.92|
|Niger|57,661|Ethiopia|4.61|Guinea‐Bissau|250.24|
|Liberia|53,253|Burundi|4.48|Cameroon|245.04|
|Congo|51,037|Sudan|4.21|Congo, Rep. of|148.69|
|Burundi|45,490|Niger|3.23|United Rep. of Tanzania|107.95|
|Burkina Faso|29,234|Central African Rep.|3.10|Burkina Faso|106.02|
|Angola|23,783|Togo<br>United Rep. of|3.02|Sudan|84.76|
|Zambia|23,594|<br>Tanzania|2.07|Ghana|77.91|
|Togo|20,613|Burkina Faso<br>|1.73|Senegal|72.14|
|||Dem. Rep. of the||||
|Djibouti|20,015|<br>Congo|1.68|South Africa|53.91|

Source: Authors’ presentation based on UNHCR (2014). We drop countries with a number of refugees lower than 100 like Madagascar (12), Lesotho (30), Cape Verde (‐), Comoros (‐), Equatorial Guinea (‐), Mauritius (‐), Sao Tome and Principe (‐). A dash (‐) means that the value is zero or not available (UNHCR 2014).

11
although the gender gap is smallest for those with post-secondary education. Conversely, for any level of educational attainment, the share of women who are employed is always lower than that for men. This may be explained in part by the fact that some women leave the labor force to be a full-time housewife.

**Figure 13. Labor force indicators by sex and educational attainment**

<!-- Start of picture text --> 80%<br>5%<br>70% 8% 12%<br>60% 10%<br>22%<br>50%<br>20% 16%<br>40% 11%<br>30% 9% 10%<br>4%<br>2%<br>20% 4% 15% 12% 2% 41%<br>34% 4%<br>10% 18% 10%<br>13% 14%<br>4%<br>0%<br>Male Female Male Female Male Female<br>Employed Unemployed Out of Labor force<br>No qualification Some/Completed Primary Secondary Post-Secondary and above<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on NHBPS 2014/15.

The labor market seems to demand low-skilled labor as there is a general pattern where there are better job opportunities for people without education than those with any education. Women and youth with some or completed primary education have 38 and 65 percent lower chances to be in the labor force and employed, respectively, compared to those with no education (Annex 2). Men with some or completed primary education have a 58 and 52 percent lower probability to be a labor force participant and employed, respectively, compared to men with no education. On the other hand, women and adults with higher education attainment (post-secondary and above) are better-off than those with no education, with greater chances of being employed and participating in the labor force. A plausible explanation for this observed pattern is that people with no education are more likely to be employed in the agriculture sector in rural areas. On the other hand, educated persons are more likely to be living in urban areas and looking for better-paid jobs. If they do not find high-skilled jobs, they will remain unemployed, with little opportunity to fall back on agriculture-related work, which is relatively hard to find in urban settings.

Among the working-age population, only 3 percent attended vocational training with women and youth being particularly disadvantaged (CBS 2017). In the context of an educational system that has weakened over the last few decades, there is often a mismatch between the skills acquired in school and those required by employers. For example, from KIIs that were conducted for this report, a human resources manager of a large pharmaceutical services company in Khartoum said, “Actually, in Sudan in general, I think there is a problem in service state, I mean as a sector [...] the service concept in Sudan doesn’t work, even in the private sector, you find that there is a governmental part so you don’t feel that it’s working in 19
|**Openness of th**<br>|**e Economy**<br>||
|---|---|---|
|Restrictions on<br>International Trade|An index constructed from the ratio of actual size of trade sector to the expected size.<br>Regression analysis is used to estimate the expected size of the trade sector given geographic<br>size, population, and location relative to potential trading partners|Fraser Institute: Economic Freedom of<br>the World 1997<br>Category VI-B.ii|
|**Structure of th**|**e Economy**||
|Government<br>Enterprises and<br>Investment|An index based on government enterprises and investment as a proportion of GDP|Fraser Institute: Economic Freedom of<br>the World 1997<br>Category II-A|
|Price Controls|An index based on the extent to which businesses are free to set their own prices|Fraser Institute: Economic Freedom of<br>the World 1997<br>Category II-B|
|State Ownership of<br>Banks|An index based on the percent of credits issued by privately owned banks|Fraser Institute: Economic Freedom of<br>the World 1997<br>Category VI-A|
|**Formal Institu**<br>|**tions**<br>“||
|Internal Control<br>Mechanism|If a government agent acts against the rules I can usually go to another officials or to his<br>superior and get the correct treatment”|World Bank: World Development<br>Report 1997<br>Q18|
|Judiciary|Legal institutions are supportive of rule of law principles and permit access to non-<br>discriminatory judiciary|Fraser Institute: Economic Freedom of<br>the World 1997|
|||<br>Category V-C|
|Institutions of Dem<br>|ocracy<br>||
|Political Rights|Average county scores for the years 1990-99|Freedom House: Annual Survey of<br>Freedom|
|Civil Liberties|Average county scores for the years 1990-99|Freedom House: Annual Survey of<br>Freedom|
|Press Freedom|Average county scores for the years 1990-99|Freedom House: Press Freedom 1999|
|Decentralization an|d Bureaucratic Culture||
|Investment Profile|A measure of government’s attitude to inward investment as determined by four sub-|Political Risk Services: International|

32
**THE PROCESS OF FINANCIAL DEVELOPMENT: A STATISTICAL VIEW FROM THE FSAP PROGRAM**<sup>**1**</sup>

**Alain Ize, Rafael Pardo, and Sarah Zekri**

> 1 This paper is based on the document “The FSAP Program: A Statistical Analysis” that was prepared as background for an FSAP Stock-Taking study co-financed by the Swiss Secretariat for Economic Affairs (SECO) and the World Bank. It benefited from comments by Ilka Funke, Susan Marcus, Larry Promisel, Roberto Rocha, and Constantinos Stephanou. We also thank Monica Dedich for her editorial assistance. The paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
household head and refugee hosting regions. The total survey sample was 5,317 households with 3,627 refugee households (837 South Sudanese, 871 Somalis, 893 Eritreans, and 1,016 Sudanese) and 1,690 host community households.

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Refugee Host South Sudanese Somali Eritrean Sudanese<br>Community Male-headed HH Female-headed HH<br><!-- End of picture text --> Source: Author’s calculation using Skills Profile Survey (2017), Ethiopia

# Figure 2: Percentage of refugee and host community households: by gender of head and refugee groups

The SPS 2017 contains detailed information on household characteristics, food and non-food consumption, assets, livestock, education and economic conditions, access to services, and perceptions. It also includes individual-level information on employment, barriers to labor force participation, livelihood structures of refugees before displacement, movement and return intentions. The data combines detailed household questionnaire information with displacement-specific information including drivers of displacement, access to resettlement mechanisms, and return intentions.

# 3.1 Livelihood Activity Variables

For the empirical analysis, we use four types of livelihood variables, defined as follows:

- _Wage employment_ refers to work where the employee receive payment regularly in the form of cash or in kind. This includes paid work for an NGO, private company, paid farm work, or paid domestic work.

- _Self-employment_ refers to people who work on their own account including shop owners, family non-farm business, street traders, or people who own non-farm business.

- _Family business_ refers to any non-farm business owned by a family member. This includes helping to prepare food or drinks to sale, helping to sell at the shop.

- _Farm activities_ include working on a farm owned by the household or member of the household, raising the household’s livestock, herding, fishering, or planting fields.

9
**3. Employment and Wages in Specific Groups during the Crisis**

In this section, we analyze changes in job creation/destruction and wages for specific groups.

# **3.1. Job creation/destruction along the cycle**

# _Year-to-year net job creation_

We can characterize the evolution of the crisis by taking the second quarter of 2009 as the trough of the recession, and the second quarters of 2008 and 2010 as the beginning and the end of this cycle. Year-to-year net job flows through the second quarter of 2009 characterize the trough of the crisis, whereas year-to-year net job flows from the second quarter of 2010 onward characterize the recovery. In addition, we include annual job flows in the second quarter of 2008 (before the crisis started) as a comparison to a noncrisis year Comparing annual performance by the second quarters avoids results being tainted by seasonal adjustments in the labor market.

During the 2009 crisis, the labor supply in Mexico accelerated, making it more difficult to cope with the fall in labor demand. In fact, the population aged over 14 (also known as the potentially active population) increased by 1.73 million by the second quarter of 2009. This was over half a million more new workers than joined the labor force annually in previous years (for example, since 2003 the average annual growth of the potentially active population has been around the 1.1 million). This massive influx of potential workers faced job destruction of a little more of 522,000 and nearly 772,000 newly unemployed people. As a consequence, the inactive population grew by nearly 1.5 million people, 1.1 million of which were available to work (see table 3.1).

**Table 3.1: Annual Changes in Main Components of the Labor Force**

|year ending the|Working|Out|of the labor fo|rce||Labor force||
|---|---|---|---|---|---|---|---|
|second quarter|age||non|||||
|<br>of...|<br>population|TOTAL|<br>available|available|TOTAL|unemployed|employed|
|2007|1,176,542|340,166|63,419|276,747|836,376|127,495|708,881|
|2008|1,233,282|185,131|365,100|(179,969)|1,048,151|88,111|960,040|
|2009|1,733,858|1,484,506|379,854|1,104,652|249,352|771,767|(522,415)|
|2010|951,655|(476,747)|(209,674)|(267,073)|1,428,402|120,851|1,307,551|

**Source: Own calculations using data from ENOE.**

It is difficult to ascertain the origin of the acceleration in working age population but a likely suspect is a change in migration patterns. Two forces may have played a role: (i) the recession in the United States may have forestalled migration from Mexico to the United States, and (ii) for the same reason, Mexican migrants in the United States may have returned to their country of origin. Preliminary evidence suggests that even though the crisis more strongly affected Hispanics in the United States, there was no net change in migration. Workers in the United States did not return home in greater numbers, and workers from Mexico did not migrate to the United States in greater numbers (see, for 12
Policy Research Working Paper 7172

# **Abstract**

This paper uses cross-section data for 107 countries to explore the relationship between gender inequality and economic growth. The paper departs from the literature by using a broad measure of gender inequality that goes well beyond gender inequality in education, which has been the focus of most studies. Another novelty of the paper lies in exploring heterogeneity in the growth-gender inequality relationship. The results confirm that greater gender inequality is strongly associated with lower economic growth. However, this negative relationship between gender inequality and growth is entirely due to the relatively poor countries, with the relatively rich countries showing no such relationship. The findings have important implications for the design and targeting of gender-specific policies.

This paper is a product of the Global Indicators Group, Development Economics. 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 mamin@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
|Table 6Dynamic effect of|Ethiopia drought onschoo|ling progression|
|---|---|---|
|Dependent: Change ingra<br>|de from 1998 to 2004<br>||
|Drought 2001<br>|-0.1903 -0.3731<br>|-0.3060 -0.4497<br>|
||(0.950) (1.860)|(1.200) (2.240)|
|* Total asset<br>|0.0007<br>(4.290)||
|* max edu<br>|<br>|0.0157 -0.1462<br>|
|||(0.390) (2.160)|
|* max educ * distance<br>|<br>|0.0192<br>(2.490)|
|* land||0.0500 0.3836|
|||(0.250) (1.890)|
|* land * distancee<br>|<br>|-0.0461<br>(2.260)|
|* household size<br>|<br>|0.0020 0.1181<br>|
|||(0.050) (4.880)|
|* household size * distan<br>|ce<br>|-0.0085<br>(3.050)|
|* livestock<br>|<br>|-0.00003 -0.0001<br>|
|||(1.270) (1.380)|
|* livestock * distance<br>|<br>|0.00001<br>(1.960)|
|* male|0.2690 0.2827|0.3053 0.3138|
||(0.760) (0.810)|(0.950) (1.040)|
|Region FE|yes yes|yes yes|
|Number of observations|846 842|815 721|
|Number of regions<br>|6 6<br>|6 6<br>|
|R squared (within)|0.0456 0.0507|0.0494 0.0671|

Numbers in parentheses are absolute t values using robust standard errors with region clusters. Age and male dummies are included in all specifications. Distance is kilometers to the nearest town.

28
<!-- Start of picture text --> EN Bangladesh: Fertility outcome<br>6<br>gFe<br>z.<br>c<br>55<br>5 Urban<br>go<br>a<br>; .<br>Fi<br>ee Higher education<br>=!<br>5<br>g<br>nh)ao<br>1950 1955 1960 1965 1970 1975 1980<br>Year of birth<br>Generated by regressing fertility outcome on urban, higher education and interactions<br>with year-oF-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> on India: Fertility outcome<br>E<br>53<br>2ifo)<br>55<br>5 Urban<br>5 O—_—_—_—_—_—_—_—_—_—_—_____SS|<br>a Higher educati<br>5 ‘gner eeucasion<br>=2<br>5<br>a<br>u<br>is<br>1950 1955 1960 1965 1970 1975 1980<br>Year of birth<br>Generated by regressing fertility outcome on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> i) Nepal: Fertility outcome<br>g<br>:<br>5<br>6<br>ks}3 =]a<br>ev ere<br>é"5Higher education<br>ec<br>5<br>4<br>&<br>i)<br>1950 1955 1960 1965 1970 1975 1980<br>Year of birth<br>Generated by regressing fertility outcome on urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Fertility outcome<br>4<br>:<br>5 Urban<br>5<br>Goa]<br>vo<br>“an5 Higher education<br>i=<br>ie)<br>4<br>a<br>is<br>1950 1955 1960 1965 1970 1975 1980<br>Year of birth<br>Generated by regressing fertility outcome on urban, higher education and interactions<br>with year-oF-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
# Surveying Informal Businesses: Methodology and Applications<sup>1</sup>

Gemechu Aga, David Francis, Filip Jolevski, Jorge Rodriguez Meza, Joshua Seth Wimpey<sup>2</sup>

**JEL Codes:** C83, O17, L22, R12 **Keywords:** Adaptive Cluster Sampling, Informality, Informal Businesses, Surveys

> 1 The authors would like to thank Norman Loayza for the guidance and constructive comments, and the participants of the seminar held by the World Bank Development Economic Indicator Group, and the participants at the Sixth International Conference on Establishment Statistics (ICES-VI). All remaining errors are those of the authors. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> 2 The authors are with the Enterprise Analysis Unit, World Bank, Washington DC., emails: <u>gayanaaga@worldbank.org; dfrancis@worldbank.org (corresponding author); fjolevski@worldbank.org; jrodriguezmeza@worldbank.org; and jwimpey@worldbank.org.</u>