rafmacalaba/datause-encoder-data huggingface dataset:
preparation of the data set, that is, by June 30, 2013. Regional projects<sup>22</sup> that do not focus on a single country and projects in several selected countries were excluded from the sample for data availability reasons. The resulting sample comprises 6,149 investment lending (IL) projects approved between FY 1990 and FY 2013. The IEG outcome ratings are available for 3,202 of these projects, that is, for about half. As shown in figure 3, the majority of these 3,202 evaluated projects (light gray) date from the 1990s and early 2000s; fewer data are available for more recently approved projects, many of which have not yet been evaluated or are still active. Given this time lag, this paper primarily covers projects approved between 1990 and 2004. It therefore has a historical focus and does not necessarily reflect the performance patterns of projects approved after 2005 (which represent only about 7.5 percent of the projects reviewed).

**Figure 3. Availability of IEG Outcome Ratings for Investment Lending Projects, by Fiscal Year Approved (1990–2013)**

<!-- Start of picture text --> 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Unavailable Available<br>400<br>300<br>200<br>Number of projects<br>100<br>0<br><!-- End of picture text --> _Source_ : Author’s own compilation, based on World Bank Project Data.

# **4.2 Project Content Measures**

Within this overall sample of 3,202 investment lending projects, PSM reform projects and other, “nonPSM” projects are distinguished. This paper employs a broad definition of PSM projects<sup>23</sup> that includes both (i) “upstream” projects primarily conducted at the center of government in core ministries and central agencies (Ministry of Finance, central HRM bodies, and so on), and (ii) “downstream,” sectorspecific projects (education, health, infrastructure) that have significant PSM reform components (at the

> 22 Regional projects concern more than one client country.

> 23 This broad definition will be used for all statistics reported hereafter, unless explicitly indicated otherwise. For a more detailed analysis of the “upstream” subset of projects focusing on the center of government and mapped to the Bank’s “Public Sector Governance Board” (PSGB) please refer to Blum (2014).

17
important to understand its consequences.

Table 1: FDI

||Share of regions<br>with FDI_>_0|Avg nb of projects<br>per region|Avg nb of sectors<br>per region|
|---|---|---|---|
|||2004||
|Brazil|0.74|14.8|2.5|
|Colombia|0.38|1.9|0.8|
|Ethiopia|0.10|0.1|0.1|
|Mexico|0.81|6.5|1.8|
|Philippines|1.00|12.0|3.0|
|South Africa|0.80|7.1|2.2|
|Vietnam|0.38|3.3|0.9|
||20|08, 2011, 2012 or 20|14*|
|Brazil|0.96|73.5|4.0|
|Colombia|0.79|21.7|2.8|
|Ethiopia|0.55|5.1|1.3|
|Mexico|0.97|29.3|3.7|
|Philippines|0.81|11.8|1.9|
|South Africa|1.00|28.9|4.2|
|Vietnam|0.52|5.3|1.1|

Source: fDiIntelligence. *Note: The end-of-period year varies by country due to the availability of the household survey data. The last years for Brazil, Colombia, and Ethiopia is 2014. For Mexico it is 2012. For the Philippines 2011. For Vietnam South Africa and it is 2008. The end-of-period year information is based on cumulative number of greenfield FDI projects over the last period interval for which the data is available: 2009-2014 for Brazil and Colombia, 2010-2014 for Ethiopia, 2009-2012 for Mexico, 2009-2012 for the Philippines, and 2005-2008 for Africa and Vietnam.

# **2.2 Household Surveys**

We use household surveys from the World Bank’s International Income Distribution Data Set (I2D2) which contains harmonized household and labor surveys. While surveys for Brazil, Colombia, Ethiopia, Mexico, the Philippines, South Africa and Vietnam are available for our baseline year, 2004, the subsequent years available vary by country. The additional years for Brazil and Colombia are 2008 and 2014. The additional years available for Ethiopia are 2009 and 2014. For Mexico they are 2008 and 2012. The additional years available for the Philippines are 2008 and 2011. For South Africa and Vietnam the only additional year we can use is 2008.

The I2D2 data contain harmonized information on wages, gender, sector of activity, location, and education. We adjust the data sets before conducting the analysis in the following way. First, we reduce the sample to individuals between 15 and 59 to focus on 6
**Table 6: Subsidies allocated per year and localty**

|Year|Phones<br>allocated|Thousand<br>inhabitants<br>benefited|<br>Available<br> <br>Funds in<br>current<br>million CH$|Subsidies<br>granted in<br>current<br>million Ch$|Average<br>subsidy per<br>localty in<br>current $|Subsidies1<br>granted as %<br>of available<br>funds|
|---|---|---|---|---|---|---|
|1995|726|240|1,728|836|||
|1996|1,632|761|<br>3,555|371|||
|1997|<br>2,146|772|<br>4,849|3,396|||
|1998|<br>858|227|<br>3,200|2,545|||
|1999|554|154|<br>2,300|__<br>2,243||_|
|Total|5,916|2,157|15,632|9,391|||

Source: Memoria 1999, Fondo de Desarrollo para las Telecomunicaciones, Memoria 1999, Subtel.

The subsidy per installed rural telephone is about US$ 3,000, compared to the US$ 12,000 spent before the Fund came into existence. The main explanation for lower subsidies than before is the introduction of competitive bidding, although the figure is rising (US$ 8,000 in 1999) as more isolated projects - with fewer collateral benefits - are put to tender. In the period 1995-1997 firms thought about 40% of the maximum subsidy on average. But this figure is raising: in 1998 firms sought 62% and 75% in 1999. Subsidies financed about 28 % of estimated total investment in 1995-97, but the figure had jumped to 49 % by 1998. The cost of administrating the Fund has been low, accounting for 1% of total expenditure.

Although the program has been successful, giving access to public phones to about 80% of the rural population at relatively low cost, it has not been trouble-free. One major problem is that it can take up to 3 years to install a phone. The bidding process lasts a whole year and then concession-holders have up to 24 months to install the corresponding equipment. The problem could be mitigated if the installation date were to be included as a bidding variable, rewarding bidders not only for seeking the lowest subsidy but also for installing the phones in the shortest time. The formula for selecting the winning bid should 30
**Figure 3: Changing Distribution of Establishments**

<!-- Start of picture text --> Panel A<br>Change in Share of Total Establishments and<br>Employment by Size Class, 1993-2007<br>0.08 Establishments<br>0.06 Employment 5.3% 5.7%<br>0.04 3.5%<br>1.9%<br>0.02<br>0<br>-0.5%<br>-0.02<br>-1.7%<br>-2.2%<br>-0.04 -2.7% -3.2%<br>-0.06<br>-6.0%<br>-0.08<br>UltraMicro (5-9 Micro (10-19 Small (20-39 Medium (40-99 Large (100+<br>employees) employees) employees) employees) employees)<br>Source: Sri Lanka Annual Survey of Industries.<br><!-- End of picture text -->

# **Panel C**

<!-- Start of picture text --> Total EPZ employment, Sri Lanka 1983-2003<br><!-- End of picture text --> <!-- Start of picture text --> 120,000<br>104,237<br>100,000 91,404<br>84,058<br>80,000<br>60,000<br>46,104<br>40,000<br>24,093<br>20,000<br>0<br>1983 1988 1993 1998 2003<br>Source: Aggarwal, Aradhna . (2005) "PERFORMANCE OF EXPORT PROCESSING ZONES:A COMPARATIVE<br>ANALYSIS OF INDIA, SRI LANKA AND BANGLADESH," WORKING PAPER NO. 155, ICRIER Working Paper Series.<br><!-- End of picture text --> <!-- Start of picture text --> Panel B<br>Total EPZ employment, Sri Lanka 1973-2003<br>Emp Change in Share of Total Establishments and Share of e share of exports<br>1973 5,876 Employment by Province, 1993-2007 1983 3.7 8.0<br>0.151983 Establishments24,093 1986 5.2 27.8 13.6%<br>0.119881993 Employment46,10484,058 9.0% 19911995 10.98.9 35.231.1<br>1998 91,404 4.5%1999 11.6 28.8<br>0.052003 104,2371.5%1.2% 2003 9.3 2.0%33.2<br>0<br>-0.6%<br>-0.05 -3.2% -2.7% -2.8% -2.3% -1.6%<br>-5.2%<br>-0.1<br>-0.15 -13.4%<br>Source: Sri Lanka Annual Survey of Industries.<br>Panel D<br>EPZ share of total manufacturing sector<br>employment and exports,<br>Sri Lanka 1983-2003<br>40.0 14<br>Share of mfg. 35.2<br>35.0 employmentShare of mfg. exports 31.1 33.2 12<br>30.0 27.8 10.9 28.8 10<br>25.0 8.9 11.6 9.3 8<br>20.0<br>6<br>15.0<br>10.0 8.0 5.2 4<br>3.7<br>5.0 2<br>0.0 0<br>1983 1986 1991 1995 1999 2003<br>Note: Exports share shows data for closest available year.<br>Source: Aggarwal, Aradhna . (2005) "PERFORMANCE OF EXPORT PROCESSING ZONES:A COMPARATIVE<br>ANALYSIS OF INDIA, SRI LANKA AND BANGLADESH," WORKING PAPER NO. 155, ICRIER Working Paper Series.<br><!-- End of picture text -->
Switzerland for less than 10 years and 14% for more than 10 years but less than 20 years. In 2003, only 38% had been in the country for less than 10 years and more than 1⁄2, for between 10 and 20 years (OFS, 2004a, Table T1117F).

The last aspect to be briefly surveyed is the regional distribution of the population from the former Republic of Yugoslavia. In Switzerland, Yugoslav immigrants are much more concentrated geographically than other communities (see Figure 1). More than 80% are settled in the German-speaking regions against less than 60% for other foreign communities. In fact, migrants from Latin countries (Italy, Spain, Portugal) for obvious cultural reasons have a much higher probability to settle in the French-speaking regions.<sup>9</sup> Furthermore, Yugoslav permanent residents are concentrated in a few cantons (i.e., states in Switzerland). Concentration ratios for 1995 calculated by the Federal Statistical Office show highest values for Central and Eastern Switzerland. Finally, when moving internally, permanent Yugoslav residents have a tendency to converge toward similar regions in larger concentrated numbers than other nationalities such as France, Germany or Austria but in smaller numbers than Italian, Portuguese and Spanish permanent residents (OFS, 2000, Section 4).

With these observations in mind we now turn to the estimation of the determinants of migration flows and the evaluation of the intensity of their impact for ethnic groups and particularly for immigrants from the former Republic of Yugoslavia.

> again since the Federal Court has ruled such system unconstitutional and, that this particular municipality now has a designated a committee to rule on naturalizations (Le Temps, 2005).

> 9  About 50%, 60% and 30% of Spaniards, Italians and Portuguese respectively are settled in the German-speaking area in 1990 and the distribution has changed little over time (OFS, 2005, Table T1123F).

14
and societal change—in the degree and persistence of group inequality, the tenacity of hostilities between warring groups, and the pressures on the European welfare state, to name just a few examples. We look forward to seeing the new areas of research that the perspective of endogenous preferences will open up.

# **References**

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20
# **4. Towards Asian Free Trade: APEC Model Results**

# **Design of Alternative Scenarios**

The scenarios begin with consideration of the impact of an Asian Free Trade Area, then consider the implications of different regions not participating in (or being excluded from) the FTA: first China, then ASEAN4, and finally the US. We conclude by comparing the gains from the Asian FTA with more comprehensive trade liberalization encompassing areas outside of Asia. We also contrast the static gains from free trade arrangements with the more substantial improvements that might occur as a result of dynamic linkages between trade expansion and productivity. We specifically model three externality channels which have been identified empirically as important in export-led development, including the effects on aggregate and sectoral productivity of increased exports and the productivity-enhancing importation of new technologies via imports of capital and intermediate goods.

For each alternative scenario, the model generates results concerning the impact on real GDP, output, trade, value added, the real wages paid to each labor category, as well as the rental rate of capital and land. Trade diversion and trade creation impacts are evaluated through data on total, intra-regional, and extra-regional trade. However, our scenarios should be interpreted as controlled experiments rather than as forecasts of performance that might occur with each option. The actual growth pattern will be the result of many more factors than just trade policy, especially macro-economic and incomes policies. Both the comparative static and dynamic-externality experiments are meant to describe the impact of different patterns of trade liberalization in the medium to long run. Use of the term "dynamics" here does not imply the actual path of the transition, but rather the net cumulative effect over time of positive productivity externalities that could potentially result from regional integration.

_Lewis, Robinson, and Wang_ 34
Table 19: Program Impact on hemoglobin levels

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Chi|ldren aged 6|-15|Chi|ldren aged 6|-12|Women|
||All|Boys|Girls|All|Boys|Girls|16-49|
|School Meals|0.081|0.138|0.036|0.118|0.249|0.006|-0.057|
||[0.173]|[0.175]|[0.189]|[0.172]|[0.176]|[0.189]|[0.151]|
|Dry rations|0.069|n.a.|0.055|0.133|n.a.|0.089|-0.008|
||[0.192]|n.a.|[0.203]|[0.196]|n.a.|[0.208]|[0.129]|
|Girl|0.003|||-0.022||||
||[0.050]|||[0.058]||||
|Constant|10.703|10.832|10.600|10.729|10.794|10.646|11.082|
||[0.242]***|[0.256]***|[0.234]***|[0.239]***|[0.251]***|[0.241]***|[0.267]***|
|Observations|2518.000|1018.000|1500.000|2032.000|831.000|1201.000|2211.000|
|R-squared|0.09|0.11|0.09|0.10|0.13|0.10|0.03|

# Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1% Dependent variable is measured level of hemoglobin in ml per liter. Regressions also control for child age, orphanhood, parents education, ethnicity, religion, household assets and village fixed effects (not reported).

42
the trade-off between the statistical precision of the poverty and inequality estimates and the level of disaggregation.

The census is assumed to enjoy complete coverage (of all rural households), such that sampling error may safely be ignored. The basic idea behind the small area estimation method is to replace a small number of exact observations of expenditure/income (using households from the survey) with a large number of estimates of expenditure/income (using households from the census) to obtain accurate estimates of aggregate poverty and inequality. This means that we will be replacing sampling error with approximation error. As approximation errors cancel out on average, the errors induced by approximation tend to be small when the number of households is large.

# _III.1 The ELL framework_

Let us provide a brief review of the ELL methodology. In the standard setup, we consider the following model: Where ln( _ych_ ) denotes the dependent variable (think of logarithmic per capita expenditure), _xch_ the vector of explanatory variables,  the vector of regression coefficients,  _c_ the cluster-specific random effect and  _ch_ the household-specific random effect. The subscript _ch_ refers to household _h_ living in cluster _c_ . The explanatory variables _xch_ must be available in both census and survey. The household specific errors are assumed to be independent from each other, and independent from the cluster error.

Once all the parameters of interest have been identified, the dependent variable is imputed into the census: where <sup>ˆ</sup> ,  ˆ and _c_  ˆ denote the estimates for _ch_  ,  _c_ and  _ch_ . Now suppose that we want to estimate poverty for a given district. As an illustrative example, let us consider the head-count index, which measures the percentage of poor households in the district: where<sup>1</sup> ( _ych_  _z_ )<sup>denotes the indicator function that equals 1 if</sup><sup>_y < z_and 0 otherwise, and</sup> where _n_ denotes the number of households living in the district. An estimate of W can be obtained by replacing _ych_ with _y_ ˆ _ch_ for all households _ch_ .

6
# **References**

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22
Consumer Surplus with Incomplete Markets: Applications to Savings and Microfinance<sup>∗</sup> John Loeser<sup>†</sup> JEL Classification Codes: D11, D14, G51, O1, O12, O16 Keywords: Consumer surplus, Incomplete financial markets, Financial inclusion

> ∗ `jloeser@worldbank.org` . This research benefited from suggestions and comments from Daniel Agness, Katy Bergstrom, Alfredo Burlando, Kieran Byrne, Alain de Janvry, Emma Frankham, Xavier Gine, Sean Higgins, Dahyeon Jeong, Erin Kelley, Cynthia Kinnan, Florence Kondylis, Greg Lane, Arianna Legovini, Jeremy Magruder, Aprajit Mahajan, David McKenzie, Elisabeth Sadoulet, Hee Kwon Seo, Ursula Wiriadinata, Brian Wright, and seminar audiences at NEUDC, UC Berkeley, WEFIDEV, and the World Bank. Financial support from the CEGA-Visa Financial Inclusion Lab is gratefully acknowledged. The views expressed do not reflect the views of the World Bank. All errors are my own.

> †Development Impact Evaluation, World Bank
# **5. Data and stylized facts on FDI in Africa**

# **5.1. Data**

The analysis is done on a sample of 16<sup>3</sup> African countries over the period 2001-2012.

Primary school enrollment<sup>4</sup> (defined as the total enrollment in primary education, regardless of age, expressed as a percentage of the population at the official primary education age), trade openness (exports plus imports as a share of GDP), fixed line telephone subscription (per 100 people), and labor productivity (output per employed individuals, generated based on 2005 constant prices) are accessed from World Bank’s World Development Indicators (2016).

Bilateral FDI stock of BRICS and G7 countries are from UNCTAD statistics (2016). BRICS’ and G7 countries’ direct investment stock to an African country _i_ at time _t,_ is calculated as the sum of FDI from individual BRICS and G7 countries at time _t_ . Since the UNCTAD data does not contain sector level bilateral FDI flows, data on Greenfield investments are used to examine the recent trends in sectoral investments. The Polity2 governance index, which is reported on a scale of -10 to +10, with -10 indicating strongly autocratic and +10 strongly democratic political systems, is obtained from the Polity IV Project (Marshall and Jaggers 2011). Tables A.1 and A.2 of the appendix contain respectively, the sample of countries used in this study and summary statistics for selected variables.

# **5.2. Stylized facts on FDI in Africa**

To get a sense of the sectoral level flows, we exploit data on Greenfield FDI in Africa, which has grown by almost five-fold since 2000. It increased from US$ 12.5 billion in 2000 to

> 3 The availability of data on BRICS FDI restricts us to 16 African countries.

> 4 Primary school gross enrollment ratio can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition

12