rafmacalaba/datause-encoder-data huggingface dataset:
SRA is at iasue. Low PE points toward the need to focus on efficiency in the use and allocation of sector resources at all levels. Direct efficiency gains and cost recovery measures at higher levels could free resources for quality improvements and expansion at the primary level. Temporary external support to recurrent budget may be required in low-income countries. Countries: Guatemala, Nigcr.

- (b) HIGH SRM: Strong but ineffective government commitmeL. to education. The situation appears characteristic of low-income countries: in view of the limited resource base (from an absolute income level and mobilization efficiency perspective), as well as the low initial educational output situation, even a relatively high apparent sector resource mobilization remains insufficien **t** o meet actual sector needs. The sustainability of the government's commitment is therefore an issue. For the rest, sector strategic priorities are similar to B.2 (a). Countries: Burkina Faso, Ghana, Mali, Rwanda.

- B3. HIGH PRM LOW SRA:

- (a) LOW SRM: Given low PE, high PRM makes this a case for increasing SRA. Incremental public financing is to be used for primary education, in part to relieve the financial burden on parents and communities. Temporary external support to recurrent budget may be required. Countries: Guinea.

- (b) HIGH SRM: High public resource mobilization generates in principle an autquate supply of resources despite the government's weak commitmen **t** o education. The low PE, however, points toward major inefficiencies in the use and allocation of sector resources. Comprehensive sector reforms are thus required, possibly combined with some increase in SRA. Countries: Jordan.

B.4 HIGH PRM/IIGH SRA: Strong but ineffective commitment by the government to education, under relatively adequate public resource mobilization conditions. This is the archetypal case for sector adjustment to improve efficiency in the use and allocation of budgetary resources, primarily in favor of primary education. For the highly indebted countries, the high SRA may not be sustainable. A sector adirstment loan may be an appropriate vehicle for required sector reforms, providing temporary budget relief until efficiency gains are realized. Countries: Central African Republic, Costa Rica, Cote d'Ivoire, Morocco, Senegal.

76
Table H2: Appeals and slant, good vs bad

||(1)<br>appealed|(2)<br>appealed|(3)<br>appealed|(4)<br>appealed|(5)<br>appealed|
|---|---|---|---|---|---|
|Slant against women, good vs bad|0.0421<sup>_⇤_</sup>|0.0394|0.0199|0.0474|0.0267|
||(0.0253)|(0.0349)|(0.0420)|(0.0333)|(0.0594)|
|Def. maj. female||0.00503<br>(0.00431)|0.00472<br>(0.00715)|||
|Pla. maj. female||||0.00448<br>(0.00537)|0.00645<br>(0.00840)|
|Def. maj. fem. X Slant against women||-0.0376<br>(0.0515)|-0.0341<br>(0.0710)|||
|Pla. maj. fem. X Slant against women||||-0.0175<br>(0.0629)|-0.0457<br>(0.0902)|
|DVmean|0.0199|0.0206|0.0203|0.0198|0.0217|
|Court-year FE|Yes|Yes|Yes|Yes|Yes|
|Restricted sample|No|No|Yes|No|Yes|
|Observations|21951|17316|9664|19785|8098|

The regressions test whether slanted judges are more likely to have case decisions appealed.The coefficients of interest are ’Slant against women, good vs bad’ and the interactions. Column 3 (5) restricts the sample to cases where the defendant (plaintiff) loses, and the interaction tests if reverals are more likely if the judges is more slanted and the defendant (plaintiff) is female. These cases have the most potentail for gender bias. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model.

62
# **Table 3 OECD regulatory questionnaire for telecommunications services**

|Weights<br>by theme|Question<br>weights<sup>**a**</sup>|Score|Question|
|---|---|---|---|
|1/3|||**_Entry regulation_**|
||w<sup>t</sup>(1-w<sup>m</sup>)||**What are the legal conditions of entry into the trunk telephony**<br>**market?**|
|||6|Franchised to 1 firm|
|||3|Franchised to 2 or more firms|
|||0|Free entry|
||(1- w<sup>t</sup>)(1-<br>w<sup>m</sup>)||**What are the legal conditions of entry into the international**<br>**market?**|
|||6|Franchised to 1 firm|
|||3|Franchised to 2 or more firms|
|||0|Free entry|
||w<sup>m</sup>||**What are the legal conditions of entry into the mobile market?**|
|||6|Franchised to 1 firm|
|||3|Franchised to 2 or more firms|
|||0|Free entry|
|1/3|||**_Public ownership_**|
||(1-w<sup>m</sup>)|% governm|**What percentage of shares in the public telecommunications**<br>**operator is owned by the government?**<br>ent ownership/100*6|
||w<sup>m</sup>|% governm|**What percentage of shares in the largest firm ion the mobile**<br>**telecommunications market is owned by the government?**<br>ent ownership/100*6|
|1/3|||**_Market structure_**|
||w<sup>t</sup>(1-w<sup>m</sup>)|6-normaliz|**What is the market share of new entrants in the trunk telephony**<br>**market?**<br>ed market share<sup>**b**</sup>|
||(1- w<sup>t</sup>)(1-<br>w<sup>m</sup>)|6-normaliz|**What is the market share of new entrants in the international**<br>**telephony market?**<br>ed market share<sup>**b**</sup>|
||w<sup>m</sup>|6-normaliz|**What is the market share of new entrants in the mobile**<br>**telephony market?**<br>ed market share<sup>**b**</sup>|

> **a** The weight wm is the OECD-wide revenue share from mobile telephony in total revenue from trunk, international and mobile. The weight w<sup>t</sup> is the OECD-wide revenue share of trunk in total revenue from trunk and international telephony.

> **b** The market share of new entrants has been normalized to be between 0 and 6 with 6 being the smallest market share over all countries and 0 being the largest.

Source: OECD (2005b).

32
**Figure 5:** Transition matrix for selected countries

<!-- Start of picture text --> fraction by parental attainment fraction by parental attainment<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>1 1<br>.8 .8<br>.6 Less than primary .6 Less than primary<br>Primary completed Primary completed<br>Secondary completed Secondary completed<br>.4 University completed .4 University completed<br>.2 .2<br>0 0<br>(a) Jamaica (b) Guatemala<br>Less than primary Primary completed Secondary completedUniversity completed Less than primary Primary completedSecondary completedUniversity completed<br>likelihood of child attainment likelihood of child attainment<br><!-- End of picture text --> Notes: The sample is constructed with individuals older than 25 that coreside with at least one individual of the generation above. The figures display the transition matrix between the educational attainment of individuals in the sample and their parents. The horizontal axis is divided according to the share of parents with each level of educational attainment. The height of each rectangle within each figure is the likelihood of child educational attainment conditional on the attainment of their parents.

intergenerational mobility that reflects the likelihood that a child completes a strictly higher or lower education level than the members of the immediately previous generation in the household (parents and/or extended family members, such as aunts and uncles).

**Upward mobility at the country level.** To estimate upward IGM, I estimate the following econometric specification, pooling observations from all the censuses and countries:

where _yicoyt_<sup>_up_is a dummy variable that takes a value equal to one when individual</sup><sup>_i_completes</sup> at least primary education and zero otherwise. The parameters _γo_<sup>_b_,</sup><sup>_γ_</sup> _y_<sup>_b_,</sup><sup>_θt_refer respectively to</sup> fixed effects by decade-cohort of the individual _i_ , decade-cohort of the generation above that co-resides with individual _i_ , and census year. This regression uses a sample of individuals with ages between 14 and 18 (or 14 to 25), for whom the generation above (parents or older relatives) have on average less than primary education. Hence, _αc_<sup>_up_</sup> is the parameter 13
page 29 However, escimates of the parameters a and b cannot be easily deduced from the results reported for that model.

- [151 Assuming the costs of this revised program would be larger than that of the program analyzed by Michigan, the smaller total LDS would make it less cost-effective than a program like that analyzed for Ghana. However, the low incidence of measles assumed by the Michigan study for unvaccinated Javanese children would have to be carefully substantiated.

- (16] All figures drawn from Sanoh (1983) are preliminary and, like the other figures presented here, are for illustrative purposes only.

- (17] With the addition of one more experimental group, one receiving both vaccination and VHW services, an interaction term could be introduced into the objective function. Equation (1) of the model would then be modified to read:

H - ho + a e + b v + d e v .

- A new version of deision rule (9) would then have to be derived accordingly.

- (181 Two modifications of Creese's (1979) costing guidelines would be helpfuL The unit of analysis should be changed from the "fully-im munised-child' to the healthy-life-day and procedures should be suggested for allowing encounters and vaccinations to be treated as joint products.

- [191 For example, Chiang and Friedlander (1984) use a translog function to specify a general multiproduct cost function.
**Table A5. Destination income and firm average input prices, LIML estimates**

||dep. var.: frm-average log r|eal input price|
|---|---|---|
||(1)<br>(2)|(3)|
|log avg. destination gdp/cap|1.15***<br>1.16**|0.96**|
||(0.44)<br>(0.51)|(0.48)|
|export share of sales|-0.56**<br>-0.58|-0.47|
||(0.23)<br>(0.71)|(0.68)|
|log avg. destination distance|-0.00<br>0.00|0.25**|
||(0.00)<br>(0.02)|(0.12)|
|log sales|0.02**<br>0.02**|-0.01|
||(0.01)<br>(0.01)|(0.02)|
|initial source interactions|Y<br>Y|Y|
|frm efects|Y<br>Y|Y|
|year efects|Y<br>Y|Y|
|N|45659<br>45659|45659|
|Kleibergen-Paap LM statistic (under-identifcation)|248.92<br>192.30|232.20|
|Kleibergen-Paap LM p-value|0.00<br>0.00|0.00|
|Kleibergen-Paap Wald rk F-stat|2.65<br>2.09|2.32|
|Anderson-Rubin Wald test F-stat|2.17<br>2.18|2.18|
|Anderson-Rubin Wald test p-value|0.00<br>0.00|0.00|

Notes: Instruments are interactions of indicators for positive exports to destination in 1997 and log relative price level (reciprocal of real-exchange rate). Initial source interactions, included directly as covariates, are defined analogously to the instruments, using indicators for initial imports. Columns 5 treats only log avg. destination GDP/cap as endogenous; Column 6 adds export share of sales, and Column 7 adds log avg. destination distance to endogenous set. Petroleum exports and imports excluded. Euro-zone countries not included in instrument set. Robust standard errors in parentheses. *10% level, **5% level, ***1% level.
suggests that following the implementation of the cap banks sought to compensate the loss in interest income by adding fees and commissions.

The forced ceiling on interest rates made an already difficult lending environment, due to external headwinds and challenges in the domestic banking sector, even more difficult (World Bank, 2017). The growth rate of aggregate credit to the private sector declined to an average of 3.8% y‐o‐y between September 2016 and August 2017, from an average of 13.6% y‐o‐y over the previous 12 months. The decline in lending is also confirmed by a survey of commercial banks conducted by the Central Bank of Kenya where 45% of the banks state that the interest rate cap reduced the amount of credit granted. A majority of 54% of the commercial banks in Kenya further reports a negative effect of the interest rate cap on lending to SMEs. Also, the Kenya Bankers Association (KBA) reports a sharp decline in loan disbursements, especially for unsecured personal loans. In June 2017, for instance, while 3.2 million loan applications were made, only about 1.1 million loans were disbursed. This presents a 34% success rate, sharply down from the 50% success rate before the law was signed in August 2016.<sup>21</sup>

**Figure 10: Impact of interest rate cap on credit supply and demand in Kenya**

<!-- Start of picture text --> Effect on credit granted Negative effects on Effect on demand for credit<br>lending to SMEs<br>10%<br>35%<br>45% 46%<br>50%<br>54%<br>45%<br>15%<br>Increased Decreased Unchanged No Yes Increased Decreased Unchanged<br><!-- End of picture text --> _Source: Central Bank of Kenya Commercial Banks’ Credit Survey April – June 2017_ The KBA further observes that as a consequence of the interest rate cap, credit is more skewed towards the secure and short‐term market end. Lenders are moving away from households and SMEs that lack collateral and instead lend to the government. Credit to the

> 21 http://www.kba.co.ke/news60.php

24
covers more than 40,000 firms. For exports we use a data set created by the foreign trade office, SECEX ( _Secretaria Comercio Exterior_ ). SECEX provides the universe of registered trade flows at the firm level.<sup>6</sup> Table 1 shows the main variables used in the analysis; and Tables 2 and 3 show, respectively, firms export activity, and average values for the main production function variables and wages, according to firms’ exporting status during the sample period. From the figures in Table 2 we observe that the proportion of exporting firms in the sample is slightly below 30%. As regards the variables in Table 3, we proxy capital with assets, and include also electricity and energy as intermediate inputs. We use sector specific producer price indices supplied by the Instituto Brasileiro de Geografia e Estatistica (IBGE) to deflate the variables in the production function and wages, with the exception of labor. The digits level of the sector deflators corresponds to the 2 digits CNAE classification. Unfortunately, the survey PIA _empresa_ has not information on firms’ prices to be able to construct firm specific deflators. As can be observed in Table 3, exporters are larger in terms of output, labor, capital and materials and pay higher wages than non-exporters.

[Table 1 around here]

[Table 2 around here] [Table 3 around here]

# **3. Production function estimation, firm trade status and TFP**

We assume that firms produce using a Cobb-Douglas technology: 6 There are some differences between PIA and SECEX in identifying exporters. Around 600-700 firms are identified in PIA as exporters and not in SECEX. Also around 1,500 firms that are exporters in the SECEX data set are surveyed in PIA claiming not to export. We used SECEX as the most reliable source of information for exporters since exports are adequately registered by the customs authority.

8
# **Figure 1. GDP growth rate (y/y, quarterly data)**

<!-- Start of picture text --> %<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text -->

**Figure 2. Consumer Price Index (CPI; y/y, monthly data) and inflation target**

<!-- Start of picture text --> CPI (y/y)<br>Inflation target of the NBP<br>% Fluctuation band<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: CSO.

Source: CSO, NBP.

**Figure 3. Warsaw Stock Exchange indices (monthly data)**

**Figure 4. Capitalization of stock market**

<!-- Start of picture text --> sWIG80 mWIG40 WIG WIG20 zl billion zloty billion % of GDP (right - hand scale) %<br>1400 600 50<br>45<br>1200 500<br>40<br>1000 35<br>400<br>30<br>800<br>300 25<br>600 20<br>200<br>400 15<br>10<br>100<br>200 5<br>0 0 0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Notes: January 2002 = 100. Notes: capitalization concerns only domestic companies.<br>Source: Bloomberg. Source: authors’ calculations.<br>Figure 5. Annual growth in residential property Figure 6. Credit-to-GDP ratio (quarterly data)<br>ask prices in the biggest cities<br>Wrocław Łódź Kraków Warszawa %<br>60<br>Poznań Gdańsk Gdynia<br>140%<br>50<br>120%<br>100% 40<br>80%<br>30<br>60%<br>40% 20<br>20%<br>0% 10<br>-20%<br>0<br>-40%<br>Source: NBP. Source: CSO.<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010<br><!-- End of picture text --> Notes: January 2002 = 100. Source: Bloomberg.

**Figure 5. Annual growth in residential property ask prices in the biggest cities**

Source: NBP.

30
# **Migration in Libya: A Spatial Network Analysis**

Michele Di Maio Valerio Leone Sciabolazza Vasco Molini<sup>∗</sup> Sapienza University of Rome University of Naples Parthenope The World Bank (Rabat, Morocco)

**JEL classification:** F22; D61; O12; N47

**Keywords:** migration, forced migration, conflicts, Libya

>  Corresponding author, mail: vmolini@worldbank.org. The authors acknowledge financial support from the World Bank. We thank colleagues from the International Organization for Migration (IOM) for making available their datasets. We also thank Francesca Recanatini, Nandini Krishnan and Maria Davalos for their comments and suggestions on an earlier version of the manuscript. The authors are solely responsible for any errors this paper may contain.
**Figure 2: KBC and Chilean wine exports (real 2012 USD)**

<!-- Start of picture text --> Fine Wine Exports/100<br>Physical K (ha.planted * $100)<br>Physical K2 (#wineries * $30K)<br>KBC (Total)<br>Innovative Property<br>Economic Competencies<br>Global Connectivity<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> <!-- Start of picture text --> Fine Wine Exports/100<br>Physical K (ha.planted * $100)<br>Physical K2 (#wineries * $30K)<br>KBC (Total)<br>Innovative Property<br>Economic Competencies<br>Global Connectivity<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> <!-- Start of picture text --> Fine Wine Exports/100<br>Physical K (ha.planted * $100)<br>Physical K2 (#wineries * $30K)<br>KBC (Total)<br>Innovative Property<br>Economic Competencies<br>Global Connectivity<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> Examples of investments in KBC that support enterprise risk management include: (i) Investments in **knowledge** . An example of adjusting to the risks of losing market share when foreign competitors innovate is Miguel Torres’ (a Spanish-owned FDI firm) first introduction in Chile of temperature-controlled stainless steel vats instead of the traditional concrete fermenting vats that did not allow temperature control and retained residues adversely affecting taste and smell, widespread in use in high-income countries. This led Chilean-owned vineyards to introduce them – and required additional investments in technical support and local learning, as no one in Chile knew how to assemble them.<sup>25</sup> To further lower the risk of falling behind the evolving technological frontier, Chile’s Production Development Corporation (CORFO, a governmental organization to promote investment and innovation) and participating enterprises co-invested $5.1 million between 2005 and 2010 in 38 foreign technological study tours (‘misiones tecnologicas’) and in 15 foreign consultancies by oenologists, viticulturalists and other global experts (‘consultorias especializadas’).<sup>26</sup> And to lower the risk of volatile exports for smaller

> 25 The investment in local assembly of stainless steel vats, and the complementary physical capital investments, “an apparently minor innovation”, allowed enterprises to export wines sanitarily safely and with reduced variability and higher quality and taste to international standards across vintages (Agosin and Bravo-Ortega, 2009).

> 26 The average private contribution to these global connectivity projects was 40%. The use of global oenologists as foreign consultants also allowed Chilean enterprises to lower the risk of changing global tastes, as they acquired knowledge of the characteristics of changing international demand and began making Chilean wines to those specifications (Agosin and Bravo-Ortega 2009).

12
**The presence of oligopolies in the traditional sectors of the economy hampers the country’s competitiveness** .<sup>39</sup> Agricultural protection creates rents, raises food prices and – by impacting wages - undermines labor competitiveness. Port and airline oligopolies make trade costly and tourism less attractive. Most banks, given their corporate ownership, lend within their conglomerate, limiting access to outsiders or unconnected companies,<sup>40</sup> which face a declining availability of credit. In the electricity and cement sectors, oligopolistic rents undermine cost competitiveness across a broad range of fixed investments, especially in infrastructure. As a result, international foreign asset holdings are below regional benchmarks (Figure 26), although at a level comparable with those of economies with similar GDP per capita (Figure 27).

**Figure 27. International Foreign Asset Holdings and GDP per Capita, 2004**

**Figure 26. International Foreign Asset Holdings, 2004**

<!-- Start of picture text --> International Investment Assets/GDP<br>1000<br>1000<br>Bahrain<br>900 900<br>Hong Kong<br>800 800<br>700 700<br>Average<br>600 EAP Total 600<br>500 Median 500 Singapore<br>400 Philippines 400 Brazil<br>300 300 Philippines Argentina<br>200 China<br>Thailand<br>200<br>100 Malaysia<br>100 Korea Japan USA<br>0<br>Mexico<br>0<br>0 10,000 20,000 30,000 40,000<br>GDP per Capita, PPP, Current International $<br>BangladeshIndonesiaTunisiaArmeniaParaguayThailandRussian FederationAzerbaijan ChileMalaysiaIcelandDenmarkBelgiumBahrain<br>International Investment Assets/GDP (%)<br><!-- End of picture text --> <!-- Start of picture text --> 0<br><!-- End of picture text --> _Sources:_ IMF, Consolidated Portfolio Investment Survey, 2007. World Development Indicators, 2007.

# **Growth is not inclusive. Over 2002-2006, higher growth did not translate into higher**

**employment** . The Philippines is characterized by jobless growth (Figure 28). Between 2004 and 2006, employment rose only by 2.5 percent on average each year. Services, primarily

> 39 In 2006, the Philippines continued to receive low rankings international competitiveness surveys, including those of the World Competitiveness Yearbook (49<sup>th</sup> of 61), the World Bank (113<sup>th</sup> of 155), and World Economic Forum (77<sup>th</sup> of 120).

> 40 In recent years, banking sector vulnerabilities were addressed but credit to the private sector has continued to decline in real terms. Asset quality is still below regional and world standards but banking sector vulnerabilities were reduced via the take out of non-performing loans and assets from commercial bank balance sheets.

33