rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 4.3: SOUTH AFRICA, comparison of the ICA method and other<br>imputation mechanisms for PF variables<br><!-- End of picture text --> <!-- Start of picture text --> I. Kernel 1 estimates of output and inputs densities<br><!-- End of picture text --> <!-- Start of picture text --> A. Sales (log) B. Materials (log)<br>5 10 15 20 25 5 10 15 20 25<br>log-Sales log-Materials<br>Complete case ICA method Complete case ICA method<br>Random ICA EM alg. [1] Random ICA EM alg. [1]<br>EM alg. [2] EM alg [3] EM alg. [2] EM alg [3]<br>C. Capital stock (log) D. Employment (log)<br>5 10 15 20 25<br>log-Capital 5 10 15 20<br>log-Employment<br>Complete case ICA method<br>Random ICA EM alg. [1] Complete case ICA method<br>EM alg. [2] EM alg [3] Random ICA EM alg. [1]<br>EM alg. [2] EM alg [3]<br>.25 .25<br>.2 .2<br>.15 .15<br>.1 .1<br>Density of Sales<br>D ensity of M aterials<br>.05 .05<br>0 0<br>.4 .4<br>.3 .3<br>.2<br>.2<br>Density of Capital<br>.1<br>.1<br>D e nsity of E m p loym e n t<br>0<br>0<br><!-- End of picture text --> Notes: 1 Epanechnikov kernel. Each point estimated within a range of 300 values. Source: Authors’ estimations with ICSs data.

57
A. Sanghi and D. Johnson <!-- Start of picture text --> 600<br>ACTION RELATING TO DEBT<br>400 ECONOMIC INFRASTRUCTURE AND SERVICES<br>Education<br>Health and Population<br>HUMANITARIAN AID<br>MULTISECTOR<br>Other social infrastructure and services<br>200 PRODUCTION SECTORS<br>PROGRAMME ASSISTANCE<br>UNALLOCATED/UNSPECIFIED<br>0<br>Country<br>ODA (USD Millions)<br><!-- End of picture text --> Figure 31: Kenya receives the most aid in health and populations services (China excluded) (2013) Source: OECD DAC aid database 2015

# **4.2 Most Chinese financing does not qualify as ODA**

The OECD Development Assistance Committee (DAC) calls export credits and loans with a less than 25 percent grant element other official flows (OOF); much of China’s financing overseas would fall under this category. The Chinese development bank, for example, created an China Africa development fund which provides equity investment capital. The ministry of finance also subsidizes the Exim bank’s preferential export credits and foreign aid loans, paying the difference between the interest rate and the actual cost of the loan. 97 percent of Chinese Exim Bank financing is in suppliers’ credits and other official flows, and the remaining three percent is in foreign aid (Br ̈autigam 2011). China gives most of its official financing at competitive commercial rates with a maturity of 12 to 15 years and a grace period of two to five years (Br ̈autigam 2011).

## **4.2.1 China loans the most to ministries of energy and petroleum and transport and infrastructure**

China’s aid to Kenya is almost entirely loans. The government of China gave a total of KSH 20.6 billion (US $200.6 million) in loans for development expenditure for the fiscal year ending in 2015, and figure 32 shows their destinations (Ministry of Finance 2014). China gave KSH 12 billion (US $120 million) to the ministry of energy and petroleum, KSH 5.6 billion (US $56 million) to the ministry of transport and infrastructure, and KSH 2.5 billion (US $25 million) to the ministry of information, communications and technology (ICT). The ministry of energy and petroleum amount includes the large geothermal loans from China EXIM bank. China’s loan breakdown matches its focus on natural resource extraction and infrastructure upgrading in many countries in SubSaharan Africa. Loans to the information, communication and technology sector go together with its investments in the communications sector (figure 23) and its strategy of using Kenya for access to the regional market. The small amount given to support devolution —the decentralization of roles and resources to the lowest levels of government —supports China’s non-interference foreign policy.

32
**Figure 19. Wages are high ...**

**Figure 20.  ... even for skilled workers**

<!-- Start of picture text --> Monthly Wages in Low and Middle Income Asia<br>5000 1800 (November 2005. In US Dollars)<br>4500 Malaysia Source: JETRO 16th<br>4000 1500 Survey of Investment Related Cost Comparison in Kuala Lumpur<br>3500 Major Cities and regions in Asia. March 2006.<br>30002500 Thailand 1200 Shanghai<br>2000 Bangalore<br>China 900<br>1500 Indonesia Qingdao Shenzen<br>1000 Philippines Jakarta Manila<br>500 BangladeshVietnam India 600 Hanoi Bangkok<br>0 Dhaka<br>300<br>0 50 100 150 200 250<br>Average monthly wage worker in USD 0<br>Source: JETRO 16th Survey of Investment Related Cost and World Bank 0 50 100 150 200 250<br>Workers (General Industry)<br>GNI per capita<br>Mid-level Managers<br><!-- End of picture text --> _Source:_ JETRO, 2006 and World Bank, 2006a.

_Source:_ JETRO, 2006 and World Bank, 2006a.

**The informal sector prevails and a quarter of the domestic labor force works abroad.** Informal activities and emigration are the major outlets for underutilized labor. The wageand-salaried workers accounted for only about 50 percent of total employment, and the proportion of unpaid family workers, prevalent in agriculture and private households, is significant (Figure 21). In 2006, over a million work-seekers left the country for employment overseas, where some 81⁄2 million Filipinos work, about a quarter of the domestic labor force and a tenth of the population (Figure 25 – F).<sup>35</sup>

**Figure 21. Fifty percent of unsalaried workers Figure 22. Rapid remittances growth**

<!-- Start of picture text --> Share of employed<br>120.0<br>100.0<br>Unpaid family workers<br>80.0<br>Own account<br>60.0 Wage and salaried<br>40.0<br>20.0<br>0.0<br>1996 1998 2000 2002 2004 2006<br>Source: National Statistics Office<br><!-- End of picture text --> _Source:_ World Bank, 2007a.

<!-- Start of picture text --> Remittances inflows<br>200<br>180<br>160 Philippines World<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>(in US$, 2000=100)<br><!-- End of picture text --> _Source:_ World Development Indicators, 2007.

35 During the first four months of 2007 the number of residents leaving the country to work abroad increased by 7.8 percent, to 343,4000 persons relative to the same period in 2006.

26
|**Table 5: Comparing short and long questionnaire. Probit‐M**<br>Dependent variable: Different answer provided in the two q|**arginal effects**<br>uestionnaires||||
|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|
|Firm is potential innovator Y:1 N:0|‐0.182**|‐0.131|‐0.282*|‐0.401**|
||(0.089)|(0.095)|(0.154)|(0.166)|
|Manager's years of experience|‐0.001||‐0.008||
||(0.004)||(0.007)||
|Respondent experience with firm||0.007||‐0.003|
|||(0.006)||(0.010)|
|Respondent is woman Y:1 N:0|0.166|0.187|0.286*|0.216|
||(0.119)|(0.127)|(0.155)|(0.168)|
|Use of show cards Y:1 N:0|||0.008|‐0.064|
||||(0.125)|(0.135)|
|Time interval btw svy (days)|0.002***|0.002***|0.002***|0.002***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Same respondent Y:1 N:0|‐0.231***|‐0.218**|‐0.055|‐0.148|
||(0.082)|(0.088)|(0.147)|(0.153)|
|Nr of interviews per supervisor|0.018***|0.017***|0.008|0.008|
||(0.004)|(0.005)|(0.013)|(0.012)|
|Enumerator's perception of truthful interview Y:1 N:0|‐0.020|‐0.026|0.058|0.169|
||(0.079)|(0.086)|(0.129)|(0.137)|
|Length ES interview (minutes)|‐0.001|‐0.002**|‐0.002|‐0.004**|
||(0.001)|(0.001)|(0.002)|(0.002)|
|Average number of meetings with tax officials|0.011|0.012|0.050*|0.047|
||(0.010)|(0.009)|(0.029)|(0.032)|
|Firm expected to pay informal payment Y:1 N:0|‐0.154*|‐0.154*|‐0.285*|‐0.323**|
||(0.084)|(0.091)|(0.148)|(0.156)|
|Employment growth (%)|0.002|0.002|‐0.002|0.001|
||(0.002)|(0.002)|(0.003)|(0.003)|
|Size (log)|‐0.140***|‐0.128***|‐0.078|‐0.077|
||(0.039)|(0.043)|(0.062)|(0.066)|
|Age (log)|0.040|‐0.034|0.063|0.039|
||(0.062)|(0.073)|(0.103)|(0.120)|
|Foreign ownership (25+%)|‐0.024|‐0.210|‐0.284|‐0.494**|
||(0.165)|(0.176)|(0.208)|(0.215)|
|Export (directly or indirectly)|‐0.019|0.036|0.180|0.232|
||(0.111)|(0.118)|(0.163)|(0.170)|
|Constant|0.054|0.241|‐0.287|‐0.067|
||(0.271)|(0.289)|(0.423)|(0.449)|
|Observations|7,090|6,281|1,592|1,431|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Country & ISIC 2‐digit FE not shown 29
Policy ReseaRch WoRking PaPeR 4698

# **Abstract**

Temporary migration programs for unskilled workers are increasingly being proposed as a way to both relieve labor shortages in developed countries and aid development in sending countries without entailing many of the costs associated with permanent migration. New Zealand’s new Recognized Seasonal Employer program is designed to enable unskilled workers from the Pacific Islands to work in horticulture and viticulture in New Zealand for a period of up to seven months. However, the development impact on a sending country will depend not only on how many workers participate, but also on who participates. This paper uses new survey data from Tonga to examine the process of selecting workers for the Recognized Seasonal Employer program, and to analyze how pro-poor the recruitment process has been to date. The findings show that recruited workers come from largely agricultural backgrounds, and have lower average incomes and schooling levels than Tongans not participating in the program. Comparing the characteristics of program workers with those of Tongans applying to permanently migrate to New Zealand through the Pacific Access Category, the program workers are more rural and less educated. The program therefore seems to have succeeded in creating new opportunities for relatively poor and unskilled Tongans to work in New Zealand.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to examine the economic effects of migration and remittances. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmckenzie@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
- 32 -

the short period under review, the overall positive evidence is modest. In general, the more significant improvements concern middle-income countries. The evidence is also more favorable ihen early loan recipients are considered (especially when only the ten intensive trade loan recipients are considered) than when all forty recipients are included in the comparisons.

Regression analyses of GDP growth rates found the growth in output to be associated with the contribution of additional imports, presumably because foreign exchange shortages had been a serious constraint. At the same time, policy reform was found to have a positive impact on growth performance. Countries that have received trade adjustment loans have experienced a stronger growth impact from additional imports than have other countries. The evidence of this policy impact is mild when all trade loan recipients are compared with nonrecipients but stronger when reformers are compared with nonreformers. Country studies corroborate this finding as well. The evidence supports the need for continued and stronger efforts to reform trade regimes as part of adjustment lending. Although not analyzed in this oaper in any detail, factors other than trade policy are also important in complementing and sustaining trade reform. Thus, greater efforts in trade reform will be beneficial for adjustment and growth, but their impacts will be stronger if attention is paid to complementary policies.
**24**

Section 4 are mostly robust to a change in the specification of the main outcome of interest.

**Table 13:** Male income and self-esteem

||(1)<br>Aggregate|(2)<br>Crops|(3)<br>Income levels (mal<br>Export-oriented crops|(4)<br>e)<br>Off-farm|(5)<br>Self-employment|
|---|---|---|---|---|---|
|Self-esteem (fem)|-131.6|-136.7|<br>-159.1<sup>_∗∗_</sup>|21.5|0.8|
||(111.4)|(95.1)|(70.8)|(31.4)|(26.6)|
|Self-esteem (male)|477.3<sup>_∗∗_</sup>|443.9<sup>_∗∗∗_</sup>|384.3<sup>_∗∗∗_</sup>|46.1|25.9|
||(184.9)|(165.6)|(146.6)|(58.9)|(43.7)|
|Outcome mean|2452.93|1867.73|1508.89|523.68|304.80|
|R-squared|0.077|0.084|0.099|0.093|0.046|
|Observations|1510|1510|1510|1510|1510|

Note: Robust standard errors are reported in parentheses. All regressions include village fixed effects and the controls listed in Table 1. Self-esteem measures are corrected for acquiescence bias. Significance levels are denoted as follows: * p<0.10, ** p<0.05, *** p<0.01.

**Table 14:** Female income and self-esteem

||(1)|(2)|(3)<br>Income levels (fem|(4)<br>ale)|(5)|
|---|---|---|---|---|---|
|=|Aggregate|Crops|Export-oriented crops|Off-farm|Self-employment|
|Self-esteem (fem)|61.1|29.2|58.0<sup>_∗∗∗_</sup>|21.6|33.1<sup>_∗∗∗_</sup>|
||(65.2)|(57.0)|(13.1)|(13.9)|(12.0)|
|Self-esteem (male)|-0.8|-17.3|-51.0<sup>_∗∗∗_</sup>|22.4|10.0|
||(51.2)|(54.5)|(16.0)|(18.8)|(16.6)|
|Outcome mean|520.55|272.26|69.11|210.05|178.27|
|R-squared|0.035|0.019|0.052|0.049|0.050|
|Observations|1510|1510|1510|1510|1510|

Note: Robust standard errors are reported in parentheses. All regressions include village fixed effects and the controls listed in Table 1. Self-esteem measures are corrected for acquiescence bias. Significance levels are denoted as follows: * p<0.10, ** p<0.05, *** p<0.01.
_Figure 2: Unskilled workers displaced by the shock come from all parts of the distribution_ <!-- Start of picture text --> 20,000<br>18,000<br>16,000<br>14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>2,048 3,262 4,152 5,014 5,986 7,115 8,568 10,686 14,552 28,369<br>Deciles (identified with their per capita consumption in 2011 rubles)<br>Number of displaced workers<br><!-- End of picture text --> Source: HBS 2011 and authors calculations using microsimulations In addition to displacement of workers, the oil shock indirectly but more significantly affects labor markets by changing relative returns to human capital and to sector specific skills. While the whole population will be negatively affected by the oil shock, in terms of incidence those depending on the returns to skills will lose about 1.5 percent more than the others, and, among the unskilled, those in urban activities will lose 7 percent more than those in expanding rural sectors. Figure 4 shows that the human capital is not uniformly distributed. Skilled employed workers (mainly in non‐agriculture sectors) represent about 31 percent of the population and the majority of them, about 18 percent, are found in the upper 60 percent of the distribution. And even within the bottom 40 and the top 60 the share of skilled workers is not uniform, as shown by the upward sloping line in the figure.<sup>18</sup> Conversely, the unskilled workers are overrepresented at the bottom of the distribution, especially those employed in the agriculture sectors. In light of the shares shown in Figure 4, the reason for a progressive impact of the reduction of the skill and the urban premia – illustrated by the downward sloping GIC 2 line in Figure 3 – becomes apparent.

> 18 On average for every 100 individuals, 9 are skilled among the bottom 40, while 21 are skilled among the top 60. However, these proportions are 5 and 26 for the bottom 10 and top 10 respectively.

17
# **Table S12. Over prescription of antibiotics**

||(1)|(2)|(3)|
|---|---|---|---|
|Prescribe antibiotics<br>for diarrhea|Independent of<br>diagnosis|Incorrect<br>diagnosis|Correct<br>diagnosis|
|Kenya|0.40|0.49|0.37|
||(.38-.41)|(.45-.52)|(.35-.39)|
||[4505]|[1097]|[3408]|
|Madagascar|0.35|0.35|0.36|
||(.29-.42)|(.27-.43)|(.22-.50)|
||[642]|[513]|[129]|
|Mozambique|0.41|0.44|0.40|
||(37.-.46)|(.36-.52)|(.36-46)|
||[725]|[189]|[536]|
|Niger|0.17|0.18|0.17|
||(.12-.22)|(.04-.31)|(.11-.22)|
||[519]|[57]|[462]|
|Nigeria|0.48|0.44|0.60|
||(.46-.50)|(.41-.46)|(.57-.64)|
||[4549]|[3313]|[1236]|
|Senegal|0.20|0.27|0.16|
||(.10-.30)|(.11-.42)|(.05-.27)|
||[152]|[64]|[88]|
|Sierra Leone|0.32|0.47|0.31|
||(.28-.35)|(.30-.63)|(.27-.35)|
||[826]|[44]|[782]|
|Tanzania|0.30|0.28|0.30|
||(.24-.33)|(.09-.48)|(.24-.36)|
||[542]|[67]|[475]|
|Togo|0.42|0.47|0.18|
||(.29-.54)|(.32-.61)|(.05-.31)|
||[302]|[233]|[69]|
|Uganda|0.29|0.27|0.31|
||(.24-.33)|(.22-.33)|(.27-.35)|

56
# **2.5 Separation of forecasts into relevant future periods**

Selection of intervals within the 100-year forecast range (2001-2100) is essentially arbitrary. For this study, we divide the 21<sup>st</sup> century into two 50-year intervals (20012050; 2051-2100) to provide a simple illustrative template.

# **2.6  Separation of weather data into scale classes**

The GCMs attempt to replicate actual climate patterns by incorporating year-to-year stochastic variation into their forecasts. At the same time, the GCMs incorporate long trends in the annual levels and monthly distributions of rainfall and temperature. For each GCM, each 50-year forecast interval therefore includes widely-varying joint observations on temperature and rainfall. The total forecast variation is compounded by the presence of 8 GCMs. For the scale-separation exercise, we use our calculated yearly temperature and rainfall pairs for each GCM in each future forecast interval. This generates 400 annual totals (8 GCMs; 50 years) for temperature and rainfall for each of 372 grid squares. These reflect anticipated future climate trends as well as broad stochastic variation from year to year. To establish the full domain for variation, we assemble annual observations as follows for each grid square: the first 40 from the CRU data (1961-2000) and the other 800 from the GCM-based forecasts (2 future periods, 50 years in each period; 8 GCMs).

For tractability, we need a simple scheme for characterizing this variation. For this paper, we establish three equal-sized intervals from minimum to maximum values (Low, Medium, High), for rainfall and temperature separately, for the 840 observations in each grid square. Then we assign each annual joint rainfall/temperature observation in each 6
productivity loss of _50_ percent. Excluding suffering and medication to treat minor RRAD, e estimated annual benefits associated with reduction of current average levels of ozone pollution to the legislation standard in MCMA are US$102 million.

# Load

Lead in air has been shown to have a strong correlation with blood lead levels associated with neurological damage in children and with high blood pressure in adults (see figure 1). In infants and children, absorption of lead can be via mouthing, air inhalation, and ingestion of food. Almost all the lead deposited in the respiratory tract is absorbed. Because children inhale a proportionally higher daily volume of air per weight measure than adults, they are more susceptible to lead in ambient air. In a population sample of children in Mexico City, the main determinant of blood lead level (BLL) was the place of residency, suggesting that lead absorption through air inhaladon is the major source of poisoning (Secretaria de Salud 1991). Nearly 95 percent of the gasoline in MCMA still contains lead.

Figure 1. Total Lead Used in Gasoline, per Six Month Period, and Blood Levels (`U.S.NHANES II Survey), After EPA (1985) <!-- Start of picture text --> b ~~~~~~~~~~~~~~~~~~~~t s<br>-so L I1 4d<br>7z. \\t ' W ~~~~~~~~~~~~~~~~~12<br>IC-,t.e^7. 17. s*1ss ,tO<br>40<br>a 1 i* GOmolUl (cia; t) + Blood Ld (o iM) d<br><!-- End of picture text --> _15. This may be a large estimate, but it partly compensates for the benefits arisingfor asthmatics, which are not included here (see appendix for estimates on asthma effects)._ 15
ECB provided massive liquidity support in the form of two exceptional 3-year LTROs worth over €1 trillion. The ECB also lowered reserve requirements, substantially loosened collateral requirements, and set up currency arrangements with other central banks to address poor foreign exchange funding conditions.

<!-- Start of picture text --> IV.1 Interbank funding conditions deteriorated IV.2 ...as did the cost of swapping Euros for US<br>significantly between 2007-09, remained elevated, Dollars<br>and spiked again in 2011...<br>Three-month EURIBOR – OIS Three-Month Cross Currency Basis Swap<br>% per annum (2005Q1-2013Q1) Bps over LIBOR, lower=more costly (2008Q1-2013Q1)<br>1.8<br>1.6<br>0<br>1.4<br>1.2<br>-20<br>1.0<br>-40<br>0.8<br>0.6<br>-60<br>0.4<br>-80<br>0.2<br>0.0<br>-100<br>-120<br>Source: Bloomberg Source: Bloomberg<br>2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1<br><!-- End of picture text -->

# **_<u>Regulatory drivers</u>_**

**First, a much needed stricter international regulatory environment has triggered medium-**

**term deleveraging which aims to put the financial sector on a strong footing.** The new Basel III frameworks call for higher capital and liquidity requirements which will gradually be phased in over the next years to accommodate the adjustment process and avoid disorderly deleveraging.<sup>25</sup> These measures are intended to strengthen and shrink the banking sector in order to make it more stable (i.e. “good” deleveraging). A recent survey of European banks found that a large majority of banks indicated higher capital and liquidity requirements as the main drivers of deleveraging and divestment plans.<sup>26</sup> Consistent with this finding, a recent ECB bank lending survey finds that regulatory requirements—Basel III, but particularly the short-term recapitalization directive by the European Banking Authority (EBA) in addition to national measures—have prompted banks to deleverage on both sides of the balance sheet and has already tightened credit conditions, particularly for large firms (Exhibits IV.3 and IV.4). Basel III was mostly designed with developed banking systems in mind although there is widespread support under EMDEs for its objectives. However, the new regulatory framework might have

> 25 There are additional considerations and implications of deleveraging for “too big to fail” institutions and global Systemically Important Financial Institutions (GSIFIs) (e.g. Blundell-Wignall and Atkinson (2012)). 26 Deloitte (2012). The survey included 18 large European financial institutions representing €11 trillion in assets.

17