rafmacalaba/datause-encoder-data huggingface dataset:
she would apply. We then used the answers to compute the share of official CPG clinical content items that the provider mentioned without prompting from the interviewer.<sup>10</sup> Providers, however, do not necessarily deliver clinical services up to their level of knowledge. We define quality of care as the actual clinical services delivered to patients. We collected information on actual clinical services provided during prenatal care from exit interviews of patients leaving facilities and from the household surveys. We measured the quality of prenatal care by computing the share of actual clinical content items delivered during a prenatal care consultation to the items that should compose a typical prenatal consultation as recommended in the Rwandan CPGs.11 For the subsequent analysis we also break out tetanus vaccine as it is also one of the directly priced services (Table 1).

The quality index and the knowledge index cover the same items and therefore are comparable. While providers know two-thirds of the appropriate protocols, they only deliver about 45 percent of the protocols (Table 3 Panel E). This implies a gap about 18 percentage points between knowledge and practice, which translated into providers delivering about twothirds of clinical services they know they should deliver. It implies that there is substantial distance from the provider’s production possibility frontier in terms of technical efficiency.

# **d. Maternal and Child Utilization**

Our utilization measures conform as closely as possible to the paid indicators in Table 1. At baseline over 95 percent of mothers had some prenatal care during the previous pregnancy, but most did not begin their care until after the first trimester and few completed 4 or more visits (Table 5 Panel B). In addition, only about one-third of women delivered in health care institutions and only about 1 in 10 were using modern contraception at the time of the survey.

We separate children’s preventive care utilization into ages 0-23 months and 24-59 months as the younger group is expected to have higher preventive visit rates (Table 5 Panels E and F). Child preventive services cover immunization, vitamin A, distribution of mosquito nets and child growth monitoring with referral of malnourished children to higher levels of care for treatment. Younger children have about .2 visits in the month prior to the survey consistent with

> 10 This measure of competency was used in Das and Hammer (2004), Kak et al (2001) and Peabody et al (2004).

> 11 This measure enters the payment formula through the general facility quality index _Q_ as specified in Table 2. It has also been used extensively in the literature to measure quality. (Barber 2006, Barber et al 2007, Peabody and Gertler 1997, Das and Hammer 2007).

17
This paper makes the case for improving the data monitoring and analytical capacity in developing countries on private capital flows, particularly portfolio investment flows (through both debt and non-debt instruments). The approach taken is to provide a survey of the recent economic literature and identify some of the unanswered policy questions that one is grappling with in developing countries and, consequently, deduce the tremendous data needs one is faced with in order to begin to rigorously analyze these issues. Section I provides an overview of recent trends in financial flows to developing countries which highlights the surge of private capital flows to a few developing countries that took place in the 1 990s. Section II traces some of the major policy issues that have been dealt with in the 1980s and mentions some of the analytical discussions in the profession at the time that went behind the policy decisions that were made in the developing countries concerned. Section III suggests some of the policy issues that one is grappling with in the context of development finance and the fruitful research agenda that emerges from the changing structure of debt and financial flows to developing countries in the 1990s. It should be noted at the outset that, in order to keep the scope of this paper manageable, it focuses on issues that are a consequence of the external debt and financial flows to developing countries rather than those arising in the trade sector or from issues related to domestic macroeconomic management in these countries.

# I. **The Changing Nature of Development Finance**

The 1 990s have seen a dramatic change in the structure of international capital inflows to developing countries. This has occurred in conjunction with a rapid and sharp increase in external financing to developing countries (Figure 1). After a virtual stagnation, there was almost a doubling of the volume of net long-term foreign financing to these countries between 1988-90 and 1991-93 (from US$88.5 billion per annum to 163.6 billion per annum, on average respectively)' and further increasing to a record $231 billion by 1995.2 There has also been a dramatic increase in private capital flows to developing

> 1 Source World Bank, _World Debt Tables, 1994-95,_ Volume 1. 2 Source: World Bank, _World Debt Tables, 1996,_ Volume 1.

3
_3'_ Table VL **INDIA:** Balce of Payments: 1987-88 to 1990-91 (millions of dollars at current prices)

||1987-88|1988-89|1989-90|1990-91|
|---|---|---|---|---|
|A. Exports of Goods and<br>Non-Factor<br>Services|16.954|18,859|20,740|21,719|
|B. Imports of Goods<br>and Non-Factor<br>Services|-21.848|-24,296|-26,267|*27,804|
|G. Resource<br>Balance|-4,895|-5.437|-5,518|-6,086|
|D. Net Factor Income and<br>Net Current Transfers<br>(excluding<br>interest payments|)<br>827|2,450|2,430|2.505|
|E. Current Balance<br>(excluding<br>interest payments|) -3,968|-2,987|-3,088|-3,581|
|F. Eaa%o#GDP<br>at marketprices|1.56|1.02|0.96|1.00|
|G3.IntereSt Payments|-1.652|-1,670|-1,712|-1.820|
|H. Repayment<br>ofLoans|-3.358|-3,474|-3,403|-3,195|
|1. G+HHsa%ofGDP<br>at marketpdrces|1.97|1.78|1 .6C|1.40|
|_Total gross Cat_<br>_Inflow_<br>_(E+G+H)_|8,978|8.131|8,203|8,596|
|_TotalgrssCapital hiflow_<br>_fGDP(Fi+)_|3.53|2.78|2.56|2.40|
|End-yew Rosrves<br>(Excluding<br>Gold)|6,354|6,095|7.091|8,237|

_Source:_ **_India:_** _Recent Development. and Medium- Terra Issues,_ Vol II; World Bank, Washington D.C. April 27, 1988; pp. 48451.
# Services, Inequality, and the Dutch Disease<sup>_∗_</sup>

Bill Battaile<sup>†</sup> , Richard Chisik<sup>‡</sup> , Harun Onder<sup>§</sup>

## **JELClassification:** F11, D31, Q33, Q31

**Keywords:** Resource-rich, Services, Inequality, Dutch Disease

> _∗_ We would like to thank Chad P. Bown, Leonardo Hernandez, Sudharshan Gooptu, Ahmad Ahsan and seminar participants at the World Bank for useful comments and suggestions.

> †The World Bank, 1818 H Street NW, Washington, DC 20433, Mail-Stop: H 4-402. E-mail: bbattaile@worldbank.org. Phone: +1-202-473-1714.

> ‡Corresponding author, Department of Economics, JOR-211, Ryerson University, Toronto, ON, M5B 2K3. E-mail: rchisik@arts.ryerson.edu. Phone: +1-416-979-5000 Ext. 4620.

> §The World Bank, 1818 H Street NW, Washington, DC 20433, Mail-Stop: H 4-402. E-mail: honder@worldbank.org. Phone: +1-202-473-5196.
The Africa Development Bank (ADB 2011) produced the first comprehensive report on middle class in Africa. The middle class is defined in the report using thresholds of consumption per capita: between $2 to $20 per day. The report subdivides middle class into three groups: the floating class (per capita consumption level of $2 to $4 per day), the lower middle class ($4-$10 per day) and the upper-middle class ($10-$20 per day). The ADB report shows that that one third of the continents’ population belongs to the middle class interval $2-$20. Yet, as most of the critics to the report point out, 50 percent of this group lays close to the $2 lower bound; indeed quite a low level of consumption to be qualified as middle class.

The Standard Bank (2014) recently published an analysis on the size of the middle class size in eleven SSA countries, constructing four different income consumption intervals based on results from South African household surveys: low income (< $3), lower middle class [$3 _,_ $4), middle class [$4 _,_ $20) and upper middle class ( _≥_ $20). It is worth noting how an investors-targeted report, in theory interested in presenting a rather rosy situation, is in fact quite critical about the general narrative of “Rising Africa” and the booming middle class. According to the Standard Bank’s estimates, the size of the middle class size has increased almost fivefold from the year 2000. Numbers nonetheless are still relatively small: only 7.6 million households (roughly 40 million people) qualify for middle class status. Nigeria gets the lion share, of these 7 million, 4.1 million are Nigerians. Looking at the shares, both the lower middle class and the middle class are around 10 percent: 20 percent of Nigerian households, thus, hover around the middle class thresholds.

Besides measuring the size of the middle class, there is growing interest in assessing the symbolic impact of the middle class and its capacity to impose a certain lifestyle. Several articles in the international press pinpoint substantial changes in Africa’s consumption patterns; they explicitly make reference to the increasing purchasing power of the local middle classes. For example, a recent Reuters article on pharmaceutical products (Berton, 2013) reports that the continent’s economic growth is luring big pharmaceutical companies to Africa, as they see potential for creating products that will treat chronic diseases which affect the middle class. This is a shift from solely supplying drugs for infectious diseases on a humanitarian basis. European drug companies are expanding in Africa due to this increase in economic wealth and demand for treatments by the urban middle class population. In the same vein, the Financial Times notes that there are luxury stores like Louis Vuitton, Richard Mille, Roger Dubuis, the Swiss company and Cartier in Nigeria, Botswana, Angola, Ghana with a stronger presence in South Africa. Africa’s market is expanding and foreign companies are steadily increasing their presence.

Increasingly, the middle class is also seen as a key political actor on the African continent.

7
a case was heard at a given chamber hearing date and what the outcome was. Hearings are scheduled on a bi-monthly basis, following a chamber-specific schedule that is set every six months by the president of the court; this allows for 21 hearing dates per chamber per year. Figure A1 shows the incoming and ongoing case volume at any hearing period.<sup>3</sup>

# **3.2 Firm data**

To document the impact of the reform on court users, we combine and leverage two sources of complementary data: the universe of firm monthly VAT filings obtained from the tax administration (together with annual corporate and individual tax returns), and primary survey data on firms involved in court cases over our study period. These cases involved 5,401 parties that are firms, which correspond to 2,154 distinct firms. Matching these to the tax administration data is only possible when we are able to obtain their unique tax ID (NINEA); this is the case for 66% of distinct firms which represent 82% of parties that are firms in the court data. We then perform the following two merges.

First, we merge the court party data with the 2012 revenue data from annual corporate and individual returns to check for balance in pre-decree revenues. This yields a match for 46% of distinct firms in our court data (993 firms), representing 70% of the parties that are firms (3,785 parties, of which 1,991 had cases before the reform); these firms are involved in a total of 2,910 cases.

Second, we merge the court party data with revenue data from monthly VAT filings.<sup>4</sup> We restrict the revenue data to include only complete time series of twelve monthly data points around the start of a parties’ pre-trial (six months before and six months after). We obtain

> 3A six-week summer break is established at the chamber level over the August–October period, on a rotating basis across chambers. All judges take leave during the period assigned to their respective chamber, and no hearings are scheduled.

> 4We focus on firm revenues and not value-added due to the frequency and granularity of the filing. While revenues are filed monthly, deductions are not, and we do not have transaction-level data to recover monthly deductions.

7
in northwestern Mozambique is considered to be the largest unexplored coal province in the world with an estimated 2.4 billion tons of reserves. In 2003, regional coal production reached 269 million short tons (Mmst), of which South Africa produced nearly 264 Mmst. South Africa also consumed the majority (97 percent) of the region’s coal in 2003.<sup>88</sup>

**Table 7  Hydro and coal resources in the Southern African Region**

||<br>**Large Hydro**|<br>**Potential (MW)**<br>|**Coal Resourc**|**es (Mil. Tones)**|
|---|---|---|---|---|
||**_Installed Capacity_**|**_Potential Capacity_**|**_Proven_**|**_Estimated_**|
|Angola|980|18,267|n.a.|n.a.|
|Botswana|n.a.|n.a.|32,112|212,000|
|DRC|2,416|97,584|n.a.|n.a.|
|Lesotho|75|3,000|n.a.|n.a.|
|Malawi|304|900|22|1,000|
|Mozambique|2,184|6,398|n.a.|3,000|
|Namibia|240|520|350|n.a.|
|**South Africa**|**668**|**n.a.**|**55,000**|**n.a.**|
|Swaziland|40|440|208|1,000|
|Tanzania|630|4,700|304|1,200|
|Zambia|1,670|6,683|30|1,000|
|Zimbabwe|750|7,200|11,000|26,625|
|Total|10,024|145,692|99,026|245,825|

_Source_ : Mathangwane (2005).<sup>89</sup> Natural gas continues to grow in importance and is a resource that exists variously throughout the region. Significant reserves are located in Angola (1 Tcf), DRC (35 Bcf), Mozambique (4.5 Tcf), Namibia (2.2 Tcf), South Africa (1 Bcf) and Tanzania (800 Bcf). Mozambique is likely to become a major producer of gas in the region in the medium term. Its three offshore gas fields Pande, Temane, and Buzi have estimated recoverable reserves in the order of 2.1 Tcf, 1.0 Tcf, and 10Bcf respectively. Actual gas production was limited to an insignificant amount from the Pande field which supplied some local villages.<sup>90</sup> In 2000, the National Hydrocarbon Company (ENH) of Mozambique and the South African company Sasol Petroleum International signed an agreement on the joint development of the Pande and Temane natural gas fields and on a gas pipeline to transport gas from these fields to the Gauteng area in South Africa. The 865 km pipeline opened in July 2004 and brought South Africa a step closer to diversifying its energy sources.<sup>91</sup> Namibia is another potentially major producer of natural gas. The Kudu gas field is located on the Namibian continental shelf offshore of southern Namibia (170 km from Oranjemund) in a water depth of 170 metres. A gas-inplace of 1.3 trillion cubic feet has been proven to 90% confidence level, which is

> 88 Energy Information Administration. 2005. _The Southern African Development Community_ .  Country Analysis Brief.

> 89 Mathangwane, F. 2005.  “Facilitating Access to Power in SADC and Ensuring the Region’s Bright Future”.  Presented in the Regional Electricity Investment Conference, Windhoek, Namibia.

> 90 Mbendi. 2005. Mozambique: Oil and Gas Industry.

> 91 http://www.southafrica.info/doing_business/economy/infrastructure/naturalgas-pipeline.htm

58
- 29 -

of the damage associated with heavy trucks, the estimate of RUC to the economy is $39-64 million. Damage costs constitute 65-80 percent of these costs. The share of costs that can be attributed to heavy trucks is _33-55_ percent.

Table 13. The Estimation of Annual Road-User Costs by Type of Vehicle and Type of Externality, 1988 ($ millions)

||||Car|Light<br>bus|Heavy<br>bus|Light<br>truck|Medium<br>truck|Heavy<br>truck|Total|
|---|---|---|---|---|---|---|---|---|---|
|1.|Vehicle fleet ('000)||68|16|7|4|5|5|100|
|2.|Vehicle-km|||||||||
||(millions)||2,720|880|525|280|400|400|5,205|
|RD|E per veh.-km (C/km)|||||||||
|3.||(a)|_0.115_|0.220|0.655|0.655|0.915|2.940||
|4.||(b)|0.078|0.138|1.090|0.138|1.805|8.583||
|Roa|d damage costs ($ mi)|||||||||
|**_5._**||(a)|3.1|1.9|3.4|1.8|3.7|11.8|25.7|
|6.||(b)|2.1|1.2|_5.7_|0.4|7.2|34.3|**_51.0_**|
|7.|Urban veh.-km (mil)||1,960|620|165|60|280|120|3,205|
|8.|Urban MCC per<br>veh. -km ((:1km)||0.30|0.45|0.60|0.45|0.54|0.75||
|9.|Urban congestion costs<br>($ mil)||5.9|2.8|1.0|0.3|**_1.5_**|0.9|12.3|
|10.|Interurban veh.-km<br>(mil)||760|260|360|220|120|280|2,000|
|11.|Interurban MCC per<br>veh.-km (Clkm)||0.030|0.045|0.060|0.045|0.054|0.075||
|12.|Interurban congestion<br>costs ($ mil)||0.2|0.1|0.2|0.1|0.1|0.2|0.9|
|13.|Total RUC ($ mil)|(a)|9.2|4.8|4.6|2.2|**5.3**|12.9|38.9|
|14.||(b)|8.2|4.1|6.9|0.8|8.8|35.4|64.2|

Sources: Lines 1, 2, 7, 10Appendix. Line 3 - Table 12, Panel A, column (2) Line 4 - Table 12, Panel B, column (8) Line 5 = [2. x 3.1. Line 6 = [2. x 4.]. Line 8 - Table 11, column (8) Line 9 = [7. x 8.1. Line I- Table 11, column (7) Line 12 [10. x 11.]. Line 13 = 15. + 9. + 12.1. Line 14 = [6. + 12. + 13.]
# **Table 6: A Direct Look at Selection**

Dependent Variable: Labour Income prior to applying/migrating

|||**Se**<br>All 18-45|**lection int**<br>year olds|**o the Lotte**|**ry**<br>Pseudo-a|<br>pplicants|**Selection in**<br>Lottery|**to Migration**<br>Winners|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Migration Dummy Variable|55.95|29.46|||28.28|21.83|13.14|7.43|
||(5.30)***|(3.13)***|||(2.16)**|(1.97)*|(0.86)|(0.51)|
|Applicant Dummy Variable|||47.12<br>(6.53)***|23.06<br>(3.06)***|||||
|Controls|No|Yes|No|Yes|No|Yes|No|Yes|
|R<sup>2</sup>|0.145|0.330|0.099|0.205|0.037|0.331|0.006|0.129|
|Number of Observations|234|221|366|350|120|117|119|117|

# Notes:

Absolute value of t-statistic in parentheses, using heteroskedasticity-consistent standard errors *, **, and *** indicate significance at the 10%, 5% and 1% levels Controls include gender, age, marital status, years of education, place of birth and height. The total number of relative types in New Zealand is also used as a control in the pseudo-applicant and lottery winner regressions.

46
|Flush toilet|||||0.129<br>**|*|0.096|***|
|---|---|---|---|---|---|---|---|---|
|Kitchen|||||0.029<br>**||0.047|*|
|Douche|||||0.140<br>**|*|0.230|***|
|Tv|||||0.163<br>**|*|0.153|***|
|Parabole|||||0.156<br>**|*|0.227|***|
|R2 x hhld size|-0.024|***|-0.006||-0.024|***|-0.004||
|R3 x hhld size|0.004||0.019|*|0.002||0.026|**|
|R4 x hhld size|-0.006||0.012||-0.001||0.013||
|R2 x unemp (count)|-0.055||-0.490||-0.122||-0.547|*|
|R3 x unemp (count)|0.487|**|-0.848|***|0.493|**|-0.946|***|
|R4 x unemp (count)|0.075||-1.146|***|0.026||-1.066|***|
|R2 x waged (count)|-0.279|***|-0.173||-0.245|**|-0.199||
|R3 x waged (count)|-0.018||-0.369|***|0.055||-0.411|***|
|R4 x waged (count)|-0.501|***|-0.392|***|-0.435|***|-0.451|***|
|R2 x public (hd)|-0.181||-0.020||-0.123||-0.021||
|R3 x public (hd)|-0.159||-0.156||-0.123||-0.045||
|R4 x public (hd)|-0.098||-0.384|***|-0.070||-0.283|**|
|R2 x drinkwater|||-0.044||||-0.070||
|R3 x drinkwater|||0.275|***|||0.233|**|
|R4 x drinkwater|||0.091||||0.041||
|R2 x sewage|||-0.588||||-0.515||
|R3 x sewage|||-0.504||||-0.507||
|R4 x sewage|||-0.441||||-0.431||
|R2 x roompc|-0.205|**|0.048||-0.224|***|0.010||
|R3 x roompc|0.224|***|0.358|***|0.124||0.280|**|
|R4 x roompc|0.171|**|0.370|***|0.168|**|0.306|***|
|R2 x roompc^2|0.041|*|-0.009||0.048|**|-0.003||
|R3 x roompc^2|-0.031||-0.087|**|-0.001||-0.064|*|
|R4 x roompc^2|-0.028|*|-0.065|**|-0.028|*|-0.051|*|
|Constant|8.168|***|8.737|***|8.199|***|8.786|***|
|adj-R2|0.429||0.404||0.478||0.469||
|Obs|6355||2796||6355||2796||

Source: HESs (2001, 2007)

# **6. Validation tests**

Before imputing expenditure poverty into all available years of the LFS, this section considers two different tests of the methodology proposed. Both tests use only 2001 and 2007 data, so that imputation-based estimates can be compared to official estimates based on observed data. The first test is within HESs samples, while the second test is cross-surveys using HESs and LFSs.

In the first test, we conduct cross-imputations using only the HESs, by estimating the expenditure model in 2001 and then imputing expenditure poverty in 2007 and vice-versa. This means that we do not have 17
# **Collecting the Dirt on Soils: Advancements in Plot-Level Soil Testing and Implications for Agricultural Statistics**

_Sydney Gourlay_<sup>_1_</sup> _, Ermias Aynekulu_<sup>_2_</sup> _, Keith Shepherd_<sup>_2_</sup> _, Calogero Carletto_<sup>_3_</sup>

> _1_ The World Bank, Washington, D.C., USA

> _2_ World Agroforestry Centre (ICRAF), Nairobi, Kenya,

> _3_ The World Bank, Rome, Italy

**Key words** : Land productivity; household survey; soil spectroscopy; soil fertility; local knowledge **JEL classification:** Q10; Q12; Q24; C81

1
Policy Research Working Paper 9144

# **Abstract**

Salinity in surface waters is on the rise throughout much of the world. Many factors contribute to this change, including increased water extraction, poor irrigation management, and sea-level rise. To date no study has attempted to quantify the impacts on global food production. This paper develops a plausibly causal model to test the sensitivity of global and regional agricultural productivity to changes in water salinity. To do so, it utilizes several local and global data sets on water quality and agricultural productivity and a model that isolates the impact of exogenous changes in water salinity on yields. The analysis trains a machine-learning model to predict salinity globally, to simulate average global food losses over 2000–13. These losses are found to be high, in the range of the equivalent of 124 trillion kilocalories, or enough to feed more than 170 million people every day, each year. Global maps building on these results show that pockets of high losses occur on all continents, but the losses can be expected to be particularly problematic in regions already experiencing malnutrition challenges.

This paper is a product of the Water Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at Jruss@worldbank.org, ezaveri@worldbank.org, sebdburo@gmail.com, rdamania@worldbank.org, jorgejoseescurra@hotmail.com and arodella@ 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
26 Table 12: Extension provision situation for the 7 ATP2 private companies as of 9/1997.

|Company|Number of<br>_"Departmentos"_<br>Served|Number of Mu-<br>nicipalities<br>Served|Number of Local<br>Communities<br>Served|Clients as<br>of<br>3097|% of Clients<br>continuing<br>from last<br>year|Number of<br>Extensionists|Number<br>of Groups|Groups per<br>Technician|Producers per<br>Technician|Clients as of<br>12-31-96|
|---|---|---|---|---|---|---|---|---|---|---|
|1|2|10|82|1710|69.4|13|**105**|<br>8.1|131|1445|
|2|3|15|92|2333|25.8|25|97|3.9|93|802|
|7a|1|6|83|1200|N/A|8|83|10.4|**_150_**|<br>1423|
|6a|2|6|39|1294|N/A|8|63|7.9|156|1124|
|3|2|_5_|<br>49|1078|58.8|8|63|7.9|135|960|
|4|1|3|63|1344|70.4|9|63|7|149|880|
|_5_|<br>1|4|47|1200|60.0|10|100|10|120|421|
|Total|12|49|455|10159|40.2|81|574|7.1|125|7055|

Source: Estrada-Rizo (1997).

aOperational **from** 10-96.