rafmacalaba/datause-encoder-data huggingface dataset:
modifications based on analysis of the CPTPP text. NTMs for goods are constructed from the 2012 update of estimates by Kee et al. (2008) and the services barriers are based on estimates by Fontagné et al. (2011). Only three-quarters of measured barriers are considered as actual trade barriers, the rest is assumed to represent quality-increasing regulations (e.g., product safety standards). Further, only three-quarters of the remaining NTMs in the case of goods and one-half in the case of services are assumed to be actionable (i.e., politically feasible in a trade agreement), the rest is assumed to be beyond the reach of politically viable trade policies.<sup>10</sup> NTMs are modeled as iceberg trade cost. These are non-revenue generating costs, which allow for trade to expand if these costs are reduced. For example, if iceberg trade costs are equal to 0.9 for some transport node, that means that if 100 units leave port r, the destination port, r', receives only 90 units.

## TPP-15 (CPTTP+)

Under this scenario TPP-11 is expanded to include Indonesia, the Philippines, Korea and Thailand in 2019. By comparing TPP-11 with TPP-15 we can evaluate the opportunity costs of Indonesia remaining outside of the agreement. In this simulation we use the same assumptions for the 11 CPTPP members as in the previous scenario. But in TPP-15 scenario the aforementioned assumptions about NTMs also hold for the new countries joining CPTPP. Tariffs in the four new CPTPP members are based on the GTAP values which are gradually reduced starting with a decline by 65% in 2019 up to 99% in 2028.

# 4. Macro-economic effects of PTAs

The results of the analysis suggest that of the PTAs under investigation, the EU-CEPA would yield the largest macro-economic gains for Indonesia in terms of income, output and exports, as illustrated in Figure 2 and Table 2. Relative to the business as usual baseline scenario, Indonesian GDP is expected to increase by 2.13% (or 54 billion USD) by 2030 as a result of the EU-IDN CEPA, compared to 0.18%, 0.58% and 0.89% in case of RCEP, TPP-15 and FTAAP, respectively.

The large impact of the EU-IDN CEPA is mainly driven by the mutual highest expected reductions in trade barriers (see e.g. Table 1 above for NTMs) and by the high share of international trade between the partners, as the EU accounts initially for 12% of total exports and 11% of total imports.

> 10 The fraction of actual NTM reductions is derived for 21 separate issues areas, based on a score from 0 to 100 with a higher score indicating larger reductions in trade barriers by TPP compared with existing PTAs. See World Bank (2016a), p. 236 (Figure A.4.1.1).

9
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 18: Pure DC reform with immediate switching decreases considerably Government expenditure for civil servants, particularly in the medium and long term**

<!-- Start of picture text --> 2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br>Year<br>ref I.1 base<br>Expenditure (% GDP)<br><!-- End of picture text -->

**Source** : WB calculations based on PROST

**Figure 19: Pure DC reform with immediate switching decreases considerably government expenditure for teachers, except for the very few first years of the simulation period***

<!-- Start of picture text --> 2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br>Year<br>ref I.1 base<br>Expenditure (% GDP)<br><!-- End of picture text -->

**Notes** : Comparisons of government expenditure for teachers – baseline with Reform I.1A (% of GDP). **Source** : WB calculations based on PROST.

- 52 -
described in more detail below and in Appendix B. While the BIS data in many cases captures much of the international financial flows we are trying to measure, it is not always an appropriate fill and so we do not want to use just a single data source for our external filling exercise. Specifically, bond inflows are measured in the BIS data as net issuance of debt securities in international markets. While this measure is appropriate for many countries, countries that have many foreigners buying domestically issued bonds or domestics buying international issued bonds will introduce error. An important example of this is government debt issued by advanced economies. The US has a substantial amount of sovereign debt that is traded abroad, but nearly all of the debt is issued domestically, making the BIS measure an inappropriate way to fill that missing series.<sup>59</sup> Thus to increase the accuracy of our filling process, we turn first to the IIP and QEDS data. To approximate flows, we first difference the stocks with a simple correction for exchange rate valuation effects.<sup>60</sup> When both IIP and QEDS data are available, we use the IIP measures for consistency with the BOP data. We use these stock measures to fill both portfolio debt and other investment debt for the government and central bank sectors. We also use these measures to fill Corporate portfolio debt in AE.

Table A4 summa- For the remaining missing data, we use our BIS constructed measures. rizes the process of constructing matching series for inflows using the BIS data.<sup>61</sup>

> 59The only national data that we include is for the United States, which has substantial capital flows that won’t be captured by the BIS data, but also a gap between the availability of QEDS and IIP data and the coverage of the BOP data. Specifically, we fill in the stock IIP measure of government portfolio debt for the US using the TIC data from the US Treasury, Securities data (B) Tables A.2.d and A.2.a, for the period 1999q1-2003q2, and then take the

> 60Data on currency composition of external debt, split by capital flow type and sector, is scarce. We assume the external debt is denominated in domestic currency. While this is not always the case, changing the assumption to denominated in USD does not appreciably change our filling accuracy.

> 61Recall that other investment debt can be decomposed into loans, currency and deposits, trade credit and advances, other accounts payable/receivable, and pension and insurance.

72
scale‖ in this dimension. In previous decades, only China was able to register among the top five contributors to global GDP growth. Over the last decade, not only did China top the list, but also fellow BRICs – Brazil and India—moved into the top five (Table 4). It is interesting to note that Korea—a relative newcomer to the club of ―advanced‖ economies<sup>9</sup> --emerged from the Asian financial crisis of the 1990s to become one of the top six contributors to global GDP growth. The developing world as a whole contributed about half of the world‘s global GDP growth in the last decade. A key question is whether this is an anomaly or whether the global economy is entering a sustained period of much faster growth among the developing countries – a ―great convergence,‖ as Martin Wolf has called it<sup>10</sup> , or the start of ―the next convergence,‖ as Nobel Laureate Michael Spence (2011) has called it. There are some signs that the underlying economic relations in trade and capital flows might support a sustained period of rapid growth in the developing world.

**Table 4:  Top Fifteen Contributors to Global Growth By Decade (%)**<sup>**11**</sup>

|**2000-2010**||**1990-2000**||**1980-199**|**0**|
|---|---|---|---|---|---|
|**_China_**|23.4%|United States|36.1%|United States|30.5%|
|United States|20.4%|**_China_**|9.6%|Japan|24.0%|
|**_India_**|5.8%|Japan|6.6%|Germany|5.0%|
|Japan|4.5%|Germany|4.5%|United Kingdom|4.3%|
|**_Brazil_**|3.1%|United Kingdom|4.2%|**_China_**|4.2%|
|Korea, Rep.|3.1%|Korea, Rep.|3.0%|France|3.7%|
|United Kingdom|2.5%|France|3.0%|Italy|3.2%|
|Germany|2.0%|**_India_**|2.4%|Korea, Rep.|2.7%|
|France|1.8%|Canada|2.3%|Canada|2.1%|
|**_Russian Federation_**|1.8%|**_Mexico_**|2.1%|**_India_**|1.8%|
|**_Argentina_**|1.7%|Italy|2.0%|Spain|1.8%|
|Canada|1.7%|**_Brazil_**|1.8%|Australia|1.3%|
|Australia|1.7%|Spain|1.8%|**_Turkey_**|1.2%|
|Spain|1.5%|Australia|1.5%|**_Brazil_**|1.1%|
|**_Turkey_**|1.4%|Netherlands|1.3%|**_Mexico_**|1.1%|

> 9 In this paper, developing countries refers to all the low and middle income countries (either lower middle or upper middle), according to World Bank definitions.

> 10 ―In the grip of a great convergence,‖ Financial Times Op-ed, January 4, 2011.

> 11 Calculation is based on the change in constant dollar GDP (at market exchange rates) of the particular country as a share of the change in constant dollar GDP of the world.

14
**Table H2: Nutritional Indicators, by age and gender**

|Age in|B|oys|G|irls|
|---|---|---|---|---|
|months|Mean|Stunting|Mean|Stunting|
||HAZ|incidence|HAZ|incidence|
|0-5|-1.09|0.26|-0.66|0.22|
|6-11|-1.53|0.39|-1.01|0.23|
|12-23|-1.80|0.56|-1.65|0.47|
|24-35|-1.86|0.58|-1.76|0.47|
|36-47|-1.79|0.45|-1.67|0.37|
|48-60|-1.49|0.36|-1.66|0.40|

87
# **Weather, Water, and Work:**

**Climatic Water Variability and Labor Market Outcomes in Sub-Saharan Africa**

Amjad M. Khan<sup>∗</sup> Landry Kuate<sup>†</sup> Roland Pongou<sup>‡</sup> Fan Zhang<sup>§</sup>

**JEL Codes:** O12; O55; Q50.

**Keywords:** : Climate change; Water scarcity; SPEI; Employment outcomes; Poverty traps; Sub-Saharan Africa.

> ∗World Bank Group, Washington D.C., akhan31@worldbank.org

> †Department of Economics, University of Ottawa, lkuat097@uottawa.ca

> ‡Department of Economics, University of Ottawa, rpongou@uottawa.ca

> §World Bank Group, Washington D.C., fzhang1@worldbank.org
ways, we assume that it affects the perceived basis risk or correlation between income and rainfall ρ . To keep matters simple, we assume that the perceived correlation ~ between rainfall and income is ρ = ρ 1( −φ ) −φ , where the parameter φ varies from 0 to 1 and measures the degree of complexity. When φ= 0 there is no complexity so that the ~ = farmer’s perception of basis risk coincides with the actual basis risk ρ ρ , but when φ= 1 there is maximum complexity and the perceived basis risk is also maximum ~ ρ = − 1 , regardless of the actual correlation ρ . Under complexity, the perceived consumption bundles under the two states of nature are Under complexity, the perceived consumption bundles under the two states of nature are _cHI_ = _YH_ − ( 1( + ρ )(1 −φ ) ) _R_ and _cLI_ = _YL_ − ( 1 +φ− ρ 1( −φ ) ) _R_ .

Thus, the higher the complexity, the higher the basis risk perceived by the farmer and as a result, the less desirable the insured loan will appear to be.

# **<u>Appendix B: Variable definitions</u>**

Data are from the Malawi Technology Adoption and Risk Initiative (MTARI) farm household survey in September-October 2006. All variables refer to respondent or respondent's household.

_Take-up_ equal to 1 if respondent signed up for hybrid seed loan, 0 otherwise. _Treatment_ equal to 1 if respondent offered insured loan, 0 if offered uninsured loan.

_Risk tolerance_ is self-reported on 0-10 scale: higher indicates greater tolerance for risk in trying new crop varieties.

_House quality_ is the first principal component of several binary asset variables. Variables are defined for housing construction materials, water source, and electricity source. In general, variables are defined such that “1” represents a higher standard of living than “0.” The binary asset variables used in this analysis are for brick housing construction, non-earthen floors, metal roofs, and running water (including water piped into the residence and water from a public tap). Additionally, we use two variables that are exceptions to the rule of “1” representing a higher standard of living. The first of these is for well water, as opposed to either running water or unimproved water sources. The second is for gas lighting, as opposed to either electricity or solar power, or firewood, candles, or no lighting.

_Net income_ is computed as the sum of farm profits and non farm income, and is reported in Malawi kwachas (MK). Farm profits are the monetary value of crops produced less the monetary cost of farming inputs. Farming inputs include irrigation, fertilizer, chemical insecticides, manure or animal penning, hired equipment such as tractors, and hired manual labor and oxen labor. Information on farm revenue and expenditure was collected for each plot; total farm profits are computed as the sum of profits over all plots farmed by an individual. Non farm income includes wages from agricultural labor (on other peoples’ farms); wages from non-agricultural labor; wages or in-kind wages from public works programs; remittances; benefits from government programs; pension income; and other sources of income. Information on these sources of
_25_

**_Ka8ga. Cdnical service ad_** _sodaa_ **_marketing_**

The economic underdevelopment of Kananga and the difficult transportation and communication systems were coupled with a high infant mortality rate. Estimated at 103 per 1000 live births, Kananga's rate was the highest of the 6 areas included in the 1982 survey.

The 1982 study showed that the women of Kananga married early, had high fertility, and wanted even higher fertiity. 'The median age at first marriage or cohabitation was just over 15 years of age. Women aged 35-44 reported an average of more than 7 children ever borne. Eighty-three percent of women with **_5_** living children said they wanted more children, as did 69 percent of women with 6 or 7 living children, and 20 percent of women with 8 or more children. Limiting the number of children was not an acceptable idea among Kananga women. A 1988 study of 47 women who had had surgical sterilization [19] found that their median number of living children was 8. When asked the main reason that they had decided on sterilization, only 5 of the 47 women stated that it was because they had enough children. The others insisted that the main reason was to maintain their own health, or because they had difficulties with their last pregnancy.

In short, Kananga women had and wanted large numbers of children, and were unfavorable to the idea of _limnting_ their births.

They were, however, using certain practices that had the effect of _spacing_ their pregnancies. Breastfeeding was universal; 100 percent of the women in the 1987 survey who had a child 10 months of age or younger were breastfeeding; nearly three-quarters were still breastfeeding at 17 months. The medians of this cross-sectional study showed that the typical Kananga woman abstained from sexual relations for about 6 months after a birth, ovulated at 12 months, became pregnant at 21 months and stopped breastfeeding at 21 months. Most of the women who had babies under 24 months of age and who were at risk of pregnancy (had begun sexual relations and had ovulated) reported that they were currently using some method of contraception. Fifty per cent of these post-partum, at-risk women were using the calendar method or traditional methods (coitus interruptus or a special belt); fewer than ten percent were using modern contraceptive methods.

Among all Kananga women ages 1349, modern contraceptives were generally known. In the 1987 survey, 99 percent knew of modern contraception, and they knew four different methods on the average. While 21 percent said they had ever used a modern method, only 4 percent of women in union were currently using one.

The attitudes of Kananga women toward contraception were largely favorable. The 1982 survey recorded 80 percent who said they approved of family planning, while 18 percent did not. The principal reasons cited by those who disapproved were that family planning was against God's will, that they did not yet have all the children they wanted, and that the methods were harmfil to a woman's health.
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40
The median quoted internediation spread between deposit and lending rates remained broadly constant during 1960-1980 but has risen rather sharply since then. A numlber of interpretations are possible. For one thing there could have been an increase in the market power of banks, possibly associated with the relaxation of interest rate controls. Another factor could be the deterioration in loan-loss experience in the latter part of the sample: equilibriurm spreads should have widened to take account of the credit risk. Finally, the degree to which the quoted rates are representative will have varied over time, with large depositors and first-rate borrowers beginning to have new non-bank opportunities.

1.2 The developing countries catch up: annual data from 1975

Data for the early years in Table 1 are sparse: the early years included vety few observations. Only from about 1980 on is there data for at least several dozen countries in each case. Table 2 and Figures 1 to 4 provide more detail for the period since 1975, distinguiishing between industrial and developing countries. These show mean and percentile figures in addition to the median on an annual basis. Along with rnoney market and treasury bill rates, we show bank deposit rates and the quoted intermediation spread, i.e. the difference between quoted deposit rates and quoted lending rates for as many countries as have sufficient annual observations included in _International Financial Statistics._<sup>_5_</sup> The general trend is summarized by three-yearly averages of the medians shovn in Table 2.

Table 2: _Median Ex-post Real World Interest Rates: 1970s to 1990s_

|%|Money<br>Industrial|Market<br>Developing|Treas<br>Industrial|ury Bill<br>Developin|De<br>g Industrial|posit<br>Developing|Lending l<br>Industrial|ess deposit<br>Developing|
|---|---|---|---|---|---|---|---|---|
|1975-77|-1.0|-0.4|-0.9|-3.1|-2.8|-4.2|2.8|4.1|
|1980-82|3.3|1.6|2.4|-1.9|0.1|-2.3|4.1|4.6|
|1985-87|5.8|3.7|5.3|0.9|3.2|1.5|4.4|5.0|
|1990-92|6.7|6.8|6.2|2.5|3.3|2.5|5.1|6.7|
|1995-96|3.1|4.4|3.5|_5.0_|1.8|3.5|4.0|6.6|

Note: The mean of the median across countries is shown. For the spread mean shown is for 1995-97.

S These figures and data are based on countries for which annual data is available for at least 12 years within 198093. It is not a balanced pool: the number of countries varies somewhat from year to year, but more according to the series, from a mean of 35 for money market and 36 for treasury bill rates to 59 for intermediation spreads and 62 for deposit rates. This sample selection strategy represents a compromise between the desirability of including as many countries as possible with the risks of too unbalanced a pool. One potentially important but hidden source of sample-selection bias could arise to the extent that reporting of data to _IFS_ is correlated with a liberalized interest rate regime.

4
In this model Xi are country-level characteristics observed before the onset of the financial crisis. Because the crisis occurred in 2008, we use the average of 2004 to 2007 to measure pre-crisis country characteristic. Note that because Xi is time-invariant, the level is subsumed into country fixed effects and only interaction with crisis is included in the regressions. We focus on two pre-crisis country characteristics: income level (Log GDP per capita) and financial development (i.e. domestic credit to GDP), both measured as averages over 2004 to 2007, i.e. representing pre-crisis levels rather than year to year variations. Interactions of crisis and pre-crisis levels of GDP and/or financial development (i.e.  coefficient) show whether countries with different income levels or different levels of financial development reacted differently to the crisis. Again, we estimate this model with country fixed effects regression with errors clustered on country level to capture any serial correlation of errors within country.

The results are presented in Table 6. In column 1 we add the interaction of the crisis with log GDP per capita and find that this interaction is significantly negative, confirming our prior observation that countries with higher GDP per capita experienced sharper declines of new registrations during the crisis. This is expected since the crisis primarily originated in developed countries and hence developed countries were most affected by the crisis. In column 2 we add interactions of the crisis and financial development and find this variable to also be very significant. One feature of the crisis was its severe impact on the functioning of financial markets, which resulted in a credit crunch and credit rationing. It is not surprising that countries in which financial markets played a larger role in the domestic economy would experience sharper contractions in new firm creation during the crisis. One plausible channel for this is through firm’s access to external finance, which is more important in countries with higher level 20
collection of information about specific health probiess, about coverage and use of particular health programmes should be tailored to country circumstances. In-depth studies of issues of importance to particular countries are also needed.

In turning to the second objective of 'impact assessment' the economic evaluation techniques of cost-benefit analysis and cost-effectiveness analysis are considered. Problems in measuring health outcomes and valuation continue to limit the application of these methods and a WHS would not be a suitable vehicle for such evaluations. On the other hand there are a range of health service concerns that can be more easily addressed, for example, access and coverage, patient costs, financing systems etc. Data related to these issues Ald be included as part of the WHS and specific suggestions are made.

Particular issues concerning the design and organisa of a WHS are considered including the selection of countries, -iaple sizes, questionnaire development, data processing and strategies for analysis.

It is concluded that a series of coordinated country health studies producing population-based estimates of health and its determinants would have a valuable role in improving the health information systems of some countries.

.4