rafmacalaba/datause-encoder-data huggingface dataset:
Clearly, the main difficulty encountered are the data themselves (available or not). This lead to several kinds of situation: <u>The data do exist but are dispersed or too plentiful: this is typically the case of the large river basins,</u> international or national ones. As regards the large transboundary basins (for example the Danube, the Rhine, etc.), the data exist at the level of the countries.

RBOs may also cover very large national river basins, or several river basins, and in that case the data may be available in a database, or several databases: this means making choices between data widely different in each point of the basin or basins.

An example can be given: it concerns the water quality parameters. The same parameters may be used in the whole basin, with perhaps some additional ones in very specific sampling points, but the results may be very different from a measurement station to another. This will depend of the station location. In the case of the Rhone basin for instance, a station located in Lyons (a very large town with an industrial complex on the main watercourse) will provide results very different (as pollution is high) from a station located on a tributary in the mountains (although some nitrates may be found in agricultural areas). Therefore, giving some exact figures characterizing the whole basin is impossible, even if all these data do exist.

<u>The river basin is small but the data do not exist or are not gathered in a database. Giving figures</u> (exact or estimated), if at all, may be difficult in that case. This might be better in the future, as river basin databases are now created when the RBOs are being set up (although this is not true everywhere).

# **Outcome**

In spite of these difficulties, many countries have responded. The following is a tally of the responses by regions:

# _Africa_

The RBOs are not yet widely developed in Africa. If we except some international commissions (Chad, Niger, Senegal, the Nile, Zambezi, etc.), there are few RBOs at the national level, so the answers were limited: Algeria, Burkina Faso, Kenya and Morocco.

48
|TableA3:Labor force participationbyrace,immigration|status, and age at arrival i<br>|nthe US.<br>|
|---|---|---|
||(1)<br>Males|(2)<br>Females|
|Dependent variable|Inthelabor force|Inthelabor force|
|White bornoutside the US, arrived youngerthan 10|-0.0035|-0.0222***|
||[0.0033]|[0.0041]|
|White bornoutside the US, arrived between 10 and20 years old|-0.0164***|-0.0388***|
||[0.0031]|[0.0038]|
|White bornoutside the US, arrived olderthan 20|-0.0395***|-0.1131***|
||[0.0018]|[0.0022]|
|Blackborn inUS|-0.0803***|-0.0263***|
||[0.0009]|[0.0010]|
|Blackbornoutside the US, arrived youngerthan 10|-0.0795***|0.0187|
||[0.0119]|[0.0119]|
|Blackbornoutside the US, arrived between 10 and20 years old|-0.0749***|0.0176***|
||[0.0067]|[0.0065]|
|Blackbornoutside the US, arrived olderthan 20|-0.0335***|0.0385***|
||[0.0031]|[0.0034]|
|Asianborn inUS|-0.0318***|0.0053|
||[0.0032]|[0.0038]|
|Asianbornoutside the US, arrived youngerthan 10|-0.0639***|-0.0328***|
||[0.0057]|[0.0065]|
|Asianbornoutside the US, arrived between 10 and20 years old|-0.0798***|-0.0492***|
||[0.0037]|[0.0041]|
|Asianbornoutside the US, arrived olderthan 20|-0.0772***|-0.1064***|
||[0.0018]|[0.0019]|
|Hispanic born inUS|-0.0511***|-0.0330***|
||[0.0012]|[0.0014]|
|Hispanic bornoutside the US, arrived youngerthan 10|-0.0491***|-0.0187***|
||[0.0034]|[0.0039]|
|Hispanic bornoutside the US, arrived between 10 and20 years old|-0.0719***|-0.0734***|
||[0.0018]|[0.0024]|
|Hispanic bornoutside the US, arrived olderthan 20|-0.0546***|-0.0985***|
||[0.0013]|[0.0017]|
|Observations|3472846|3649194|
|R-squared|0.15|0.10|

Robust standard errors in brackets. * Significant at 10%; ** significant at 5%; *** significant at 1%.

Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The probit regressions (marginal effects shown) also include 5 year age group dummies, region dummies, education (HS drop out, HS graduate, some college, college and above), indicators for being married, disabled and in school, the number of children under age 5 in the household and the logarithm of total personal income earned by the other household members. The regressions use the sampling weights provided with the IPUMS data.

35
concessional terms, and foreign direct investment (FDI) in the energy sector in low- and middleincome countries. Other categories of finance under this label are international funds to promote clean technologies, and for renewable energy development. As of today, few such commitments have been made; see World Bank, chapter 6, for an overview of recent commitments.<sup>6</sup> Clearly, the current situation is inadequate; but may be changed following the December, 2009 Copenhagen meeting.

# **2.5 Finance to LICs resulting from international quota trading or tax revenue transfers**

An international c-a-t scheme, with free quota trading and allocation of emissions rights by population, could lead to rather massive financial transfers between major groups of countries. For the foreseeable future, current high-income countries are likely to retain a per capita GHG emissions level that is (far) higher than that of most current low-income countries. This will lead to financial flows out of highincome countries to lower-income countries, as rights to the former group’s excess GHG consumption need to be purchased from the latter group.

Allocating emissions rights by population is a “radical” mechanism that is likely to be resisted by countries that are currently high per-capita emitters (the OECD). An alternative principle, namely allocating emissions rights in proportion to the “initial baseline” (in proportion to relative emissions in some baseline year, or to some hypothetical “business-as-usual” emissions path) is more attractive to these, in particular as it is likely to lead to a smaller financial outflow from this group of countries.

A study by the IMF (2008a) has attempted to assess the magnitudes of such future flows, under specified paths for mitigation policy, and growth paths for the global economy. These calculations, the main results from which are given in Tables 2.3-2.4 below, are based on the MiniCAM model as applied to global mitigation policy up to 2100, assuming complete participation in the c-a-t scheme, and under two long-run targets for the atmospheric CO2 concentration, namely 450 ppm (in Table 2.3), and 550 ppm (in Table 2.4).<sup>7</sup> The tables show, as expected, that finance is generally expected to flow from high-income countries (HICs) and to LICs, more so in the 450 ppm alternative (when mitigation policy is more aggressive), and more when emissions rights are allocated by population. We see however that there could be some slight modifications of this pattern: even with population-based allocation, both China and Latin America will need to purchase emissions rights, later in the century.

> 6 The most important category of committed funds of this type are the multilateral Climate Investment Funds, with commitments of US$6.3 billion for the 2009-2012 period.

> 7 These model runs were developed for the Climate Change Science Program sponsored by the U.S. Department of Energy. For more information about the MiniCAM model and these model runs, see U.S. Department of Energy (2007).

8
# **2. Overview of Previous Work**

The empirical literature on trade facilitation is limited, as outlined in Wilson, Mann, and Otsuki (2003) (henceforth WMO, 2003). In the past, researcher used a single measure of trade facilitation to estimate its effect on trade, finding large gains from trade facilitation efforts. For instance a 3% reduction in landed costs applied to intra-APEC merchandise trade, as might be obtained by electronic documentation, reduces trade costs by US$60 billion (APEC, 2001). A 1 percent reduction in import prices for the industrial countries and the newly industrializing countries of the Republic of Korea, Chinese Taipei and Singapore, and a 2 percent reduction for the other developing countries yields an increase in APEC merchandise trade of 3.3 percent— meaning the elasticity of trade facilitation efforts to trade flows is greater than 1 (Economic Committee,1999). Considering global estimates, a 1 percent reduction in the cost of maritime and air transport services in the developing countries could increase global GDP some US$7 billion (1997 dollars).

If trade facilitation is considered in a broader sense to include an improvement in wholesale and retail trade services, an additional US$7 billion could be gained by a 1 percent improvement in the productivity of that sector (UNCTAD, 2001). Djankov, Freund, and Pham (2006) examine how time delays affect trade, using World Bank data from the Doing Business 2006 report on the days it takes to move standard cargo from the factory gate to the ship in 126 countries. The authors find that on average, each additional day that a product is delayed prior to being shipped reduces trade by at least 1 percent. Each day is equivalent to a country distancing itself from its trade partners by 85 km on average. Delays have an even greater impact on developing country exports and exports of time-sensitive goods, such as perishable agricultural products.

Other authors consider more specific categories of trade facilitation effort or a more limited country set. Hertel, Walmsley and Itakura (2001) find that greater standards harmonization for e-business and automating customs procedures between Japan and Singapore increase trade flows in overall between these countries as well as their trade flows with the rest of the world. Hummels (2001) finds that each day saved in shipping time in part due to a faster customs clearance is worth 0.5 percentage point reduction of ad-valorem tariff. Fink, Mattoo, and Neagu (2002a) examine the effect of anticompetitive practices in port services and other transport services on unit shipping cost. Fink, Mattoo, and Neagu (2002b) find that a 10 percent 5
that in the developing world, climate change in regions reliant on subsistence farming is a prominent reason behind migration to urban areas.

That said, existing literature also finds evidence for the trapping effect we discussed in section 2. This literature argues that where there is extreme poverty and migration is costly, climatic shocks might trap people rather than induce them to abandon their homes. This insight is consistent with the heterogeneous effect we observe when investigating how droughts affect urban growth across different levels of average annual precipitation. Our measure of precipitation captures the yearly total amount of precipitation (in mm) in a given cell, based on monthly meteorological statistics from the GPCP v.2.2 Combined Precipitation Data Set. We find that the effect of drought is stronger in grid cells that fall above the developing world median in terms of precipitation. In line with the logic suggested by the trapping effect, we interpret this as evidence that people migrate _before_ recurrent droughts make migration too costly an option for many households to consider. It also suggests that people in grid cells located above the precipitation median might have developed fewer coping strategies that could be deployed once extreme weather shocks arrive.

To zoom in on the last push factor suggested by extant research, we examine the role of conflict. We do this by calculating the average number of conflict events (as recorded by the UCDP-PRIO dataset) in the neighboring cells. We then distinguish between cells where the number of events falls above the national mean and conflict cells that fall below country-level means.<sup>13</sup> We find evidence that conflict amplifies the effect of drought even if our analyses cannot ascertain whether climatic shocks cause conflict which then leads to migration or whether conflict and migration are two discrete consequences of global climate change. To sum up, we find relatively consistent evidence for the importance of push factors, indicating that what happens in neighboring cells does not there but rather the of urban in destination cells. stay intensifies pace growth We find evidence that is broadly supportive of the trapping effect reported by previous studies.

> 13We use mean rather than median because most cells do not experience any conflict at all. We calculate _country_ -level means because conflict dynamics and the threshold at which they induce migration are likely to be country-specific.

20
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**<u>Table 5. Impact of Land Tenure on Productivity (Fixed Effect Estimation)</u>**

||Gross r<br>Rs|evenue<br>/ac|Net re<br>Rs/|venue<br>ac|
|---|---|---|---|---|
|Barga land|-0.193***|-0.194***|-0.202***|-0.203***|
||(7.42)|(7.36)|(7.08)|(7.05)|
|Plot size (acre)|0.007|0.009|0.090***|0.092***|
||(0.44)|(0.60)|(4.42)|(4.50)|
|Distance to homestead (km)|-0.112***|-0.106***|-0.135***|-0.131***|
||(4.48)|(4.34)|(4.65)|(4.64)|
|Soil/plot characteristics incl.|No|Yes|No|Yes|
|No. of households|1,772|1,772|1,761|1,761|
|No. of observations (plots)|9,053|9,010|8,818|8,777|
|R-squared|0.62|0.63|0.59|0.59|

_Notes:_ All regressions are household level fixed effects estimates. Standard errors corrected for village level clustering. Robust t statistics in parentheses. *, **, and *** denote significance at 10%, 5%, and 1%, respectively.

20
<!-- Start of picture text --> Main Middle Latin ;<br>Energy Swaziland_ | Lower S84 Festa South; Asia gyorica FUMPEECentral toy dae Middle«Tope IncomeHl World<br>; Moth == Asia : & . ‘Tneome Income<br>Middle ; Pacific Asia Income OECD<br>Income Africa Caribhean<br>Access to Flecticity Network (4 ofPopulation) 0 0 5 98 "i 4 19 9 "i 85 Fa ; 60<br>Electricity Average End-User Prices (1S cents/kW'h) -<br>(Residential Non-Residential 43 aff 6/3 . . 5) WR 6/6 ofp | BY oT 13/6 Of6<br><!-- End of picture text -->

Note: Data used isfront tke latest observation available beiweer 1997 and 2682, Source: A "Research" Database ofInfrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,
_Brazilian spatial labor dynamics Page 37_

# Transport Cost to São Paulo.  Brazil.  1995.

<!-- Start of picture text --> Custo de transporte para São Paulo - 1995<br>10 - 500<br>500 - 1500<br>1500 - 2500<br>2500 - 5000<br>5000 - 10512<br>N<br>W E<br>0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Kilometers<br>S<br><!-- End of picture text -->

# Transport Cost to Capital.  Brazil.  1995.

<!-- Start of picture text --> Custo de transporte à capital - 1995<br>0 - 300<br>300 - 600<br>600 - 1000<br>1000 - 3000<br>3000 - 5949<br>N<br>W E<br>0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Kilometers<br>S<br><!-- End of picture text --> <!-- Start of picture text --> Map 10<br><!-- End of picture text -->
- 33 -

57. Scondrlo l: The status quo assumes that the pattern of growth - In terms of the crop mix, Irrigated versus ralnfed, and area versus yield growth - will be the same as observed In the period 1968/69-1970/71 to 1976/77-1978/79. For this scenario, we use Tyagl's (1981) estimate of the agricultural employment elasticity of 0.75.

58. Scenario II: Irrigation assumes that futLre agricultural growth will ar'se from greater emphasis on irrigation, a concomitant shift towards more paddy and wheat, and a modest Increase In per hectare yields. SpecifIcally, we assume that 50 percent of future agricultural growth will arise from Increases In the gross cropped area, 20 percent from Increases In the crop mix In favor of rice and wheat, and 30 percent 17 from higher yields. Then, using relevant employment elasticities reported by Tyagi each one percent growth in agricultural output will lead to:

1.0((1.05)(0.5) + (0.76)(0.2) + (0.66)(0.3)] - 0.88 percent

growth In agricultural employment.

59. Scenario liI: rainfed agriculture assumes future growth will depend on rainfed agriculture, with a shift away from sorghum and millets and Into ollseeds, sugarcane, and less traditional crops. In the absence of any estimated employment elasticitles for most of these crops, we simply assume that the growth will be predominantly yield driven and use Tyagl's (1981) aggregate yleld employment elasticity of 0.6.

60. Since per capita agricultural Income is assumed to Increase over time, so too do the Income multipliers In Tables 14a-14c. As per capita Income rises, consumers Increasingly diversify their consumption Into nonfoods. rhe strength of this relatlonship Is shown In Table A-19, where states with higher agricultural incomes spend

**17 Tyagi** (1961) **gives** employment elast;citieo for solected crops; paddy 0.73; **wheat** 0.79, sorghum 0.44, **sugarcane 0.30, milleto S 15** percent. **He also** givoc **aggrogate** slasticitie with **rospect** to **changes** in **the gross** cropped **area (1.65), a** cropping pattern index (0.47) **and** yield **(0.66).**
through BulkSMS, a web-based bulk text-messaging platform. The VMA used BulkSMS’s service for program administration, including notifying and confirming payments with SPs, making program announcements, coordinating site visits, and confirming acceptance of contractual changes (Densmore 2012a).

SPs were reimbursed for services rendered through electronic transfer of funds to their bank accounts. This was done after the VMA had verified the submitted claims for compliance with program regulations and guidelines. The verification process went through various stages. Initially, the VMA outsourced claims processing to a private insurer, Microcare. The database that was developed by the company was intended to flag questionable claims for manual review. However, the system identified a very high proportion of problematic claims, which reduced the usefulness of the program because a great majority of claims required manual review. By late 2006 the regional VMA office in Mbarara had hired a medical expert to vet the claims. When the program was expanded in 2009, a new system—claims processing database management system— was developed (Densmore 2012b, 1833–42). In addition, vetting teams carried out spot checks on samples of claims for compliance with medical and financial standards in an effort to control fraud. In cases where fraud was evident or highly probable, the claims were rejected and the SPs were paid a fraction of the claimed amount or were suspended from the program.

# **<<A>>** Evaluation Objectives

The RHVP program design included an impact evaluation. The evaluation of the SD component is reported in this paper. The evaluation had two main objectives: (1) assess the effect of the program on improving access to, and the quality of, reproductive health services while reducing inequities in their use; and (2) evaluate the impact of the program on improving reproductive health behaviors and outcomes at the population level. The evaluation aimed to assess the effect of the program on targeting beneficiaries; improving knowledge of reproductive health services; facilitating health service use and access, particularly among new users; assessing equity; ascertaining costs (service costs, program costs, and out-of-pocket expenses); and ensuring quality of care (clinical quality, client and SP satisfaction). In 2008 GPOBA contracted Venture Strategies for Health and Development (VSHD) to undertake a population-based evaluation of the program.

# **<<A>>** Evaluation Design

11
Figure 5 presents the same graph separately for men (left graph) and women (right graph).

The stock of men in formal wage employment and upper tier informal self-employment increases as countries develop more than women, while women have a more pronounced movement out of the labor force than men<sup>8</sup> . However, men also exhibit slower movement out of lower-tier wage employment than women do, though both groups see similar decreases in lower-tier selfemployment – the category with the sharpest decreases across the GDP distribution<sup>9</sup> .

Table 4: Jobs ladder and extreme poverty

||(2)|(4)|(6)|
|---|---|---|---|
||$1.90<br>Headcount|$3.20 Headcount|$5.50 Headcount|
|Upper-tier self-employed|0.163|0.312|-0.048|
||(0.188)|(0.206)|(0.166)|
|Formal wage|0.053|0.023|-0.405***|
||(0.092)|(0.098)|(0.107)|
|Upper-tier informal wage|-1.938**|-2.307**|-0.253|
||(0.825)|(0.943)|(0.618)|
|Lower-tier Informal wage|-0.008|0.131|0.160|
||(0.170)|(0.128)|(0.117)|
|Unemployed|0.463*|0.751**|-0.059|
||(0.276)|(0.343)|(0.315)|
|Inactive|0.008|-0.069|-0.135|
||(0.189)|(0.200)|(0.119)|
|Total pop. (log)|-0.016|0.033|0.350**|
||(0.169)|(0.179)|(0.146)|
|GDP p.c. (log)|-0.042|-0.064|-0.118|
||(0.052)|(0.064)|(0.079)|
|Observations|338|338|338|

Notes. Standard errors are in parentheses and are clustered at the country level. Country-survey type and year fixed effects are included in all specifications. The country-survey type fixed effect takes into account significant changes in the survey used to measure poverty such that poverty rates are comparable across time. The omitted category is lower-tier self-employment. * p<0.10 ** p<0.05 *** p<0.01.

> 8 High female inac�vity rates in our sample are driven by Saudi Arabia and Türkiye.

9 As we measure net changes, not gross, we cannot distinguish transition in and out. The correlation captures levels, not individual transitions.

23
26 Table 8: Prospective effects of GM cotton adoption by non-adopters as of 2001 on national economic welfare, without and with cotton subsidies and tariffs removed first (equivalent variation in income, 2001 US$m)

||Without<br>subsidy and<br>tariff reform|With cotton<br>subsidies and<br>tariffs first<br>removed|With<br>simultaneous<br>cotton<br>subsidy/tariff<br>removal and<br>GMcatch-up|
|---|---|---|---|
|**All high-income countries**|366|279|744|
|Australia|-28|-58|80|
|United States|57|-25|404|
|EU25|269|281|295|
|Japan|36|37|14|
|Korea-Taiwan|-14|-6|-68|
|**All developing countries**|1957|2043|1866|
|E. Europe and Central Asia|317|317|303|
|China|100|94|144|
|Other Southeast Asia|63|83|-48|
|India|822|855|771|
|Other South Asia|148|151|140|
|Middle East & Nth Africa|175|211|194|
|Sub-Saharan Africa|199|223|370|
|Latin American & Carib.|135|146|-8|
|**World**|**2323**|**2322**|**2610**|

Source: Authors’ GTAP model simulation results