rafmacalaba/datause-encoder-data huggingface dataset:
and Slater, 2003; Shepherd, 2007) there is a lack of systematic comparisons of experiences with respect to when and where participation in high‐value markets is beneficial to smallholders, the constraints that impede their participation and the effectiveness of alternative means for overcoming these constraints. There is also a lack of consensus on measures to be used in judging the ‘success’ of interventions such that it is possible to contrast between those that ‘work’ and those that do not. This paper attempts to throw some light on these issues. Specifically, it reports on the results of a survey of practitioners involved in schemes to better link African smallholders with higher‐value markets. Before highlighting those results, we provide an overview of the existing literature.

# **Benefits and challenges of smallholder participation in higher‐value markets**

The existing literature on impacts of smallholder participation in high‐value markets is diverse in terms of mode of investigation and primary focus. The contract farming literature sheds some light on the impact of smallholder participation in high‐value markets (see for example Glover and Kusterer, 1990; Key and Runsten, 1999; MCCulloch and Ota, 2002; Humphrey _et al_ ., 2004; Minten _et al_ ., 2005; Maerterns and Swinnen, 2007), often comparing participant and non‐participant farmers across a range of measures. Positive impacts of participation are generally associated with income generation, employment and improving access to credit and technical assistance (Glover and Kusterer, 1990; Grosh, 1994; Humphrey _et al_ ., 2004). Other studies extend the analysis to include farm‐level and/or regional spill‐overs related to productivity of food crop production, food security and/or development of business service markets (see for example Kennedy and Cogill, 1987; Von Braun, _et al_ ., 1989; Govereh and Jayne, 2003). Less tangible impacts, including social status and capacity development, are also reported (Reynolds _et al_ ., 2004; Masakure and Henson, 2005).

The potential negative aspects of smallholder participation in high‐value markets generally relate to the institutional characteristics of supply chains and wider socio‐ economic and environmental impacts. Thus, concerns are raised that patterns of specialization can lock smallholders into relationships that are more favorable to downstream buyers than to the farmers themselves (see for example Little and Watts, 1994; Singh, 2002). There are also concerns that the requirements of discerning buyers can act to exclude less endowed producers, with consequences for income disparities and more general social differentiation at the community level (Singh, 2002; Simmons, _et al_ ., 2005; van der Meer, 2006). Such exclusion of poorer smallholders would limit the achievement of broader economic development objectives (Reardon and Barrett, 2000; Humphrey, 2006). There are also concerns that such arrangements do not present a viable long‐term approach to poverty alleviation, as market leaders will only commit to 2
# **Figure 8: Trends in age- and gender-specific mortality rates in rural areas of four sub-Saharan African countries.**

<!-- Start of picture text --> Swaziland<br>Period<br>Female 15-24 Female 25-34 Female 35-44<br>Male 15-24 Male 25-34 Male 35-44<br>Benin<br>Period<br>Female 15-24 Female 25-34 Female 35-44<br>Male 15-24 Male 25-34 Male 35-44<br>.15<br>Mortality .1 Mortality<br>.05<br>0<br>1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>.06<br>.05<br>.04<br>.03<br>Mortality Mortality<br>.02<br>.01<br>0<br>1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br><!-- End of picture text --> <!-- Start of picture text --> Zimbabwe<br>Period<br>Female 15-24 Female 25-34 Female 35-44<br>Male 15-24 Male 25-34 Male 35-44<br>Nigeria<br>Period<br>Female 15-24 Female 25-34 Female 35-44<br>Male 15-24 Male 25-34 Male 35-44<br>.15<br>.1<br>.05<br>0<br>1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>.06<br>.04<br>.02<br>0<br>1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br><!-- End of picture text --> 41
# **III.  The Design of the Experiment**

# **A.  Sample**

In order to evaluate the program, we randomly assigned 100 interested and eligible schools into a treatment and a control group. Interested schools were selected as follows. First, to minimize program implementation and data collection costs, the schools were chosen to be in close geographic proximity: we chose schools in the north part of Antioquia, Caldas, Choco, Cordoba, Quindio y Risaralda. For all interested schools in this area, we further reduced the sample to schools to those with 80 or more students in order to facilitate the collection of data. From this list of 100 schools a CPE team visited each school to verify the number of students, the existence of a classroom that could be refurbish for the computers, and the type of school (public or private).

Once the final sample was created, we conducted a stratified randomization, stratifying on department and type of school—basic education, basic plus lower secondary, high secondary.<sup>2</sup> In this process half of the schools received the program and half were assigned to a control group which did not receive the program. The lottery was performed at the beginning of August 2006, and the list of schools, with their status, was given to the Ministry of Communication for the implementation of the program.

# **B.  Data Collection**

Data was collected in two phases: a baseline survey conducted immediately after the randomization but before the start of the treatment and a follow-up survey conducted two years later. The baseline was conducted between August 14 and September 29,

> 2 Type refers to the grades covered by each school.  Basic includes grades one through five.  Lower secondary includes grades six through eight, and high secondary includes grades nine through eleven.

- 8 -
2 _Fiscal Crisis in Central Europe_ several authors have pointed out, there are more serious, structural reasons underiying the crisis, which require the policy-makers' attention.' At least four factors are commonly cited:

- On the revcnue side, the traditional tax base of RESEs was the state-owned enterprise (SQE) sector. The sector has bome the main brunt of the recession, thus driving down tax collection, and is likely to further lose relative importance in the economy, as privatization advances and industry is downsized.

- In addition, the loss of the traditional tax base cannot easily be compensated for by increasing contributions from other sectors, since either these are traditionally difficult to tax (some services, small businesses), or the administration is not prepared to implement the new tax laws that are approved by the legislature.

- On the expenditure side, despite the relief provided by the sharp cuts in govermnent subsidies, the central government is likely to be burdened with many of the social costs of adjustment, which were previously hidden within the SOE system. These include, in addition to unemployment benefits which are likely to be high for a number of years, the cost of supporting, either through the pension system or the social assist.ance system, the people displaced from the work-force by the transformation.

Finally, the switch to positive real interest rate policies, together with the emergence of large deficits and non-bank financing of the budget, has led to increasing debt-service pressures.

These factors are present, to a certain extent, in all countries, and can therefore be used to provide partial explanations for the overall crisis. In this paper we examine relevant data for Poland, Hungary and Czechoslovakia, and assess their congruence with the theses enumerated ' See, for example, Tanzi (1991 and 1993).
end of November 1994. Without that increase in private investment, power shortages could have been higher.

Table 3: Power sector performance indicators in South Asia (selected countries)

||Installed<br>capacity<br>(MW)|Peak<br>demand<br>met(MW)|Peak<br>demand<br>(MW)|IPPs/Private<br>Sector share<br>in installed<br>capacity<br>(%)|Electricity<br>access rate<br>(%)<sup>*</sup>|Technical<br>losses (% of<br>generation)|Per capita<br>electricity<br>consumpti<br>on (kWh)|
|---|---|---|---|---|---|---|---|
|Bangladesh|8537|6434|8349|16.35|60|14.36|213|
|India #|243028|126793 $|131943 $|34.0|75|23.65|917|
|Nepal|720|569.6<sup>**</sup>|1094.6|33.33|76|25.03|106|
|Pakistan|23412|13445|18467|35.56|69|17|450|
|Sri Lanka|3312|2112<sup>***</sup>|2146|33.15|85|14|490|

_Sources_ : Bangladesh (BPDB, 2014); Nepal (NEA, 2013); Sri Lanka (CBSL, 2013), India (CEA, 2014); Pakistan (Kessides, 2013),<sup>*</sup> IEA (2011),<sup>**</sup> excludes electricity imports capacity from India,<sup>***</sup> based on 1.2 GW hydro plant not running during drought seasons, # As on March 2014, $ For March 2014 There are estimates indicating that inadequate power supply imposes significant costs on South Asian economies. For example, the cost of load shedding to the Pakistani economy was estimated to be PKR 1,272 billion (Pakistani Rupees) in 2011-12, equivalent to 6% of the economy (Saeed, 2013). Table 4, based on data from World Bank enterprise surveys, provides an assessment of the direct loss of economic output by electricity users due to electricity outages in South Asian countries, along with additional statistics indicating the degree of service unreliability in the region.

# _(ii) Low operational efficiency and service quality._

Technical losses (both transmission and distribution) are estimated to average 14– 25% of electricity generation in the region (Smith, 2004). The technical losses are particularly high in Nepal, Afghanistan and Pakistan. The poor physical condition of the transmission and distribution (T&D) networks in the face of networks investment inadequacy is one of the important causes of high network losses (Palit and Chaurey, 2011). Lower T&D losses have been achieved by the relatively more efficient distribution utilities in India, and in Bangladesh and Sri Lanka. This indicates in turn thatmuch of the observed T&D losses of 35-40% across states in India are due to nontechnical reasons. Lack of metering of all consumers remains a challenge for the 15
# **Stages of Development of Payment Systems: Leapfrogging across Countries and MENA’s Place in the World**

Clément Gévaudan and Daniel Lederman<sup>1</sup> Office of the Chief Economist Middle East and North Africa Region The World Bank Group JEL classification: E42, G21, O00, O11, O30 Keywords: payment systems, innovation, banking, digital payment, mobile money, leapfrogging, digital technology, digital economy, MENA 1 Clément Gévaudan is a Consultant with the World Bank’s Office of the Chief Economist for the Middle East and North Africa Region. Daniel Lederman is the region’s Deputy Chief Economist. The authors gratefully acknowledge insightful discussions on issues related to the development of payment systems in developing countries with Davide Salvatore, Robert Cull, and Harish Natarajan. The opinions expressed in this note do not necessarily reflect the opinions of the World Bank Group, its management or its Executive Directors. All remaining errors are the authors’ responsibility.
In summary, our results for the Madrid GGU indicate significant reduction in methane emissions during the past four years, coupled with continuing progress during the most recent year. While the Madrid landfills undoubtedly remain high methane emitters among Western European sites, the evidence from our database suggests that their emissions are at least headed in the right direction.

# **5.2 Irrigated Rice Production in Karnal, Haryana, India**

Our second case focuses on irrigated production of rice, the staple crop for the majority of the world’s population (Adhya et al. 2014). Table 1 shows that Agricultural Soils account for about 10% of global methane emissions, and irrigated rice production accounts for most of those (Smith, Reay and Smith 2021). In 2013, rice was harvested on 165 million hectares of land in 100 countries, with 90 percent of global production in Asia. Irrigated fields occupy about 80 million hectares and produce 75 percent of the global crop (FAO 2014; Fischer et al. 2014). Rice production in flooded fields produces methane because oxygen does not penetrate the soil when it is blocked by water. This promotes the growth of methane-producing bacteria.

We identify rice paddy flooding months using RiceAtlas, a spatial database of global rice calendars developed by Laborte et al. (2017). RiceAtlas distinguishes up to three rice cultivation seasons and identifies the first flooding and harvest days for each season by day-of-year. We have chosen Karnal district in India for this case because it is identified by RiceAtlas as an area with only one of three potential rice cultivation seasons. This permits a clear illustration of the utility of the 5 km methane database for tracking methane emissions across seasons and years. In Karnal, fields are typically flooded from early summer through October.

Figure 9 provides summary information about Karnal. Figure 9(a) displays the GGU (Karnal District, Haryana) along with the sampling area for Sentinel-5P data (bordered in blue). Figure 9(b) draws on our 5-km global database to display mean methane concentration anomalies for the GGU sampling area from January 2019 to November 2022.

Our 5-km database contains 2,747 observations for the Karnal GGU sampling area. Figure 10 plots the observations against time, with mean monthly observations superposed. The plot reveals the close correspondence between rice paddy flooding months and annual peak periods for mean CH4 anomalies.

Table 8 reports estimates for the Karnal GGU from OLS, Random Effects and Fixed Effects models. Results for the two models align in all cases. For Karnal the monthly trend (γ1) is positive and highly significant, yielding an estimated increase of about 0.40 ppb/month (or about 19.2 ppb over four years). The final-year deviation is also positive and highly significant, yielding an estimated difference of about 7.8 parts per billion between 2022 and the previous three years.

21
# **IV. PCR Objectives and Design Parameters: A Policy Discussion**

As reviewed, the original motivations for the development of PCRs around the world have been many and varied. However, in many instances PCRs are now used for purposes going beyond their original objectives. In particular we argue below that irrespective of the original motivation, it is likely that an established PCR can be useful to bank regulators to analyze, more scientifically, banking regulations pertaining to capital and provisioning. However, we provide a broader policy discussion here on the often-competing objectives of PCRs and the implications and tensions in their design thus created. We focus the discussion on 5 basic objectives: (1) to improve credit-\ access, (2) to strengthen bank supervision, (3) to promote competition, (4) economic research to inform macroeconomic policy making, and (5) to improve bank regulation. The final objective is dealt with in somewhat more depth. A theme throughout is also the relation between private credit bureaus and a PCR.

# **_1. Improving access to credit_**

Perhaps surprisingly, according to World Bank survey results, two-thirds of the respondents (39/57) consider supervised financial institutions the primary users of PCR data, while bank supervisors were identified by only one-third (18/57) as the primary users. Moreover, more than three-quarters of supervisors (44/57), indicated that the PCR was the primary source of credit data for financial institutions – Miller (2003). There are several ways PCRs may enhance credit access. First, and as illustrated in the empirical results above, the use of a PCR may, for a constant level of risk, allow a financial institution to expand its loan portfolio. The marginal increase in the loan portfolio will particularly benefit those companies or individuals that have a sound repayment history but lack other obvious indicators of creditworthiness such as wealth or that lack guarantees.

The use of PCR data may then enable lenders to predict credit risk more accurately and more efficiently. Credit registry data facilitates the evaluation of prospective borrowers, reducing the need for more costly and intrusive background and reference checks. If an adequate supply of data is available for statistical analysis, then automated or semi-automated credit decision tools 12
households in the first two Selben quintiles, so expansion of the coverage of benefits has been gradual.

BDH transfers are made to women, and can be collected at any branch office from the largest network of private banks ( _Banred_ ) or from the National Agricultural Bank ( _Banco Nacional de Fomento_ ). In terms of magnitude, the monthly $15 BDH transfer accounts for approximately 7 percent of pre-transfer expenditures of the mean household in the study sample. As a point of comparison, PROGRESA transfers accounted for about 20 percent of average household expenditures in Mexico (Skoufias 2005). Although the BDH program and the _Bono Solidario_ program that preceded it have been the subject of much controversy and policy discussion in Ecuador, there has been no systematic evaluation of the impact of these programs on schooling and child labor.<sup>1</sup>

# _B. Sample frame_

The sample for the evaluation of the BDH program in this paper was drawn from the Selben rosters of four of the twenty-two provinces in the country: Carchi, Imbabura, Cotopaxi, and Tungurahua. All four provinces are in the sierra (or highlands) region of the country. The sampling framework followed a two-stage process. Within the provinces in the evaluation, parishes were randomly drawn and, within these parishes, a sample of 1,391 households was selected. At the request of program administrators, households near the cut-off between the first and second Selben quintiles were over-represented, as were households with older children. None of the households in the sample had received transfers from the BDH or the _Bono Solidario_ prior to the evaluation.

At the time the evaluation was launched, the BDH budget was insufficient to cover all households in the first and second Selben quintiles. This is the basis for the identification strategy in this paper. One-half of households in the evaluation sample were randomly assigned to a treatment group that would be eligible for BDH transfers, and the other half were assigned to a control group that would not be eligible for transfers for the first two years. We refer to the

> 1 León, Vos, and Brborich (2001) analyze the impact of the _Bono Solidario_ program on consumption poverty, and León and Younger (2005) focus on the impact of the _Bono Solidario_ on child health.  Both papers find significant but modest program effects.  However, the analysis is not experimental.  Rather, it is based on comparisons between _Bono Solidario_ recipients and non-recipients in the 1998/99 _Encuesta de Condiciones de Vida_ (ECV), a nationally representative household survey, and may therefore be subject to biases associated with endogenous program placement or take-up.

5
agribusiness sell mostly to the US, EU, and Japan while exporter-imports in all sectors as a whole and exporter-importers in apparel have a more diversified portfolio of destination markets.

**Figure 4. Portfolios of Sourcing and Destination Markets for Exporter-Importers in Peru Panel A. Portfolio of Sourcing Markets**

<!-- Start of picture text --> Sources of Imports, Exporter‐Importers (X‐M)<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000 2004 2008 2012 2000 2004 2008 2012 2000 2004 2008 2012<br>Agribusiness Apparel All<br>Andean China Other LAC Rest of World USA EU JP<br>Panel B. Portfolio of Destination Markets<br>Destinations for Exporter‐Importers (X‐M)<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000 2004 2008 2012 2000 2004 2008 2012 2000 2004 2008 2012<br>Agribusiness Apparel All<br>Andean China Other LAC Rest of World USA EU JP<br><!-- End of picture text -->

# **3. Baseline Export Performance Premia for Exporter-Importers**

To investigate the importance of imports of intermediate inputs for Peruvian exporters we follow the approach - initially proposed by Bernard and Jensen (1999) in studying the productivity 10
These findings together suggest that greater dynamism has a strong payoff in that a higher pace of firm turnover is associated both with a higher rate of productivity growth of incumbents and a larger difference in the productivity gap between entering and exiting businesses. Appropriate caution needs to be used in drawing causal inferences as these results reflect correlations, not causality. Still, these findings suggest that understanding within firm productivity gains is closely connected with understanding the pace of turnover and the gap in productivity between entering and exiting businesses.

**Figure 13   The relationship between net entry contribution and the productivity growth of incumbents.**

<!-- Start of picture text --> Labor Productivity - Pooled Manufacturing<br>Five-Year Differencing, Real Gross Output<br>Country and Industry Time Averages<br>1.5 Correlation Coefficient: 0.5800***<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0 -0.5 0.0 0.5 1.0 1.5<br>Net Entry Productivity Growth<br>Note: Excluding Brazil and Venezuela. Outliers Excluded.<br>Incumbents' Productivity Growth<br><!-- End of picture text -->

**Figure 14   The relationship between the firm turnover rate and the productivity growth of incumbents**

<!-- Start of picture text --> Labor Productivity - Pooled Manufacturing<br>Five-Year Differencing, Real Gross Output<br>Country and Industry Time Averages<br>1.5 Correlation Coefficient: 0.3349***<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>0.0 0.5 1.0 1.5<br>Firm Turnover Rate(f)<br>Note: Excluding Brazil and Venezuela. Outliers Excluded.<br>Incumbents' Productivity Growth<br><!-- End of picture text --> 43