rafmacalaba/data-use-mentions-extended huggingface dataset:
prioritized projects for each village. The following sections provide further information on the sources of data on _ex-ante_ project preferences (III.1); selection process (III.2); selection outcomes (III.3); and villagers‘ satisfaction (III.4).

# IV.1. **Ex-Ante Project Preferences**

Data on _ex-ante_ preferences come from a survey administered prior to village council elections and project selection. In each village, the survey was administered to three groups of villagers: (1) ten randomly-selected male heads-of household; (2) a focus group of male village leaders; (3) a focus group of leading village women. The overall number of respondents surveyed exceeded 13,000 (see Table 2 for more detailed information).<sup>9</sup>

## [TABLE 2 HERE]

A question in the survey asked all respondents to indicate, from a list of potential projects, the three projects that should be implemented if the village was provided with a $60,000 grant.<sup>10</sup> From this data, we construct village-level dummy variables indicating the project most frequently preferred by each of the three groups,<sup>11</sup> providing measures of the preferences of male villagers, male elites, and female elites, respectively.

## [TABLE 3 HERE]

Table 3 presents the preferences for the three groups of villagers. Drinking water projects are the first choice of male household respondents and female elites, while preferences of male elites are more evenly distributed across different projects. A comparison of villages assigned to different

> 9 We did not administer the baseline survey to a random sample of female villagers due to financial and logistical constraints, so we do not have information on their preferences.

> 10 Male and female focus group respondents were asked to identify one project from a list of 15 possible projects, while individual male household and female respondents were asked to select and prioritize three projects from the same
electronic business registries and credit bureaus make it more difficult to identify these problems. An alternative usually used in emerging markets is for the factor to buy receivables “with recourse”, which means that the seller is accountable in the case that a buyer does not pay its invoice, and that the seller of the receivables retains the credit risk. However, this may not successfully reduce the factor’s exposure to the credit risk of the seller’s customers, since in the case of a customer’s default, the seller may not have sufficient capital reserves to repay the factor.

One solution to these barriers to factoring is the technology often referred to as “Reverse Factoring”. In this case, the lender purchases accounts receivables only from high-quality buyers (e.g. any receivable from a specific informationally transparent buyer _)._ The factor only needs to collect credit information and calculate the credit risk for a few selected buyers (such as a large, internationally accredited firm). The main advantage of reverse factoring is that the credit risk is equal to the default risk of the high-quality customer, and not the risky SME. This arrangement allows creditors in developing countries to factor “without recourse” and provide low-risk loans to high-risk suppliers.

Reverse factoring may be particularly beneficial for SMEs for a number of reasons. First as previously discussed, ordinary factoring requires comprehensive credit information on all the borrower’s customers, which may be particularly difficult and costly to determine for SMEs in countries with weak credit information systems. Second, reverse factoring make possible for firms to factor without recourse, which allows SMEs to transfer their credit risk and borrow on the credit risk of its creditworthy customers. This may allow firms to borrow greater amounts at lower costs.

16
value-added in agriculture would fall not only in economies where it has been highly protected (Europe, Northeast Asia and the US) but also in South Asia – whose trade policies have a slightly pro-agricultural bias according to the GTAP database, and which enjoys expanding market access abroad for those non-agricultural products in which the region has a strong comparative advantage.<sup>7</sup> All other developing country regions would see a rise in net farm income though (as would the developed country Cairns Group members of Canada, Australia and New Zealand). That is true of China not only because it has already reduced much of its agricultural protection as part of its reforms associated with its accession to WTO at the end of 2001, but also because it faces extremely high tariff barriers in it export markets for farm products (Jean, Laborde and Martin 2005). And it is true in particular of the Sub-Saharan African region, even when southern Africa is separated out, despite the fact that there are numerous net food importing and preference-receiving exporting countries in that region (see Anderson, Martin and van der Mensbrugghe (2005a) for more details). Table 12 shows separately the contributions of high-income and developing countries’ farm policy reforms to that outcome, from which it is clear that most of the effect comes from one or other of those reforms, with only a minor contribution to the residual from non-agricultural sectors. Those value added changes are due in considerable part to the changes in import and export prices for farm and other products, which are summarized in Table 13.

Of particular importance to Brazil and some Sub-Saharan African countries is the case of cotton, which is receiving special attention in the WTO’s Doha Development Agenda following the Cancun Trade Ministerial in 2003 and the Dispute Settlement case that went against the US

> 7 The move to free trade would boost South Asia’s ratio of production to consumption in textiles and clothing from 1.51 to 1.66, for example.

15
130 The question of how to price contraceptives is an important issue too, if the premise is that the private sector vill eventually take over and continue programs. Vhere management has decided to provide family planning as an employee benefit free of charge, the issue is decided, but where free-standing projects are planned or NGOs are assessing how to become more efficient and solvent, how to price services to both earn revenues and yet retain users are important questions. There is some evidence that the price elasticity of demand varies across contraceptives and therefore cross subsidization may be possible. Host of the projects reviewed in section IV assume an inability to pay or an ability and willingness to pay only nominal amounts. Some experimentation and documentation is varranted in measuring the elasticity of demand for contraceptives with respect to price, and to establish how much of a trade off there is between convenience and price. Although this is really an adjunct issue, it is nonetheless key to the continuation of private sector projects and will be important in future endeavors. The sustainability of many of these projects over the long term depends on recovering some share of costs.

Determinants of Private Sector Use. The available data on source of contraceptives was reviewed in Section III. The analysis provided an overview of the topic and indicated regionai patterns and disparities. Hovever, the CPS and DOS data are somevhat limiting in not being complete enough to *apture some of the issues of interest, such as the difference in reliance on NGO, social marketing and commercial sources of contraception. Horeover, better information on supply would help interpretation of the source information, and vould allow demand analyses to control for supply characteristics.

Host important, however, is the need to analyze these data in greater depth, controlling for the many factors that affect consumers' decisions. Comparative multivariate analysis is key to a better understanding of the ad.wraa1ly **Planninq** Paper lwhole)
Norway has also resulted in wage increases for high-skill workers. Czernich et al. (2011) find significant and positive correlations between internet adoption and wages in rich countries. Atalay et al. (2018) use newspaper ads that have been published between 1960 and 2000 in three main U.S. journals with wide readership nationally to measure the correlation between job tasks and ICT adoption. The authors find that ICT adoption has boosted analytic activities while decreasing manual tasks. Such an effect could potentially damage the employment prospects of unskilled low-income workers. This effect is also discussed by Eichhorst et al. (2016), who focus on the role of adoption in the transformation of occupations in Germany and other developed countries. The authors, however, note that the latest changes in the job market (e.g., new employment opportunities from internetbased marketplaces and self-employment opportunities) have been fostered not only by ICT adoption but also by globalization.

There is also an open debate identified across the literature on the effects of the internet regarding job creation and job matching. Although Czernich (2014) initially finds a negative relationship between Digital Subscriber Line (DSL) availability and unemployment in German municipalities, the results only hold under a linear model specification and therefore, the author concludes that the availability of broadband internet has no effect on unemployment. However, such results do not imply that the internet does not affect the overall labor market (e.g., through speeding the matching process and increasing employment, especially of offsite jobs). More recently, Hjort and Poulsen (2019) study the causal relationship between fast internet and employment rates using individual and firm-level data from 12 African countries, and compare individuals and firms located 16
in the Red River Delta appear more drought prone, and to a lesser extent also those in the Mekong Delta.

# _Localized excess rain maps_

Systematic and accurate information about floods at geographically disaggregated levels is notoriously hard to come by or to construct. One of the reasons is that satellite imagery relies on visibility, and during floods there is usually much cloud cover. Radars can see through clouds, but have not been set aside for this purpose. Yet, floods are part and parcel of livelihoods in many parts of Vietnam and it is thus key to explore their welfare effects.<sup>18</sup> This study distinguishes between localized and regional events. Localized events are based on large amounts of rainfall in short periods of time. They may also induce landslides. Regional events arise when rivers exceed their banks, potentially due to heavy rainfall upstream or snowmelt, or in coastal areas following storm surge.

Inspired by Zubair et al. (2006), who use rainfall intensity in a short period to proxy localized flooding, daily rainfall maps were generated, obtained through spatial extrapolation of the daily rainfall records in the rainfall stations (as opposed to the monthly rainfall data used before) using 17 Using 4‐months rainfall deficits instead, excluding the 4 driest months of the year, yielded very similar maps. Given the existence of multiple cropping seasons in Vietnam, an alternative approach would be to focus on rainfall shortages during particular periods of the year. This was not pursued here, as it would greatly increase the need for location specific information, and thereby reduce the practical applicability of the approach in other settings. Nonetheless, the approach is sufficiently flexible to accommodate such considerations. 18 Vietnam has 2,360 rivers and streams that are more than 10 km long. Severe floods were recorded in 1945, 1961, 1964, 1966, 1969, 1971, 1996, 1998, 1999, 2000, and 2001. As a result, the government of Vietnam has actively applied a number of flood control methods,
Indicators (SPI) is a framework of 5 pillars and 22 dimensions to assess the maturity of national statistical systems. The five pillars include variety of data users ( _Data use_ ); the provision of services that connect users and producers such as online access, IMF SDSS subscription, and NADA data catalogue ( _Data services_ ); production of indicators that track the SDGs ( _data products_ ); availability and frequency of micro data ( _data sources_ ); and _data infrastructure_ , which encompasses adherence to international standards, legislation, skills, and partnerships. The SPI is only available from 2016 and is still a work in progress. For instance, the pillar on data use only covers the variety of international organizations as users of data. The pillar on data infrastructure only covers legislation. The data sources pillar is limited in coverage of administrative data. The SPI has built on the SCI in several meaningful ways. It is global in coverage, includes several additional sources of micro data, and includes additional dimensions of openness under the category of _data services_ (see table A5). See Table A3 in the annex for definitions of SPI subcomponents.

The Open Data Inventory (ODIN) measures how complete a country’s statistical offerings are and whether their data meet international standards of openness. Data assessed in ODIN must be official country data that are published on the national statistics office’s (NSO) website or any other official country website that is linked from the NSO website. Coverage scores are based on the availability of key indicators and appropriate disaggregation over time and for geographic subdivisions. These encompass social statistics, economic and financial statistics, and environmental statistics. Openness scores are based on whether data can be downloaded in machine-readable and non-proprietary formats, are accompanied by metadata and whether download options exist such as bulk download and user-selection or APIs, and have an open terms of use or data license. Thus, this openness measure is really one of online access restricted to NSO or NSOlinked websites. Access to microdata is excluded and so is
_The method used to calculate changes in trends is illustrated using data from Mexico._ Panel A of Table 2 gives the employment ratios for low educated male youth in urban areas, for each quarter between 2007 and 2009. We then calculate the change of employment ratio relative to the same quarter in the previous year. The employment ratio changed from 57.3 percent in the first quarter of 2007 to 57.8 percent in the same quarter in 2008, yielding a slight increase by 0.5 percentage points. For each cell, we then take the average of these changes across all pre-crisis periods (up until 4<sup>th</sup> quarter of 2008) and all post-crisis periods (from 1<sup>st</sup> quarter of 2009 and onwards).In this cell, the average year on year change in the employment rate after the crisis trend in employment ratio is -3.6 percentage points, and the average pre crisis trend is -0.3 percentage points, indicating that for this cell, growth in the employment ratio slowed by 3.3 percentage points. Taking a weighted average over all 16 cells, with weights equal to the cell population in this case, gives the overall change in the employment ratio. Among all Mexican workers, the employment ratio slowed by 1.7 percentage points (Panel B of Table 2). This difference, between the pre-crisis and post-crisis averages of year-on-year changes of the indicator, is used to measure workers’ labor market adjustment throughout this paper.

# **_Group Comparison_**

_The analysis examines differences in labor market adjustment by group, controlling for selected observable characteristics_ . In particular, we regress the change in the trend, described above, for each cell in each country on dummy variables for the four characteristics, separately for each outcome and country.<sup>18</sup> There are therefore 16 observations in each regression. Using regression coefficients rather than simple tabulations isolates changes in the returns to
in nonfarm household enterprises, most of which are lower productivity jobs that do not yield secure earnings (Lain et al., 2021). Vietnam offers another example of a country where signs of weaknesses have persisted amid a robust recovery in aggregate employment. High initial losses in jobs, hours worked and wages – and large gaps by gender, skill level and sector of workers – largely dissipated by the fourth quarter of 2020. However, the highest rate of job growth was occurring in more precarious forms of off-farm selfemployment rather than in wage work (World Bank, 2021 _a_ ). Job quality, as measured by the probability of having employment with a labor contract, is estimated to have declined as a result of the pandemic in second and third quarters of 2020 (Dang and Nguyen, 2020).

Our paper adds to this body of evidence by deepening the understanding of the shortterm as well as longer-term impacts of the shock, such as on labor market outcomes for youth, for a developing economy. Access to several rounds of panel data with detailed information on labor market outcomes and individual characteristics, spanning several years before and during the pandemic, offers us the unique opportunity to explore causal impacts of the shock on labor market dynamics, after accounting for the effects of confounding factors like seasonality, time trends and worker characteristics to a reasonable 4
month. The resulting increase in household consumption lowers the national poverty measures. We see that removing the Samurdhi payments from households would increase the poverty headcount index by 0.79 and 1.19 percentage points, respectively, under the national and $3.20 poverty lines. However, results from the third scenario suggest that poverty would drop to 1.63 percent and 5.50 percent, under the national and $3.20 lines, respectively, if these budget‐neutral transfers were made to beneficiary households identified by the PMT formula. These results suggest that using the PMT method would significantly improve the poverty impact of Samurdhi transfers by effectively targeting benefits towards the most needy households.

# V. Robustness

This section examines the robustness of our PMT model to various issues. First, we examine performance of the PMT model when the analysis is conducted with different sample sizes. Second, we examine how much the performance of the model varies by whether individual survey quarters or the full annual sample is used to estimate the PMT coefficients. We finally examine PMT performance at sub‐national levels and for certain demographic groups.

# _PMT performance and sample size_

We often rely on household surveys to design, verify, and refine the PMT model. Cost and logistical considerations are often an important factor when implementing household surveys. This raises the following question: how much does changing the sample size of the household survey affect the performance of the PMT model built from it? Having the answer to this question would help relevant organizations optimize scarce resources. We address this question by taking random subsamples of HIES 2016, which surveyed 21,765 households. We do not change variables in the PMT model presented in Table 1, although we recognize that the variables in the PMT model would depend on the specific sample used to design it. We estimate the PMT coefficients, assign PMT scores to households, and simulate the beneficiaries based on the cutoffs also computed from samples of varying sizes. We then examine the performance of the PMT model
# **III. Context: Sectoral Reallocation of Resources and Enterprise Dynamics**

Over the past thirty years since 1980, there have been significant changes in the underlying structure of the Sri Lanka economy from agriculture to manufacturing and services. In terms of value added, manufacturing has remained roughly constant at 29 percent of GDP, while the value addition of agriculture has declined by 17 percentage points to 12 percent as the services sector has increased its share to 59 percent of GDP (Figure 1, panel A). The proportion of the largely non-traded services sector in Sri Lanka is large even by South Asian standards, where the size of the services sector stands out relative to other developing regions of the world. Services-led growth has become more pronounced in recent years putting it closer to the composition of economic growth in OECD countries than other developing countries. Over 60 percent of real GDP growth recorded from 2000 is accounted for by growth in the services sector (Figure 1, panel B).

Sectoral reallocation from agriculture to manufacturing has played an important but less significant role in Sri Lanka’s labor productivity growth than for other South Asia economies. Over the past several decades, Sri Lanka has not benefitted from sectoral resource reallocation to the same degree as its South Asian neighbors. Table 1 provides estimates of economy-wide productivity enhancements from sectoral reallocation based on national accounts data. The contribution of sectoral reallocation to overall growth is given by: where GDP1980−2008 is the annual growth rate of GDP from 1980 to 2008, ki,1980 is the share of employment in sector _i_ (indexed by agriculture, manufacturing and services) in 1980, and LaborProdi,1980−2008 is the average annual growth in labor productivity from 1980 to 2008. Over this time period, broad sectoral reallocation contributed approximately 15 percent to average annual growth in output per worker in Sri Lanka (0.5/3.1), as compared to 25 percent in India (1.1/4.4), just under 40 percent in
income countries and Latin America & the Caribbean.  However _cellular teledencity_ is reasonably comparable to upper middle-income countries though it still lags behind high-income OECD levels.  In terms of household _ownership of telephones_ , only 28% of South African households own a telephone, which is much higher than the upper middle-income benchmark of 13% and comparable figures for SSA and South Asia regions, both at 4%, and East Asia Pacific (9%). But it is still a long way from Europe and Central Asia (ECA) at 43%, suggesting considerable remaining room for growth. The ownership rates in rural areas, however, are seven times lower (6%) than in urban areas (43%) in South Africa, making the rural rate more comparable to those in entire group of middle-income countries.

**_Affordability—pricing_** _._ A 3-minute local call in U.S. cents is one of the most expensive in the world in South Africa at 15 cents, which is three times the cost in South Asia and East Asia Pacific and equal to the high-income OECD countries. This reflects monopolistic market structures in local services. Local debate in South Africa has also emphasized the high cost structure, including on the international call market segment.  Nevertheless, within the cellular and international segment, some calls are very competitive. For example, the database used for this benchmarking shows that in terms of the cost of three off-peak minute cellular local calls (25 cents), South Africa has one of the cheapest rates when compared with upper middle-income (54 cents) and high-income OECD (59 cents) countries where costs are twice as much. In addition, according to the database (with original data from the ITU—World Telecommunications Development Report) an international, 3-minute, peak rate, fixed line call to the United States in 2001 appeared lower than in upper middle-income countries and high-income OECD countries, but this needs to be checked against most recent data within the country and a more detailed look at the