rafmacalaba/data-use-mentions-extended huggingface dataset:
decades, they typically have not affected the consumer end of the business. Instead in water private participation was mostly introduced in water treatment plants and in electricity in generation according to the private participation in infrastructure database of the World Bank <u>( http://ppi.worldbank.org/).</u>

**Figure 6:   Cost Recovery by Public Utilities in Developing Countries**

<!-- Start of picture text --> Ratio of Revenue<br>to Costs<br>1.8<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>Telecom Gas Pow er Water<br><!-- End of picture text --> Source: World Bank (1994) In other infrastructure sectors prices are low relative to costs and access for citizens is limited. Where access exists quality is often low, particularly under public ownership<sup>6</sup> . Citizens, who do not have access, typically lower income groups, pay higher unit prices for non-networked substitutes than the rich pay for their networked services. It is unsurprising that studies on the incidence of subsidies in infrastructure suggest redistribution tends to favor the better off (Brook and Irwin, 2003). Among non-payers of utility bills, government authorities stick out. The few studies that link infrastructure service provision to corruption perceptions are consistent with this. Corruption is associated with low quality and low maintenance expenditures (Tanzi and Davoodi, 1998) and inefficient operations (Bo and Rossi, 2007 for 13 Latin American electricity distribution companies).

When a system does not supply users with demonstrated high willingness to pay (including the poor) and when important customers do not pay, corruption does not principally operate by exploiting the pricing power of infrastructure services. Instead of taking rents out
|Sindh|54.75%|13.51%|18.99%|16.67%|31.58%|18.87%|
|---|---|---|---|---|---|---|
|Khyber-Pakhtunkhwa|4.70%|20.26%|3.80%|1.85%|0.00%|15.09%|
|Islamabad|3.22%|10.92%|3.80%|1.85%|0.00%|1.89%|
|_Herfindahl concentration_|0.26|0.17|0.44|0.55|0.42|0.29|

Source: Author’s elaboration from Enterprise Survey (2014)

# 6. Exploring the Causal Links Between Innovation and Productivity

As Paul Krugman famously claimed, "Productivity isn't everything, but in the long run it is almost everything. A country's ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker.” (Krugman, 1994). Although several studies have been conducted to explore the links between innovation and productivity, most of the evidence available relies on the findings for developed countries, with little evidence for developing economies. It is therefore the objective of this section to fill this gap by focusing the analysis on the South Asia region.

## 6. 1 Methodology

In order to examine the empirical relationship between innovation and productivity, we follow a conceptual framework based on Figure 1, which forms the basis of the CDM model. The framework holds that firms invest in knowledge inputs that can be transformed into innovation outputs according to an innovation function. At a later stage, these innovation results impact on firm-level productivity, contingent on the capacity of firms to transform innovation outputs into improvements in product quality and efficiency.

As a
discrimination observed in the DRC. Section 5 presents the empirical approach and the results; finally, section 6 concludes.

# **2. Context: The Democratic Republic of Congo**

The DRC is one of the poorest countries in the world; it is the fourth most populous (67.5 million inhabitants) and the largest country in terms of land area in Sub-Saharan Africa. In 2012, the time period covered by the survey, it had an estimated GNI per capita of US$335.48 in constant 2010 US$ and was ranked 176 of 189 economies in the UNDP’s Human Development Index. Fertility rates were quite high at 6.4 births per woman and gross secondary school enrollment was still low at 43.3 percent. The economy suffers from low levels of human capital. The recent World Bank’s Human Capital Index ranked the DRC 146<sup>th</sup> of 157 economies in 2017.<sup>7</sup> The score for the DRC was 0.37, implying that the productivity as a future worker for a child born in 2017 in the DRC is 63 percent below what could be achieved with full health and education. Both the human capital index and primary school enrollment rates are similar between men and women in the DRC. However, in 2012 secondary school enrollment rates were much higher for males than females (54.2 percent versus 32.1 percent), and tertiary school enrollment for males was almost double that of females (10.6 percent versus 5.9 percent).<sup>8</sup> Women are as likely as men to work, but they are over-represented in the agricultural sector. Women made up 48 percent of the labor force all throughout 2012-2018. However, in 2012, 80 percent were employed in agriculture, 4 percent in industry, and 16 percent in services.<sup>9</sup> Informal employment is quite high with the most recent available estimate in
assessments, consistent with the approach proposed by Ceriani et al. (2019). There is also information on physical housing characteristics, including type of construction, floors, water and electricity connections, sanitation and the like.

Information on district characteristics is taken from the 2011 population census, which was conducted roughly at the same time as the NLSS III. The resulting housing stock measure is combined with official figures to estimate the proportion of dwellings destroyed in each district (Government of Nepal 2015).

A detailed description of the variables used in the regression analysis can be found in the annex (table A.1). Their summary statistics are reported there as well (table A.2). Among other insights, these summary statistics show that self-assessed housing rents before the Gorkha earthquakes were higher in the most affected districts (the ones where at least 5 percent of the dwellings have been fully destroyed) than in the rest of the country.

# **6. The hedonic pricing regression**

The hedonic price function in equation (8) is estimated with self-assessed rent as the dependent variable. The alternative would be to use self-assessed housing price, but there are much fewer observations available for this variable. Given the characteristics of the data and the conceptual options available, four specifications are considered (table 1). A first choice is whether to define neighborhood effects at the district level (first two columns) or at the narrower VDC level (last two). A second choice is whether to assume that the impact of neighborhood effects is linear (columns one and three) or quadratic (columns two and four).

The overall significance of the model is strong in all cases. Adjusted R<sup>2</sup> statistics are in the range of 0.58-0.64, highlighting the good fit of the model. The reliability of the estimates is further confirmed by the F-statistics, which are statistically significant at the 5-percent level for all specifications and at the 1-percent level for most of
enrollment in grades 6–8 in government schools by an average of 6 girls per school (or by 9 percent relative to initial enrollment) over the period 2003–05. Using government school census data and difference-in-differences estimation,<sup>4</sup> Hasan (2010a) finds that the program increased female enrollment in grades 6–8 in government schools by an average of 11 girls per school over the period 2003–08. Hasan also finds that the program increased corresponding male enrollment by an average of 10 boys per school. In another study, Hasan (2010b) uses nonrepresentative panel household sample survey data for three districts (one program district, two nonprogram districts) with adult literacy rates some distance from the cutoff and applies within-family difference-in-differences estimation.<sup>5</sup> In this study, Hasan finds that the program had no effect on the likelihood of government school participation by girls ages 10–14 in 2005 or 2006 but had a significant positive effect of 3 percentage points on this outcome in 2007. Finally, using government school census data from 2003–09 and parametric RD estimation, Alam, Baez, and Del Carpio (2011) find that the program had significant positive effects on girls’ enrollment in grades 6–8 from 2005 to 2009. They find average gains in girls’ enrollment by grade ranging from 10 to 32 percent.

# **3. Program context**

Pakistan, a lower-middle-income economy with a per capita income in 2018 of $4,928 (in 2011 purchasing power parity U.S. dollars), is the sixth most populous country in the world (World Bank 2019). The country is divided into four large provinces (Balochistan, Khyber Pakhtunkhwa, Punjab, and Sindh) as well as small territories.

Punjab is a major province. It accounted for 53 percent of Pakistan’s population of almost 208 million in 2017 (Pakistan Bureau of Statistics 2017a) and for 55 percent of the

> 4 The difference-in-differences approach compares
Second, gender has limited explanatory power and gender differences in labor market vulnerability vary across countries. In Europe and Mexico, women are around 10 percentage points more likely than men to have a job amenable to home-based work, whereas in Brazil the gap widens to 19.4 percentage points. In India, by contrast, there are no gender differences, and in Turkey women are 12.7 percentage points _less_ likely to be able to work from home. However, it is important to bear in mind that these estimates only reflect whether a job can be performed from home and do not consider other dimensions of gender differences in susceptibility to COVID-19 induced labor market stress. Women are disproportionately shouldering caregiving and childcare needs stemming from the shutdown of schools and childcare centers (Alon et al., 2020, Sevilla and Smith, 2020, Wenham, 2020) or the illness of their family members, which constrains their labor supply (Heath et al., forthcoming).

Third, and most important, labor market vulnerability is inversely correlated with educational attainment. Workers with tertiary education are much more likely to be able to work from home in all countries and regions. Education explains a large share of the variation in the ability to work from home as is evidenced by the R2s which consistently exceed 0.2; it is the strongest predictor of who has a relatively safe job among the set of explanatory variables we consider here. While education offers protection in all countries, the probability of having a job amenable to working from home increases least with additional education in India, which is not surprising given that India has fewer jobs that can be performed from home to start with. Interestingly, when using the DN2020 telework variable instead of our home-based work measure, the coefficient of education level in India becomes similar to the one in the other studied countries (see annex 1), which attests to a lack of internet access being the binding constraint on highly educated Indians’ ability to work from home.

Fourth, workers in temporary
>Land Service: Land Cover 100m:<br>Collection 3: epoch 2019: Globe 2020.|
|Population – GHS-<br>POP R2019A|This spatial raster data set depicts the<br>distribution of population, expressed as the<br>number ofpeopleper cell.|https://ghsl.jrc.ec.europa.eu/ghs_pop20<br>19.php|GHS population grid multi-temporal<br>(1975–1990–2000–2015).|

36
The scope of provisions that aim at protecting forest resources varies significantly across agreements.<sup>5</sup> RTAs can include more general language to promote the conservation and sustainable use of biological diversity and sustainable forestry management. Or they can commit members to specific actions such as measures to combat illegal logging and related trade. For instance, the RTA between the United States and Peru contains an annex on forest sector governance setting out detailed commitments promoting sustainable management of forest resources and combating trade associated with illegal logging, among other things. The agreement has specific requirements such as the identification within the Peruvian government of a focal point, with appropriate and sufficient authority and staff to investigate violations of laws and regulations for forest sector governance. The RTA between the EU and Cameroon contains similar, although not as detailed, provisions on forestry governance and trade in timber and forest products. Both agreements also include references to other international instruments that will support the objective of improved conservation, such as members’ participation to multilateral environmental agreements (MEAs). Finally, both agreements foresee provisions on technical assistance and capacity building. In the case of the EU-Peru RTA, the agreement recommended a voluntary partnership agreement under the EU’s action plan on forest law enforcement, governance and trade (FLEGT), which Cameroon signed shortly after the entry into force of the RTA.

# **2.2 Deforestation**

Forest loss around the globe is driven by agricultural land expansion (both for small holder agriculture as well as commercial production), forestry practices, fires, and urban expansion (Curtis _et al._ , 2018). Tropical deforestation is overwhelmingly driven by agricultural expansion (Gibbs _et al._ , 2010), and accounts for a large share of global greenhouse gas emissions (Baccini _et al._ , 2012).

To measure the impact of forest loss arising from RTAs, we utilize the Global Forest Change dataset by Hansen _et al._ (2013). These data provide high resolution estimates of year
# Introduction

Pacific Island Countries (PICs) are often characterized as being both remote (in a global context) and highly dispersed, but within each country there is enormous variation in the remoteness of individual communities, and the extent to which communities are clustered or dispersed from one another. This paper seeks to better understand, and to empirically measure, the nature of dispersion and remoteness within individual PICs.

This paper defines a new measure of local-area population remoteness which is highly sensitive to variation in settlement patterns. Remoteness of a community is calculated as the population-weighted average distance between the community of interest, and all communities across the country. It is designed to be evaluated using commonly available population census data. A community is measured to be more remote if it is home to a smaller share of the population, relative to other communities further afield. It is also sensitive to the distance between the community of interest and any other community (in particular, a community is measured to be more remote, the more distant it is from large population centers). The remoteness measure for a particular community is unaffected by the overall population level of the country as a whole.

Our new measures of community-level remoteness can be aggregated to derive country-level indicators of (i) the geographical scale of population dispersion, and (ii) a dispersion-adjusted measure of population density, or inversely, sparsity. The geographical scale of dispersion is defined as the population-weighted average remoteness. This indicator is again highly sensitive to variation in settlement patterns (unlike more macro measures of geographical dispersion, such as the size of a country’s Exclusive Economic Zone). The dispersion indicator will be higher when a larger share of the population live in remote communities. Like the community-level remoteness indicator, the dispersion indicator is populationneutral, and unaffected by the overall level of a country’s population. The dispersion-adjusted measure of population density is defined as being proportional to the country’s population divided by the square of the dispersion indicator (with the square operation applied to transform
borrowers, by effectively stopping access to capital, affects policy choices and the pace of adjustment in each country. However, even when reforms are adopted, economic outcomes take time to change in the “real” sectors and market perceptions can continue to move in ways that worsen outcomes on the ground, despite reform initiatives having been begun.

**Figure 1: Sovereign credit default swap (CDS) spreads (basis points)**

<!-- Start of picture text --> 2,000<br>25,000<br>1,600<br>20,000<br>1,200 15,000<br>800 10,000<br>400 5,000<br>0 0<br>Ireland Portugal Spain Greece<br>2007‐Jan 2008‐Jan 2009‐Jan 2010‐Jan MoU‐GRC (1st) MoU‐IRL2011‐Jan MoU‐PRT 2012‐Jan MoU‐GRC (2nd) MoU‐ESP 2013‐Jan 2014‐Jan 2015‐Jan MoU‐GRC (3rd) 2016‐Jan<br><!-- End of picture text --> Monthly data for 5‐year tenor. Values for Greece are on the right axis. Source: Bloomberg, CMA.

The interest differential with the German 10 year bund shows that spreads for the GIPS decreased dramatically until 2007 so that by that year, the interest rate differential was .28, .09, .21 and .09 respectively. Following the 2008 crisis, during the first part of 2009, the VIX index, reflecting global risk aversion and CDS spreads for GIPS had both fallen. Surprisingly perhaps, Greece’s risk premium, as measured by CDS spreads, was lower than Ireland’s until the end of 2009. Yet suddenly, from the end of 2009 until the middle of 2011, global risk aversion as measured by the VIX and CDS spreads for GIPS were moving in opposite directions, the latter having risen dramatically.
12 firms (8 new entrants). The final step assumes the impact of moving to perfect competition in the long run (although, in the mobile telecommunication market, the number of firms tends to be rather small). For the price elasticity of demand, the simulation relies on the empirical estimations reported in Hausman and Ros (2013) for mobile telecommunication services (η ��0.476).

**Table 3. Estimated price change and related parameters**

|**Oligopoly (Nash)**<br>**market**|**Step 1**|**Step 2**|**Step 3**|**Competitive market**|
|---|---|---|---|---|
|Firms�4|Firms� 6|Firms�8|Firms�12|Firms� ∞|
|η��0. 476|η��0.377|η��0.328|η��0.279|η<sup>∗</sup>� �0.1808|
|p1� 1. 344|p�� 1.306|p�� 1.275|p�� 1.229|p�� 1.0000|

_Source:_ World Bank estimates using ENIGH 2014; Hausman and Ros 2013.

_Note:_ Changes in elasticity occur due to the simulation of a large price drop in the item of interest, resulting in a quantity demanded of larger magnitude and decreasing the absolute value of the elasticity.

Table 3 shows illustrates the WELCOM prediction that market concentration increases prices in the mobile communication market in Mexico by about 34.4 percent with respect to a scenario of perfect

> 24 In this section the term telecommunications and communications will be used to refer to mobile communications services.

> 25 The rule that WELCOM follows is to multiply the original number of firms by 1.5 in
representative food basket in Bulgaria<sup>12</sup> , as a relative poverty line is used for official poverty measurement in the EU<sup>13</sup> , we use the Bulgarian food basket recently estimated by the EC within the European Reference Budget network **.** This food basket outlines the monthly budget for adequate food intake for three reference households (children and working-age individuals in good health, without disabilities, and residing in Sofia). The basket covers expenses for food and kitchen equipment essential for preparing, serving, consuming, and preserving the food. Additionally, it considers the budget required for physical activity and other aspects of food, such as its social function—the estimation of the Bulgarian basket under the European Reference Budget network<sup>14</sup> in 2015. The administrative data is used to model the program beneficiaries in the surveys. Frequently, the survey figures related to population variables, income, consumption, fiscal interventions, etc., do not align with the totals derived from administrative accounts.

# Commitment to Equity Model (CEQ) and Modeling of Social Transfers

**This study uses the CEQ model and infrastructure developed by Robayo and Cabrera (2024).** The fiscal incidence approach aims to help through a comprehensive tax/benefit system assessment using a diagnostic tool called the Commitment to Equity Assessment (CEQ). The CEQ approach is among the first efforts to comprehensively assess the tax/benefit system in developing countries (including indirect subsidies and taxes and in-kind benefits in the form of free education and health care) and to make the assessment comparable across countries and over time.

**The analysis uses a national poverty measure, specifically the "at-risk of poverty" indicator, as its primary metric and investigates how policy reforms can influence this measure** . In the European Union context, this evaluation generally involves a comparative analysis of the "at-risk-of-poverty" rates with and without the proposed policy reforms. Both poverty rates are determined
worked with the non-profit advertising team to exclude 40 randomly-picked districts from the delivery of the campaign. Relying on a set of selfreported attitudes and behavioral outcomes, we found evidence that the ad campaign increased the use of bed nets among individuals living in solid houses (by 11% compared to control), while it was ineffective for households living in non-solid dwellings. Moreover, self-reported household malaria incidence decreased by 44-53% among those living in solid houses compared to control, but not among non-solid dwellers. The reduction in malaria incidence was predominantly driven by bed net owners. Consistently, the analysis of administrative health facility data showed that monthly malaria incidence in urban areas decreased by 6.2 cases per million people. This is about 30% of the overall monthly malaria incidence rate in the pre-treatment period and implies an upper bound cost per averted _reported_ urban case of USD 3.4. By contrast, rural incidence was not significantly affected.

Was the ad content ineffective for households at greater malaria risk or did it fail to sufficiently reach them? To answer this question, in a second trial we experimentally varied exposure to the same ads at the individual level, using the re-marketing tools of the ad platform. We found that bed net use and timely treatment seeking increased for both types of households, suggesting that social media campaigns need to invest in improved targeting strategies to reach their development objectives.

35
3 FOREIGN DIRECT INVESTMENT IN KENYA FDI has grown rapidly, reaching US $23 million in 2008 from just US $1 million in 2003. Between 2009 and 2010, Chinese FDI increased 261 percent, the largest gain during the 2003-2012 period before returning to US $68 million in 2011. China’s FDI stock in Kenya has also grown 35.6 percent annually from US $26 million in 2003 to US $403 million in 2012. Figure 20 shows Kenya’s top FDI inflow sources in 2011 (UNCTAD 2015). China comes in second to the United Kingdom, and its share of Kenya’s FDI total inflows has also grown. Reaching a peak of US $101 million in 2010, or 57 percent of Kenya’s total FDI flows, it then fell to US $79 million in 2012, representing 31 percent of total flows (figure 18). China’s large FDI profile will lead to greater cooperation with Kenya, but Kenya must continue to lower the costs of business and investment and curb corruption to squeeze the most from foreign investment.

<!-- Start of picture text --> 100 400<br>75 300<br>50 200<br>25<br>100<br>0<br>2003 2006 2009 2012 2003 2006 2009 2012<br>Year Year<br>FDI Inflow (USD Millions) FDI Instock (USD Millions)<br><!-- End of picture text -->

- (a) Kenya’s FDI inflow from China (2003-2012) (b) Kenya’s FDI instock from China (2003-2012) Figure 17: Chinese FDI in Kenya is growing quickly since 2009 (2003-2012)

Source: UNCTAD FDI/TNC database 2015 <!-- Start of picture text --> 40<br>20<br>0<br>2003 2006 2009 2012<br>Year<br>China FDI (% of total FDI)<br><!-