rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9659

# **Abstract**

This paper uses the World Bank database on deep trade agreements to demonstrate the rapid increase in preferential trade agreements with standards of intellectual property protection that are enforceable and elevated beyond the minimums required in the World Trade Organization Trade-Related Aspects of Intellectual Property Rights Agreement. These accords are referred to as intellectual property–related preferential trade agreements. The paper sets out a treatment-control econometric approach, in which treated agreements are defined by various characteristics and the control group is other preferential trade agreements. This approach is used to study whether membership in intellectual property–related preferential trade agreements affects a country’s trade with nonmember countries. For this purpose, the paper defines a set of industries that intensively use intellectual property rights (the high-intellectual property group) and a set of industries that do not (the low-intellectual property group). There is evidence that countries in these agreements with the United States, the European Union, or the European Free Trade Association experience significant increases in third-country aggregated exports of biopharmaceuticals at all levels of income, while exports of low-intellectual property goods are relatively diminished, compared with the control preferential trade agreements. This result is reinforced using detailed bilateral sectoral trade and holds also for exports of medical devices from higher-income economies. Because these industries are the target of many elevated standards in intellectual property–related preferential trade agreements, the result suggests that these policies affect trade volumes. Further exploratory analysis suggests that these impacts are associated with higher local sales of affiliates of multinational firms, using US data. These are viewed as preliminary findings that point to the need for further analysis.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp
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forward processing and distribution links would be at least double the backward linkages in Kenya and over 15 times as great in Zambia.

Second, food processing clearly achieves prominent stature among forward linkages. Although bread baking requires imported wheat flour, virtually all other processing activities involve transformation of local agricultural production. Furthermore, oil extraction, sugar production, tea drying and packaging, coffee and cocoa dehulling and drying are often performed in rural areas by large scale enterprises. While we know these large scale activities take place predominantly in rural areas, the small scale orientation of much of the past research causes those activities to elude the statistical net. Hence even the substantial figures in Table 8 understate the magnitude of rural agroprocessing.

Finally, distribution of agricultural products generates, after food processing, the second largest of the forward linkages from agriculture. If retailing of local agricultural produce is approximately proportional to the share of marketed farm production in total rural consumer cash expenditure, then about 50 percent of rural retailing represents a forward distribution link from agriculture. 31/ Applying this proportion to data from Zambia, Table 8 indicates that distribution furnishes between 15 and 50 percent of total agricultural linkages, the higher proportion prevailing in rural towns.

E. Consumption Links As per capita farm incomes rise, the demand for local services, housing, durables, and higher quality foods typically increases more rapidly than the demand for foodgrains. The Asian experience suggests that the production of these commodities and services is labor intensive, hence rural employment in the non-foodgrain sector increases quite rapidly with per capita farm incomes (Mellor, 1976; Gibb, 1974; Bell, Hazell and Slade, 1982). Bs.t these patterns of growth may depend on a combination of high
# **1 Introduction**

Access to jobs, particularly to better jobs, is crucial for economic development. Proximity to urban areas has always been considered an important aspect for rural households for accessing jobs, goods, and services, and it is widely studied in the literature. The studies show that living closer to urban areas increases non-farm employment and market oriented activities (Sheng et al., 2022; Deichmann et al., 2009; Fafchamps and Shilpi, 2005, 2003), improves the economic well-being and nutrition of rural households (Stifel and Minten, 2017; Sharma, 2016), and overall, positively affects spatial dimensions of development (Sharma, 2016). However, proximity alone cannot be a good measure of accessibility because it does not take into account road condition - which can have a large impact on travel time and costs to urban centers.

Poor transportation infrastructure restricts accessibility to markets and jobs for the rural population in low- and middle-income countries (World Bank, 2009). Improved roads tend to decrease travel time and travel costs, and promote mobility. Better transportation infrastructure might stimulate mobility of goods and labor, connecting people to jobs.

A large majority of households in low- and middle-income countries are still mainly employed in agriculture, often in self-subsistence farming. According to the accounts data, the average agricultural productivity gap is 3.5, meaning that value added per worker in agriculture is more than three times lower than the one of the non-agricultural sector. There are higher disparities in different income levels of the countries. In low-income countries labor productivity in non-agricultural employment is 4.5 times higher than in agricultural employment, it is 3.2 times higher in middle-income countries, and 2.2 higher in high-income countries (Gollin, 2023; Gollin et al., 2014). Structural transformation has been a key policy issue in low- and middle-income countries in recent decades. One of the ways to address this problem is to give rural households opportunities to be employed in more
Policy Research Working Paper 10967

# **Abstract**

This study uses a cluster randomized controlled trial to evaluate the impact of a nationwide malaria prevention advertising campaign delivered through social media in India. Ads were randomly assigned at the district level, and the study relies on data from two independently recruited samples (8,257 individuals) and administrative records. Among users residing in solid (concrete) dwellings, where malaria risk is lower, the campaign led to an 11 percent increase in mosquito net usage and a 13 percent increase in timely treatment seeking. Self-reported malaria incidence decreased by 44 percent. Consistently, recorded health facility data indicate a reduction in urban monthly incidence of 6.2 cases per million people, corresponding to 30 percent of the overall monthly incidence rate of malaria. Conversely, the study finds no impact on households living in non-solid dwellings, which face higher malaria risk, nor among rural settlements where such dwellings are more prevalent. To disentangle if this lack of impact stems from ineffective content or insufficient reach, an individual-level trial was conducted (1,542 individuals), ensuring campaign exposure for both household types. The findings indicate an increase in bed net usage and timely treatment seeking for both groups, underscoring the need for improved targeting in social media campaigns to fulfill public health goals..

This paper is a product of the Development Impact Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dd3137@gsb.columbia.edu, amunozboudet@worldbank.org, vorozco@worldbank.org or nandan@vlab.digital. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture
Although various theoretical models suggest quite opposing roles for agriculture in development, they do not necessarily contradict each other. The models are derived under different economic assumptions (e.g., openness to trade). Therefore, it is not surprising that they derive different policy implications. Because developing countries differ with respect to their economic environments, the role of agriculture for development might be re-evaluated in each specific case. This is in line with the 2008 World Development Report‘s message (World Bank 2007), which suggests that in agriculture-based economies, agriculture can be the main engine of growth, whereas in transforming countries, agriculture is already less important as an economic activity but is still a major instrument to reduce rural poverty. In urban countries, by contrast, agriculture plays the same role as other tradable sectors and subsectors with a comparative advantage can help to generate economic growth.

So far our discussion has mostly considered theoretical models. We now turn to the empirical investigation of the relation between the agricultural sector and economic growth, which has a long history. Early contributions by Kuznets, Chenery and others focused on sector changes accompanying economic development. In 1966, Kuznets observed that as economies develop, the share of agriculture in output and employment diminishes, which later empirical data have reconfirmed. Other important early contributions include Chenery and Syrquin (1975), who combined cross-section and time-series data over 1950 – 1970.

More recently, Timmer (2002) used a panel of 65 developing countries over 1960 – 1985 to show a positive correlation between growth in agricultural GDP and its lagged values and nonagricultural GDP growth. He suggests that this correlation can be explained by ―first-order‖ effects of agricultural growth on lower food prices, labor migration and capital flows from agriculture, as well as ―second-order‖ effects such as improved nutritional intake, which improves workers‘ productivity. Similarly, Self and Grabowksi (2007) established a positive relation between different measures of agricultural productivity and average growth of real GDP per capita over 1960 – 1995 for a
in rainfall by 1 SD during the South West monsoon season, which is equivalent to 130 mm of rainfall, reduces child WAZ by 0.63 SD in India.

In terms of statistically insignificant effects, Block et al. (2004) examine the effect of Indonesia’s drought and financial crisis of 1997–98 using time-age-cohort decomposition analyses employing high-frequency nutrition data. They find that child WAZ remained constant throughout the crisis period despite a rapid increase in food prices and reduction in household consumption of rice, eggs, and dark, leafy vegetables. However, the authors note that the absence of effect on child WAZ was the result of an intrahousehold resource reallocation and a coping response that protected child caloric intake at the expense of their food quality. Deuchert and Felfe (2015) exploit variation in Super Typhoon Mike’s effect on housing damage in 1990 in the Philippines to estimate the effect on child WAZ and find that it was insignificant. Children whose housing was not affected were a weak counterfactual as the disaster likely also impacted the local economy, among other things. Moreover, the study notes that households that experienced housing damages were more likely to have received disaster relief, which might have masked the true effect of the shock.

_Child height-for-age z-scores or stunting status_

> 4 Bengtsson (2007), not included in this review, uses rainfall variation as an instrumental variable for income changes and finds that, for children below age 10 in rural Tanzania, a decrease in income by 10 percent increases the probability of being malnourished (defined as child WAZ less than 1 SD below the international reference median) by 7 percent.

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ments to competition law defines the market affected at the 4-digit ISIC level and the year for which the correction of competition was enforced. Most cases did not specify a particular region, which suggests that the anti-competitive behavior was not limited to a narrow location, so each case is assumed to have national impact. Moreover, the limited number of observations per region in the firm database reduced the possibility of studying the few cases that had a region-specific scope. When a resolution applied to a sub-region rather than the whole national market, our coefficients would be picking up a diluted, underestimated effect that would represent a lower bound of what the actual effect is.

We assume that competition enforcement is exogenous to firm performance. Osiptel and Indecopi are independent and autonomous bodies that receive cases initiated externally by complaint from any physical or moral persons and internally based on monitoring and control organs. This ensures that competition enforcement is applied nationwide regardless of firm or regional idiosyncrasies. Violation of competition law needs to be legally confirmed, following pre-defined requirements, for a case to be accepted. Rulings can be appealed to independent courts. All these elements support the assumption that interventions are exogenous.

The information about _de jure_ and _de facto_ regulations was collected from the digital legislative archive from OSIPTEL and INDECOPI, following the criteria that the degree or resolution had to be relevant (i.e. pertain to at least one of the ICT branches of hardware, software, telecom or ICT infrastructure), timely (i.e. had to be published and implemented in the 2007-2017 period) and had to be related to competition (e.g., encouraging firm entry, reducing misallocation and administrative barriers that hinder innovation).

# **4.2 Empirical Approach**

The methodology described above requires that productivity and markups be identified consistently if the economic impacts of regulation and enforcement are to be accurately characterized. A widely used methodology to estimate productivity is Ackerberg et al. (2015) and a popular extension for estimating markups is De Loecker et al
# **Figure  1: Panel survey: sub-sample of  HIECS**

<!-- Start of picture text --> Jul-04<br>Aug-04<br>Sep-04<br>Oct-04<br>Nov-04<br>Dec-04<br>Jan-05 Panel<br>Feb-05 Feb-08<br>Mar-05 HIECS<br>2008/2009<br>Apr-05<br>Apr-08<br>May-05<br>May-08<br>Jun-05<br>Jun-08<br>HIECS Jul-08<br>Aug-08<br>2004/2005<br>Sep-08<br>48,000 HH Oct-08<br>Nov-08<br>Dec-08<br>Jan-09<br>Feb-09<br>Mar-09<br>Q2<br>Q3<br>Q4<br>Q1<br><!-- End of picture text --> The data used in this analysis rely on a one-month panel sample of the full HIECS 2004/05, containing the same (matched) households. Out of the 3,903 addresses from February 2005 sample revisited in 2008, 3,690 participated in the survey, of which 3,552 households were the same (panel) households as interviewed in 2005 and 138 were new households (at the old addresses). These 138 households do not form a panel and are not used for any analysis presented in this paper.

**_Main living standards indicator: consumption._** In this paper, as in the previous studies of poverty and living standards in Egypt (World Bank 2007), the analysis relies on actual consumption expenditure, including all money spending on consumer goods and services (durables included), and non-monetary parts, such as imputed rents, own production and in-kind transfers received by households. Food consumption includes food that the household has purchased, grown and received from other sources for 279 items. Non-food consumption is the sum of expenditure on 298 non-food items, including expenditure on fuel, clothing, schooling, health
data, implying that the labor market looks more dynamic than it really is. Even a simple classification of workers by industry suffers from non‐random measurement error (Bassi et al., 2008). Retrospective data suffer, on the other hand, from recall error as respondents may not accurately remember the details of previous employment spells. For this reason, scholars have argued that retrospective questions can be used to gather qualitative data on labor market outcomes in previous periods, such as the occupation and the sector, but not for quantitative data such as earnings or hours worked (Grosh & Glewwe, 2000, chapter 9).

Although the LSMS reference work retrospective questions, they seem to have fallen out of favor. DHS, LSMS‐ISA surveys and most LSMS surveys do not include retrospective questions, while LFS cover them. For instance, the 2014 Guatemala LFS includes the question _‘What was your last employment?’._ The 2012 Indonesia LFS, instead, includes several retrospective questions, such as _‘Did (name) ever have a job/business before?’, ‘Did (name) quit or moved out into another job during the last one year?’, ‘What was (name’s) previous job main industry before quit or moved into the last job?’, ‘What is employment status of the previous job of (name) before quit or moved into the last job?’. T_ he 2007 Nepal and 2013 Malawi LFS include the same questions for the previous job as for the current job. Finally, the 2014 Tanzania LFS includes the following questions: _‘In which year did you start working in economic activities for wage job or self‐employment to support your life for the first time?’, ‘What type of work/activity were you doing at your first employment?’, ‘Have you ever worked or having any economic activity?’, ‘What type of work did you do in your last job?’._
As shown in the table, the unbanked population in Mexico is somewhat less educated than the banked population, is less likely to be working, and has a lower household income. These patterns are broadly consistent with those found in the U.S. survey.

# B.  Where do the unbanked get financial services?

Individuals who do not use banks still need financial services. If they have any financial savings, they need a means to safeguard their savings. If they receive income in the form of a check, they need a means to convert the check into cash. They need to pay bills. Finally, they many need to borrow periodically. This subsection draws on the survey data to examine where and how the unbanked obtain these financial services.

Table 14 presents data on the forms in which the unbanked keep financial savings and contrasts this with the banked. As noted in Table 13 above, 74.9 % of the unbanked said that they had no formal sector financial savings. The survey also asked about the use of informal means of financial savings, such as holding cash, money orders, uncashed checks, jewelry that can be sold, etc. As shown in Table 14, 18 percent of the unbanked respondents said that they use such means; only 1.1 % of the banked did. Allowing for both formal and informal means of financial savings, 56% of the unbanked had no financial savings and 0.7% of the banked had none.

|**Savins methods or sort of**<br>**Table 14. Use of inform**|**al savings meth**|**ods**|
|---|---|---|
|**g**<br>**savings**|**Banked**|**Unbanked**|
|**Informal savings**|**1.1%**|**18.7%*
in 2010–12, to be consistent with the population data from the 2011 Census of India. However, when the images for these three years were of poor quality, we complemented them with images from more recent years. Similarly, when Google Earth images were not available, we used Google Maps images that were composites from recent years.

**Figure 3. Examples of Google images overlaid with administrative boundaries**

_Note_ : The yellow lines depict the digitized administrative boundaries for the respective administrative units.

# _Diverse groups of assessors_

To collect subjective assessments of Google images of the 1,277 places in the sample we mobilized a broad pool of assessors, emphasizing heterogeneity along two dimensions: familiarity with India and knowledge of urban issues. Heterogeneity is important to explore whether the assessors’ characteristics matter. The two dimensions considered lead to the constitution of four distinct groups of assessors.

Our first group of assessors were three in-house research analysts who were both familiar with India and knowledgeable on urban issues. These analysts all had graduate-level degrees in economics and were given background information on the purpose of the exercise. While they were originally from three different developing countries – China, India and Peru – they were all familiar with India as they worked at the time at the South Asia region at the World Bank.

The second group of assessors comprised 15 university students who were knowledgeable on urban issues but unfamiliar with India. They all had experience in land use classification using satellite images, because of their activities at the Center for Urban and Environmental Research at George Washington University (GWU). These students participated on a voluntary basis, for a fixed payment upon completing the classification of all the places assigned to them for judgment. The students were all from the US.

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# 1. **Introduction**

Firm financing patterns have long been the object of study of the corporate finance literature. Financing patterns have traditionally been analyzed in the Modigliani-Miller framework, expanded to incorporate taxes and bankruptcy costs. More recently, asymmetric information issues have drawn attention to agency costs and their impact on firm financing choices. An important literature also exists relating financing patterns to firm performance and governance.

The financial structure of the corporate sector has proven relevant in some other areas of economic research. Several recent studies have focused on identifying systematic cross-country differences in firm financing patterns. Those studies have identified the effects of such differences on financial sector development and economic growth. They have also examined the causes for different financing patterns, and in particular countries' legal and institutional environments.<sup>2</sup> Finally, firm financing choices have emerged as an important factor in the literature on predicting and explaining financial instability.<sup>3</sup> Corporate sector risk characteristics have, however, not been much examined in the literature, aside from leverage and debt maturity considerations. Even these measures have been the object of few empirical investigations, mainly due to a paucity of data on corporate sectors around the world. Building on data which have recently become available, we fill this gap in the literature and shed light on the risk characteristics of corporate sectors around the world. We use data for 11,000 firms from 46 countries over the period 1995-96, and calculate 12 indicators typically used by financial analysts to gauge a firm's risk. We also analyze three corporate accounting profitability 2
Table 1: Summary statistics

|Variable|Mean|Std. Dev.|Min.|Max.|
|---|---|---|---|---|
|**Education sector: 1082 observation**|**s (108 countries, an**|**nual data for 1990-2003)**|||
|Public education expenditure|4.02|1.92|0.38|13.61|
|Education aid|1.13|1.45|0.01|14.19|
|Education IP|0.13|0.23|0|3.6|
|Education SP|0.04|0.09|0|0.95|
|Education TC|0.81|1.1|0|10.85|
|Education ONM|0.16|0.34|0|5.83|
|General aid|1.2|1.92|0|22.78|
|Support to NGOs|0.13|0.24|0|3.02|
|Other non-education aid|5.84|6.78|0.01|62.84|
|Real GDP per capita|3.63|2.98|0.47|17.96|
|Real GDP per capita growth|1.6|5.46|-30.28|49.86|
|Urbanization|42.4|20.36|6.3|91.56|
|Trade|78.11|41.06|10.83|280.36|
|PV debt|52.15|60.07|0.09|892.12|
|Public debt service|4.02|3.47|0|35.24|
|**Health sector: 1087 observations (1**|*