rafmacalaba/datause-encoder-data huggingface dataset:
more volatile sectors, in sectors with a higher R&D intensity, and in sectors with a lower capacity to pledge collateral for external financing. Importantly, these sector-specific predictions permit a generalized difference-in-difference specification as in Rajan and Zingales (1998) to address any remaining endogeneity concerns.

We identify firms whose projects have more upside potential in the sense of hypothesis (ii) of Proposition 1 through their economic activity in the U.S., which serves as a benchmark country. This is done by computing the average value added growth across two-digit ISIC Rev 3.1 sectors for all years in the UNIDO (2017) Industrial Statistics Database and, alternatively, by the average R&D intensity of two-digit sectors provided by Samaniego and Sun (2016). Similarly, we record the volatility of economic activity as measured by the standard deviation of value added in two-digit ISIC Rev 3.1 sectors for all years in UNIDO (2017) to identify more risky projects as in hypothesis (iii) of Proposition 1. Finally, we approximate the pledgeable income of entrepreneurs as in hypothesis (iv) of Proposition 1 by the industry-level asset tangibility taken from Samaniego and Sun (2016), which is measured as the ratio of fixed assets to total assets. That is, firms in sectors with higher asset tangibility can pledge more collateral and thus have better access to external

**_Descriptive statistics._** The descriptive statistics, summarized in Table 1 show that about half (53%) of all formal establishments in developing countries engage in some form of innovation activity; 39% introduced a product which was new to the firms’ main market, and 43% introduced a new production process. But only relatively few establishments (35%) have access to formal bank loans. Similarly, the share of bank finance for firms’ working capital expenditure amounts to only 13%. Instead, the predominant form of working capital finance is internal funds/retained earnings; their share accounts for 72%. Moreover, about half (46%) of the establishments report internal funds/retained earnings as their only source of working capital financing, and for 78% they are the primary source (financing more than half of their working capital expenditure). These statistics are consistent with the view that investment in innovation is largely intangible and risky; the associated working capital expenditure is therefore difficult to finance externally and must be covered by corporate liquidity holdings in the form of internal funds/retained earnings.

17
57. Real estate and equity investment increased rapidly, and the housing boom turned into a bubble, with housing prices reaching well beyond what economic fundamentals would otherwise indicate.<sup>36</sup> The increased availability of mortgages led to a departure of the housing prices from fundamentals. Between 1997 and 2006, the price of the typical American house increased by 124 percent. During the two decades ending in 2001, the national median home price ranged from 2.9 to 3.1 times median household income. This ratio rose to 4.0 in 2004, and 4.6 in 2006.<sup>37</sup> Schiller in his article on ―Irrational Exuberance‖ shows how far housing prices deviated from what would have been suggested by economic fundamentals (Figure 37).

**Figure 37: Home Prices and Economic Fundamentals**

<!-- Start of picture text --> 250 1000<br>900<br>200 800<br>700<br>150 600<br>Home Prices<br>500<br>100 400<br>Building Costs 300<br>50 200<br>Population Real Interest 100<br>0 0<br>1880 1900 1920 1940 1960 1980 2000 2020<br>Source: Schiller (2005) .<br>Index or Interest Rate Population, millions<br><!-- End of picture text -->

> 36 World Economic Outlook, October 2008 (Chapter 1), International Monetary Fund. According to the WEO: ― _The countries that have experienced the largest unexplained increases in house prices over the past decade are Australia, Ireland, and the United Kingdom; house prices in these countries were 20 to 30 percent higher in 2007 than can be attributed to fundamentals. A group of other countries -including France, Italy, the Netherlands, and Spain- have house price gaps of between 10 percent and 20 percent. The gap estimate for the United States –per about 7 percent- is smaller than for most other countries and has been narrowing compared with earlier estimates, partly reflecting the decline in U.S. house prices over the past 18 months._ ‖ 37 US Census Bureau.

44
during the period under consideration. Indeed, the coefficient associated with OIL is negative though not statistically significant.

<u>Table 2: Probit Estimations for Inflation Starts</u><sup>a</sup>

|Independent Variables|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|
|---|---|---|---|---|---|---|
|CONSTANT|-1.381|-1.591|-1.497|-1.292|-1.374|-1.088|
||(12.191)|(10.370)|(7.808)|(5.424)|(6.614)|(4.153)|
|GAP|0.076|0.090|0.088|0.097|0.084|0.097|
||(2.418)|(2.916)|(2.922)|(3.011)|(2.804)|(3.005)|
|FP|-0.049|-0.051|-0.051|-0.058|-0.043|-0.051|
||(2.194)|(2.308)|(2.312)|(2.431)|(1.928)|(2.094)|
|OIL|-0.004|-0.004|-0.004|-0.004|||
||(0.955)|(0.914)|(0.948)|(0.861)|||
|DEMOC||||-0.052||-0.067|
|||||(1.512)||(1.834)|
|DURABLE|||-0.006||-0.007||
||||(0.891)||(1.031)||
|BS||-0.050|-0.048|-0.058|-0.058|-0.067|
|||(2.233)|(1.987)|(2.501)|(2.277)|(2.461)|
|CF|||||-0.077|-0.086|
||||||(2.785)|(2.900)|
|Log likelihood|-81.6|-79.2|-78.7|-77.7|-76.8|-75.12|
|McFadden R<sup>2</sup>|0.052|0.079|0.084|0.093|0.107|0.123|
|Number of observations (0/1)|306/24|306/24|306/24|301/24<sup>b</sup>|306/24|301/24<sup>b</sup>|

a Absolute values of z-statistics, calculated using the Huber/White procedure, are given in parentheses under the corresponding coefficient estimates.<sup>b</sup> Five observations are missing since the DEMOC variable for South Africa (1992 and 1993), Korea (1987), Peru (2000), and for the Philippines (1986) were indetermined.

Model 2 incorporates budget surplus as a percentage of GDP (BS) to assess the role of fiscal policy in inflation starts.<sup>15</sup> The results underline the important link between fiscal policy and inflation starts: a 1 percent rise in BS reduces the probability of an inflation start by 5 percent. The output gap and FP continue to be statistically significant with their expected signs for inflation starts in this specification.

> 15 The lack of data for the majority of countries considered did not let us to examine the effect of the government debt on inflation starts.

13
under a range of assumptions drawing from the literature linking service uptake with health outcomes (Tura, Fantahun, and Worku 2013).

In studies of vouchers, knowledge outcomes can be measured among facility clients, SPs, and the general population. Common metrics include knowledge of disease symptoms, program characteristics (i.e., where to find a voucher or clinic location), and SP adherence to patient safety and treatment guidelines. Improvements in behaviors in the context of a health systems intervention largely encompass health-seeking behaviors. Other barriers to health care—such as distance to health facilities, poor roads, and difficulty in securing transport—are taken into account because they may keep clients from seeking care. However, if cost is the principal barrier, it follows that use of the facilities should increase with voucher distribution. If the burden of the untreated health conditions is high among the general population, it may be possible to detect a change in utilization patterns among the general population following the introduction of vouchers. Quality is measured by improvements in facility infrastructure, in the service delivery process (i.e., how clients are medically treated), and in client satisfaction with services that also contribute to reductions in maternal and neonatal morbidity and mortality.

Cost metrics are another important area to monitor. To gain insight into whether OBA subsidies are effective in improving health care delivery and health outcomes, it is important to monitor client out-of-pocket spending, facility revenue and costs, the ratio of program subsidies to the number of clients served, and related expenses. The final area to monitor is population health. The incidence of new cases per month or quarter in a population served by contracted facilities may be one measure of health outcomes. The odds of exposure in clinic-based cases and controls may be another approach. The change in prevalence in a difference-in-differences design may constitute yet another metric that makes it possible for administrators, funders, and other stakeholders to know whether OBA subsidies are a success.

# **<<A>>** Country Context

Uganda is among the world’s poorest countries, with a GDP US$6777 and a life expectancy of 59 years (World Development Indicators 2014). According to the 2013 United Nations Development Program (UNDP) Human Development Report, Uganda is ranked 161 out of 185 countries (UNDP 2013). Although the country is on track to achieve the Millennium Development Goal (MDG) targets of halving poverty, improving gender equality, and empowerment of women, it is significantly lagging on reducing child mortality (MDG 4) and improving maternal health (MDG 7
- 41 -

reduction in costs of bidding documents could increase the number of bidders by 5 percent or invite 0.3 bidders more to public tenders.

The longer bid preparation period helps to enhance competition. The coefficient of _bidpreptime_ is significant and positive in the negative binomial model. The implied elasticity is 0.64. Thus, if the bid preparation period is extended by 10 percent, 6 percent more bidders would apply.

According to the first stage regression of the instrumental variable (IV) estimator (presented above), more bidders would participate in the competition, if more firms are attracted at the initial stage of the public procurement. The coefficient of _bdnum_ turned out to be significantly positive. This confirms a basic principle of the public procurement systems: The competition should open and nondiscriminatory with free entry. It is important to invite as many firms as possible to public tenders at the beginning.

Larger contracts are more attractive to potential bidders. It is not straightforward to assess the scale effect in our model, because the size of contracts is measured by more than one variable. But when focusing on the marginal effect of engineering cost estimates, the coefficient is 0.36. A 10 percent increase in cost estimates, holding everything else constant, would result in a 3-4 percent increase in bidder participation. It means that too small contracts are not profitable enough to motivate potential contractors to participate in public tendering. Recall that that the average contract amount is relatively small at about NRs 7.7 million or $110,000 in Nepal and that bidders have to bear some (implicit) entry costs regardless of whether they win or lose the contract.

Distance does matter to the bidder participation. According to our bid estimation results (Table 5), the firm location does not really matter to bid prices. But it does affect the bidders’ entry strategy. The first stage of the IV regression indicates that firms located far from a project location are less likely to decide to enter the market. The estimated elasticity is small at -0.06 but statistically significant. If the distance between a project site and potential
# **1. Introduction**

In a context of high global cashew prices and tight budget constraints, the Government of GuineaBissau introduced a surcharge on raw cashew exports, which came into effect in early May of 2011. This surcharge is designed to generate revenue for a concurrently-created Fund to Promote the Industrialization of Agricultural Products ( _Fundo de Promoção à Industrialização de Produtos Agricolas,_ FUNPI). The main objective of FUNPI is the support of the agro-industry, and specifically of the cashew processing industry. As argued in the 2011 draft Country Economic Memorandum, Guinea-Bissau needs to find efficient instruments to promote cashew processing as a way to promote value-added, to create jobs and to reduce poverty. However, the current surcharge can negatively affect poor farmers through lower farm-gate prices (while generating a large fund outside the budget with uncertain governance mechanisms). While the nominal farm-gate prices rose during recent years due to extremely high world prices, there is a general concern about the potential poverty impact of the tax in the presence of reduced world prices.

This paper assesses the impact of a change in the price of cashew received by exporters in general –and FUNPI in particular– on farm-gate prices and on poverty in Guinea-Bissau. In order to pursue this, we design a theoretical model of supply chains in export agriculture with three players – exporters, traders, and farmers– competing in a bilateral oligopoly fashion, adapted so that cashew processors interact as buyers in the final stage of the value chain. Then, we calibrate the model with data from the Bissau-Guinean cashew sector (to represent the market) and micro-level surveys (to study poverty). We use this model to answer four questions. First, we analyze the effect of i) a standard shock to export prices and ii) a tax on cashew exports on margins along the value chain, and within farmers along the income distribution. Second, we simulate the effects of increasing prices (either because of global shocks or of a tax like FUNPI), combined with improvements in competition in cashew trading. Third, given that one of the government’s goals is to stimulate cashew processing in Guinea-Bissau by using part of the raw cashews export tax revenues, we explore the impacts of an interaction between the FUNPI contribution and an increase in processing capacity. Fourth, we investigate the impacts an interaction between the FUNPI tax and the provision of services to farmers (for example, cash transfers to finance inputs such as seeds, fertilizers, etc.).

The paper is organized as follows. Section 2 presents some details on the cashew sector in GuineaBissau. Section 3 introduces a new model that captures interactions of bilateral oligopoly-like transactions among farmers, traders and exporters. This model is calibrated to the cashew sector in Guinea-Bissau and is used to run simulations, which are presented in Section 4. Section 5 analyzes the effects of FUNPI contribution and complementary policies on poverty using household survey data. Finally, Section 6 summarizes our findings and presents the policy implications.

2
process for the RAMV census and PEP, including whether each member had PEP (in any version), its issue date, perceived benefits, and reasons why they had registered in RAMV and PEP or had not. Next, the questionnaire included a labor module following the design of the Colombian Labor Force Survey ( _Gran Encuesta Integrada de Hogares_ ) to make it comparable to existing administrative data on monthly and weekly income; this module also collected data on labor history in Venezuela and Colombia. Fourth, the survey included a module on health and access to healthcare that included the EQ-5D-3L, a standardized scale used to assess health across different dimensions, including physical and mental health, via a Likert scale.<sup>9</sup> The final module offered information at household level on these dimensions: (i) migration, (ii) integration into Colombian society and connections with migrant networks, (iii) prosocial preferences, (iv) housing, and (v) expenditure and remittances.

The qualitative findings informed the survey design and data collection protocols. First, during the focus groups, forced migrants reported that although Venezuelans and Colombians both speak Spanish, there are important differences in everyday words and terms that make it difficult for Venezuelans to understand information from local authorities and NGOs. For this reason, Venezuelans reviewed the survey to ensure appropriate language usage. Second, forced migrants also reported high levels of mistrust because they fear deportation and are often targeted by scams and misinformation via text and social media. To build trust and enhance participation, all surveys were administered by Venezuelan enumerators, many of them forced migrants themselves. Furthermore, Venezuelan migrant organizations disseminated information on the objectives and scope of the survey.

On average, the survey was administered over an average of one hour and 40 minutes, and respondents received an incentive of 27,000 Colombian pesos (about $USD 9) for participating. As most forced migrants are excluded from the financial system, it was hard to deliver the incentives during data collection. For this reason, different delivery options ex-

> 9The questionnaire has been adapted to different settings including Colombia and Venezuela, and it has demonstrated appropriate psychometric properties and validity. The Spanish-language version adapted to the Venezuelan population was administered to elicit severe symptoms of anxiety and depression.

14
# **References**

- Alatas, Hamidah, Julia Granata, and Josefina Posadas. 2020. “Indonesia’s Occupational Tasks and Skills: From Occupational Employment Demand to Tasks and Skills Requirements.” World Bank, Washington, D.C.

- Caunedo, J., E. Keller, and Y. Shin. 2021. “Technology and the Task Content of Jobs across the Development Spectrum.” NBER Working Paper 28681, National Bureau of Economic Research, Cambridge, MA.

- CSC (Critical Skills Monitoring Committee). 2019. _Critical Occupations List 2018/2019: Technical Report_ . Kuala Lumpur: CSC.

- Cunningham, Wendy and Obert Pimhidzai. 2018. Vietnam’s Future Jobs: Leveraging Mega-Trends for Greater Prosperity. Washington, D.C.: World Bank.

- Cunningham, Wendy, Harry Moroz, Noël Muller, and Aivin Solatorio. 2022. “The Demand for Digital and Complementary Skills in Southeast Asia.” World Bank, Washington, D.C.

- Dierdorff, E., and J. Norton. 2011. _Summary of Procedures for O*NET Task Updating and New Task Generation_ . Report created and presented to the National Center for O*NET Development.

- Doan, Dung, Trang Luu, Nga Thi Nguyen, and Abla Safir. 2023. “Green Jobs: Upskilling and Reskilling Vietnam’s Workforce for a Greener Economy.” World Bank, Washington, D.C.

- DOL (United States Department of Labor). 2018. “O*NET Data Collection Program: Office of Management and Budget Clearance Package Supporting Statement Part A: Justification.” Washington, DC.

- Fleisher, Matthew and Suzanne Tsacoumis. 2012b. “O*NET Analyst Occupational Skills Ratings: Procedures Update,” Human Resource Research Organization, Alexandria.

- Frey, Carl Benedikt and Michael A. Osborne. 2017. “The Future of Employment: How Susceptible Are Jobs to Computerisation?” _Technological Forecasting & Social Change_ 114:254-80.

<mark>GSO (General Statistics Office). 2008.</mark> _<mark>List of Occupations 2008</mark>_ <mark>. Hanoi: GSO.</mark>

- Handel, Michael J. 2016. “The O*NET content model: strengths and limitations,” _Journal of Labour Market Research_ 49(2):157-76.

- Harvard’s Growth Lab’s. _Atlas of Economic Complexity_ . Available at <u>https://atlas.cid.harvard.edu/countries/239/export-basket</u>

- ILO (International Labour Organization). 2012. _International Standard Classification of Occupations: ISCO-08. Volume I_ . Geneva: ILO.

- Lewandowski, P., A. Park, W. Hardy, and Y. Du. 2021. “Technology, Skills, and Globalization: Explaining International Differences in Routine and Nonroutine Work Using Survey Data. _World Bank Economic Review (forthcoming)_ .

26
real-time traffic anomalies that arise from accidents, roadworks, and other disruptive events. Similar approaches have been used to develop an intelligent interface platform in Dublin (Daly et al., 2013) and a smartphone application in the United Kingdom (see Hampson et al., 2017) that provide real-time information on traffic congestion sources. In the case of the United a mobile known as Kingdom, application Twitraffic provides real-time traffic information from users’ tweets. On average, Twitraffic is believed to detect traffic incidents about 7.1 minutes before they are picked up by the UK government’s highway agency data (Hampson et al., 2017).

# C. Urban Sensing Technology

In addition to mobile and social sensors, credit cards, smart cards in public transit, retail scanners, and digital toll systems, are increasingly being used as “urban sensors” (Blazquez and Domenech, 2018). Data generated through the urban sensors can be used to measure broad patterns of urban mobility and to some extent traffic flows and congestion.

Transponders at toll plazas can be used to measure personal and commercial vehicle flows along a road segment (Kitchin, 2014), and eventually track variations in economic activities. For example, Askitas and Zimmermann (2013) explore data from an electronic toll collection system in Germany to generate a monthly Toll Index, which they use to nowcast business cycles. Smart cards in public transit, such as SmarTrip in the Washington metropolitan area and Oyster in London, can be used to track individual travel behavior, and generate useful information on economic and social outcomes. It is possible, for example, to characterize a cardholder’s commuting trajectory while generating information on his/her place of residence and employment status by combining smart cards with credit card data (Wang and Moriarty, 2018). Smart cards also allow for granular analyses of transport behavior. As an example, Tao et al. (2014) explore a large smart card database to examine the spatial-temporal dynamics of Bus Rapid Transit (BRT) sys5
**Table 2** <u>(number of specifications in parentheses)</u>

||-1|0|1|
|---|---|---|---|
|Overall results(140)|5.7%(8)|31.4%(44)|62.9%(88)|
|Sample type||||
|Developed (47)|6.4%|31.9%|61.7%|
|Developing (54)|7.4%|25.9%|66.7%|
|Mixed(39)|2.6%|38.5%|59.0%|
|Dependent variable||||
|Output (67)|1.5%|44.8%|53.7%|
|Output growth (24)|16.7%|29.2%|54.2%|
|Productivity (18)|5.6%|27.8%|66.7%|
|Other *(31)|6.5%|6.5%|87.1%|
|Independent variable||||
|Public Capital (65)|10.8%|40.0%|49.2%|
|Physical Indicator(75)|1.3%|24.0%|74.7%|
|Public capital||||
|Aggregate (48)|12.5%|41.7%|45.8%|
|Transport (9)|11.1%|33.3%|55.6%|
|Telecom (4)|0.0%|0.0%|100.0%|
|Energy (1)|0.0%|0.0%|100.0%|
|Water(3)|0.0%|100.0%|0.0%|
|Physical indicators||||
|Electricity (20)|0.0%|30.0%|70.0%|
|Roads (27)|0.0%|18.5%|81.5%|
|Telecom (17)|0.0%|29.4%|70.6%|
|Water (2)|0.0%|0.0%|100.0%|
|Sanitation (2)|0.0%|50.0%|50.0%|
|Synthetic (6)|0.0%|16.7%|83.3%|
|Other(1)|100.0%|0.0%|0.0%|
|Theoretical framework||||
|Prod function (69)|2.9%|36.2%|60.9%|
|Cross-country reg (29)|13.8%|37.9%|48.3%|
|Cost function (13)|7.7%|15.4%|76.9%|
|Growth accounting (4)|0.0%|25.0%|75.0%|
|Other (Firm-level survey) (18)|5.6%|27.8%|66.7%|
|household surveydata(7)|0.0%|0.0%|100.0%|
|Aggregation level||||
|Country (81)|4.9%|35.8%|59.3%|
|State / region / district (35)|5.7%|34.3%|60.0%|
|Industry (9)|11.1%|22.2%|66.7%|
|Firms /households(15)|6.7%|6.7%|86.7%|

* Poverty, inequality, individual income, child height, asset or product prices, exports, investment, etc.

Different types of dependent variables have been used, the main ones being output, growth and productivity. Only about half of the analyses using either out put or output growth have produced conclusive positive results, while about two thirds of the studies trying to explain productivity have done so. Finally, studies using some alternative dependent variable (see table footnote) have been much more successful in proving a positive association between infrastructure and development outcomes.

An important question that has been addressed in the literature is to determine the more appropriate proxies for infrastructure. These have either been some measure of public capital (i.e. investment in infrastructure, generally from public sources 20