rafmacalaba/datause-encoder-data huggingface dataset:
and tax evasion and responses from surveys implemented in 2002 and 2005. We find the responses to be highly correlated across the two years for the various variables. Similar data have been used by several papers including Svensson (2005) and Fisman and Svensson (2007). Hallward-Driemeier and Aterido (2009) examine how well firm responses in the ES surveys on questions related to obstacles in the business environment correspond to other data sources and find a high degree of correlation between firm responses and measured objective outcomes from external data sources.

The use of self-reported measures to study criminal behavior is very common in criminology. Several researchers (e.g. Chaiken and Chaiken 1982; Mande and English 1987; Homey and Marshall 1991) have shown that self-reports used to estimate the prevalence and frequency of offending among incarcerated adults provide more detailed data than do police and court records and cross- validation of these self-reports with formal records indicates a reasonable degree of validity in the responses of adult inmates (Marquis with Ebener 1981). Junger-Tas and Marshall (1999) report that despite problems related to sampling and international data collection methods, the reliability and validity of data from self-report surveys are higher than for police data collected within each particular country.

All of the above give us confidence that the ES surveys are an important first step in understanding firm’s illegal activities. As a measure of bribe payments we construct the variable, **Bribes** , which are firm responses to the question – “ _What percent of annual sales value does a typical firm like yours spend on gifts or informal payments to public officials to “get things done” with regard to customs, taxes, licenses, regulations, services etc?_ ”<sup>19</sup> The variable, **Tax Evasion,** is constructed from firm responses to the following question – “ _Recognizing the difficulties many enterprises face in fully complying with taxes and regulations, what percentage of total sales would you estimate the typical establishment in your area of activity reports for tax purposes?”_<sup>20</sup>

> 19 While this is a general variable proxying for the extent of corruption in the economy, in separate questions, the survey also asks firms to report on bribes paid to specific government agencies (tax inspectorate, labor and social security, fire and building safety, sanitation, police, and environmental). All our results exploring the link between corruption and tax evasion are robust to using these alternate measures to examine a sample of firms that report paying bribes in general but not to tax officials.

> 20 Since informal firms often misreport taxes, an existing economics literature on informality uses this variable as a measure of the extent of informal or unofficial activity in the economy (e.g Friedman et al., 2000; Dabla-Norris et al., 2008; Gatti and Honoratti, 2008; La Porta and Shleifer, 2008), However, since our sample consists entirely of

14
# **Analysis of red flags: definition, interpretation and outcomes**

**To develop a list of red flags to analyze, we took a sub-sample of flags from recent publications** including Ware (2006), World Bank (2007) and World Bank (2006).<sup>3</sup> For the purposes of this project, the flags in these publications can be allocated into four categories:

1. _Unobservable red flags_ . These are red flags that might be unobservable in the course of normal supervision (let alone from a desk review).  Many such ‘red flags’ might be better viewed as suitable questions to be asked during an investigation of credible allegations of corruption.  Some are not so much red flags as direct evidence of fraud or collusion or strong circumstantial evidence of corrupt activities.  Examples from our sources might include:

- Identification of pressures on Bid Evaluation Committee members.

- Falsification of curricula vitae in consultant services proposals.

- False certification/ information of contractor - size of business certification, financial capabilities.

- Government personnel or their families acquiring a financial interest or employment in a contractor or subcontractor.

2. _Uncollectible red flags_ . These are flags that are difficult to evaluate using objective criteria and/or are difficult to detect in the course of a desk review, even though many should be observable during the course of standard supervision. Examples include:

- Advertising in local or regional publications with small local circulation, or national gazettes that have a restricted circulation.

- Excessive prior experience requirements that are disproportionate when compared to the size of tendered goods/ services.

- Failure to answer requests for clarification in good time.

- Failure to provide secure storage of, and restricted access to, bids received.

3. _Potentially irrelevant red flags or “red herrings”_ . The warning signs in this group tend to be found in a great number of contracts, but are perhaps more likely indicators of weak reporting or confusing Bank procurement requirements. Examples include:

- Failure to include the names of the Bid evaluation commission in the bid evaluation report.  This was observed in 20 out of 55 contracts in our database, but it appears to be a peculiarity of local procurement practices rather than deliberate exclusion.

> 3 The LAC publication “ _Is your project at risk_ ” also highlights important warning signs which may appear throughout the project cycle, however the language was difficult to convert into objective measures simple to evaluate during a desk review.

6
10 pressure grew. In 2008, pellets exports to the EU had regrown to 989,000 tonnes, still only ten percent of the 1989 peak (COMTRADE, accessed 03 December 2009).

# <u>China</u>

The demand for imported cassava chips into China reflects the related desire by the Chinese government for food self-sufficiency, the growth in the demand for meat by consumers, and the development of its bio-fuels sector. As we saw above, Chinese policy privileges the production of foodgrains for domestic consumers, which means that the requirements for animal feed and feedstock for bio-fuels have increasingly had to be met via imports. This has led to the growing importation of soya products from Latin America and cassava from Thailand. Alternative feedstocks into biofuels such as molasses have been discouraged due to environmental concerns (OAE, 2006).

The bulk of Thailand’s cassava exports to China occurs in the form of dried cassava (cassava chips rather than pellets), and is used as an input in the production of biofuels (Table 1). These imports began at a small scale in the mid 1990s, but grew rapidly in the 2000s, following a wheat harvest failure in 2001 and the liberalization of trade barriers in agricultural products under the China ASEAN Free Trade Agreement (FTA) in 2003. The Early Harvest Programme (EHP) of this FTA necessitated the removal of a six percent tariff previously imposed by China on Thai cassava products, boosting their price competitiveness.

# **Table 1: Thai Cassava Exports** **<u>to China (millions US$)</u>**

||**2002 **|**2003**|**2004 **|**2005 **|**2006**|**2007 **|**2008**|
|---|---|---|---|---|---|---|---|
|**Total ($m)**|109.02|137.44|236.74|329.87|474.81|417.62|288.30|
|||**_Of_**|**_which (_**|**_%)_**||||
|**Dried cassava**|94.10|94.66|90.04|89.44|87.61|83.26|72.57|
|**Cassava starch**|5.90|5.34|9.96|10.56|12.39|16.74|27.43|

_Source:_ COMTRADE, accessed 26 November 2009 China’s demand for starch as an intermediate input into non-food industries has increased rapidly in recent years and the share of starches in cassava imports from Thailand grew from only six percent in 1998 to 27 percent in 2008.

# <u>Korea</u>

South Korea mostly imports residue pellets made out of starch waste with a low starch content of 55 percent, compared to the pellets imported by the EU, which are made mostly (70% to 80%) from chips and which has 65 percent starch content. Korea is a very new entrant into the Thai export market, importing virtually nothing until 2007, and then taking 16 percent of dried cassava imports (mostly low-grade i.e. residue pellets) in 2008.

# **_2.3. Changes in Market Destinations and Product Composition_**

Two major changes can be observed in the export composition of Thai cassava products. The first is the change in destination. The Thai dried cassava industry– essentially a “creation” of the EU CAP after 1962 - was potentially devastated after the reforms to EU agricultural policy in the early 1990s. This reform of the cereal price support system reduced the price competitiveness of cassava pellets imports as an animal-feed. Fortuitously for the Thai industry, the Chinese market began to grow
traditional areas (carrots) with sufficient mechanisms to recognize failures and quickly phaseout support (sticks); and (iii) _accountability_ , whether the intended beneficiary of innovation policy, society at large, has the information and ability to monitor the policymakers and bureaucrats to ensure that policy is responsive to its needs.<sup>14</sup>

# **III.2 Description of SBIRI and BIPP output achievements**

This subsection describes what we were able to find out about SBIRI and BIPP. The analysis is largely descriptive, given that available DBT data coupled with interviews with DBT staff, DBT partners and beneficiaries were not sufficient to allow an assessment of the benefit-cost of resources spent.

# **_(1) SBIRI: Early-stage funding of SMEs_**

The first PPP initiative created by DBT for domestic early-stage technology development and commercialization, launched in 2005, was the Small Business Innovation Research Initiative. It was modeled on the US SBIR (Small Business Innovation Research) program. A stated objective was to attract a greater number of SMEs to accelerate innovation by engaging in peer-reviewed quality research. Box 3 describes eligibility criteria for public support.

## **Box 3: Funding structure of SBIRI**

SBIRI is open to SME Indian registered and majority-owned enterprises (start-ups and existing firms) that have an in-house DSIR (Department of Scientific and Industrial Research)-certified R&D unit, groups of such firms, and collaborations of such firm(s) with public R&D institutions. The enterprise must not have more than 500 employees engaged in R&D.<sup>15</sup> SBIRI operates in 2 phases, focusing on early-stage, pre-proof of concept funding:

|Phase|Description and funding|
|---|---|
|I<br>II|For establishment of pre-proof of concept of innovation<br><br>80% grant for project costs up to Rs 2.5 million (about $50,000)<br><br>50% grant up to a maximum of Rs 5 mn for larger projects, with interest free<br>loans up to 50% of remainder amount for total project costs exceeding Rs 10 mn<br>For product and process development|
||<br>Soft loan with 1% simple interest rate for project cost up to Rs 10 mn<br><br>Soft loan with 2% simple interest rate for project cost up to Rs 100 mn (about $2<br>million)<br><br>Fullgrant to cover R&D costs ofpublic R&D institutions.|

SBIRI established a transparent, structured and time-bound evaluation process for the review of grant and loan applications. SBIRI widely advertised the 15 completed funding announcements to mid-2011, typically open for two months. The first call for proposals closed at end-October 2005, there were two in 2006, and subsequently three announcements

> 14 See Rodrik (2007). The authors are grateful to John Speakman for highlighting the relevance of this threepoint test for good policy design for evaluating these programs.

> 15 Since the size limit of 500 employees applies only to the enterprise‘s DSIR-certified R&D unit, the overall size of supported enterprises can in principle be very large, significantly larger than any definition of SMEs.

20
**Figure 8a: Income share on subsistence consumption by LAC urban quintiles (current US$)**

<!-- Start of picture text --> 10%<br>9%<br>8%<br>7% Quintile 1<br>6% Quintile 2<br>5% Quintile 3<br>4% Quintile 4<br>3% Quintile 5<br>2%<br>1%<br>0%<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19<br>Cost of subsistence consumption (US$/m)<br>Source: Own elaboration from LSMS household survey data<br>Figure 8b: Income share on subsistence consumption by LAC urban quintiles (PPP)<br>10%<br>9%<br>8%<br>7% Quintile 1<br>6% Quintile 2<br>5% Quintile 3<br>4% Quintile 4<br>3% Quintile 5<br>2%<br>1%<br>0%<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19<br>Cost of subsistence consumption (US$/m)<br>Percentage of household income<br>Percentage of household income<br><!-- End of picture text --> Source: Own elaboration from LSMS household survey data

**Figure 8b: Income share on subsistence consumption by LAC urban quintiles (PPP)**

Source: Own elaboration from LSMS household survey data 19
# **What Is the Impact of Weather Shocks on Prices? Evidence from Ethiopia**

Ruth Hill<sup>*</sup> and Habtamu Fuje<sup>†</sup>

**Keywords** : Weather shocks, grain markets, seasonality, transfers, roads JEL Classification: <mark>R11, F18, O13, J43</mark>

> * RHill@worldbank.org

> † Habtamu.Fuje@columbia.edu
**Table 7: long-term impacts of the 4P program on empowerment - socio-economic proxies and direct measures - ITT**

||Age at<br>Marriage<br>|Age at First<br>Birth<br>|Prevalence of<br>transactional sex<br>**W**|Generalized self<br>efficacy (raw<br>values 8-32)<br>**omen**|Generalized self-<br>efficacy (KKL<br>zscore avg)|Relative<br>Autonomy Index|
|---|---|---|---|---|---|---|
|ITT|0.933**|0.457**|-0.004|-0.118|-0.010|-0.044|
||(0.01)|(0.05)|(0.74)|(0.84)|(0.87)|(0.84)|
|Observations|176|299|437|436|436|428|
|Adj. R2|0.04|0.00|-0.00|-0.00|-0.00|0.05|
|Control mean|20.650|20.138|0.023|22.470|0.053|3.072|
|||||**Men**|||
|ITT|0.260|||0.367|0.039|-0.179|
||(0.56)|||(0.54)|(0.54)|(0.52)|
|Observations|89|||435|435|408|
|Adj. R2|0.09|||0.04|0.04|-0.01|
|Control mean|21.694|||22.350|0.039|2.076|
|P-values inparent|heses: *p<0.1 **|p<0.05 ***p<0.01|||||

30
(Government of Jordan, 2015). Humanitarian assistance supported by the United Nations, including UNHCR and the World Food Programme and international donors such as USAID, the International Rescue Committee, and CARE, is also a crucial element of support to refugees in Jordan. A significant component of this assistance is cash-based programming. By 2018, Jordan was UNHCR’s second largest cash operation worldwide (after Lebanon) (UNHCR, 2019b), providing US$98 million in assistance to over 435,000 refugees (Brown et al, 2019).

In 2016, Jordan became the first country in the Arab region to facilitate Syrian refugees’ access to the labor market (ILO, 2019). The Jordan Compact sought to advance long-term solutions through promoting access to education and to employment opportunities for Syrian refugees – international donors pledged $700 million in the form of grants, $1.9 million in concessional loans from the Multilateral Development Banks at concessionary rates, trade benefits with the European Union, and investments in the Jordanian economy (OECD, 2016). The payment of grants and loans was tied to specific targets for refugees that included the provision of an additional 50,000 school places in 2017, the issue of 200,000 work permits by 2020 (United Nations, 2016), and employment quotas in particular sectors (Kelberer, 2017; Barbelet et al., 2018). In 2021, the Jordanian government issued a record 62,000 work permits to Syrians, increasing opportunities for employment (UNCHR, 2022a). Work permits have increased wages and employment stability for Syrian refugees. In 2020, 25 percent of Syrian refugees with a work permit had a written contract with their employers and were covered by social security, compared to 9 percent of those without a permit (Stave, Kebede, & Kattaa, 2021).

Although the number of Syrians with work permits has increased, they still mainly find jobs in the informal sector since there is reportedly a mismatch between their skills profiles and the sectors in which they are allowed to work (Barbaret, Hagen-Zanker, & Mansour-Ille, 2018). Interview and survey data of Syrian refugees further revealed that a majority of workers, including those with higher skill levels, felt unsafe in their jobs due to frequent harassment, low bargaining power, harsh labor conditions, and little agency to change their situation due to fear of the authorities (IRC & Airbel Center). In this context, women are still disadvantaged. Given the direct and indirect costs of acquiring work permits (such as the direct cost of the transaction, the costs of providing all required documents at different institutions, transportation costs to complete those transactions, time and resources forgone that would have been used to work or care for children), it is often only male family members that are in a position to apply for work permits. Refugee women’s options in the labor 7
We assume that distance, scale economies and policy barriers jointly determine costs for shipping good _k_ between country-pair _o_ and _d_ by affecting marginal costs or mark-ups. This gives rise to the following reduced form equation, which incorporates all principal determinants previously discussed: The dependent variable _mtc_ odk represents the per-unit cost (in dollars per tonne) paid by the service’s consumers, including the price of the transport, insurance costs and cargo handling but excluding customs charges. The product index _k_ corresponds to containerizable goods disaggregated at the 2-digit level of the Harmonized System (HS) classification.<sup>9</sup> In order to obtain the most precise price data, we restrict the sample to two importing countries (and all their trade partners) which report import values in CIF _as well as_ “value for duty,” thereby allowing us to compute ‘true’ MTCs and sidestepping the problems afflicting CIF-FOB ratios (Hummels and Lugovskyy, 2006).<sup>10</sup> We use the data computed by Korinek (2011) who uses Hummels’ (1999) methodology to compute transport costs. The _distanceod_ variable measures maritime distance between the two main container ports of trading partners, corresponding to the shortest way by capes, straits or canals expressed in nautical miles (AXS Marine, 2010).

Our main interest lies in the policy measure _STRIod,q_ which represent dummy variables corresponding to quartiles of the bilateral liner shipping STRI score. In addition, we control for the presence of cargo reservations ( _CRod_ ) and liner conferences ( _LCod_ ) on a particular route as well as the terminal fees ( _TermCostsod_ ) at origin and destination ports, respectively.

We include full sets of origin, destination, and commodity fixed effects (θo, λd and δk). It is important to emphasize that the coefficients of interest in equation (1) are identified off

> 9  Following the OECD Maritime Transport Costs Database, we assume that containerisable cargo corresponds to all lines except 10, 12, 15, 25-29, 31, 72, and 99 in the Harmonized System (HS) disaggregated at 2-digits. 10  “Value for duty” is defined by Statistics New Zealand as the value of imports before the addition of insurance and freight costs.  The equivalent statistical concept in the United States is “Customs value” which is defined as the price actually paid or payable for merchandise when sold for exportation to the United States, excluding U.S. import duties, freight, insurance, and other charges incurred in bringing the merchandise to the United States.  In contrast, the CIF value of imports is the actual cost paid by importers, so that the difference of both valuations yields a measure of ‘true’ maritime transport costs.

10
In what follows, the paper describes the methodology for this study and the empirical results. We then review the deployment and uptake of ICT in LAC and compute measures of relative distortions. Section 3 describes economic framework that allows us to gauge how removing distortions can lead to competition and productivity improvements in the ICT sector. Section 4 looks at the empirical links of _de jure_ and _de facto_ policies on productivity outcomes in Peru. It maps firm-level data to regulations and enforcement data at the four-digit level where markets are delineated as segments of the ICT sector. Collection of regulation and enforcement data is important for studying how regulations may improve growth and reduce market distortions.

# **2 ICT Distortions in LAC**

The uptake of computers and the internet is evidence of the importance of ICT for both work and personal activities. Computer and internet penetration rates have increased across the globe. According to the International Telecommunication Union (ITU), in 2020 the share of LAC households that own a computer ranged from a high of 68% in Uruguay to a low of 17% in Honduras (ITU (2020)). The rates are slightly higher for access to the internet (households that do not own a computer may own a cellphone with internet capabilities; see Figures 1 and 2). ICT adoption varies widely in LAC: Uruguay, Argentina, and Brazil have the highest adoption rates; Honduras, Nicaragua, and Guyana the lowest (Figure 3).

Unfortunately, the benefits of ICT are not shared between and within countries in LAC. For instance download speeds in some countries are slow relative to peers and developing countries generally (Figure 4).

Slow uptake of ICT could be the result of several factors, including the current development context and the regulatory environment of each country. The ITU’s ICT Regulatory Tracker provides comparable scores for both regulatory and competition frameworks (ITU (2020)). The Tracker traces private vs. public ownership, the degree of competition for broadband, basic and leased line services, and whether the law recognizes the concept of significant market power. These are classified as G1, which cannot be considered a competition model, to G5, which features independent regulators, a pro-competition environment, and working together across agencies and firms to improve the deployment of ICT. The ITU scores each country according to these criteria. Table 1 summarizes the set of scores for Peru as an example of the different measurements.

LAC seems representative of a competitive environment. Figure 5 ranks the competitiveness of ICT from a monopoly (score = 4) to one that is fully competitive (score = 1). Because these scores are based on self-reporting, they do not necessarily relate to outcomes. Despite seeming to be pro-competition, in terms of costs LAC is more discouraging. The 4