rafmacalaba/datause-encoder-data huggingface dataset:
determinant of exports to this market in the context of the treatment effects regressions, where the probability of being eligible for preferential access is endogenously determined by U.S. geostrategic interests. The significance of distance reappeared in the Heckman-selection models where program utilization is determined by U.S. non-economic motivations, but in this case the estimated effect of the Andean trade preferences was not significant.

Of particular current interest is the finding that FTA members have significantly larger exports to the U.S. compared to excluded countries. And this effect seems to be larger than the also positive effects of CBI, ANDEAN, and AGOA. Another important finding is that the estimated benefits of preferential market access programs decline considerably when we take into account the rules of origin requirements and other bureaucratic barriers by using utilization ratios in the estimations. GSP is the only exception in terms of providing benefits to beneficiaries and seems to have negative effects in several specifications.

Among the interesting methodological conclusions, the presence of zeroes in the productlevel exports data tends to produce severe biases in linear estimations. These biases are particularly influential on the estimated coefficients of some preferential programs, AGOA in particular, and they also severely biased the impact of distance in the OLS linear regression. Thus truncated data techniques seem to be more appropriate in this type of application. The inclusion of a direct proxy for international transport and transactions costs seems to be quite important in these models. Indeed, all of our estimates of the impact of these costs on trade volumes indicate that they are economically and statistically important. Moreover, in the treatment regressions, which also control for the endogeneity of program eligibility, the distance variable became statistically irrelevant, whereas the direct proxy of international transport and transactions costs remained robustly significant with economically large coefUndoubtedly, there is need for further analysis of the effects of preference programs and more to be learned from gravity models in this context. One main limitation of this study is the focus on U.S. programs. The European Union has similar reciprocal and unilateral 20
Mehta, B. S., Awasthi, I. C., Mehta, B. S., & Awasthi, I. C. (2019). Measurement of Women’s Work. _Women and Labour Market Dynamics: New Insights and Evidences_ , 33-54.

- <mark>Muller, M., & Sousa, L. D. (2020).</mark> _<mark>“She Helps Me All the Time”: Underestimating Women's Economic Engagement in Rural Honduras</mark>_ <mark>. The World Bank.</mark>

- ONEC (2012). Encuesta de Hogares de Propósitos Múltiples - Preliminar 2022 [Data set]. October 2023 - Oficina Nacional de Estadística y Censos. https://onec.bcr.gob.sv/Repositorio_archivos/

<mark>UN Women (2023). Empoderamiento económico de las mujeres. URL:</mark> <u><mark>https://elsalvador.unwomen.org/es/nuestro-trabajo/empoderamiento-economico</mark></u> <mark>Accessed on October 25, 2023.</mark>

- <mark>Urminsky, O., Hansen, C., & Chernozhukov, V. (2016). Using double-lasso regression for principled variable selection.</mark> _<mark>Available at SSRN 2733374</mark>_ <mark>.</mark>

- <mark>World Bank (2011).</mark> _<mark>World Development Report 2012: Gender Equality and Development.</mark>_ <mark>Washington, DC: World Bank.</mark>

<mark>World Bank (2023). Gender Data Portal. URL:</mark> <u><mark>https://genderdata.worldbank.org/indicators/sl-uem-zs/?gender=total. Accessed on</mark></u> <mark>October 25, 2023.</mark> 27
# **5.2.Instrumentation of Knowledge in the Production Function**

<mark>Our procedure consists of using generalized method of moments (GMM) to re-estimate the equation presented in column 4 in table 1. As mentioned, we use as instruments both the dummy indicating knowledge of public support for innovation and the sector-region-size intensity of past perceived barriers to import key goods for innovation. IV results are presented in table 2. Column 1 presents the first-stage results and column 2 presents the GMM results (which repeat the specification in column 7, in table 1). First-stage results reveal the expected signs and significance for both instruments.</mark> <mark>The test for weak instruments (Montiel-Pflueger test) (Olea and Pflueger 2013), which considers the clustered error structure of the models, gives evidence of rejecting weak instruments hypotheses.</mark><sup>28</sup> <mark>In addition, because our IV model is overidentified—that is, the number of instruments exceeds the number of endogenous regressors—we can test whether instruments are uncorrelated with the error term by means of a Sargan (1958) test. In this procedure, a significant test statistic represents either an invalid instrument or an incorrectly specified structural equation. Table 2 shows that the statistic is not significant, supporting the validity of our instruments.</mark>

**Table 2. Instrumental Variables Estimation**

||(1)|(2)|
|---|---|---|
|Variables|First Stage|GMMestimates|
|Prop. of firms affected by|0.89***||
|barriers to imports|(0.27)||
|Knowledge of financial programs|0.22**<br>(0.094)||
|RD&D||0.097**|
|||(0.048)|
|Capital|0.13|0.31***|
||(0.11)|(0.042)|
|Labor|-0.21**|-0.014|
||(0.089)|(0.048)|
|Missing dummy|-0.27*|0.066**|
||(0.15)|(0.029)|
|Constant|0.0049|0.30***|
||(0.11)|(0.028)|
|Observations|2,949|2,949|
|R-squared|0.010||
|**Overidentif restric test**|||
|Sargan statistic|1.79||
|Pvalue|0.1809||
|**Weak IV test**|||
|M-P Statistic|8.825||
|tau=5% CriticalValue|8.48||

_Note:_ Standard errors are clustered at the sector-size level. For weak IV test, tau = % of worst case bias. RD&D = research design and development.

> 28 The test rejects the null hypothesis that the Nagar bias of the second-stage coefficient of RD&D exceeds 5% of the “worst case bias”: that is, the case in which instruments are completely uninformative and first and second stage errors are perfectly correlated.

21
|Liberia Renewable EnergyAccess Project|Zubair K.M. Sadeque|
|---|---|
|Liberia Renewable EnergyAccess Project|Zubair K.M. Sadeque|
|Liberia Renewable EnergyAccess Project|Zubair K.M. Sadeque|
|Liberia Renewable EnergyAccess Project|Zubair K.M. Sadeque|
|Liberia Renewable EnergyAccess Project|Zubair K.M. Sadeque|
|Liberia Renewable EnergyAccess Project|Zubair K.M. Sadeque|
|Liberia Renewable EnergyAccess Project|Zubair K.M. Sadeque|
|Support for Promotion of Sustainable Livelihoods in the Pool Dep|artKaori Oshima|
|Support for Promotion of Sustainable Livelihoods in the Pool Dep|artKaori Oshima|
|Support for Promotion of Sustainable Livelihoods in the Pool Dep|artKaori Oshima|
|Transit Corridor Improvement Project|Nijat Valiyev|
|Transit Corridor Improvement Project|Nijat Valiyev|
|Transit Corridor Improvement Project|Nijat Valiyev|
|Transit Corridor Improvement Project|Nijat Valiyev|
|Transit Corridor Improvement Project|Nijat Valiyev|
|Transit Corridor Improvement Project|Nijat Valiyev|
|Transit Corridor Improvement Project|Nijat Valiyev|
|Transit Corridor Improvement Project|Nijat Valiyev|
|Transit Corridor Improvement Project|Nijat Valiyev|
|Transit Corridor Improvement Project|Nijat Valiyev|
|Transit Corridor Improvement Project|Nijat Valiyev|
|Southern Sudan EmergencyFood Crisis Response Project AF IV|Abel Lufafa|
|Southern Sudan EmergencyFood Crisis Response Project AF IV|Abel Lufafa|
|Southern Sudan EmergencyFood Crisis Response Project AF IV|Abel Lufafa|
|Southern Sudan EmergencyFood Crisis Response Project AF IV|Abel Lufafa|
|Southern Sudan EmergencyFood Crisis Response Project AF IV|Abel Lufafa|
|Southern Sudan EmergencyFood Crisis Response Project AF IV|Abel Lufafa|
|Niger River Basin Management Project|Pierrick Fraval|
|Niger River Basin Management Project|Pierrick Fraval|
|Niger River Basin Management Project|Pierrick Fraval|
|Niger River Basin Management Project|Pierrick Fraval|
|Niger River Basin Management Project|Pierrick Fraval|
|Niger River Basin Management Project|Pierrick Fraval|
|Niger River Basin Management Project|Pierrick Fraval|
|Niger River Basin Management Project|Pierrick Fraval|
|Niger River Basin Management Project|Pierrick Fraval|
|Niger River Basin Management Project|Pierrick Fraval|
|Niger River Basin Management Project|Pierrick Fraval|
|Kenya Statistics Program for Results|Utz Johann Pape|
|Kenya Statistics Program for Results|Utz Johann Pape|
|Kenya Statistics Program for Results|Utz Johann Pape|
|Kenya Statistics Program for Results|Utz Johann Pape|
|Kenya Statistics Program for Results|Utz Johann Pape|
|Kenya Statistics Program for Results|Utz Johann Pape|
|Kenya Statistics Program for Results|Utz Johann Pape|
|Kenya Statistics Program for Results|Utz Johann Pape|
|Kenya Statistics Program for Results|Utz Johann Pape|
Figure 24. Skill Shares Pre- and Post-TA Relative to Non-TA Firms

|15.9388S-<br>I<br>-2<br>SHAREO|F HIGHLY SKILLED WORKERS|I<br>2<br>AND ADOPTION|
|---|---|---|
|71.0987<br>22.4253<br>-2<br>SHARE OFSKI|time<br>LLEDAND SEMI-SKILLEDWOR|2<br>KERS ANDADOPTION|
|859857<br>o<br>20.3028-<br>-2<br>SHARE|~~~~~~<br>0<br>S<br>time<br> OFUNSKILLEDWORKERSAN|<br>2<br>DADOPTION|
**Figure 12. Effective tax rates faced by firms**

<!-- Start of picture text --> 80.0<br>60.0<br>40.0<br>20.0<br>0.0<br>0 to 1 10 to 50 to 100 to 500 to<br>25 75 250 1000<br>Net income reported on 2004 tax statements (million MNT)<br>% of reported income)<br>Gross tax rate (CIT paid as<br><!-- End of picture text --> Source: World Bank (2006b).

Corruption in Mongolia has worsened since 2001, according to Transparency International and the World Bank’s Governance Indicator, and perceptions of corruption as an obstacle to growth are more widespread in Mongolia than in a number of comparator countries. Mongolia ranks 9<sup>th</sup> out of 62 countries, for which firm level survey data were available, in terms of its share of firms reporting corruption as a major obstacle to their growth (World Bank 2006b). Mongolia’s Country Policy and Institutional Assessment (CPIA) score on transparency, accountability and corruption of the public sector is much lower than that of most countries in East Asia.<sup>60</sup> But Mongolia’s rank on the Transparency International Corruption Perception Index is 3, slightly better than that of Fitch’s ‘B’ median of 2.9.

Corruption appears to be a major impediment to large, successful firms in Mongolia, especially those based in the capital city (World Bank 2006b). The process of obtaining permits is nontransparent and many companies have to employ expensive mediators whose job is to negotiate the process of obtaining a permit. Inspectors, most commonly from the Tax Offices of the Government and the State Professional Inspection Agency, visited firms frequently. According to calculations in World Bank (2006b) the total cost of fines, fees and unofficial payments made up over half of the direct financial costs incurred by firms.

More than half of the firms surveyed by the World Bank as part of the 2006 Investment Climate Assessment said that they are expected to make an unofficial payment to government officials, which on average is over 4 percent of the value of a government contract. This average payment is higher than in most of the countries for which such data exist (World Bank 2006b). An even larger share of firms surveyed (64 percent) are concerned with the openness and fairness of the bidding process. There is little understanding of conflict of interest issues, and although the Law on Civil Service asks for income and asset declaration by public officials, the law has not been enforced. In addition, the perception of nearly half of the firms surveyed is that individuals with

> 60 Only Tonga and Lao, PDR have scored lower than Mongolia on this dimension of the CPIA ratings.

32
Tanzania (HBS 2018) or Uganda (UNHS 2016), for example, include these questions in their questionnaire, but the data is missing for virtually all HHs.

Likewise, variables on cultural adequacy, a criterion outlined in the CESCR, are largely absent in HCES surveys. Further, cultural adequacy is too context specific for a study that aims at crosscountry comparability and largely depends on HHs’ preferences, which are often not captured in available micro-level data.

Finally, on affordability, we are limited by the dearth of information from HCES surveys on housing expenditure in a consistent manner. To account for this important element, we propose pursuing a twofold approach: first, estimate the adequacy of a given housing unit and the related housing deficit and then (in future research) assess if alternate adequate housing is affordable to HHs based on existing housing finance conditions, HHs incomes, and comparable house price data.<sup>7</sup>

# **_3.2 Data: Multi-Purpose HH Surveys_**

For the measurement of these seven adequacy dimensions and complying with our aim to construct a micro-founded _Housing Adequacy Index_ , we rely on the 64 countries’ most recent multi-purpose household surveys.

## 3.2.1 Household Consumption and Expenditure Surveys (HCES)

Our main data sources are household consumption and expenditure surveys (HCES) which are often carried out with support of the Living Standard Measurement Study (LSMS) program that was initiated by the World Bank Group in 1980. Many HCES, which are usually national representative surveys, collect data on a core set of questions on household consumption, expenditures, and income. The principal implementing agency of these surveys is usually the national statistical office (NSO). Most of these HCES have by now become multi-topic and multilevel surveys using several questionnaires to measure different aspects of living standards. While unique in their efforts to collect representative information on HHs’ living standards, HCES might unintentionally undersample unregistered or vulnerable populations which are often excluded from the standard census on which HCES often rely for sampling. As a result, certain population groups might be inadvertently omitted or underrepresented from surveys including the homeless, internally displaced people, refugees, informal slum dwellers, or nomadic populations (e.g., CarrHill 2013; Lucci et al. 2018). While the _AHI_ is bound by the available micro-level data, we undertake robustness checks, to account for this potential underrepresentation of e.g., slum areas in the HCES.

7 A draft working paper is available from the authors on request.
Considering that Brazil is a megadiverse country, the level of research and development activity involving prospecting the potential of this immense gene pool is either insufficient, or continues to be carried out with impunity by biopirates. In contrast, the regulatory procedures established by law and the considerable delays and transactions costs involved in securing permits have impeded the generation of royalties and taxes from potential bioprospecting discoveries and associated technical innovation. Cases of accusations of biopiracy, including apprehensions of collections and preventive arrests over a long period effectively paralyzed scientific work focused on native biodiversity with traditional populations. Business projects based on biodiversity have been similarly affected (of a total of 86 permits granted by the genetic research and development board CGEN as of August, 2009 (Figure 1), only 17 requests, of which 6 were renewals, were presented by companies); and only three private companies – Natura, Extracta and Quest Pharmaceuticals – had secured approvals for access. Embrapa – the national agricultural research corporation – submitted a similar number of requests for permission. At the same time, over 150 requests for such approvals were still on the docket as of November 2011 (May & Vinha, 2013).

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>2003 2004 2005 2006 2007 2008 2009<br>Research Bioprospecting<br>Traditional Knowledge Ex Situ Germplasm Bank<br><!-- End of picture text -->

**Fig. 1** . Biodiversity use licenses authorized by CGEN by purpose: 2003–2009. Source: May & Vinha, in press; www.mma.gov.br, CGEN, _processos autorizados_

All evidence indicates that the complex procedures and the delay in granting permits have discouraged innovation in the biotechnology sector based on natural assets, which encourages more R&D based on biochemical synthesis. Other companies in the cosmetics and essential oils sector have expressed a lack of interest in subjecting themselves to CGEN administrative procedures, since going through such procedures for obtaining an access permit publicizes 17 | P a g e
according to the post-reform rules in a complex assessment process carried out by a team of physicians and rehabilitation experts. Individuals whose health impairment was classified higher than 40% during this review retained eligibility to benefits. Mainly due to capacity constraints, it took several years to undertake all the reviews, so the process was completed only in 2016.

About 18,000 beneficiaries (9% of the reassessed beneficiaries and 5% of all beneficiaries) who underwent the review lost benefit eligibility. The total number of recipients decreased much more, from 473,000 in January 2012 to 355,000 in January 2017 (Hungarian Central Statistical Office, 2022), due to a large drop in inflows, that started from the early 2000s and gained new momentum after 2012. This drop in the number of beneficiaries after the reform suggests that while in principle the eligibility conditions (expressed as percent of health damage) did not change, the assessment process became more stringent. On top of the large drop in the number of beneficiaries, the benefits of 12,000 beneficiaries decreased in inflation-adjusted terms.<sup>2</sup> The pre-reform disability benefit categories were consolidated into two benefit programs called Disability Allowance and Rehabilitation Allowance. Beneficiaries not recommended for vocational rehabilitation became eligible for the Disability Allowance while those who were deemed able to return to the labor market following rehabilitation became eligible for the Rehabilitation Allowance, which was paid for up to 3 years and set at a much lower rate than the Disability Allowance.<sup>3</sup> Rehabilitation Allowance recipients were required to cooperate with the rehabilitation authority and fulfill obligations set out in the employment rehabilitation plan. At the same time, recipients over 62 years of age were reclassified as old-age pensioners.

Although the comprehensive reevaluation of a large subgroup of DI recipients is uncommon, it is not without precedent: the majority of DI recipients under age 44 were reassessed under more stringent rules in the Netherlands in 2004. Garcia-Mandicó, García-Gómez, Gielen and O’Donnell (2020) estimate that the reassessment removed 17 percent of beneficiaries from the program and reduced benefit income by 20 percent, on average. However, in contrast to the Netherlands, where support for labor market reintegration was substantially expanded between 1997 and 2002, beneficiaries in Hungary who lost part or all of their benefit received little support in returning to the labor market. The capacity of rehabilitation services at the time was very limited and intensive, personalized services were only provided

> 2Other factors also contributed to the drop in DI claims: the cohorts in their 50s where shrinking in size during this period, their level of education was increasing, and the economy was recovering.

> 3Those subject to the health review were also placed on this temporary allowance until the date of their review but received their original income until the review.

6
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" Ghana, Nicaragua, Albania (not including Tirana), and Vietnam were the countries in our sample where the use of unimproved water sources was most prevalent. In Pakistan, Jamaica, Kazakhstan, Kyrgyz Republic, and Bulgaria, very few households (even in rural areas) obtained water from rivers or streams. It is interesting to note that in countries where coverage of in-house taps was high, the number of households still relying on rivers, streams, or springs was not necessarily low. In Albania (excluding Tirana), for example, 32 percent of households used in-house water taps and yet 42 percent still relied on basic water sources (relatively few households use other improved sources). In Cote d'Ivoire, on the other hand, the majority of households obtained water from informal, private, or community sources. Although only a minority of households in Cote d'Ivoire had in-house taps at the time of the LSMS survey, few households relied on rivers or streams as their primary water source.

Figures 11 and 12 examine the relationship between income and household water source choice in the poolec urban and rural samples from all countries except Nepal.<sup>3 2</sup> In both urban and rural areas, a smaller percentage of the poorest households than households in other income deciles had in-house taps, and a greater percentage of the poor used informal, private, or community sources. In urban areas very few households at any income level were using a river or stream as their primary water (or drinking water) source. In rural areas between 20 percent and 30 percent of households in all but Cle richest deciles relied on unimproved water sources.

Water vendo.-s are an informal source that has recently attracted much attention in discussions of water service and the poor. Information about water vendors is available in four of our fifteen sample countries: Cote d'Ivoire, Ghana, Pakistan, and Nicaragua. Only 2.4 percent of households in these countries reported using water vendors as their primary source of drinking water.<sup>33</sup> Over 15

> 3' Even households with water connections may obtain water from more than one source. LSMS surveys generally ask only for the primary water source or drinking water source.

> 32 The Nepal LSMS does not permnit analysis of those households using basic sources.

> 33 Other households could be using vendors as a supplement to their primary water service.

16
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Figure 8: Estimated Relative Risk of Conflict Events for Locations in Figure 3, based on Model B3
_Total net transfers made_ is the difference between “Total transfers made” and “Total transfers received” defined above.

_Transfers made, biggest gift bf. Oct09_ is the same variable as _“_ Transfers made _”_ above with the sum of transfers restricted to categories in which the biggest gift was made before or in October 2009.

_Transfers received, biggest gift bf. Oct09_ is the same variable as _“_ Transfers received _”_ above with the sum of transfers restricted to categories in which the biggest gift was made before or in October 2009.

_Total net transfers made, biggest gift bf. Oct09 (MK)_ is the difference between “Transfers made, biggest gift bf. Oct09” and “Transfers received, biggest gift bf. Oct09” as defined above. _Has fixed deposit account_ is 1 if the respondent has a fixed deposit account with any bank, and 0 if not.

_Not interviewed in endline_ is 1 if the respondent was not interviewed and is 0 if the respondent was interviewed during the endline survey of July to September 2010.

40