rafmacalaba/datause-encoder-data huggingface dataset:
considered the post-treaty period. Since country dyads may sign more than one treaty, we also recorded the number of treaties signed until 2008 (end year of our study) or any other appropriate end year (e.g. if the country ceases to exist at a certain year prior to 2008). While it may be true that one successful agreement may preclude the negotiation of additional agreements, we contend that increased variability across time may require additional agreements between the protagonists to solve lingering environmental change. On a whole, we expect those country dyads governed by more treaties to exhibit additional resilience. Treaty data are retrieved from the Transboundary Freshwater Dispute Database (TFDD) international treaty database. In terms of variables, we used a dummy variable indicating the presence or absence of a treaty between dyad partners ( **_Treaty_** ), as well as a continuous variable indicating the number of treaties ( **_Number of Treaties_** ) signed between them.

# _<u>Basin Events of Conflict and Cooperation</u>_

To identify whether a river basin governed by a treaty tends to be more resilient to the impacts of water variability, we utilize a combined measure of conflict and cooperation. We use this proxy because river riparians most often exhibit both types of relations throughout time (Zeitoun and Mirumachi, 2008).<sup>4</sup> We develop our combined conflict-cooperation indicator based on the water events data stored in the International Water Events Database at Oregon State University (OSU), created under the framework of the Basins at Risk project (BAR) and covering the period 1948-2008 (Yoffe et al., 2003; De Stefano et al., 2010).<sup>5</sup> Each event includes a brief summary and source of information coded by date, country pair (dyad), basin, and issue area. Each event is also coded according to

> 4 Such a measure provides a comprehensive assessment of hydropolitical relations in the basin which could potentially be inaccurate if one only considered conflictive or cooperative behavior alone. For the sake of completeness, however, we also ran separate regressions using only cooperative events and only conflictive events. Results from these regressions were not qualitatively different from those obtained using the combination of both types of events.

> 5 Other databases utilized in large-n studies pertaining to conflict and cooperation over water include the Correlates of War (used by Tir and Stinnett, 2012) and Issues Correlates of War (used by Brochmann and Hensel, 2009). Despite their merits, the Correlates of War database pertains to all forms of militarized conflicts (so the reason for the conflict may not necessarily be water related) and the Issues Correlates of War only pertains to conflicting claims (contentious events) and research is still underway to collect claims data for basins/countries in Asia, Africa, and parts of Europe.

6
suggest that new export products account for a quarter of the value of the extensive margin of trade.

**Figure 6: New products by sector and by region in developing countries**

<!-- Start of picture text --> Sectoral distribution of the extensive margin<br>ASIA ECAMENA<br>LAC SSA<br>Agriculture Chemicals<br>Food Machinery<br>Machinery(P&C) Mining<br>Services Textiles<br>Wood<br>Source: Author's calculations using Comtrade data.<br><!-- End of picture text --> Figure 6 shows the importance of the different sectors at the extensive margin by regions. Several features emerge clearly from these graphs. First, the sectoral distribution of the extensive margin varies among developing countries. In particular, higher income developing countries such as those in ASIA and ECA have a higher share of their extensive margin on machinery than the rest of the countries. Second, agricultural products are more important for the countries in Latin America and Africa than for those in Asia and ECA+ MENA.

To conclude, these charts suggest that there are some clearly defined diversification patterns. That is, the intensive margin is the most important category for all regions; and within the extensive margin, geographical diversification seems to matter more for developing countries than it does for developed ones. Moreover, it seems that the poorer the region, the more important the geographic and product dimensions of the extensive margin. We acknowledge that these are partial results since other factors that affect export diversification are not controlled for in the charts. Undertaking these controls requires more formal theoretical and statistical procedures that will be presented in the following sections.

# **3. THE THEORY**

From the previous section we found that, although the intensive margin explains the bulk of the growth in exports for all countries in the sample, diversification seems to be non-trivial for developing countries and this in turn seems to be determined by–at least- the geographic region where the exporting country is, the size of the destination market and the directionality in trade. Thus, in this section we turn to a presentation of 10
**Figure 27: Current Account Global Imbalances**

<!-- Start of picture text --> 3%<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>-3%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>United States Oil Exporters Germany and Japan OCADC<br>China and EMA Rest of World Discrepancy<br>Percent of world GDP<br><!-- End of picture text --> Source: International Monetary Fund, World Economic Outlook.

Note: OCADC is composed of Bulgaria, Croatia, Czech Republic, Estonia, Greece, Hungary, Ireland, Latvia, Lithuania, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain Turkey, and the United Kingdom.; EMA is composed of Hong Kong SAR, China; Indonesia; Korea; Malaysia; Phiippines; SIngapore; Taiwan, China; and Thailand.

34
reading on the Programme for International Student Assessment (PISA) – as an indicator of educational quality (OECD 2016a, 2016b, 2016c).

_Market Efficiency._ To construct a subcomponent index for market efficiency ( _Effi_ ), we classify markets into output, financial, and labor markets. We select the World Bank Doing Business scores as an indicator of output market efficiency, which measure the regulatory environment in terms of ease for firms to start a business, trade across borders, register property, get credit, and the like (World Bank 2017a). We choose the International Monetary Fund (IMF) Financial Development Index as an indicator of financial market efficiency, which measures the level of financial development by including the size and liquidity of financial markets, ease for individuals and firms to access financial services, and the ability of financial institutions to provide services at low costs with sustainable revenues (Svirydzenka 2016). As indicators of labor market efficiency, we construct an composite index, using factor analysis, consisting of minimum wage (% of value added per worker), severance pay for redundancy dismissals (weeks of salary), and the share of women in wage employment in the nonagricultural sector from World Bank databases (World Bank 2017h, 2017q).

_Infrastructure._ For a subcomponent index for infrastructure ( _Infra_ ), we select fixed-telephone and mobile subscriptions (per 100 people) (World Bank 2017c, 2017i); the length of paved roads (km per 100 people) (International Road Federation 2017a, 2017b); electricity production (kw per 100 people) (OECD/IEA 2017); and access to an improved water source and improved sanitation facilities (% of population) (WHO/UNICEF 2017b, 2017a).

_Institutions._ To construct a subcomponent index for institutions ( _Inst_ ), we select the World Bank Worldwide Governance Indicators. These include measures of voice and accountability (citizens’ participation in selecting their government and freedom of expression); control of corruption (the extent to which public power is exercised for personal gain); government effectiveness (the quality of public services and policy formulation and implementation); political stability (the absence of politically motivated conflict); regulatory quality (the ability of 16
Peri, Giovanni, and Chad Sparber. 2011. “Highly Educated Immigrants and Native Occupational Choice,” _Industrial Relations: a journal of economy and society_ , 50 (3): 385-411.

Piracha, Matloob and Florin Vadean. 2013. “Migrant educational mismatch and the labour market.” In A. F. Constant & K. F. Zimmermann (Eds.), _International Handbook on Economics of Migration_ (pp. 176-192). Edward Elgar Publishing Ltd.: Cheltenham, UK.

<mark>Premji, Stephanie, Patrice Duguay, Karen Messing and Katherine Lippel. 2010. “Are immigrants, ethnic and linguistic minorities over‐represented in jobs with a high level of compensated risk? Results from a Montréal, Canada study using census and workers' compensation data.”</mark> _<mark>American journal of industrial medicine</mark>_ <mark>,</mark> _<mark>53</mark>_ <mark>(9): 875-885.</mark> <mark>Rojas, Felipe Lozano, Xuan Jiang, Laura Montenovo, Kosali I. Simon, Bruce A. Weinberg, and Coady Wing. 2020. “Is the cure worse than the problem itself? Immediate labor market effects of COVID-19 case rates and school closures in the US.”</mark> _<mark>National Bureau of Economic Research</mark>_ <mark>Working Paper No.27127.</mark> Rich, Judith. 2014. “What Do Field Experiments of Discrimination in Markets Tell Us? A Meta Analysis of Studies Conducted since 2000.” _Institute for the Study of Labor (IZA) Discussion Paper_ 8584.

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<mark>Smith, Peter M., Agnieszka A. Kosny, and Cameron A. Mustard. 2009. “Differences in access to wage replacement benefits for absences due to work‐related injury or illness in Canada.”</mark> _<mark>American Journal of Industrial Medicine</mark>_ <mark>,</mark> _<mark>52</mark>_ <mark>(4), pp.341-349.</mark> Solé, Meritxell, Luis Díaz-Serrano, and Marisol Rodríguez. 2010. “Work, Risk and Health: Differences between Immigrants and Natives in Spain.” _IZA Discussion Paper_ No. 5338.

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Yasenov, Vasil. 2020. “Who Can Work from Home?” _IZA Discussion Paper_ No. 13197.

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32
visit. The stratification thus ensured that each credit officer handled roughly the same number of treatment and control clubs.<sup>8</sup> Club visits began with private administration of the baseline survey to individual farmers, and were followed by a training session. Both treatment and control groups were given a presentation on the importance of credit history in ensuring future access to credit. The training emphasized that defaulters would face exclusion from future borrowing, while borrowers in good standing could be rewarded with larger loans in the future. Then, in treatment clubs only, individual participants’ fingerprints were collected. Our project staff explained how their fingerprint uniquely identified them for credit reporting to all major Malawian rural lenders, and that future credit providers would be able to access the applicant’s credit history simply by checking his or her fingerprint.<sup>9</sup> Online Appendix A provides the script used during the training. See Online Appendix B for further technical details on the biometric technology used.

After fingerprints were collected, a demonstration program was used to show participants that the laptop computer was now able to identify an individual with only his or her fingerprint. One farmer was chosen at random to have his right thumb scanned again, and the club was shown that the individual’s name and demographic information (entered earlier alongside the original fingerprint scan) subsequently was retrieved by the computer program. During these demonstration sessions all farmers whose fingerprints were re-scanned were correctly identified. The control group was not fingerprinted, but as mentioned previously, also received the same training emphasizing the importance of one’s credit history and how it influences one’s future credit access.<sup>10</sup> The baseline survey administered prior to the training and the collection of fingerprints included questions on individual demographics (education, household size, religion), income generating activities and assets including detailed information on crop production and crop choice, livestock and other assets, risk preferences, past and current borrowing activities, and

> 8 There are 16 localities or “extension planning areas” (EPAs) in the study. EPAs are administrative boundaries set up for the delivery of agricultural services by Malawi’s agriculture ministry.

> 9 Our team of enumerators encountered essentially no opposition to fingerprint collection.

> 10 Because we provided education on the importance of credit history to our control group as well, we can estimate neither the impact of fingerprinting without such education, nor the impact of the credit history education alone.

7
27 Table 4: Comparison between Historic and Simulated Series (1962-86)

|VarLables|<br>correlation coefficient|**Theil's**coefficient|
|---|---|---|
||0.829|0.323|
|**cp**|0.915|0.079|
|**lp**|0.752|0.266|
|ig|0.937|0.108|
|x|0.932|0.110|
|y|0.873|0.098|
|ff|0.997|0.053|
|_o0_|0.679|0.794|
|OLp|0.904|0.361|

table 5: Simulation Results for External Financing Policies on Domestic Variables Deviations from Baseline in terms of Elasticity

||<br>||debt-r<br>without<br>effe|eduction<br>liquidity<br>ct<br>|| debt-re<br>Iwith liq<br>I<br>effect<br>|duction<br>uidity<br>|Iaddition<br>I<br>to th<br>I<br>sect|al financin8 i<br>e private<br>I<br>or<br>I|additio<br> <br>to th<br>secto|nal financing<br>e public<br>r|I|
|---|---|---|---|---|---|---|---|---|---|
||F|~~~~~~~|~~~~~I|||.||.|**I**.... ...|
|I|Ilyear|5<br>syearsa r|<br>year|<br>years|Iyear|_<br>5 years|I<br>year|5 years||
||t--|--|-||--|1<br>|<br>--<br>|-|!|
|Iip1|____<br> <br>0.476|<br>0.584|1.129|2.159<br>|-0.928<br>|-0.097|0 397|0.210||
|isI|<br>0|0.208|1 0.725<br>|3.134<br>|I<br>0<br>|-0.132<br>1|<br>0.475|0.369||
|cpI|<br>0.018|0.022|1 -0.0054|-0.055|I<br>-0.037|-0.001<br>1|-0.017|-0.003|1|
|ICjI|-1.088|-1.006|1 -0.960|-0.463|I<br>0.313|-0.001<br>1|<br>0.152|0.057|I|
|y<br>1|<br>0.041|0.058|1 0.081|0.180|I<br>-0.081|-0.011<br>1|<br>0.023|0.022|I|
|Cv 1|<br>0.004|0.008|1 0.022|0.046|1<br>-0.005|-0.005<br>I|0.011|0.009||
|i<br>1|<br>-0.047|-0.112|1 -0.266|-0.678|1<br>0.064|0.031<br>1|-0.140|-0.07|I|
|a<br>I|<br>-0.028|-0.008|1 -0.158|-0.054|1<br>0.038|0.002<br>1|-0.083|-0.004|1|
|OlpI|<br>-0.505|-0.491|1 -0.514|-0.547|1<br>0.034|0.008<br>1|<br>0.0255|-0.013|1|
|bLgI|<br>0|-0.177|1 -0.653|-1.486|1<br>0|-0.113<br>1|-0.973|0.315|1|
|1OR/PI|0.936|0.875|1 1.120|0.716|1<br>0.068|-0.006<br>1|<br>0.184|-0.038|I|

Calculated with 1986 values Table 6: The optimal debt-reduction liquidity combination **_(S_** billion, unless otherwise indicated)

|f|0.6|1|3.2|5|
|---|---|---|---|---|
|n<br>i|0.164|0.237|0.463|0.423|
|pdebt|<br>|20.507|20.77|22.31|23.35|
|DR<br>i|2.072|3.166|9.438|13.690|
|O(OD$g)o<br>|0.232<br>|0.355|1.059|1.536|
|y(%)R|0.347|0.531|1.582|2.300|

Note **:** q-0.20, * - 0.547
Table <u>3. Performance of the models of dropout in the primary to lower secondary transition</u>

|**Country and**<br>**specification**|**Sensitivity**<br>**(percent)**|<br>**False-positive**<br>**proportion**<br>**(percent)**|**Overall accuracy**<sup>**1**</sup><br>**(proportion)**|
|---|---|---|---|
|_Guatemala_||||
|Model 1|67.8|32.9|0.67|
|Model 2|71.6|30.7|0.70|
|Model 3|80.0|21.2|0.79|
|_Honduras_||||
|Model 1|68.8|28.9|0.70|
|Model 2|69.1|27.2|0.71|
|Model3|78.2|19.8|0.79|

Source: Author calculations on administrative data from Guatemala and Honduras. Note: This is the area-under-the-curve statistic, which ranges from .50 to 1.00, with higher values associated with higher accuracy (higher sensitivity and lower false-positive proportion).

25
- 7 -

to collusive bidding and corruption. The lengthy bid evaluation process is of particular concern. It will reduce transparency of the process and increase the risk of anticompetitive practices and cost overruns (Ware _et al_ ., 2007).

The current paper primarily aims at examining the firms’ bidding and entry strategy to establish an efficient public road procurement system. However, it also casts light on a series of policy issues related to contract management and project quality assurance, because public contracts for infrastructure projects are often far from complete. In addition, firms can take the low-balling strategy—which is to submit an unrealistic bid and initiate renegotiation afterward (Ware and others, 2007). If this strategy cannot be excluded, the procurement performance needs to be analyzed along with assessment of ex post contract amendments as well as the quality of projects delivered.

The paper collected new data on procurement and contract performance from 155 rural road upgrading works in Nepal, on which about 820 bids were submitted. The quality of roads was also assessed by road specialists who visited every road location. The remaining paper is organized as follows: Section II describes recent road developments and other relevant issues in Nepal. Section III develops the empirical models with all possible hypotheses taken into account. The section also presents our data in an informal way. Section IV summarizes our formal empirical results, and Section V discusses policy implications. Then, Section VI concludes.

# **II. RECENT ROAD DEVELOPMENTS AND RELEVANT ISSUES IN NEPAL**

Nepal is one of the least developed countries in the world. About 29 million people live in the country, out of which half still live on less than $1.25 a day at 2005 international prices. Gross domestic product (GDP) per capita was about $430 in 2009. In the country, there exists some 17,000 km of road network. This is about 12 km per 100 sq. km of land area, which is compared unfavorably to other developing countries in the region. Not only quantity
There is heterogeneity in the type of fiscal injections used across countries. In Bulgaria, Indonesia, Romania, and Uruguay, operational subsidies are the main instrument of support. In Albania, government support is channeled mainly through government loans. Croatia and Kosovo use both operational subsidies and government loans. Loans from SOEs are important in Bhutan and Bulgaria; equity injections are important in Argentina, Bhutan, and Ghana. On average, deferred tax liabilities are small relative to GDP, although some countries, such as Bhutan, Kosovo, and Peru, used them in some years (figure 7).

# **Figure 7 Fiscal injections to SOEs as percent to GDP in Bhutan, Kosovo, and Peru, by type of support 2008–18**

<!-- Start of picture text --> a. Bhutan b. Kosovo<br>1.00<br>1.00<br>0.80<br>0.80<br>0.60<br>0.60<br>0.40 0.40<br>0.20 0.20<br>0.00 0.00<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Fiscal injections as percent of GDP Fiscal injections as percent of GDP<br><!-- End of picture text --> <!-- Start of picture text --> c. Peru<br><!-- End of picture text --> <!-- Start of picture text --> 0.08<br>0.06<br>0.04<br>0.02<br>0.00<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Operations subsidies Government equity injections<br>Deferred tax liabilities Government loans<br>SOE loans<br>Fiscal injections as percent of GDP<br><!-- End of picture text --> _Source_ : Authors, based on data from the World Bank Infrastructure SOE Database.

19
9 country was set to $1, so that log of lobbying (LLOB) equaled zero. That is, there was no informational lobbying by those Caribbean principals.

Per capita GDP is taken from the CTO. The real exchange rate (RER) is measured as the nominal exchange rate (island’s currency per dollar) times the ratio of the U.S. CPI (1995 base) to the island’s price level (1995 base). The RER is standardized to a value 1 in the 1995. The RER is taken from World Development Indicators (2003) and CTO.

Complete data on the lobbying and tourism variables were available for one or more years during the ten -year period for these thirteen countries: Aruba, Barbados, Netherlands Antilles (Bonaire and Curaçao), Belize, Bermuda, Cayman Is., Dominica, Dominican Rep., Grenada, Jamaica, Montserrat, St. Lucia, Trinidad and Tobago. Our sample is thus a balanced panel consisting of 130 observations.

Over the 1990-2000 period the 13 destinations accounted for 38 percent of the total tourist arrivals in the Caribbean, and 32 percent of the U.S.-Caribbean tourism (U.S. tourists account for more than a half of total arrivals in the Caribbean). These destinations accounted for about 70 percent of the total reported foreign lobbying spent by all the Caribbean destinations (about $306 mn.).

# **4. Results**

Our empirical strategy is to first estimate a baseline model with and without fixed-effects in order to observe whether, all else held constant, foreign lobbying is positively associated with tourism. Next, we take care of the endogeneity of lobbying spending. If decisions about the amount of foreign lobbying expenditures are based on expectations about how they might impact on tourism, then it is likely that the variable LLOB is endogenous. Thus, we estimate the fixedeffects models using instruments. Finally, we present estimates from the gravity model using a random effects specification.

Table 1 presents OLS and fixed effects (FE) estimation of the baseline model in equation (1). The OLS model estimate the elasticity of lobbying to be 0.12, implying that a 10 percent increase in foreign lobbying expenditure increase tourism arrivals by 1.2 percent. Including fixed-effects indicates that the OLS model overestimates the lobbying elasticity. The FE model yields a smaller estimate of 0.032, implying that a 10 percent increase in foreign lobbying expenditure increase tourism arrivals by 0.32 percent. Although small, the elasticity is estimated