rafmacalaba/datause-encoder-data huggingface dataset:
to surgeons seek to use the right instrument for the right problem—and do not regard, for example, a hammer as inherently “more rigorous” than a screwdriver—so too must evaluators of development interventions seek to cultivate a comprehensive awareness of how to craft the right combinations of methods for the task at hand. This sensibility, as much as the technical skill associated with mastering a particular method, is what is required to generate robust, useful and useable insights regarding the effectiveness of investment climate reforms. The use of qualitative and mixed methods approaches has made a promising start in this domain; it is our hope that this summary note can encourage and inform the important next steps.

# **References**

Abadie, Alberto, Alexis Diamond and Jens Hainmueller. “Synthetic control methods for comparative case studies: Estimating the effect of California's Tobacco Control Program.” _Journal of the American Statistical Association_ 105(490): 493-505, 2010.

Adler, Daniel, Doug Porter and Michael Woolcock. “Legal pluralism and equity: Some reflections on land reform in Cambodia.” World Bank, 2008. Available at <u>http://siteresources.worldbank.org/INTJUSFORPOOR/Resources/J4PBriefingNoteVolume2Issu e2.pdf</u> Alcázar Lorena and Miguel Jaramillo. “The real barriers for municipal formalization: A qualitative case study in Lima Cercado.” GRADE, 2011.

Alcázar Lorena and Miguel Jaramillo. ”Panel /tracer study on the impact of business facilitation processes on microenterprises and identification of priorities for future business enabling environment projects in Lima, Peru” GRADE, 2011.

Ananthpur, Kripa, Kabur Malik and Vijayendra Rao. “The anatomy of failure: An ethnography of a randomized trial to deepen democracy in rural India.” Policy Research Working Paper No. 6958, Washington, DC: World Bank, 2014.

31
# **Table A - 9 :  Traders' Estimates of Trade Costs (Percent of the Value of Imports)**

|**Country**|**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|
|---|---|---|
|**Egypt**|8.5-15|11.0|
|**Jordan**|2-6.0|4.0|
|**Lebanon**|2.5-11|7.5|
|**Morocco**|3 - 17|7.0|
|**Saudi Arabia**|3 - 10|5.0|
|**Syria**|4-11.5|9.5|
|**Tunisia**|1.5-6.0|4.0|
|**UAE**|1.0-8.5|3.5|
|**Yemen**|2.0-10|7.0|
|**Average (2008 Survey)**|**2 - 11**|**6.0**|
|**Average (2001 Survey)**|**-**|**10.6**|

*  See Footnote in Table A-1.

27
related syntax.

Table 1: A summary of the available perspectives and approaches for each trade

||**Perspective**<br>**in line with**<br>**the trade fow**<br>**(1)**|**Perspective**<br>**consistent with**<br>**more aggregate fows**<br>**(i.e. additive)**<br>**(2)**|
|---|---|---|
|**1. Total exports**|||
|**1a. Aggregate**|Exporting-country|World (source/sink)|
|**1b. Sectoral**|Sectoral-exporter|Exporting-country (source/sink)|
|**2. Bilateral exports**|||
|**2a. Aggregate**|Bilateral|Exporting-country (source/sink)|
|**2b. Sectoral**|Sectoral-bilateral|Exporting-country (source/sink)|
|**3. Total imports**|||
|**3a. Aggregate**|Importing-country|n.a.|
|**3b. Sectoral**|Sectoral-importer|n.a.|

13
FIGURE 8 Effects of recessions on potential growth A. World: Response of potential output growth after recessions B. Advanced economies: Response of potential output growth after recessions <!-- Start of picture text --> Percentage points Percentage points<br>0.0 0.0<br>-0.5 -0.5<br>-1.0 -1.0<br>-1.5 -1.5<br>-2.0 -2.0<br>-2.5 -2.5<br>Year 1 Year 3 Year 5 Year 1 Year 3 Year 5<br>C. EMDEs: Response of potential output D. Share of adverse events associated with<br>growth after recessions recessions<br>Percentage points Percent<br>0.0 With recessions Without recessions<br>100<br>-0.5<br>80<br>-1.0<br>60<br>-1.5<br>40<br>-2.0<br>20<br>-2.5<br>0<br>Year 1 Year 3 Year 5<br>Banking crises Epidemics<br><!-- End of picture text --> D. Share of adverse events associated with recessions Source: World Bank.

Note: Recessions are defined as the period from the peak preceding a business cycle trough to the trough, with the troughs defined as years in which output growth is both negative and one standard deviation below the long-term average. Banking crises are identified as in Laeven and Valencia (2020). Epidemics include SARS (2003), swine flu (2009), MERS (2012), Ebola (2014), and Zika (2016).

A.-C. Blue bars are coefficient estimates from local projections model. Orange whiskers indicate 90 percent confidence interval. Methodological details are in annex E. Sample includes unbalanced panel of 28 advanced economies 50 EMDEs for 1998-2020.

D. Share of events associated with recessions is the share of events that coincide with a recession in a 3- year window, out of the total number of events. Sample includes unbalanced panel of 33 advanced economies and 98 EMDEs for 1981-2020.

28
- 28 -

45. Note that other factors Influencing the level of nonfarm activity are captured in the error term. Raw material avallabillty, historical accident, location, othnicity, and differentlal policles all undoubtedly Influence nonfarm activity to some extent. But they are difficult to measure and It seems reasonable to model then as varying randomly across districts.

46. The same model can be used to estimate rural nonfarm employment (RNFL) multipilers by substituting employment for Income as follows: RNFL - a + b AGY + c AGY*INFR + d AGY*POPDEN + a AGY*AGYCAP + f AGY*IRRIG [4) 47. Both equatlons (2] and [4] have been estimated separately using state and district-level data. For the distrwcts, we have used the same 85-dIstrict sample used by Blnswanger and Khandker<sup>1 2</sup> (1988) and Khandker (1988). It Is a representative, India-wide sample Including districts from Andhra Pradesh, Bihar, Gujarat, Haryana, Jammu and Kashmir, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Punjab, Rajasthan, Tamil Nadu and Uttar Pradesh. Since Income data are not available at the district level, farm and nonfarm Income (Y) are estimated as employment (L) times the wage rate (w) divided by the wage share (S,) In total Income (Y - wL/S,,).<sup>13</sup> curthermore, since only agrieljtural wage data are avallable, we must assume wage rates are equal In farm and nonfarm activities. For Infrastructure, we have used road density per square kilometer, although rural bank branches and rural electrification are highly correlated and produce similar results. Farm and nonfarm Income data are available for each 12 **We are** grateful to Shahid..- Khandkor **for access** to **the** agricultural **and** infrastructural variables necessary for **estimating** this **model. We have** supplemented **the** employment figures by returning to the population **census** (India, 181b) to **break** out nonfarm employment **in** rural towna **as** woll **as** rural **areas and** large urban **centers.** 1<sup>3</sup> Transtating from **wage** to income **multiplieor quires** the following **adjustment. We** wish to **estimate the** Income multiplier (dRNFY/dACY **_*_** b) from RNFY a a * b ACY. ut d?te constraints **require us** to **estimate** a **wage** -ultiplier, bl, **Instead** from RNFW a **n'** + b AGW. **The wag*** equstion **can be rewritten as** RNFY*Swn **u a*** **_*_** bl AGf * S n So long **as** **_wage_** pharee of total income, S , remain constnt, **we can comput** b **from** bl **as** follows: b **_*_** bl s Sw/Swn Calculations **bosed** on Hazell **and Rmasa my** (19i1) **place Swa/Swn** at e.e2. **hence the wage share** adjustment **described** in **Table** A.13.
woman was a victim of domestic violence. Clearly our interview had made her husband angry enough that he had come back home from the fields to punish his wife publically and make the point that talking to us would have adverse consequences. The very act of conducting the interviews had raised the risk of violence against our respondents.

Could this have been prevented? It is not clear how. The survey team was experienced with women’s issues. The sample size was small (less than 200 women) so the PI was able to personally supervise the survey. All existing protocols on informed consent had been followed. Every effort was made to keep the interviews (especially the individual interviews) confidential. But, the very act of asking questions of this nature is fraught with risk for the respondent. And asking about traumatic events makes concerns about the nature of informed consent in low literacy populations even more acute.

Such concerns are common to any effort to collect information on domestic violence and other issues that are private, involve some shame, and are embedded within power relationships. Cognizant of these concerns, and also of the importance of collecting representative data on domestic violence, the World Health Organization (2001) has developed a set of ethical guidelines which they follow in collecting their influential data on domestic violence in several developing countries<sup>4</sup> . There has been an explosion of research on the topic and a vast expansion in the availability of data on domestic violence over the last decade. The

> 4 The guidelines are: 1) The safety of respondents and the research team is paramount, and should guide all project decisions. 2)  Prevalence studies need to be methodologically sound and to build upon current research experience about how to minimize the under-reporting of violence. 3)  Protecting confidentiality is essential to ensure both women’s safety and data quality. 4) All research team members should be carefully selected and receive specialized training and on-going support. 5)  The study design must include actions aimed at reducing any possible distress caused to the participants by the research. 6)  Fieldworkers should be trained to refer women requesting assistance to available local services and sources of support. Where few resources exist, it may be necessary for the study to create short term support mechanisms. 7)  Researchers and donors have an ethical obligation to help ensure that their findings are properly interpreted and used to advance policy and intervention development. 8)  Violence questions should only be incorporated into surveys designed for other purposes when ethical and methodological requirements can be met.

10
23 percent are still in school, the majority in post-secondary institutions. The same is true of females.

Figure 4 Average Years of Education for Males and Females by Birth Cohort

**asrs** of schooling

<!-- Start of picture text --> 8.3 8.7 8.3<br>7.0<br>6.8<br>6.6 6<br>4.8 4.8<br>3.7<br>3.1<br>1925-39 1940-44 1945-49 1950-54 1955-59 1960-68<br>Birth cohort<br><!-- End of picture text --> Females * Males This rising trend in the educational attainment reflects an increasing propensity both to enroll in school and to continue on to secondary and higher education. As shown in Table 3, the proportion of females who never attended school has fallen dramatically from over 40 percent in the two oldest cohorts to only 8 percent in the youngest cohort. For males, the proportion **never** enrolled in school fell from 15 percent of the oldest to 3 percent of the youngest cohort. The proportion of adults with some secondary schooling haJ increased more than threefold. The effect of the expansion of educational opportunLties during the late 50s and throughout the 60s is 28
by sake of gravity forces. Barbados is an extreme example of such country, whereby exports would increase by 3.45 percent and GDP by 1.11 percent even by adopting the less ambitious trade agreement configuration.<sup>15</sup> Reciprocally, there is little gain for large countries such as Argentina signing with small ones. More interestingly for our exercise, singing deep agreements (of cluster #1) would magnify those gains for small countries. Keeping the extreme example of Barbados, exports would increase by 7.96 percent with a deep agreement and GDP by 2.59 percent (columns 5 and 6).

This set of results point to two main policy implications on the path forward for regional integration in the Latin America and the Caribbean region. The first policy message is that relatively large LAC countries already engaged in networks of PTAs would benefit more from a deepening of existing agreements than from extending such network to new partners. The second policy message is that small LAC countries would gain from signing new agreements, even of a shallow type, although gains would be higher with deeper agreements.

Table 7: General-Equilibrium effects of deeper trade integration for LAC countries, year 2017.

|||Within|_Intensi_<br>_PT_<br> LAC|_ve Margin_<br>_A_<sup>_CF L_</sup><br>_Deep_<br>Within LAC|plus ROW|_Extensiv_<br>_PTA_<br>Within LAC|_e Margin_<br><sup>_CF L_</sup><br>_All_<br>, Cluster #1|
|---|---|---|---|---|---|---|---|
|Country Name|Iso3|∆Export<br>(1)|∆GDP<br>(2)|∆Export<br>(3)|∆GDP<br>(4)|∆Export<br>(5)|∆GDP<br>(6)|
|Argentina|ARG|6.20|0.82|8.35|1.11|0.28|0.03|
|Bahamas|BHS|0.00|0.01|2.97|1.69|1.77|0.96|
|Barbados|BRB|-0.19|-0.06|2.19|0.67|7.96|2.59|
|Belize|BLZ|0.28|0.09|3.45|1.49|3.85|2.42|
|Bolivia|BOL|7.84|1.43|9.24|1.67|0.13|0.02|
|Brazil|BRA|3.56|0.18|5.81|0.28|0.30|0.02|
|Chile|CHL|2.86|0.62|13.78|3.15|0.04|0.01|
|Colombia|COL|8.58|0.43|19.68|0.99|1.55|0.08|
|Costa Rica|CRI|2.91|0.66|11.46|2.78|1.11|0.28|
|Cuba|CUB|3.51|0.17|5.86|0.28|1.30|0.06|
|Dominican Republic|DOM|0.13|0.17|3.90|4.75|1.64|2.01|
|Ecuador|ECU|4.83|0.55|7.24|0.82|3.19|0.36|
|El Salvador|SLV|3.06|2.19|5.90|4.53|0.26|0.31|
|Guatemala|GTM|4.33|1.58|8.74|3.60|0.95|0.38|
|Haiti|HTI|0.02|0.00|-0.71|-0.43|3.91|2.51|
|Honduras|HND|1.38|1.87|4.89|5.91|0.25|0.43|
|Jamaica|JAM|0.76|0.17|4.28|0.90|2.89|0.71|
|Mexico|MEX|0.24|0.32|0.92|2.12|0.02|0.02|
|Nicaragua|NIC|0.86|2.78|3.30|6.66|-0.02|-0.01|
|Panama|PAN|1.25|0.78|2.34|1.90|5.89|3.11|
|Paraguay|PRY|8.95|1.39|8.83|1.39|0.21|0.03|
|Peru|PER|4.15|0.40|17.40|1.75|0.32|0.03|
|Saint Lucia|LCA|3.08|1.39|3.68|1.81|3.15|1.55|
|Suriname|SUR|0.87|0.00|3.60|0.01|1.79|0.00|
|Trinidad and Tobago|TTO|1.69|0.50|3.35|1.13|1.53|0.43|
|Uruguay|URY|5.19|1.09|4.97|1.06|0.41|0.07|
|Venezuela, RB|VEN|4.10|0.24|6.53|0.39|2.28|0.14|
|LAC||1.96|0.31|4.44|1.01|0.29|0.05|
|RoW||-0.02|0.00|0.08|0.01|0.00|0.00|

_Note_ : percent change compared to the baseline in total exports and GDP of participating countries.

> 15Results for the counterfactuals addressing less ambitious agreement types are not reported in the table but available upon request.

24
domestic production and thus higher incomes. On the other hand, policy managers will be prudent at times to provide protection against short-run shocks so as to ensure that competitive producers survive over the long run. Temporary protection might be justified as well for uncompetitive producers whose livelihood depends on agriculture and for whom there are no immediate alternative sources of employment/income. Also entering into the equation are the interests of the non-agriculture poor, for whom higher food prices mean lower real income.

# **The roles of international trade agreements**

The longer-run benefits of commitment to a program of liberalization through negotiation and bound commitments are familiar. The mercantilist quid pro quo for domestic reforms – improved access to foreign markets – will help to provide alternative uses for domestic resources whose comparative advantage is not in agriculture. Moreover, such commitment to reform through international agreement will provide continuing pressure on the sector to reform as well as support achievement of longer-term objectives against the many pressures that will arise to go in other directions.

While the long-run objective implies a general momentum toward liberalization, natural events and shifts of agricultural policies in other countries imply that any country will face considerable short-run volatility in international markets. Thus international commitments to long-term reform usually include ‘safeguard’ provisions that provide flexibility to deal with shorter term events. Such provisions allow new trade restrictions but at the same time attempt to discipline against their overuse so that a longer-run momentum toward liberalization is maintained.

# **Dimensions of a safeguard/flexibility provision**

Membership in the WTO commits a government to use only approved instruments of trade control, principally tariffs; to apply them in a generally nondiscriminatory manner and to subject them to a long-time process of binding and reduction through negotiation.<sup>5</sup> Safeguards, being a part of the overall agreement, reflect each Member’s overall and long-term commitment to liberalization. Flexibility in this context does not refer to the freedom a government would maintain by remaining outside the agreement or by not binding its tariff rates through the agreement. It refers to disciplined or guided flexibility intended to keep its application by each Member in line with the overall and long-run objectives implicit in membership.

I will review safeguard mechanisms from the perspectives of three characteristics: flexibility, discipline, and guidance toward good policy choices.

> 5 Hudec 1987, p. 133.

3
40 investing across borders: do fdi-specific regulations matter?

I formally test for the exogeneity of FDI regulations with three alternative instrumental variables related to land. The first index on Access to Land Information measures aspects of whether the land registry or cadastre have a publicly accessible inventory of private and public land. The second index on the Availability of Land Information scores the richness of 18 pieces of land-related information (for example, plot size, land value, address, previous contracts). And the third index captures the time taken (number of days) to lease public or private land in the host country on average (World Bank Group 2010). These instrumental variables are chosen because they are associated with the soundness of domestic institutions; but they are not a determinant of FDI in their own right because a substantial share of aggregate global FDI is in services for which access to industrial land is not as important as for FDI in manufacturing.

The tests follow a three-stage process. _First_ , I conduct the Durbin-Wu-Hausman (DWH) test for the endogeneity of each of the Openness, Arbitration, and Procedures variables. After confirming a high degree of correlation between the potentially endogenous variable and its instrument, Openness is instrumented by the Access to Land Information index, FDI Arbitration is instrumented by the Availability of Land Information index, and FDI Procedures is instrumented by the time it takes to lease private land. I also instrument for all the three variables together. In all the four cases, the p-value of the DWH test is greater than 0.15, which fails to reject the null hypothesis of exogeneity of regressors.

_Second_ , I check for the validity of the instruments in an over-identified model. None of the p-values of the Hansen’s _J_ chi-squared is less than 0.44; the null hypothesis that the overidentifying restriction is valid is not rejected. Finally, the results reject the null hypothesis of weak instruments for the Openness and Arbitration variables, but not the Procedures variable. However, when the Arbitration and Procedures variables are instrumented together in a just-identified model, the
or agricultural productivity beyond their ability to explain cross-country variation in private credit to GDP. Under the null, the instruments are valid. We report the p-value of the test as “OIR test”. The second test assesses whether the instruments are able to explain cross-country differences in financial development or agricultural productivity. This test is provided as an F test in the first stage of the IV regressions. Under the null, the excluded instruments do not explain variation in the dependent variable. We report the p-values of the test as “F Test”.

Most importantly, to ameliorate further concerns about endogeneity problems and omitted variable bias we use fixed effects panel estimations whenever sufficient data are available. Some data are limited, however, and not always available annually. Specifically, most data from the FAO are only provided as averages for following five periods: 1979-81, 1990-92, 1993-95, 1995-97 and 2001-03. Consequently, we can not do panel regressions for the specific agricultural productivity indexes, cereal and crop yields.

16
-107-

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- W. Tyler, S. Goldberg, and B. Blazic-Metzner (1984), "Brazil On Improving the National Income Accounting Syscem", World Bank, Washington D.C., October, processed.

- United Nations (1981), Recommendations for the 1983 World Programme of Industrial Statistics, Part I, General Statistical Objectives, Statistical Papers, Series M, No. 71 (Part I), New York.

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- U.S. Dept. of Commerce (1981c), The NationRl Income and Product Accounts of the United States 1929-76: Statistical Tables, BEA, Washington DC, September.

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- M. Ward (1985), Purchasing Power Parities and Real Expenditures in the OECD, Paris.
16 FICURE 1A <!-- Start of picture text --> BRAZIL POPULATION PROFILE 1985<br>o lo.ox .<br>.::102~~00E2 E §04J<br><!-- End of picture text --> <!-- Start of picture text --> NIGERIA POPULATION PROFILE 1985<br>20OXo<br>20-2. 40-44 a_<br>JAPAN POPULATION PROFILE 1985<br>0.02 H<br>2D-24 40-" 4-<br><!-- End of picture text -->