rafmacalaba/datause-encoder-data huggingface dataset:
45 adequate index of trade orientation. The subsection that follows deals with this issue.

V.2 An Index of Trade Intervention There is little, if any, hope that researchers will ever be able to construct the ideal index of trade intervention that will capture all the subtleties of reality. Empirical research, then, has to proceed on the basis of finding proxies for this ideal index. If better, more reliable, and less controveraial proxies to this index are developed, the quality of that research will tend to improve.

In a recent paper on openness Edward Leamer (1987) has used a large cross country data set to compute patterns of trade according to comparative advantage. He then takes "[t]he difference between the 'predicted' and actual trade intensity ratios ... as an indicator of trade barriers" (p. 7). In this paper we use these intervention indexes as a proxy for the extent of trade intervention.

Leamer uses a traditional Heckscher-Ohlin general equilibrium model of trade as his theoretical framework. "Predicted" comparative advantage trade ratios are computed using a regression analysis that considers three goods aggregates and seven factor of production. Leamer then defines a rate of intervention which "measures the extent to which trade is distorted by policy, positively or negatively" (p. 26). For every country this intervention index is defined as the ratio of the sum of the absolute value of the residuals from the regression to GNP.<sup>33</sup> 33Leamer also constructs an alternative intervention index where the denominator is the sum of the absolute value of predicted net trade. In private communication, however, Leamer has argued that for the purpose at hand using GNP as the denominator is more reasonable.
-18-

# VI. Possible omitted variable and sample selection bias.

This sample of projects financed by the World Bank group may suffer from selection bias; it is neither random nor necessarily representative of public or private projects in any given country. Consider what may occur in countries where the World Bank project presence is not large relative to overall investment--and where the Bank is not a residual lender. Given its special lending role, the World Bank in such cases may attempt to 'skim-and-insulate': to identify the best possible projects and then try to insulate them from national policy and institutional deficiencies. By contrast, in countries where the Bank project presence is large, one could expect that projects financed by the World Bank Group to have about average performance. In such cases, insulating projects from policy inadequacies would be less feasible.

Thus, we might expect that the World Bank 'project presence' would be inversely related to ERRs. Further, if project presence were negatively related to the quality of the policy framework, the impact of policies on ERRs may have been overestimated in the specifications above. Omitted variable bias--in an upward direction for the estimates of policy effects--would arise if settings where the Bank can skim and-insulate also happen to have better economic policies.

# A. Econometric specifications with World Bank proiect presence.

To test for possible mis-specifications due to such omitted variable bias<sup>27</sup> , we constructed a World Bank 'project presence' variable: the Bank's accumulated project disbursements as a share of the total capital stock. Using this variable (a single observation for each country), we tested for mis-specification in two ways. First, this variable was included as an additional independent variable in the primary set of Tobit estimations (table 8, column 1). Second, the project presence variable was also used to truncate the sample for low and high values of World Bank presence. After removing the outliers, we tested whether the policy coefficients behaved differently for the remaining sample, where the Bank presence was within a more 'normal' range. In all cases (specifications 2-4, for left-, right- and double-tail truncations, respectively), the robustness of the policy coefficients was maintained.<sup>28</sup>

> 27 Instrumental variable estimation was also considered to test for mis-specifications. In this case, however, the challenge of finding an appropriate instrument--correlated with policy variables but not with the error term of the 'second stage' regression _(ERR =p* i8 + a*Xi + a * Z_ + g)--seems insurmountable. Possible candidates, including variables on economic conditions and govemance, for example, must be ruled out because of the likelihood of endogeneity. 28 In specifications including the other main policy variables utilized in our analysis, the results--not reported here--are also very similar.
(S1 indicator) is the adjustment needed to bring public debt to the SGP target of 60 percent of GDP or less by 2060. The second (S2 indicator) is the fiscal adjustment needed to satisfy the government’s intertemporal budget constraint over an infinite horizon.

The Ageing Working Group estimates that age related expenditures will increase by 8.5 percent of GDP in Romania by 2060, almost double the EU average of 4.4 percent of GDP (figure 6). It estimates that the upfront fiscal adjustment needed to ensure fiscal sustainability, as a result of the fiscal costs of demographic aging alone, are between 3.2 percent and 4.9 percent of GDP.

**Figure 6: Increase in Age-related Public Expenditures in the European Union, 20102060, percent of GDP**

<!-- Start of picture text --> 21<br>unemployment long-term care healthcare pension spending total<br>18<br>15<br>12<br>9<br>6<br>3<br>0<br>-3<br>LU GR SI CY NL MT IE RO ES BE CZ LT FI SK DE EA UK EU27 AT BG PT SE DK FR HU IT LV EE PL<br><!-- End of picture text --> _Source: European Commission (2009), “2009 Ageing Report: Economic and budgetary projections for the EU-27 Member States (2008-2060)”, Brussels_

# **4.3 The state pension system**

16
_Social Capital, Household Welfare and Poverty in Indonesia_ _74_

# **Table A8: Composition of Households' Three Most Important Groups, by Region and Household Characteristics**

||Government/<br>National<br>**Groups**|Religious<br>Groups|Production<br>Groups|Social Groups|
|---|---|---|---|---|
|_Province_|||||
|Jambi|0.7|0.9|_0.5_|_0.5_|
|Jawa Tengah|1.3|0.4|0.7|0.1|
|<br>NTT|1.3|0.8|0.3|0.5|
|_Head of Household_|||||
|<br>Male|1.1|0.7|_0.5_|0.3|
|Female|0.8|0.7|_0.5_|0.4|
|_Religion_|||||
|Muslim|1.0|0.6|0.6|0.3|
|Catholic|1.1|0.8|0.4|0.6|
|Protestant|1.5|0.8|0.1|0.3|
|_Education_|||||
|None|1.0|0.6|0.4|0.3|
|Primary School-<br>Incomplete|1.0|0.7|_0.5_|0.3|
|<br> <br>Primary School-<br>Complete|1.2|0.7|0.5|0.4|
|Secondary School- Incomplete|1.4|0.6|0.3|0.3|
|<br>Secondary School -Complete|1.3|0.7|0.4|0.3|
|<br>Vocational|1.3|0.6|0.4|0.3|
|University/Other|1.3|_0.5_|0.6|0.3|
|<br>_Quintile_|||||
|Poorest|1.1|0.7|0.4|0.3|
|2|1.1|0.8|0.5|0.3|
|3|1.1|0.6|0.5|0.4|
|4|1.1|0.7|_0.5_|0.3|
|Richest|1.1|0.6|_0.5_|0.3|
|All|I<br>1.1|0.7|_0.5_|0.3|

Note: Entries are average number of times each category was listed among the 3 most important groups. "All" line does not add to 3 because some households reported fewer than 3 most important groups.
# **References**

- Angrist, J. D., & Pischke, J. (2009). _Mostly Harmless Econometrics: An Empiricist’s Companion_ . New Jersey: Princeton University Press.

- <mark>Asher, S., & Novosad, P. (2020). Rural roads and local economic development.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>110</mark>_ <mark>(3), 797-823.</mark>

- Augsburg, B., De Haas, R., Harmgart, H., & Meghir, C. (2015). The impacts of microcredit: Evidence from Bosnia and Herzegovina. _American Economic Journal: Applied Economics_ , 7(1), 183-203.

- <mark>Bahal, G. (2020). Estimating the impact of welfare programs on agricultural output: evidence from India.</mark> _<mark>American Journal of Agricultural Economics</mark>_ <mark>,</mark> _<mark>102</mark>_ <mark>(3), 982-998.</mark>

- Balboni, C., Bandiera, O., Burgess, R., Ghatak, M., & Heil, A. (2022). Why do people stay poor?. _Quarterly Journal of Economics_ , 137(2), 785-844.

- <mark>Bandiera, O., Burgess, R., Das, N., Gulesci, S., Rasul, I., & Sulaiman, M. (2017). Labor markets and poverty in village economies.</mark> _<mark>Quarterly Journal of Economics</mark>_ <mark>,</mark> _<mark>132</mark>_ <mark>(2), 811870.</mark>

- <mark>Banerjee, A., Duflo, E., Goldberg, N., Karlan, D., Osei, R., Parienté, W., ... & Udry, C. (2015). A multifaceted program causes lasting progress for the very poor: Evidence from six countries.</mark> _<mark>Science</mark>_ <mark>, 348(6236), 1260799.</mark>

- <mark>Banerjee, Abhijit, Esther Duflo, and Garima Sharma. 2021. "Long-Term Effects of the Targeting the Ultra Poor Program."</mark> _<mark>American Economic Review: Insights</mark>_ , <mark>3 (4): 471-86.</mark>

- <mark>Baum-Snow, N., & Lutz, B. F. (2011). School desegregation, school choice, and changes in residential location patterns by race.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>101</mark>_ <mark>(7), 3019-46.</mark>

- Benjamin, D., Brandt, L., & McCaig, B. (2017). Growth with equity: income inequality in Vietnam, 2002–14. _Journal of Economic Inequality_ , 15(1), 25-46.

- <mark>Blakeslee, D., Chaurey, R., Fishman, R., & Malik, S. (2022). Land rezoning and structural transformation in rural india: Evidence from the industrial areas program.</mark> _<mark>World Bank Economic Review</mark>_ <mark>,</mark> _<mark>36</mark>_ <mark>(2), 488-513.</mark>

- <mark>Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2019).</mark> _<mark>A practical introduction to regression discontinuity designs: Foundations</mark>_ <mark>. Cambridge University Press.</mark>

CEMA and UNDP (2009), Reviewing the past responding to new challenges: Mid-term review of programme 135-Ii, 2006 – 2008, Committe For Ethnic Minority Affairs and the United Nations in Vietnam. Available at: <u>https://www.undp.org/sites/g/files/zskgke326/files/migration/vn/21361_P135_Report_E N.pdf</u>

- Chaurey, R., & Le, D. T. (2022). Infrastructure maintenance and rural economic activity: Evidence from India. _Journal of Public Economics_ , _214_ , 104725.

29
Table 2: Robustness I, Survival and Alternative Measures of Financial Development. The dependent variable is the hazard rate of trade relationships for product k of ...rm i operating in country c to destination country j: All regressions are estimated using the Cox Proportional Hazard Model. We control for destination country, year, ...rm and exporting country x HS2 ...xed e¤ects, and allow the baseline hazard to vary across HS8 product (strata). The variables of interest are de...ned in Table 1. Additional controls include an alternative measure of sanitary risk, the sanitary risk index computed using Poisson regression (alt_sanitary_riskk, see Jaud et al.,2009a). We use as alternative measures of ...nancial development proxied by trade credit over GDP in country c (TCc), by trade credit insurance over GDP in country c (ICc) and by the Ease of Getting Credit index (EGCc). Robust standard errors clustered at (exporting country) � HS4 sector level are in parentheses. *, **, and *** denote statistical signi...cance at the 10%, 5%, and 1% levels, respectively.

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|FDc x sanitary_riskk|-0.263**<br>(0.106)|||||
|initial_exportcikj|-0.088***<br>(0.007)|-0.088***<br>(0.007)|-0.088***<br>(0.007)|-0.089***<br>(0.007)|-0.089***<br>(0.007)|
|NProductsci|-0.004**|-0.004**|-0.004**|-0.004**|-0.004**|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|NDestinationscik|-0.057***|-0.057***|-0.057***|-0.057***|-0.056***|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|Contiguitycj|-0.150|-0.151|-0.147|-0.147|-0.150|
||(0.104)|(0.104)|(0.104)|(0.105)|(0.104)|
|Com_languagecj|-0.126***<br>(0.039)|-0.126***<br>(0.039)|-0.126***<br>(0.039)|-0.126***<br>(0.039)|-0.126***<br>(0.039)|
|Colonycj|-0.086|-0.086|-0.093|-0.094|-0.086|
||(0.084)|(0.084)|(0.085)|(0.085)|(0.084)|
|Distancecj|0.090*|0.089*|0.092*|0.090*|0.089*|
||(0.048)|(0.048)|(0.048)|(0.048)|(0.048)|
|FDc x alt_sanitary_riskk||-0.202**<br>(0.088)||||
|TCc x sanitary_riskk|||-0.118*<br>(0.062)|||
|ICc x sanitary_riskk||||-0.138<br>(0.120)||
|EGCc x sanitary_riskk|||||-0.0003**<br>(0.0001)|
|Observations|14870|14870<br>~~20~~|14870|14870|14870|
perceptions deviate from fundamentals, sometimes abruptly and significantly. An implied default probability measure can capture the market’s views as they change, which would otherwise not have been picked up in low-frequency variables connected to fundamentals. As will be shown below, the index captures a key ingredient in a DSA by essentially inverting a ”fan chart” to produce a measure of how far a country is from its maximal effort to implement discretionary fiscal policies in an attempt to reduce debt.

The third aspect is expert evaluation of risks. Using standard DSA debt indicators, we evaluate for each country the number of variables that exceed thresholds of risk as defined by the DSAs into a **Count Index** . DSAs have been a staple input for evaluating debt risks. Economists at the IMF and the WBG use judgment and history as a guiding principle to assess whether a country is in debt distress. The Count Index reflects some of this judgment.

The fourth and final aspect captures what ought to be. Using a theoretical framework that embeds endogenous default, we calculate time varying debt default thresholds for each country to construct our **Theory Index** . The theoretical model is used to find debt ratios that are indicative of risk. These theoretical thresholds are endogenous and hence different than DSA guidelines on debt. They are time varying and embed the possibility of a self-fulfilling debt crisis - i.e., when market perceptions deteriorate significantly from fundamentals.

These four indices are constructed for each country and then used as explanatory variables for debt default. Figure 4 illustrates the objective. The red circle represents the unit disk (i.e. 1 = default), while the blue area represents the space spanned by the four angles of debt.

**Figure 4:** Angles of debt vulnerability

<!-- Start of picture text --> Vulnerabilityindex<br>Implieddefaultindex<br>Capturesfundamentals<br>Capturesperceptions<br>Theoryindex Capturesjudgement<br>Captureswhatoughttobe<br>Countindex<br>Unitcircle= Default<br>Parabola= Fitteddefaultprobability<br><!-- End of picture text --> Since a key aim is predicting default, the analysis attempts to incorporate as much information as possible (reduce omitted variables), while not trying to overfit relationships. To this end, the indices are used as explanatory variables in estimating observed or historical default episodes. The strength of each of the indices in terms of prediction is assessed empirically. Given that this part of the work is one related to a prediction (i.e., to default or not), several advances in econometrics and machine learning are employed. The meth6
FIGURE 1: STYLIZED ORGANIZATIONAL STRUCTURE OF LOCAL GOVERNMENT ENGINEERING DEPARTMENT11 <!-- Start of picture text --> Agency Head Office Chief Engineer<br>Divisional Offices Additional Additional<br>Chief Engineer Chief Engineer<br>(8 Divisions) (Division 1) (Division 8)<br>Regional Offices Superintending Superintending Superintending<br>Engineer Engineer Engineer<br>(20 Regions) (Region 1) (Region 2) (Region 20)<br>Units of District Offices Executive Executive Executive<br>Engineer Engineer Engineer<br>primary<br>(64 Districts) (District 1) (District 2) (District 64)<br>interest<br>Upazilla Offices Upazila Upazila Upazila<br>Engineer Engineer Engineer<br>(492 Upazillas) (Upazilla 1) (Upazilla 2) (Upazilla 492)<br><!-- End of picture text --> <!-- Start of picture text --> Units of<br>primary<br>interest<br><!-- End of picture text --> Source: Authors 19. Uptake of e-procurement has skyrocketed since 2012 in the three agencies under investigation. At the end of our observation period, virtually all the tenders in LGED, BWDB and RHD were processed through e-procurement (Figure 2). Similarly, on the bidder side, the number of firms registered in the e- procurement system has grown exponentially. As of the beginning of 2022, about 95,000 firms had registered in the e-procurement system.

> 11 A complete organogram of Local Government Engineering Department (LGED) can be found here: Local Government Engineering Department, Organogram, Approved in Year 2018, available at https://www.lged.gov.bd/site/page/e66db6f5-aca3488c-92d7-1c833ff4448e/

7
# 6. Annex

## 6.1. The road network

We constructed a GIS road network for Peru and allocated the RUC and the vehicle speed (km/hour) produced by HDM-4 based on the roads’ attributes—class, condition, type of pavement, number of lanes, terrain and traffic level. For road segments in urban areas, we recalculated speed with the Google Directions Application program interface (API) to capture the impact of congestion.

_Table 2. Coverage of GIS Road Data_

|**Network**<br>**Type**|**GIS Coverage of**<br>**Reported Network**<br>**KMs**<br>**%**|**Surface Type**<br>**(Paved/Unpaved)**|**Condition**<br>**(Good/Fair/Poor)**|**# of**<br>**Lanes**|**Traffic**|
|---|---|---|---|---|---|
|Primary|100|√|√|----|√|
|Secondary|100|√|√|----|Some|
|Tertiary|100|√|√|----|----|

_Source:_ Ministerio de Transportes y Comunicaciones (2013). _Note:_ GIS = geographic information system.

_Table 3. Condition as a percent of the total primary network_

|_able 3._|_Condition as a_|_percent_|_of the t_|_otal primary_|_Table 4. Surface type_|_as a percent_|_of the total_|
|---|---|---|---|---|---|---|---|
|_etwork_|||||_primary network_|||
||Good|Fair|Bad|No info|Paved|Not Paved|No info|
||%|%|%|%|%|%|%|
|Peru|55|20|16|9|Peru<br>85|8|7|

_Source:_ World Bank based on GIS data. _Source:_ World Bank based on GIS data.

The Ministry of Transport and Communication (MTC) provided a georeferenced road network data set that contained data on surface type and road condition for 93 percent of the country’s roads, updated in May 2015. However, it lacked information on terrain, which is an input to HDM-4. Thus, we used a Digital Elevation Model (DEM) to classify the country into 15 geographies and extracted elevation data at fixed intervals along the line of the road network. We calculated terrain class for each link and grouped them into the seven terrain types used by the HDM-4 model. Though we obtained traffic volume data (AADT) from the MTC, it was not georeferenced or included as one of the attributes in the GIS network. Hence, based on the origin and destination pair of the AADT, we identified the location of origin and destination (OD) and attributed the primary roads that can connect the OD with the shortest distance (the simulation is based on the shortest path routing algorithm). Once we had all the needed information to match the physical characteristics of the GIS network with the outputs of HDM-4, we appended the corresponding road user cost and vehicle speed to each link.

We then selected ODs using national and provincial capital cities, population centers of over 25,000 inhabitants, main ports and airports, and border crossing, generating a total of 93 nodes total. Based on 25
13 tables 2, 6, and 8 (the results in table 6 are calculated from figures in Appendix tables 1 and 2). Table 2 presents information on food subsidies, table 6 on feeding programs, and table 8 has summary information for a range of programs also including micronutrients and food for work.

There are no clear guidelines as to what a desirable level of non-food costs is: high values may either indicate administrative inefficiencies, or a high level of complementary inputs. The nonfood cost share of a targeted food subsidy (particularly a food coupon scheme) is a somewhat nebulous concept. For the Jamaican food stamp project the costs are those of printing the stamps, security, and mailing them out: costs of identification and screening beneficiaries are omitted (these are incurred by other agencies). Thus the non-food cost share would decline if the face value of the stamps were for example doubled. This would however not imply that the program had become more efficient. Thus non-food cost share data have to be interpreted somewhat cautiously.

Table 2 shows that food costs range from 63-88% of program costs for targeted food subsidies (with a mean of 79% and a median of 83%). Non-food costs are a slightly higher fraction for feeding programs (table 6), and range from 11-95% (with a mean of 70% and a median of 75%: note however that data are available for a far greater number of feeding programs than food subsidies). Some of the outliers in the feeding program data likely reflect bad data, where non-food costs are inadequately quantified, or cases where the programs have a large share of complementary inputs.

There exist some interesting variations across feeding programs. Programs in Latin America have larger non-food cost shares, and Africa smaller ones, than for other regions. Bearing in mind that no consistent region,l differences in unit costs were found (table 3), one possibility is that the food costs alone in Africa are higher (in particular transportation costs), whereas in Latin America due to better infrastructure there are larger complementary inputs. As regards program scale, the medium-size programs which were earlier found to have lower unit costs, also have lower non-food
reasoning test. But take-up is unrelated to household wealth, expressed willingness to pay for training, marital status, or willingness to take risks. As with existing enterprises, take-up is lowest in Colombo.

In both qualitative interviews and direct survey questions, those who attended appeared satisfied with the course. Among current enterprise owners surveyed in round two who had taken the course, 78 percent said they would strongly recommend, and 17 percent said they would somewhat recommend, the course to a friend currently running a business; 86 percent said that the course was more helpful than they had expected. Similarly, 81 (17) percent of potential owners said they would strongly (somewhat) recommend the training to someone starting a business, and 85 percent said it had been more helpful than they had expected.

# **3.3 Follow-up surveys**

Four rounds of follow-up surveys were conducted in September 2009, January 2010, September 2010, and June 2011 - corresponding to 3-4 months, 7-8 months, 15-16, and 24-25 months after the training. We refer to these as rounds 2, 3, 4 and 5 surveys, respectively. The follow-up surveys asked detailed information about business outcomes, including the key performance measures of business profits in the last month, sales in the last month, and capital stock (including raw materials and inventories). Business profits were asked directly, following the recommendations of de Mel et al. (2009b). Nominal values were converted into real values using the Colombo consumer price index. Appendix 3 addresses the possibility that training affected how profits were reported; we find that this is not driving any of our results.

Overall attrition was low – of the 624 (628) current owners (potential owners) selected for the experiment, 584 (588) were interviewed in the second round, 591 (587) in the third round, 580 (560) in the fourth round, and 575 (556) in the fifth round surveys. We cannot reject equality of attrition rates across treatment groups by round 5 for the current enterprises (p=0.25), but attrition rates are slightly lower for the training only group amongst the potential enterprises – a test of equality of attrition rates by round 5 has p-value of 0.085 for this group. Appendix 2 shows that the results from the sample of potential owners are robust to this attrition. In addition, in the case of refusals or inability to conduct an interview in the fifth round, proxy reports and direct observation were used to assess whether or not the owner was running a business, with information on this outcome available for 97 percent of firms.

12