rafmacalaba/data-use-mentions-extended huggingface dataset:
of these measures, Mexico also has performed comparatively well over the last few decades. These measures are not very satisfactory, however, as they reflect many other influences in addition to the quality of instruction as reflected in the acquisition of knowledge and skills. A comprehensive education survey was conducted in Mexico in 1995 as part of the Third International Mathematics and Science Study (TIMMS) that may have yielded important data to permit correlating the preceding education sector characteristics with academic achievement levels to obtain a better understanding of the determinants of education quality in Mexico. That data set has not been made publicly available.

- 2 The scatter diagram in Figure 1 is based on 317 observations from five different years. The observations pertaining to Mexico, ordered by date, are as follows:

Ave. Schooling (years) Ln (GDP per capita; 1980 US$) 1960 2.76 7.95 1970 3.68 8.29 1980 4.77 8.71 1985 5.20 8.63 1990 6.72 8.67 The trend line represents the least square regression line given by: S = -13.17 + 2.28 Ln(GDPcap) Adj. R<sup>2</sup> _=_ 0.68 (-18.7) (26.0) t-values in parentheses

- The application of Ramsey's RESET test to this regression equation failed to detect a specification error; unlike with the alternative specification of type: S = a + bX + cX<sup>2</sup> . However, coefficient stability tests indicated that the trend line is not constant across decades. This is reflected in the upward drift of the coefficients associated with the dummies in the following equation:

- S = -13.20 + 2.21 Ln(GDPcap) + 0.023DUM70 + 0.525DUM80 + 0.833DUM 85 + 0.997DUM90 (-19.0) (25.4) (0.1) (1.7
<!-- Start of picture text --> Min scores in HS GPA or national entry test is an admission requirement e<br>Research skills are very important when hiring faculty<br>* Percent of faculty working in the industry<br>HEI provides job market information e<br>Somebody is in charge of industry relations e<br>HEI trains students for job interviews @<br>Test required for graduation @<br>Government standards are very important to update program e<br>Collect student satisfaction data more than once per year @<br>Teaches numerical competencies @<br>-200 0 200 400 600<br>Percent<br>@ Training and curriculum = @ Link with productive sector Faculty @ Other Practices<br><!-- End of picture text -->
poor live in cities with fewer than 50,000 inhabitants. Only around 10% live in cities with populations greater than a million. In Kazakhstan, the incidence of poverty in smaller towns is six times larger than in Almaty. And there are large differences in access to local public goods and services too: in Morocco, average access to sewerage is over 80% in cities greater than a million, but less than 50% in the smallest towns.

A greater understanding of how poverty – both in terms of incomes or consumption expenditures and in terms of access to public services – varies across different types of cities should help inform the discussion of appropriate poverty reduction strategies in most countries. Yet, the evidence base needed for this disaggregated analysis is seldom available, since household surveys – on which most
Table A4: Characteristics of Schools in 2009 that joined Mais Educação in 2010, by treatment status

||Schools in<br>Panel A|: Fifth-grade sc|hools|Schools in<br>Panel|B: Ninth-grade sch|ools|
|---|---|---|---|---|---|---|
|Variable|_Mais_<br>_Educação_<br>(2010–2011)|Nonparticipa<br>ting schools|Difference<br>(1)−(2)|_Mais_<br>_Educação_<br>(2010–2011)|Nonparticipating<br>schools|Difference<br>(4)−(5)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|∆ Portuguese grades|11.0|7.7|3.3***|11.5|8.4|3.0***|
|∆ Mathematics grades|13.2|10.6|2.6***|3.7|1.7|2.0***|
|IDEB Index|4.3|4.6|-0.3***|3.5|3.9|-0.4***|
|∆ IDEB|0.5|0.4|0.1***|0.3|0.2|0.1***|
|Municipal population|959,776|716,126|243,650***|1,110,275|732,763|377,512***|
|Students' enrollments|701|543|158***|989|752|237***|
|IDHM|0.7|0.7|0.0
with randomization assignment and successful delivery of the program components. This is notable given the challenging implementation context.

## Attrition

The lottery assigned 491 households to treatment and 728 households to control. The followup survey was successfully completed among 458 treatment households (93%) and 689 control households (95%). The difference in attrition rates across treatment and control groups is not statistically significant.<sup>15</sup>

# **Data analysis**

## Pre-analysis Plan

To present a comprehensive overview of impacts on a large set of variables while balancing this with the threat of false positives, we registered the trial and published a PAP before analysis began.<sup>16</sup> The results presented in this report follow the variable construction and econometric specifications defined in the PAP, with a few exceptions. First, we include an index generated through principal component analysis of number and type of assets, rather than value of assets, since we did not collect asset values at follow-up due to a questionnaire design oversight. Second, we present both the pre-specified measure of women’s empowerment (aligned with other studies) along with a measure that incorporates a broader set of empowerment dimensions to highlight the differences in results. Third, conditional average treatment effects compare bottom, middle, and top thirds of the distribution for each respective outcome of interest, rather than median splits, to better understand the distribution. Beyond these deviations, all other analysis follows the PAP.

## Econometric Specification

Since treatment compliance with randomization was not perfect, we use the intention-totreat (ITT) estimator – simply, the difference between average outcomes across treatment and control groups at follow-up. The basic specification is: Here, Yi is the outcome of interest for household i at follow-up, Ti is a dummy variable equal to 1 if household i is assigned to receive treatment and 0 otherwise. β is the estimate of the average effect of the TUP intervention at follow-up. Since randomization is stratified by community, we follow Bruhn
|0.28|0.31|0.32|0.32|0.31|
||[0.097]|[0.11]|[0.11]|[0.10]|[0.11]|
|**District Government x Management Practice: Monitoring**||||-0.19<br>[0.14]||
|**Manager x Management Practice: Monitoring**|||||-0.10<br>[0.090]|
|**Sector, indicator, percentile fixed effects**|Yes|Yes|Yes|Yes|Yes|
|**District government fixed effects**|No|No|No|No|No|
|**Region fixed effects**|Yes|Yes|Yes|Yes|Yes|
|**Individual controls**|Yes|Yes|Yes|Yes|Yes|
|**Marginal costs and noise controls**|No|Yes|Yes|Yes|Yes|
|**Adjusted R-squared**|0.034|0.061|0.063|0.064|0.063|
|**Observations [clusters]**|1455 [92]|1455 [92]|1455 [92]|1455 [92]|1455 [92]|
|**Central government respondents [percentage]**|429 [62%]|429 [62%]|429 [62%]|429 [62%]|429 [62%]|
|**Districtgovernment respondents[percentage]**|258[38%]|258[38%]|258[38%]|258[38%]|258[38%]|

Notes: Clustered standard errors are in parentheses. Standard errors clustered at the district government-sector interacted level, allowing for error correlation within regions
and _ψ_ returning to its normal value thereafter. In terms of disciplining the choice of _ψt_ =1, we appeal to Bick and Blandin (2020), who provide survey evidence on the rest unemployed’s expectations of being recalled by their former employer. Since the persistence parameter _ψ_ has a direct mapping to the fraction of firms that recall resting unemployed workers—see Equation (4), this empirical moment is a useful calibration target. Following this strategy, we obtain 1 _− ψt_ =1 = 0 _._ 36, which is larger than 1 _− ψ_ = 0 _._ 03.

Before going into the results, two comments are in order. First, the reallocation shock has aggregate effects in our model only if there are financial or labor market frictions. In a perfect-credit benchmark with frictionless labor markets, a reshuffling of entrepreneurs, who re-draw idiosyncratic productivity from the same distribution, has no bearing on aggregate quantities. Second, as a result of the same model assumptions, while the reallocation shock lives for only one period, it carries persistent effects. This is because it takes time for the frictional labor market to reallocate workers and for firms that become suddenly productive to build up their internal funding and overcome the financial constraint.

**Fig. 15: Lockdown, Demand, and Reallocation Shocks**

In figure 15, the solid lines are the benchmark case with the lockdown and the demand shock, and the lines with circles are the result of adding the reallocation shock. By construction, there is no difference between the two cases on impact, because the reallocation shock 31
6|3.0|
|**All**|0.05|0.34|0.12|4.5|2.7|3.1|

_Source:_ Authors’ calculations.

# **Pakistan (HIES 2004–2005)**

The data are from the 2004–2005 Household Integrated Economic Survey (HIES), administered by the Federal Bureau of Statistics of the Government of Pakistan. The fieldwork for this survey was carried out between July 2004 and June 2005.

Non-purchased items had an average value of 37 percent of cash expenditure, and nonpurchased food accounted for about one third of all non-purchased items. Both ratios were approximately constant across the expenditure distribution. All expenditures on food items were deflated using a Paasche index provided in the survey data set.

The questionnaire asked three separate questions related to the use of LPG. The first two were related to the source of energy for cooking and lighting, respectively, but the categories of possible replies aggregated natural gas (piped gas) and LPG (bottled gas). The third question asked whether the household had consumed any LPG during the previous month and, if so, how much the household had paid and consumed. There was a separate question on the expenditure on natural gas. The availability of natural gas as a household fuel complicates the analysis because natural gas is a superior fuel for cooking and is a clear substitute for LPG. Combining responses from different parts of the survey showed that the number of households who had purchased LPG was considerably larger than the number of LPG-purchasing households that reported that gas (in some form) was their primary cooking fuel. Households may have used LPG as a secondary cooking fuel or for other purposes, but only 0.7 percent used it as the primary lighting source. As expected, few households purchased both natural gas and LPG. Firewood was the most commonly used primary fuel for cooking, while electricity, charcoal, and kerosene were scarcely used as the primary cooking source (
its public returns. This paper examines two features of the banking market – competition and concentration – that can influence private returns to joining a credit bureau and that can thus affect the probability of a credit bureau emerging.

Pagano and Jappelli (1993) develop a model which predicts that the incentive to share information is greater when lenders are protected from competition by barriers to entry. Brown and Zehnder (2010) find empirical evidence for a negative relationship between competition and

> 2 See also Padilla and Pagano (2000) 3 Based on the Doing Business dataset that is described in more detail in section 3.

2
complex mosaics of land use and land cover areas, forests and urban fringes where the boundaries between forest and non-forest areas were not clear.

We also conducted accuracy assessment for each individual image using the maximum likelihood method. In order to assess classification accuracy, we generated contingency matrices and the corresponding Kappa statistics. Kappa values quantify how much better a particular classification is in comparison to a random classification, making it possible to calculate a confidence interval for comparing two or more classifications. In addition we also assess procedural accuracy to measure the percentage of pixels of a given land cover type that is correctly classified. User accuracy is generated to measure the commission errors useful for examining whether or not a pixel classified into a given class actually represents that class on the ground. We used a subjective scale in accuracy assessment and found Kappa values greater than 80 percent for all the images.<sup>12</sup> After checking their accuracies, all individual images are subset to the actual classified areas and mosaics made to the base images to cover the entire study area. These mosaic images are re-sampled to 60m x 60m to bring them to the same resolution. For example, all MSS images are upgraded to 60m x 60m from 80m x 80m, while the TM and ETM+ images are degraded to 60m x 60m from 28.5m x 28.5 m. The decision to resample the images into 60m x 60m was made only after ensuring that no significant information would be lost during the exercise. After these re-samplings, the land use and land cover classes were generated by overlaying the village development committee (VDC)<sup>13</sup> polygons. All road lengths were calculated in meters for each individual VDC. These road lengths were calculated by using the Spatial Analyst Zonal method.

# **4.2 Household Survey Data**

The primary economic data we use come from a repeated cross-section of the Nepal Living Standards Survey (NLSS) which is a nationally-representative survey of households and communities conducted by
# **6.   Conclusions**

We learn two things from this analysis. First, (contrary to previous work) we find that the CF allocations have, in fact, been successful in inducing entry into lagging regions, conditioning on the location of firms’ headquarters. This result however, can be easily lost among the confounding effects of local unobservables that are negatively correlated with CF allocations. It is hard to capture these determinants of entry with available data describing agglomeration attributes, but without properly accounting for these factors, there is a bias toward finding a negative effect of the CF’s. The RAIS data, which describe spatial entry behavior on an annual basis, provide a unique opportunity to overcome this problem non-parametrically.

Second, we learn that, while the CF’s were successful, headquarter proximity is a significant determinant of entry behavior (conditional upon having a parent) that works to offset this success. Simply “turning-off” the effect of parent firm location significantly raises overall entry into lagging regions. These effects are, moreover, likely a lower bound on the effect of removing the pull of firm headquarters if we believe that there are positive spillovers between entrants.

The conclusions of this paper for policy-makers are, unfortunately, limited. While we find that the CF’s were successful in inducing firms to locate in Brazil’s lagging regions (and that the CF’s may yield more “bang for the buck” if they are used to induce entry by firms’ headquarters into those regions), we are unable to determine whether the CF’s were good policy or not. In further research, we plan to move from the limited question of “do subsidies affect firm location decisions” to the more policy-relevant

> 18 These findings are consistent with Lall, Funderburg, and Yepes (2005) who examine the effect of transport costs and agglomeration economies on manufacturing wages in Brazil. They find that while wages are higher

25
quality”. Identical analysis performed in the 1990s found that roughly 70 percent met the “acceptable” or “good” quality standard (IEG, 2010).

Even so, there are recent examples of influential projects that sought to integrate impact evaluation results with cost-effectiveness analysis to collectively set policy priorities, as with the Disease Control Priorities Project (Jamison et al. 2006). Arguably, as impact evaluation methods continue to mature in sectors like Education, Governance, Agriculture, and Health and Nutrition, evaluators may consider how to re-institutionalize VFM practices.

A major goal of IE studies is to provide scientific evidence on the policies and programs that do and do not work to improve development outcomes. However, if IEs do not include program or policy cost information, resource-constrained policymakers will have limited evidence to guide their selection of efficient programs and policies, or to consider the cost implications of scaling, replicating or reproducing programs and policies found to be effective.

Cost-benefit analysis (CBA) and cost-effectiveness analysis (CEA) would be useful for (i) identifying whether social benefits exceed social costs of a single intervention; (ii) comparing worth of interventions with different (monetizable) outcomes; and (iii) comparing the relative worth of interventions that share common outcome(s). CEA and CBA help identify which intervention produces a given amount of outcomes for the least cost. Including VFM analysis may help policymakers understand and use evidence from the growing number of IE field studies in their program decisions by identifying which interventions produce the most outcome for a given cost (White 2014; Dhaliwal et al. 2013; McEwan 2012). Such evidence can provide insight that is “counter to common sense, popular appeal, and traditional ideas” (Levin 2001).

Including VFM directly in IEs yields at least two advantages over producing them separately. First, collecting cost data at the time of the intervention and data collection on the IE outcomes is likely to be less onerous in terms of expense and time than reconstructing program costs ex post
27

> _QiZ<QW2_ BGT must buy the missing gas in the flexibility market to restore system balance. BGT accepts flexibility bids 1-3.

SMPbuy, The system marginal price of gas bought by BGT from winning bidders. It is equal to the price of the last accepted flexibility bid.

Data for July 1996 support the relation between flexibility and spot market prices predicted above, although the period is short (table 4). During 10 days of that month BGT sold gas at a system marginal price below the prevailing spot market price and bought gas at a system marginal price above the spot market price. The system average price was close to the system marginal price on all but one day of trade, reflecting the small spread of prices in flexibility bids. The exception was July 28, when the system average price exceeded the spot market price even though the system was selling. This exception was caused by the high price of £3.11 per therm for 170,847 therms sold to National Power, compared with the prevailing system marginal price for system sell of 10.07 pence per therm. According to British Spot Gas Markets (July 29, 1996), the trader for National Power who placed this expensive bid had confused therms with kilowatt-hours.

**Table 4 Trading and Prices in the Flexibility Market, July 19-28, 1996** (1 pence per them, unless otherwise indicated)

||Volumea||||SAP|Spot market|
|---|---|---|---|---|---|---|
|Dav|(millions of therms)|SAP|SMP buy|SMP sell|30-day|price|
|19|-2.56|**15.04**|**15.05**|_8.90_|12.45|12.1275|
|20|2.81|**11.72**|_34.87_|*
_5_ Using a sample of daily data from January 1997 to March 2000 we find that Ras Gas yield spreads exhibit a very high degree of persistence. By far the most important explanatory variable for both levels and changes in credit spreads is the Kepco credit spread: investors rationally anticipate on the incidence of the off-taker's financial and economic condition on the riskiness of their bond. However, we also find evidence for over-reaction: while Ras Gas spreads widen with contemporeanous Kepco spread movements, they narrow in lagged ones. The price of crude oil, which serves as a reference for LNG pricing, comes out largely insignificant: investors seem to disregard commodity price risk.. While rising energy prices increase revenue and debt service capacity it also raises the cost of electricity generation for Kepco leading to higher breach of contract risk. Further investigation shows that the direct energy price impact on Ras Gas is insignificant but that the indirect impact via Kepco's financial position is highly significant: revenue and off-taker quality effects off-set each other but the indirect effect via Kepco yields survives.

In terms of Korean exposure variables we find evidence of Ras Gas exposure to the Korean currency both directly and indirectly through Kepco yields despite the fact that the offtake agreement is USD based. Also, Ras Gas bond holders acquire significant Korea country risk which, in light of the energy dependence of the Korean economy, makes sense. Finally, we find significant evidence for "guilt by association" effects related to financial contagion. As returns in European, Asian, Middle Eastern and Latin American emerging debt markets fall Ras Gas spreads are predicted to widen considerably. In particular, the impact of contemporaneous and past events in European emerging debt markets stands out. This responsiveness reflects the turmoil in global debt markets following the Russian financial crisis of summer and fall 1998 which heavily affected other market segments. Similarly, Ras Gas spreads exhibit a delayed reaction to Asian, Middle Eastern and Latin American emerging debt markets with only a few but highly significant lagged returns accounting for most of the explanatory
**Compute the counterfactuals** The system of equations that define the general equilibrium (Eqs. 3.12, 3.7 and 3.9) can be used to undertake model-based counterfactuals for new trade costs, new productivity or new amenity levels that uses only parameters and the values of enˆ dogenous variables in the initial equilibrium. We use the hat notation such that _x_ = _x_<sup>′</sup> / _x_ with _x_<sup>′</sup> the counterfactual value. The system of equations for the counterfactual equilibrium (Eqs. 3.12, 3.7 and 3.9) can be re-written as follows:

where _Yi_ = _ωiLi_ denotes labor income and _λn_ = _Ln_ / _L_<sup> ̄</sup> denotes the population share of each location in the initial equilibrium. Given the observed variables in the initial equilibrium { _λn,Yn,πni_ } and an assumed comparative static for trade costs.

# **4 New transport and trade investments**

## **4.1 Calibration of the model**

The information needed to calibrate the model comes from traditional data sources and satelite images. Four types of data at the state and district level are used: residential land size, labor force, wages, and transport costs. Labor force includes active population as calculated by GHSL for 2015 (Figure A5 in the Appendix). All wages are monthly and expressed in dollars based on estimates from Tilottama et al. (2010) (Figure A7 in the Appendix). Land covers urban areas using CIESIN data (Figure A6 in the Appendix).

We assume labor mobility within country but not across countries. Following Redding (2016) and the residential choice Equation 3.9, workers can move in a location with higher wages, lower consumption and housing prices depending on their initial preferences across the amenities that are offered across locations. Such assumptions smooth the movement of workers across locations, otherwise all workers would move to the locations