rafmacalaba/data-use-mentions-extended huggingface dataset:
construct both the total number of junior secondary schools and the percentage of district junior secondary schools that are public in the district.

We obtain village-level data on the percentage of schools that are private using the 2000 IFLS, which like earlier IFLS rounds contains a complete roster of neighborhood schools. This roster contains the schools that household members report attending, as well as schools identified by community leaders but which are not attended by the sample of survey households in the community. These community leaders were not asked to provide information regarding the school type, however. Therefore, we inferred the public or private status of schools listed on the roster, using the school name.<sup>3</sup> As a first step towards investigating how a student’s test score is affected by the type of junior secondary school, Figure 1 shows students’ test scores exiting junior high schools, smoothed against their test scores exiting elementary school, separately for public and private junior high students. In Figure 1 and throughout the paper, students’ test scores are normalized using the scores of other students that took the national test in the same year. The shaded regions indicate 95% confidence intervals. Conditional on elementary test scores, students at public junior secondary schools score higher upon exiting junior secondary school. Moreover, the difference in exiting test scores appears to be greatest for students at the tails of the elementary test score distribution.

To further probe this initial finding, we control for other observed child and family characteristics in a regression framework. Including control variables, however,

> 3 Public junior secondary schools in Indonesia are typically assigned a registration number. Therefore, any school name containing the Indonesian acronym for junior secondary school ( _SMP_ ) followed by a number is designated as public. In addition, a school was also considered to be public if its name identified it as a

11
Policy Research Working Paper 5545

# **Abstract**

Scaling-up adoption of renewable energy technology, such as solar home systems, to expand electricity access in developing countries can accelerate the transition to low-carbon economic development. Using a purposely collected national household survey, this study quantifies the carbon and distributional benefits of solar home system programs in Bangladesh. Three key findings are generated from the study. First, dissemination of solar home systems brings about significant carbon benefits: the total carbon emissions avoided from replacing kerosene use for lighting by solar home systems in nonelectrified rural households was equivalent to about 4 percent of total annual carbon emissions in Bangladesh in 2007. This figure increases to about 15 percent if the grid-electricity generation is used as the energy baseline to estimate the carbon avoided from the installation of solar home systems. Second, solar home system subsidies in rural Bangladesh are progressive when the program is geographically targeted. Third, there exists a market potential for solar home systems in many rural areas if micro-credit schemes are made available and the propensity to install solar home systems is very responsive to income, with a 1-percent increase in per capita income increasing the probability of installing solar home systems by 12 percent, controlling for other factors.

This paper is a product of the Environment Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lwang1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily
587|
|Within R-squared|0.206|0.360|0.302|0.346|
|Between R-squared|0.025|0.062|0.011|0.001|
|Overall R-squared|0.010|0.012|0.005|0.018|

_Notes:_ Robust standard errors in brackets;<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

_Source:_ Authors’ own elaboration using World Bank’s data.

26
|Dimension|Brief Description|
|---|---|
||This set of indicators is based on countries’ use of internationally accepted and<br>recommended methodologies, classifications and standards regarding data integration.<br>These indicators help facilitate data exchange and provide the foundation for the<br>preparation of relevant statistical indicators. The following methods and standards are<br>considered: System of national accounts in use, National Accounts base year,|
|Dimension 5.2:<br>Standards and Methods|Classification of national industry, CPI base year, Classification of household<br>consumption, Classification of status of employment, Central government accounting<br>status, Compilation of government finance statistics, Compilation of monetary and<br>financial statistics, Business process. Further work could improve the validity of this<br>indicator and reduce the risk that countries may be incentivized to adopt only<br>traditional standards and methods and neglect innovative solutions that may be more<br>valid in the current context.|
||Not included because of lack of established methodology or suitable data sources. A|
|Dimension 5.3: Skills|new indicator drawing on PARIS21 indicators such as statistical society presence and<br>data literacy could be developed and is an area of future work.|
||Not included because of lack of established methodology or suitable data sources. A|
|Dimension 5.4:|new indicator based on textual analysis of NSS reports/websites for references to|
|Partnerships|partner organizations could be developed. This is an area of future work.|
||The indicator is based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical|
|Dimension 5.5: Finance|plan that is fully funded and under implementation). It is included in dashboard, but not<br>in the overall SPI score or index because of insufficient country coverage.|

**Table S3.
Decarolis, F. (2014). Awarding Price, Contract Performance, and Bids Screening: Evidence from Procurement Auctions. American Economic Journal: Applied Economics, 6(1), 108–132. http://www.aeaweb.org/articles.php?doi=10.1257/app.6.1.108

- Decarolis, F., & Palumbo, G. (2015). Renegotiation of public contracts: An empirical analysis. Economics Letters, 132, 77–81.

- Di Tella, R., & Schargrodsky, E. (2003). The Role of Wages and Auditing during a Crackdown on Corruption in the City of Buenos Aires. Journal of Law and Economics, 46(1), 269–292. http://ideas.repec.org/a/ucp/jlawec/y2003v46i1p269-92.html

Fazekas, M., & Blum, J. R. (2021). Improving public procurement outcomes: review of tools and the state of evidence base (No. 9690; Policy Research Working Papers).

https://openknowledge.worldbank.org/handle/10986/35727 Fazekas, M., & Kocsis, G. (2020). Uncovering High-Level Corruption: Cross-National Corruption Proxies Using Government Contracting Data. British Journal of Political Science, 50(1), 155–164. https://doi.org/10.1017/S0007123417000461 Fazekas, M., & Tóth, B. (2016). Assessing the potential for detecting collusion in Swedish public procurement.

http://www.konkurrensverket.se/globalassets/publikationer/uppdragsforskning/forsk_rapport_20163.pdf Fazekas, M., Tóth, I. J., & King, L. P. (2016). An Objective Corruption Risk Index Using Public Procurement Data. European Journal of Criminal Policy and Research, 22(3), 369–397. https://doi.org/10.1007/s10610-016-9308-z
country assembly of firms across time which would give us a collective view of the policy choices countries made in the region. The latter data sets look specifically within Malaysia, Vietnam and China and see whether those policies that are significant drivers for the cross‐country firm results are also validated for firms within the three countries separately.

# **3.1 Data Linkage**

The data linkage index builds on the methodology pioneered by Arnold _et al_ . (2011; 2015). Their approach in which the authors create a so‐called services linkage index has been widely used in the empirical field. In our case, we develop a data linkage index variable for digital innovation and use this composite indicator in our regressions. For each country, we interact the country‐specific data policy index with software use as proxy for measuring the extent to which a sector uses data in their production process. This identification strategy relies on the assumption that sectors more reliant on the use of software are more affected by data restrictions. This weighted method is a more refined way of measuring the impact of restrictive data policies rather than simply taking an unweighted approach of regressing our data policy index on any outcome variable of innovation.

In doing so, the country‐specific index of restrictive data policies we develop is multiplied with sector‐specific data‐intensity proxied by software use for each downstream industry _j_ in country _c_ . This is how the data linkage (DL) variable is set up. In this variable, data‐intensities are expressed as (D/L) which is measured by the sector’s software use over labor (see below). In equation (1), therefore, the term ϛ�� denotes the software use for each sector _j_ for which data is retrieved from the US Census ICT survey. Then in equation (1), the data‐intensities are stated as a ratio over labor, called LAB� , that is employed in each downstream sector _j._ The data for labor is
***|2.1377***|0.3160***|0.2208***|
|education|(0.1830)|(0.1437)|(0.0264)|(0.0240)|
|=1 if Head has higher education|4.1748***|3.2704***|0.4228***|0.3586***|
||(0.2728)|(0.2726)|(0.0331)|(0.0442)|
|Constant|3.9172***|5.4001***|0.5180***|0.7323***|
||(0.2240)|(0.1952)|(0.0312)|(0.0274)|
|Panel variable|District|District|District|District|
|Age at Interview dummies|Yes|Yes|Yes|Yes|
|DHS 2006 x Region x Age<br>dummies|Yes|Yes|Yes|Yes|
|Observations|3912|3420|3912|3420|
|No. of Clusters|69<sup>a</sup>|69<sup>a</sup>|69<sup>a</sup>|69<sup>a</sup>|
|R-Squared|0.2339|0.2100|0.1685|0.1450|

All specifications are estimated using the panel fixed-effects estimator. District casualties are expressed per 1000 inhabitants. Sample only includes individuals surveyed in the Nepal DHS 2001 and 2006 and aged 15-18 at the time of the survey.<sup>a</sup> DHS data collection was somewhat affected by the conflict in 2001, and so a number of districts were not covered, namely Dolpa, Jajarkot, Rolpa and Rukhum. The small districts of Manang and Mustang were not surveyed either
Overall, these descriptive findings suggest that the impact of DSM on seed purchases and yields may be greater for maize than for wheat and more evident in the 2019 survey round than in 2016. However, it is essential to account for changes in the comparison groups relative to the treated cohorts, rather than focusing solely on the progression of the outcome variables for a cohort over time. We explore this in the next section.

# **4 Empirical Model**

The ACC survey covers districts introduced to DSM at different times: five in 2013, eight in 2014, seven each in 2015 and 2016, nine in 2017, and 20 each in 2018 and 2019. This staggered rollout allows us to use the Callaway and Sant’Anna (2021) Difference-in-Differences method (CSDID), which accommodates variation in treatment timing, a defining characteristic of our sample. CSDID also allows the parallel trends assumption to hold, conditional on a vector of pre-treatment covariates that explain the evolution of outcomes without DSM treatment.

Following Callaway and Sant’Anna (2021), the basic CSDID framework is presented as follows. Let i denote the outcome variable (such as seed purchases or yield) for household _i_ at time _t_ , for _i=1, 2, ..., N_ and _t=1, 2, ..., T_ . Define i as the treatment status, taking a value of 1 if household YY _i_ resides in a DSM district at time _t_ , and 0 otherwise. i indicates treatment cohorts or groups, DD equaling 1 if the household lives in a district first treated to DSM at time _g_ , and 0 otherwise. _C_ GG represents a “never-treated” comparison group, identifying households that were never treated between 2012 and 2019. Once treated, districts remain under DSM, satisfying the CSDID assumption of treatment persistence. denotes pre-treatment control variables (fertilizer use, oxen ownership, household size, gender, and age of the household head) that are either time-invariant or, in the case of continuous
, community level data as well as additional parts of the survey.

The remainder of the paper proceeds as follows. Section I anchors the paper in the literature. Section II provides information on Moldova and corruption in education. Section III describes the data used, and section IV presents our empirical strategy. The main results are presented in section V. Section VI tests alternative explanations and the robustness of the main results before section VII concludes.

> 3. For example, see the contributions of Cameron et al. <u>(2015), Beine et al. (2013), Barsbai et al.</u> (forthcoming), Spilimbergo (2009), Batista and Vicente (2011), <u>Ivlevs and King (2014).</u>

3
# **Land Price Effects of Informality, Farm Size, and Land Reform: Evidence from More Than One Million Transactions in Ukraine**

## **1. Introduction**

Land and house prices in high-income countries have increased significantly in real terms since the 1950s (Knoll _et al._ 2017), allowing owners to realize collateral benefits (Kumar and Liang 2024; Zevelev 2021) and take up entrepreneurial activities (Schmalz _et al._ 2017). A growing literature on urban land markets shows that market prices of property are affected by public goods such as connectivity (Ahlfeldt _et al._ 2017), road maintenance (Gertler _et al._ 2024), trees (Han _et al._ 2021), proximity to the coast (Severen and Plantinga 2018), or flood risk exposure (Bosker _et al._ 2019). Data on land prices is instrumental to estimate the cost of agglomeration (Combes _et al._ 2019) and analyze impacts of policies such as urban regeneration (Rossi-Hansberg _et al._ 2010) and low-income housing construction (Asquith _et al._ 2023), density restrictions (Hilber and Vermeulen 2016), building standards (Baylis and Boomhower 2023), disaster risk <mark>(Gourevitch</mark> _<mark>et al.</mark>_ <mark>2023; Hino and Burke 2021) and</mark> insurance subsidies (Garbarino _et al._ 2023).

In rural areas, factor mis-allocation that is not related to endowments (Adamopoulos and Restuccia 2022) has been shown to be widespread (Gollin and Udry 2020) and to negatively affect productivity (Maue _et al._ 2020) and occupational choice (Adamopoulos _et al._ 2022). Measures to improve market functioning such as issuance of transferable certificates (Bu and Liao 2022; Chen _et al._ 2022) or removal of restrictions on land (Chari _et al._ 2021; Chen _et al._ 2023; de Janvry
di¤erent DSDs. It is reassuring that the regression results for actual yield con...rm the conclusions based on potential yield in Panel A of Table 1. The evidence in Table 1 is thus very strong in favor of the conclusion that the LDO lands are more productive compared to the other lands in Sri Lanka and that any possible adverse e¤ect of low labor quality is clearly dominated by strong positive correlations between land productivity and incidence of LDO restrictions in a DSD.

The evidence in Table 1 is also consistent with the evidence on overall crop yields at the district level reported by the Statistical Abstracts of Sri Lanka. According to Statistical Abstract, 2009, paddy yields during the monsoon season in Mahaweli system H in the Dry zone is about 30 to 40 percent higher than average yield in Sri Lanka. Mahaweli annual reports also indicate signi...cant productivity advantage of settlement schemes for nearly all ...eld crops.<sup>17</sup> The striking productivity advantage of the LDO areas implies that the OLS estimate of the

> 17 The crop productivity statistics in Mahaweli area are posted in http://www.mahaweli.gov.lk/.

15
Regarding health impacts, individuals who live closer to the camps have a higher probability of contracting viral diseases than those who live farther from the camps. Estimates indicate that a decrease in distance to refugee camps by 30 km is associated with a 2.1 pp increase in the likelihood of experiencing diarrhea, a 3.7 pp increase in the likelihood of experiencing fever, and a 2.7 pp increase in the likelihood of experiencing a cough. Adindividuals closer to are more to seek health care from ditionally, refugee camps likely NGO providers. These results are in line with those documented by Dagnelie, Mayda and Maystadt (2023), who use DHS data to examine the effects of 30 refugee camps in Africa. The authors report negative effects on height-for-age and weight-for-age scores of children under five years of age and suggest that the effects stem from heightened rates of malaria transmission. Importantly, this mediating factor might also affect our context as Zhao et al. (2023) note that Myanmar bore the heaviest malaria burden in the Greater Mekong subregion between 2011 and 2017.

Greater proximity to refugee camps is also evident in heightened self-reported safety concerns. The estimates indicate that an increase in proximity to the camps by 30 km is associated with a higher likelihood of experiencing serious injuries by 2.6 pp, encountering combat situations by 2.1 pp, facing near-death experiences by one pp, and witnessing homicides of strangers (one pp). These correspond to large and meaningful effects that represent increments of at least 20 percent in the mean values of these outcomes. The results underscore unease about the effects of to on the proximity refugee camps safety and security of individuals in surrounding areas.

Finally, a decrease in distance to the camps is associated with more positive perceptions among hosts regarding the benefits their families receive from the presence of the Rohingya. Additionally, those living closer to the camps are less likely to believe that support for camp residents should come only from external sources. However, respondents nearer the camps are also more worried about
Policy Research Working Paper 6226

# **Abstract**

The failure of trade economists to anticipate the extreme drop in trade post Lehman Brothers bankruptcy suggests that the behavior of trade in exceptional circumstances may still be poorly understood. This paper explores whether uncertainty shocks have explanatory power for movements in trade. VAR estimations on United States data suggest that domestic uncertainty is a strong predictor of movements in imports, but has little effect on exports. Guided by these results, the paper estimates a bilateral model with focus on the impact of importer uncertainty on foreign suppliers. It finds that there is a strong negative relationship between uncertainty and trade and that this relationship is non-linear. Uncertainty matters most when its levels are exceptionally high.

The paper does not find evidence of learning from past turmoils, suggesting that prior experience with major uncertainty shocks does not reduce the effect on trade. In line with the expectations, the negative effect of uncertainty shocks on trade is higher for trade relationships more intensive in durable goods. Surprisingly, however, the effect of durability is nonlinear. Supply chain considerations or the possibility that the relationships with the highest durability lead to important compositional effects may have a bearing on the results. The results are robust to excluding the post Lehman shock, suggesting that the trade response during the 2008–2009 crisis has been similar to past uncertainty events.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at dtaglioni@worldbank.org or veronika.zavacka@graduateinstitute,ch.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations
indicators such as weight and height of child and whether the child has been immunized. Computing HOI for access to basic infrastructure, such as safe drinking water, electricity and sanitation, would require household-level information for these indicators. With regard to circumstances, the minimum information needed to make the analysis meaningful would be gender, age, and location (urban/rural and/or regional) of the child; demographic characteristics of the household (size and composition); characteristics of parents (gender, age, and education); and some measure of household income, consumption, or wealth.

In practical terms, computing HOI for a particular opportunity when the number of circumstances is relatively large (more than three) requires an econometric exercise, which involves obtaining a _prediction_ of the D-index from observed access to opportunities and circumstances among children. In simple terms, the exercise consists of running a logistic regression model on the full sample of children for whom the HOI measure will be constructed to estimate the relationship between access to a particular opportunity and circumstances of the child. The estimated coefficients of the regression are used to obtain, for each child, his or her predicted _probability_ of access to the opportunity, which is then in turn used to estimate the D-index, the coverage rate, and eventually the HOI (see box 3 for more technical details on the econometric exercise).

# **Box 3. Computing the Human Opportunity Index from Household Survey Data**

To construct the HOI, the conditional probabilities of access to opportunities for each child based on his or her circumstances must be obtained. To do so, one can estimate a logistic model, linear in the parameters β , where event I corresponds to accessing the opportunity (for example, access to clean water), and _x_ the set of circumstances (for example, gender of the child, education, gender of the head of the household, and the like). One can fit the logistic regression using survey data: where _xk_ denotes the row vector of