rafmacalaba/data-use-mentions-extended huggingface dataset:
restricting our analysis to other types of violence (one-sided violence in UCDP or battles in ACLED). In addition, increased gender-based violence due to the influx of workers might be considered as orthogonal to the reasons for local conflicts in DRC. While it is possible for other countries and other non-World Bank projects, none of the World Bank ICR documents lists the eruption of local violence as a reason for stopping or delaying the project. While World Bank projects are only a subset of all projects, their completion would be more likely to be disrupted in case of increased violence compared to projects led by the government. Therefore, it is unlikely that the completion date of such long and often delayed projects is explained by a sharp decrease in violence.

# **3.3 Using Satellite Images to Infer Road Quality**

The road project data detailed in the previous section gives us a start and end year for the project. We will maintain the assumption that between these dates, the project is ongoing, delivering improved road quality only towards the end, where implementation tends to concentrate. What cannot be said, however, is how long the increased road quality is maintained after the end of the project–what one could call the perishability of the peace dividend of road rehabilitation. This matters to identify the impacts of road rehabilitation on conflicts. We expect the average quality on rehabilitated roads to increase until the completion of the project and then gradually fall again, given the harsh tropical conditions reducing the lifespan of newly rehabilitated unpaved earth roads (see discussion in section 2.1).

We use machine learning models for assessing the quality of roads using satellite imagery (Cadamuro et al. 2018; Thegeya et al. 2022; Conner et al. 2023). We first classify the images as described in Muhebwa et al. (2023) to then find a parameter that describes the decay of road quality over time in a particular region of the DRC. Each image is given predicted quality labels from a 2-class and 5-class classifier
inefficiently low volumes). Thus, as a country’s relative specialization in a sector rises, diversification in that sector will occur more through exporting existing products to new markets. The finding by others (Amurgo-Pacheco & Pierola 2008; Brenton & Newfarmer 2009) that export growth along the extensive margin is weighted toward the geographic, rather than discovery, channel is thus consistent with the results presented here.

We find an analogous result for the trade volume variable, albeit in the opposite direction: Larger trade volumes are associated both with a higher number of products exported and more concentration in the sector’s exports. Intuitively, it makes sense that a larger sector is better able to discover new products; it is perhaps surprising, however, that the dominating effect appears to be one that strengthens subsectors that are already 22The fact that a full tariff schedule for Syria was only published in 2002 (from the Unctad Trains database) and 2007 (directly from Syrian Customs) determined the starting and ending point of our sample.

> 23Unfortunately, actual production output data were only available at the sectoral level at a highly disaggregated level—for agriculture and mining/manufacturing—and so were not usable for the analysis.

24
Appendix Table 24). However, differentials associated with Jurmala, Liepaja and Daugavpils are almost unchanged from specifications which do not account for local unemployment, suggesting region-specific effects dominate those of local unemployment for people in these towns.

Some of the regional effects depend on gender or ethnicity. The advantage of working in Jurmala, Liepaja or Ventspils is much more pronounced for men than for women. The effects of working in Riga district and Liepaja on non-Latvians’ earnings are two times larger than on Latvians’ earnings, but it is the other way around in Jurmala. Finally, except for Ventspils and Daugavpils, all above-mentioned location effects on earnings are almost completely concentrated in the private sector.

**The further from Riga, the lower are wages.** Another way to look at regional wage effects is to link them with the distance between job location and Riga. It turns out that, Ventspils and Liepaja aside, every 10 kilometers of distance from Riga reduce wages by 1.8 percent for workers without higher education, and by 1.2 percent for employees with higher education. This model is estimated using LFS 2001 data, and is not shown. The main effect is significant at 0.001 level, interaction with higher education – at 0.05 level. All described regional effects remain almost unchanged when (the major group of) occupation is controlled for. In our terminology, these are pure “reward” rather than “access” effects.

**Small firms pay less.** Since 2002 the Latvian LFS has included a question on the number of employees in the local unit where the respondent actually works.  This indicator, usually referred to as plant size, is obviously correlated with firm size. Small firms cannot have big plants. All else equal, an individual working in an establishment with 50 or more employees earns 15% more than an otherwise similar employee in a small unit with 1 to 10 workers.  The ‘small firm’ effect is more pronounced in rural areas and among non-Latvians
progress, and therefore there would be no issue with LAYS conversion. However, in practice studies and interventions vary widely in the test used, making comparability an issue. LAYS can currently be constructed only with the data as they are, so a separate effort is needed to produce comparable underlying learning assessments.<sup>15</sup> Once that effort bears fruit, it will enhance the comparability of LAYS.

For now, therefore, we rely on standardized effect sizes. Standardized effect sizes are used to account for differences across measurement scales and express those effects in relative terms. This should prove useful when comparing effect sizes in education across various assessments and scales. However, standard deviations will not account for whether a given test is either “too hard” or “too easy,” causing floor or ceiling effects. We test for this possibility empirically by comparing standard deviations from tests on nationally representative samples, chosen to ensure that the same underlying population is represented. We focus on primary-level tests for countries that have participated in multiple tests and that have interventions featured in this paper. Appendix Figure B5 compares standard deviations for Tanzania, Malawi, and Indonesia using various assessments: HLO scores derived from EGRA, Progress in International Reading Literacy Study (PIRLS), raw EGRA or Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) tests. We find only small differences of a few points, and as a result, the estimated learning rates per year across assessments are quite similar, as shown in Appendix Figure B6. As a robustness check, Appendix Figure B6 also shows learning rates per year using raw data from the assessments before they are converted to HLO scores.

In terms of standard deviations across samples, it is well known there is high variation across sample populations. As a result, when students answer five questions correctly in a country with less variation, this will appear as a larger standardized effect size than if they had gotten the same questions correct in a country with more variation. This is
- 6 -

growth to real interest rates is very clear when compariri, Table 1 and Figure 1 which shows the ratio of M2 to GNP over 25 years._ 13. Comparing levels of financial development in Korea, Thailand and Japan, Figure 2 shows that Thailand achieved a higher level of financial level development throughout the entire period due to considerably less government intervention in the financial markets. The depth of Thai financial development measured by the ratio of M2 to GNP is greater than in Korea in spite of a per capita income that was at first half and then soon only one-third of Korea's.

Table 1: REAL DEPOSIT RATES IN KOREA, 1960-1986

|Period|Deposits Rate|
|---|---|
|1960-64|-6.7%|
|1965-69|26.9%|
|1970|1.8%|
|1971|-2.0%|
|1972|5.0%|
|19.3|-19.1%|
|1974|-15.8%|
|1975|0.6%|
|1976|1.5%|
|1977|-6.4%|
|1978|-8.8%|
|1979|-16.3%|
|1980|-0.4%|
|1981|7.0%|
|1982|4*0%|
|1983|1.3%|
|1984|5.3%|
|1985|5.8%|
|1986|7.7%|

Sources: Hanson and Neal (1985) and Dornbush and Park (1987). Their calculations were based on official statistics. See also Figures 4 and 5 which are directly based on the original sources.

7/ M is one of the simplest if imperfect measure of the willingness the public to maintain time and savings deposits with financial institutions. M2 over GNP is very convenient to compare different countries and follow changes over Lime.
c-othperserv|1.35|3.375|-7.5|

Note: sigmaq = Armington-elasticity of substitution between imports and domestic output in domestic demand; sigmat = CET-elasticity of transformation for domestic marketed output between exports and domestic supplies; rhoe = constant price elasticity of export demand. For sector (commodity) notation, see Table 3. For a survey of elasticity values, see Annabi, Cockburn and Decaluwé (2006).

41
typically prudential in their nature,<sup>14</sup> or the discretionary warnings on the elevation of risk articulated in NBRM speeches or publications (e.g. Financial Stability Reports) are excluded from the focus of the following analysis.

# <u>Regulation on FX lending</u>

As with many other emerging markets and small economies, the presence of foreign currency in the everyday operation of individual business entities is considerably high. The banking system is not an exception to this pattern. The lending and deposit-taking in foreign currency are highly present in the banks’ balance sheets. Looking at the data from 2004 to 2006, the share of FX lending<sup>15</sup> has grown by 15.7 percentage points (Chart 12).<sup>16</sup> At the end of 2006, for the first time the share of these loans was higher than the share of the denar loans.

**Chart 12. Share of foreign currency (FX) lending in the total amount of loans**

<!-- Start of picture text --> 25.9%<br>26.3%<br>26.1%<br>29.4%<br>25.4%<br>23.4% 26.4%<br>20.0% 23.5%<br>16.1%<br>20.6% 22.1% 23.8%<br>21.0%<br>44.7%<br>50.4% 47.3%<br>62.9% 59.4% 54.5% 50.8%<br><!-- End of picture text --> Source: NBRM Despite the growth of the FX lending, the banking system was still among the systems with moderate participation in FX lending.<sup>17</sup> However
Loecker, J. and J. Eeckhout (2018): “Global Market Power,” Working Paper 24768, National Bureau of Economic Research.

- De Loecker, J., J. Eeckhout, and G. Unger (2020): “The Rise of Market Power and the Macroeconomic Implications,” _The Quarterly Journal of Economics_ , 135, 561–644.

- Department of Social Services (2019): “Household, Income and Labour Dynamics in Australia Survey, HILDA,” Department of Social Services, Government of Australia.

- EUROSTAT (2019): “European Union Statistics on Income and Living Conditions, EU-SILC,” EUROSTAT.

- Fairfield, T. (2015): _Private Wealth and Public Revenue in Latin America: Business Power and Tax Politics_ , Cambridge University Press.

- Gindling, T. H. and D. Newhouse (2014): “Self-employment in the developing world,” _World development_ , 56, 313–331.

21
027)|(0.032)|
|Observations|2,200|2,200|2,200|2,200|2,200|

Note: Time Period: Investment regressions use annual data for the years 2007 to 2013. Cloud regressions use data for 2007 and 2013. Panel A displays separate regressions for the different types of capital investment while Panel B presents estimations for each type of could services. Capital investment variables are in (logged) thousands of pounds, while the cloud variables are a dummy variable. Treated firms are identified taking the average investment values for firms in 2005/06 and calculating if this lagged investment average is above or below the AIA threshold in any given year. Regressions use a binary AIA eligibility indicator. The regressions in Panel A are estimated using the method outlined in Callaway and Sant’Anna (2021) and those in Panel B use difference-in-differences. All regressions include year and firm fixed effects, as well as firm controls of lagged employment, a multi-plant dummy, foreign owned dummy and log age. These are not reported for brevity. Robust standard errors in parenthesis are clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% level, respectively.
. (2012, May). Physician Agency and Adoption of Generic Pharmaceuticals. _American Economic Review 102_ (6), 2826–58.

- Kling, J. R., J. B. Liebman, and L. F. Katz (2007). Experimental analysis of neighborhood effects. _Econometrica 75_ (1), 83–119.

- Koenker, H., M. K. Coulibaly, and I. Bouare (2020). _Trends in and factors associated with malaria prevention in Mali: Further analysis of the Mali Demographic and Health Surveys and Malaria Indicator Surveys 2006-2018_ . Demographic and Health Surveys.

- Koné, D., D. Coulibaly, O. Doumbo, I.-S. Fall, E. Kibuchi, B. Mitto, G. Sang, D. Kyalo, R. W. Snow, and A. M. Noor (2015). _An epidemiological profile of malaria in Mali_ . Programme National de Lutte contre le Paludisme (PNLP).

- Kravitz, R. L., R. M. Epstein, M. D. Feldman, C. E. Franz, R. Azari, M. S. Wilkes, L. Hinton, and P. Franks (2005). Influence of patients’ requests for direct-to-consumer advertised antidepressants: A randomized controlled trial. _JAMA 293_ (16), 1995–2002.

- Lagarde, M. and D. Blaauw (2022). Overtreatment and benevolent provider moral hazard: evidence from South African doctors. _Journal of Development Economics_ .

- Lee, S., U. Kara, E. Koay, M. Lee, S. Lam, and D. Teo (2002). New strategies for the diagnosis and screening of malaria. _76_ (S1), 291–293.

- Lopez, C., A. Sautmann, and S. Schaner (2022). Does patient demand contribute to the overuse of prescription drugs? _American Economic Journal: Applied Economics 14_ (1), 225
Finally, district-level data for the variables on resource quality based on soil and water testing were collected from the Punjab Soil Fertility Department. These variables represent values, averaged by district and year, from thousands of soil tests (organic matter, phosphorus content, pH, and soluble salts) conducted by the department for scientists and farners. While not strictly a random sample, we have no reason to believe there will be systematic biases by district or over time. There has also been considerable concern about secondary salinity and sodicity caused by use of low quality tubewell water (Siddiq, 1994; Byerlee and Siddiq, 1994). This was captured by a similar data set on tubewell water test values (residual carbonate and electroconductivity) by district and year.

Growth in TFP was analyzed for three periods corresponding to different phases of Green Revolution technical change (Byerlee, 1992): the Green Revolution period, 1966-74, when modem varieties were widely adopted with associated inputs, the input-intensification period, 1975-84, when input use increased rapidly, and a post-Green Revolution period, 1985-94, when input use leveled off. However, the cost function analysis was restricted to the whole period, 1971-94, because of the non-availability of resource quality data prior to 1971.

# **Major Trends in Punjab's Agriculture**

The major characteristics of Punjab agriculture are described in table 1. Farm size which now averages 3.9 ha has continuously declined over the past three decades, with a decreasing share of that land farmed by the tenant. At the same time, human resource investments and infrastructure have steadily improved over this period; however, rural literacy remains very low.

**Table 1. Physical and human resource base, size of holding, and land ownership type in the irrigated Pakistan's Punjab**

_By region and period (1966-94)_

||**_Period_**<br>|**_Wheat-_**<br>**_mixed_**<br>|**_Wheat-rice_**|
The paper is structured as follows. The next section provides a brief review of relevant literature, while the following section describes what’s new in the analytical framework and the empirical methodology used in this global update. The two subsequent sections present the results and discuss the main findings, respectively. The final section concludes. The appendices provide methodological details and a detailed description of the main indicators.

# **What Do We Know about School Closures, Mitigation Effectiveness, and Learning Losses?**

This section reviews what we have learned so far about the extent of school closures during COVID-19, the likely effectiveness of remote learning, and the learning losses that resulted. This information all feeds into the data and assumptions used in the simulation model.

## Schools Closures Varied across Contexts

During peak school closures in April 2020, 1.6 billion children, or 94% of the world’s students, were out of school. Over a billion of these children live in low- and middle-income countries. According to the latest results from the UNESCO school closure tracker, documented in Figure A1 in the annex, the length of school closures has varied greatly across regions, with full closures in low- and middle-income countries lasting longer than those in high-income countries. Globally, between February 2020 and February 2022, schools were fully closed for 141 days on average, while partial school closures also lasted for 141 days (Table A1). Partial closures may involve schools offering face-to-face instruction only in certain areas, or for certain grades, or via a hybrid model where only some students receive in-person instruction at a time.

## Remote Learning Provision Varied across Contexts

After COVID-19 prompted school closures in early 2020, many countries pivoted towards providing remote instruction. A 2021 World Bank report documents countries’ experiences with remote learning during the pandemic, and concludes that many countries used multi-modal strategies by combining the use of, for example, internet, TV, radio, as well as printed materials, but there were differences across income groups
. This inherent flexibility makes this new data source particularly valuable for detecting and analyzing (im)mobility responses that are triggered by rapid-onset climate or extreme nature events that, for example, could evolve into short-term, transitory moves that would be difficult or impossible to capture in a timely manner using data from traditional sources. Despite their recognized potential, big data remain vastly untested and only sporadically adopted in official statistics. Applications that try to combine new data sources are still in their early stages and important ethical and privacy challenges connected to their use are yet to be resolved. Nascent “data collaboratives” ( <mark>Young and Verhulst, 2018) such as the Development Data Partnership, a collaboration between various international organizations and technology companies for an efficient use of third-party data for international development,</mark><sup>13</sup> <mark>offer concrete promises in this respect, while also supporting the argument that the solution to the migration data problem must come from increasing integration and interoperability of different data sources.</mark><sup>14</sup>

> <mark>that have attempted to track individuals (or a subset of them) beyond country borders, some examples include the tracking of international migrants between Mexico and the US as part of the Mexican Family Life Survey (Rubalcava and Teruel, 2007), between Albania and Greece as part of the Albania Panel Survey 2004 (INSTAT, 2005), and from Tonga to New Zealand as part of a migration lottery experiment (McKenzie et al., 2006).</mark>

> 11 Further discussion on this is provided in section 2.4.

> 12 <mark>For a comprehensive review of the existing digital data sources and their recent application for measuring migration, see Tjaden (2021).</mark>

> 13 For additional information on the Development Data Partnership, see https://datapartnership.org/.

> 14 In a similar manner, establishing linkages between household and mobility
distribution, such as the seemingly strong assumption of welfare equality within the household (Haddad and Kanbur, 1990). This data constraint points to the desirability of supplementary indicators of individual attainments. Data on child health and mortality has understandably been given high priority in looking for such indicators.

So why aggregate in the form of a MIP? Alkire and Santos (2010b, p.7) argue that their index “... goes beyond previous international measures of poverty to identify the poorest people and aspects in which they are deprived. Such information is vital to allocate resources where they are likely to be most effective.” But is it the MIP or its components that matter for this purpose? Following Alkire and Foster (2007), the Alkire-Santos MIP has a neat decomposability; we can reverse the aggregation. This is useful, for only then will we have any idea how to go about addressing the poverty problem in that specific setting. But the question remains: why do we need the aggregation in the first place?

Consider the following stylized example of a policy problem. Suppose that there are two dimensions of welfare, “income” and “access to services.” Assume that an “income-poor” but “services-rich” household attaches a high value to extra income but a low value to extra services, while the opposite holds for an “income-rich” but “services-poor” household.<sup>8</sup> There are two policy instruments, a transfer payment and service provision. The economy is divided into geographic areas (which could be countries) and a given area gets either the service or the transfer. We then calculate a composite index like the Alkire-Santos MIP based on survey data on incomes and access to services. There is bound to be a positive correlation between average income and service provision, but (nonetheless) some places have high income poverty but adequate services, while others have low income poverty but poor services. The policy maker then decides whether each area gets the transfer or
16 investing across borders: do fdi-specific regulations matter?

rates are measured by the weighted average of applied tariffs on manufactured imports. The effect of high tariffs on FDI, a priori, is ambiguous: it may encourage tariff-jumping horizontal FDI, but discourage vertical FDI that relies on repeated flows of parts and components across borders. The general skill level in the country is assessed by the average number of years of schooling undertaken by adults aged 25 and over. Other determinants of FDI that are not included as regressors are subsumed under the unobserved term, e. The description, source, and summary statistics of the variables are in Tables 13 and 14.

# 1.4 data

I average outward FDI position (stock) in US dollars for 2007 and 2008 from 30 OECD countries into 87 OECD and non-OECD countries belonging to the IAB sample (Table 16). The years 2007 and 2008 represent the latest and most stable number of observations in the OECD’s FDI database, prior to the plunge in crossborder flows in 2009 because of the global financial crisis. Two-year averages are used to smooth out annual fluctuations. For robustness, I also use un-averaged annual data for 2006, 2007, and 2008. Bilateral FDI from each source country, instead of aggregate FDI from all source countries, is used to cast the relationship in a gravity framework yielding a richer set of observations than would be the case for a cross-country regression with aggregate FDI. Source countries include only members of the OECD because of data constraints; however, they have historically accounted for the majority of global FDI outflows.<sup>18</sup> The choice of FDI stock as the dependent variable, preferred to FDI flows, needs elaboration. The model in Melitz (2003) which inspired Helpman et al. (2008) is inherently cross-section because it assumes steady state productivity levels for

> 18 FDI outflows from non-OECD countries are increasing. For example, in 2003, 12.3 percent of total FDI inflow into the 10 member countries of the