rafmacalaba/data-use-mentions-extended huggingface dataset:
First, the costs of rigorous impact evaluation would have been similar to the costs of the evaluation the project is already doing. The changes needed to make the evaluation rigorous— per treated village—would have been mostly free. The project is already carrying out baseline surveys and multiple follow-up surveys in the treated villages, and a number of surveys (though no baseline survey) in the nonrandom comparison villages. The only additional costs involved in making the impact evaluation design rigorous would be 1) the very low cost of identifying the matched pairs within which to randomize treatment, using rainfall and census data, and 2) the additional cost of a baseline survey in the randomized comparison villages, which would only add one round of surveys, in half the villages, to the multiple rounds of surveys the project is already planning. These additional costs would only represent a small percentage increase in the project’s existing evaluation budget per treated village.

Second, the overall cost of any impact evaluation, even a rigorous one, would represent only a small percentage of the project’s costs. This is important because it is not too late to rigorously evaluate the effects of future expansion of the Millennium Villages. For example, it would only take about 20 matched pairs of village clusters, or fewer, to allow high confidence of detecting a treatment effect of about ten percentage points in some indicator of proportion.<sup>28</sup> That is, if the intervention had about a ten percentage-point effect on the proportion of children in each household attending school or receiving vaccinations, this could be reliably detected with only 20 matched pairs of village clusters or fewer. Conservatively overestimating that it would cost US$100 per household per survey round to survey 300 households per cluster, four rounds of surveys in one pair of village clusters (0, 5, 10, and 15 years from start) would cost US$240,000. This is the cost per treated village. Even this conservatively high estimate is only 16
would be reduced to 60 or below with addition of new teachers

- Except districts where more than 50 percent of schools meet T1 are excluded from receiving teachers based on T1, unless their overall district PTR is above 75:1.

Target 2 (T2): deploys the least number of teachers needed so that PTR reduces to below 90 (in 2017) or 80 (in 2018 and 2019).

# **Estimating need under the revised rules**

Employing these revised rules, we then estimate the need for teachers to fulfill these targets in each year from 2017 to 2019. **Table 2.1** shows the number of teachers required in each district in each year following the application of both rules, along with the number of schools needing teachers. In each year, the data on school-level PTRs is drawn from EMIS data, reflecting the previous year’s allocations of new teachers as well as other movements, deaths and retirements of teachers in the system.

7
port measures from the government and may need to maintain some means of livelihood to cover basic necessities (Franklin 2018).

We do not observe job quality directly in the HFPS data, so we rely on several proxies to analyze the quality of net employment changes. In particular, we examine transitions between sectors and types of employment (wage employment vs. self-employment) across different population groups before and after the onset of the pandemic. In the case of agriculture, in poorer countries the sector oftentimes acts as a shock absorber and workers unable to find work elsewhere may resort to jobs in the agriculture sector. As the economic fallout caused by the pandemic lingers over a longer period, households may rely on agricultural employment as a coping strategy. Since we observe the prepandemic sector of employment for each respondent, the analysis here relies on the plausible assumption that the movement from non-agricultural employment immediately preceding the COVID-19 pandemic to agricultural employment after the onset of the pandemic is a push factor related to the need to mitigate income losses, rather than a pull factor associated with better remunerated employment in agriculture. Similarly, self-employment which is oftentimes in the informal sector can serve as a buffer against the labor market shock, particularly among households who do not have access to formal or informal safety nets that would allow them to wait it out for a better job. As such, it could be argued that transitions from prepandemic wage employment to pandemic self-employment are primarily push factor transitions into immediate, but more precarious and lower-quality employment vis-`a-vis the jobs held previously.

HFPS data record a significant share of workers changing jobs during the COVID-19 pandemic. In the early phase of the pandemic, a period also characterized by substantial work stoppages, an additional 9 percent of workers reported working in jobs different from their prepandemic employment. Over time, this figure rose to more than 20 percent of workers in the second half of 2020, before slowing in 2021, at which point around one in four workers reported having a different job than before the pandemic (
for the period 1961-2021. Together, these commodities represent over 98 percent of the global agricultural land use during the sample period. Calorific data for most commodities are sourced from the FAO’s Food Balance Sheets. Figures 1 and 2 present the calories per kilogram and global production shares of the 15 most significant commodities in 2021, which collectively represented nearly 90% of global food production in terms of calorific output.

While various data sources are available for commodity production and nutritional values, considerable variation exists in data collection methods (e.g., dry vs. raw weight). To ensure consistency, all data used in the analysis are sourced from the FAO. In addition to the crop-level data, we also collect the regional-level data for each crop. Important to note that the FAO Food Balance Sheets provide the calorific content of a specific commodity per 100 grams of edible portion in terms of the retail weight ("as purchased"). As such, it does not consider the calories from the non-edible portion of the crop _— 11 —_
resources. While understanding the error of the poverty estimates is useful in this regard, the ultimate concern is to understand how different methodological choices affect poverty alleviation by influencing the efficiency of targeting. Consequently, we also consider how machine-learning methods perform in terms of reducing poverty in the context of the Census created from the Mexican Intercensal Survey. Similar to Elbers et al. (2007), we propose simulating a hypothetical poverty-alleviation program that distributes resources to households according to a given set of small area estimates.<sup>25</sup> In addition to the estimates, the procedure requires true household-level incomes from a census-type dataset.

The procedure we use in our simulations is as follows:

1. Using the true household-level incomes, calculate the poverty headcount and poverty gap for the country as a whole for some chosen poverty line. Using the poverty gap measure, determine the budget required to completely eradicate poverty.<sup>26</sup> Calculate an individuallevel transfer amount by dividing the total budget by the number of people living in poverty.

2. Using a given set of small area estimates, order the municipalities according to their estimated poverty headcounts, from most to least poor. For the poorest municipality, augment the true per capita income of all households by an amount equal to the transfer (i.e., each household receives an amount equal to the transfer times the number of household members). Continue this distribution procedure for the second poorest municipality, the third poorest municipality, and so on until there are not enough resources to cover all households in a given municipality. For the marginal municipality, distribute the remaining budget equally among individuals in that municipality.

3. Using the post-transfer household incomes, calculate an updated poverty headcount to assess the effects of the distribution scheme on poverty alleviation.

> 24See Hastie et al. (2009) for derivation and detailed discussion of the bias-variance decomposition.

> 25See Yeh et al. (2020),Chi et al. (2022), and Aiken
Particularly among developing economies, once micro firms (including sole proprietorships) are included, the predominance of the smallest firms as the source of formal, private sector employment increases (World Bank 2014). However, in terms of employment growth — that is the expansion of the overall stock of employment — the evidence has been mixed. Particularly, while some have found (Ayyagari et al 2014, for example) that young and small firms are the driving force of growth, others have argued that it is the specific dynamism of young, start-up firms that accounts for this growth (Haltiwanger et al 2013).

Parallel to this has been a focus on the lifecycle of firms. Potential entrepreneurs must assess their competition and understand their relative productivity after competing in the market (Lucas 1978; Melitz 2003); if they find themselves to be profitable in these markets, they will continue operations — including the requisite investments needed — if not, they will exit those same markets ( _Ibid_ ; Olley Pakes 1996). This is the modeled mechanism in markets that are competitive and allocatively efficient, under which both revenue and employment shares will flow to more productive firms (Olley and Pakes 1996; Melitz and Polanec 2012). Where those conditions are less ideal, investment among incumbents can be discouraged, average firm size will be dampened downward, and new entrants are less dynamic and productive. The agesize correlation has been widely reported: Hsieh and Klenow (2014) do show stunted lifecycle growth for plants in Mexico and India. Simultaneously, firms may face costs at entry that demand higher initial investment, inducing a higher starting size, which can have lasting effects for firms that are “born large”. Based on a sample of 120 developing countries, Ayyagari et al (2015) find a consistent trend over the lifecycle of firms; however, start size explains the vast majority of variation in current size. Indeed, a specific advantage of using micro-data is to see if these effects are heterogeneous within economies.

A positive correlation between
study the firm-level determinants of firm survival. Unlike previous studies, with the exception of Shiferaw (2007, 2009) and Bedi and Shiferaw (2009), we rely on census-based panel data, which allows for better tracking of firm entry and exit. However, we go beyond existing studies since we do not face a stock sampling problem (survivorship bias) and we use a long panel that allows firms sufficient time to follow their natural lifecycle and therefore minimize right-censoring. We also include firms outside of the manufacturing sector, which is important given the historically small size of manufacturing sectors in Sub-Saharan Africa.

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have less concentrated import markets, independent of their commodity export intensity.

Our paper provides general insight into differences in market concentration across countries. Leone, Macchiavello and Reed (2024) describe how concentration leading to high markups has raised prices in Africa’s domestic cement industry, and conclude the reason for concentration is small national market size, rather than entry costs that are higher in Africa. In contrast, the present paper provides evidence of entry costs in importing that are unique to commodity export intensive economies and can account for higher costs in these economies.

The remainder of the paper is organized as follows. Section 2 describes the data and measurement of import market concentration. Section 3 shows the main result in Figure 1a is robust to a battery of controls and alternative regression specifications. Section 4 presents evidence that tariffs, non-tariff measures, and tariff evasion are mechanisms that concentrate import markets. Section 5 documents the positive association between domestic prices and import market concentration. Section 6 concludes.

# 2 Data and measurement

Import Market Concentration. Import market concentration is measured using a novel database of all firm-level import transactions recorded by customs authorities in 53 countries, geographically and institutionally diverse, and broadly representative of middle-income economies (see Table S1). The database has the same source as the World Bank Exporter Dynamics Database described by Fernandes et al. (2016) but includes import rather than export transactions. The sample period covers 1997-2021 but with different year coverage for each country. We eliminate observations in HS27 (oil, petroleum, natural gas, and coal) as their trade is not uniformly reported across countries’ customs data. Country-year total superstar exporters. Gaubert, Itskhoki and Vogler (2021) discuss the policy implications of such “granular” comparative advantage in exports. Our evidence highlights that import markets can also be granular, with implications for the price level.

6
to a broader positive impact on poverty reduction. Additionally, the model does not estimate the potential disincentive effects of the MSA reform. The evidence on the disincentive effects of Monthly Social Allowance programs across Europe is mixed. Terracol (2009) reveals that the French guaranteed income program, the RMI, initially exerts a strong disincentive effect, particularly in the first few months of participation. Still, this effect tends to diminish after approximately six months. Moreover, household composition appears to influence how program participation impacts individuals. Coady (2021) delves into the design of means-tested Monthly Social Allowance schemes in various European nations, highlighting the delicate balance between alleviating poverty and managing work disincentives. Many countries prioritize employment incentives over poverty reduction by combining low-benefit generosity with modest benefit withdrawal rates. Lastly, Gouveia (1999) examines the impact of the Portuguese Minimum Guaranteed Income Program (RMIG) and identifies a small, positive effect on reducing inequality and poverty. However, the gains are somewhat offset by labor supply effects.

19 These include restrictions on assets, which are also subject to a test, and registration of the unemployed in the Employment Agency (see Annexes 2 and 3 for detailed eligibility criteria). In the SILC survey data, we cannot identify the registered unemployed, or the number of months this group is registered in Public Employment Services. In addition, we cannot identify whether the number of homes the household owns, and consequently the number of rooms in the first home. It was not possible to use another survey (e.g., Household budget survey) to estimate the likely magnitude of these limitations, as the income-poor from the HBS are not the same income-poor from the SILC. There are sizable divergences in the incidence of income poverty using both surveys.

18
shortest paths.</mark><sup>16</sup> <mark>Usually, a ‘bottleneck’ node or edge in a transportation network will have higher betweenness than other peripheral nodes or edges. The conventional betweenness centrality derived from network theory is applied to the multi-modal network model in Kinshasa. We also adapted this metric to one that is able to capture network flows based on simulated travel trajectories leveraging the 8,866 trip OD information provided in the JICA commuters travel survey.</mark> Table 6: Summary table of criticality metrics (adapted from Jafino et al. 2020).

|**No**|**Metric name**|**Description**|**Function**|**Scale**|
|---|---|---|---|---|
|1|Change in weighted total<br>travel cost|Increase in total travel cost (distance and traffic flow) among all<br>origin-destination pairs due to disruptions on an element|Travel cost|Global|
|2|Change in expected user<br>exposure|The average impact of disruptions experienced by all users in the<br>transport system|Travel cost|Global|
|3|Change in worst-case user<br>exposure|The maximum impact of disruptions among all users in the<br>transport system|Travel cost|Global|
|4|Change in unweighted<br>total travel cost|Increase in total travel cost (only distance) among all origin-<br>destination pairs due to disruptions on an element|Travel cost|Global|
|5|Change in region-based<br>unweighted total travel<br>cost|Increase in total travel distance among all origin-destination<br>pairs within a certain sub-area where the element is located due<br>to disruptions on an element|Travel cost|Local|
|6|Change in weighted<br
Estimating education production functions based on such observational data certainly has its limitations (cf., e.g., Glewwe and Kremer 2005). While the extensive PIRLS data does allow for several important features, including the estimation of a limited value-added specification, it cannot solve all the problems imminent to retrospective studies using observational data. What the presented results provide are informative knowledge on the relationship between students’ reading performance and a host of student, family and school characteristics, conditional on all the other characteristics considered. These are useful descriptive results which can inform policymaking when properly – and thus cautiously – interpreted. It should be clear from the start that they do _not_ necessarily always reflect a causal relationship in the sense that the student or school characteristics causally influenced reading performance by the amount of the estimated parameters. Given that observational data always emanate from individual choices, unbiased causal empirical estimates – if ever possible – can ultimately only come from experimental setups of one form or the other. It is hoped that the descriptive results presented here can serve as a first step that leads towards a better-informed quest for causal identifications in the future.

So far, little research has been done on the determinants of educational quality in Argentina and Colombia, and actually in Latin America more widely. The existing empirical research on educational production in Latin America is ably reviewed in Mizala and Romaguera (2002). For Argentina, the only work they cite is Llach et al. (1999), who analyze a national achievement test. Galiani and Schargrodsky (2002) provide an analysis of secondary school decentralization on educational test performance in Argentina. Research on Colombian educational quality focuses mainly on the relative performance of private and public schools (Cox and Jimenez 1991; Piñeros and Rodríguez 1998) and the Colombian voucher system (King et al. 1999; Angrist et al. 2002). With respect to other Latin American countries, somewhat more research has been done on Chile (cf. Sapelli 2002 and the contributions introduced therein), again with special
Ivanic, Maros, and Will Martin (2008), "Implications of Higher Global Food Prices for Poverty in Low-Income Countries," Policy Research Working Paper 4594. Washington, DC: World Bank.

Liu H. et al., (2001), “A comparison study of multiple measures of adherence to HIV Protease inhibitors”, _Annals of Internal Medicine_ , 134:968-977.

Macro International, Inc, (2008), _HIV Prevalence Estimates from the Demographic and Health Surveys._ Calverton, Maryland: Macro International, Inc.

Orrel C., D. Bangsberg, M. Badri & R. Wood, (2003a), “Predictors of adherence to antiretroviral therapy among HIV-infected persons: a prospective study in Southwest Ethiopia”, _BMC Public Health_ , 8:265.

Orrel C., D. Bangsberg, M. Badri & R. Wood, (2003b), “Adherence is not a barrier to successful antiretroviral therapy in South Africa”, _AIDS_ , 17:1369-1375.

Paterson D.L., Swindells S., Mohr J., Brester M., Vergis E.N., Squier C., Wagener M.M., Singh N., (2000), “Adherence to protease inhibitor therapy and outcomes in patients with HIV infection,” _Ann Intern Med_ , 133(1):21-30.

Republic of Mozambique National AIDS Council, (2008), _Progress Report for United Nations General Assembly Special Session on HIV/AIDS_ 2006-2007.

UNAIDS/WHO, (2008), _Report on the global HIV/AIDS epidemic._ Geneva, Joint United Nations Programme on HIV/AIDS.

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Table 2: Topics of advice

|Description<sup>_a_</sup>|Full Sample|Malawi|Nigeria|Uganda|
|---|---|---|---|---|
|Agricultural production and processing|91.3|92.6|80.1|96.3|
|New seed varieties|50.7|51.3|49.2||
|Fertilizer|52.6|50.0|58.9||
|Pest control<br>|29.0|28.3|30.8||
|Composting/manure|41.8|50.3|21.0||
|Irrigation|26.5|34.5|6.9||
|Marketing and crop sales|25.9|17.2|24.6|39.9|
|Livestock production<br>|32.8|20.2|26.7|55.8|
|Animal diseases and vaccination|30.5|18.8|28.1|50.0|
|Fishery|8.1|7.9|1.9|12.4|
|Forestry|15.7|21.3|2.3||
|Access to credit|12.8|15.8|5.5||
|Other|2.1|2.8|0.5||
|Observations (HHs who received advice)|6,877|3,336|1,369|2,172|

_Source:_ Authors’ calculation based on survey data from the LSMS-ISA database (World Bank, 2018). _Notes:_ Numbers are percentages of households who received advice on indicated topic, conditional on having received in-person advice (i.e., excluding advice received through media).<sup>_a_</sup> Exact wording differs
indicator variables for these two groups so that we can compare against both. Of the 76 districts lying with 0-10 km of NS-EW, 40 districts were to be covered in the 48 NS-EW projects identified for Phase I. The empirical appendix provides greater detail on this division.

The powerful result from Table 4 is that none of the outcomes we measure for the GQ system in the postupgrade period are observed for districts along the NS-EW corridor. The placebo-like coefficients from the interactions of post-GQ upgrades with districts lying between 0-10 km from the NS-EW highway are mostly negative and never statistically significant. The lack of precision is not due to too few districts along the NS-EW system, as the district counts are comparable and the standard errors are of very similar magnitude. In Appendix Table 1, we show that null results continue to hold when we combine the NSEW indicator variables and that the coefficients are well estimated. Said differently, with the precision that we estimate the positive responses along the GQ network, we estimate a lack of a change along the 17