rafmacalaba/data-use-mentions-extended huggingface dataset:
countries, irrigation levels are very low, typically less than five percent.

Panel B presents summary statistics on the primary crop production (millet in Niger, maize in all other countries). The patterns present in our cross country comparisons of total farm production are mostly borne out in our comparison of primary crop production. Nigeria has, on average, the largest harvests, highest yields, and the highest use of purchased inputs like fertilizer, pesticide, and herbicide. On average, Niger has the largest land area dedicated to the primary crop and Ethiopia, on average, expends the most labor per ha. Uganda has, on average, the smallest harvests and yields and applies the least amount of fertilizer.

Since total farm values are given in constant USD and primary crop values are in kg, we cannot directly compare most values. However, we can compare land area for the farm with land area dedicated to the primary crop. In Ethiopia, Nigeria, and Uganda, land area to the primary crop makes up around 30% of the total farm area, implying fairly diversified production practices. By contrast, the primary crop in Malawi, maize, commands 92% of total farm-land, suggesting Malawian agriculture is highly specialized. Niger (44%) and Tanzania (58%) are in between these two extremes. The relative degree of specialization (mono-cropping) compared to diversified agricultural production has implications for the use of remote sensing weather data to predict agricultural production as well as serve as an instrument for other economic indicators. In Malawi, where 92% of the farm is dedicated to producing a single crop, it may be fairly easy to settle on a single rainfall and temperature variable that do a good job at predicting yields. However, in highly diversified agricultural settings, like Ethiopia, Nigeria, and Uganda, it may be difficult to find a single set of variables that predict outcomes, since the rainfall and temperature metrics that are highly correlated with maize production may not be correlated with teff, cassava, or
inefficiency ≈ 1- OTE. For reporting purposes, for each indicator one can use either the mean of overall inefficiency for each country, which is , or the average of OTE for each country. Either the average inefficiency or efficiency scores can be used to rank countries for each indicator. Note that the ranking shows the scope ii ii ii for improvement for each country and indicator. The inefficiency scores show potential for improvement uu� = ηη + ττ̅ for the indicator in question and not the value of the indicator, which might confuse some readers. We clarify this issue using a graph, which is used for illustration purposes. Since the model we estimate for each indicator is i 0 i′ ′ i i , the country average is 0 ′ ′ �= 0 since i . If we denote 0 + ′ ′ � ii ii ii � , yy = ββ + xx. Now we use the information on ββ+ zz γγ+ αα −ηη −ττ + vv and in ii ii ii ii ii ii ıı yȳ = ββ + xx̄ ββ+ zz γγ+ αα −ηη −ττ̄ assuming vv EE(vv ) = 0 ∀ii ββ Figure 1 and measure the difference between them as the potential for improvement. ii ii ii ii ıı ii ıı ıı ii ıı ıı ii xx̄ ββ+ zz γγ+ αα bbyy mm yy�= mm −uu� where uu� �= ηη + ττ yy mm To be more specific, let us assume that is educational quality, measured by the PISA reading performance, and the points A and B in figure 1 represent � for two countries, say Vietnam and the ıı Republic of Korea. The points C and D represent their respective yy _m_ values, that is, their own potential. ıı Clearly both actual and potential values for Korea are higher than those of Vietnam. However, Vietnam yy is much closer to its frontier value (distance AC < BD) and therefore has smaller scope for improvement. Thus,
around one in four students drop out prior to Grade 5 (Ministry of Education, 2022).

Large class sizes may prove detrimental to learning by reducing the level of attention received from teachers by each student, reducing students’ ability to ask and answer questions and seek guidance, and reducing the likelihood that teachers engage in time-intensive teaching tasks such as marking homework. In Malawi, evidence from the Malawi Longitudinal Schools Survey (see section ) suggests that, while primary school teachers are as likely as those in neighboring countries to correct mistakes and give positive reinforcement, they are substantially less likely to set and mark homework and to be available to support students after class, activities which increase in time commitment as a result of large class sizes (Asim and Casley Gera, 2024).

In addition to overall poor conditions in lower primary, many low-income countries have wide variations in conditions between schools, with poorer and more remote districts and sub-district areas typically having larger class sizes than wealthier districts and those closer to capital cities, particularly as a result of inefficiencies in the distribution of teachers (Mulkeen, 2010; Bashir et al., 2018). These inequitable conditions are associated with inequities in learning outcomes, with the least well-equipped districts and schools typically having lower learning outcomes. Evidence from cross-sectional data derived from a range of primary level standardized tests suggests that ‘raising the floor’ on test scores by improving the performance of the lowest-performing students and schools is likely to be the most cost-effective way for low-income countries to raise overall learning levels in the short to medium term (Crouch and Rolleston, 2017). In Tanzania, a recent set of reforms targeted to seven disadvan2
(public) basic education and 214,663 students enrolled in (public) secondary education. In the case of (public) basic education, the inference approach from the PECS resulted in a larger number of students than the administrative target<sup>61</sup> ; hence, a random simulation within the eligible pool was performed to sub-select the number of (public) basic students in the PECS that matched the administrative number.<sup>62</sup> Third for the students identified in the PECS as those enrolled in (public) basic and secondary education (calculated in the second step), the study imputes the average in-kind benefits differentiated for each education level (calculated in the first step).

# **Health**

**The analysis uses the “insurance approach” to allocate in-kind public health benefits.** First, the in-kind benefits from public health services (per individual) were calculated based on executed health expenditure data from administrative accounts (primary care versus hospitals) divided by the number of individuals insured under government insurance self-reported in the PECS. Second, the analysis tries to

> 59 All households’ expenditure items that have used gasoline or diesel as a production input will carry indirect effects from fuel subsidies.

> 60 The PECS does not have information on the current grade of education. We estimated this variable based on the highest grade of education achieved, plus one year for individuals currently enrolled in public schools. It is only possible to estimate the current grade for primary and secondary schooling.

> 61 Originally, the inference approach from the PECS resulted in 716,822 students enrolled in (public) basic education in the survey, 122 percent times higher than the admin target. After the random allocation, the number of (public) basic education students simulated in the PECS came down to 580,558 (99 percent of the admin target).

> 62 The random allocation approach was not needed for secondary education since the inference approach from the PECS resulted in
provide a useful snapshot of individuals' labor market positions in a given point in time. But labor market positions are dynamic, i.e. they change and evolve over time. Such evolution is as important as an accurate description of a specific point in time. For example, it is very useful to record the unemployment rate in a given month and year but it is very different if most of those unemployed in that month and year have been in the state for a long or a short period of time.

To describe labor market dynamics, we construct transition matrices over the relevant labor market states. Transition matrices link the labor market state of a given individual at a given date with the labor market state of the _same_ individual at a future date. As a result, transition matrices

> 8 Source: ILO, Malaysia Labour Force Survey, and Indonesia National Labour Force Survey. Data on the year of 2020. Retrieved Jan 2022.

> 9 Source: ILO and national statistical offices. Data on the year of 2020. Retrieved Jan 2022.

7
groups of localities and populations with different characteristics, since our experimental sample was selected through a series of filters and not a random sampling of eligible localities. In this exploratory analysis, we analyze each specific application, rather than the aggregated number of applications in each locality (i.e., the unit is the application, rather than the locality). Given T0 only had one application (which was not selected for a grant), comparing the characteristics of this unique application to average characteristics of applications from T1 and T2 (which had 214 applications) could be too noisy and not meaningful. While we cannot derive conclusive evidence from this exploratory analysis, it can help generate hypotheses as to why the experimental sample had a low number of applications that were selected for funding.

Only one application that was submitted to this call for proposals from localities in the RCT sample was selected for grants (0.4% - this application came from T2). While localities outside the RCT sample submitted a similar total number of applications (215 in the sample and 226 outside), the rate of applications selected was much higher outside the sample (11% – 26 applications from outside the RCT sample). Furthermore, administrative data shows that 93% of the applications submitted from the RCT sample lacked important details, while only 48% of applications from outside our localities had this characteristic. Unfortunately, we do not have more detailed data to assess and compare the “quality” of the proposals or to isolate the specific issue that caused the difference in success rate, although we include some hypotheses below. Also, the average number of applications per in-RCT-sample locality was much higher (mean from inside sample = 6.52, mean from outside sample = 1.39, t = -10.81, df = 179, p-value < 0.001), with 76% of localities that submitted an application from outside the sample submitting only one application. Furthermore, when comparing the demographic characteristics of these localities, the
of the trip for about 15 percent of foreign visitors, while cultural tourism is still in a nascent stage (Table 1).

**Table 1. Activities realized during the trip.**

|_Forei_|_gn tourists_|_Domestic to_|_urists_|
|---|---|---|---|
|Shopping|82%|Recreation|32%|
|Entertainment|46%|Vacation|24%|
|Beach|22%|Family visit|24%|
|Ecotourism|15%|Rest (_descanso_)|10%|
|Etnotourism|2%|Religion|5%|
|Other|11%|Health|2%|
|||Other|3%|

Source: Authors’ calculations using the data from tourism surveys for Satellite Tourism Accounts, 2008. For foreign tourists, 1,626 tourists (and more than one response was permissible); for domestic tourists-1,423 trips (states only the main purpose).

Investments in infrastructure and hotel accommodations, which are not yet sufficient for meeting the growing tourism demand in most areas outside of Panama City, are expected 4 Results for October and November 2007 of the ongoing tourism survey conducted by PSM SIGMADOS. Each month’s sample size is 500 tourists (with the total sample size of 6,000 once all rounds of the survey are completed).

5 Tourism survey conducted in June 2006 by Dichter & Neira Latin Research Network. Sample size: 843 foreign tourists.

3
if the individual lives in a dwelling made of stones, bricks, cement or concrete. State fixed-effects are included in all specifications. Individual controls include gender, age, household size, caste, education, distance to the medical center, employment and student status, religion, presence of pregnant woman in the house, ownership of bednet and air conditioner, and 5-year malaria incidence at baseline. Cluster controls are reported in Table 1. Standard errors are clustered at the district level.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 theoretically more likely to be exposed to and remember the campaign.<sup>36</sup> While the table confirms both hypotheses, the effect is only evident for respondents living in solid dwellings. Among them, ad recall significantly increased only for individuals under 26 (by 16 p.p.) and for those expressing at least some concern about malaria (by 9 p.p.). By contrast, among residents of non-solid dwellings, the coefficients for both younger respondents and those more concerned about malaria were close to zero and statistically insignificant. This lack of recall effect, including those theoretically more receptive to the campaign, suggests the ad content may not have effectively reached this population. To substantiate this hypothesis, we conducted the following _feed_ experiment.

# **6.2 Individual-level** **_Feed_ Experiment**

To differentiate between limited ad effectiveness and limited reach of the ad campaign, in April 2021 we nested an individual-level study at the end of the cross-sectional survey (January-March 2021, see timeline 4.3). This study ensured that the campaign ads were distributed equally to both solid and non-solid dwellers (balancing also across gender, caste, education, as well as the treatment assignment of
reproductive equipment, reaching 114 male births per 100 female births around 2005 (UNFPA, 2012). Hence, if the parents who prefer to have sons or the mothers who want to return to work are willing to engage in sex selection, the instrument will not be randomly assigned. Despite the fact that Albania has a high male-female ratio, the evidence from the sample of mothers and fathers used in this paper suggests that the sex-ratio at first and second order births is very close to the natural rate, which makes this issue a minor concern (see Tables 1 and 4).<sup>17</sup> Another simple way to check whether the instrument is as good as random is to examine whether parents differ in demographic characteristics by the sex-compositions of their two first children (Angrist and Evans, 1998; Ag ̈uero and Marks, 2008). The idea is that if there is no correlation between the instrument and other variables affecting parental labor supply, then there should be no systematic differences in demographic characteristics between parents who have same-sex and mixed-sex sibling compositions, and similarly between those who have two girls and another sibling composition or two boys and otherwise. Examples of such variables are _age, age at first birth, years of education, rural area_ and _religion_ .

Table 5 reports the difference in means in demographic characteristics between parents (mothers and fathers) with different sibling’s sex-composition (same-sex, two girls and two boys). Columns (1) and (4) indicate that mothers (fathers) with children of the same sex and those with children of mixed-sex, have similar demographic characteristics before the treatment, that is, the arrival of the third child. The rest of the estimates in Table 5 show that in general there are no statistically significant differences in demographic characteristics between parents with two boys and those with another sibling composition, and also between those with two girls and otherwise, except for the variable _age_ . Parents with two girls and just the fathers with two boys seem to
**Figure 9. The gender gap in monthly wages by years of age**

<!-- Start of picture text --> (a) Unconditional (b) Conditional<br>0.2 0.2<br>0.1<br>0<br>0 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29<br>15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 ‐0.2<br>‐0.1<br>‐0.2<br>y = ‐0.0131x + 0.0256 ‐0.4<br>‐0.3 R2 = 0.2429<br>Unconditional<br>‐0.4 Conditional on: log hours<br>Unconditional Linear (Unconditional) Conditional on: log hours, education<br><!-- End of picture text --> _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 38
numerical scale that ranges from 1 to 21, with 1 corresponding to the highest rating (“Aaa” for Moody’s and “AAA” for S&P and Fitch) and 21 to the lowest rating. A higher value of the numerical index is thus associated with a worse rating.

An important threshold is that separating investment grade (“BBB-” or higher for Fitch and S&P, and “Baa3” or higher for Moody’s) and sub-investment grade countries. In our numerical scale, investment grade countries have a rating of 10 and below. As not all agencies rate all countries, we maximize the number of observations by averaging the ratings of the three agencies for each country-year and only use one or two agencies for countries that are not rated by all three agencies.<sup>22</sup> Table 3.1 reports the correspondence between the alpha-numeric ratings, their numerical counterparts, and our combined numeric sovereign rating.

The data on 5-year CDS spreads are sourced from the World Bank’s Fiscal Space database (Kose et al. 2017). Higher spreads convey higher premium demanded by investors, i.e., higher sovereign risk. Out two sovereign risk measures are highly correlated: the correlation between sovereign ratings and the CDS spreads is 0.7 and is statistically significant at the 1-percent level.

Other variables are sourced from various publicly accessible databases. Data on the share of government debt in foreign currency in total government debt is from the World Bank’s Fiscal Space database. Data on public investment are from the IMF Investment and Capital Stock Dataset. The measures of institutional development are from the International Country Risk Guide (ICRG), World Bank’s Worldwide Governance Indicators (WGI), Country Policy and Institutional Assessment (CPIA), and World Economic Forum (WEF). Most macroeconomic data (general government debt-to-GDP ratio, exchange rate, inflation, foreign reserves as a share of GDP, real GDP growth, per capita GDP, net real
YES|YES|YES|YES|

_Note:_ Dependent variables are household (log) health and education expenditures. Data used are from the three waves of the GHS and telephone survey for conflict. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level.

Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 38
ISIC Rev 4 coding, the 4-, 3-, 2- and 1- digit levels. In cases where NACE Rev 2 value mapped to more than one ISIC code, particularly at the 4- and 3- digit levels, the same value was used for each ISIC code. In cases where more than NACE Rev 2 value mapped to a single ISIC code, then the average of the values from the NACE Rev 2 codes was applied. Values at the 4-, 3-, 2- and 1- digit ISIC coding were then merged with PIAAC country datasets based on the finest ISIC coding reported by the individual (e.g., in some cases the 4- and 3- digit ISIC code was missing for an individual but a 2- digit level was reported). Table 1 presents summary statistics from the resulting skills and industrial emissions data set.

11
# **Appendix F: Balance across Treatment Conditions**

The analyses in this paper rely on randomization, which guarantees that treatment and control areas are similar in expectation. In practice, however, it is possible for them to differ simply by virtue of unlucky draws. To test this, we compare Tuungane treatment and control areas, and – for those areas that partook in the parity lottery – gender parity and control areas. Because we do not have baseline data for the villages, we make use of the data collected in 2010 and 2011 by Humphreys et al. (2019) in 816 randomly selected villages. We limit ourselves to pre-treatment information and variables that do not change due to the treatment. We analyze the following variables. Distance from a set of (nearest) points of importance that are unlikely to change due to the program: mine, post office, and the kingdom headquarters. Distance data (measured in hours of walking) are based on individual responses, mean aggregated to the village level. Note that in a large number of villages, individuals do not know the distance to these locations, and thus we have fewer observations than the 816 visited villages. We also use data on ethnic and religious composition of the village, measured as the probability that two individuals, selected at random from the village, will be of different ethnicities or religious groups. These data come from a survey conducted with village chiefs. In total, 773 of the 816 village chiefs were interviewed. Data were also collected about the characteristics of the previous chief: his year of birth, and whether he was democratically elected. Note that many chiefs responded with “Don’t know”, resulting in considerably fewer than 773 observations for the balance test. We also collected data on the principal economic activities undertaken in the village (as a percentage of the village population). In addition, data were collected on the presence of infrastructure in the village in 2006: wells, schools, clinics, churches and meeting halls
37 This pace of price adjustments may not be , however, optimal for policy makers. The same issue analyzed in the context of political economy yields a different outcome: from the point of view of the government it might be better to avoid frequent price increases. Any decision to raise prices is likely to be costly for the authorities in terms of the loss of popularity and the cost is clearly minimized by lowering the frequency of adjustments. These political costs may be thought of as an important part of menu costs introduced earlier in the paper. Just like standard menu costs, they make (administered) prices more sticky in that they discourage the government from making frequent inflation-driven adjustments. Keeping in mind that all governments whether populist or not are subject to those same constraints, it may turn out that the economically rational option of small repeated adjustments is not politically feasible. Therefore, with only limited frequency of administered increases available, the revised policy recommendation could suggest bigger adjustments because only they can ensure that necessary relative price realignments take place. In other words, when political economy rules out the feasibility of regularly repeated increases, sizable adjustments might be preferred to small ones.

# Section 8: SUMMARY AND CONCLUSIONS

This paper has been designed as an attempt to estimate the magnitude of relative price shifts on the overall price level in Poland during the transition period 1989-1997. For that purpose, the theoretical model has been found that builds on menu costs and trend inflation to derive a positive relationship between variance and skewness of the distribution of relative price changes and the general inflation. The model allowed to estimate the effect of relative price shifts within the framework controlling for nominal and real shocks. Using Polish data, a set of 3 versions of the model were estimated. All of them yielded high explanatory power and statistically significant coefficients on most variance and skewness variables thus giving a strong empirical suppor **t** o the theoretical relationship. Larger shifts in relative prices accompanying the adjustment process and detected by higher variance in the equation were proven