rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2 Data and empirical strategy**

## **2.1 Data**

The database consists of sectoral and aggregate labor productivity statistics for 91 countries, and 8 sectors covering the period up to 2018.<sup>4</sup> Compared with the literature using sectoral datasets, it employs a large and diverse sample of countries.<sup>5</sup> The database combines data from the APO Productivity Database, the OECD STAN database, the OECD National Accounts, and the GGDC/UNU-WIDER Economic Transformation Database (ETD, de Vries et al. (2021)) for value-added data and employment. In addition, this database is extrapolated backwards using annual growth rates from KLEMS, the GGDC 10-Sector database (GGDC, de Vries et al. (2015)) or the Expanded Africa Sector Database (EASD, Mensah and Szirmai (2018)) to construct a long time series dataset. Also, if there is no available employment data, ILO modelled estimates are supplementary employed. See the Appendix for more details.

## **2.2 Within sector and between sector effects**

Following de Vries et al. (2012), McMillan et al. (2014) and Diao et al. (2017), we start by employing a shift-share-analysis which decomposes aggregate labor productivity into the within sector and between sector effects: where _y_ is aggregate labor productivity, _y j_ is labor productivity of sector _j_ , _Yj_ is initial value-added of sector _j_ , _s j_ is the employment share of sector _j_ . Between sector effects are driven by the change in employment share. They are further decomposed into those which are due to the reallocation of sources to sectors with higher productivity levels (static sectoral effect), and those due to reallocation toward sectors with higher productivity growth (dynamic sectoral effect). Within-sector productivity growth may reflect the effects of improvements in

> 4The eight sectors distinguished in the dataset are agriculture, mining, manufacturing, utilities, construction, trade services
capitalization costs and the eventual losses associated with liquidating the residual entity will be the realized costs of privatization.12 Importantly, even if _no_ residual entity assets are recovered, the re-capitalization simulations indicate that the typical province would generate large fiscal savings from privatizing its public provincial bank.

Another important fiscal issue is the future solvency of the privatized provincial banks. If privatized provincial banks continue to operate as poorly as public provincial banks, they will go bankrupt. Although predicting the future solvency of any bank, public or private, is a speculative endeavor, the available post-privatization data strongly suggest that the privatized provincial banks operate quite differently from public provincial banks. In fact, data presented in Section IV on credit allocation by sector, operating income and costs, and portfolio quality indicate that, by 1996, privatized provincial banks operated similarly to the ten largest private banks in Argentina. A case can be made, therefore, that the solvency risks posed by privatized provincial banks are no worse than those posed by the typical private bank -- although it should be emphasized that the post-privatization experience has not been long, and future data are required before firmer conclusions can be drawn. Finally, in Section V, we conclude and draw lessons from Argentina's bank privatization experience to date.

# II THE COST OF NOT PRIVATIZING

Between 1991 and 1996, the net worth of most public provincial banks fell, even in nominal terms (See Table II in Appendix II). The notable exception was Banco de La Provincia de Buenos Aires (hereafter Buenos Aires), whose net worth increased at a pace commensurate with many well-established private banks. Given its location and the resulting implications for

- 12 To some small extent, they were defrayed by the sales price of the privatized entity.

4
with the dynamic results discussed above, temporary democratization episodes (i.e., in countries that revert to dictatorships after a brief democratization episode) have a significantly lower effect on agricultural protection.

# **<<B>>** _Economic and political crises_

As noted in the recent political economy literature, the implemented policies may be related to both economic and political (in)stability (see North _et al._ 2009; Besley and Persson 2009 among others). Therefore, we complement our earlier specifications with three variables designed to capture the effect of economic and political crises. An economic crisis is measured with a dummy equal to one for every year that the real GDP per capita growth rate from the Penn World Table is negative (zero otherwise). A political crisis is measured with two dummies equal to one in every year a country is involved in a domestic war or international conflict (zero otherwise). All three variables are used in the regressions with several lags.<sup>28</sup> The effect of democratic reform on policy outcomes is very robust to the inclusion of these additional covariates (see table S.6).

# **<<A>>** _VII. CONCLUSIONS_

In this paper, we investigate how democratization affects agricultural and food policies. On the basis of the unique data set collected by the World Bank, we empirically analyze the impact of political regime transitions on agricultural taxation and subsidization.

We find a significant positive (negative) effect of a democratic transition on agricultural protection (taxation). The transition to democracy increases agricultural protection by 10 to 18 percentage points, depending on the indicator and the model employed. This measured effect primarily reflects changes in poor countries, where the vast majority of the transitions from autocracy to democracy occurred and where farmers 28
Alexis, Samba Mbaye, Justin Sandefur, and Laura Moscoviz (2020), “Phone survey on the Covid crisis in Senegal", April 23, "report_ENG_v1.pdf”, https://doi.org/10.7910/DVN/9XE95F/95RW9C, Harvard Dataverse, V3.

Ministerio de Salud Pública (2020) “Actualización de casos de coronavirus en Ecuador”, <u>https://www.salud.gob.ec/actualizacion-de-casos-de-coronavirus-en-ecuador/</u> [accessed May 4, 2020] Reuters (2020) “Ecuador confirma primera muerte por coronavirus”, March 13, <u>https://www.infobae.com/america/agencias/2020/03/13/ecuador-confirma-primera-muerte-porcoronavirus/</u> [accessed 29 April, 2020] Torres, Claudia, Patricia Otero, Byron Bustamante, Vanessa Blanco, Olga Diaz and Fernando Vázquez (2017) “Mental Health Problems and Related Factors in Ecuadorian College Students”, _International Journal of Environmental Research and Public Health_ 14(5): 530.

UNESCO (2020) “COVID-19 Educational Disruption and Response”, <u>https://en.unesco.org/covid19/educationresponse</u> [Data from April 4, 2020].

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severe business For this buffers should be available abnormally cycle. purpose, capital and used.

We will outline how the macroeconomic scenarios can be constructed before we first describe how these macroeconomic scenarios are translated into the risk factors that are . shown in the flow chart of Figure 1

# **3.1. TTC and PIT Scenarios**

Through-the-cycle (TTC) values of the macroeconomic variables of interest can be constructed in a number of different The is to use the arithmetic mean ways. simplest way computed from historical data. When this strategy is followed, care needs to be taken that the average is calculated over a sufficiently long horizon, covering preferably several business cycles, including normal as well as abnormal ones. It is particularly important to account for structural breaks and transitional convergence when these averages are computed for emerging market economies, as it is likely that steady-state values have changed over time. This can be achieved by employing statistical methods that are robust to structural changes and outliers. It may also be appropriate to consult expert judgment when dealing with substantial transitional or structural changes.

Another way to compute TTC values would be to use the unconditional means implied by a statistical model of the macroeconomy. The benefit of using this approach is the flexibility of being able to include structural shifts and dummy variables in the model if needed. Nonetheless, if the data span is too short, the cost of this approach is a decline in the precision of the estimated parameters needed to construct the unconditional means.

Point-in-time (PIT) values can also be obtained in a number of different ways. One possibility is to use consensus forecasts for the next four quarters from the relevant na14
# **2.4 Stage Two: Revisiting the Decision**

In stage one of the experiment, one of each household’s 20 decisions (10 of the husband’s and 10 of the wife’s decisions) was randomly selected to be implemented. If the selected decision concerned an allocation in the near time frame, between tomorrow and 30 days from tomorrow, which happened with probability one-third by design, then the experimental intervention was finished for that household. The chosen individual in the household redeemed its allocation and was not interviewed again. Stage two of the experiment was only carried out with those households whose randomly selected decision concerned an allocation in the far time frame, between 60 and 90 days from tomorrow.

In stage two, this group of households was unexpectedly revisited. The targeted date for revisiting was randomly selected from the interval between 16 and 2 days prior to the first possible disbursement of funds in the far period. Thus, these households were targeted to be revisited between 45 and 59 days from their baseline interview. Revisits occurred even if the household chose an allocation involving no disbursement of funds at 61 days from their interview.<sup>25</sup> Revisit dates occurred in March and April 2010.

At the revisit, the household head and his spouse were physically separated and the survey of wealth, income and expenditure was performed again. After the interview was conducted, the participant whose choice had been selected to be implemented was again presented with a cup containing 20 tokens. This time, however, four dishes were placed in front of the participant: dishes _A_ , _B_ , _A_  and _B_  . Dishes _A_ and _B_ contained a total of 20 tokens allocated to reflect the participant’s original decision at baseline. Dishes _A_  and _B_  were empty. The participant was told that the first set of dishes showed his or her baseline choice; an allocation between what was effectively one to 16 days from the revisit and 30 days from then. This information
Table C.5: Result of the regression on amenities

||Residual utility (log)|
|---|---|
|Proximity to a district park (< 1 km)|-0.015 (0.02)|
|Proximity to the ocean (< 2 km)|0.11<sup>_∗∗∗_</sup>(0.02)|
|Proximity to the ocean (2< . < 4 km)|0.08<sup>_∗∗∗_</sup>(0.02)|
|Proximity to Urban Heritage Site (< 2 km)|-0.01 (0.02)|
|Airport Noise Cone (within)|-0.04 (0.03)|
|Slope (between 1 and 5%)|0.09<sup>_∗∗∗_</sup>(0.02)|
|Slope (_>_5%)|0.15<sup>_∗∗∗_</sup>(0.02)|
|Proximity to a biosphere reserve (< 2 km)|0.004(0.02)|
|Proximity to a train station (< 2 km)|-0.013 (0.02)|
|Constant|-1.96<sup>_∗∗∗_</sup>(0.02)|
|Observations|307|
|R<sup>2</sup>|0.30|
|Adjusted R<sup>2</sup>|0.28|
|F Statistic|14.2<sup>_∗∗∗_</sup>(df = 297)|

_Note:_<sup>_<u>∗</u>_</sup> p _<_ 0.1;<sup>_<u>∗∗
generates a representative sample of informal businesses in the respective city. In addition to efficiency gains in fieldwork, the sampling method provides greater precision in the population estimates as compared to alternative sampling methods such as non-adaptive area sampling.

# **2.2 Questionnaire Design to Survey Informal Businesses**

Surveying informal businesses requires a well-developed instrument to be used for the interview. The questionnaire used in the Informal Sector Enterprise Surveys is a result of trial-anderror through implementation. The World Bank Enterprise Analysis unit has explored various approaches of surveying informal businesses around the world for several years. The process 6
impacts of acute illness during the experimental survey than the 300 households in the original observational survey. These results are reassuring in terms of the comparability of the two samples and the possibility that repeated surveys may have altered health behavior; at least these broad indicators suggest that households were behaving very similarly when we first visited them and when we followed up eight years later.

# **Section II: Recall period and health measures**

Table 2 shows the differences in means for the main outcome variables between the weekly and monthly recall for the observational and experimental samples. It provides a benchmark from which progressively more detailed specifications deal with, in Tables 3 through 5, respectively: 1) time invariant individual fixedeffects and seasonal effects in a difference in differences specification, 2) variation in recall effects by total number of episodes, and 3) possible biases introduced by modeling alternative patterns of missing values that are more frequent in weekly than monthly data.<sup>9</sup> As is clear from the table, the response from the monthly-recall group is lower than that of the weekly-recall group for every outcome variable. Furthermore, with only the exception of time spent sick with an acute illness, the estimated differences between the experimental and observational results are virtually identical at similar levels of statistical significance. The tests between the weekly and monthly data are precisely estimated, and the differences between the methods are robust. For time spent suffering from an acute illness, both methods show that the results from monthly recall are significantly different from those from weekly recall, just at slightly different rates.<sup>10</sup> On every dimension, the declines due to monthly reporting are large and important behaviorally. The number of acute illnesses declines by 0.6 illnesses per person-month or just over 60 percent. This measure may be contaminated if episodes last longer than a week (and are thus double-counted in the weekly but not the monthly data). However, consistent with the total acute illness result, we find that
logical upgrading in response to fuel price is reduced - as well as for foreign plants, which are more likely to use technology closer to the frontier (Blackman and Wu, 1999; Guadalupe et al., 2012; Brucal et al., 2018).

Unlike for fuels, we do not find a positive impact of electricity prices on plants’ performance. This result is consistent with the fact that electricity prices do not incentivize productivity-enhancing capital replacement, because electricity powers technologies that are already on the efficiency frontier. Hence plants have less room to improve the capital vintage as a result of electricity price movements. In the absence of the capital replacement channel, rising electricity prices turn out to have a negative impact on plants’ performance and profitability, in line with previous literature (Abeberese, 2017; Marin and Vona, 2017). In fact this effect is more muted in Indonesia than Mexico, which supports the hypothesis that Indonesia’s manufacturing industry may use electricity-powered capital less efficiently than Mexico’s.

One advantage of the Indonesian and Mexican manufacturing data is that they include detailed information on outputs and inputs for each plant over time. However only the Indonesian data includes value and quantity of energy consumed, which allows estimating plant-specific unit prices for electricity and fuels. The Mexican data includes only the total plant-level expenditures on electricity and on other fuels. To estimate plant-level energy prices, we combine various sources of data. For electricity we combine monthly data on regional electricity tariffs by firm size with plant-specific sales, electricity expenditures and monthly production; to construct fuel prices we match sector-specific fuels’ consumption with monthly fuel price data and plant-specific monthly production data. A challenge with these energy prices - particularly for Indonesia - is that they are endogenous with respect to plant-level outcomes. This problem is less severe for Mexico as the plant-level data are constructed using national, sectoral and regional data. To address these endogeneity concerns we instrument energy prices in both countries using energy prices of neighboring plants for Indonesia and, in the case of Mexico
with the gender difference in time spent on household tasks, this may indicate a higher risk of fatigue resulting from home schooling for female students.

A potential concern is that students may start off attempting to carry out educational activities, but that their enthusiasm and usage may wane over time, especially once it becomes clear that they will not be heading back into classrooms this school year. Since we randomly ordered students, the groups of students called in each week are similar, and so we can compare time-use of students interviewed in different weeks. Figure 5 shows that this fear does not appear to have been realized during April (covering weeks 3, 4, 5 and 6 since schools closed). Exactly the same proportions of students are engaged in education at each hour in the morning for each of the four weeks of April, and although there is more variation in the afternoon, it is not the case that the proportion doing schooling falls as the month progresses.

# **5. Mental Health**

The COVID-19 pandemic is likely to both directly cause anxiety to students as they worry about the potential health consequences of its spread, as well as indirectly affect their mental well-being through concerns about family finances, their ability to continue their education, and through social isolation. Giannini (2020) reports that a survey conducted by the United Nations in Thailand found more than 7 in 10 children and young people said the pandemic is causing stress, worry, and anxiety.

Our phone surveys measured mental well-being using the 5-item MHI-5 index of Veit and Ware (1983). This asks students to answer on a five-point scale how often in the past month they have been happy, felt calm and peaceful, been very nervous, felt downhearted and blue, and felt so down in the dumps that nothing could cheer them up. Scores range from 5 to 25, where higher scores indicate better mental health. We follow several studies (Urban Institute, 1999; Yamazaki et al. 2005) in using a cutoff of 17 as
_Mathematics_ (Panel A), improvements are observed across the whole distribution of grades. Specifically, we observe both reductions in the proportion of students in the lowest grade category (‘Before beginning’) and improvements in the proportion in the ‘In process’ and ‘Satisfactory’ categories. As before, results are driven by the _regional_ sample. Point estimates obtained for the ‘Satisfactory’ category (ITT coefficients of 1 p.p. in the full sample and 2.3 p.p. in the regional sample) represent improvement by 17% and 38%, respectively. Similar results are observed for _Reading Comprehension_ (Panel B) but in this case, point estimates are smaller and, in some cases, marginally insignificant.

Overall, our estimates show strong evidence of the substantial impact of the intervention in the _regional_ sample, but not in the _metropolitan_ sample. It is difficult to single out a specific reason that might drive these results. One possible explanation is that “ _¡Expande tu Mente!_ ” is more likely to succeed among students with low-achievement—as evidence from other countries has also shown. At the same time, schools in the _metropolitan_ sample are larger and better equipped. It is likely that that the GYM intervention might have been more salient in a regional context, where teachers have fewer teaching tools at their disposal. Besides, the fact that the average classroom size is lower in the _regional_ sample might have contributed to the success of the GYM sessions—everything else equal, a session that requires all students to read an article is more likely to be effectively delivered in a smaller classroom.

# _<u>Robustness checks and heterogeneous estimates</u>_

There might be concerns that remote schools in the treatment group were more likely not to receive the ‘GYM package’, thereby potentially generating sample selectivity. To deal with this, we report results excluding remote schools from the control group. We do this by excluding schools from the control group located in areas
**Figure 1:** _Share of married women aged 20-24, who married before the age of 15 and 18, respectively, by state_

<!-- Start of picture text --> Panel A: Marriage before the age of 15 Panel B: Marriage before the age of 18<br>% women married before age 15 % women married before age 18<br>(32% - 40%] (77% - 86.8%]<br>(16% - 32%] (62.19% - 77%]<br>(6% - 16%] (50% - 62.19%]<br>[1% - 6%] (37.56% - 50%]<br>No data (28.44% - 37.56%]<br>[17.16% - 28.44%]<br>No data<br><!-- End of picture text --> Notes _: This figure shows the proportion of married, women (or in cohabitation wit a man or have been married once) aged 20-24 who married before 15 (Panel A) and 18 (Panel B). The data source is the 2018 Nigerian DHS._ Figure 1 reveals the existence of an internal child marriage belt along the Sahel part of the country stretching from North-West to North-East Nigeria. In particular, marriage before the age of 15 (Panel A), although still widespread country-wise, is more prevalent in Northern states, with rates ranging from 32% to 40%. Marriage before the age of 18 is much more widespread in Nigeria, as shown in Panel B. However, in this case as well, northern states are clearly more affected. In some areas, the prevalence rate is well above 80%—which is huge even by Sub-Saharan African standards. For example, despite not being directly affected by the security crisis involving _Boko Haram_ , North-West Nigeria
mostly flat and located at low altitudes but will need to be considered if land for personal farming is included. Third, no information was available on access to irrigation facilities as responsible authorities are still in the process of establishing a registry of public irrigation assets. Cadastral blocks’ high level of granularity of (with an average size of less than 2 km<sup>2</sup> ) together with the rather high volume of land market activity in areas suitable for irrigation (not all of which may currently be operational) suggest that cadastral block fixed effects may absorb most of the land value effect of public irrigation infrastructure, though testing this with actual data, once available, is desirable.

It will also be important to expand our analysis to include other types of agricultural and non-agricultural land use.<sup>28</sup> Doing so will require to overcome the legal separation of land parcels and buildings or structures on them that is common in post-socialist countries. This not only implies that prices for land and buildings

> 27 Owing to idiosyncrasies in the way NMVs at parcel level were recorded, NMV values are available for only about 1.2 million land parcels. SGC is currently working on establishing a database with API connectivity that should provide NMVs for all parcels in the country by early 2025. 28 Land in Ukraine is classified in a wide number of land use classes (e.g., as crop land, hay fields, pastures, or unsuitable for agriculture) that may or may not correspond to actual use. If such data were available, overlaying them with actual land use and soil quality would be desirable.

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|
|Firm-level Adoption of Technology Survey|287|59|Yes|
|Survey of Advanced Technology (SAT)|57|3|No|
|Community Survey on ICT Usage and E-Commerce in Enterprises|9|0|No|
|Information & Communication Technology Survey (ICTS)|4|0|No|
|Annual Business Survey (ABS) 2019|5|0|No|

## **A.1 Sampling frame**

The sampling frames for the Brazilian state of Cear ́a, Senegal, and Vietnam were based on the most comprehensive and latest establishment census available from national statistical

> 51The survey is currently planned to be implemented in other countries including Bangladesh, India (the states of Tamil Nadu and Uttar Pradesh), Malawi, Kenya, the Philippines, Poland, and the Republic of Korea.

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