rafmacalaba/data-use-mentions-extended huggingface dataset:
aged 6 to 24 fell by around two percentage points. The decrease in enrollment in private education is also in this order of magnitude in absolute terms, but larger in relative terms because of the lower average baseline level. These results are in line with simulations

> 1 Psacharopoulos _et al_ . (2020) estimate that at the global level, the present value of a four-month school interruption implies an earnings loss of more than US$10,000 over the course of a lifetime. Using a structural model matched to US data, Fuchs-Schündeln _et al_ . (2020) find that earning losses amount to about 1% over the lifetimes.

2
. These percentages are very low, considering that they are included in impact evaluations where at least one measure of impact is supposed to be very available to be used in these comparisons.

We find that just 25 percent of the CEAs and CBAs reported opportunity costs. However, because policy makers craft decisions based on real budgets and financial costs, excluding opportunity costs may be theoretically imperfect but practically correct.

The transparency of reporting is very low among the 30 World Bank IEs that included any kind of VFM analysis. Descriptions of the Costing data, methods and assumptions are often vague or incomplete. Just 13 percent reported unit costs of inputs, and only 17 percent reported unit costs per impact. Only 8 percent of IE projects conducted over a year reported discount rates and only 21 percent reported adjusting for inflation. There was also a lack of consistency in reporting discount rates, currency conversions, and inflation adjustments. For example, 58 percent of the analyses did not report the exchange rate used to convert from local to the currency of analysis. In addition, very few studies—around 13 percent—provided 14
outcomes measured in ENAHO. _εipt_ is an error term clustered at the province level as we measure the number of Venezuelan immigrants at this level and suspect there is strong serial correlation in many of our outcomes.

_β_ identifies the effect of the number of Venezuelan immigrants in year _t_ in region _p_ . Importantly, we also control in all models for time (either year or month*year) fixed effect ( _αt_ ) and province fixed effects ( _αp_ ). Hence, we control for any time-invariant differences in outcomes across provinces and aggregate changes in outcomes, both of which may be related to the location choice decisions of Venezuelans. In our preferred specification, we also control for province-specific time-trends ( _time ∗ αp_ ) which account for any local trends in the outcome variable. In this model, the impact of the presence of Venezuelans is identified by examining how outcomes for Peruvians change when more Venezuelans arrive in an area conditional on the trend in that outcome.<sup>8</sup> It is possible that local shocks impact both the destination choice of Venezuelans and outcomes for Peruvians, hence we also use an instrumental variable strategy where we exploit the intuition that immigrants are more likely to move to localities where immigrants from the same nationality are located. We therefore instrument our measure of the number of Venezuelans in a province _p_ with the presence of Venezuelans in that province as recorded in the 2007 census interacted with year dummy variables. This is a semi-parametric version of the traditional immigrant network instrument as recommended by Goldsmith-Pinkham et al. (2020) as it allows the strength of the network effect to potentially vary in each year. An overidentification test can be used to examine whether the instrument has a consistent relationship over time.

# **5 Results**

## **5.1 Labor Market Conditions and Discrimination**

Table 2 shows our main results on the effects of local labor market conditions on self reported discrimination. We first present the OLS results, and then turn to provide the estimates from our IV
# **1 Introduction**

Citizens in developing countries often distrust state institutions and their ability to provide public services. When citizens distrust public institutions, they may fail to cooperate with – the state unless the state coerces them to cooperate (Besley, 2020a; Levi, 1988). For example, distrustful citizens might avoid paying taxes for public goods or settling disputes – through the judicial system actions that entail individual, immediate costs in exchange for public, long-term benefits. Increasing public trust becomes ever more critical in times of crises such as pandemics or wars when states must act swiftly and rely on its citizens to cooperate with potentially costly state measures.

The Covid-19 pandemic provides a rare opportunity to study the linkages between citizen cooperation, perceptions of state capacity, and trust in the state as a crisis unfolds. Containing the outbreak depends crucially on whether citizens voluntarily comply with government directives that limit civil liberties and thus on whether citizens trust the government and the state. While it is too early to judge whether the Covid-19 pandemic will become a pivotal moment on the scale of a war, Besley (2020b) argues that events similar to the pandemic led to substantial social and economic changes in the past. Importantly, the pandemic underscores trust in state institutions as a key outcome at stake. Governments’ management of the crisis will likely carry a lasting impact on this dimension of trust.<sup>1</sup> Communication offers an important link between public trust in the state and governments’ pandemic response. In this paper, we provide evidence on the extent to which communication designed to _(i)_ encourage support for government policy and _(ii)_ improve perceptions of state capacity can redress deep-rooted mistrust in elected governments and state institutions. Estimating the causal link between such communication and attitudes towards the state from observational data poses challenges as governments strategically adjust their communication in response to public opinion.

To address our research questions, we leverage randomly assigned informational treatments shared with Pakistani citizens through a phone survey.
.8 in weekly phone households, these data are much closer to but still somewhat lower than the 2.8 workers per household reported in recall surveys. Table 10 shows this finding more straightforwardly. Those who work infrequently during the season are dramatically underrepresented whether at the personplot-day level or the person-day level. For instance, according to the recall NPS, 13 percent of the household labor force works fewer than 10 days per person-plot, while weekly visit surveys report that 56 percent of household workers fall within this category. The total seasonal hours reported in weekly visit and weekly phone interviews that correspond to work for an above-mean number of weeks are similar to the number of hours reported in recall surveys overall (not reported), adding to support for the idea that those who work infrequently are likelier to be forgotten in recall surveys. Because of the paucity of person-plot weeks and days outlined in table 3 and because of the wide spacing of work events they suggest, the work schedules of people who work infrequently are almost certainly highly irregular and difficult both to remember and to make inferences about. Accordingly, it may be that the bulk of household members whom recall-surveyed households remember to report as active in agriculture are those who make large or consistent labor contributions during the season; meanwhile, there appears to be a subset of workers—perhaps those working little, infrequently, or irregularly—who risk being forgotten.

Here (by forgetting), as in the case of the mismeasurement of working time (by inference), it appears that the lack of a typical pattern of work serves to make end-of-season surveys more 18
and near-zero coeffi cient on PTA in column (4) as an encouraging indication that the selected provisions completely explain the average PTA effect reported in column (1).

Next, column (5) returns to our original simple model from column (1) but adds a dummy variable for the EU.<sup>24</sup> Our reasons for treating the EU separately from other agreements are three-fold. First, we suspect that not all of the EU’s efforts to promote trade are captured in how their provisions variables are coded in our data. There could also be unobserved effects that are channeled through the EU’s secondary law process, in which the EU’s governing institutions are empowered to pass new regulations and directives on an ongoing basis. Second, our provisions data set does not include agreements that are no longer in effect. For the most part, the agreements that cannot be included are EU pre-accession agreements, which obviously are subsumed by the EU agreement once each new member joins the EU. As discussed in Section 2, we deal with this data issue in practice by dropping all observations associated with obsolete agreements. Nonetheless, this could lead to biased estimates of the EU agreement and the provisions associated with it. Third, the latest EU agreement has in place six of the eight provisions selected in column 2 (all except AD14 and TBT7); thus, we want to make sure we are not simply picking up an “EU effect”in the provisions that are selected.

As the PPML results in column (5) show, the estimated EU effect is large, several times that of non-EU PTAs in fact. However, when we treat the EU as a possible predictor in the lasso, we find that is not selected and consequently the set of provision variables selected is identical to that in column (2), which is our preferred set to work with in the subsequent iceberg lasso and bootstrap lasso analyses.

# 5.2 Cross-Validation Lasso Results

As discussed before,
Due to the specialty of the data availability, we run our regressions on two data samples. Firstly, we only work on U.S. data, with 50 states across 11 years (2006-2016). As U.S. data have the most important variables and best quality, we are able to take advantage of these estimations to better understand the determinants of property tax revenue. We use both OLS and Fixed Effects models as shown in Equation (1) - (4).<sup>8</sup> Secondly, to utilize all the data we have, we combine data from all the countries, including Australia, Canada, Chile, USA, and OECD. State and province level data from Australia, Canada, and the United States are also pooled together to construct our panel data set. Since our data availability is limited, we can only use a few control variables in these specifications, such as GDP, population, and household size (we use government transfer for both the United States and Canada). Details are shown in Equations (5) - (8)<sup>9</sup> as below. Property Tax Revenue = constant + 1 2. Population 3. Government Transfer + 4. Property Tax Rate + 5. Household Size (1) ββ . GDP + ββ + ββ Property Tax Revenue = constant + ββ ββ 1 + εε2. Population 3. Government Transfer + 4. Property Tax Rate + 5. Household Size + 6. Governance ββ . GDP + ββ + ββ Property Tax Revenue = constant + ββ ββ 1 2 ββ. Population + εε3. Government Transfer + 4. Property Tax Rate + 5. Household Size + (3) ββ . GDP + ββ + ββ Property Tax Revenue = constant + ββ ββ 1 2 Year Dummy + State Dummy + εε. Population 3. Government Transfer + 4. Property Tax Rate + 5. Household Size + 6. Governance + Year Dummy + (4)ββ . GDP
these were converted into raster grids denoting distance from the water and sewerage networks. Two further types of data were assembled that reflected aspects of the quality of water and sewerage provision across these networks. First, data were obtained on recorded complaints made by Lusaka residents to LWSC about aspects of the sewer and piped water network. Complaints were aggregated by Township (the administrative level below the District Metering Area (DMA) used to administer water across the city, with 78 units across Lusaka District) and categorized as relating to water quality, water supply, or sewerage. Second, data were obtained from the Zambian office of the Centres for Disease Control ((CDC), 2018) who undertook water sampling at 290 randomly selected water source locations (taps, boreholes, tanks etc.) across the city and recorded whether each had evidence of contamination with _Escherichia coli_ bacteria, which served as a proxy for unsafe drinking water.

(iii) Household water and sanitation access and other characteristics. Data were compiled from two household surveys conducted in Lusaka: the World Bank Lusaka Sanitation Assessment survey (2015) and the Lusaka Sanitation Program (LSP) baseline survey undertaken by Vision RI (2016). Both were representative sample, questionnaire-based surveys in which selected households were asked broad ranging questions, including on their access to and use of improved water and sanitation facilities, and latitude and longitude coordinates of households were recorded via GPS. In order to use these detailed survey data as putative covariates of cholera risk across the city, two further steps were undertaken. First, questionnaire responses from each household were condensed into an index of risk for both water and sanitation, as detailed in Table 2. Second, the set of resulting risk level data at household locations was used in a Bayesian geostatistical model (P W Gething, Dasgupta, and Andres 2017; Peter W. Gething and Joseph 2017) to yield an interpolated raster grid (one each for sanitation and water risk). Three other geospatial covariates were also derived from the household survey
increase the returns from formal education and enhance the impact of education on growth. Using a model incorporating the interaction of education, its distribution, and policy reforms we found strong evidence supporting the hypothesis that the interaction of education and policy reform matters.

# **II. Where Education Worked or Did Not Work**

Many developing countries have made significant progress in providing better access to eclucation, as evidenced by improved literacy and enrollment rates and higher quality and more equitable distribution of educational services. But the returns from investment in education vary a great deal. In this section, we compare countries where education has worked to boost growth with those where it has not. Most striking in these comparisons across countries are the differences in equality of education, the policy environment for education, and the impact on growth (table I).2 This section highlights cases where the distribution of education was key to development, and cases where the interaction of policy reform and education promoted economic growth.

- 2 In this paper, we employ a panel data set of 12 countries from 1970 to 1994 consisting of variables on GDP per capita measured by purchasing power parity (PPP) and by constant dollars, physical capital stock, labor, level of education of the labor force, and the distribution of education in the labor force, terms of trade, and a few dummy variables. (See appendix I for data and methods).

5
- 10 -

**Figure 3. Meter installation rate and electricity consumption in Albania**

<!-- Start of picture text --> Estimated power<br>consumption (kWh)<br>by district<br>(LSMS 2005)<br>!( !( !(!( !( Power consumption (kWh) !(!( 112 - 127128 - 170<br>!( !( !(!( 171 - 194195 - 213<br>!( !( !( !( !( 214 - 241<br>!( !( !( !( 242 - 270<br>!( !( 271 - 303<br>!( !( !(<br>!(<br>!( !( !( !(<br>!( !( !( !( !( No-metered !(<br>!( !( !( customers (%)<br>!( !( - 0.08 %<br>!( !( 0.09 - 1.11.2 - 4.5<br>4.6 - 20.3<br>!( 20.4 - 50.1<br>50.2 - 91.1<br>4<br><!-- End of picture text --> Source: Author’s drawing based on LSMS 2005 and ERE data.

**Table 1. Household** **<u>characteristics by electricity meter installation</u>**

||W|ith met|er|Wi|thout m|eter|Wilcoxon|
|---|---|---|---|---|---|---|---|
|Variable|||Std|||Std|k tt|
||Obs.|Mean|.<br>Dev
online appendix COVID-19 in Bangladesh.

# **D of licenses to masks on Impacts produce firms**

## **D.1 Data**

**Sample** As discussed in Section 2.2, the FDA provided licenses to garment manufacturers for the production of barrier masks. We use the published list of licensed manufacturers to identify 38 licensed manufacturers and identify an additional 43 garment manufacturers using a combination of firm registration data and EBM product descriptions. Firm names, as published by the FDA, were matched to TINs by RRA staff.

**Data** We construct a balanced panel of firms and aggregate outcomes to the firmby-quarter level. We construct outcomes from VAT, PAYE, and EBM data, and also use customs data to construct pre-period firm characteristics.

## **D.2 Empirical strategy**

To avert concerns pertaining to the impact of several heterogeneous economic shocks at the pandemic’s outset, we study the impact of licensing on firms within the garment manufacturing sector, only. For a discussion of the sectoral heterogeneity underlying the COVID-19 shock in Rwanda, see Byrne et al. (2021).

We consider two approaches to identifying the impact of licensing on firms. Firstly, we assume parallel trends in outcomes for licensed and non-licensed textile manufacturers absent licensing. Under this assumption, we estimate the impact of licensing A17
**Table 1: Student Reading Achievement Trends and Estimated Decline in Scores due to School Closures, EU Countries Only**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Departure from linear trend in 2021|-9.11***|-14.86***||-0.40|-2.25|
|_Principal report on how many weeks sc_<br>>8 weeks|_hool was affected_||-18.75***|||
|0-2 weeks(difference from_>8 weeks_)|||11.59***|||
|2-8 weeks(difference from >_8 weeks_)|||4.25|||
|_UNESCO data on school closures_<br>weeks with full closures||||-1.14***||
|weeks full or partial closures|||||-0.41***|
|_Additional controls_||||||
|country fixed effects|Yes|Yes|Yes|Yes|Yes|
|country*year|Yes|Yes|Yes|Yes|Yes|
|student grade|No|Yes|Yes|Yes|Yes|
|student age|No|Yes|Yes|Yes|Yes|
|country average student age|No|Yes|Yes|Yes|Yes|
|N of students|459039|458869|449734|458869|458869|

* p<0.05; ** p<0.01; *** p<0.001

Another way of estimating the impact of school closures is to compare the departure from the learning achievement trend between countries with shorter and longer school closures, according to UNESCO country-level data. These results are presented in columns (
MacDonald, R. and P. Dias (2007) “Behavioural equilibrium exchange rate estimates and implied exchange rate adjustments for ten countries”, Peterson Institute of International Economics, Washington DC.

MacDonald, R. and L. Ricci (2003) “Estimation of the equilibrium real exchange rate for South Africa”, IMF Working Paper WP/03/44.

Maeso-Fernandez, F., Osbat, C., and B. Schnatz (2004) “Towards the estimation of equilibrium exchange rates for CEE acceding countries: methodological issues and a panel cointegration perspective”, European Central Bank Working Paper Series No. 353.

Montiel, P. and J. D. Ostry (1993) “Is the parallel market premium a reliable indicator of real exchange rate misalignment in developing countries?”, IMF Working Papers 93/70, International Monetary Fund.

Murray, C. J and D. H. Papell (2002) “The purchasing power parity persistence paradigm”, _Journal of International Economics,_ Vol. 56, 1–19.

M.P. Timmer, G.J. de Vries, and K. de Vries (2014). “Patterns of structural change in developing countries.” GGDC research memorandum 149.

Nilsson, K. (2004) “Do fundamentals explain the behaviour of the Swedish real effective exchange rate?”, _Scandinavian Journal of Economics_ , 106(4), 603–622.

Oh, K-Y (1996) “Purchasing power parity and unit root tests using panel data”, _Journal of International Money and Finance_ , Vol. 15, 405–418.

Onour, I. and N. Cameron (1997) “Parallel market premia and misalignment of official exchange rates”, _Journal of Economic Development_ , Vol. 22, No. 1.

Papell, D. H. (1997) “Searching for stationarity: purchasing power parity under the current float”, _Journal of International Economics_ , Vol. 43, 313–332.

Paruolo, P. (
2003; Hall et al., 1992; Kahsay et al., 2018; Kalogirou, 2002). This method is also applicable to the plot-level MAPS data, however, as the data includes precise measures of soil parameters that are often extrapolated from lower resolution geospatial data. In this paper, the unit of analysis is the _plot_ rather than the _pixel_ as in geospatial analysis.

The fuzzy membership method, drawn heavily from Ahamed et al. (2000) and Hall et al. (1992), begins with an identification of the similarity, or Euclidean distance, between the vector of soil properties on each plot, _x_ , and the representative vector for a given suitability class. After normalizing values over the interval [0,10] for each property to eliminate unit-sensitivity (following Hall et al., 1992), the “distance measure” is constructed as follows: where: ~~x~~ 1 2 ) is the vector of soil parameters on a given plot; and ~~μ~~ nn = (xx , xx , ... , xx suitability class, _c_ . cc<sup>= (μμcc1, μμcc2, ... , μμccnn) is the representative vector of soil properties that corresponds to</sup> Equation 1 results in a _distance measure_ for each suitability class, where a higher score reflects greater divergence (less similarity) between the properties on a given plot and the respective suitability class. Subsequently, a _membership grade_ is computed for each suitability class, which indicates the relative fit of a given plot to the specific class, ranging from zero to one, allowing for comparison of fit across suitability classes.

Equation 2 results in a plot-level membership grade for each suitability class based on a given crop’s representative vectors for each class. Each plot is then assigned the overall suitability class of that with the highest membership grade. It is important to note that the method above assumes equal weights for each of the soil properties, which may be a
of government/state ownership in a firm (%)|118|-0.404|-0.220|0.279|-0.087|-0.015|0.702|-0.561|<br>-0.384|0.232|
|Percent of firms not needing a loan|116|-0.196|-0.311|0.516|-0.073|-0.072|0.996|-0.275|<br>-0.466|0.253|
|Percent of firms whose recent loan application was rejected|69|-0.536|-0.370|0.405|-0.200|-0.115|0.731|-0.555|<br>-0.443|0.553|
|**Chinn and Ito Financial Openness Index**|150|-0.118|-0.414|**0.053**|-0.086|0.053|0.402|-0.144|<br>-0.484|**0.024**|
|Quality of supervision|117|-0.392|-0.375|0.918|0.224|0.019|0.278|-0.524|<br>-0.447|0.604|

26
.3, 3.5]|
|University education|345|6.7|23.1 (33.1)|18.0 (12.0)|15|16.4|2.5 [1.1, 3.8]|
|No‐university|170|7.4|23.5 (27.7)|19.0 (13.5)|15|16.1|2.8 [0.9, 4.6]|
|Digitspan recall >=7|279|7.1|24.1 (35.6)|19.0 (13.3)|15|17.0|3.2 [1.7, 4.8]|
|Digitspan recall <=6|176|7.1|24.5 (29.4)|18.3 (12.4)|15|17.4|2.1 [0.5, 3.7]|
|Abuja and Lagos|107|6.5|25.4 (30.5)|19.5 (13.1)|15|18.9|1.2 [‐1.3, 3.8]|
|Other Cities<br>Notes:Trimmed mean drops count|408<br>erfactuals abov|7.1<br>e the 95th percentile|22.6(31.6)|18.0(12.4)|15|15.5|2.7[1.5,3.9]|

22