rafmacalaba/data-use-mentions-extended huggingface dataset:
18 Latin American countries in 2020. The Prejudice Prevalence variable is constructed using the following indicator variables: (i) Acceptance of Venezuelan migrants having a negative impact [=1], (ii) Belief that migrants negatively affect the host economy [=1], Belief that migrants compete with hosts for jobs [=1], (iii)Belief that migrants contribute to increased crime [=1], and (iv) Belief that migrants impose a burden on the state [=1]. The At Least One Variable Equals 1 Index equals 1 if any of the aforementioned variables is 1, and the ”Average Index of Variables” represents the mean of these variables. The data for prejudice prevalence is sourced from the Latinobarometro survey for the year 2020, an annual public opinion survey representing the populations of 18 Latin American countries. The data for the share of migrants is calculated by dividing the total number of migrants in each country by the total population in the same year. The migrant variable represents forced migrants and is sourced from the United Nations Refugee Agency data portal, which includes refugees, peoples of concern, and asylum seekers. Total population data is obtained from the World Bank data portal.

41
to the age breakdown data from the Zimbabwe census from 2012. This generated a mean probability of severe disease across the population in Zimbabwe of 2.9%. After testing a range of different intercepts, an intercept of -4.2 was chosen as this produced a mean district level probability of severe disease of 2.9%.</mark>

### 3. Generating counterfactual scenarios

We produce two counterfactual scenarios for probability of severe COVID-19. These are based on predictions of prevalence of respiratory illness under different levels of handwashing risk, which affect the probability of having a comorbidity and in turn the final probability score. The first scenario uses a handwashing risk of 0.25 throughout the country, equivalent to the lowest level of handwashing risk (i.e. the highest level of handwashing access) currently observed at the district level. This is a realistic scenario where it is assumed that all the districts can improve their handwashing access levels _at least_ to the level of the best performing district. The second scenario, which shows an ideal case, uses a handwashing risk of 0, equivalent to assuming that _every_ household has access to a hand washing facility, soap and water.

# Results

## Handwashing Risk Estimates

Figure 1 shows the cluster level handwashing risk and the district level estimates. There is considerable variation across Zimbabwe, with the lowest cluster level handwashing risk of 0.036 and highest of 0.923. At the district level, hand washing risk varied from 0.25 in Bulawayo to 0.69 in Mberengwa.
Table 6 shows all coefficients in the relevant regressions are insignificant, suggesting that treatment and control groups were not affected differently by the war in other systematic ways.

# **5. Conclusion and policy implications**

We find that mandatory use of a farmer registry with a spatial element allows not only quick implementation and transparent targeting of cash transfers in a conflict setting with low administrative capacity and a long history of endemic corruption, but also use of satellite imagery to assess farm-level impacts on aspects of land use that can be observed remotely.

While <mark>we find a positive overall impact of the</mark> support to bridge working capital gaps, estimated effects are modest overall and largest close to the frontline and for the smallest farm size group. Beyond showing the usefulness of evaluating interventions in real time using administrative data, this suggests that, even with an ongoing conflict, needs and the most effective responses may differ by location and farm type. A similar package of working capital support may be appropriate close to the frontline and possibly even in areas officially classified as affected by hostilities. In areas farther from the frontline, working capital support as provided under PSG may be insufficient to offset the drop in grain and oilseed profitability and use of grant resources to leverage access to investment credit may offer farms in the size group targeted by the PSG a better chance to diversify into higher value products in line with their factor endowments. <mark>Combined with</mark> 15
# **6 The Role of Expectations for Investment**

An important factor in ensuring recovery is capital accumulation in the post war period. It is here that political risk of the resurgence of violence could have a very direct impact. If potential investors believe that future disruptions through violence are likely then they will stay back.<sup>36</sup> The lack of investment then makes the economy less resilient.

Measuring expectations and their impact is difficult. We therefore approach the topic in four ways. First, we discuss the literature which has found a direct impact of changes in expectations and asset prices and investment. Second, we turn towards four cases of strong recoveries and show their that economic activity is strongly correlated with the return of refugees to the country. Third we show, using several surveys, that firms report political risk and political instability as a major obstacle to their economic activity. There is some evidence that the perception of this risk is related to past violence. Forth, we use a model of expected violence to show that this can explain patterns of foreign investments in a post conflict context quite well.<sup>37</sup>

## **6.1 The Literature**

It is an accepted truth in macroeconomics that expectations are an important driver of economic activity. Empirical work has, for example, used news shocks to identify their effect. Ramey (2011) argues, for example, that economic activity reacts to the news about government spending shocks. Taking news into account is therefore crucial to understand the effect of these spending shocks. Bruckner and Pappa (2015) analyze bidding for the Olympic Games using panel data for 188 countries during the period 1950-2009. They argue that the bid attracts considerable inflows of investment several years before the Games. In other words, it is not the Olympic games that drive an improvement in economic activity but the expectation of the Games coming to a country.

More specifically, there is mounting evidence for the role of expectations in
more signals are brought to bear on choices than in an autocratic government (Coates et al., 2008).

# **4. Empirical Analysis**

The empirical analysis tests the following set of hypotheses:

- (i) Public investment levels are higher in countries with low institutional quality. (ii) Public investment volatility is higher in countries with low institutional quality. (iii) Infrastructure quality is higher in countries with high institutional quality. (iv) High aid flows, revenues, and natural resources positively affect both the levels and volatility of public investment.

- (v) Public investment levels are higher in resource rich countries with low institutional quality.

- (vi) Corruption is an important channel through which quality of governance affects public investment levels (following Tanzi and Davoodi, 1997, and Mauro, 1998).

# **4.1. Data**

We construct an annual panel dataset for 144 countries over the period 1984-2008 to exploit both cross-sectional and time series variation. The dataset encompasses country-level public investment and quality of infrastructure data, several measures of institutional quality, and a set of control variables (see Table 4). A brief description of the variables used in the analysis is provided below, while a more comprehensive list with sources and descriptive statistics is found in Table A of the Appendix.

# [Table 4 about here]

Deviating from Keefer and Knack (2007), we use gross public fixed capital formation from the IMF’s World Economic Outlook (WEO) database as a measure of public investment. Public gross fixed capital formation is referred to the general government sector, excluding public corporations.<sup>10</sup> We normalize public investment by GDP and by total investment.<sup>11</sup> Although some countries have missing observations, the majority of countries have the full panel set and this ensures that all regions of the world are well represented.

In order to understand how the variance of public investment is affected by institutional quality
that the evidence for this has been mixed in existing studies. We examine this channel in columns 1 and 2 of Table 7. We see that although businesses are more likely to regularly use business records to know if sales of a particular product are increasing or decreasing, this does not translate into any impact on reducing fluctuations in profits in bad months relative to usual months.

Columns 3, 4, and 5 of Table 7 examine the inventory management and purchasing outcomes of the firms. We see no significant impacts of treatment on the fraction of stock lost to spoilage, the receipt of bulk discounts in purchasing, or in the cost of producing the firm’s most profitable item. The latter is measured with considerable noise, so the insignificant point estimate admits a wide confidence interval.

Better business practices may enable firms to obtain more financing. This may work through either the demand or supply side for formal finance. On the demand side, individuals who have gone through training may have more plans for expansion or feel more confident approaching banks. On the supply side, banks may be more willing to lend to firms which keep better records, or which have higher profitability and sales. However, column 6 of Table 7 shows that access to finance is limited in our sample, with only 17 percent of the pure control group receiving a loan from a bank or microfinance organization in the past year, and that treatment does not have a significant impact on this. This is consistent with the qualitative work, which found a number of challenges on both the demand and supply side for formal finance: women were often afraid of what might happen if they fell behind on payments, found the loan application process cumbersome and timeconsuming, and also faced challenges in terms of ability to provide documentation and/or collateral in some cases (ICRW, 2015).

We do find firms to be 5.8 percentage points more likely to be using business bank accounts (column 7). Columns 8 and 9 then look at whether firms
total monthly income for each individual. Finally, the computer sums this individual total monthly income across all household members and divides it by the number of household members.

The government then transfers a monthly, unconditional benefit (referred to as the “basic benefit”) to all households reporting a per-capita income below the “extreme-poverty threshold” and transfers an additional benefit (referred to as the “variable benefit”) to households with children who have a reported per-capita income below the higher “poverty threshold” conditional on these households making health and education investments in their children.<sup>24</sup> Between July 2009 and June 2014, the extreme-poverty threshold was R$70 per-capita, per-month and the poverty threshold was R$140 per-capita, per-month.

Finally, the MDS has several enforcement mechanisms to prevent income misreporting. First, during the interview, the income questions come at the end of the questionnaire so that questions on expenditures and assets can help the interviewer asses the veracity of the reported income (Bastagli, 2008). Second, during the interview, the applicant is reminded of her responsibility to provide true statements under penalty of losing the right to be eligible for government programs (Gazola Hellmann, 2015). Third, the ministry conducts audits, which can be triggered by citizens’ complaints and cross-checks of registry data with other datasets such as administrative data on formal employment, deaths, or automobile purchases (Gazola Hellmann, 2015). However, despite these attempts, the large informal sector in the Brazilian economy leaves substantial scope for misreporting, which we conjecture is an important response margin to the reform.

# _3.1 Data Sources and Sample Description_

We have access to the Cadastro Unico<sup> ́</sup> household registry, which is used to determine the eligibility of households for BF as well as all other targeted federal social programs (Veras Soares, 2011). Many of these other programs have eligibility criteria above the BF thresholds which explains the large number of ineligible applicants in
summarize the main patterns in the poverty reduction data, Figure 2 displays the share of five-year periods which achieved sustained extreme poverty reduction, over time across the world and five continents. Notably, the share is above 50% for most years in most continents. This reflects the tremendous progress that has been made against extreme poverty in the last four decades. For instance, sustained poverty reduction occurs in more than 70 percent of countries in 06-10 and 11-15 in Africa and Asia, and in 06-10 in Europe. Interestingly, the period of 06-10, which spans much of the recent crisis originating in the United States, does not seem to have been a particularly bad year for poverty reduction globally. This highlights a separation of cycles between advanced and developing economies. Two eras of poor performance stand out, 1981 to 1995 in Africa, and 1985

> 8On the other hand, the use of the indicator variable means that we lose variation in poverty reduction rates. We examine the relationship between these poverty reduction rates and our key explanatory variables in Table A2 in the Appendix and show that this relationship is in line with the mechanisms postulated by the theoretical framework. However, these results are not useful in inferring threshold market sizes.

14
% pop|23.5|12.6|15.6|
|Access to wells/boreholes*|% pop|15.3|49.0|41.5|
|Access to surface water*|% pop|30.3|23.7|32.7|
|Flush toilets*|% pop|5.3|11.2|9.8|
|Improved latrines*|% pop|15.1|6.4|9.2|
|Traditional latrines*|% pop|69.8|54.8|52.4|
|Open defecation*|% pop|9.5|27.6|34.2|
|Domestic water consumption**|liter/capita/day|25|78.9|102.9|
|Collection ratio**|% sales|88.0|69.7|94.4|
|Distribution losses**|% production|45|43.9|33.4|
|Cost recovery of tariffs**|% total costs|51.6|59.3|44.2|
|Tariff (US cents per m3) **|Republic of Congo|Scarce water resources|Other|developing regions|
|Residential tariff (at 30 m3) **|48.6|60.26||30600|
|Non-residential tariff (at100 m3/mo**|48.6|120.74||.–.|

Sources: * Household survey, 2001; refers to national access. ** AICD database, 2005; refers only to SNDE.

23
* p _<_ 0.1). IGMA is the slope of Conditional Expectation Function (CEF). ∆ _IGMAy_ = _θ_ 7 + 2 _θ_ 11 _Eiy_<sup>_p× Inpres_, where</sup><sup>_E_</sup> _iy_<sup>_p_represents father’s years of</sup> schooling for _y_ = 0 _,_ 6 _,_ 9 _,_ 12 _,_ 16. The IGMA values are based on coefficients reported in Table 3. The variable Inpres intensity measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. The Normalized IGMA is the IGMA value relative to IGMA of children for father’s with average years of education (5.57 years), comparison cohort (born between 1957 to 1962), and zero Inpres intensity for the full sample (combined sample of sons and daughters). Normalized IGMA values are reported in percentage. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

41
educational establishment (for example, pre-primary, primary or high school) must send information on initial enrollment by grade, age and gender. Non-response is very low (less than 1%). Using this information it is possible to construct a panel at the communitycohort level. Unfortunately, we do not have access to data on measures of school readiness and learning performance among students in the sample. Counting with data on these dimensions would have allowed us to provide a more comprehensive analysis of the shortand medium-run effects of attending a pre-primary. Additionally, we have data on private pre-primaries and their exact location for 2005 but not for prior years.

We have also gained access to comprehensive data from the 1994 and 2002 Population Census. These data contain basic socioeconomic characteristics at the household and individual level. Importantly for our purposes, the geographic location of the household is identified at the community level, and it is possible to match communities from the 1994 to the 2002 Population Census. Additionally, the National Statistics Institute provides geo-coordinates for communities in the 2002 Population Census.

# **4 Research design**

## **4.1 Sample construction**

We focus on a sample of rural communities that had a primary school during the period of analysis. We exploit variation in access to pre-primaries across cohorts within communities to identify the impacts on enrollment and progression rates.

To be selected into the estimation sample, a rural community has to meet 4 conditions. First, it must have a population of between 300 and 2,500 in 2002. This condition ensures that rural communities under analysis are large enough to have a pre-primary school. Second, it needs to have one primary school operating during the 1995 to 2008 period. This makes it possible to measure impacts on enrollment and progression in primary school. Third, the community should not have other communities located within a 0.5 kilometers radius from its center This restriction reduces the likelihood that the opening of a pre-primary in one community might induce children from neighboring communities to start
from potential AD duties. This finding demonstrates that exporters are willing to avoid the threat of retaliation by decreasing their dumping margins in a particular country with importers that are named in AD petitions in the exporters’ home country. We also show that the pricing effect of retaliation threats is increasing in firms' shares of each industry's product in their total exports. This price increase is higher for exporters serving the developed world and heavy AD users.

1 See Blonigen and Prusa (2003) for an extensive survey.

2 India and the United Stated are the top two AD users in the world.

3 Studies on retaliation and AD also use filing behavior as the dependent variable.

- 4 See table 1 for the AD duties of importing countries.

5 Most of the AD cases are filed for multiple products. Therefore, the number of cases does not always represent the number of six-digit HS products investigated.

6 See Molina and Muendler (2009) and Hirakawa, Muendler and Rauch (2010) for more information about SECEX and RAIS data.

7 http://econ.worldbank.org/ttbd/gad.

8 See Feinberg and Reynolds (2006).

9 Given the large costs associated with initiating AD investigations, firms often come together via lobbies to file petitions and share the cost of filing. 10 Our results are insensitive to treating each European Union country as a different market and to removing European Union countries from the sample.

11 The linked employment data also report the wage bill paid by the firm. However, the availability of this variable is so limited in our sample that it is unusable.

12 Although we do not report the results here, the alternative measure of the use of total revenue that a firm earns from the same product in the destination does not alter our findings.

13 The linked employment data also report the wage bill paid by the firm. However, the availability of this variable is limited in our sample, which does not allow us to utilize the data.

14 While we
‐0.221***|
||||(0.000)||(0.000)|(0.000)|
|FDI Overallt‐2||||0.085*||‐0.074|
|||||(0.078)||(0.102)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐
39 Khwaja, Asim Ijaz. Forthcoming. Can Good Projects Succeed in Bad Communities? Forthcoming, _Journal of Public Economics_ .

- Kingdon, G. and M. Muzammil. 2001. “A Political Economy of Education in India,” _Economic and Political Weekly_ , 36(32): 3052-3063

- Kitschelt, Herbert and Steven I. Wilkinson. 2008. _Patrons, Clients and Policies. Patterns of Democratic Accountability and Political Competition._ Cambridge and New York: Cambridge University Press.

- Kudamatsu, Masayuki. 2011. “Has Democratization Reduced Infant Mortality in SubSaharan Africa? Evidence from Micro Data.” Forthcoming in _Journal of the European Economic Association_

- La Ferrara, Eliana. 2002. “Inequality and group participation: theory and evidence from rural Tanzania,” Journal of Public Economics, 85(2): 235-73.

- Maskin, Eric, and Jean Tirole. 2004. “The Politician and the Judge: Accountability in Government”, American Economic Review, 94(4): 1034-1054

- Mann, Michael.  1986. _The Sources of Social Power: A History of Power from the Beginning to AD 1760._ Cambridge, UK: Cambridge University Press.

- Mansuri, Ghazala and Vijayendra Rao, Forthcoming. “Localizing Development: Does Participation Work?” Policy Research Report, Development Research Group, The World Bank

- McGee, R. and J. Gaventa. 2010. Synthesis Report: Review of impact and effectiveness of transparency and accountability initiatives. Transparency and Accountability Initiative, Open Society Foundation, London, UK

- Montalvo, José G. and Marta Reynal-Querol. 2010. “Ethnic Polarization and the Duration of Civil Wars.” _Economics of Governance_ 11(2).

- Mullainathan, S. and A. Shleifer, 2002, Media Bias,  (National Bureau of Economic Research, Inc, NBER Working Papers: 9295).

- Nguyen, Trang and Gerard Lassibille. 2008. “Improving management in education: Evidence from a randomized experiment in Madagascar,” MIT Poverty Action Lab Working Paper
in each category, whereby recently is defined based on the data at hand. For establishment, consumption, labor force and health surveys, we expect data to be collected at least once every 5 years. This is lenient: the 2016 State of Development Data Funding (SDDF) report published by the Global Partnership for Sustainable Development Data proposes a frequency of 2-3 years for health surveys, 5 years for consumption surveys and annually for labor force and establishment surveys.<sup>12</sup> The World Bank expects welfare surveys to be updated every three years. We expect price survey data to be collected multiple times annually – typically monthly- but examine NSOs for data within the past year. Census data is expected to be collected at least once every decade. Although the exercise of examining NSO websites for recent microdata was carried out between February and April 2021, we use year 2019 as the reference year. This because of COVID-19 related disruption in data collection which often prevented face-to-face interviews from being conducted. Hence recent establishment, consumption, labor force and health surveys are considered to be those

> 12 See <mark>http://opendatawatch.com/knowledge-partnership/state-of-development-data-funding-2016/</mark>

7