rafmacalaba/data-use-mentions-extended huggingface dataset:
macroeconomic effects of tariffs. As a robustness check, we also bring to bear sector-level data, which provides insight into the channels through which the effects of tariffs are transmitted, while also addressing some of the inherent limitations of the macroeconomic analysis (by controlling for country shocks that may be correlated with tariff changes).

# A.   Country-Level Analysis

We use the local projection method (LPM, Jordà, 2005) to estimate impulse-response functions. LPM allows us to retrieve the dynamics of the dependent variable after a shock, and has been widely used in empirical macro, for example: Auerbach and Gorodnichenko (2013); Jordà and Taylor (2016); Ramey and Zubairy (2018); Alesina et al. (2019); Furceri et al. (2019). LPM seems relatively attractive in our context because it does not impose the dynamic restrictions embedded in models like vector autoregressions or autoregressivedistributed lag specifications. The framework is flexible enough to accommodate a panel structure. It is particularly suited to estimating nonlinearities in the dynamic responses and state-dependencies that may be impractical in a multivariate context (Ramey and Zubairy 2018, IMF 2019). The estimates generated by LPM are more robust to misspecification of the data generating process since each impulse response is estimated separately for each horizon (Ramey 2016, Biljanovska et al. 2017). For our purpose, this approach provides a simple and flexible way of estimating the dynamic effects on a panel with large number of observations. The state-dependent properties allow to understand the asymmetric effects of tariffs across various dimensions. The baseline regression is specified as follows: where: 7
the WB’s website,<sup>3</sup> with the aim of identifying existing gaps by country, fostering countries’ efforts to improve data dissemination, and informing technical assistance strategies.

A country’s performance is evaluated across nine indicators and addresses three main areas: (i) public debt statistics dissemination practices; (ii) publication of key debt management documents; and (iii) reporting on risks stemming from contingent liabilities. Each indicator is evaluated according to a fourcategory scale, which ranks standards from low (red) to high (green), according to the criteria presented in Figure 1.

Dissemination practices for public debt statistics are assessed according to the following three dimensions: accessibility, completeness, and timeliness.<sup>4</sup> Debt data should cover debt stocks at the cut-

> 2 This assessment cannot differentiate between intentional and non-intentional under-reporting, nor can it identify “hidden debt”, as this would require granular data from creditors or other stakeholders involved in the transactions. As discussed in Section 3, these records are rarely available.

> 3 - - <u>https://www.worldbank.org/en/topic/debt/brief/debt transparency report</u>

> 4 Completeness is evaluated relative to the prevailing legal framework and countries’ current borrowing practices. For instance, if SOEs in one country are not legally allowed to borrow or do not have outstanding debt, they are not expected to be covered in the public debt reports.
254 wards, depriving us of any electoral outcome data besides the party of the victor, meaning that regressions using vote shares and turnout are conducted on the 2,202 wards for which both poverty and electoral data are available.<sup>11</sup> 67 of these wards submitted no applications to TASAF II, and so the analysis of the percent of applications funded is conducted in 2,135 wards. Table 1 provides summary statistics on the ward-level sample in the left panel, and on the household survey data in the right.

# **4. Estimation Strategy**

Our estimation strategy is influenced by Galasso and Ravallion (2005) who provide an empirical structure for testing the additional contribution of local information gained through decentralization. In particular, they define the information set held by the central planners and then use a household dataset to construct a much richer definition of „eligibility‟ for the program than was available to central bureaucrats. They then attribute the additional poverty targeting achieved above and beyond that coming from the planners‟ information set as the benefits arising from decentralized targeting. Our approach is inspired by this structure in the sense that the only component of TASAF II that was centrally dictated was the allocation of funds to the districts, and therefore all within-district targeting arises from the actions of decentralized agents. We therefore decompose the variation in targeting efficiency into a cross-district (centralized) and a within-district (decentralized) component. Using this structure, we can separately isolate the role of the clearly defined funding formula that drives allocation to the districts, and the complex decentralized process through which the districts allocate funding to the lower administrative levels.

For each specification we present the univariate correlation between the headcount index and the outcome of interest. We then present a multivariate linear regression using an additional battery of controls, and we econometrically decompose the overall targeting efficiency into a between-district and a within-district component. Specifically, for ward _i_ in district _j,_ we estimate:

> 11 Uncontested wards are slightly less likely to submit
13(8):765–791.

- Bouët, A., Bureau, J.-C., Decreux, Y., and Jean, S. (2005). Multilateral agricultural trade liberalisation: The contrasting fortunes of developing countries in the doha round. _World Economy_ , 28(9):1329–1354.

- Brandsma, A., Kancs, d., Monfort, P., and Rillaers, A. (2015). Rhomolo: A dynamic spatial general equilibrium model for assessing the impact of cohesion policy. _Papers in Regional Science_ , 94:S197–S221.

- Britz, W. (2020). Comparing three penalty functions-cross entropy, quadratic and linear loss–in sam splitting applications.

- Canning, P. N. and Tsigas, M. E. (2000). Regionalism, federalism, and taxation: A food and farm perspective. Technical report.

- Cherubini, L. and Los, B. (2016). Regional employment patterns in a globalizing world: A tale of four italies. _Global value chains: new evidence and implications_ , pages 213–235.

- Cicowiez, M. and Lofgren, H. (2017). Building macro sams from cross-country databases: method and matrices for 133 countries. _World Bank Policy Research Working Paper_ , (8273).

- Cirillo, C., Imperioli, L., Manzo, M., et al. (2021). The value added tax simulation model: Vatsim-df (ii). Technical report.

26
they want to do, and what they actually do) and exploring additional channels to finding work are further barriers to finding work for women in KRI. We find evidence of these barriers through responses in the social norms sample, as well as an additional sample of job seekers registered at a job agency in KRI called Rwanga Foundation.

In an initial diagnostic exercise with 100 randomly selected job seekers with women who were registered with Rwanga, women who are currently working, had to on average look for a job for 7.5 months before they were able to successfully find a job. However, non-working women had stopped looking for work after 2.76 months. Working women respondents (N=175) in the social norm sample reported a similar number of an average of almost 9 months looking for jobs before they found their current job (the median was 3 months, and 62% had found jobs within 6 months).

We also observe “intention-action” gaps for non-working women who want to work in the social norms sample as well. For example, while 68% of non-working women said they want to work, only 20% of these women (who want to work) had either spoken to a friend or relative about work or looked for work in the past 6 months. Even fewer, 13.8%, had contacted a job
04||
||(0.015)|(0.013)|(0.019)|(0.05)|(0.021)|7|
|Public assistance (cash or in-kind)|0.07|0.03|0.03|0.05|0.05||
||(0.035)|(0.018)|(0.01)|(0.026)|(0.023)|7|
|Any assistance from any source|0.22|0.21|0.18|0.23|0.21||
||(0.055)|(0.132)|(0.118)|(0.117)|(0.134)|8|

Source: Own calculations based on HFPS-Wave 1.

Notes: Urban observations only. Standard errors in parentheses. n/a indicates that the statistic is not relevant for that cell, namely households that do not own an enterprise cannot report on income declines in a household enterprise.

> 29 The survey questions are: (i) "Has your Total Household Income changed since the pandemic started?" (ii) "Has this source of household income changed since last 12 month: Family farming, livestock or fishing?”, (iii) "Has this source of household income changed since the pandemic started: Non-farm family business?”, (iv) "Has this source of household income changed since the pandemic started: Wage employment of household members?”, and (v) “If someone in the household own a business, has the revenue from that business decreased since the pandemic started?”

> 30 Countries included: **Total income decline** (Ethiopia, Malawi, Nigeria), **Farm/nonfarm, wage** income and **business revenue** decline (Ethiopia, Madagascar, Malawi, Nigeria, Sudan
Appendix 4. Definition of variables and data sources

|Variable|Definition|Source|
|---|---|---|
|Crisis|Sovereign debt, banking, or currency crisis|Laeven and Valencia (2018)|
|Change in U.S. real interest<br>rate|Percentage point change in U.S. real lending interest rate (deflated by GDP<br>deflator)|WDI|
|GDP growth|Annual percentage growth rate of GDP at market prices based on constant<br>local currency|WDI|
|Short-term debt|Share of short-term debt (with a maturity of 1 year or less) in external debt|IDS|
|Debt service|Ratio of debt service on external debt to exports|IDS|
|Reserve cover|International reserves in months of imports|IDS|
|Change in government debt|Percentage point change in public debt to GDP ratio|GDD|
|Change in private debt|Percentage point change in private debt to GDP ratio|GDD|
|Concessional debt|Share of concessional debt in external debt|IDS|
|Funding ratio|Ratio of credit provided to private sector to total deposits|GFDD|
|Currency overvaluation|Percentage deviation of real effective exchange rate from HP-filtered trend|Darvas (2018), World Bank|
|Currency mismatch|Ratio of foreign liabilities to foreign assets|Lane and Milesi-Ferretti (2018)|
|FDI|Net inflows of foreign direct investment as a share of GNI|IDS|

Note: GFDD – Global Financial Development Database; GDD – Global Debt Database, IDS – International Debt Statistics; WDI – World Development Indicators.

43
it (Fazekas & Saussier, 2018). On the one hand, building integrated data systems even if they only encompass the already collected information requires considerable investment. Linking disparate procurement data systems, standardizing data formats, and delivering reliable data management infrastructures are typically far from straightforward exercises with many frontline civil servants opposing change. Capturing past data, typically recorded in semi-structured text files, and building state-of-the-art data infrastructures going forward both require IT expertise and the understanding of complex data systems typically lacking in public sectors. On the other hand, systematizing data collection and publication had frequently revealed that reporting requirements are grossly neglected, making the most essential bits of data erroneous, missing or incomprehensible. The demand for using such data across the globe, as examples from Canada,<sup>7</sup> Czech Republic,<sup>8</sup> the European Union,<sup>9</sup> and Hungary reveal, makes the challenge of improving data quality even more pressing (Czibik et al., 2015). In addition, linking contract data to databases holding related information is also challenging but promises considerable benefits, most notably the ability to trace development outcomes back to procurement performance. Potentially linked databases include for example public financial management systems, treasury accounts of public bodies, company registry, financial and ownership data (including beneficial ownership registries gaining prominence recently), and information on sectoral outcomes such as student achievement or mortality (Fazekas & Tóth, 2016, 2014).

Big Data in public procurement also gives rise to the need for new, more advanced indicators which help diverse users in making sense of the often daunting variety of data; recall, public procurement includes purchases for anything ranging from nuclear power plants to school meals. Such new indicators of value for money and open access can potentially complement or in some cases replace traditional indicators of

> 6 - <u>http://www.open contracting.org/iodc15_open_contracting_reflections</u>

> 7 <u>https:
<mark>other stakeholders, sewage and organic solid wastes are some of the fastest growing sources of nitrogen pollution in the country (INA, 2017).</mark> Most previous work that provides causally interpretable estimates has primarily focused on short‐run and immediate birth outcomes (Brainerd and Menon, 2014; Jones, 2019).<sup>4</sup> These studies show that early‐life exposure to nitrogen‐related pollution can lead to infant mortality (Brainerd and Menon, 2014) and low birthweight (Jones, 2019). The well‐established fetal origins literature suggests that intrauterine health impacts can lead to lasting health damages, and that low birthweight is associated with shorter height in adulthood (Barker, 1990; Almond and Currie, 2011; Currie and Vogl, 2013; Christian et al., 2013; Almond, Currie and Duque, 2018). And low birthweight is a well‐known marker for many health problems later in life, including coronary heart disease (Barker 1995), decreased glucose tolerance (and thus a higher propensity for obesity) (Ravelli et al. 1998), and increased rates of all‐cause mortality (Risnes et al. 2011). Still, there has been no attempt to quantify the full extent of health damages, especially the irreversible and lagged human capital impacts, as a result of nitrogen pollution in water.

Evidence for lagged human capital impacts in environmentally vulnerable and poor locations is severely limited due to the paucity of longitudinal data that can trace long‐term impacts. This is particularly challenging for water pollution, since monitoring of water quality is sparse in space and time and is site‐specific. In this paper, we exploit temporal and geographic variation in nitrogen pollution exposure with a newly constructed database of water quality that combines in situ monitoring station data with a geospatial statistical model for stream networks developed by ver Hoef and Peterson (2010). We carefully integrate early‐life exposure to nitrogen pollution between the critical years of development
informative sampling. Finally, the few studies that have considered the prediction of changes over time have found that this is much harder than prediction across space, even for medium to long-run changes. Seeing short-run changes in welfare from space may prove challenging but is a challenge worth taking on.

The recent literature has offered more discussion than evidence regarding the pros and cons of different methodologies. One dividing line has been the choice of “direct CNNs” trained directly to survey data as opposed to utilizing interpretable geospatial features in a mixed linear or tree-based machine learning model. In Uganda and Sri Lanka, where both approaches have been compared, it seems that the interpretable features approach does at least as well as directly training CNNs, but the evidence on this question remains scant. A second fault line has emerged over the level at which to specify linear models. As a general rule, predictions typically benefit from using the most spatially disaggregated data possible, so if sub-area level data are available, area-level models should be only used as a last resort. This is particularly true when considering an evaluation criterion that combines accuracy and precision, such as mean squared error, since the use of more granular auxiliary data appears to have a larger
in three ways. First, the survey firm’s software identified outliers in terms of length taken to answer each question as well as suspicious patterns in the answers. Second, the firm called back 2,416 respondents to confirm that they had participated in the survey. Finally, an in-depth call back review of 229 surveys stratified by interviewer was done in close collaboration with the research team to validate the e↵ort of each interviewer.

The survey included 47 questions, with some questions containing multiple sub-questions. The length of the interview meant that it was necessary to provide a monetary incentive to increase the proportion of respondents who finished the survey. This was done through a mobile credit transferred directly to the phone of the participant.

The survey questions covered di↵erent dimensions of social and institutional trust, as well as many possible drivers and correlates of trust. The survey also captured geographic and socio-demographic information of respondents to assess how key variables varied at the 12According to the World Inequality Database (https://wid.world/country/morocco/), the top 10 percent’s share of the total wealth increased from 62.9 percent in 2020 to 63.2 percent in 2021. 13AUC/OECD. 2018. Africa’s Development Dynamics 2018: Growth, Jobs and Inequalities, OECD Publishing, Paris/AUC, Addis Ababa, https://doi.org/10.1787/9789264302501-en.

> 14Clementi, Fabio et al. Polarization and Its Discontents: Morocco before and after the Arab Spring. Policy Research Working Paper 9049. Washington, D.C.: World Bank Group.

7
# Diagram 1

_Categories of the dependent variable in the multinomial regression model_ <!-- Start of picture text --> Did not<br>Economic Crime and<br>Family reasons Other reasons migrate<br>reasons violence<br>internally<br>Job search Study<br>Family Criminal<br>reunification insecurity People who<br>lived in<br>Mexico in<br>Natural 2015 and<br>Job change<br>disasters still resided<br>in the same<br>state in<br>Getting Violence 2020<br>married<br>Job offer Other<br><!-- End of picture text --> Source: Own elaboration.

**However, using the Census data for estimating the propensity to migrate has the limitation that individual characteristics are observed post-migration** . The Census has no information on the characteristics of individuals and their households before the migration decision. This is a challenge when modeling the migration choice since determinants of migration are likely to be related to individual and household socioeconomic conditions before choosing to migrate.

**Time-invariant characteristics and individual factors inferred at the time of migration are used as explanatory variables to overcome Census data limitations.** Time-invariant characteristics include ethnicity and race, and individual factors inferred at the time of the migration choice are age and if the person had children under the age of 14. Under the assumption that the highest level of educational attainment achieved does not change significantly after 25 years old for most individuals,<sup>13</sup> the sample is constrained to individuals 25 and over at the time of migration to allow for the inclusion of educational attainment as a regressor.

**Variables capturing socioeconomic conditions of the state of origin at the time of migration (2015) are also included as regressors.** Among these variables are labor market conditions (unemployment rate, employment rate, and informality rate), deprivations in non-monetary dimensions (deprivation index), the
immigrants’ returns as the difference between the initial inflow of a cohort arrived in year _t_ and the stock remaining at a later date _t_ + _k_ . Following OECD (2008), we identify a cohort size entering at year _t_ on the basis of respondents present in the ACS data in year _t_ + 1 and reporting to have arrived in the preceding year. We then use the ACS in year _t_ + _k_ to identify migrants who entered in year _t_ and are still present in _t_ + _k_ . To reduce statistical noise due to the small sample size of the ACS (1 percent of the population), we group together cohorts arriving between 2007 and 2012 and examine their return migration up to 9 years after migration (e.g., until 2021 for the 2012 cohort and until 2016 for the 2007 cohort). As with the analysis in section 4.1, we focus on individuals who migrated from a developing country and moved when they were adults between 18 and 40.

Figure 3 shows that migrants married before migration are more likely to return to their home country compared to migrants unmarried before migration: 24 percent of initially married migrants have returned after 9 years, compared to 14 percent for those who migrated unmarried. This is true both among men and women, with the former being more likely to return than the latter (34.9 percent of initially married men have returned after 9 years compared to 14.2 percent of initially married women).<sup>20</sup> Note that this form of selection into return migration by initial marital status makes only a marginal difference to our Table 1 finding that a majority (55.9 percent) of migrants are unmarried before migrating. In fact, for migrants who report to have arrived in the U.S. within one year (and hence are unlikely to have already returned home), this share stands at 52.9 pecent, and it is relatively stable over the years since migration (58.3 percent after
.28 ha vs. 0.76 by „full land users‟)<sup>14</sup> and are less poor (12% vs. 19%). They enjoy better access to infrastructure and extension and -somewhat paradoxically given that a lack of resources is a key reason for leaving land idle- live in places with much better credit access. At the same time, they are smaller and have lower levels of agricultural assets. They rely on non-agricultural sources of income more heavily than the mean farmer; in fact the share of their agricultural income is 10 points below the national average. Their level of productivity, in terms of total output or profit per ha, is half or less of the national average. Both the size of area affected -with the amount of idle land more than three times what is currently transferred through leases- and characteristics of households with fallow land thus suggest that land leasing could significantly improve productivity in rural areas. Exploring the reasons for the apparent failure of land rental markets on such a massive scale will thus be of great policy relevance.

# **4.3 Descriptive evidence on land markets**

As a first step towards such analysis, table 5 displays descriptive statistics for households depending on whether they remained in autarky, demanded, or supplied land to rental (col. 1-3) and sales (col. 4-6)

> 13. We report figures for land left uncultivated because of lack of resources (economic reason) and inappropriate location (structural reason). 14. The fact that that households leaving land idle have slightly larger plots on average than those cultivating all of their land (0.31 vs. 0.28 ha) casts doubt on potential of consolidation programs to effectively address the causes underlying abandonment of productive land.

14