rafmacalaba/data-use-mentions-extended huggingface dataset:
multiple types of fertilizer, we take the average price across all fertilizer types.<sup>6</sup> We winsorized the fertilizer price per kilogram at both the 5<sup>th</sup> and 95<sup>th</sup> percentiles within each country. In addition to quantitative data on fertilizer prices, we collected self-reported data on the change in fertilizer prices compared to the previous agricultural season and, for those reporting higher prices than last season, the strategies they used to cope with high fertilizer prices.

# _2.3. Estimation_

Our analysis is mostly descriptive, focusing on the estimation of national-level means by survey wave. To examine the correlates of fertilizer use and shortfall, we estimate linear probability models using OLS. When examining trends over time, we fit a linear trend for each country by regressing the dependent variable on a discrete variable taking the value of 1 for the first round we collected data on the outcome in that country, a value of 2 for the second data point, and so on. Throughout our analysis, we weight our estimates using the HFPS survey weights that are designed to correct for selection bias in the HFPS sample compared to a nationally representative sample (Ambel, McGee, and Tsegay 2021; Zezza et al. 2023).

# **3. Results**

# _3.1. Agricultural Engagement_

To provide context, we first show the share of households engaged in crop farming over time (Figure 1). For five of our study countries – Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda – we observe this before the COVID-19 pandemic and then at various points during and after it, from 2020 to 2024. In the pre-COVID period, between 56 percent (Ethiopia) and 80 percent (Malawi) of households engaged in crop farming. The first data points during the COVID-19 pandemic suggest there is a significant increase in the household-level incidence of crop farming, as pandemic lockdowns limited other income generating activities, of 5.
7 if, following Baumol (1967), it is assumed that technological change is faster in the relatively less service-intensive private sector.6 Under such assumptions about relative price changes for public and private goods, government expenditure ratios based on current prices bias upwards the trend in the "quantity" of governLen* output since relative price effects are not accounted for. An opposing position argues that nominal and not "real" shares of government expenditures actually are what is important as long as relative 7 prices of public and private goods reflect consumer valuations. If this is the case, the share of output in value terms is the appropriate index of government's claim on income.

Whether nominal or real shares of government expenditure **s** hould be employed in analyzing t,ie "size" of government clearly depends on the question being raised. Dctermination of the income elasticity of the demand for public goods would seem to require a real measure of government output. However, assessment of the resource requirements of government would favor a nominal index. Ultimately, getting a handle on the reasons behind the increasing (or decreasing) cost of government would benefit from a comparison of both measures. In any event, estimates of the growth in government expenditures reveal considerable differences depending on the choice of a constant versus current dollars measure.

# 4. The constraints of the data.

For OECD nations an array of government expenditure data are available and have been reported on. The opposite is the case for most developing nations.
the pandemic was impossible and most safety net programing during the pandemic had to rely on information collected a few years ago. For example, in Egypt, the national cash transfer program that was expanded in response to the COVID-19 crisis targets households based on survey data that was collected up to four years ago.

The COVID-19 pandemic also emerged at a stage when social protection programming in some African countries was striving to be shock-responsive. These institutional arrangements in countries such as Kenya and Uganda were being oriented to rapidly scale up programs in certain geographical areas to respond to weather shocks but were not yet widespread in Africa and were poorly prepared to respond to a new type of shock (Beasley et al., 2021). Shock-responsiveness requires investments in data infrastructure and social registry which can aggregate administrative data and program beneficiary status across a variety of social protection programs, allowing for efficient coordination and identification of new beneficiaries without the necessity of collecting household data in the field. Only 12 countries in Africa were classified in 2018 as having a unified social registry operating at a medium to large scale. Even if some of these data may be available, the lack of digitalization and data sharing procedures may complicate their use for efficiently targeting safety net programs. In a case study of Kenya’s social protection expansion, it was noted that the value of social registry was compromised by incomplete data and bureaucratic delays with sharing access with the necessary stakeholders to facilitate the roll-out of new programs (Doyle and <mark>Ikutwa,</mark> 2021).

# **_Targeting based on existing listings and associated limitations_**

Even where the use of existing listings for scaling worked most smoothly, basing short-term aid responses for COVID-19 on eligibility for long-term existing programs or social registries risks excluding certain groups, such as migrants and the undocumented. Migrant-receiving countries in southern Africa such as Botswana, Namibia, and South Africa did not have any formal program during the COVID-19 lockdowns to support migrant workers who suddenly found themselves unemployed and cut-off from their home country (
Moreover, in table 2 I estimate line B’s effect on the overall log number of individuals and disentangle the effect from the previous regression between formal and informal workers. In panel A, I report the results for the pool of workers, while panels B and C report the results for the number of formal and informal workers. The dependent variable in the first and third columns is the log number of workers. On the one hand, the point estimates suggest that the effect is very small on the number of individuals. For instance, it is only 1.7% in the case of the pool of workers, and 2.2% for low-skilled. On the other hand, the second and fourth columns show the estimates for the log number of formal workers. The results suggest that the locations affected by the shock experienced an increase in formal workers between 3% and 6%. This effect is larger than the estimate on the number of individuals. Finally, the third and sixth column reports the results for the log number of informal workers. In the case of municipality fixed effects, the number of informal workers decreased by around 3% in the locations that experienced the shock.

There are two main potential concerns of the interpretation of this effect. First, line B may be endogenous; I address this concern in section 3.2.3 by comparing line B with planned lines. Second, worker sorting maybe explaining the results. I address this concern in section 3.2.4.

Second, I use data from the Economic Censuses and test whether the shock also generated an indirect effect affecting the “treated” location in terms of jobs. I estimate the following specification to study whether areas close to the new subway lines experienced changes in workers’ informality rates: where _yi,t_ is one of the outcomes of interest of census tract _i_ at moment _t_ . I estimate equation 3.3 for the following outcomes: the share of informal workers and the share of informal firms
stated preferences, etc.), subjective well-being data or external sources in the literature. For instance, the dimension-specific statistics feeding synthetic indices are sometimes aggregated using a parametric utility function. Its parameters can be calibrated using empirical values taken from specialized literatures. For the trade-off between income and longevity, one can draw on empirical estimates for the elasticity of inter-temporal substitution and the value of a statistical life (see section 3.3).

> 12 In the case of multidimensional poverty measures, the equal-weights procedure is more nuanced than what its name may suggest. The reason is that, in practice, each dimension is captured by several dimension-specific variables. The equal-weights practice attributes equal weights between dimensions and also attributes equal weights among the variables capturing the same dimension. Hence, the weight 1 1 received by each variable is given by ndim<sup>∗</sup> nvar<sup>wherendimdenotesthenumberofdimensionsand</sup> nvar denotes the number of variables capturing the dimension. For instance, the global MPI has three dimensions: education, health and living standards. Health is captured by two variables (nutrition and child mortality) that each receives a weight of 6<sup>1=</sup> 3<sup>1∗</sup> 2<sup>1.Livingstandardsiscapturedbysixvariables</sup> that each receives a weight of 181<sup>.</sup>

11
20 Despite its already high value (85.6% in 1992), by 1996 the average share of local governments in consolidated regional expenditures on "Education" gained additional 2 percentage points. This expenditure category has been a firm prerogative of local governments in practically all regions - between 1992 and 1996 the values of the respective coefficient of variation remained exceptionally low at around 0.09. Also, compared to other types of expenditures, the minimum value across regions of the average share of local governments in public spending on "Education" was remarkably high: 56% in 1992 and 59.2% in 1996.

The single most notable expansion of local governments' expenditure responsibilities was in the sphere of "Social Protection". Between 1992 and 1996, the average share of local governments in consolidated regional expenditures on "Social Protection" rose from 22.3% to 70.8%. As noted by Lavrov (1996a) however, this seemingly drastic change in policies may at least in part might reflect a 1994 change in the budget classification, which moved the formerly separate category "Allowances for Children and Other Social Transfers to the Population" in the expenditure item "Social Protection". Between 1992 and 1996, the regional disparities in local governments involvement in "Social Protection" diminished substantially, as the respective coefficient of variation fell by nearly 70 percentage points right after the steep rise of the average share of local governments in consolidated regional expenditures on "Social Protection" in 1995.

A more detailed look at local governments' involvement in "Social Protection" by economic region'<sup>0</sup> however, shows that the Northern Region did not participate in the observed rapid decentralization of this type of expenditures. As the data in Table 11 shows, up to 1994 the Northern Region was in line with the rest of the country by degree of decentralization of the "Social Protection" expenditures. But in 1994, it did not follow the countrywide rapid expansion of local governments' involvement in
relation between household characteristics and expenditure in training data. This is a particularly valid concern in the case of phone surveys since phone ownership positively correlates with higher expenditure.

The comparability of the AWMS sample with the IE-LFS 2019-20 sample relies on the reweighting process that corrects a possible sampling bias induced by phone ownership and differences in survey response. As stated in the data section, the AWMS R3 listing comes from households that provided a cellphone number

> 9 To avoid over-fitting, Cross-validation (CV) is a standard empirical test in machine learning literature. The Q3 urban and rural samples of the IE-LFS 2019-20 are randomly split into five subsamples each; then, the model is estimated on four folds (training dataset) and evaluated on the remaining one (testing dataset). Since the remaining fold is not included when the model is trained, all performance indicators in the remaining fold are not subject to the over-fitting problem. This exercise is repeated iteratively for different levels of statistical significance to maximize model performance.

10
Albania (2015)<br>|
|Guatemala (2014)<br>|
|Paraguay (2014)|
|Serbia (2016)|
|Indonesia (2017)|
|Spain (2017)<br>|
|United States (2016)<br>|
|<br>Panama (2016)<br>|
|Mauritius (2017)<br>Croatia (2018)|
|Romania (2016)|
|0<br>5<br>10<br>15<br>20<br>25|

_Notes_ : Original estimates based on data from CEQ Institute, CEQ Data Center on Fiscal Redistribution, https://commitmentoequity.org /datacenter; OECD data; World Bank data. Countries are grouped according to World Bank Income classification system starting with Low <u>Income Countries (LIC), Lower Middle Income Countries (LMIC), Upper Middle Income Countries (UMIC) and High Income Countries (HIC)</u> 20
Science Research Network.

- Bettinger, E. (2004). How financial aid affects persistence. In _College choices: The economics of where to go, when to go, and how to pay for it_ (pp. 207–238). University of Chicago Press.

- Bettinger, E. (2015). Need‐Based Aid and College Persistence The Effects of the Ohio College Opportunity Grant. _Educational Evaluation and Policy Analysis_ , _37_ (1 suppl), 102S‐119S.

- Bettinger, E., Long, B. T., Oreopoulos, P., & Sanbonmatsu, L. (2012). The Role of Application Assistance and Information in College Decisions: Results from the H&R Block Fafsa Experiment*. _The Quarterly Journal of Economics_ , _127_ (3), 1205–1242.

- Binder, M., Krause, K., Miller, C., & Cerna, O. (2015). _Providing Incentives for Timely Progress Toward Earning a College Degree_ . Retrieved from http://www.mdrc.org/publication/providing‐incentives‐timely‐progress‐toward‐ earning‐college‐degree

- Bird, K. A., Castleman, B., Goodman, J., & Lamberton, C. (2017). _Nudging at a National Scale: Experimental Evidence from a FAFSA Completion Campaign_ (No. 54; p. 34).

- Blagg, K., Chingos, M., Graves, C., & Nicotera, A. (2017). _Rethinking Consumer Information in higher education_ (p. 59). Urban Institute.

- Bleemer, Z., & Zafar, B. (2018). Intended college attendance: Evidence from an experiment on college returns and costs. _Journal of Public Economics_ , _157_ , 184–211.

- Bonilla, L., Bottan, N. L., & Ham, A. (2017). _Information Policies and Higher Education Choices: Experimental Evidence
entrepreneurship to become informal.<sup>42</sup> Similarly, tax policy should anticipate distortions to firm growth that happen when firms of different sizes face differential enforcement.<sup>43</sup> These are examples that illustrate the breadth of the importance of understanding the aggregate impact of policies at a more disaggregated level.

# **Micro evidence on the aggregate welfare effects of trade**

Micro‐level evidence yields important new insights on aggregate outcomes in the field of international trade as well. Influential recent research shows <mark>that the aggregate welfare gains from trade may not be as big as previously thought, particularly for large countries with a smaller share of trade in GDP.</mark><sup>44</sup> <mark>However, subsequent research based on micro data on products, firms, and industries shows that there are subtle but important nuances in the calculations of aggregate welfare gains from trade that could depend on sectoral linkages and the structure of the tariffs. If the products of one sector are used as inputs in other sectors, then the aggregate w</mark> elfare gains from trade could be higher as expansion in one sector indirectly benefits the other sectors through these linkages.<sup>45</sup> On the other hand, for those countries where higher tariffs are imposed on imported products that respond less to tariff changes, the aggregate welfare gains from trade could be less.<sup>46</sup> This could be because firms are locked into fixed import patterns in global value chains and may not switch inputs in response to tariff changes in the short run. All these nuances are missing when we only focus on aggregate statistics such as average tariff rates.

<mark>The bottom‐up approach yields insights on how trade affects workers, firms, and localities differently. For example, low‐cost imports from China increase unemployment, lower labor force participation, and reduce wages in some local labor markets in the
_Comprehension_ , and _History, Economics, and Geography_ , respectively. Consistent with previous findings, no impact is observed in the _metropolitan_ sample.<sup>21</sup>

# _<u>Mechanisms</u>_

The administrative data available do not allow us to fully uncover the mechanisms through which the intervention had an impact on students’ test scores, however, there is some information available on the self-administered questionnaire that students assessed by MINEDU have to answer. In **Table 8** we provide evidence of the impact of the intervention on educational expectations (in the short and medium term), as well as on students’ self-perception of their own abilities in math and reading comprehension (in the short-term). In the short-term, students in schools assigned to receive the ‘GYM package’ improved their expectation to attend university by 1.1 p.p. Consistent with previous findings, the ITT impact on expectations is virtually zero in the _metropolitan_ sample, and approximately 1.8 p.p. in the _regional_ sample. Results are very similar in the medium-term estimates, albeit marginally insignificant. Finally, our results do not suggest the intervention had an impact on students’ beliefs about their own abilities in the short-term. It is possible that the intervention might have had an impact on this domain in the medium-term, once students realized they had improved their performance on both subjects—potentially thanks to an increased academic effort. Unfortunately, this hypothesis cannot be tested because the scales used to measure these self-perceptions were discontinued from the MINEDU questionnaire.

> 21 By 2016, the research team knew the intervention had had an impact in the _regional_ sample and not in the _metropolitan_ sample. Because of this, we decided to randomly allocate half the schools from the _regional_ sample to receive the ‘GYM package’ so that they could repeat the session at approximately the same month of the year. The instruments and protocols were identical, and the same monitoring procedure was put in place. The only difference
Li (2007) Care for Girls Support girl-only families; China censuses (1950Descriptive statistics; Success of benefits program is Program (benefits improve investments in 2005), survey data from literature review contingent on local component), girl children; change government bureaus, development. Scope to improve China preference for sons previous studies design, and implementation of legislative measures that indirectly raise value of girls Murphy Care for Girls (See above) Key informant Qualitative Program has failed to deal with (2014) Program (benefits interviews; analysis of the underlying causes of gender component), policy documents and inequity. Material benefits are China newspaper articles conditional on local development 24
. WorldPop 250m is used for both the DOU and DB methods.

# **4.3 Analysis with the new global dataset**

To examine the relationship between household welfare and population density, we conduct regression analysis using household consumption expenditures as a proxy measure of household welfare. It is important to consider that the association between welfare and location may be influenced by household characteristics. For example, if more educated households tend to reside in urban rather than in rural areas, urban areas may have lower poverty rates simply due to the composition of more educated households (that is, “sorting”). Therefore, we investigate the extent to which household welfare is determined by location characteristics after controlling for household characteristics. Our main specifications are as follows: and rural areas for six regions). In the case of the DOU classification, we reclassify them into urban centers, urban clusters, and rural areas for six regions. Then, the ratio of the regional poverty lines is calculated as a new spatial price deflator that is applied to the consumption aggregate when measuring poverty with international poverty lines.

> 24However, compared to richer countries, households in poor countries tend to allocate less budget to housing (see Figure A3 in Appendix A).

> 25The housing price index is calculated by estimating the following hedonic regression model with HBS data:

ln ( _RENT ij_ ) = _α_ + _β_ 1 _Xij_ + _β_ 2 _LOCATION j_ (1) + _εij_

> where _RENT ij_ is either observed or imputed rents for household _i_ in location _j_ , _Xij_ is a vector of housing characteristics, and _LOCATION j_ (1) is the geographic domain to distinguish prices relative to the base location _j_ =1. _β_<sup>�</sup> 2 is the log of a housing price index. Distinguishing housing prices between urban and rural areas is challenging if rent observations are limited in the latter.

12
heterogeneity in wealth and liquidity constraints.<sup>18</sup> The difference is that the consumption data capture short- and medium-term deprivation, whereas assets (as also emphasized in the conceptual framework) represent a better indicator of long-term wealth (Aiken et al., 2023). Indeed, the correlation between these two variables is relatively low in our sample (0.37). The lagged values of the reversed GS-SPEI are included to capture lagged responses and cumulative or nonlinear effects due to repeated shock exposure over time.<sup>19</sup> Previous survey literature has made a strong case for investigating the way populations respond to the risk of cumulative shocks (Cattaneo et al., 2019), especially since climate change is the reason for the increased frequency and intensity of extreme weather events (Stott, 2016). The adaptive capacity score is included to capture heterogeneity due to a household's pre-shock ability to adapt _in situ_ . This variable is of particular relevance since the dynamics of the relationship between on-farm adaptation and migration remains under-explored (Cattaneo et al., 2019), and is built by leveraging information on farmers’ adoption of agricultural inputs and practices that are relevant in adapting to climate risk.<sup>20</sup> The education variable captures the role of human capital of household members in determining the household-level decision of dealing with climate risk by sending out individual migrants, another key mediating channel emphasized in the literature on climate migration ( <mark>Burzyński et al., 2022)</mark> and a main determinant of migration in the NELM framework (Stark & Bloom, 1985).

The choice of including only a limited number of conditioning variables for the estimation of CATEs is consistent with the adoption of the double orthogonalization approach discussed above 18 Monetary consumption values have been temporally and spatially deflated.

> 19 The SPEI variable we built captures growing-season weather anomalies occurring during the entire between-wave interval across different survey rounds. Including a lagged value of this variable