rafmacalaba/data-use-mentions-extended huggingface dataset:
regions. More recently, Azzoni and Almeida (2016) show that overall price-level differences in Brazilian metro areas can diverge, with levels as low as −19 percent and as high as +14 percent from the national average. We hence use the data from POF to calculate Paasche price indices that we use to spatially deflate the expenditures registered in the survey.

13 There are several differences between our consumption aggregate and other studies using POF data. In one of the most recent ones (Oliveira et al. 2016), the authors aim to measure and analyze welfare, poverty (using the administrative “poverty’’ line equivalent to half a minimum wage), inequality, and vulnerability. Thus, the differences found with our work are somewhat expected. For instance, while we exclude observations with zero food expenditures from our analysis, Oliveira et al. (2016) impute food expenditures for these households to be able to include them in the estimations. Our main objective is to estimate a poverty line and not necessarily to reproduce the population distribution of expenditures. Any imputation method chosen would potentially bias our results because it would necessarily rely on observed characteristics such as family composition and location. Instead, we want the line to rest on actual data. Other differences emerge as we apply closely the recommendations in Deaton and Zaidi (2002): Oliveira et al. (2016) include all education expenditures, without distinguishing by mode of acquisition as we do; the authors also exclude costs of mass transportation (bus, subway, train, ferryboat, alternative transportation and their connection), often used to go to and return from work and often higher for those who live far away.

11
mapping methods to alleviate poverty in the context of the Mexican data.

The results of our simulations can be summarized as follows. First, we find that the magnitude of the downward bias of the _R_<sup>2</sup> based on direct estimates is large, with the true _R_<sup>2</sup> being around 35 to 50 percent higher depending on the model considered. We further find that this bias has important implications for model selection in that the direct _R_<sup>2</sup> incorrectly identifies the appropriate level of estimation in the vast majority of our simulations. Second, when assessing model performance based on the true poverty rates using the mean squared error, we find that our closest approximation to the standard machine-learning implementation performs poorly relative to several benchmark models. We find that these performance issues are largely due to the limited predictive power of remotely-sensed covariates relative to census-based covariates. Finally, our targeting simulations show that none of our machine-learning implementations outperform more traditional poverty mapping methods that are feasible with the same data.

Our paper builds on the work of Corral et al. (2021a) and Corral et al. (2022), who similarly used the Mexican Intercensal Survey to examine the performance of several poverty mapping methods. Corral et al. (2021a) focused exclusively on traditional poverty mapping approaches and did not consider the performance of machine-learning methods. While Corral et al. (2022) consider the performance of machine-learning approaches, their implementation only uses census-based covariates and thus does not examine the performance of the standard implementation that relies on remotely-sensed covariates. Importantly, we are not the first paper to evaluate the performance of machine-learning methods relative to census data. While Yeh et al. (2020) and Chi et al. (2022) validate their models relative to censuses, both papers use the _R_<sup>2</sup> as a performance metric and focus on wealth estimates rather than poverty. Finally, van der Weide et al. (2022
are SMEs (see appendix tables A1).<sup>4,5</sup> At the onset of the global financial crisis in 2007, privately held firms, accounted for 30 percent of total assets and almost 40 percent of total employment in our sample. Moreover, about 60 percent of the firms, or about 160,000 firms, used long‐term debt at some point in the years preceding the financial crisis. Because we are interested in examining the evolution of debt use by firms over time, this subset of firms will constitute our main sample.

Our three main variables of interest are the ratio of total debt to total assets (TDTA), the ratio of long‐term debt to total asset (LTDTA), and the ratio of long‐term debt to total debt (LTDTD). The first two variables capture the extent to which firms finance their assets with debt and long‐term debt, respectively. The last variable captures the maturity composition of debt. A standard approach in the literature is to focus on a narrow definition of total financial debt, defined as the sum of short‐term financial debt (including trade credit),<sup>6</sup> plus long‐term financial debt (defined as the portion of long‐term debt maturing after one year). This approach has often been used in the literature, for instance by Rajan and Zingales (1995), because it is consistent with the focus on external finance in the theoretical corporate finance literature.<sup>7</sup>

> 4 We follow the World Bank Group Enterprise Survey definition, according to which a firm is defined as a small firm if it has between 5 and 19 employees, a medium firm if it has between 20 and 99 employees, and a large firm if it has more than 100 employees. In robustness tests, we will use a threshold of 250 employees to define a firm as an SME, consistent with the EU SME employment threshold.
over the long run (Espey and Espey, 2004). With regional aggregate data, Bernstein and Griffin (2005) find that the residential electricity elasticities in the United States are –0.24 and –0.32 in the short and long run, respectively. With micro data, Wilder and Willenborg (1975) estimate the elasticity at –1.31 in South Carolina in 1973. Reiss and White (2005) report an elasticity of –0.39 for California households during the period: 1993-1997. With more detailed micro data on an hourly basis, the time-of-day price elasticity is estimated to ranges from –0.13 during peak hours and –0.22 during non-peak hours in Connecticut

> (Hausman _et al._ , 1978).

**Nordin’s difference.** Second, net positive (negative) consumer welfare caused by the increasing (decreasing) block tariff should be included in _X_ 1, but it will be another endogenous variable for the same reason as above mentioned. Note that the price variable in the demand function must be a marginal tariff rate of the final block that each consumer faces, rather than the average price (computed as the total bill divided by total consumption). This is basically because neoclassical economic theory assumes that people consume goods or
manufacturing firm in Croatia is better managed than its counterparts in Russia (average score, 0.43) and Mexico (average score, 0.39).<sup>4,5</sup> _Second,_ weaknesses in management practices particularly relating to data-driven performance monitoring cripples the overall management score in Croatia as well as Eastern Croatia. Data-driven performance monitoring includes specific practices that help managers define their key performance indicators (e.g. inventory, sales, absenteeism) and appropriately track them periodically over time, while incentives management practices relate to how managers and non-managers are awarded bonuses and promotion. The result for Croatia regarding the weaknesses of these subindices is similar to that in Russian manufacturing, that is, the monitoring aspect of structured management is weaker. By comparison, this finding is different from that in the United States and Mexico, where manufacturing firms are stronger in monitoring vis-à-vis structured management practices relating to workers’ incentives and target setting.

_Third,_ an interesting finding of this survey data is that services firms are farther from the frontier when it comes to adoption of structured management practices. Services firms were included in a MOPS framework survey for the first time in Mexico, where, as in the case of Slavonia and Croatia, it was also found that adoption of structured management practices is weaker relative to that in manufacturing (Bloom et al., 2019b). They attribute the lower management score in services to a lack of pro-competitive environment, which lowers the pay-off from adopting better management practices. In Eastern Croatia, an average services firm scores 0.519 on the adoption of the most advanced management practices, compared to 0.535 in manufacturing. As in Bloom et al. (2019b), we also conjecture

> 3 We use the terms Eastern Croatia and Slavonia interchangeably. ‘Slavonia’ is used as a shorthand for the region of Slavonija, Baranja & Srijem, consisting of five counties in Eastern Croatia. These five counties are: Brod-Posavina county, Osijek-Baranja county, Pozega-Slavonia county,
10 Our sample comprises 24 emerging market economies: nine are inflation targeters and 15 are non-targeters. The period analyzed extends from the first quarter of 2000 to the second quarter of 2015, following periods of turmoil in the late 1990s for emerging markets, including the Asian crisis of 1997-98, the Russian debt crisis of 1998 and the Brazilian abandon of its pegged exchange rate regime in early 1999. The main source for our data is the IMF International Financial Statistics; only the interest rates were taken from individual country sources. Interest rates are the benchmark policy rates (or, alternatively, the money market, discount or short-term T-bill rates if the benchmark is not available), inflation is measured by consumer price index (CPI) changes in all countries, and real gross domestic product (RGDP) gap is RGDP growth compared to trend generated by HodrikPrescott (HP) filter. Exchange rates are the bilateral currency of each emerging market against the U.S. dollar (USD). We also collected real exchange rates for currencies of emerging markets against a basket of currencies but there were less data availability in this case to operate consistently across countries. For this reason, we will focus below on the estimates of each currency against the USD.

Table 1 lists the countries and their respective averages of inflation, interest rates, GDP growth and nominal exchange rates against the U.S. dollar. In terms of inflation, two digit rates are reported only across non-IT countries (Dominican Republic, Ecuador and Nigeria) and the highest average inflation among ITers is observed in Brazil with 6.62%. Two digits average interest rates are observed in five non-IT countries (Argentina, Costa Rica, Dominican Republic, Nigeria and Pakistan) but only in one inflation targeter (Brazil). The four fastest growing economies in the sample are part of the group of non-targeters: China (9.87%), Nigeria (8.05%) and Panama (7.27%). Across the inflation targeters the two with
The remainder of the paper is structured as follows. Section 2 discusses our data sources, section 3 details our methodology, section 4 introduces a theoretical framework, section 5 presents our empirical results, section 6 conducts robustness checks, and section 7 concludes.

# **2 Data**

Our data source of academic articles is the Semantic Scholar Open Research Corpus (S2ORC) (Lo et al. 2020). The corpus contains more than 130 million English language academic papers across multiple disciplines. The papers included in the Semantic Scholar corpus are gathered directly from publishers, from open archives such as arXiv or PubMed, and crawled from the internet.

We placed some restrictions on the articles to make them usable and relevant for our purposes. First, only articles with an abstract and parsed PDF or latex file are included in the analysis. The full text of the abstract is necessary to classify the country of study and whether the article uses data. The parsed PDF and latex file are important for extracting important information like the date of publication and field of study. This restriction eliminated a large number of articles in the original corpus. Around 30 million articles remain after keeping only articles with a parsable (i.e., suitable for digital processing) PDF, and around 26% of those 30 million are eliminated when removing articles without an abstract. Second, only articles from the years 2000 to 2020 were considered. This restriction eliminated an additional 9% of the remaining articles. Finally, articles from the following fields of study were excluded, as we aim to focus on fields that are likely to use data produced by countries’ national statistical system: Biology, Chemistry, Engineering, Physics, Materials Science, Environmental Science, Geology, History, Philosophy, Math, Computer Science, and Art. Fields that _are_ included are: Economics, Political Science, Business, Sociology, Medicine, and Psychology. This third restriction eliminated around 34% of the remaining articles. From an initial corpus of 136 million articles,
............ 92|
|Table A7: Quality of service data ................................................................................................................ 94|
|Table A8.1: Multiyear analysis for countries with a low QFD (less than 1 percent of GDP in the reference|
|year) ............................................................................................................................................................ 95|
|Table A8.2: Multiyear analysis for countries with a medium QFD (1-2 percent of GDP in the reference<br>year) ...................................
the equator). Following ii,tt jj mm this simple methodology, flooding depth for the 28 coastal provinces are calculated for events with return periods of 5, 25, 50, 100, 250, and 500 years (figure 2.1.b). Just as with the pluvial and fluvial flooding maps no information on existing flood protection measures has been incorporated into this modelling.

However, extensive public protection infrastructure such as dikes exist in many provinces in Vietnam, aimed the mitigation of both at riverine and at coastal flooding. Given the absence of flood protection modelling in the flood maps described above, using them as they are would result in an overestimation of flood exposure. Unfortunately, incorporating flood protection infrastructure into flood models at a national scale is still prohibitively difficult. This analysis thus uses a novel approach based on knowledge collected for the main report combined with qualitative expert assessment to estimate the flood risk, while accounting for existing protection infrastructure (section 4.1).

**Figure 2.1: Riverine and coastal flood risk in Vietnam**

_a) Riverine (fluvial and pluvial) flooding_ _b) Coastal flooding_ 5
the sophisticated nature of these enterprises in one village in rural Taiwan (Niehoff 1987). In that setting, all but one of the enterprises were self-financed with no formal guidance apart from production and export brokers; these brokers were identified as an important source of ideas regarding types of factories and commodities suitable for household entrepreneurship. We have some evidence of linkages between domestic and foreign firms in Ethiopia (Abebe et al. 2021). However, survey data from Ethiopia indicates that less than 5 percent of large firms do any sub-contracting. Overall, we have very limited knowledge about linkages between small and large firms in Africa.

The African Center for Economic Transformation (ACET) is taking business-to-business linkages very seriously. In early 2021, ACET launched the ACET Private Sector Development Program. The program has two symbiotic objectives. The first – at the macro level - is to promote evidence informed private-sector friendly public policies and regulations to strengthen the ecosystem for small and 18
“Does the job involve driving?” (“Operating vehicles”), and; 2) “Does the job involve repairing items/instruments?” (“Control/Feel objects”; “Manual dexterity”).

**Table 1. Tasks to skill mapping using STEP skill measurement surveys**

|**Skill**<br>**Bracket**|**STEP Task**|**Questio**<br>**n**|**Corresponding**<br>**O*Net Task **|**Coding**|
|---|---|---|---|---|
||Type of document<br>read|m5a_q0<br>5||Summation of<br>"Yes"(0-5)|
|Non-<br>routine|Length of longest<br>document typically<br>read|m5a_q0<br>4*m5a_<br>q06|Analyzing<br>data/information|Categorical (0-5)|
|<br>Analytical|Math tasks|m5a_q1<br>8||Summation of<br>"Yes"(0-5)|
||Thinking for at least<br>30 minutes to do<br>tasks.|m5b_q1<br>0|Thinking creatively|Categorical (1-5)|
|Non-<br>routine|Supervising<br>coworkers|m5b_q1<br>3|Guiding subordinates|Dummy|
|<br>Interperson<br>al|Contact with clients|m5b_q0<br>5*m5b_<br>q06|Establishing personal<br>relationships|Categorical(0-10)|
||How often your work<br>involves learning new<br>things|m5b_q1<br>7|Importance of<br>repeating the same<br>task(inverse)|Categorical(0-5)|
|
that preschool availability increases the probability of women to be employed, particularly those with small children.

The second mechanism that we explore is related to the evidence that availability of childcare services increases job stability (Angeles et al., 2011) and employee attendance (Ranganathan and Pedulla, 2020). That is because it provides more flexibility in terms of working arrangements to workers with childcare responsibilities. For the plants, lower turnover in turn would raise productivity (Brown, 1989; Brown and Medoff, 1978; Shaw, 2011; Strober, 1990; Yanadori and Kato, 2007; Giesing and Laurentsyeva, 2017; Kacmar et al., 2006; Grinza and Rycx, 2020). We find suggestive evidence in support of this channel using SAKERNAS data: expansion of preschool availability reduces the turnover of women in manufacturing, particularly for women living in households with small children.

A further implication of both channels is that wages may increase as a result of increased labor supply. Unlike in a classical supply and demand framework in a perfectly competitive labor market, access to a larger pool of workers may enable the firms to find better matches between workers and jobs, possibly leading to increase in wages (Pissarides, 2000; Beerly et al. 2021). Similarly, increased job stability may lengthen the average tenure of workers in the firm, which would also increase average wages. We test these implications using SI data and find that indeed an increase in preschool density had positive impact on the average worker’s wages.

Our paper contributes to several strands of literature. First, we add to the literature on allocation of talent and productivity, building on Hsieh et al. (2019), by empirically demonstrating the role of one specific barrier to allocative efficiency between the market and home production sector: women’s disproportionate childcare responsibilities. Second, we contribute to the empirical evidence testing Pissarides’s theory of imperfect labor market with firm-employee matching and frictional search. Beerly et al. (2021) recently demonstrated empirical evidence to this
that treated sections connected to more controls via the road network see a larger increase in total cases.

Finally, information or people may move more easily between areas that are proximate in both geographic and cultural terms. We use the household survey to determine the plurality ethnic group in each section. Sections tend to be homogeneous: in the median section, 95 percent of respondents report the same ethnicity. For each section, we then count the number of control sections with the same plurality ethnic group and within 10 kilometers. Table E.24 provides no indication that spillovers occur due to movement of patients between proximate co-ethnic areas.

# **4.2.2 Mechanisms**

Concerns about sub-standard care deterred patients from utilizing clinics during the Ebola crisis. Fearful that seeking care would condemn their loved ones to death, households “engaged in practices of hiding sick family members, running away from local communities, or attempting to manage the course of Ebola within local households and communities” (Abramowitz et al. 2016, 24). If the CM and NFA interventions generated persistent improvements in the perceived quality of healthcare and utilization, this would help explain increased reporting in treated sections.<sup>38</sup> Using our endline surveys but restricting attention to the 160 clinics in the Ebola sample (see Table D.20 for estimates using our full sample), in Table 5 we estimate treatment effects on general utilization; satisfaction with public health workers; and households’ beliefs about the efficacy of western (“white-man”) medicine relative to traditional or religious remedies, the primary alternatives to government-run clinics in rural Sierra Leone. The effects on general utilization remain positive and significant when we pool the treatments and in CM alone; the effect attenuates in the NFA arm relative to the full sample (see Table 1). We continue to find positive effects on satisfaction, focusing here on satisfaction with public health workers, which is asked of all households.<sup>39</sup> Both treatment arms
- December 2017, and November 2018 - February 2019. In each survey, we asked for up to a year of recall data on agricultural production; based on the timing of our surveys we therefore have production for all agricultural seasons from June 2014 through August 2018, with the exception of 2015 Dry (June - August 2015) and 2016 Rainy 1 (September 2015 - February 2016).

The household sample for the follow up surveys consists of all the baseline respondents, while the plot sample for the follow up surveys consists of the sample plots and most important plots. To maintain a panel of plots, we ran a “tracking survey”. This survey was triggered whenever a household’s sample plot or most important plot was sold or rented out to another household, or a household stopped renting in that plot if it was not the owner (“transacted”). Specifically, we tracked and interviewed the new household responsible for cultivation decisions on that plot to record information about cultivation and production, along with household characteristics when the new household was not already in our baseline sample. Data from this tracking survey is incorporated in all our plot level analysis, limiting plot attrition.

Attrition in our survey is low, and details on attrition are presented in Table A8. Only 6.0% (6.4%) of plot-by-season observations for sample plots outside the command area in our primary analysis sample (defined in Section 3.1) are missing during the dry season (rainy season). There are three sources of attrition: household attrition, plots transacted to other farmers that we were not successful in tracking, and plots rented out to commercial farmers who were based in the capital or internationally (from whom we were unable to collect agricultural production data). We do not find evidence of differential attrition of sample plots due to household attrition or plots transacted to other farmers that we did not track, however we do find access to irrigation causes an additional 6.4 - 10.2pp of