rafmacalaba/data-use-mentions-extended huggingface dataset:
main challenges.

**Family responsibilities and household work are the biggest challenges for growth reported by both genders, but**

**more so for women.** This can be explained by the fact that women spend disproportionately more time on household work and looking after family than their male counterparts. On average, women spend almost two more hours per week caring for dependents and six more hours doing household chores, as can be seen in the descriptive statistics table (Table 3a) reported below. More than twice as many women entrepreneurs report gender as a barrier to business growth than male entrepreneurs.

**Conflict, a key aspect in the context of Myanmar, affects respondents in different and non-uniform ways.** As mentioned above, conflict areas in Shan have more access to banking than those in non-conflict areas, while Kachin presents the opposite scenario. Moreover, education attainment is lower in conflict-affected areas; indeed, entrepreneurs operating in these areas report fewer years of schooling. In the conflict areas of Kachin and Kayin, business owners on average have completed less than high school. Shan is once again the exception as respondents in these conflict areas report quite high levels of education completed: on average respondents in Shan conflict areas have completed high school and some university. This is most likely due to Lashio, a conflict area in Shan, having a university and teachers’ college, while Mogaung, in Kachin, has neither.

> 24 736 USD as of August 2020.

> 25 ILO and Sasakawa, National Assessment of Women’s Entrepreneurship Development Framework Conditions – Myanmar, Draft executive summary. 26 Ibid.

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shows that such negative impact arises from the observations with above-median (but not extreme) poverty rates. Indeed, the indirect impact is negligible when poverty is below the median, and it even turns positive at extremely high levels of poverty. Importantly, these empirical findings are fully consistent with the analytical model sketched in Section II.

# **VI. Conclusions**

This paper has examined two issues that have received limited attention in the otherwise extensive empirical literature of growth, inequality and poverty. First, the paper provides an empirical assessment of the impact of poverty on growth, building on the earlier work by López and Servén (2009). Second, as a byproduct, the paper also highlights the indirect effect of inequality on growth accruing through poverty.

To guide its empirical work, the paper uses a simple analytical model in which growth is driven by aggregate investment, but poor consumers lack the resources to save and invest. In the model, the impact of poverty on growth is unambiguously negative, and its magnitude increases with the extent of poverty. In contrast, the impact of inequality on growth can go either way, as it combines two different effects that may be mutually opposing. The first one is the indirect impact of inequality on growth accruing through poverty, which is uniformly negative except when extremely high poverty is coupled with very low levels of inequality. The second is due to the impact of inequality on the aggregate investment of non-poor individuals, which is also shown to be ambiguous.

To test these predictions, the paper uses a large panel data set including 804 observations covering 158 countries and spanning the years 1960-2010. The empirical strategy involves including inequality and poverty indicators among the explanatory variables in an otherwise standard empirical growth equation.

On the whole, the results reveal a consistently negative and strongly significant correlation of poverty with subsequent growth. Its magnitude is economically significant too: a 10 percentagepoint decrease in the headcount poverty rate is associated with a rise in annual per capita real growth of 0.5% to
to the backbone. We assume that access before 2008 was null everywhere, an assumption that is supported by World Bank data on access to Internet, which reports that less than 4 percent of individuals in Sub-Saharan African countries (including high-income countries) had access to Internet in 2008. We confirmed our figures by cross-checking them against World Bank indicators reporting the percentage of the population using Internet.<sup>8</sup> Figures A6 and A7 in the appendix show the access to Internet in Ethiopia and Kenya.

**Isolated vs Bundled investments** Tables A1 reports the number of districts for Ethiopia and Kenya with bundled and isolated investments for all years. Given that the small number of districts with electricity but no paved road, we assume that there is no district with electricity but no paved road.<sup>9</sup>

**Employment** We are interested in structural transformation, which we interpret as changes in sectoral employment, in line with the literature (Herrendorf et al., 2014). We derive sectoral employment shares from Demographic and Health Surveys (DHSs), which produce harmonized survey data with GPS coordinates for most surveys and are available for several rounds per country. The DHS is a repeated cross-section of enumeration areas (EA), with approximately 20–30 households enumerated per EA. Four rounds of survey data are covered in Ethiopia (2000, 2005, 2011, 2016) and four in Kenya (1991, 2003, 2009, 2018). For Ethiopia, the DHS rounds included 12,751 individuals in 2000, 14,052 in 2005, 21,080 in 2011, and 19,157 in 2016, from approximately 650 EAs, which differ per round. Djibouti and Somalia did not conduct DHSs.

We use DHS data for which we have access to the occupation of the individuals as well as a proxy for their location. In order to construct the shares of employment per sector, we use respondents’ answers to questions about their current occupation.
inside and outside the government. This includes making use of typical data collection methods like censuses and surveys, but also administrative data, geospatial data, and data generated from the private sector and from citizens. The fourth pillar of the SPI is therefore data sources. For the cycle to be complete, capability needs continuously to be reviewed to ensure that it is enough to deliver the products, services and ultimately data use required. The fifth pillar of the SPI is therefore data infrastructure. In summary, a successful statistical system offers highly valued and well-used statistical services, generates high quality statistical indicators that can also track progress for the SDGs, draws on all types of data sources relevant to the indicators that are to be produced, develops both hard infrastructure (legislation, governance, standards) and soft infrastructure (skills, partnerships), and has the financial resources to deliver.

Figure B.1 in Appendix B offers an alternative visual description of the beneficial interactions of the different data pillars, which reinforce each other through stakeholders’ partnership, joint accountability, better capacity, and meeting user needs. Improvements in performance can be represented as a virtuous data cycle that can become self-sustaining.

# **2.2. Construction of the SPI**

We follow Cameron _et al._ ’s (2021) nested weighting structure to construct the Statistical Performance Index (overall score). Compared to other weighting schemes, this weighting structure offers properties such as symmetry, monotonicity, and subgroup decomposability. It is based on Atkinson’s (2003) counting method, which was employed to construct a social exclusion index (Chakravarty and D’Ambrosio, 2006) and to measure adjusted multi-dimensional poverty (Alkire and Foster, 2011). It was also proposed to be potentially useful for constructing an SDG index (Dang and Serajuddin, 2020). Our statistical performance indicators have a three-level structure, and the SPI overall score is formed by sequentially aggregating the indicators at each level.

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migration rate is an extremely strong determinant of whether a household has a migrant member, with the F-statistic on the 1924 migration rate above 30. Therefore our estimation does not appear to be subject to weak instrument concerns.

# **3.1 Threats to Instrument Validity**

Our identifying assumption is that the historic community migration rates, reflecting the pattern of early development of the railroad system in Mexico, do not affect child health outcomes over 70 years later apart from their influence through current migration. Our instrumental variables estimation relies on this exogeneity assumption, and therefore it is important to consider possible threats reasons to its validity. We consider two such threats. The first is that different states in Mexico may have different disease environments which have persisted through time. Then, for example, households in the 1920s may have migrated to escape from states with poor health conditions, and households today may continue to do the same. If this were true, we would find households in high migration states having worse health outcomes, not as a result of migration, but as a consequence of the persistent poor health conditions in those states. We examine this possibility in Table 3 by testing for independence between the infant mortality rates in 1930 taken from INEGI (2001), the earliest available at the state level, and the 1924 state level migration rates. The Spearman rank correlation coefficient of 0.23 is insignificantly different from zero, so we cannot reject the null of independence. That is, there is no significant relationship between historic infant mortality and historic migration. Nevertheless, we will include the 1930 state infant mortality as an additional control in our analysis.

A second potential threat is that in addition to spurring migration, the pattern and timing of the historical development of the railroads led to more economic development and, in particular, was accompanied by greater development of health infrastructure. As such, the historic migration rate in a state could be positively correlated with the current level of health infrastructure in that state. Table 3 examines the
, Korniyenko, Loukoianova, and Pivovarsky,

> Using data from 45 of the largest multinational banks from 1991 to 2004, de Haas and van Lelyveld (2010) find that their lending did not slow during systemic crises in host countries, which they attribute to the support received by subsidiaries from their parent banks. Because lending by domestic banks did slow during crises, multinational banks could be viewed as stabilizing forces in turbulent times. For a more thorough review of the literature on crises and foreign bank participation see Cull and Martinez Peria (2010).

4
an LMIC setting in Africa.

Kenya closed all schools nationwide soon after its first COVID-19 cases in March 2020 and partially reopened schools for specific grades in October 2020, prior to fully reopening in January 2021. School closures increased household childcare needs—more so for households without young children already at home due to economies of scale—creating trade-offs in adults’ time allocation across childcare, work in different sectors, and leisure. We exploit the quasi-random variation in the timing that children enrolled in different grades were eligible to return to school and use data from the nationally-representative panel Kenyan Rapid Response Phone Survey to implement a difference-in-differences analysis comparing changes in labor supply after the partial reopening for adults in ‘treatment’ households with children in grades 4 or 8—eligible to return (99% did return)—against those with children in adjacent grades.

Having a child eligible to return to school increases adults’ labor supply in the weeks after reopening. Labor supply effects of the partial reopening are concentrated on the intensive margin: total work hours in the past 7 days increase by 3.6 (22%), driven by a 26% increase in household agriculture hours. We find no effects on labor participation or on wage employment or household enterprise hours, which may take longer to adjust.<sup>1</sup> Household agriculture is likely more flexible

> 1. Analysis of longer-term impacts is complicated by the fact that schools fully reopened in January 2021, meaning our comparison group also becomes ‘treated.’

2
contribute to economic growth.<sup>18</sup> Households in Belize, Dominica, Saint Lucia, and Suriname were asked which emergency supplies they own (see Table 2). This question was not included in the Haiti survey. Emergency supplies commonly mentioned by households include packaged food, flashlights,

> 16 See, e.g., the website of Belize’s and Saint Lucia’s National Emergency Management Organizations: <u>https://site.nemo.org.bz/preparedness-guidelines/assemble-disaster-kit/</u> and https://nemo.gov.lc/Tips/To-DoChecklist. (last accessed on April 30, 2024).

> 17 - See, e.g., https://nemo.gov.lc/Shelter <u>Listing. (last accessed on April 30, 2024).</u>

> 18 Prior evidence supports the hypothesis that catastrophic meteorological events have a negative impact on economic growth (Hsiang and Jina, 2014).

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datasets, we use an array of rule-based (de jure) measures of financial integration. We also check the robustness of our findings by controlling for outcome-based (de facto) measures.

Our database includes multiple de jure measures of financial integration that are based on the information drawn from the International Monetary Fund’s Exchange Restrictions and Exchange Arrangements (AREAER). The data series are mainly coded as a series of binary variables capturing whether a country has any restriction on its external (current and financial) accounts. A zero (0) indicates that there are restrictions in place and a one (1) suggests that the country is fully liberalized. The data distinguish between the existence of multiple exchange rate practices, restrictions on the capital account, the current account or the ability to repatriate proceeds from exports. The original data do not distinguish between restrictions on inflows and outflows, and are available for the period 1966-2011.

We focus on two de jure measures of financial integration for our baseline results. Our first measure, based on the work by Chinn and Ito (2006), tracks the first standardized principle component of the major categories in the AREAER tables, including current account restrictions,

> 6 The sample length is dictated by data availability. Most measures of financial integration are available only up to 2011: (i) IMF AREAER indicators are available for the 1966-2011 period; (ii) Quinn openness indicator is available for 1940-2011; (iii) Data series from Lane and Milesi-Ferretti and Chinn-Ito are available for the 1970-2011 period.

> 7 We employ the tests offered by Levin, Lin and Chu (2002), Im, Pesaran and Shin (2003), and Hadri (2000). They all point to the same conclusions. The results of these tests are available upon request.

4
The paper makes several contributions to the minimum wage literature. First, it is one of very few papers to investigate firm responses going beyond employment cuts for a developing country.<sup>3</sup> Compared to the prior contributions that looked at firm-level responses to the minimum wage in developing economies (Hau, Huang, and Wang, 2016; Mayneris, Poncet, and Zhang, 2018), we take advantage of a very rich linked employer-employee dataset that enables us to link firm destruction to boarder employment and reallocation effects, as well as informality. Overall, our paper is also one of the very few in the minimum-wage literature to jointly examine impacts on both firm- and workerlevel outcomes, which has been even more rare for developing economies.<sup>4</sup> To the best of our knowledge, it is also the first paper to assess impacts on firm destruction for the universe of registered firms in the economy, including small firms which predominate in developing countries.<sup>5</sup> Compared to prior contributions, the granularity of the data allows us to shed light on heterogeneous effects across firm types – an area of study that is critical for understanding productivity and allocative efficiency – which may have been missed by prior contributions.

Our paper also contributes to the very thin literature on the efficiency gains and reallocation effects of the minimum wage. It is only the second paper to examine reallocations among workers as a result of the minimum wage by tracking workers who leave exiting firms, and it is the first to address this issue in a developing-country economy.<sup>6</sup> Our results suggest that productivity gains from the minimum wage in the formal sector emerge through the elimination of firms with low levels of productivity. However, these productivity gains are somewhat offset by a lack of worker reallocation into more-productive firms, at least in the short run. One year after the minimum-wage increase, a minority of workers from exiting firms were employed in formal firms, and
effects. Most importantly, it provides a framework to disentangle these quantities and assess external validity.

We focus on effects on farmer investment decisions and examine five outcomes: cultivated area, fertilizers, pesticides, seeds, and a risk index.<sup>3</sup> We construct these outcomes using original data from each study.<sup>4</sup> We estimate intention-to-treat effects – that is the effects of being offered insurance regardless of the decision to subscribe or not – because it is arguably the most relevant parameter for a policymaker interested in the economy-wide effects of index insurance programs.<sup>5</sup> Using the traditional pooling model, we find that farmers who are offered index insurance cultivate more land (+0.09 SD), and use more seeds (+0.10 SD), pesticides (+0.09 SD), and fertilizers (+0.07 SD). The effect on the risk index is smaller and non-significant. Using the Bayesian hierarchical model, we find similar point estimates (around 0.06–0.11 SD), however, effects are much more uncertain and can be close to zero or negative. The 95% posterior intervals are [ _−_ 0.02 SD, +0.26 SD] for cultivated area, [ _−_ 0.02 SD, +0.25 SD] for seeds, [ _−_ 0.02 SD, _− −_ +0.15 SD] for pesticides, [ 0.05 SD, +0.21 SD] for fertilizers, and [ 0.09 SD, +0.09 SD] for the risk index. These results tend to confirm the potential of index insurance to foster smallholder farmers’ productive investments, however, they also show that effects are more uncertain than suggested by the pooling model. In fact, the pooling model is misleading because it considers that impacts are homogeneous across studies (thus understating the
.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|1994|86.1|78.5|81.5|81.2|87.2|87.8|84.2|76.1|82.3|84.6|
|1995|85.4|74.6|78.6|78.8|90.0|100.5|79.0|85.0|68.6|96.7|

Note: Price data obtained the office d'Aide A la Commercialization des Produits Vivriers. Price data covers 10 markets in C6te d'Ivoire and is collected monthly. Mean price for each commodity was obtained by regressing logarithm of the price on dummies for years, markets, and month of observation. Nominal prices were deflated by the CPI series shown in Table 10.

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of all fraud as underreporting of value, 27.4 percent as underreporting of quantities, and the remainder as misclassification (4.9 percent) and misreporting country of origin (0.5 percent).<sup>15</sup> However, only 38 percent of inspectors said they knew which firms were likely to commit customs fraud, suggesting that asymmetric

> 10 In 2017, 15,000 candidates applied for a 300-position recruitment program launched by Madagascar customs. The best-ranked graduates from the national school of administration usually select customs as their preferred administration post.

> 11 Madagascar ranked 158th out of 180 countries in Transparency International’s 2019 Corruption Perception Index.

> 12 According to a very senior civil servant, informal payments to some inspectors are north of 200,000 USD each year. 13 Cantens et al. (2010) show similar environments with a culture of impunity across customs agencies in African countries.

> 14 Due to a close collaboration with the World Bank, the Director General of Customs made the response to our survey be mandatory for all inspectors.

> 15 The importance of undervaluation as the main form of tariff evasion in contrast to product and especially country of origin misclassification is also consistent with the very simple import tariff structure imposed by Madagascar with only four ad-valorem tariff rates (0, 5, 10, and 20) and no specific tariffs except on petroleum and gas (WTO, 2015).

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et al., 2021; Arag ́on et al., 2021; Tesfaye et al., 2021), and extreme weather impacts (Wineman et al., 2017; Michler et al., 2019; McCarthy et al., 2021). Our findings suggest that economists should exercise caution when seeking to combine EO data with socioeconomic survey data, as the selection of different EO sources may produce very different results, thus contributing to this literature on the use of EO data in economic applications.

Our paper also contributes to the nascent, but growing, literature on the economic implications of measurement error. Much of the research in this stream has focused on mismeasurement in the context of agricultural production and focuses on mismeasurement in self-reported data (Carletto et al., 2017; Abay et al., 2019; Gourley et al., 2019; Kosmowski et al., 2019; Lobell et al., 2020; Abay, 2020; Abay et al., 2021; Gollin and Udry, 2021; Kilic et al., 2021; Michler et al., 2022). A related literature focuses on mismeasurement in everything from prices (Fitzpatrick, 2023), to consumption (Abate et al., 2023), to time use (Field et al., 2023), to willingness to pay (Jack et al., 2022). A smaller literature has begun to document, for economists, the presences of measurement error in EO data (Gibson, 2021; Gibson et al., 2021; Alix-Garcia and Millimet, 2023), an issue long known and discussed in GIS, remote sensing, and geophysical science (Wilkinson, 1996). We add to this literature as the first attempt, to our knowledge, at understanding the impact of mismeasurement in EO products in estimating policy relevant relationships between climate and development outcomes in low- and middle-income countries.

Understanding the differences in results that arise when a researcher chooses one EO product over another also has practical consequences, due to the
industries only affects the change in the outcome through the endogenous variable of interest, and not through any potential confounding channel (Goldsmith-Pinkham et al. 2020; Adao et al. 2019; Borusyak et al. 2018). In this paper’s case, FDI growth across industries may reflect both domestic supply and demand factors, which could violate the validity assumption of Bartik instruments. To mitigate this concern, we use the component of industry level foreign employment share growth explained by China’s FDI deregulation during 1990-2005. Specifically, we use the “Catalogue of Industries for Guiding Foreign Investment” in 1997 and 2002 to measure FDI regulatory shocks at the 3-digit industry level. The process is explained below: (i) We first map each industry and product in the 1997 and 2002 Catalogues to the 4-digit industry level based on 1994 industry classification. In the 1997 Catalogue, FDI was encouraged or permitted in 368 out of 426 4-digit industries. As FDI was not allowed in almost all industries, we consider FDI became more 9