rafmacalaba/data-use-mentions-extended huggingface dataset:
in the ESR group are 5.0 pp less likely to report working relative to other older men assigned to group C.

We provide suggestive evidence on potential mechanisms driving these results. We test two potential channels: the prevalence of peers’ informal employment and social norms related to domestic obligations. First, our findings suggest that the incidence of peers’ informality in a community influences the effectiveness of using the ESR survey method in capturing female and youth work and youth employment. Second, we also show that peers’ informality in a community has no differential effect on the impacts of using the LOA module. Third, we document that the LOA module works better to address women’s underreporting of labor market indicators when they live in communities where other women spend more time on domestic work. These findings on mechanisms may indicate that the LOA survey method is needed in settings that are more likely to affect women’s own assessment of what constitutes work or employment, which is the case of incidence of norms around domestic obligations. Furthermore, the ESR survey method seems to be more relevant in contexts where the proxy respondent is exposed to more informal employment, which may affect his assessment of other household members’ labor market participation, particularly those of women and youths. Although these findings provide only suggestive insights, we believe they warrant further exploration, as they can serve as a starting point for new avenues of methodological and policy-relevant research.

Our paper contributes to the literature in several important ways. First, we provide evidence on how different survey methods can help address women’s and youths’ underreporting of labor market indicators, which results in a mis-measured employment and work gaps by gender and age. The majority of published studies focus primarily on understanding how women underreport their labor outcomes when responding to standard survey methods and how alternative survey methods improve women’s reporting of labor indicators relative to men (Ambler et al., 2021; Arthi et al., 2018; Bardasi et al., 2011; Dillon et al
> The selfemployed in developing economies can, for the vast majority, be categorized as self-employed without employees or unpaid family workers. As such, this high prevalence of self-employment may be indicative of limited opportunities in the formal sector and, thus, a large informal economy. A key question is what factors determine a woman’s choice to engage in the informal versus the formal sector. More specifically, what influences the choice to start a business in the informal or the formal sector. This choice is likely tied to a woman’s motivations for starting the business, as well as the economic, cultural, and legal environment in which she finds herself.

Evidence suggests that institutions matter. There has been burgeoning research showing that unequal or discriminatory laws affect the economic status of women, using data from the World Bank’s Women

> 3 --- --- --- <u>https://www.ilo.org/wcmsp5/groups/public/ ed_protect/ protrav/ travail/documents/publication/wcms_711798.pdf</u>

> 4 https://www.unwomen.org/en/news/in-focus/csw61/women-in-informal-economy

> 5 World Development Indicators [accessed: July 2, 2021].

2
There is also work on panel data-based analysis of the impact of the crisis for Indonesia. For instance, Beegle, Frankenberg and Thomas (1999) estimate how changes in per capita consumption and transitions into and out of poverty during 1997 and 1998 were related to a set of household and community covariates in 1997. Such panel data analysis however also comes with its own set of problems. First, a potential advantage of panel data is that we can eliminate potential bias due to any omitted observed or unobserved household level determinants of welfare, using household fixed or random effects. However, without an independent measure of the household-specific shock, the presumption is that everyone was hit by the crisis-induced shock, and this rules out the use of "difference-in-differences" estimation that has often been used in impact evaluation analysis.<sup>8</sup> Second, while panel data holds the promise of providing a direct measure of welfare change, there is also the thorny issue of measurement error. It is not clear how much of the observed change in household welfare or transitions into or out of poverty are signal rather than noise. Thus, even with panel data, empirical determination of the distributional impact of the crisis is not easily resolved.

There have also been estimates of self-rated poverty for the Philippines based on quarterly surveys conducted by the Social Weather Stations (Mangahas 1999). According to these surveys, the incidence of poverty<sup>9</sup> averaged 59% for the period 1996-97 while the average for 1998 was 61%. Similarly, Reyes et al. (1999) reported an increase in self-rated poverty from 40% just before the crisis to 43% in January l999."0 Quite apart from the use of a very different

> **8** A useful discussion of the properties of this and other estimators commonly used in the impact evaluation literature can be found in Angrist and Krueger (1999).

> 9 The incidence of self-rated poverty is not calculated using a pre-determined poverty line but
0.005)|(0.005)|(0.003)|(0.008)|(0.008)|(0.008)|(0.003)|
|<br>Super Index of Overall|0.049|0.046|0.121|-0.079|-0.159*|-0.156*|-0.079|-0.260***|
|<br>Empowerment (Standardized)|(0.082)|(0.082)|(0.085)|(0.072)|(0.081)|(0.082)|(0.089)|(0.071)|
|Change in Subjective Wellbeing|0.276|0.275|0.706***|-0.142|0.176|0.174|0.515***|-0.108|
|from Five Years Ago to Today|(0.187)|(0.189)|(0.173)|(0.157)|(0.190)|(0.190)|(0.179)|(0.190)|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, an indicator for never had sex, and whether the respondent participated in the pilot phase of the development of the testing instruments. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**)
|**Country**|**Survey(s)**|**Year(s)**|
|---|---|---|
|South Africa|Income and Expenditure Survey|2000,05|
||LivingConditions Survey|2008|
|Sri Lanka|Income and Expenditure Survey|2002,06|
|St. Lucia|Household Budget Survey|2005|
|Suriname|Income and Expenditure Survey|2001|
|Swaziland|Income and Expenditure Survey|2000|
|Tajikistan|Household Budget Survey|2003,05-06|
||LivingStandard Measurement Survey|2003,07|
|Tanzania|Household Budget Survey|2000|
|Thailand|Socioeconomic Survey|2002,06|
|Timor-Leste|LivingStandard Survey|2001,06|
|Tonga|Income and Expenditure Survey|2000|
|Tunisia|LivingStandard Survey|2000|
||Enquête Budget-Consommation|2000|
|Turkey|Household Budget Survey|2003-06|
||Income and Expenditure Survey|2002|
|Uganda|Integrated Household Survey|2002,05|
|Ukraine|Household Budget Survey|2000-01|
||LivingConditions Survey|2003|
|Uruguay|Encuesta de Hogares|2000-06|
|Uzbekistan|Household Budget Survey|2000,03|
|Vanuatu|Income and Expenditure Survey|2006|
|Venezuela|Encuesta de Hogares|2000-06|
|Vietnam|LivingStandard Survey|2002,04,06|
|Westbank & Gaza|Income and Expenditure Survey|2004-07|
|Yemen|Household Budget Survey|2005|
|Zambia|LivingConditions Survey|2002,04|

20
data. The advalorem tariff rates for the EU before and after the Uruguay Round (UR) are available through the WTO schedules of concessions at the 8-digit HS level and the 8-digit preferential tariff rates are obtained from UNCTAD.<sup>34</sup> The intra and extra-EU bilateral trade data is available at the same level of disaggregation through Eurostat. In order to construct the reciprocity variable for a given country we employ the data in Finger et al. (1999) who use the available tariff reductions for each WTO member for each product during the UR aggregated by country into averages and then weighted by own imports. They calculate both the tariff reductions from 1986-95 and 1992-95, which tend to differ mainly for the countries that undertook considerable unilateral liberalization between 1986-92. This is an important point in identifying the reciprocity effect as we describe above. From these we construct our own product weighted measures of reciprocity that account for the share of each main exporter to the EU in a particular good.

We also require data on production and other industry variables that we use as instruments for x. This data is typically available for EU members and we aggregate it in the way that is suggested by the theoretical model.<sup>35</sup> UNIDO’s industrial database provides the most comprehensive source covering all EU members and dating back to 1978. UNIDO’s data is collected at the industry level and hence it is more aggregated than the trade and tariff data, which could potentially introduce some measurement error. Although we can not rule out this possibility, we note that it may not be such an important concern for the following reason. If the EU negotiators use the data at the most disaggregated level available for most of its members, as we do, then our measure is actually the relevant one.<sup>36</sup> Furthermore, we argue that our use of instrumental variables reduces any measurement error problem generated by using industry data for this variable.

Using industry level data for x
- Jaslowitzer, Patrick, William L. Megginson, and Marc S. Rapp. 2018. “State Ownership and Corporate Investment”, August 22

- Jiang, Jie, Jack Hou, Cangyu Wang, and HaiYue Liu. 2021. "COVID-19 Impact on Firm Investment— Evidence from Chinese Publicly Listed Firms," _Journal of Asian Economics_ 75(101320).

- Jurzyk, Emilia M. and Cian Ruane. 2021. “Resource Misallocation Among Listed Firms in China: The Evolving Role of State-Owned Enterprises,” IMF Working Papers, Working Paper No. WP/21/75 https://www.imf.org/en/Publications/WP/Issues/2021/03/12/Resource-Misallocation-AmongListed-Firms-in-China-The-Evolving-Role-of-State-Owned-50167

- Kopelman, Jason L. and Harvey S. Rosen. 2014. “Are Public Sector Jobs Recession-Proof? Were They Ever?”, Griswold Center for Economic Policy Studies, October.

- Lazzarini, Sergio G. and Musacchio, Aldo. 2018. “State Ownership Reinvented? Explaining Performance Differences between State‐Owned and Private Firms”, _Corporate Governance: An International Review_ , Vol. 26, Issue 4, pp. 255-272.

- Marrez. Helena. 2015. “The role of state-owned enterprises in Romania,” ECFIN Country Focus 12(1), European Commission, Brussels.

- OECD (Organisation for Economic Co-operation and Development). 2023. “OECD Review of the Corporate Governance of State-Owned Enterprises in Romania,” Corporate Governance, Paris: OECD Publishing.

- Olley, G. Steven, and Ariel Pakes. 1996. “The Dynamics of Productivity in the Telecommunications Equipment Industry. _Econometrica_ 64(6): 1263-1297.

- Sakha, Sahra, and Yosuke Jin. 2022. “Strengthening the business environment for productivity convergence,” in OECD Economic Surveys: Romania 2022, OECD Publishing, Paris, https://doi.org/10.1787/63318cf5-en.

- Szarzec, Katarzyna, Ákos Dombi, and Piotr Matuszak. 2021. “State-owned enterprises and economic
, the collected survey is corrected for nonresponse through sample grossing-up weights. However, these weights take into account and correct only for the original data sample design probabilities and do not reflect the additional bias in survey participation given the characteristics of participating households. For example, the survey over-represents children in the survey and people who live abroad for more than 12 months, so that the age structure does not match that of the census. In order to match the age structure of the population along with critical characteristics of the tax and benefit system, we follow Myck and Najsztub (2015) to correct population weights in the PHBS.<sup>8</sup> Household survey data are combined with data from GUS Statistical Yearbooks, National Income Accounts and public finance accounts from the Ministry of Finance. This information is complemented with administrative data from the Social Insurance Institution (ZUS), National

> 8 In particular, in addition to correcting for the age structure, weights are calibrated to ensure that the number of taxpayers, those paying health insurance, receiving pensions, unemployment benefits, and those benefiting from joint taxation are in line with administrative data.

11
is very unclear precisely what it is we know.” The authors show that seasonality in staple crop prices can be substantial. This is achieved using trigonometric and sawtooth models to overcome some of the systematic upward bias in seasonal gap estimates from the common monthly dummy variable approach, which arises especially in shorter samples of 10‐15 years and which has so far gone unnoticed.<sup>14</sup> Maize prices in the 193 markets from the seven African countries studied are on average 33 percent higher during the peak months than during the troughs. For rice the price gap is on average 16.5. These seasonal markups are two and a half times larger than in the international reference markets. Seasonality varies substantially across markets, but in virtually none of them is it lower than in the reference markets. Seasonality does not explain much of overall price volatility over the year.

> 14 Unlike most of the studies reported here, the main data sources for this study are not the LSMS‐ISA household surveys, but rather price data obtained in key markets across the seven countries covered.
|0.7683*|0.6992**|0.2458*|0.7802*|
||(0.225)|(0.107)|(0.338)|(0.225)|(0.108)|(0.340)|
|Avg Temp Sq.|-0.0211**|-0.0129***|-0.0238**|-0.0220***|-0.0130***|-0.0250**|
||(0.006)|(0.004)|(0.008)|(0.006)|(0.004)|(0.008)|
|N|869447|201466|667981|869447|201466|667981|
|Cell FE|y|y|y|y|Y|Y|
|Year FE|y|y|y|y|Y|Y|
|Country Trends|y|y|y|y|Y|Y|
|R-sq|0.319|0.392|0.305|0.319|0.393|0.305|

_Note: Dependent variable is change in gridcell log(GDP p.c.). Observations are weighted by population. Standard errors in parentheses are clustered at the Administrative 1 level. Statistical significance is given by + p<0.1, * p<0.05, ** p<0.01, *** p<0.001._ Is the relationship between dry shocks and GDP in the developing world explained by agriculture, the sector that is more directly affected by rainfall variability? We use data from the ESA CCI project to determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split the sample based on different shares of cropland ranging from less than 20 percent to
following the intervention (follow-up). The baseline survey collected information on appliance ownership and use, lightbulb ownership (type, wattage, etc) and use (room of use, hours used in a typical day, etc.), electricity-related behaviors, and various household demographics. Importantly, we asked households to report the number of days in the past month that they did not have electricity due to outages.<sup>37</sup> Implementation of both survey rounds began in the month of March, specifically so that, when asking about the outages in the month prior, we were capturing winter months.<sup>38</sup> The number of CFLs distributed to each treatment household was tracked. The follow-up survey repeated questions from the baseline and also asked new questions on perception and understanding of CFLs.

**Spatial data:** GPS data were collected on the location of each participating house during the baseline survey. These data points permit various calculations relevant to potential spillovers in adoption, including the distance to nearest treated household and the number of treated households within certain radii. In addition, we use GIS data from OpenStreetMap on locations of all buildings in the study villages. These data permit calculations of the total number of households located within various radii (e.g., 100 meters, 200 meters, and so on) of each participating household.<sup>39</sup>

# **4.2 Household characteristics**

Households in the study sample are small, with an average of just under 4 members. Household monthly income per capita is on average 76 USD per month (2.45 USD per person per day) and household heads are educated, with 84 percent having finished secondary school. Most houses (91 percent) are owner-occupied, the majority of which were constructed during the period of the Soviet Union. They comprise an average of 4.3 rooms, and are typically constructed of brick (54 percent) or a hay/adobe
# **5 Conclusions**

Major trade reforms and infrastructure investments are essential levers of economic integration for Africa. Quantifying their contribution can be challenging because of the lack of data and the presence of general equilibrium effects. In this paper, we start by constructing a new granular dataset of transport times faced by African exporters to quantify the reduction in times from the implementation of the Program for Infrastructure Development in Africa, an ambitious list of transport investments for the next decade. We then use data from the World Bank’s Deep Trade Agreements project to assess the response of trade to different degrees of agreement ambition using a structural gravity model. We recover the elasticity of domestic and international sales to transportation time for African countries, with a functional form for unobserved bilateral frictions, making them depend on transport time and other unobservable characteristics of the origin and destination countries. Finally, we build on our calibrated model to simulate the trade and economic impacts of our investment scenarios in roads, ports, and land borders, and the consolidation of various trade agreements signed in Africa into the African Continental Free Trade Area.

Our results show that trade reforms and infrastructure improvements complement each other. The combination of tariff reduction and trade facilitation, which improves border delays and behind-the-border frictions, can substantially reduce trade costs and increase exports by 11.5 percent and GDP by 2 percent for countries in the continent.

29
The remainder of the paper is organized as follows. In Section 2, we draw on the empirical literature to specify regression models for child wasting and maternal anemia. We introduce the data in Section 3 and present our regression results in Section 4. Section 5 uses the results to explore the impact magnitudes of the regression variables; state/province differences in regression predictions; and districtlevel spatial clustering of regression residuals. Given the striking spatial clusters revealed by this exercise, Section 6 offers some initial thoughts about their sources. Section 7 summarizes and concludes the paper.

# **2.  Model Specification**

The DHS data record individual conditions that relate to maternal and child health outcomes, and individual and household characteristics and behaviors that are directly or indirectly linked to health outcomes. These links are frequently explored in empirical research based on DHS variables, or equivalent variables from other sample-based surveys. Such exercises implicitly rely on a model of interdependent household utility, in which adult caretakers make decisions about food, sanitation and health treatments that will benefit themselves and children in their care, subject to limited household financial resources; cultural preferences; the costs of food, sanitation and health measures; knowledge of expected links between their decisions and health outcomes; and uncertainty about the strength of those links.

DHS surveys provide information on some of the requisite variables for a full analysis of healthrelated decisions and outcomes, including maternal and child health measures, household financial resources, sanitation measures, demographics, religion, foods given to children, health-related knowledge (proxied by schooling), and interactions with health treatment facilities. They do not provide information on relative prices, the effectiveness of health and nutrition education, the efficiency of available treatment facilities, or the myriad micro-actions that influence the translation of caregivers’ 4
are calculated by comparing COVID-related deaths and baseline deaths for all or top causes with respect to the period of one single day. The discrete daily data shows significant noise, but they allow us to examine patterns related to the speed and sustained strength of the progression of the pandemic at the country level. The raw data also allows us to detect sudden adjustments due to the statistical methods and other one-off country-specific factors.

The global numbers in Figure 4 illustrate the steady rise in daily severity, followed by a significant tapering in recent weeks. Around end-March, COVID-related deaths exceeded the total number of daily deaths from Alzheimer’s – the top fifth cause of death globally. The daily additions continued to rise through the middle of April when they started to exceed the global third cause of death, which is chronic obstructive pulmonary disease.

10
not appropriate to compare levels or changes over time of any economic indicator, as it does not express the current cost of living of an economy based on the prices of a fixed basket of commonly purchased goods and services.

> 17  NSO’s statistics and SEDLAC numbers serve different purposes. The first ones are the best possible representation of individual countries, while the second ones represent regional comparable indicators. Therefore, numbers presented in this paper should not be interpreted as a claim of methodological superiority over official poverty estimates. 18  The ICP includes nearly 200 countries. Some countries did not participate in the program. As stated in the official report, Afghanistan, Argentina, Lebanon, Libya, South Sudan, and the Syrian Arab Republic are the only large economies that did not take part in the 2011 ICP round. 19  One of the new features of the 2011 ICP round is the availability of global PPP values in addition to USD PPP. For this paper, we still consider the USD PPP values.

9
welfare. At the same time, the need to finance transport infrastructure leads to higher taxes that reduce real consumption. The net welfare effect for each country results from the combination of the trade gains and the share of the costs of the common infrastructure.

We then use this framework to estimate the trade, GDP and welfare effects of the transport infrastructure related to the Belt and Road Initiative for 55 participating countries and a total of 107 countries/regions in the world (Figure 1). We use a combination of geographical data and network algorithms to compute the reduction in shipping time and trade costs between all country pairs in the world.<sup>5</sup> The computations are based on the Shortest Path Algorithm on both the current

> 4 Hummels and Schaur (2013) estimate the “value of time” both at the sectoral level as well as for all goods together. When including all goods and controlling for product fixed effects, they find that a one-day delay in shipping time is equivalent to an ad-valorem tariff of 0.6 to 2.3 percent. Separating each HS2 in different regressions, the average across all products is around 5 percent. de Soyres et al (2018) use the rich heterogeneity of Hummels and Schaur’s (2013) estimates at the HS2 level in order to account for each sector’s specificity in their sensitivity to time barriers.

5 The infrastructure projects considered in this study are the ones currently being constructed, planned or proposed as part of the BRI (see de Soyres et al., 2018, for the full list). We do not consider the question of 3