rafmacalaba/data-use-mentions-extended huggingface dataset:
We explore this heterogeneity using the YL data by separating the sample in two groups: mothers with less than primary education and mothers with at least primary education completed. Our estimations are presented in Table VI. Unlike the existing literature, there are no apparent differences in the effect of maternal mental health for mothers who have completed or not primary education.

However, when we combine the heterogeneity by household wealth and maternal education, we find out that our results are still in line with the existing literature for two reasons. First, the papers finding that lower maternal education exacerbates the negative effect of maternal mental health on early vocabulary argue that this low maternal education can be associated to sub-optimal childcare practices or to restricted access to quality material inputs and opportunities. Access to quality inputs that help to improve children’s vocabulary is restricted to less wealthy households as well. Second, there is extensive evidence of a strong correlation between mother’s education and socioeconomic status. In this sense, the expected differences in terms of lack of resources that allow to overcome the negative effects of maternal mental health on child language are captured not by maternal education but by household wealth in this particular context.

In addition, we explore whether the effect of exposure to maternal mental health issues during a child’s young age varies depending on whether the mother has a partner, given that this factor may modulate the impact of depression. The literature suggests that the presence of other members in the household that provide support to the mother can buffer the effect of depression on children. Our results are presented in Table VII. Our estimates suggest that having a partner actually worsens the negative effects of maternal mental health issues on child vocabulary (Columns 1-3).

To understand these unexpected results, we further explore the characteristics of the partner. First, we separate the sample of women living with a drinking (columns 4-6) and heavilydrinking partner (columns 7-9). Our estimations indicate that Z-scores of children with mothers whose mental health was 1 sd worse at _t-1_
- Auffhammer, Maximilian, Solomon M Hsiang, Wolfram Schlenker, and Adam Sobel (2013), “Using weather data and climate model output in economic analyses of climate change.” _Review of Environmental Economics and Policy_ , 7, 181–198.

- Azam, Mehtabul (2011), “The impact of indian job guarantee scheme on labor market outcomes: Evidence from a natural experiment.” _Available at SSRN 1941959_ .

- Balakrishnan, Kalpana, Sagnik Dey, Tarun Gupta, RS Dhaliwal, Michael Brauer, Aaron J Cohen, Jeffrey D Stanaway, Gufran Beig, Tushar K Joshi, Ashutosh N Aggarwal, et al. (2019), “The impact of air pollution on deaths, disease burden, and life expectancy across the states of india: the global burden of disease study 2017.” _The Lancet Planetary Health_ , 3, e26–e39.

- Barbier, Edward B (2010), “Poverty, development, and environment.” _Environment and Development Economics_ , 15, 635–660.

- Barrows, Geoffrey, Teevrat Garg, and Akshaya Jha (2018), “The economic benefits versus environmental costs of india’s coal fired power plants.” _Available at SSRN 3281904_ .

- Behrer, Arnold Patrick and Meagan Mauter (2017), “Allocating damage compensation in a federalist system: Lessons from spatially resolved air emissions in the marcellus.” _Environmental Science and Technology_ , 51.

- Berg, Erlend, Sambit Bhattacharyya, Rajasekhar Durgam, and Manjula Ramachandra (2012), “Can rural public works affect agricultural wages? evidence from india.”

- Bhargava, Anil K (2014), “The impact of india’s rural employment guarantee on demand for agricultural technology.”

- Bhuvaneshwari, S, Hiroshan Hettiarachchi, and Jay N Meegoda (2019), “Crop residue burning in india: Policy challenges and potential solutions.” _International journal of environmental research and public health_ , 16, 832.

- Bikkina, Srinivas, August Andersson, Elena N Kirillova, Henry
Household Budget<br>Survey|2015/16|Yes|
|topic<br>surveys<br>(LSMS-<br>d<br>Kyrgyz<br>Republic|ECA|Lower middle<br>income|Integrated Household Survey|2010, 2011,<br>2012, 2013,<br>2014,2015|No|
|tagge)<br>Lesotho|AFR|Lower middle<br>income|Continuous Multipurpose<br>Household Survey|2012/13,<br>2013/14,<br>2014/15,<br>2017/18|No|
|Mauritius|AFR|Upper middle<br>income|Continuous Multi-Purpose<br>Household Survey|2014, 2015,<br>2016,2017|No|
|Mongolia|EAP|Lower middle<br>income|Household Socio Economic<br>Survey|2010, 2011,<br>2014,2016|No|
|Nicaragua|LAC|Lower middle<br>income|National Household Survey on<br>Living Standards Measurement<br>(EMNV)|2014|No|
|Pakistan|SAR|Lower middle<br>income|Social and Living Standards<br>Measurement Survey|2010/11,<br>2011/12,<br>2013/14|No|
|Sierra Leone|AFR|Low income|Integrated Household Survey|2011, 2018|No|
|South Africa|AFR|Upper middle<br>income|Living Conditions Survey|2014/15|No|

_Notes:_

1. The rounds in bold were supported by the LSMS+ program. The LSMS+ program aims to improve the availability and quality of individual-disaggregated household survey data, and support 6 IDA countries in collecting intra-household, gender-disaggregated household survey data

2. Community questionnaire gathers the information on local conditions common to all households in the same community.

6
|(0.0466)|(0.0167)|(0.0180)|
|Lag 2 Rain in cm|0.0319**|0.0319**|0.0812**|0.0380**|0.0318**|
||(0.0156)|(0.0156)|(0.0398)|(0.0150)|(0.0156)|
|Constant|-0.0688**|-0.0688**|-0.181**||-0.0663**|
||(0.0290)|(0.0290)|(0.0756)||(0.0288)|
|Health Post x Month Obs|432|432|432|432|432|
||Robust s<br>*** p|tandard errors<br>_<_0.01, ** p_<_0|in parenthes<br>.05, * p_<_0.1|es||

Notes: Augmented version of the Arellano-Bond Generalized Method of Moments estimator used in all specications except for Column 4. In Column 4, I use Conley standard errors, which account for spatial autocorrelation and serial autocorrelation over time, but do not cluster at the health post catchment area level. In Column 2, I do not weight movement observations to scale up to the full population of Senegal. In Column 3, I scale the expected imported incidence and total incidence by the utilization of the region based on DHS data. In Column 5, I calculate net imported incidence by subtracting out the expected cases leaving a health facility area divided by the population of the area.
for human capital investment, the average ratio of private credit to GDP as a measure of financial depth, the average inflation rate to account for macroeconomic price stability, and the average number of main telephone lines per capita as proxy for public infrastructure. Among the variables that vary only across-countries, we have a governance index from _International Country Risk Guide_ (Political Risk Services), labor-market and firm-exit flexibility indices from _Doing Business_ (the World Bank), and a firm-entry flexibility index from _Doing Business_ (the 16
Babu et al., 2018). As shown in Table 1, aid distribution rates were much higher in 2015 and 2016 versus 2013, with a TA-level average of more than one distribution per household. The summary statistics in 2016 provide a rough proxy for aid

> 18 We use the nominal values in the analysis below, and control for inflation with time and location fixed effects. Given Malawi’s currency devaluation in 2012, it is difficult to adequately convert nominal to real Malawi kwacha with publicly available data.  We did calculate an inflation correction factor to generate real values, but given data issues, we felt it was better to use nominal values with fixed effects.

18
- MDG 1c: halve between 1990 and 2015, the proportion of people who suffer from hunger (Malnutrition prevalence, weight for age, percent of children under 5).

- MDG 2.a: ensure that by 2015, children everywhere, boys and girls alike, will be able to complete a full course of primary schooling (Primary completion rate, total, percent).

- MDG 3.a: eliminate gender disparity in primary and secondary education, preferably by 2005, and at all levels of education no later than 2015 (Ratio of female to male in primary and secondary enrollment).

- MDG 4.a: reduce by two-thirds, between 1990 and 2015, the under-five mortality rate (Mortality rate, under-five, per 1,000).

- MDG 5.a: reduce by three-quarters, between 1990 and 2015, the maternal mortality ratio (Maternal mortality ratio, per 100,000 live births).

- MDG 7.c: halve by 2015 the proportion of people without sustainable access to safe drinking water and basic sanitation (Improved water source and sanitation facilities, percent of population without access).

In what follows, we take a close look at MDG performance in developing countries, with a particular focus on those countries facing the larger gaps in terms of MDG achievement.

**_Several low-income countries are doing well._** A look beneath the aggregate global statistics shows not just middle-income countries doing well, but many low-income countries, too (table 1).  This confirmed that progress in individual African and poor countries was indeed strong.<sup>9</sup>

**_Although the variation among lagging countries is large, the average gap is not._** Lagging countries are, on average, only 23 percent away from being on track to achieve all the MDGs (table 2). They are especially close to the targets for gender parity in primary education (average gap is 7 percent); gender parity in secondary education (16 percent gap
necessary. (2) Other exports require the support of these services. (3) Finally, services as final exports can be developed if supportive policies are adopted (see section 5).

# **5 Services and Regional Development**

Russia is the largest country in the world. It spans nine time zones, has a complex geography and a variety of climate types, and shares land borders with nine other countries. Thus services are critical not only for Russia’s connections to the world economy but also to connect its regions with each other. This section explores the characteristics of regional services sectors using firm‐level data and assesses the extent to which internationalization of services differs by region.

Firm‐level data make it possible to explore more deeply the regional trade dynamics of Russian firms.<sup>18</sup> Figure 22 shows the revenue generated by each firm exporting services as part of their total services revenue, averaged across each regional services sector. It shows that the export propensity of firms is rather small, between 1.6 (North Caucasus) and 4.5 (Far East). Moreover, average export revenue is 2.9 percent of total services firm revenue in most regions. The high share of the Far East is mainly due to high revenue for transportation (12.2 percent).

> 17 The distribution services includes ISIC 50 Sales, maintenance and repair of motor vehicles and motorcycles; retail sale of automotive fuel; ISIC 51 Wholesale trade and commission trade, except of motor vehicles and motorcycles; ISIC 521 Non-specialized retail trade in stores; ISIC 522 Retail sale of food, beverages and tobacco in specialized stores; ISIC 523 Other retail trade of new goods in specialized stores; ISIC 524 Retail sale of second-hand goods in stores; and ISIC 525 Retail trade not in stores; ISIC 526 Repair of personal and household goods; ISIC 55 Hotels and restaurants.

> 18 The firm-level analysis applies both to Russia’s eight greater regions (Northwest, Central,
Each EA was assigned to a specific enumerator.P12F<sup>13</sup> As noted above, the fieldwork ran for 12 months. During the first month, all sampled households were visited for the first time. In this first visit, the enumerators administered a light multi-topic questionnaire to all sampled households in a given EA, collecting basic information on household demographics, economic activities, dwelling attributes, farm and durable asset ownership, along with detailed information on all gardens and (within-garden) plots owned and/or cultivated.P13F<sup>14</sup> These questionnaire modules did not vary by survey treatment, and were based on the comparable Malawi Integrated Household Survey (IHS) questionnaire modules. In addition, irrespective of the survey treatment, the enumerator obtained the plot areas and the outlines for the cassava plots cultivated by the CVIP households, using a handheld Garmin eTrex 30 GPS unit.P14F<sup>15</sup> The respondent reported the area of each cassava plot,P15F<sup>16</sup> and irrespective of the survey treatment, one cassava plot was selected at random in each household for crop cutting, as explained in section 3.2. During the first visit, the enumerator also laid down a 5x5m crop cutting sub-plot, following a strict randomization protocol, on the selected plot in each household.P16F<sup>17</sup>

# **3.1. Survey Treatments**

CVIP implemented two diary- and two recall-based survey treatments. The enumerators scheduled visits to the CVIP households in strict observance of the requirements of each survey treatment arm (in terms of timing and frequency).P17F<sup>18</sup> For all questions regarding agriculture and cassava migration and the respondent died in 2 1-member households. There was no other loss of sampled households due to refusal and all diary households kept their diaries for as long as they had cassava standing on their plots during the fieldwork period.

13 The experiment used
has data on city size by core city and urban agglomeration, and we did not find any differences when either of the two measures were used. In any case, the data has measurement errors as any data base, and this is why we used similar indicators from different sources (e.g. access to telephones in UN databases and the same indicator in EOS database) to check the sensitivity of the results across databases. These results are robust and independent of the definition of city boundaries. We did control for various definitions of city boundaries by running regressions both on core city population and metropolitan population, with same results.

# **TABLE 4: DETERMINANTS OF CITY PERFORMANCE – SYNTHESIS OF ECONOMETRIC REGRESSION RESULTS**

|**Source**<br>**Dependent Variables **|**UN 1998**<br> **Access to**<br>**Water**|**UN 1998**<br>**Access to**<br>**Electricity**|**UN 1998**<br>**Access to**<br>**Sewerage**|**UN 1998**<br>**Access to**<br>**Telephones**<br>**[1]**|**EOS 2003**<br>**Access to**<br>**Telephones**<br>**[2]**|**EOS 2003**<br>**Access to**<br>**Cell Phones**|**EOS 2003**<br>**Internet**<br>**Access**|**EOS 2003**<br>**Quality of**<br>**Infrastructure**|**EOS 2003**<br>**Quality of**<br>
Policy Research Working Paper 10172

# **Abstract**

Quantifying the impact of the COVID-19 pandemic on poverty in Africa has been as difficult as predicting the path of the pandemic, mainly due to data limitations. The advent of new data sources, including national accounts and phone survey data, provides an opportunity for a thorough reassessment of the impact of the pandemic and the subsequent expansion of social protection systems on the evolution of poverty in Africa. This paper combines per capita gross domestic product growth from national accounts with data from High-Frequency Phone Surveys for several countries to estimate the net impact of the pandemic on poverty. It finds that the pandemic increased poverty in Africa by 1.5–1.7 percentage points in 2020, relatively smaller than early estimates and projections. The paper also finds that countries affected by fragility, conflict, and violence experienced the greatest increases in poverty, about 2.1 percentage points in 2020. Furthermore, the paper assesses and synthesizes empirical evidence on the role that social protection systems played in mitigating the adverse impact of the COVID-19 crisis in Africa. It reviews social protection responses in various African countries, mainly focusing on the impact of these programs and effectiveness of targeting systems. Although the evidence base on the protective role of social protection programs during the pandemic remains scarce, the paper highlights important findings on the impacts of these programs while also uncovering some vulnerabilities in social protection programming in Africa. Finally, the paper draws important lessons related to the delivery, targeting, and impact of various social protection programs launched in Africa in response to the pandemic.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at nyonzan@worldbank.org.

_The Policy Research Working Paper
Ireland, the most inaccurate of grade A countries. Overall, we judge that the Republic of Yemen had the most inaccurate GDP data over this period, and Australia the most accurate.

One potential issue with the Penn World Tables A through E grades is that they may not be reflective of GDP data accuracy. To further explore the implications of our rankings, we also considered the World Bank’s subjective ranking of GDP accuracy for the 114 countries in our sample of 178 which it grades. The analysis is presented in Online Appendix B.3. While there is a positive correlation between the Penn World Tables and World Bank subjective rankings, there are important differences. Regardless, our ordering entails a significant reordering of the World Bank, rankings as well.

In the following section, we consider some of the countries with the most inaccurate GDP data at the top of Table 7, in each grade. In many cases, the ranking is due in part to a single, cyclical episode of especially erroneous data. As we have previously shown, being able 26
issue. Finally, in order to be sure that the choice of PCT applications to build the coinventorship variable does not bias the results, the baseline regression is replicated using alternative patent data sources, such as the USPTO, the EPO, and “Triadic Patent Families” (TPF) (OECD Triadic Patent Families database, January 2014). TPF consist of a set of patents filed at the EPO, the Japan Patent Office (JPO), and granted by the USPTO that share one or more priority applications. If anything (columns [4] through [6]), it seems that using alternative sources of patent applications may overestimate the relationship between high-skilled migration and international co-patenting.

Appendix S7 replicates the baseline regressions but splits the count of co-patents and foreign inventors into five broad technology fields (Schmoch 2008). Appendix S8 uses alternative count data methods intended to zero-inflated dependent variables, with no important differences with respect to the main results.

TABLE 7. Robustness Checks. Co-inventorship

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS||Citation||||
||<br>co-inv.+1|NegBin|<br>weighted|USPTO|EPO|TPF|
||||<br>Co-inve<br>|ntorship<br>|||
|ln(Diaspora)|0.232***|0.221***|0.165***|0.229***|0.174***|0.219***|
||(0.0136)|(0.0267)|(0.0268)|(0.0230)|(0.0234)|(0.0263)|
|ln(Distance
68, because either no direct-to-indirect tax revenue breakdown was available, or no income tax revenue data was available, or because data was significantly outdated (prior to 2011).<sup>32</sup> Additional data quality issues were found with data for some of the countries included. For each country, the data provides a total tax revenue figure, total direct and indirect tax revenue figures, and more detailed breakdowns across individual tax types. However, in a number of cases, the total tax revenue figure exceeds the sum of the sub-components. This may occur, for example, where some revenue was not allocated to a specific category. Perhaps of more concern, in a smaller number of countries, the sum of the components exceeds the total tax revenue figure. This includes, in several countries, the addition of the direct and/or indirect tax subcomponents not equaling the direct and indirect tax aggregates. A judgement call was therefore necessary in calculating the indicators for several countries as to whether to rely on the provided direct/indirect tax figures, or the addition of their subcomponents. To provide consistency across the three indicators, the subcomponents were used rather than the aggregates. This ensures that any differences in the country rankings in Figure 5 are due purely to the inclusion or exclusion of property taxes from the numerator.

Figure 5 clearly shows the strong reliance of IDA countries on indirect as opposed to direct taxes. Only five countries have a direct-to-indirect tax ration greater than 1. The highest of these is Papua New Guinea, where income taxes are the dominant revenue source. In contrast, Vanuatu – which employs no personal or corporate income tax – has the lowest direct-to-indirect tax ratio. While there is also no PIT in the Maldives, the CIT increases the indicator slightly. The greater reliance on indirect taxes in most IDA countries is likely driven to a significant extent by administrative capacity constraints that result in greater emphasis being placed on indirect taxes that may be easier to collect than direct taxes. Additionally, the low
11 We also estimate a variant of equation (1) where we include the explanatory variables in lagged forms using both province FE and GMM models. The results are qualitatively similar (Dang et al., 2023). We further show the estimation results with additional control variables for public infrastructure such as the share of villages with yearround passable roads, the share of villages with a daily market, and the distance from the village to the nearest town in Appendix A, Tables A.3 and A.4. Since these variables are computed from the commune module of the VHLSSs, we do not have complete data for these control variables for the whole period of our study. Yet, these tables show that provinces with more villages that have year-round passable roads could reduce poverty and have faster economic growth.

12