rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 2: Test for balance on measures of fishing productivity

||DistToCoast<br>(1)|TonsPerSet<br>(2)|TonsPerArea<br>(3)|FittedVals<br>(4)|
|---|---|---|---|---|
|Treatment fraction|-0.362|5.559|3.238|0.046|
||(1.797)|(4.319)|(1.203)|(0.033)|
|Mean dep. var.|30.748|54.506|6.804|0.791|

All regressions have 34,272 observations and control for two-week-of-sample by two-degree-grid-cell fixed effects, day-of-sample fixed effects, and potential closure-level length distribution. Standard errors clustered at level of two-week-of-sample by two-degree grid cell.

balanced on pre-period juvenile catch trends in Figure A1.

To test whether potential closures are balanced on observables, I focus on three of the control variables in Equation 1 that are likely correlated with fishing productivity: distance to the coast, tons per set, and tons per area (km<sup>2</sup> ).<sup>21</sup> If treated potential closures are more desirable to fish near, juvenile catch inside the treatment window will be mechanically higher for treated potential closures than for control potential closures, all else equal, because there will be more fishing near treated potential closures. Indeed, in Table A1 I find positive, significant correlations between juvenile catch inside potential closure-treatment bins and each of these three variables (Columns 1 to 3). When I regress juvenile catch on all three variables together in Column 4, all three coefficients remain positive, though tons per area is no longer significantly correlated with juvenile catch. I record these fitted values and also use them to test for balance in Table 2.

In Table 2, I test whether potential closures are balanced
and spend more on sophisticated services.

The findings from the analysis of the firm data show that: _First_ , firms integrated in GVCs have a productivity premium between 13 and 22 percent relative to domestic firms, with the productivity premium increasing with the extent of integration into GVCs. Double and triple mode firms, i.e., firms integrated at an intermediate and high level have a much higher productivity premium relative to firms participating in GVCs in a limited or low level. There is some variation across industries, with the productivity premium from deeper levels of GVC participation being higher in the auto, electronics and IT/ITeS industries compared to textiles and chemicals. _Second_ , intensifying the share of spending on service inputs is not necessarily associated with an increase in productivity. The composition of service use matters more for productivity. _Third_ , an increase in the share of service expenditures on complex services by 1 percent is associated with a 10 percent higher productivity. Thus, a firm’s purchase of technological know-how, licenses or sophisticated professional services is associated with a higher productivity as these services involve using more sophisticated production licenses, better management tools or digital value-added services. In particular, the intensive use of IT and IT-enabling services, which are highly sophisticated, is associated with higher productivity. _Fourth_ , the origin of services (domestic vs. foreign) also matters. Firms importing services have a 10 percent higher productivity compared to firms not importing services. Around three percentage points of this productivity premium can be attributed to imports of complex services. Complex services are characterized by a higher level of value-added that is created in their production 16
Headed|0.10|0.10|0.11|0.00|0.00|0.24|
|People per Room|2.37|2.48|2.30|-0.19|0.02|0.00|
|**Head's characteristics**|||||||
|Age|45.76|45.92|45.66|-0.26|0.17|0.13|
|Literate|0.75|0.78|0.73|-0.06|0.01|0.00|
|No Education|0.59|0.58|0.59|0.01|0.01|0.08|
|Primary Educ.|0.12|0.13|0.12|-0.01|0.00|0.01|
|lower secondary|0.10|0.10|0.11|0.00|0.00|0.31|
|upper secondary|0.06|0.07|0.06|-0.01|0.00|0.03|
|Tertiary|0.13|0.12|0.13|0.00|0.00|0.52|
|**Samples**|**29,192**|**17,480**|**11,712**||||

**Note** : “Difference” captures the raw difference between the post-Covid-19 sample (treatment) and the pre-Covid-19 sample (control). Statistical significance: *** p < 0.01, ** p < 0.05, * p < 0.1

The survey data provides important individual and household characteristics like age, gender, marital status, location of settlement, educational attainment, access to
**Risks to Child Development and School Readiness among Children under Six in Pakistan: Findings from a Nationally Representative Phone Survey**

Elizabeth Hentschel, Heather Tomlinson, Amer Hasan, Aisha Yousafzai, Amna Ansari, Mahreen Tahir-Chowdhry and Mina Zamand

# **JEL Classification:** J13, J24, I31

**Keywords:** Early Childhood Development, School Readiness, Risk Factors, Pakistan, Children under 6
**Figure 4: Exports, imports and migration**

<!-- Start of picture text --> Exports Imports<br>Number of immigrants from other Mexican Number of immigrants from other Mexican 158.3<br>160 142.1 states 160 states<br>140 140<br>120 120<br>100 100<br>80 63.3 High exports 80 High imports<br>60 60 47.1<br>Low exports Low imports<br>40 40<br>20 20<br>0 0<br>Number of immigrants from other countries Number of immigrants from other countries<br>160 160<br>140 140<br>120 120<br>100 100<br>High exports High imports<br>80 80<br>60 42.4 Low exports 60 43.3 Low imports<br>40 24.7 40 23.8<br>20 20<br>0 0<br><!-- End of picture text --> Source: Own elaboration based on Population Census, Integrated Public Use Microdata Series and Customs data. Notes: (1) The sample is restricted to the urban and semi-urban municipalities with complete data in the whole period. (2) A municipality has relatively high exports (imports) if its exports (imports) per 2000 worker ratio in 2015 is above the median across municipalities and low exports (imports) if its ratio lies below the median.

**Figure 5: Exports, imports and informality rate**

<!-- Start of picture text --> Exports Imports<br>Informality rate Informality rate<br>100 100<br>87.9<br>85.0<br>80 72.0 80 69.1<br>60 60<br>High exports High imports<br>40 Low exports 40 Low imports<br>20 20<br>0 0
and formal workers have jobs more amenable to working from home, and that home-based-work amenability is higher in richer countries. Their measures of working-from-home amenability are positively correlated with those of DN2020, but their geographic coverage is constrained by the availability of skills surveys.

The remainder of this paper is organized as follows. The next section briefly reviews existing measures of home-based-work, explains why they may not be appropriate for developing countries, and how we adjust them to appropriately reflect ICT constraints. Section 3 presents estimates of the prevalence of jobs amenable to working from home across countries and shows that poorer countries have more jobs at risk. Section 4 explores implications of COVID-19 for inequality, showing that the pandemic will likely exacerbate both spatial and income inequality since lagging regions have more jobs at risk, and because poorer workers are less likely to be able to work from home. Section 5 presents robustness tests. Section 6 examines which workers are most at risk and demonstrates that labor market risk is inversely correlated with education; skilled workers are more likely to have jobs amenable to home-based work. Workers on temporary contracts, who are more vulnerable to start with, are less likely to have jobs that can be performed from home. Section 7 concludes and points out that our analysis suggests that COVID-19 will likely exacerbate pre-existing socio-economic disparities, both within and across countries.

# **2 Data and methods**

Labor market vulnerability depends on the nature of the jobs that workers have. The main criterion used in the literature is the feasibility of home-based work. Dingel and Neiman (2020) use information from characteristics of more than 900 occupations based on two surveys from the US Department of Labor/Employment and Training Administration’s Occupational Information Network (O*NET). When answers reveal that an occupation requires daily activities such as “working outdoors” or “operating vehicles, mechanized devices,” or “contact with the public,” they determine that the occupation cannot be performed entirely from home. DN2020’s measure, which is based on
pandemic. The informal sector accounts for a large share of the economy in Cambodia, hence taxes are largely collected indirectly. Poorer households pay indirect taxes as part of the purchase price of goods and services, which reduces their purchasing power, even with tax exemptions on necessities. In the absence of offsetting transfers, their net cash position is negative on average. Short-term poverty can increase following execution of fiscal policy because direct transfers are too small to compensate for the impact of indirect taxes on poor and vulnerable households. Note, households also benefit from in-kind services from spending on health, education, and infrastructure, but they do not receive these services in cash and therefore do not increase their incomes today. However, it is possible for fiscal policy to reduce poverty in the long run through investments in human capital and infrastructure. Other components of spending not captured in the CEQ exercise, such as public infrastructure, can make the overall system more progressive through their indirect effects on inclusive economic growth.

Cambodia is not alone among developing countries in seeing a short-term poverty increase due to fiscal policy. Several peer lower middle-income countries also experience net poverty increases due to their 31
*

While the registry provides data on registered transactions, assessing levels and determinants of informality requires linking household to registry data at the parcel level. To do so in a way that is representative for rural areas in Rwanda, we use the 2015 round of a three-round panel of 3,600 households in 300 rural villages covering 23 districts that were interviewed in 2011, 2012, and early 2015.<sup>12</sup> This survey was administered using tablets and the questionnaire was pre-populated with a list of all parcels owned or cultivated by a household in 2012, requiring the enumerator to in each case check for changes in status (beyond asking for new acquisitions). The unique parcel identifier (UPI) was used to match with LAIS data, in particular the name of the registered owner, to ascertain that households who claimed to have completed the process of registering a transaction did actually do so.

> 12 The survey originally aimed to evaluate the impact of the LTR program’s national roll-out. 100 sectors, 4 in each of the country’s 25 districts, were randomly chosen. In each selected sector, three enumeration areas with 12 households to be interviewed in each were then selected randomly. In 2015, 3,348 of the original households could be re-interviewed.

11
8|
|**Access to electricity**|195|person|4|
|**Ports**|348|TEU|4|
|**Wastewater treatment**|120|person|3|

Source: Yepes (2008). *Varies by region.

In order to assess the full budgetary allocation needed by the various sectors, estimates of the associated maintenance needs are included as well. Maintenance is needed for any investment to meet its assumed lifetime so a commitment to maintenance is built into the costbenefit analysis and the calculation of the social rate of return of the investments. In this paper, maintenance costs are estimated by multiplying the stock value in the previous period by a depreciation rate. A fixed annual depreciation rate is assumed for each sector and shown in Table 4. Estimates of total annual expenditure needs by sector, sub-group and region are presented in Table 5. In the absence of data for a sector or subsector in a country, regional averages of investment were imputed to obtain total investment needs.<sup>5</sup>

> 3 In some cases recent trends of annual growth of the same series are used to fill in missing values. United Nations National Accounts Main Aggregates Database is used for historical GDP and for the value added components of agriculture and manufacturing. Additional data on container port traffic is taken from the Containerization International Yearbook (1970-2006). Extra information on roads and paved roads is available for Oman. GDP projections are from the World Bank.

> 4 When past growth rates were not available, a Hodrick-Prescott filter (with a smoothing factor of 100) was used to obtain the long-run growth rate trend of the past ten years (1999-2009) resulting in an implied annual growth rate equivalent to that of the World Bank projections. Growth rates are then used to obtain 2010-2020 GDP scenarios. 5 Regional averages of investment as a share of GDP were used in the following cases: roads in Syria, electricity in West Bank and Gaza, water and sanitation
SNA, 1968).10 When these accounts are incomplete (e.g., a component of GDP is missing, or there is no data on depreciation), we recover missing values using accounting identities or by following the imputation procedures used in the World Inequality Database (WID, 2020). To ensure comparability with the more recent data, we recast the historical series into the 2008 System of National Accounts framework.11 To our knowledge, this is the first factor income shares dataset that harmonizes data from the 2008 and 1968 System of National Accounts. In countries and years when the two systems overlap (typically in the 1970s, when countries transitioned from the old to the new framework), the series match well.

Our work expands the dataset in Karabarbounis and Neiman (2014) along two dimensions. First, the integration of the 1968 System of National Accounts data extends coverage in time and space.12 Second, while Karabarbounis and Neiman (2014) focuses on factor shares in the corporate sector, we compute factor shares of total domestic output.

# **3.2.2 Tax revenue data**

We construct a new tax revenue dataset that includes disaggregated tax revenue data by type of tax. Our database includes all taxes—personal income taxes, corporate income taxes, Social Security payroll taxes, property taxes, wealth taxes, estate and inheritance taxes, consumption and other indirect taxes —at all levels of government. We integrate previously unused historical data from developing countries to obtain a global coverage.

We first gathered existing high-quality data from OECD (2020) and ICTD/UNU-WIDER (2020) for recent years, and from the IMF GFS (2005) for older years. Second, we retrieved thousands of country-year observations of historical revenue data from the Harvard University Library archives,13 as well as online data from national statistical offices and

> 10The variables include value added; compensation of employees; operating surplus of corporations; operating surplus of unincorporated enterprises; consumption of fixed capital; and indirect taxes
also contextual and depend on several factors, most importantly, the previous WBG country engagement. Typical in-country partners are members of civil society, of associations of excluded groups, and of private sector organizations. While time and resources are often constrained in the SCD process, a few meetings and field visits to consult with communities and other partners can go a long way in anchoring the SCD to reality. Such efforts should ideally include core members of the SCD team, and may be linked to ongoing projects, so as to minimize transaction and other costs.

5. **Ensuring thorough reviews of extant material** . Reviews of the existing literature that also taps into the country discourse is an essential step to a robust diagnostic. If this step is incomplete or narrow, it will color the extent to which social inclusion is integrated into the SCD. If done well on the other hand, it will help to answer many questions. Such a review also helps to build in-country partnerships that provide invaluable support to the SCD and the CPF process.

6. **Using mixed methods and multi-disciplinary perspectives** . There are several ways of identifying and understanding exclusion. Analysis of survey data is often an essential step in framing the questions. But it is often qualitative data, derived from focus groups, consultations or interviews with key stakeholders, which provides a granular understanding. A task team may also use other innovative tools. Some SCDs covered by this review, for example, have used methods like client satisfaction surveys or surveys of subjective well-being, that reveal the underlying causes of exclusion, including the less discernible ones such as lack of trust, feelings of stigmatization, stereotyping and discrimination. The Lesotho SCD, for example, draws on client satisfaction surveys to explain why the utilization of health facilities is low despite the high prevalence of HIV infections, citing provider problems such as a lack of medicines and supplies, absent and disrespectful doctors, and long waiting times at facilities as main reasons for dissatisfaction.

7. **Identifying
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may prevent many of the original recipients from formally exiting farming and transferring their land to younger farmers able to make better use of it and mechanisms to ensure such transfer will be important.

As smaller farms rely disproportionately on land held under use rights, high levels of tenure insecurity and limited investment incentives for land held under permanent use could be a plausible explanation for the low levels of TFP by farms below 50 ha. Using the cadastral designation of land can provide a quick check and suggests that 58% of land by farms below 20 ha and 36% for those below 50 ha, as compared to 20% for farms in the 50-120 ha class, is for ‘personal farming’ rather than ‘commercial agriculture’, a difference that is significant at 1% in both cases. Making use rights more transferable, possibly by helping the smallest farm size group acquire full ownership rights to the land they farm thus seems to have potential to boost Ukraine’s future development by increasing the welfare of this group, competitiveness of the land market, land-related investment and use of land as collateral, as well as the amount of local land tax receipts.

# **5. Conclusion and policy implications**

This paper aimed to complement existing studies by assessing impacts of Russia’s invasion of Ukraine on agricultural performance and food supply based on a micro-level economic data and by including informal farms, a segment of Ukraine’s agriculture sector that is often overlooked in official statistics. Two findings are noteworthy. First, our data show that surveyed farmers kept their cultivated area in 2022 almost unchanged, a sign of farmers’ resilience. At the same time, worsening terms of trade for agriculture led to a precipitous regionally differentiated decline in farm profitability and productivity. Monitoring changes in planted area for winter as well as summer crops in 2023 based on satellite imagery will be important to identify the extent to which such changes in profitability triggered longer-term adjustments like decapitalization. Second, our data suggest that, especially with the country undergoing a significant
struggle for control of strategic and war-economy areas, and the pressures from the army’s military strategies (González, Bolívar, and Vásquez 2003; Gutiérrez and Sánchez 2006). The peak of violence in the late 1990s, for instance, resulted from a mix of the military strengthening of guerrillas, the expansion of paramilitary groups, the weakness of the state, the economic crisis and the reshuffling of drug cartels’ structure (CNMH 2013).

Different armed groups have preferred different types of violent actions, although these have also varied though time. In the case of violent attacks against civilians, the main perpetrator since the mid-1990s have been guerrilla groups, though that does not imply this was the main type of violence they perpetrated. Between the late 1980s and 1993 attacks were highly associated with drug cartels,<sup>20</sup> but since then, the cartels favored a low profile, which resulted in a reduction in their use, and a parallel pick-up of this strategy by guerrilla groups (CNMH 2013). The peak of violent attacks in the early 2000s was linked to the guerrilla’s desire to demonstrate their growing power, as well as being a contention strategy against the paramilitary growth and, later on, the state’s military recovery (CNMH 2013). These attacks have been, with some notable exceptions, aimed at the local sphere.

# **IV. Empirical strategy**

# **A. Violence data**

A key challenge for identifying the effect of violence is to find a measure of a _shock_ instead of one reflecting persistent _levels_ of violence in a municipality. Violent attacks against civilians have

> 19 31,000 combatants demobilized but former paramilitary leaders have stated that these numbers were inflated and that the actual number of combatants was about half.

> 20 Particularly the Medellin cartel.

8