rafmacalaba/data-use-mentions-extended huggingface dataset:
- _Growth of permanent employment_ : this is a dummy variable that takes the value of 1 if there was an increase in the number of permanent employees over the preceding three years.<sup>4</sup>

- _Labor productivity_ : the log of value added per worker, where value added is defined as sales less spending on raw materials and deflated using a PPP-based year-specific exchange rate.

- _Total factor productivity_ : this variable is constructed as a simple residual using logs of sales, employment, fixed capital and material, with weights on the latter three variables set to 0.25, 0.10 and 0.65, respectively.

- _R&D-firm_ : this is a dummy variable that takes the value of 1 if the firm reports that it does R&D.

- _Process or product innovation_ : this is a dummy variable that take the value of 1 if the reports that it introduced a new process or product during the previous three years.

- _Sales to MNCs_ : the percentage of sales reported to have been made to multinational companies. We interpret this as an indicator of external evaluation of the firm’s quality.

- _Education top level of the manager_ : this is measured in years of education.

- _Average education level of the production workforce_ : this is also measured in years of education.

- _Training offered_ : this is a dummy variable that indicates whether the firm has an in-firm training program.

- _Per cent of production workers trained_ : this relates to the firm’s in-firm training.

# _The reported costs of public input constraints_

Measures of _Rij_ , the reported cost or shadow price of public inputs, are available in the SAR data: “How much of an obstacle is XX to the operation and growth of your business?” The model predicts that these will be positively related to measures of productivity and growth: they will be increasing in _Aij_ and positively correlated with

> 4 If permanent employment data is not available for a firm for 3 years earlier, a shorter period is
electricity. This is particularly relevant in the context of Bangladesh where the government has determined to use SHS as one of the alternatives to the grid option in its efforts to achieve the target of a universal access to electricity by 2020.

The grid electrification as an alternative baseline for quantifying the carbon benefit of SHS is also proposed in Kaufman et al (2000). Basically, the carbon benefits come from the comparison between two electrification options: grid connection using conventional fossil fuel and solar systems. It should be noted that the direct comparison can be problematic as there exists a substantial difference in the quality of electricity service received from the two options, with SHS providing much limited capacity.

In quantifying the carbon avoided using grid-electrification as an alternative baseline, the average level of electricity consumption of grid-connected household is proposed as the benchmark to estimate electrification generation (Kaufman et al, 2000). The underlying assumption is that non-electrified households would consume electricity at the same rate as the grid-connected households if they were connected to a grid. The availability of the Bangladesh household survey data allows us to predict the electricity consumption of non-electrified households based on the electricity consumption model estimated using the grid-connected households, controlling for household characteristics, such as incomes, family size and location.

The predicted average monthly electricity consumption among non-electrified households, based on this electricity model, is about 28 kwh per month when connected to a grid. The average consumption is about 26 kwh per month for the bottom two income groups and about 31kwh per month for the top two income groups. Using the grid-emission rate of 0.8 kg CO2/kwh, the carbon emissions avoided from SHS is equivalent to about 269 kg CO2 per SHS per year. This estimate is about 3.5 times that estimated using the kerosene displacement baseline (76 kg CO2 per SHS per year).

The scale of the carbon emissions avoided from SHS adoption can be better illustrated by putting these estimates in the national context of total number of households currently without electricity access in Bangladesh (about 23
**|10.767***|10.749***|10.650***|10.671***|<br>10.672***|<br>10.571***|
||(0.034)|(0.046)|(0.034)|(0.057)|(0.035)|(0.039)|(0.044)|(0.053)|(0.066)|
|Observations|2,118|2,108|2,118|2,118|2,118|2,118|2,118|2,108|2,108|
|R-squared|0.017|0.179|0.079|0.023|0.041|0.135|0.072|0.074|0.192|

Note: Dependent variable is the log of per capita food consumption expenditure. Standard errors in parentheses. _Differences found to be statistically significant are indicated by level:_<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. The reference category is the 14-day diary. Model 2 includes household head and household characteristics as controls, model 9 includes the interaction of all characteristics with treatment dummies.

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5, Panel C we restrict our sample to only first-time winner markets. Again, the results remain stable.

Lastly, the onset of the global financial crisis in 2007 depressed house prices and affected local demand considerably during the period 2007-2012.<sup>14</sup> One potential concern with our results is that it may capture the differential response of prices to household demand shocks stemming from house price shocks and household wealth fluctuations (Stroebel and Vavra,

> 13The IRI data also contains product prices from pharmacy stores, such as CVS.

> 14See, for example, Mian and Sufi (2011); Mian et al. (2013); Mian and Sufi (2014).

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population or life expectancy. The World Bank (2002) is the source for the data on total secondary school enrollment and population of the age group corresponding officially to that level of education.

Financial depth is computed as the ratio to GDP of the stock of claims on the private sector by deposit money banks and other financial institutions from Beck, Demirguc-Kunt and Levine (2000). This measure of financial depth is significantly correlated with other proxies such as the traditional measure of financial depth (M2/GDP). Indicators of other aspects of financial markets, such as the size and activity of the stock markets, can also be used as proxies for financial depth. However, data availability and the fact that incentives to perform efficiently are clearer and stronger for private agents make this measure the preferred proxy for the size and activity of financial markets in recent empirical studies (Levine, Loayza and Beck, 2000).

Trade openness is the residual of a regression of the log of the trade (exports and imports) to GDP ratio on the logs of area and population, and dummies for oil exporting and landlocked countries. The variables in the regression are constructed from data in the World Bank (2002). This structure-adjusted trade openness measure ensures that one would not be attributing to trade policy outcomes resulting from structural country characteristics.<sup>16</sup> Government burden is measured with the ratio of government consumption to GDP (World Bank, 2002). The rationale for this choice is that current government consumption is devoted mainly to covering the bureaucracy’s wage bill. One might argue that not all of government consumption is wasteful as expenditures on health, education and law and order promote growth. Loayza _et al._ , however, did not adjust for these growth promoting

> 16 For instance, landlocked countries may trade less than other countries as they face high transport costs. For oil exporters, large trade volumes may co-exist with high import tariffs.

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Figure 6.0: Standard Errors for Original and HLO Scores by testing regime <!-- Start of picture text --> EGRA<br>LLECE<br>PASEC<br>PIRLS<br>PISA<br>SACMEQ<br>TIMSS<br>0 5 10<br>Original Score HLO Score<br><!-- End of picture text --> Scale. Our methodology enables us to link countries participating in regional standardized achievement tests (RSATs) on an international scale. On this scale, 625 represents advanced attainment and 300 represents minimum attainment. This interpretation is derived by taking an ISAT benchmark for high performance on the upper end of the distribution and an RSAT benchmark (expressed in HLO units) on the lower end of the distribution. This approach enables us to capture performance across the entire distribution and accounts for floor and ceiling effects that would be introduced by just taking the ISAT or RSAT benchmarks alone on both ends of the spectrum.

# 4. Illustrative Analysis

We include a few key figures from our database as motivation for the utility of the database. We analyze disaggregated data by subject and schooling levels to explore the full texture of the data.

Figure 7.0 shows select countries on a global scale in reading in 2016. We see a few notable trends. Russia has taken the lead – a new development. Chile nearly outperforms France and has started to outperform Eastern European countries such as Georgia. Saudi Arabia places near the bottom outperforming only African, Pacific or war-affected countries. Rwanda outperforms the regional behemoth, South Africa. The performance gap between Egypt and Russia is nearly double.

Figure 8.0 shows the relationship between average harmonized learning outcomes in science from 2000-2017 and 2015 GDP per capita.

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including information from nominal growth from eleven economic sectors to model labor income and household-specific inflation as described in the methodology. All other projections rely on headline inflation and nominal growth rates. Growth and poverty changes between FY16 and FY19 are used to estimate the elasticity for the Poverty Growth Elasticity and the Growth Incidence Curve for the Quintile growth contribution. Estimate poverty growth elasticity for real GDP per capita and poverty between FY16 and FY19 is -1.06. The neutral distribution model uses a passthrough of 0.86 to reflect the consumption share on total GDP; results using higher (lower) passthrough rates present marginally more (less) pronounced variations of poverty to growth. Quintile growth contribution projections include a Democratic approach, where households within a quantile receive the same per-capita income amount, and a Plutocratic Approach, where each household receives an amount of income based on its share in the total income within its quantile. Projections using sectorlevel growth rely on the sector of employment of the member with the highest reported labor income.

# **5. CONCLUSION**

Since 2019, the last year when data to estimate poverty and inequality levels were available, Pakistan has experienced large macroeconomic and natural shocks. However, the absence of new household survey information limits the ability to assess the implications of the different shocks on household welfare to formulate appropriate policy responses.

> 16 Slight deviations from the observed poverty rates are introduced by changes in the sampling frameworks due to urban households being oversampled in HIICS 2015/16 and the inclusion of the Federally Administered Tribal Areas (FATA) within the Khyber Pakhtunkhwa province. FATA areas are affected by violence and economically disadvantaged when compared with the rest of the country. We implement additional reweighting tests with no qualitative change in the results.

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lower bound.

The log of the last month’s sales (R�) is the main measure of having resources to pay. The data include a monthly measure of revenues, as without formal accounting, businesses’ recall of the last month’s sales is considered reliable and readily available in respondents’ minds. D� is a measure of the opportunity cost of staying informal, though clearly difficult to measure. The survey instrument

> 25 Note that ACS with or without stratification can implement various types of expansion rules, including to squares on the cardinal (N,S,E,W) axes or diagonals.

> 26 In the literature on ACS, “network” is defined to mean all contiguous squares meeting or exceeding the threshold. Networks may be bordered by adjacent “edge units” where informal businesses may be encountered. While such accommodations were used in data collection, for the sake of not introducing further confusion, “network” is used here to include all squares enumerated from the selection of the same initial PSU.

> 27 The actual number of networks enumerated was higher as there were some non-unit networks.

> 28 The exact wording of questions is presented in Appendix A.II.

(17)
There are a number of reasons why policy makers and development workers will benefit from improved measurement of actual remittance flows. First, if policy is being designed to encourage remittances or stimulate investment, it is important to know the true size of the flows. Inaccurate information may lead to inappropriate initiatives. Second, from an efficiency standpoint, a large share of informal remittances in an economy suggests that rents to banks and money transfer providers in the official market are very large, and there may be simple ways to improve competition and increase the remittances received. Third, there may be positive externalities from using formal channels (and especially financial institutions such as banks) to transfer money, including increased access to credit and the use of financial institutions for savings.

No existing work uses an empirical methodology to estimate informal remittance flows globally. A handful of country studies use household survey data to estimate remittances, but these estimates cannot be compared with national accounts to compute the size of the informal sector because of issues in reporting. Indeed, the implied estimates of national remittances are well below the values recorded in the balance of payments (BOP). Surveys that ask how remittances are sent are likely to provide the best estimates of the size of the informal sector, but such surveys are only available for a handful of countries.

In addition to estimating informal flows, our results also provide information on the determinants of migrants’ remittances and transactions costs. Not surprisingly, the stock of migrants in OECD countries is the primary determinant of official remittances. We estimate that a doubling of the migrant stock in OECD countries would lead to about a 75 percent increase in recorded remittances. We also find that transaction costs are 3
data) and other traditional data sources (e.g., census data, administrative records and registries, labor mobility data).

- c. Broadening the perspective for some key research questions to make better use of existing household survey data and provide complementary solutions to the ‘rare event’ problem (e.g., look at climate-induced immobility other than mobility).

In an ideal scenario all these tools and integrations would be operationalized and the longitudinal and multitopic nature of household surveys would: i) allow investigating the prevailing outcomes of slow-onset changes, namely migration _vs._ immobility, and their interactions with contextual factors; ii) shed light on the leading mediating channels at play; iii) facilitate assessing the potential existence of relevant migration chains and the role of climate change as an indirect push factor for cross-border mobility; and iv) gauge final impacts on welfare and how these are distributed across different types of households and individuals.

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not adjust _p_ values for these five comparisons. The most conservative approach, a Bonferroni correction, would substitute .02 and .01 for the usual 0.1 and 0.05 critical values. In general, we will see the main findings pass even this conservative test.

This theory and the main outcomes were pre-specified but not formally registered. Our study predated the social science registry, but we outlined predictions for the main measures in a 2012 National Science Foundation proposal 1225697.<sup>31</sup> Naturally these five are not the only conceivable outcomes of interest. There are alternative mechanisms (e.g. therapy could affect behavior by changing peers, post-traumatic stress, or substance abuse). Other outcomes enhance our interpretation (such as the effects of cash on economic inputs and outputs that generate income). We collected survey data for many such outcomes, and every one of these is reported in this paper’s tables. But note that the treatment effects on these other outcomes are exploratory and suggestive, primarily meant for for interpretation and hypothesis generation.

**Self-reported data** One threat to identification comes from systematic measurement error in self-reported data, especially measurement error correlated with treatment status. In the absence of administrative data such as arrest records, we developed a technique to validate select survey variables through intensive observation. Blattman et al. (2015) reports the approach in detail, and we summarize in Section 7 and Appendix F.

**Spillovers** Another threat to identification comes from spillovers. Our recruiting strategy— working in large neighborhoods, recruiting less than 1% of adult men in those areas, and less than 15% of high-risk men we could identify on the street—was designed to reduce equilibrium effects such as a change in the returns to illicit work. We do not have the data or research design, however, to confirm that these equilibrium effects were minimized.

Another potential spillover involves interactions within and between treatment arms, especially therapy. For example, because
interested in improving the signal-to-noise ratio of their multidimensional poverty measure when aiming at Objective 2.

# **3.4 Combining data from separate surveys**

One of the main data constraint at global level is the missing distribution between incomes and key non-monetary outcomes such as health (Section 2.4). A priori, this constraint is very consequential because both health and income are arguably among the most important dimensions of well-being.<sup>41</sup> The classical approaches to deal with this data constraint “waste” substantial amount of available data. One popular solution consists in constructing an objective well-being indicator based on a single survey. If income data comes from one survey and health data comes from another survey, this solution wastes all the data in the ignored survey. This is the solution used by global multidimensional poverty measures.<sup>42</sup> An alternative solution is to construct a synthetic indicator that ignores inequalities in health and inequalities in incomes. This is the solution used by the HDI, at least when interpreting mortality data as a signal on a population’s health. This solution wastes the data on income inequalities that is available in the income survey.

In this section, we outline three alternative methods that waste less data by combining information from separate surveys using a hybrid index. The first two methods rely on making assumptions on the missing joint distribution. These methods seem relevant when cross-regional well-being comparisons are expected to depend mainly on the partial distributions, with the joint distribution playing a limited role. The third method assumes equal non-monetary outcomes on small areas to compute household’s “total” consumption. This method seems relevant when non-monetary inequalities across areas are expected to be larger than non-monetary inequalities within areas.

## **3.4.1 Assuming the overlap between two types of poverty**

We start with the simplest method to estimate the fraction of multidimensionally poor individuals from two separate surveys. We first present the method and then show that it works surprisingly
# 3. Methodology

Constructing a non-COVID counterfactual household income distribution for FY20 The first part of the estimation strategy adopted in this paper aims to generate an updated baseline household income distribution for FY20, in the absence of the COVID-19 shock. The starting point is Egypt’s Household Expenditure and Income Survey (HIECS) 2017/18, which collected information about households’ incomes and consumption and is the official data source for poverty estimation. The survey is representative of the Egyptian population, and data were collected over a full year to capture seasonality in income patterns. The survey covers three quarters of FY18; data collection started in Q2 of 2017 and was completed in Q3 of 2018. For this analysis, we treat this baseline information as FY18.

To create a counterfactual income distribution for FY20, we start from the observed trends provided by the Labor Force Survey (LFS) for the period 2017 to June 2020. We take the trends in labor force participation, unemployment, and earnings across different sectors to update the HIECS 2017/18 employment and labor income distributions. This analysis focuses on the early pandemic impacts for FY20. That means that the income shock considered covers the last quarter (Q4) of FY20 (April to June 2020). The LFS data already captures the impact of COVID-19 in that last quarter. Therefore, to create the counterfactual non-COVID income distribution, we assume that, in the absence of the COVID-19 shock, labor market conditions would have been like those observed in Q3 of FY20. Projections done before the pandemic indicate that there was no expectation of another economic shock or break for Q4 (see Annex A, Figure A.1). Trends using LFS also show that the structure of Egypt’s labor market changes little from quarter to quarter, so using employment and income measures for Q3 may be a sensible way to model the counterfactual labor market situation. However, one concern is seasonality. When we compare trends in FY19 across quarters, the indication is that Q4 employment composition and earnings are
Whether farms have less than 120 ha of agricultural land registered in their name in non-conflict areas is easily verifiable.<sup>14</sup> Those that have more land registered may be eligible if they did not cultivate all their land in 2022. Although in the PSG program, area cultivated was based on farmers’ declaration, with random checks only, it can be easily verified by overlaying parcel boundaries from the public cadastral map with publicly available crop maps although technical challenges of quickly establishing real time interoperability between registries did not allow doing so in this case.<sup>15</sup> Appendix table A1 provides information on disbursement under the PSG land window for the universe of beneficiaries. The program paid a total of 35.1 US$ million in grants to 21,050 farmers. Of these, 73% had an area smaller than 20 ha, 15% were from 20 to 50 ha in size, and 12% had between 50 and 120 ha. As the grant was proportional to area, those in the three groups received 24%, 27% and 49% of total resources respectively. Of the farms receiving PSG support, 20% were organized as legal entity, 12% as sole proprietorship (FOP), and 68% as unincorporated individuals, receiving 50%, 22%, and 28% of resources, respectively. Table 1 displays the same figures for 19,660 beneficiary farms in the regression sample used for subsequent analysis.<sup>16</sup>

# **3. Farm-level effects of the war and PSG access**

Survey data, together with market price information, show that, while total area cultivated was remarkably resilient to the war, profits per ha for Ukraine’s traditional agricultural export crops halved due to lower output and higher input prices, though land prices suffered only modest declines. High levels of unsatisfied credit demand are one factor limiting small producers’ ability to respond to the associated relative price changes by diversifying into higher value