rafmacalaba/data-use-mentions-extended huggingface dataset:
:**<br>**Rural**<br>**Workers**|||
|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|Unemployed|0.01|0.09|0|1|167582|
|Farm|0.53|0.5|0|1|167582|
|Non-Farm|0.47|0.5|0|1|167582|
|Non-Farm Informal|0.3|0.46|0|1|167582|
|Non-Farm Formal|0.16|0.37|0|1|167582|
|Wage Labour|0.31|0.46|0|1|167582|
|Small Business|0.02|0.15|0|1|167582|
|Business Owner|0.04|0.2|0|1|167582|
|Self-Employed Professional|0.04|0.18|0|1|167582|
|White Collar|0.05|0.22|0|1|167582|

_Notes_ : This table presents summary statistics based on the Consumer Pyramids data in 2015. Panel A restricts the sample to rural individuals aged between 15 and 65; Panel B restricts the sample to rural individuals aged 15-65, who also participate in the labour force. _Wage Labour_ combines wage labourers and industrial workers; _Non-Farm Informal_ includes wage labourers and small business owners; _Non-Farm Formal_ includes industrial workers, business owners, self-employed profesions and white collar workers.

42
opportunities for income diversification along the coast should therefore increase consumption levels and reduce poverty.

Households with more assets also have higher consumption levels, despite the very low level of ownership of productive assets. Surprisingly, land ownership and the amount of land farmed do not significantly impact household consumption. Despite experimenting with several alternative measures for social capital, in general, social capital does appear to have a

> 17 We do not report results with village distance variables as in nearly all instances these variables were statistically insignificant. We drop the distance variables from the analysis because missing distance data for some villages resulted in a large number of observations being excluded from the analysis.

33
|(0.003)||(0.010)||(0.016)|
|R2|0.00|0.00|0.10|0.10|0.01|0.01|
|N|558,222|558,222|538,130|538,130|468,366|468,366|
|Average outcome among Greek natives|0.984|0.984|0.718|0.718|0.192|0.192|
|Age andgender FE|Y|Y|Y|Y|Y|Y|

**Notes** The table presents regression estimates measuring the educational gap between Albanian immigrants and Greek natives. The sample consists of 16-60 individuals in the 2001 Greek census who are either native-born or born in Albania (26,681). Albanian immigrants who arrived when adults are 19,879 individuals who report to have migrated when they were older than 20. There are 4,188 and 2,614 Albanian immigrants who arrived at an age between 6 and 12 and below 5. The sample is further resstricted to migrants who have eat least spend 5 years in Greece. The outcome tertiatry educaion is only defined for indivdiuals older than 25. All regressions estimates include both gender and age fixed-effects Notes: *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; standard errors are robust to heteroskedasticity. Source: Author’s elaboration on the 2001 Greek census.

35
suggests a number of solutions to this problem, most notably the Arrelano-Bond estimator. Such estimators, however, need at least three periods to estimate the model, using the first period to instrument for the initial conditions of the second period which explain growth between period two and three. Since we have only two periods, we cannot follow this approach. However, although the bias on the ‘convergence coefficient’ may be significant, Monte Carlo experiments indicate that the bias on the other RHS coefficients tends to be small (Forbes, 2000).

The second problem is endogeneity. Even though our model does not contain ‘flow’ variables but only beginning-of-period ‘stocks’, initial expenditure y(92) has been used to construct the growth variable and is thus correlated with the error term. Initial inequality may also be an endogenous variable, as the literature suggests that growth affects inequality (e.g. Aghion et al., 1999; Lundberg and Squire, 2003). One would expect this to be more problematic for changes in inequality (which we do not use) rather than for initial inequality. Put to scrutiny, a Hausman test rejects exogeneity of expenditure inequality, but cannot reject exogeneity of education. Consequently we deal with the endoge neity of initial expenditure and expenditure inequality.

Since we do not have lagged values, e.g. _y_ (t-1), to use as instruments, we have to find instruments amongst the (few) available sub-county census means. We have chosen as instruments for income a variable that measures the ‘education deficit’ (the number of school years missed) of children below the age of 13. The (initial) education deficit for children in this age group is strongly negatively correlated with initial income, but, arguably, does not affect growth in the period analysed. The instruments for expenditure inequality are the maximum education deficit for children below 13 and 14
<br>Medium-Range<br>Weather<br>Forecasts|1979-current|0.28 deg|hourly|mean temp|K|
|(ECMWF) ERA5<br>31||||||
|-Modern-Era Retrospective analysis for Research and Applica-<br>tions, version 2 (MERRA-2) statD|1980-current|0.625x0.5 deg|daily|mean temp|K|

_Note_ : The table summarizes the Earth observation sources and related details for precipitation and temperature data.
The global food crisis of 2007–08 involved approximately a doubling of international wheat and maize prices in the space of two years and a tripling of international rice prices in the space of just a few months. Understandably, such rapid increases in the international prices of staple foods have raised concerns about the impact on the world’s poor. Household surveys suggest that most poor people earn significant shares of their incomes from agriculture but are nevertheless often net food consumers (World Bank 2008b). Consistent with this stylized fact, several multicountry World Bank simulation studies find that poverty typically increases when food prices increase (holding all else equal), with much of the increase in poverty taking place in poorer rural areas (Ivanic and Martin 2008; de Hoyos and Medvedev 2009; Ivanic, Martin and Zaman 2011). Likewise, the U.S. Department of Agriculture’s (USDA 2009) simulation found that approximately 75–80 million people went hungry during the 2008 food crisis, a number that the Food and Agriculture Organization (FOA) of the UN (FAO 2009) applied to its precrisis baseline numbers in the absence of an FAO model that could simulate a food price shock.<sup>2</sup> Subsequent USDA simulations were used by the FAO to estimate that over one billion people went hungry in 2009, up from 873 million in 2005–06.<sup>3</sup> 2 Some basic problems with the FAO model are reviewed in Headey (2011a) and FAO (2002). In the 2008 crisis, the FAO had an underlying model that only incorporated quantities, not prices, so the FAO’s capacity to simulate the effects of food price increases was very limited. Therefore, the FAO relied on a USDA trade model (USDA 2009). A major shortcoming of the USDA model was that it did not include middle-income countries, including large ones such as China, Mexico, and Brazil. Headey (2011a) also shows that the USDA (2009)
2015). Quantifying seasonal population uxes driving rubella transmission dynamics using mobile phone data. Proceedings of the National Academy of Sciences 112.35, pp. 1111411119.

- Wesolowski, Amy, Taimur Qureshi, et al. (2015). Impact of human mobility on the emergence of dengue epidemics in Pakistan. Proceedings of the National Academy of Sciences 112.38, pp. 1188711892.

- White, NJ (1997). Assessment of the pharmacodynamic properties of antimalarial drugs in vivo. Antimicrobial agents and chemotherapy 41.7, p. 1413.

46
cross-sectional regression of the initial capital-output ratio on (log) real GDP per worker.<sup>9</sup> As a robustness check, we also constructed an alternative ̳back-cast‘ projection of capital per worker. Specifically, to construct an initial capital stock for the year 1960, we assume a zero capital stock in the distant past. Using the average in-sample growth rate of real investment

> 8 Country coverage is dictated by the availability of information. In particular, time coverage is limited by the human capital indicator, which is not available after 2000.

> 9 The regression used for extrapolation is: K/Y = -1.1257+0.2727*ln(Y/L), where K is the capital stock, Y is real GDP, and L is the labor force. The depreciation rate employed in the perpetual inventory calculations is 6%.

5
of Finan, Olken, and Pande (2015) who estimate public sector wage differentials for 32 countries and find a public sector wage premium in almost every country, which is highest in low‐income countries and then falls with income per capita. This paper differs from Finan, et al (2015) by including a significantly larger number of countries, including countries from all regions and income classes, and by considering how using alternative private sector comparators affects the estimated public sector premiums.

The results inform the debate about the methodology to correctly compare public and private sector earnings. Our approach differs from the many benchmarking studies done usually by human resource consulting firms that use the “jobs approach.” The jobs approach compares pay for a sample of similar public and private sector jobs and ignores the characteristics of the workers employed in the jobs. This approach necessarily entails limiting the benchmarking to large, formal sector firms, often multinationals, that explicitly classify jobs, and tends to find large public sector wage penalties. One problem with this methodology is that the alternative employment opportunities for public sector workers in developing countries are not limited to the formal private sector and may include self‐ employment and informal activities which pay far less (Alderman et al., 1996, Rama and MacIsaac, 1999). A second problem is that wage premiums are inferred from a relatively small, and not necessarily representative, selection of jobs included in the study.

This study instead follows the more common “worker approach” to estimating public sector premiums, which compares the earnings of public and private sector workers reported in household surveys after controlling for observed predetermined worker characteristics. This approach, however, suffers from significant limitations, including the omission of important determinants of worker productivity such as skills and motivation that are not observed in the data. We therefore consider three alternative estimates of public sector earnings differentials that compare the wages of public sector workers first to all private employees (formal and informal), and then to only private formal employees, and finally to
In search of evidence on the impact of such trade interventions, we conducted a thorough review of the evaluation methods for 85 World Bank trade-related investment lending projects undertaken during the 1995–2005 period. The source of data was the World Bank’s Operations portal website and in particular the Project Appraisal Documents (PADs) and the Implementation Completion Reports (ICRs).<sup>19</sup> The evaluation methods used can be classified into five distinct categories: (a) only economic or financial internal rates of return, net present value or effectiveness calculations; (b) beneficiary surveys and stakeholder workshops; (c) both a and b, (d) both a and b, with a comparison of beneficiaries to a control group; and (e) no formal evaluation methods used.<sup>20</sup> One key aspect to note is that the implementation of a beneficiary survey does not guarantee that a rigorous impact evaluation can be conducted since in most cases the survey covers only outcomes pertaining to beneficiaries of the project, and no control group is covered (more details on these methods will be provided in section 4).

Figure 3 shows that evaluation using only economic or financial rates of return was the most commonly used method for the trade-related projects, while 10 percent of the projects involved no formal evaluation method.<sup>21</sup> Included in the latter category is a trade competitiveness project that described the impact of the project in purely subjective terms: ―While the impact on the firms assisted had not yet been determined, a visit to two beneficiaries by a supervision mission confirmed that there had been an impressive impact on the firms' quality of products and skills.‖ Another example of the latter is a trade competitiveness project where the achievement of the overall goal was measured in terms of the higher average annual growth rate of exports during the project duration and increases in exports' share of GDP compared with the initial year of the project.

Figure 3 Evaluation of
matched observations. Appendix A explains the matching process in more detail.

We were able to uniquely match 1,312 client households to the SBM data, 47% of the 2,772 clients interviewed for our endline survey. These live in 75 of the 81 study GPs (78 of which we were able to identify in the SBM data). There are a number of possible explanations for the achieved match rate. First, the matching was based on the name and surname of the head of the household and his/her father’s name.<sup>16</sup> We encountered a number of duplicate name records in the SBM dataset, each of which had unique card identifiers and unique data on toilet ownership and subsidy uptake . This was the case for 7% of all SBM data observations in our study area. Since we would not be able to uniquely match these to our survey data, these observations were dropped from the SBM data sample before matching. Second, the survey data were collected at least 2-3 years after the SBM baseline. Households might have experienced a change in the head of household over this period, or have split or merged, undermining our ability to match them to the SBM data.

The matching result was balanced across treatment and control groups. Of the matched client households, 959 (73%) did not have a toilet at survey baseline. Our analysis focuses on this sample of households, as these are explicitly targeted by the SBM subsidy program (which does not cover toilet repairs or upgrade) and our objective is to assess the extent to which micro-credit can support SBM in its mission.

> 14It was not possible to obtain data for the remaining 12 percent of clients, since our partner MFI no longer had all the information required by the credit bureau in order to avail of these records.

> 15If household head had no spouse than the name of the eldest female household member was provided instead.

> 16In a minority of cases, where the father of
Niger, Nigeria, Tanzania, and Uganda.

LSMS-ISA survey panels are administered approximately every 1 to 3 years. In the LSMS-ISA program, not only original households are revisited each wave, even if they relocate within the country, but also individual household members who split off from previously selected households are tracked and included in subsequent waves. This intertemporal aspect of LSMS-ISA surveys, therefore, unlocks the potential for the analysis of internal and rural–urban migration patterns, among other things (Carletto and Gourlay, 2019).

23
resources towards the most productive firms can be written as in equation (16). These gains are equivalent to aggregate productivity (TFP) gains as sector-level debt and equity remain invariant.<sup>6</sup> While equation (16) allows us to calculate the total gains an economy can obtain from eliminating the distortions and reallocating resources towards the most productive firms, a similar expression, together with equations (8) and (9), can be used to calculate the counterfactual real gains from pursuing different types of SOEs-related structural reforms.

# **4. Data**

This section describes the firm-level and dynamic database we constructed to empirically identify the effect of state ownership on the marginal cost of production through the financial channel.

We implement the theory on the ORBIS database, working with the data’s raw files to assess the role of firm ownership on the cost of finance. These files are collected by the Bureau van Dijk (BvD) and compiled into two separate modules, a financial This is derived by combining the expressions for the sector-level demand of debt, sector-level demand of equity, sector-level price, _Ps_ , and aggregate output price, _P_ . As sector-level debt and equity remain constant between the efficient economy and the economy with distortions, aggregate output gains in equation (16) are equivalent to aggregate productivity gains.

14
# **2.3 Design of Supply-Side Experiment**

In November 2023, alongside our demand-side experiment, we conducted a supply-side experiment at the same four restaurants to explore how changes in the price of Moambe Chicken affect wild meat consumption. Moambe Chicken, a dish specific to Congolese cuisine, is popular in the region and may fill a similar cultural role as wild meat. Our field team, primarily composed of Kinshasa residents, advised us that Moambe Chicken was the most likely substitute dish for wild meat at restaurants in Kinshasa. On randomly selected days, the price of Moambe Chicken was reduced by $2 (5,000 Congolese Francs). On these days, we compensated restaurants $2 for every plate of Moambe Chicken they sold (to all customers, not only those participating in our demand-side experiment). The goal of this experiment was to determine whether making a non-wild meat option more affordable would reduce customers’ decisions to order wild meat. This relationship between wild meat consumption and the price of alternatives is the rationale for supply-side alternative protein programs, such as promoting poultry production, where the aim is to reduce wild meat consumption by increasing the availability and affordability of substitutes (Foerster et al., 2012; Moro et al., 2015).

The restaurants provided daily sales data, which included all customers, not only those participating in our demand-side experiment. These data encompassed the number of plates sold in three categories: wild meat, Moambe Chicken, and all other dishes, along with the corresponding revenue for each category. For the purpose of our analysis, “wild meat dishes” referred to all types of wild meat combined into a single category. For instance, if a restaurant sold 2 dishes of antelope and 2 dishes of monkey on a day, it was recorded as 4 dishes of wild meat sold that day.

During the supply-side experiment, we gathered data across 68 restaurant-day observations, comprising 17 days for each of the four participating restaurants. Among these, 11 restaurantdays were randomly chosen
when looking at the aggregate distribution of land cultivated for maize, Figure 9 shows that producers belonging to the richest quintile cultivated 14 percent of the total maize hectares using inputs from the program. Producers in the poorest quintile cultivated a very similar share of that type of land, 16 percent. This again calls into question the targeting of the program, which does not seem to concentrate on the most vulnerable producers but rather focuses on producers in the middle quintiles, as they represent the lion‘s share of maize production in Zambia.

> 16 The average area cultivated for maize with inputs from government programs for the second to fourth quintiles were 1.23, 1.39 and 1.43 hectares, respectively. Average cultivated areas for maize (with or without inputs from public programs) are: 1.05, 1.02, 1.15, 1.39 and 2.17 hectares respectively. Source: Authors‘ estimates from LCMS VI.

27
out of pocket to reach a bank branch (Barasa and Mwirigi, 2013). Strong legislation fosters a positive customer experience that creates trust in the system. At present, the agent banking indicator examines the extent to which countries have enacted some good legal/regulatory practices to better enable third-party agents to provide financial services on behalf of financial institutions. It includes the minimum standards to qualify and operate as an agent, type of contracts that can be signed between financial institutions and agents, the range of financial services agents can provide and financial institutions’ liability for agent actions.

Nonbank e-money issuers (NEMI) can play an important role in providing an array of financial services — particularly payments, transfers, and savings—for those who are currently excluded from the formal financial system (Lauer and Tarazi, 2012). It is important for governments to adequately supervise non6
of the open issues and research gaps in the literature, the great potential that LSMS-type surveys hold to help address some of the most policy-relevant research questions in the field. Our proposals are twofold: conceptual and operational. Conceptually, we call for researchers to make use of these data to investigate some of the most important but still unclear aspects of this nexus, such as the role of immobility, the complementarity or substitutability between migration and other adaptation strategies, the migration potential of cumulative slow-onset events. Operationally, we propose to integrate multi-topic, multi-purpose, longitudinal household surveys with additional pieces of information enhancing both the quality and quantity of the information collected. In particular, with only slight modifications to the current questionnaires, such as short modules on adaptation and intention to migrate, the collection of migration histories or associate modules, and the integration of face-to-face surveys with phone surveys to increase the frequency of the longitudinal information, it would be possible to maximize the potential of these tools in a cost-effective way.</mark> 35
- Visited at least one of its competitor’s businesses to see what products its competitors have available for sale

- Asked existing customers whether there are any other products the customers would like the business to sell or produce

- Talked with at least one former customer to find out why former customers have stopped buying from this business

- Asked a supplier about which products are selling well in this business’ industry

- Attracted customers with a special offer

-

- Advertised in any form (last 6 months)

The _stock score_ ranges from -1 to 2, and it is calculated by subtracting one point

- If the business runs out of stock once a month or more

And adding one point for each of the following that the business has done in the last 3 months

-

- Attempted to negotiate with a supplier for a lower price on raw material

- Compared the prices or quality offered by alternate suppliers or sources of raw materials to the business’ current suppliers or sources of raw material

The _records score_ ranges from 0 to 8, and it is calculated by adding one point for each of the following that the business does

- Keeps written business records

- Records every purchase and sale made by the business

- Able to use records to see how much cash the business has on hand at any point in time

- Uses records regularly to know whether sales of a particular product are increasing or decreasing from one month to another

- Works out the cost to the business of each main product it sells

- Knows  which goods you make the most profit per item selling

- Has a written budget, which states how much is owed each month for rent, electricity, equipment maintenance, transport, advertising, and other indirect costs to business

- Has records documenting that there exists enough money each month after paying business expenses to repay a loan in the hypothetical  situation that this business wants a bank loan

The _financial planning score_ ranges from 0-12, and it is calculated by adding up to three points for each of the following two questions

- How frequently do