rafmacalaba/data-use-mentions-extended huggingface dataset:
Given these value-added trade statistics in addition to total exports, we measured the share of each country’s exports that is part of a multi-stage trade process (i.e. a global value chain) using the definition from Aslam et al. (2017). Aslam et al. (2017) define country c’s GVC participation in sector s at time t as: ������ The first component measures the share of country c’s total exports of products in ����� ��������� sector s that are produced within the country (indirect value-added, DVX���). This is “forward participation” as it computes domestic value-added incorporated in the exports of other countries (the downstream perspective). The second component ������ is the share of imported ����� ��������� (foreign) value-added that is incorporated into the exports of country c, also referred to as “backward participation” (the upstream perspective). Whereas the first component is the share of a country’s exports that contributes to GDP, the second component specifies the share of a country’s gross exports that is made up of inputs produced in other countries. This GVC participation rate measures the extent to which a country’s exports are linked to international production networks. Consequently, the larger is the participation ratio for a given country, the greater is its involvement in GVC.

In conclusion, our GVC data capture backward and forward participation at sector level for 115 countries worldwide including 23 SSA countries (see the Appendix for the list of SSA countries included in the data). We also focus on manufacturing GVCs and classify exports into 19 subcategories as presented in a concordance table in the Appendix.

# **_3.1.2 Skill data_**

Human capital is potentially an important determinant of country participation in global markets. However, data on human capital are
# **Table 3:  Regressions Correcting for Differences in the Number of Firms Across Countries**

The regressions below are estimated using country fixed effects and robust standard errors clustered at the country-year level. The dependent variable _Access to finance_ is a dummy variable that indicates whether the firm has access to a loan, overdraft, or a line of credit. The _Lerner index_ is a measure of competition (higher values imply lower levels of competition). The _Herfindahl index (HI)_ and _Concentration 3_ are measures of concentration. _Log firm size_ is the logarithm of the number of permanent employees. _Log firm age_ is the logarithm of the firm’s age in years. _Government-owned_ and _Foreign-owned_ are dummy variables that equal one if the firm has government or foreign ownership, respectively. _Exporter_ is a dummy variable that indicates if the firm is an exporting firm. _Manufacturing_ is a dummy variable that takes value 1 if the firm is in the manufacturing industry. _Financial development is_ measured as domestic credit to the private sector as a fraction of GDP. The _inflation rate_ is measured as the growth rate of the GDP deflator (annual). Results in columns (a), (c), and (d) are weighted by the inverse of the square root of the number of firms; those in column (b) are weighted by the inverse of (the square root of the number of firms  the inverse of the standard deviation of the Lerner Index). *** <u>p<0.01, ** p<0.05, * p<0.1</u>

|Variables||Acc|ess to finance||
|---|---|---|---|---|
||(a)|(b)|(c)|(d)|
|Lerner index|-0.434**|-0.387**|||
||[0.
each other’s savings. One third of clubs that was assigned to either ordinary or commitment savings accounts was randomly determined to be ineligible to receive raffle tickets (and was not told about the raffle). Another one third of clubs with savings accounts was randomly selected to have raffle tickets distributed privately. The final third of clubs with savings accounts was randomly selected for public distribution of raffle tickets. In these clubs, each participant’s name and the number of tickets he received was announced to everyone that attended the raffle meeting.

Because of the simple formula for determining the number of tickets, farmers in clubs where tickets were distributed publicly could easily estimate how much other members of the club had saved. Private distribution of tickets, though, did not reveal information about individuals’ account balances. The raffle scheme was explained to participants at the time of the baseline survey using a simulation. Members were first given hypothetical balances, and then given raffle tickets in a manner that corresponded to the distribution mechanism for the treatment condition to which the club was assigned. In clubs assigned to private distribution, members were called up one by one and given tickets in private (out of sight of other club members). In clubs assigned to public distribution, members were called up and their number of tickets was announced to the group.

Thus, the final design of the project includes seven treatment conditions: a pure control condition without savings account offers or raffles; ordinary savings accounts with no raffles, with private distribution of raffle tickets, and with public distribution of raffle tickets; and commitment savings accounts with no raffles, with private distribution of raffle tickets, and with public distribution of raffle tickets (see Table 2).

The randomization was carried out at the club level. The list of tobacco clubs in central Malawi (all of which had existing production loans with OIBM) was provided by OIBM in cooperation with the two tobacco buyer companies. Prior to randomization, treatment clubs were stratified by location<sup>11<
based on US inflation rates. On using GDP growth rates to proxy GNI growth rates, our calculations based on pre-conflict data suggest the the growth rates of GDP per capita in current USD and GNI per capita based on the Atlas Methodology on average are quite close (e.g. 1998 - 2007: 7 percent versus & percent; 1993-2007: 5 percent versus 4 percent).

The post-conflict scenarios (pessimistic, moderate and optimistic) reflect the voluntary mability case.
nearest health facility would be one option (see, for instance, Schmidt and Kedir [31] who estimate travel distance to nearest major urban area in Ethiopia, using GIS data). Access to food as proxied through wealth is another example, as is mothers’ education. The two latter examples are not great targeting metrics, as you need to observe each household, but due to their consistent high correlation to undernutrition, they are also included to gauge the SAE results merits.

To investigate if imputed woreda levels of stunting and underweight are a useful targeting metric, an OLS regression of z-scores is estimated in the EMDHS with addition of the SAE estimates. The regression includes the following potential targeting metrics as control variables: a) access to health services approximated by households utilizing any pre- or post-natal services [32], as physical access to health care could be used as a targeting mechanism through GIS analysis of traveling distance; b) access to clean water approximated through households utilizing protected water source; c) lack of access to toilet facilities; d) access to food and other necessities, and or knowledge approximated by wealth measured by the EMDHS asset index; and e)mothers’ education. Note that the alternative targeting mechanisms are allowed to vary for each household, which should give them a comparative advantage in correlation to the z-score, compared to imputed undernutrition levels at woreda level, which are averages across thousands of children.

Use of protected water sources, lack of access to toilet facilities, utilization of health services for both stunting (in height for age regressions) and underweight (in weight for age regressions) have the expected signs, with lack of access to toilets and access to health facilities being significantly related to the z-score (column 1 and 5, Table 2). Controlling for wealth and mothers’ education, however, render these variables insignificant with only wealth and mothers’ education remaining significant (columns 2,3,6 and 7, Table 2). Similar patterns have been observed in
|_Western Balkans_|55|65|63|
|---|---|---|---|
|Albania|28|38|40|
|Bosnia and Herzegovina|56|53|59|
|Kosovo|44|48|52|
|Republic of North Macedonia|74|72|77|
|Montenegro|50|60|68|
|Serbia|62|83|71|
|_Western Europe_|97|98|97|
|Austria|97|97|98|
|Belgium|96|98|99|
|France|97|97|94|
|Germany|98|99|99|
|Ireland|94|95|95|
|Luxembourg|95|96|99|
|Netherlands|99|99|100|
|United Kingdom|97|99|96|
|Total|69|77|81|

_Source_ : Global Findex database.

_Note_ : Table shows percentage of adults 15 and older who own an account at a financial institution or through a mobile money provider.

# Inequality in Account Ownership Is High in Parts of the Region

Globally, inequality in account ownership along gender, income, and other dimensions persists, despite continued growth in account ownership. Gender inequality is especially persistent. While 72 percent of men around the world have an account, just 65 percent of women do. The gender gap of 7 percentage points was also present in 2014 and 2011. In developing economies, the gender gap remained constant at 9 percentage points (figure 2).

The gap between richer and poorer households also failed to narrow. Among adults in the richest 60 percent of households within economies, 74 percent have an account. Among the poorest 40 percent, only 61 percent have an account. The global average gap between richer and poorer households stood at 13 percentage points in 2014 and 2017. Account ownership is also lower among young adults, the less educated, and people who are out of the
Rodrik found that ethno-linguistic fragmentation was not strongly linked to income inequality (r=0.13), but remained significant even after controlling for institutional quality. Hence the evidence suggests that ethnic cleavages matter in the ability to manage shocks even after the quality of institutions is controlled for. To test the robustness of this conclusion, he uses additional measure of social divisions, such as home language, the proportion of people who speak the country's official language at home, racial tension, lack of trust, and the Gini coefficient for land. The relation between these social conflict proxy measures and the growth differential between 1960-75 and 1975-89 was negative and significant. The correlations between the measures varied, thus each measure is capturing a different social characteristic. For example, he found that "no trust" is highly correlated with income inequality, but not with ethnic and linguistic measures.

Fedderke and Klitgaard (1998) found significant correlations between income inequality, degree of hierarchical steepness in society and degree of individualism (Hofstede cultural variables) and levels of per capital GDP, but not with growth. Knack and Keefer (1997) demonstrate that the Gini coefficient for income inequality is strongly associated with lower trust and civic co-operation. They also demonstrate that limits on executive power increase trust.

When income inequality is considered a proxy (albeit imperfect) of social cleavages and social exclusion, two findings are worth noting. First is the fact that measures of income inequality are quite stable, even during periods of rapid growth. A possible interpretation is that since the dominant social groups make policy and resource allocation decisions, these decisions reinforce their power and prosperity, and hence policy interventions do not result in major changes for those excluded. The second consistent finding is that countries with initial unequal income distributions experience slower growth. This finding is even stronger for initial unequal distribution of land in developing countries (Deininger and Squire, 1997). Birdsall and Sabot (1994) come to similar conclusions in comparing high inequality and low growth in Latin America and
materials and supplies can also be computed from most government financial accounts. Each of these measures captures a diffsrent aspect of the wage bill's claim on government resources. For example, the share of government wages in GDP, reported for many nations in Heller and Tait (1983), primarily serves as a proxy for the size of government in the economy. The other measures come closer to assessing the wage bill's claim on resources relative to other inputs.

When the wage bill begins to claim incr'asing amounts of expenditures, or revenues, this may signal changing factor proportions in government production. An extreme example is offered by Liberia where the wage bill is estimated to have jumped from 36% to 66% of revenues between 1977 and 1981. Reports of widespread shortages of materials throughou **t** he government followed these events.16 Shortages in complpmwntary inputs, however, may be endemic and not the result of sudden changes in the wage bill's claim on revenues. In the Central African Republic the wage bill/revenue ratio actually fell from 79% to 59% between 1976 and 1984, suggesting some increase in input use relative to labor. However, an independent study of at least one ministry, the Ministry of Rural Development, found 95% of its budget going to wage expenditures leaving a mere 1570 CFAF (roughly $6) per person per month to cover non-labor operating costs.17 Real factor proportions may, therefore, be poorly indexed by a simple expenditure ratio due both to changing relative prices and questionable data.

Rwanda provides another example. From 1979 to 1981 the ratio of wages and salaries to current revenues rose dramatically from 34% to 49%. By comparison, the ratio of the wage bill to current expenditures increased only 3%, from 51% to 54%, suggesting a less drastic picture of changing factor 22
Further, few studies have applied these skills measures to examine earnings differences between men and women.

This study aims to conduct a comparative analysis across seven low- and middle-income countries, examining the gender wage gap in these countries using data from the STEP Skills Measurement surveys. It is the first study to use measures of cognitive and noncognitive skills in wage decomposition analysis in this set of countries. The multi-dimensional conceptualization of skills in the STEP survey data offers a broader definition of human capital and thereby allows us to explore the potential role of other dimensions of human capital in understanding the gender wage gap.

> 4 Additionally, the structure of the labor market and women’s legal (property) rights have also been examined in gender wage discrimination research (Blau and Kahn, 2003; Doepke, Tertilt, and Voena, 2011).

> 5 Mueller and Plug (2006) hypothesize that personality can either directly (through productivity differences) or indirectly (through occupational segregation) affect gender differences in earnings. Further, personality differences can also affect occupational choices and thus lead to differences in wages.

4
realized some returns, and possibly also disinvested in response to adverse shocks and other factors.

# _B. Data_

The study uses two rounds of household data: a baseline survey was collected in 2010 before the project was implemented and a follow-up survey was collected in 2013, 18 months after households received the last transfers. In September 2010, a PMT (baseline) questionnaire was administered to all households in the pilot villages. The information collected is limited to the variables necessary to calculate the PMT formula.<sup>13</sup> In November 2013, approximately 18 months after the end of the program, 2,000 households were sampled to participate in a comprehensive follow-up survey. In each project village, 20 beneficiaries and 20 nonbeneficiaries were randomly sampled based on the baseline PMT data. The follow-up survey includes all the variables in the 2010 baseline questionnaire, and additional modules on investments (durables, local credit, household enterprises, and agriculture), education, health and consumption.

As further discussed below, the quasi-experimental empirical strategy employed to evaluate the impact of the cash transfer program on productive investments is largely driven by the structure of the data. Notably, most investment variables were collected only in the follow-up survey.

# _C. Empirical identification strategy_

We estimate the effect of cash transfers on household �’s investments and other outcomes of interest ��. Specifically, we consider a range of outcomes including livestock (stock at the follow-up survey in 2013, stock 12 months before, consumption and sales); housing quality (house material, access to water, toilets, etc.); durable goods (number and value of durable goods); engagement in household enterprises (HEs) (number of HEs, revenues, charges and profits, value of equipment); and agricultural investments (surface cultivated, quantity produced and yields, input spending, type of crops). These outcomes cover a wide range of potential investments. We also consider the participation in _tontines_ (number of _tontines_ , amount received,
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**Figure 5. Examples of 30-meter resolution Landsat 8–based LCLU map for northern Nigeria**

_Note:_ Google Earth high resolution imagery (left side) and the corresponding 30-meter resolution Landsat 8- based land-cover and land-use (LCLU) map for northern Nigeria (right side). Dark green: stable natural vegetation; light green: nonstable natural vegetation; light yellow: rain-fed agriculture; red: irrigation agriculture.

# **Results**

We created a 30-meter spatial resolution LCLU map focusing on the agricultural landscapes of northern Nigeria for the year 2015. Validating the map against an independent data set of ground points resulted in an overall accuracy of 0.91 and a kappa coefficient of 0.89. Accuracies were consistent throughout the classes, with individual user and producer accuracies above 0.82 and 0.85, respectively (table 2).
**4.1. Estimation method**

To measure the impact of disability of parents on child education, we assume the follow function: where _Y_ is an indicator of educational performance of children, _D_ is the dummy indicating the disability of parents of the children, and _X_ is a vector of control variables including individual and household characteristics which can affect educational performance. In this paper we use two education indicators. The first is school enrollment, which is equal 1 for children attending school, and 0 otherwise. The second is their academic record, which is equal to 1, 2 and 3 for „excellent‟ record, „good‟ record and „normal and bad‟ record, respectively.<sup>2</sup> When the dependent variable is school enrollment, we will use a probit model to estimate equation (1). A problem in non-linear functions is that the meaning of the coefficients is not clear. Thus to estimate the impact of parental disability on school

> 2 As mentioned, we combine pupils with the „bad‟ record and pupils with the „normal‟ record, since there are only around 1 percent of pupils having the „bad‟ record.

9
created by ending the PDMS. The most prominent of these are the Aid Transparency Index produced by the NGO “Publish What You Fund”<sup>21</sup> and the Quality of Official Development Assistance (QuODA) indexes produced by the Center for Global Development and the Brookings Institution (Birdsall and Kharas, 2011). These indicators borrow from the PD, but go beyond it to incorporate other aid transparency and quality indicators. They also differ from the PD in implicitly assigning full responsibility for quality of aid to donors. The PD indicators on use of country systems, included in the QuODA indexes, represent a mixture of donor and recipient country performance, consistent with the PD’s message that aid effectiveness is a shared responsibility. As mentioned above, recipient fixed effects statistically explain a slightly larger part of the variation in use of country PFM systems in our donor-recipient observations than do donor fixed effects. In measuring the quality of aid and ranking donors, it is therefore important to adjust for characteristics of the recipient countries in which donors are operating. Otherwise, donors with larger shares of their aid portfolios allocated to riskier countries are implicitly penalized in the rankings. The one-off ranking of donor performance by Knack, Rogers and Eubank (2011) includes such adjustments, but the more prominent and permanent QuODA project does not (despite being impressively designed and executed in most other respects), and thus risks creating the wrong incentives for donors.

> 21 See http://www.publishwhatyoufund.org/index/2013-index-changes/

31
countries. Almost all countries experience declines in aggregate demand for maize imports. However, since a country’s decision to import Tanzanian maize depends on both the substitution and the expansion effect in equation 1, a key factor is the change in relative prices of maize from Tanzania versus other exporters. For example, in the case of Tanzanian maize exports to Latin America and the Caribbean – an extremely minor destination for Tanzanian maize exports in the benchmark database – the substitution effect is positive and dominates the demand effect, and Latin America increases its imports of Tanzanian maize.

Let us now turn to the impacts of the Export Restriction Regime – reported in the second group of columns in Table 4 as well as the price effects reported in Table 5.

In the years when production is above trend and an export restriction is in place, the maize price may drop below the price that would have prevailed if there was no restriction. This can be seen in column III of Table 5. In the case of the years with positive shocks to Tanzanian maize production (Scenarios 1995 and 1971), the domestic price of maize falls even more under the export restriction. With the export restriction in place, no increases in exports are possible (columns VI and VII in Table 4). In the case of Scenario 1982, when Tanzanian maize production was below-trend, and maize exports decreased for all countries (since the modeling of the restriction allows for decreases from 2001 benchmark flows).

The intuition behind these results can be illustrated by Figure 1, which considers the case of a simple net-trade model (not modeled in this paper) and the strong restriction of an export ban. In the base period, domestic grain demand and domestic grain supply are characterized by D and S0. If the domestic price of grain is P0 then the quantity demanded is Q0, with there being no imports or exports of grain. PM and PX are the import and export parity prices respectively, within which prices normally vary in an open economy. In a