rafmacalaba/data-use-mentions-extended huggingface dataset:
and 4) are based on quarterly data for Ethiopia from 2005 to 2013. As discussed above, a 1 percent devaluation increases total exports by 0.5 percent. Disaggregation of exports uncovers that an additional depreciation induces a larger increase in exports in the manufacturing sector than in the other sectors of the economy. This is to be expected as depreciation expands manufacturing exports via resource reallocation in the export sector and particularly through incentivizing firms to enter into new export products and new markets. Conversely, real appreciation has a more negative impact on manufacturing than agricultural exports. This is in line with the well-established empirical finding that an overvalued currency is particularly damaging to the manufacturing industry. Despite the fact that the manufacturing sector in Ethiopia remains relatively nascent and might be currently less important than the agriculture and services sectors, this was also the case for many historically fast-growing countries when they first kick-started development. Expansion of manufacturing activities served as an important engine of their growth take-off.

# **5.3    The impact of devaluation on the current account balance**

In Ethiopia, most of the external current account deficit could be arrested by allowing for a significant real exchange rate depreciation (Portugal and Zildovic, 2014). Figure 11 shows that REER appreciation accounts for most of the current account deterioration over the period 2010-13. This is not too surprising given the previous finding that an appreciation of the REER has a fairly large negative and positive impacts on exports and imports, respectively. As mentioned earlier, the country’s rapidly expanding current account deficit is principally attributed to a worsening trade deficit, which is in turn on account of weak export performance and soaring imports of capital goods for public investment programs. The significant contribution of changes in the effective exchange rate to movements in the current account balance indicates that devaluation would be an important step toward reversing the downward spiral in the current account balance.

# **6.    Major Policy Trade-offs of Nominal Devaluation**

# **6.1    Will a devaluation of the birr cause inflation
Table A.1: Summary Statistics

||Mean|Median|standard Dev.|Minimum|Maximum|
|---|---|---|---|---|---|
|Real annual Wage (Rs/year)|57065|44871|48982|421|1576665|
|% Area Under LDO|0.097|0.048|0.122|0.002|0.626|
|Travel Time to Large City (hour)|2.479|1.883|2.348|0.078|15.182|
|Area LDO*Travel Time|0.350|0.087|0.617|0.001|4.947|
|Area of DSD (0000 sqkm)|0.015|0.011|0.013|0.002|0.087|
|% area >1000 feet of elevation|0.090|0.000|0.232|0.000|1.000|
|Gabalon's Malaria Endimicity Index|3.407|1.480|3.410|0.340|11.500|
|Plasmodium Vivax (000 cases)|0.040|0.009|0.107|0.000|0.959|
|Plasmodium Falciparum (000 cases)|0.013|0.004|0.030|0.000|0.368|
|% of lab. Forc. w. education>= primary|0.675|0.674|0.151|0.293|0.961|
|% of area under inland water bodies|0.066|0.000|0.157|0.000|0.960|
|Land quality (% Excellent )|0.040|0.000|0.148|0.000|1.000|
|Land
# Table 7: Average establishment traits by <u>gender of owner</u>

||Total<br>shipments|Total<br>employment|Fixed assets|Output per<br>employee|Assets per<br>employee|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|||A. Averag|es over 1994-20|05 period||
|Female-owned establishments|1614|1.35|909|810|635|
|Male-owned establishments|8802|2.26|2841|2818|1151|
|Female-to-male ratio|18%|60%|32%|29%|55%|
|||B. Trend in rati|o during the 199|4-2005 period||
|Ratio in 1994|29%|67%|31%|37%|38%|
|Ratio in 2000|17%|58%|34%|29%|58%|
|Ratio in 2005|15%|54%|31%|24%|53%|

Notes: Tabulations depict traits of establishments by gender of business owner taken from NSS.
2) The predictions of our model suggest that _unorganized groups_ will ascribe negative consequences to rising healthcare litigation if the following conditions are met: This set of conditions implies a potentially ambiguous effect of rising litigation effect on unorganized groups (the net effect will depend on whether the welfare gain from a reduction in rent-extraction is greater or smaller than the welfare gain from the increased power of lobbies).

If we treat patient/consumer groups as less organized than corporate lobbies, we notice indeed a statistically significant difference between consumers, on one hand, and regulators and producers, on the other hand, with consumers seeing the effects as being particularly positive (a mean of 3, compared to a mean of 1.87 – lower than neutral for the non-consumer group).

3) _Organized groups (lobbies)_ favor rising judicialization if: In general, lobby groups will have a positive perception of the effects of rising litigation as it reduces rent extraction of the public sector agency and allows their indirect influence on adjustments of the Benefit Plan.

One of the problems commonly associated with perception surveys is self-identification of incentives and stakes. In other words, lobbies might not publicly state what their true gains following litigation are, while having strong private beliefs about their sign and magnitude. Therefore, in order to correct the self-identification bias, we also include a control ( _Perception of Lobby Influence)_ that captures the overall assessment of the intensity with which “members of the private sector with commercial interests mobilize or help litigants” (question 13 of the survey). This variable is ordinal and ranges from 1 (never) to 5 (continuous mobilization efforts).

33
ing the merger of data from both waves and the exclusion of the Rohingya, the resulting dataset included 12,533 individuals in 2019 and 4,854 individuals in 2023.

2. Descriptive statistics for the variables employed in this study are in Table The analysis suggests that the population around the refugee camps is disproportionately young (on average 26 years of age) and mostly balanced in terms of gender composition (48 percent males). Additionally, household size is on average six individuals.

# **III.B.4 Individual-Level Outcomes**

Table 2, spanning Panels C to H, provides descriptive statistics for each of the individuallevel outcomes. These outcomes are grouped in several categories including: (i) adult and household welfare, (ii) labor market dynamics, (iii) health and education outcomes, (iv) safety perceptions, and (v) hosts’ attitudes toward the displaced population. A detailed B. definition of all the variables is included in Appendix _Adult and household welfare._ Adult’s welfare comprises measures of wealth, income, expenditures, consumption, and assistance. The impacts of camps are first assessed in a wealth index constructed as a composite measure of a household’s cumulative living standards the formula from the and Health The com- following Demographic Surveys.<sup>8</sup> analysis prehends the effects of camps on the total wealth index and on each subcomponent, including the proportion of service access, the proportion of adequate shelter materials, and the proportion of assets consumed. Second, the effects of refugee camps are assessed on total income and expenditures (including food and non-food). Third, the analysis evaluates outcomes related to food consumption, including: food consumption variety, share of self-produced, the share of food products for which aid was received, and indicator variables for bartering food. Fourth, since the Rohingya are completely dependent on humanitarian aid, the effects of camps are assessed on aid and assistance, including the following variables: receiving vouchers, food
with the sole exception of Venezuela. Of the 33 PCRs which collect ratings, 26 distribute these ratings back to the financial institutions. As public credit registries become tools to monitor and analyze forward-looking provisions, they may also be used to analyze capital.<sup>20</sup> Many public credit registries, including the world’s oldest PCR in Germany, were established in response to a banking crisis prompted by defaults of large borrowers. Monitoring credit risk concentrations and informing individual banks of these risks remains a key objective for most public credit registries. At the same time, in many developing countries improving access to credit also has high priority. Information from the public credit registry can serve both objectives by allowing banks to better assess the creditworthiness of borrowers at the same time that supervisors use the data to monitor for risks. However, there are tensions which are created by these different objectives and which can affect the design of the public registry and the data collected.

One of these tensions concerns the size of loans to be included in the PCR. From a lender’s perspective it would be preferable to have data available on all loans regardless of size, since credit reports are particularly important for consumer and small business lending. Supervisors, however, may prefer to focus the database on large loans which pose a systemic threat. Also, by establishing a minimum loan size for inclusion in the PCR, supervisors can drastically reduce the size of the database, making it easier to manage and to enforce quality standards.<sup>21</sup> Figure 1 provides information on the minimum loan sizes included in PCRs by region measured by the ratio of the cutoff amount to GNI per capita. In the World Bank survey 23 countries did not have any minimum loan size or had a very low minimum loan size indicating the important role these registries play as a provider of information to financial institutions. Virtually all

> 19 In some PCRs where the rating is on the credit line, collateral may also affect the rating
mainly in the poor areas.

In order to draw a picture of where each country stands, we use the 2011 NUTS 3 level data for this analysis. The CAP funds distribution data are from the DG AGRI Clearance Audit Trail System database (CATS), provided by the European Commission. The 2010 agricultural indicators from the EU Farm Structure Survey are used to represent the regional agricultural situations. Since the German agricultural data are only available at the NUTS 2 level, they are used as proxies for the NUTS 3 level agricultural variables.

22
.|
|||50 if an English translation/version for NSO’s website is available but<br>does not reasonably reflect the local language(s)and/ or not available<br>for other pages besides the landing page.<br>100 if English translation/version for NSO’s website is available,<br>reasonably reflects the local language and available for other pages<br>besides the landing page.|
||Micro data tab/dashboard/library|0 if no micro data tab/dashboard/library is available on NSO’s website<br>100 if micro data tab/dashboard/libraryis available on NSO’s website|
||Survey calendar|0 if no survey calendar is available on NSO’s website<br>50 if survey calendar is available on NSO’s website but not up to date.|
|||100 if surveycalendar is available on NSO’s website and upto date.|

# 5.2 Data openness element

In section 3, we describe and present results of an exercise that examines both the website of NSOs and international repositories such as web-portals of the MICS and DHS surveys and microdata libraries maintained by the World Bank (WB), International Household Survey Network (IHSN), IPUMS and Eurostat to determine the degree of accessibility of microdata. For each data 23
11,164|12,051|
|**Mean(with censoring)**|10,863|.|10,191|
|**P1**|2,968|322|1,364|
|**P5**|5,223|1,374|5,531|
|**P10**|5,741|2,321|5,531|
|**P25**|7,419|5,531|6,186|
|**P50**|9,539|7,813|8,644|
|**P75**|13,779|13,376|13,862|
|**P90**|18,019|21,446|21,823|
|**P95**|22,258|28,416|29,108|
|**P99**|29,678|57,253|56,975|

_Note: To limit the impact of the underrepresentation of top incomes in survey data on the estimate of mean tax compliance, we censor the distribution of survey income (“true income”) and imputed tax income (“reported income”) at the 99_<sup>_th_</sup> _percentile of the distribution of survey income._ _Source: Observed and imputed values based on EU-SILC and tax admin data. P1-P99 refers to the first and 99_<sup>_th_</sup> _income percentiles based on the uncensored distribution._ 11 Theoretically, we can imagine a scenario in which the distribution of imputed tax income is completely different from the distribution of income observed in this survey. In such a case, the outcomes of the imputation would be difficult to interpret.

18
data from them, including weekly data on quality, output, and inventories. They find that adopting these management practices raised productivity by 17% in the first year through improved quality and efficiency and reduced inventory, and some evidence that within three years this led to the opening of more production plants. The results thus show that in large firms, at least, changing management practices can lead to substantial improvements in firm performance. However, they can only indirectly estimate the changes in profits from this effort.

# **6. What We Do Not Know**

There are now a range of studies which look a variety of business training programs, and examine impacts on business practices, business outcomes, and potentially outcomes for microfinance institutions. However, there are still a number of open questions that existing studies have barely touched upon, but that are important for thinking about the case for policy action to support business training.

# _Who does training help most?_

Our discussion above has touched on heterogeneity in outcomes by gender of the owner and, to some extent across studies, by firm size. Several studies have examined heterogeneity in other dimensions such as owner’s education and baseline business skill levels, business sector, and interest in training. However, the low power of most studies to find average effects for the full sample also means there is relatively little power for examining the heterogeneity of effects.

As a result, who benefits most from training (or which types of training are most suitable for which types of firms) is still an open question. On one hand, poor subsistence firms whose owners are only running the business because they cannot find a wage job may have very low business skills. This suggests that it should be relatively easy to make improvements, but perhaps the owners will be less interested or able to implement the practices taught, or perhaps these practices only start having an effect as businesses reach a larger scale. There is much talk of targeting gazelles- firms that grow rapidly – but
in agricultural services (such as sales of fertilizers and tools and farm maintenance).

How can these effects be estimated? With a household survey database containing information on expenditures and quantities of agricultural goods, a naive model to estimate the responses of wages to changes in agricultural prices would be to run a regression of wages on unit values. Let ahc be the agricultural wage income of household h living in region (cluster) c. Let ln vc<sup>gbe the log unit values ofgood greported by households in cluster c.A</sup> simple model would be where mhc is a vector of controls such as gender, age and marital status of the household head, his/her education, household size and demographic composition, and time and regional dummies.

There are three concerns with a regression model such as (1): endogeneity of unit values, bias due to proxy variables, and measurement error. Endogeneity may arise because households simultaneously choose quantity and quality. Therefore, unit values are not a perfect measure of prices. Even when unit values are a good proxy for prices, the model may estimate the vector γ consistently, but λ inconsistently (i.e., the proxy bias). Measurement error arises if there are inaccurate responses, mainly on quantities consumed. In all these cases, OLS estimation of (1) will lead to inconsistent estimates of the wage agricultural income price-elasticities. The joint estimator developed in this section takes care of all these problems.

In a general equilibrium model with wage and supply responses, there may be additional problems in the estimation of own- and cross-price elasticities using standard empirical models of demand (such as those discussed in Deaton and Muellbauer, 1980a). Concretely, when a fraction of household income responds to price changes, the elasticities will be inconsistently estimated. To see this, let household preferences over agricultural and non-agricultural goods be defined by a utility function uh = uh(ch), where ch is a vector of consumption. Utility is maximized
<br>– Demograph|Yes<br>ic and Health Su|Yes<br>rvey (DHS) – Egy|Yes<br>pt 1992, 1993,|Yes<br>2005, 2008, 20|Yes<br>14.|

Source: Authors calculations – Demographic and Health Survey (DHS) – Egypt 1992, 1993, 2005, 2008, 2014. Notes: The dependent variable is a binary indicator for whether the interviewed woman is currently working. Columns 1 to 3 report results for a restricted period representing 13 years around the reform year between 1992 and 2005. Columns 4 to 6 report results for the full covered period between 1992 and 2014. The estimation samples are restricted to married women whose youngest child's age is around 5 years window around the custody cutoff and whom did not have children after the reform. The custody cutoffs are set to 10 for the first window (1992 to 2005), and to 15 for the second window to account for the custody. Wild bootstrap (WB) p-value using one thousand replications. *p<0.10, **p<0.05, ***p<0.01.

In the case of The Arab Republic of Egypt, in the first post-reform window period, between 1992 and 2005, the effect is positive for the sample of married mothers of children aged below the age-cutoff for child custody (10 years for boys and 12 for girls), but it is not statistically significant. While multiple controls for age, time trends and socio-economic position of the woman and her spouse are included, it seems that the variable corresponding to the World Bank’s WBLaw_index has the most positive effect during this first period. These results are also in line with Hassani-Nezhad and Sjögren (2014). The increase in women’s labor outcomes likely happened through a power-shift effect (described above), meaning that married women sought their employment increase thanks to gaining higher bargaining24
facilitate the travel of administration officials and military troops from the capital city to other regions of the country, and the movement of people and goods between different ethnic homelands. Leveraging this premise for our analysis, we construct hypothetical roads linking ancestral ethnic homelands from the capital of each country to instrument for the actual road network. We use a least-cost algorithm, assuming that land altitude is the main geographic determinant of construction costs for roads. The exclusion restriction is likely to be met because these hypothetical roads are conditional only on geographic costs of construction, not demand-side factors. Moreover, the set of ethnic groups and homelands in each country is exogenously determined, given the arbitrary partition of Africa.

Estimates suggest positive and significant employment effects of proximity to roads, proximity to electricity grids, and the interaction of the two. For people in locations sufficiently close to an electric grid, bringing the main road 10 km closer increases the probability of being employed by between 2.5 percentage points (based on the LSMS sample) and 26 percentage points (based on the DHS sample). Greater road access also increases the number of hours worked by 1.2 hours per week (DHS sample). For locations sufficiently close to a main road, bringing an electric grid 10 km closer increases the likelihood of being employed by between 6 percentage points (LSMS sample) and 8.4 percentage points (DHS sample). Greater proximity to an electric grid also raises the number of hours worked by 12.5 hours per week (DHS sample).

Our estimates also reveal complementarity. We show that bringing an electricity grid closer to a location increases the positive marginal impact of bringing a main road closer; and, in turn, bringing a main road closer to a location increases the positive marginal impact of bringing an electric grid closer. Specifically, we find that bringing an electric grid 10 km closer increases the probability of working by more than 8.4 percentage points in locations that have direct road access, but by only 4
already so severe in those countries before the pandemic. This result aligns with the findings in Samiego et al. (2022), who document greater losses in human capital accumulation in developed countries than in developing countries. But children with the weakest foundational literacy before school closures are likely to fall further behind, especially if their families are not literate. Therefore, though the absolute or relative increase in Learning Poverty may not be large in the regions with the highest rates before the pandemic, the many children already below minimum proficiency in those regions may fall further behind. This phenomenon is examined further in simulation results using the Learning Poverty Gap and Severity simulations,<sup>20</sup> described below.

The Learning Poverty estimates treat all students below the minimum proficiency threshold as being equally learning deprived, even though countries with similar Learning Poverty rates have different learning levels among those who are learning poor. Changes in the Learning Poverty rate also do not capture changes in

> 20 The **Learning Poverty Gap** measure captures the depth of learning deprivation and can distinguish students that have different levels of learning deprivation. However, this measure assumes that the effort required to improve an extremely learning-deprived and a moderately learning-deprived student are equivalent. The **Learning Poverty Severity** measure extends the concept of the Learning Poverty Gap by incorporating the student’s initial level of learning and gives greater importance (weight) to students that are in greater learning deprivation. See Azevedo (2020) for a detailed discussion on the concepts of Learning Poverty Gap and Learning Poverty Severity.

10
industries in the 2000 Census, we classi...ed business sectors into male-dominated industries, identi...ed as construction, repair services, manufacturing, and shoe making and repair; femaledominated industries, identi...ed as hair and beauty care, and food and restaurant sales; and mixed industries, identi...ed as trade and retail, and sewing and tailoring. These industries cover the vast majority of the industries in which the self-employed work in Ghana. The 4.6 percent of those screened who worked in other industries such as communication services, pharmacy, photography, ...shing, and agriculture were not included in the sample.

Our aim was then to arrive at a sample of roughly 900 baseline ...rms strati...ed by gender and sector. In order to minimize the spillovers from the treatments to be carried out, we limited the sample from each EA to no more than 5 males in male-dominated and 5 males in mixed industries, and no more than 3 females in female-dominated and 3 females in mixed industries. We also ensured that only one individual was chosen from any given household. This resulted in an initial sample of 907 ...rms, consisting of 538 females and 369 males. A baseline survey of these ...rms was conducted in October and November 2008. The ...rm owners were asked for details of both their ...rm and their household.

A second pre-treatment survey of these ...rms was conducted in February 2009. The purpose of a second pre-treatment round was to eliminate ...rms most likely to attrit. In particular, 55 of the initial 907 ...rms could not be found on at least three attempts, 15 ...rm owners refused this second round, 24 ...rm owners were no longer operating a business, and 20 ...rms that did not provide details on their ...rm pro...ts,