rafmacalaba/data-use-mentions-extended huggingface dataset:
31 match those in the developed economy – which are lower. See the SW panel. Changing the returns has less impact than changing the quantity of schooling alone, increasing output to 67 _._ 7 percent of the developed economy. Again, a portion of this is due to the experience premium. If we remove the premium, _pcGDP_ only rises to 63 _._ 6 percent.

The SE panel shows what happens if we increase the returns to experience only. This increases _pcGDP_ to 66 _._ 4 percent. This is similar to the impact of increasing the quantity of education. The panel also compares the impact from changing the returns to experience to changing the quantity of education (NE panel): the impact on income of changing the returns to experience is faster. This is notable as significant reform to the schooling system is likely costly and slow. On the other hand, if reforms that improve the returns to experience are institutional, costs may be lower.

**Robustness.** Web Appx. Section A.7 shows that our quantitative results hold when we perform a number of robustness experiments with different parameter values. For example, the results are robust to alterations in demographics, retirement ages, labor market conditions or sets of returns. Furthermore, the findings are unchanged if specific types of workers or countries are excluded, more prominence is given to youth unemployment, experience is allowed to be accumulated before age 18, human capital spillovers are accounted for, or the sample is divided into three income groups (low, middle- and high-income countries). Web Appx. Section A.8 discusses the longrun welfare impacts of reforms. These impacts depend on the chosen discount rate, especially as reforms to experience have a faster impact than reforms to education. The same section then describes how our results can shed light on the long-run impact of reforms to the retirement age (since they affect the period of time over which the returns to experience or education pay off) or reforms that increase
MENA is a puzzling case. It has the highest inequality and second highest level of corruption, and it is, on average, not procyclical despite its resource wealth. The level of credit to the private sector is the second highest, suggesting that financial depth may be compensating the negative impact of the other two factors. Maybe the extreme cases of the United Arab Emirates, Saudi Arabia, and Kuwait, that are strongly countercyclical drive the results for the region. This result needs further analysis, probably by introducing the level of international reserves as a control variable, which provides a cushion that allows the policy to be managed countercyclically.

# **Across countries**

We compute individual coefficients, and individual standard errors for the 38 SSA countries, which are available upon request. Using such a methodology, we find five to six countries which are countercyclical over the whole period: Guinea, Lesotho and Democratic Republic of Congo, Mauritius, South Africa, and Botswana.<sup>17</sup> Based on the individual country-coefficients, we examine whether procyclicality is different in the sample of SSA countries for resource-dependent economies.<sup>18</sup> We find that procyclical bias is higher in resource-rich or resource-dependent economies. The average coefficient for SSA economies is .31; for resource-dependent economies, it is .33; and for the other SSA economies, it is .27 (Figure D.1). The comparison between resource-dependent (and resource-rich) countries in SSA and resourcedependent countries in other regions shows that the procyclicality bias is bigger in SSA. While the SSA resource-dependent economies have a procyclicality coefficient of .33 the analogous category in other regions has .23. However, the resource-rich countries show an even larger contrast: the SSA resourcerich show a procyclicality coefficient of .33 while those of other regions register .08. This is due to the one-to-one mapping between the two groups of countries (resource-rich and resource-dependent), while in other regions the correspondence is not as clear (Figure D.
to rely so much on external finance. These results again point to limitations in external finance access in Egypt, even for this very selective sample of largest listed firms.

We find little or no effect of cash flows or net income on financial savings, while there is a clear positive relationship between net income and cash stocks (firms with higher cash flows are able to hold more cash).

# **8.2 Determinants of Investment**

In Table 7 we report similar regressions for investment to capital ratios. We find that larger firms make more investment into physical assets. This result parallels what we found earlier in the ICA data. Firm size is a dominant determinant of investment in our data as the regression with firm size produces much larger R squared.

We also find that sales growth is positively related to investment as sales growth is one proxy for good growth opportunities. It is expected that firms with good growth opportunities will make more investment. However, if we include firm size in this regression, sales growth becomes not significant (the p-value becomes close to 15%, which could be considered marginally significant).

The interest rate and net income both are positive and marginally significant (at about 15%). We do not find any other significant determinants of investment in our data.

Finally, we investigate how physical savings are related to financial savings over time. As discussed above, total savings can be allocated either toward physical savings or financial savings. Therefore, the contemporaneous relationship between financial savings and physical savings is likely to be negative – i.e. if firm spends money on new machinery, there is less money to save as financial assets. There is likely to be a decrease in cash stocks as well, since investment into PPE uses up liquid assets that firm has accumulated.

However, one of the key reasons firms accumulate financial savings is to invest in physical assets in the future. Therefore, overtime we should find that an increase in financial savings in one year is likely to
# Figure 7: Average Outcomes by Group

<!-- Start of picture text --> Legally married [18+) Economically active [18-65]<br>Formal employment [18-65] Unemployment [18-65]<br>.8<br>.6<br>.6<br>.4<br>.4<br>.2<br>.2<br>0 0<br>Sanavirón Huarpe Comechingón Pampa Rankulche Charrúa Diaguita-Calchaquí Otros Tehuelche Querandí Maimará Atacama Chané Quechua Ona Mapuche Aymara Tupí Guaraní Guaraní Kolla Omaguaca Lule Tonocoté Mocoví Vilela Toba-Qom Ava Guaraní Chorote Chulupí Wichí Mbyá Guaraní Tapiete Pilagá Ona Querandí Maimará Rankulche Aymara Tehuelche Comechingón Pampa Quechua Charrúa Mapuche Guaraní Otros Lule Diaguita-Calchaquí Huarpe Sanavirón Tupí Guaraní Atacama Omaguaca Mocoví Toba-Qom Kolla Vilela Ava Guaraní Chulupí Tapiete Chané Mbyá Guaraní Wichí Chorote Pilagá Tonocoté<br>.8 .15<br>.6<br>.1<br>.4<br>.05<br>.2<br>0 0<br>Querandí Tehuelche Charrúa Sanavirón Comechingón Pampa Diaguita-Calchaquí Mapuche Rankulche Ona Tupí Guaraní Huarpe Chané Lule Tapiete Kolla Atacama Guaraní Otros Maimará Toba-Qom Tonocoté Quechua Wichí Chulupí Omaguaca Mocoví Pilagá Mbyá Guaraní Aymara Chorote Ava Guaraní Vilela Chorote Tupí Guaraní Chané Ona Wichí Ava Guaraní Toba-Qom Huarpe Pilagá Lule Rankulche Pampa Mapuche Guaraní Tehuelche Diaguita-Calchaquí Mocoví Quechua Tonocoté Querandí Sanavirón Kolla Charrúa Comechingón Tapiete Chulupí Otros Atacama Aymara Maimará Omaguaca Mbyá Guaraní Vilela<br><!-- End of picture text --> _Sources:_ REDATAM INDEC Census Argentina 2010 (Instituto Nacional de Estad ́ıstica y Censos, 2010). _Notes:_ Solid horizontal lines show average for Natives, dashed horizontal lines show the average for non-Natives, and the dotted horizontal lines show the first and ninth decile by department.
types of paid-extension.

> The debate on public vs. private extension continues to rage among experts in the field. The debate includes issues such as should extension be publicly funded? Who should pay and how much should be paid for extension services, and which segments in the farming population can afford to pay for extension? (See Cary, 1993; Schwartz, 1994; and Rivera, 1996a,b). Although significant, this debate is beyond the scope of this paper.

> 2The purpose of this paper is not to evaluate the paid-extension performance in Nicaragua but rather to illustrate, using available data from Nicaragua, the application of the indicators developed in the paper.
# **3.3** **<u>Children’s Education and Reduction in Gender Disparity</u>**

Human capital investment is another cornerstone of the success of microcredit programs. Earlier studies show that microcredit borrowing, especially by women, has a positive effect on children’s schooling (Pitt and Khandker 1998). Earlier findings also suggest that microcredit improves health and nutrition (Pitt, Khandker, Chowdhury, and Millimet 2003) and empowers women (Pitt, Khandker, and Cartwright 2006), thereby playing a pivotal role in improving social and human development. The question is whether these benefits are sustained over time.

In terms of trends in the rate of children’s school enrollment over time, we find that, for both participants and non-participants in microcredit programs, there is incremental growth in schooling investment for both boys and girls. However, as earlier research has shown, the schooling gains are higher for girls than for boys among microcredit program participants. Among participants, the school participation rate for girls (ages 5–18) was 71 percent in 2010/11, about 20 percent higher than in 1991/92; while the corresponding rate for boys was 69.6 percent in 2010/11, 14.7 percent higher than in 1991/92. Among non-participants, by contrast, the school participation rate for girls was similar to that of boys in 1991/92; however, boys gained 5.8 percent more than girls over time (70.1 percent versus 64.3 percent) (Table 4). Therefore, we can say that microcredit expansion has helped to reduce gender disparity in rural Bangladesh, at least in terms of children’s education. This finding is consistent with earlier ones (e.g., Pitt et al. 2006), which showed that microcredit, by helping to improve women’s empowerment, has in turn contributed to the social upliftment of girls in society.

# **3.4*
>12</sup> Meanwhile, population aging has contributed to a decline in labor force participation, which tends to be lower among older workers (Maestas, Mullen, and Powell 2016).

In the past five decades, growth was supported by rapidly growing working-age populations—until the mid-1980s in advanced economies and around 2010 in EMDEs (Figure 7). Since 2000, countries with rising working-age population shares accounted for half of global output growth and three-quarters of global GDP levels. With the retirement of the baby boom generation and lower fertility rates, demographic trends have turned less favorable to growth and will continue to do so over the next decade (Kose and Ohnsorge 2019). In advanced economies, the working-age share of the population is set to

> 11 The benefits from a rising working-age population have been particularly pronounced in Asia (Bloom et al. 2010; Bloom et al. 2007; Aiyar and Mody 2011). Demographic change over the period 1960-1995 for 86 countries has been estimated to have accounted for approximately 20 percent of per capita output growth, and more in Asia and Europe (Kelly and Schmidt 2005). Cruz and Ahmed (2016) estimated that a 1-percentage-point increase in the working-age population share was associated with a more-than-proportional increase in GDP per capita growth in 160 countries over 1960-2010. Other studies of the relationship between demographics and growth include Higgins and Williamson (1997); Eastwood and Lipton (2011); and Kelley and Schmidt (1995 and 2007).

> 12 These factors have been explored in Mincer (1962); Goldin (1994); Hill (1983); Killingsworth and Heckman (1986); and Connelly (1992).

8
We first classify the images as described in Muhebwa et al. (2023) to then find a parameter that describes the decay of road quality over time in a particular region of the DRC. Each image is given predicted quality labels from a 2-class and 5-class classifier. We encountered two main problems. First, extensive road quality data is not available to train the machine learning models. We had to use road quality data from other African contexts (Kenya and Liberia) to train the model and then use satellite imagery for the DRC (Muhebwa et al. 2023).

Second, getting remote sensing data for the entire sample period and road network in DRC is not possible due to the limited image availability. We, therefore, focus on estimating a road discount factor from three large road projects taken from our database that were completed after 2010. These projects were large road rehabilitation projects in conflictridden regions in eastern DRC. Muhebwa et al. (2023) provides a road quality classification for 7,121 images of project roads.<sup>5</sup> Each image is a 256 by 256-pixel satellite image at 60 cm/px resolution. We, therefore, obtain a road quality class index for short road segments of 150m by 150m based on a classifier. The classifier was trained on groupings based on the International Roughness Index (Sayers et al. 1986), and we use these evaluations to classify

> 5We start with 55,719 images from the project areas. To clean the data, we first filtered out any roads that were not the three main project roads. This leaves 7,121 images. Of these images, 6,061 are classified as ‘bad’ roads by the binary classifier.

13
|Sudan|IDP<br>Profiling<br>Survey<br>(2018)|Stratified cluster<br>sampling|Abu Shouk and El Salam<br>camps, and neighboring and<br>non-neighboring Al-Fashir|IDPs and host<br>communities|17,645<br>(95.2%)|
|---|---|---|---|---|---|
|South<br>Sudan|High<br>Frequency<br>Survey<br>Wave 4<br>(2017)|Stratified two-<br>stage cluster<br>design|Urban areas of seven of the<br>ten pre-war states (Western<br>Equatoria, Central<br>Equatoria, Eastern<br>Equatoria, Northern Bahr-<br>El-Ghazl, Western Bahr-El-<br>Ghazal, Warrap, Lakes<br>state).|IDPs and host<br>communities|4,554<br>(92.8%)|

Data was available for all selected MPI indicators in every country except South Sudan, where the question on access to a bank account was not included in the survey. The MPI for South Sudan is thus constructed without this indicator, and the remaining indicators in the financial security dimension are re-weighted accordingly, receiving 1/6 of the weight each.

# **5. Findings**

## **5.1 Key findings**

The focus of this study is to analyze the level and composition of multidimensional poverty among forcibly displaced communities across countries and compared to host communities, respecting each of the survey designs. Table 3 shows the headline figures for the MPI disaggregated by displacement status. Overall, the highest level of poverty can be found in the Somalia population covered, where over 63% of IDPs and nearly 45% of the host community are identified as multidimensionally poor, while the lowest overall poverty is
Provided that key identifying assumptions are met,<sup>15</sup> the size of the estimated demand-robust confidence interval on the vaccination rate specifies the range of values a survey-based estimate of vaccine take-up that is free from experimenter demand would fall into.

Finally, we can compare the point estimate of vaccine uptake under the standard questionnaire design (T1), along with its demand-robust confidence interval, to the vaccination rate as reported in administrative data sources. The larger the demand-robust confidence interval on our surveybased estimate of vaccine uptake, the larger the potential importance of enumerator demand in explaining misalignment between both data sources. Statistically significant differences between the administrative data and our survey-based estimate, with demand-robust confidence intervals, would suggest that enumerator demand effects alone cannot account for the difference between our survey-based estimates and the administrative data.

# 4 Results

## **4.1 Estimated vaccine coverage in survey and administrative data**

We find substantial misalignment between survey-based estimates and administrative records of COVID-19 vaccine coverage. Comparing phone survey estimates to administrative coverage figures from the same time<sup>16</sup> in a sample of 36 LMICs, we find survey estimates that suggest coverage rates that are between 11.5 percentage points lower and 37.1 percentage points higher than those reported in administrative sources (Figure 1). Vaccination rates are statistically different in 28 out of 36 countries (46 out of 57 survey waves). In 21 out of these 28 countries (35 survey waves), survey estimates suggest higher vaccine coverage than is reported in administrative data (in 7 countries survey estimates are lower). On average, survey estimates exceed the administrative data by 47% when excluding extreme positive outliers occurring below 5% reported administrative coverage. However, this pattern displays noticeable regional variation: While estimated survey rates are fairly aligned with administrative figures in Latin America and the Caribbean (LAC), they systematically exceed administrative reports by large margins in Sub-Saharan Africa.

> 15 Most notably, monotonicity implies that negative reinforcement should
|1,149|1,149|1,149|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Model with fixed effects by the municipality and by year for 2005, 2010, and 2014, also using the FAIS formulated as the instrumental variable. The nonmonetary dimensions are expressed in 0–100, understanding 100 as 100% of coverage. Include observation from semiurban and rural municipalities only, defined as municipalities with fewer than 15,000 inhabitants.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total occupied and active economic units in each municipality.

Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 38
grew twice as fast as credit to corporations<sup>75</sup> and Greece was the country with the fastest increase in construction. In 2007, Greece and Portugal had around 45% of the stock of total credit with households, with Ireland and Spain close behind.

**Lesson 10a: Structural reforms before and after crises have different effects.**

**Lesson 10b: Wage cuts in a downturn and during fiscal consolidation may not promote growth recovery (particularly when employment also falls).**

_<u>While the short run effects of negative shocks may be equally strong in countries with more and less flexible economic systems and strong institutions (see Section 1), the recovery is faster in the former. This is because resources reallocate faster and creditor perceptions are more favorable, and it often takes time for reforms to have a net positive impact on output. Structural reforms to make markets more flexible during or in the immediate aftermath of a downturn can have unexpected (even negative) effects.</u>_ _<u>Falling real wages during fiscal consolidation and household deleveraging, aggravate declines in domestic demand and worsen the downturn.</u>_

**Figure 14.a: Global competitiveness ranking**

**Figure 14.b: Resolving insolvency ranking**

<!-- Start of picture text --> 0<br>20<br>40<br>60<br>80<br>100<br>Greece Ireland Portugal Spain<br>Source: World Economic Forum,<br>Global Competitiveness Reports.<br>100<br>95<br>90<br>85<br>80<br>75<br>70<br>65<br>60<br>55<br>50<br>Greece Ireland Portugal Spain<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>2000 2002 2003 2004
main towns, with Addis Ababa hosting over 61 percent of plants in 1996, while the other four towns being Dire Dawa, Bahir Dar, Hawassa and Nazreth add another 10 percent to total plant count. By 2009, the importance of the capital city declined to less than 40 percent, while the share of top five locations dropped to 51 percent during the same period. Indeed, between 2000 and 2011, a number of cities have managed to expand their manufacturing base – in particular, Adwa, Sebeta, Debreziet, Awassa, and Mekele, witnessed an increase in the share of the total manufacturing plants from 12.2 percent in 2000 to 22.2 percent in 2011 (Mukim, 2016).

# **_Defining High-Growth Firms_**

The literature on high growth firms considers various indicators and methods to identify HGFs. The OECD definition, for example, imposes a percentage cut-off on growth rate, typically on a three-year period. In practice, OECD defines HGFs as those firms that experience an annualized compound growth rate in employment by at least 20 percent over a three-year period. A criticism of this definition is that it is more likely to assign HGF status to smaller firms that start from a lower base. This shortcoming is addressed by the “Birch Index”, which scales the relative growth rate by absolute change in employment to reflect the differences in initial size. In the context of Ethiopian data that includes plants with a threshold of 10 employees, using the OECD definition does not seem to be problematic. Nevertheless, the paper will also consider robustness checks with the Birch qualified HGFs.

In terms of the choice of indicators, employment is the chosen metric for two reasons. First, from a development perspective, factory employment is often a desirable economic outcome at the household level and is associated with income growth and development. Second, given the possibility of negative

> 24 In practice, however, some plant-year observations have recorded fewer than ten workers. This could perhaps be because once an establishment crosses the ten-employee
<br>-1.0<br>-1.5<br>-2.0<br>Fastest-growing Slowest-growing<br>F. Distribution of poverty<br>Percent<br>EAP ECA LAC MNA SAR SSA<br>100<br>80<br>60<br>40<br>20<br>0<br>1999 2008 2015<br><!-- End of picture text --> Source: World Bank PovcalNet.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa. A.B.F. Regional aggregation based on 2011 Purchasing Power Parity (PPP) and $1.90 per day poverty line. C. Shared prosperity is the average growth in household per capita income or consumption of the bottom 40 percent between 2010 and 2015. The shared prosperity premium measures the difference between income growth of the poorest 40 percent of households and the average household income growth.

D. Chart shows unweighted average of the average annual change in poverty headcount rates between two poverty estimates in each group of countries. “Fastest-growing” includes the quartile of EMDE country-year pairs with the highest average annual real GDP per capita growth between two poverty estimates; “slowest-growing” includes the quartile with the slowest average annual real GDP per capita growth. Based on data available from 1981.