rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Technical Efficiency**

Figure 3 shows the production frontier as estimated through the SFA for ECA for 2000 and 2016.<sup>10</sup> As it shows, not only did capital stock rise, but the frontier shifted upward over time.

<!-- Start of picture text --> 130<br>110<br>90<br>70<br>50<br>30<br>0 100 200 300 400 500 600<br>Capital per worker<br>Production frontier in 2000 Production frontier in 2016<br>Output per worker<br><!-- End of picture text --> Table 6 reports regression results for the baseline specification for the efficiency model.

First, the institutional environment of Capital per worker the country, which depends on the efficiency and the behavior of both Production frontier in 2000 Production frontier in 2016 public and private actors (proxied by the index of government effectiveness), is a **Figure 3. Production Frontier for ECA** determinant of productivity, robust across different model specifications and to the inclusion of more countries in the sample (Section 4; Annex 3).

Infrastructure, captured by the extent of energy consumption and Internet adoption or by infrastructure quality (WEF), is also critical for productivity, especially in the extended model (Section 4; Annex 3).

The results show that also the quality of education matters for the efficiency level at which an economy operates, as good health and education outcomes enhance the efficiency and productivity of each individual worker. The results are robust to enlargement of the sample (Section 4; Annex 3). Human capital has a central role in endogenous growth theory, either as an accumulative factor along with physical capital that affects income per capita, or by facilitating the production or absorption of new technologies. Several papers, starting with Barro (1997), have provided robust evidence of this positive link. More recently, researchers have instead shown
fertilizer response functions for maize based on agronomic field experiments in Kenya, indicates that achieving technical efficiency could improve average yields by about 60 percent.

Low agronomic efficiency results from poor soil and moisture conditions, which can be remedied by adding organic sources of nitrogen. Fertilizers cannot profitably increase crop yields if soils are severely degraded. Recent research in Kenya has confirmed that removing fertilizer supply constraints will encourage use by wealthier farmers who cultivate better soils but have no impact on use by poorer farmers who grow maize on degraded land where fertilizer response is not enough to make its use profitable (Marenya and Barrett 2009a,b). Survey data commonly indicate that the contribution of fertilizer to food grain yields varies tremendously across farms even within the same villages. Households in Xu et al.‘s (2009) Zambia study are characterized by a great variation in the marginal productivity of nitrogen, even in the same agro-ecological and soil conditions, which most likely reflects differences in farmers‘ management ability, knowledge about appropriate application rates, and whether they are able to acquire fertilizer in a timely manner. Simply bringing fertilizer response rates among farmers in the bottom half of the distribution up to the mean would contribute substantially to household and national food security (Nyoro et al., 2004).

Experts recommend greater emphasis on integrating organic matter, such as manure from livestock or post-harvest crop waste, to raise soil carbon levels and make nutrients from fertilizers more available to plants. In Malawi, Sauer and Tchale (2009) found that controlling for other factors, maize yield response to fertilizer was higher with integrated soil fertility management. Similarly, a decade of experimentation in Malawi by Snapp et al. (2010) provides evidence that integration of semi-perennial legumes (such as pigeon pea, which produces grain) can provide a foundation for sustainable crop management. Modest application of nitrogenous fertilizer to monoculture maize was effective at doubling yield, but a rotation system with semiperennial legumes reduced the variability of yields, produced grain with 45 to 70 percent higher protein, and
|
|Years of executive in office in democracyi,t|0/|Party of executive controls all houses in no<br>democraciesi,t|n-||
|Years of executive in office in<br>non-democracyi,t||Herfindahl Index of government party seats<br>parliament in democraciesi,t|in|<br>0/|
|Regime<br>change<br>from<br>non-democracy<br>to<br>democracyi,t||Herfindahl Index of government party seats<br>parliament in non-democraciesi,t|in|<br>|
|Regime<br>change<br>from<br>democracy<br>to<br>non-<br>democracyi,t|||||

# **4. The Data**

The data are assembled on the basis of several data sets, related to energy pricing, and to economic and political characteristics of countries. The data for the dependent variable in equation (1) are annual diesel and premium gasoline prices, measured in November, with up to 10 observations for each country during the 1991-2010 period (GTZ, several years). We have merged these with data for premium gasoline, diesel and kerosene prices, for a large set of countries, for the period 2002- 2008, from the IMF, some of which overlap with the GTZ data (Coady _et.al_ , 2010). We also have a set of data for prices on regular gasoline, but these cover far fewer countries than those for premium gasoline, and will not be further analyzed in this paper.

Table 2 sums up our available fuel data. We see that mean fuel prices are highest in high income OECD nations, except that average prices of regular gasoline in OECD and nonOECD high-income countries are very close. Second, prices in middle income countries are below those in the least developing countries; the latter presumably have less means to subsidize (most often imported)
physical capital is Granger-caused by the levels and changes in economic freedom, in the case of levels this relation is bilateral. The authors also find bilateral Granger-causality relationship between the level of investment in human capital and economic freedom.

# _Aggregated index and its components_

Several authors have made the case against aggregation (see e.g. Heckelman and Stroup, 2000 or Lundström, 2003). Apart from the problems with the adoption of various weighting schemes discussed above, one can argue that some elements of economic freedom might not be positively and robustly related to economic growth. Ayal and Karras (1998) find that out of 13 components of economic freedom, six are statistically significant and related to economic growth according to the predictions of economic theory (money growth, government enterprises, negative interest rate, black exchange rate, trade size and foreign capital). Inflation variability impact is not robust to different model specifications.

Carlsson and Lundström (2002) analyze seven categories from the 2000 edition of the Economic Freedom of the World index. They found a positive and robust relationship between two categories of economic freedom i.e. freedom to use alternative currencies, legal structure and security of private ownership and economic growth. The impact of the freedom to trade with foreigners is counterintuitive as authors find a robust negative relationship with growth. The authors find the hump-shaped relationship between economic growth and the size of government consumption and transfers. Increased freedom in terms of lower level of government consumption and transfers decreases the growth rate for the index values lower than 8.46; at higher values the impact is reversed.

Heckelman and Stroup (2000) analyze 14 elements of economic freedom and find that only four of them have a positive and significant effect on growth in bivariate regressions, while only two remain significant in multivariate regressions. These elements of economic freedom with a robust impact on economic growth are the difference between the official exchange rate and the black market rate with a maximum for countries with no black market for their currencies (positive impact) and top
WPS3639

# **FINANCES OF EGYPTIAN LISTED FIRMS**

## **Inessa Love**<sup>**1**</sup>

### **Abstract**

This paper presents an analysis of the finances of Egyptian listed companies evaluating recent trends in growth and profitability during the 1995-2001 period. The data from financial statements reveal the effect of the economic slowing of the past few years, especially in the construction and real estate sectors and especially in smaller companies. We find that smaller firms appear to be less profitable and enjoy lower growth, likely because of being particularly adversely affected by many of the sources of the high costs of doing business in Egypt. While the Egyptian firms are not very highly leveraged on average, we find that smaller firms have significantly less access to bank finance than larger firms do. This confirms the widely held view that there is a need to improve the availability of credit for small enterprises.

### World Bank Policy Research Working Paper 3639, June 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Inessa Love is with the World Bank, DECRG.  I thank Patrick Honohan for extremely helpful discussions and advice,  Sahar Nasr and the Capital Market Authority for assistance,  Abdel Meguid (CMA) for providing the data and Victor Sulla for excellent research assistance.
# **2.2 Data**

We obtained data on 1,883 SMEs that applied for a working capital loan with our partner bank, from two sources: administrative data from our partner bank and Equifax Peru. The administrative data include the EFL score and the date when the SME applied for the loan, as well as the applicant’s age, gender, business sales, and whether or not our partner bank approved them for a loan. The applicants in our sample were on average 39 years old and 50 percent of them were female. Average annual business revenues were about US$12,000 (see Column 1 in Table 1). In addition, for each loan applicant, the data include the national ID number (DNI) and, if their business is registered under the business name instead of the individual’s name, it also includes the business’s tax payer number (RUC).<sup>2</sup> Our second data source is Equifax Peru, the largest credit bureau in the country. For the DNIs and RUCs in the EFL data, we purchased five years of monthly information on borrowing from regulated financial institutions, covering the period from May 2011 to April 2016.<sup>3</sup> Equifax collects this information from the Peruvian Bank Supervisor’s (SBS) credit registry ( _Central de Riesgos_ ). SBS collects data directly from all regulated financial institutions monthly, covering the universe of commercial banks, as well as all regulated NBFIs. For each ID number in any given month, we obtained the total amount borrowed (current and delinquent) from each SBS supervised financial institution in Peru, for the three loan types most relevant in our sample: microloans, loans to small firms, loans to medium firms. If a borrower has more than one loan of the same type with the same institution, Equifax reports only the sum of these loans, with no information on how many loans constitute this total amount.

Our primary outcome
br>T<br>as<br>il<br>er<br>n<br>i<br>h<br>C<br>o<br>V<br>a<br>n<br>c<br>o<br>u<br>v<br>e<br>r<br>I<br>sl<br>a<br>n<br>l<br>u<br>d<br>m<br>s<br>b<br>C<br>i<br>e<br>a<br>n<br>u<br>r<br>Q<br>e<br>s<br>a<br>F<br>r<br>JANUARY 2005 IBRD 33725<br><!-- End of picture text -->
(Percentage of women in each labor status)

||**2001**|**Rural**<br>**2010**|**2018**|**2001**|**Urban**<br>**2010**|**2018**|
|---|---|---|---|---|---|---|
|Employed|32.9|36.2|29|15.1|14.7|13.7|
|Unemployed|.54|.743|.73|4.96|3.64|4.39|
|Inactive|66.5|63.1|70.3|79.9|81.6|81.9|
|Paid workers|17.3|25.2|29.4|93.2|94.7|96|
|Formal workers|.549|1.75|3.05|32.9|44.3|48.1|
|Public Salaried|.|7.09|10.7|.|25.3|22|
|Private Salaried|.|88.1|82.8|.|65.6|68.2|

Source: HCP Enquête National sur l’emploi. Paid workers category includes wage workers, independent, employers and others; it excludes apprentices and cooperatives workers. Formal workers is the share of employed that have health insurance (CNSS). Public and Private categories -unavailable in 2001- are calculated as shares of employed (they do not sum up to 100 because of a third “other” marginal category).

In urban areas, the decline in female participation rates is less accentuated, smoother, and spread across age groups (figure 10 panel b). A phenomenon specific to urban areas is the increase in
distribution to compare the situation in 2013 to the reference situation in 2001 at the household level, and linking it to household perception, it is possible to test whether absolute polarization is linked to household perception. To our knowledge this is the first attempt to explain, at such granular level, the correlation of absolute polarization with well-being perception.

Results from the multi-logit regression where we also control for standard household-level characteristics, for example, such as education, employment, location, etc., indicate that polarization is significantly linked to perception, and most importantly that this relation is asymmetric. By interacting the polarization indicator with dummy variables defining classes of welfare (poor, vulnerable, etc.), we find that the poorer the household, the more polarization is perceived to be linked to the well-being of the household, while the richer it is the more polarization will positively link to its perceived well-being. Results are robust with respect to the use of classes or quintiles for ranking social groups from the poorest to the richest and other specifications.

In conclusion, while absolute polarization is growing in many countries (Nissanov and Pittau, 2016; Petrarca and Ricciuti, 2016; Clementi et al., 2017), few analytical works explicitly link this pattern to a respondent’s unhappiness about his or her socioeconomic condition. Morocco offers a unique opportunity in this regard for two related reasons. First, the Moroccan household surveys collect reliable and comparable data on perception, and second because these data made it possible to document that, despite making important economic progress, Moroccan citizens appeared dissatisfied with their socioeconomic situation and were very concerned about their future. At first glance, there was no apparent link between this negative perception and welfare distribution; poverty rates fell and inequality, as measured by the Gini index, did not vary. In contrast, exercising the theoretical option to link people’s perception and an underlying household level measure of absolute polarization, enabled us to show that perception is indeed significantly linked to welfare distribution.

The potential applications of
Figure 8: Heterogeneity in the Prevalence of Shocks in Sudan: Consumption Per Capita Quintiles <!-- Start of picture text --> 2009<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Poorest Poor Middle Rich Richest<br>Floods/Drought Crop Disease Livestock Loss Severe Illness/Accident Death of a hh. Member<br>Fire Robbery/Assult Dwelling Damaged Severe Water Shortage Other Shocks<br>2014/15<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Poorest Poor Middle Rich Richest<br>Floods/Drought Crop Disease Livestock Loss Severe Illness/Accident Death of a hh. Member Fire<br>Robbery/Assult Dwelling Damaged Severe Water Shortage Other Shocks Death of a hh. Head<br><!-- End of picture text --> _Source:_ Authors’ calculation using 2009 NBHS and 2014/15 NHPBS.

21
in 2014 that was constructed prior to 1975 or during 19751990, 1975-1990, and 2000-2014.

The Sri Lanka indicators were also supplemented by a variety of spatial “texture” features derived from a cloud-free mosaic of 2017-2018 Sentinel-2 imagery, which is collected every 5 days by Sentinel 2 sensors on board two satellites, Sentinel 2A and 2B. This imagery is made publicly available by the European Space Agency and is the highest spatial resolution (10m per pixel) optical data that are publicly available. The “texture” features were calculated using Jordan Graesser’s Sp.Feas package, an open-source Python library for processing contextual image features from satellite imagery. These contextual features have been shown to be strongly correlated with poverty and population density (Engstrom et al, 2017 and 2019a). The following contextual features were considered as candidate predictors:

- **Fourier Transform** (FT) which is used to detect high or low frequency of lines.

- **Gabor Filter** , a linear Gaussian filter used for edge detection (Mehrota et al, 1992)

- **Histogram of Oriented Gradients** (HOG), which captures the orientation and magnitude of the shades of the image. (Dalal and Triggs, 2005)

- **Lacunarity** (LAC), which describes the extent of gaps and holes in a texture. Low-lacunarity geometric objects are homogeneous because all gap sizes are the same, whereas highlacunarity objects are heterogeneous. (Myint et al, 2006)

- **Line Support Regions** (LSR), which characterize line attributes (Ünsalan and Boyer, 2004)

- **Normalized Difference Vegetation Index** (NDVI), the most widely used vegetation index, which provides information about the health and amount of vegetation

7
The potential effect of re-raking is illustrated by Table 7. It represents the share of the population belonging to households with different types of welfare dynamics: chronically poor (below low poverty line in both periods),<sup>18</sup> those who have experienced sharp and slight falls in living standards, and those who moved up a ladder of welfare. Table 7 suggests that while 36 percent of the population (22+14 percent) have experienced improvement in their position, and a further 16 percent managed to gain in line with the average, thus preserving their position as non-poor, as many as 48 percent of the population have either stayed in poverty or experienced losses – and for 17 percent there were very deep losses (movement down by more than two deciles).

**Table 7: Distribution of Population by Welfare Dynamics, 2005-2008**

**Percent of population (Total=100)**

|**Category**|**Percent of**<br>**population**|
|---|---|
|Chronic poor*|10|
|Deep falls in welfare (down by >2 deciles)|17|
|Slight falls in welfare (a fall to a next decile)|21|
|Preserving ranks as non-poor|16|
|Slight improvement in welfare over 2005-08|22|
|Big jump ahead in welfare (up by >2 deciles)|14|

_*Note: Lower poverty line. Source: Authors estimates based on panel HIECPS survey 2005-2008._ Moreover, when only one third of those belonging to the lowest decile in the initial period preserve their ranks three years later, the meaning of ‘the poor’ in the definition of pro-poor growth from the cross – section perspective simply does not make sense: these are predominantly different people in 2008 compared to 2005. Hence, other measures are required to capture the effect of growth on the non-poor in terms of degree of their vulnerability to become
|0.008|-0.157*|0.100|-0.165|
||(0.118)|(0.125)|(0.274)|(0.091)|(0.204)|(0.402)|
|Home repair|-0.043|-0.072|0.116|-0.001|-0.109|-0.280|
||(0.083)|(0.086)|(0.314)|(0.131)|(0.149)|(0.707)|
|Gifts|0.061|0.073|0.454|0.013|0.210*|0.281|
||(0.107)|(0.099)|(0.276)|(0.140)|(0.120)|(0.545)|

Note: The model of estimation is the same as Table 1, the dependent variable is the logarithmic form of household per capita consumption growth, and the variables of control include lagged log value of household expenditure per capita, average age/10, age squared/100, dependency ratio, urban-rural dummies, and regional dummies. For readability, we present only the results of interest - the coefficients of the z-score of online purchase / population in 2013. Fully results available upon request. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

First, for goods and services regularly consumed by most households,<sup>22</sup> including expenditures on cosmetics and beauty, food, clothes, utilities, communication, local transportation, and entertainment, we see a positive association between the z-score of online purchases over

> 22 From the CFPS data,
<mark>As there is no unobserved component, equation (2) gives an unbiased estimate of the impact of electrification.</mark> It is possible, however, that the unobserved factors are not fixed over time. Changes in the perceived benefits of electrification could affect both households’ decision about whether to be connected to the grid and the outcome variables.

<mark>Taking into account the existence of time‐varying heterogeneity, we can rewrite equation (1) as follows:</mark> <mark>where the additional term η</mark> �� represent <mark>s time‐varying unobserved household‐ and village‐ level determinants of the outcomes of interest. Taking the difference in equation (3) between the two periods yields</mark> <mark>The unobserved factor is not eliminated. Hence, a simple fixed‐effects estimation of equation (4) would be biased.</mark> <mark>We use propensity score matching with fixed effects to control for the potential endogeneity concern. A fixed‐effects model controls for time‐invariant unobserved factors, as shown in equation (2); propensity score matching based on observables in the first‐round survey can be used to control for time‐varying unobserved factors by balancing treated and untreated units (see Angelucci and Attanasio 2013). Applied this way, propensity score matching exploits the fact that unobserved heterogeneity is often correlated with initial conditions (Heckman 1981; Chamberlain 1984; Arulampalam, Booth, and Taylor 2000), which are assumed to be captured by the base‐year survey.</mark> 9
%|24%|51%|38%|49%|57%|41%|76%|65%|
|**Developing**<br>**countries**|44%|59%|57%|70%|38%|42%|78%|16%|42%|37%|43%|49%|42%|64%|51%|
|**OIC-GCC**|35%|59%|71%|50%|32%|26%|71%|24%|50%|38%|50%|62%|41%|79%|71%|
|**low-**<br>**income**<br>**countries**|41%|72%|86%|52%|21%|31%|76%|21%|34%|28%|45%|72%|52%|79%|55%|

Source: CGAP Financial Access Report Data 2010 Note: The CGAP Financial Access Database covers questionnaires sent to 151 economies: 13 in East Asia and the Pacific, 27 in Europe and Central Asia, 20 in Latin America and the Caribbean, 14 in the Middle East and North Africa, 6 in South Asia, 40 in Sub- Saharan Africa, and 23 in the high-income OECD countries. Among them, 37 OIC countries are covered. For practical purposes, most small islands and economies at war were not included. The sample covers more than 94 percent of the world’s population and nearly 98 percent of global GDP.

Selected questions are presented in the first and second rows of the table. The row for OIC countries means the percentage of OIC countries that replied yes to the questions. The same calculation applied to the groups of developing countries, OIC-GCC, and low incoming countries. OIC-GCC means OIC countries excluding the GCC countries