rafmacalaba/datause-encoder-data huggingface dataset:
sides of the same variable. Are people less bonding also invariably more bridging (and vice-versa) or can people be more (or less) bonding and bridging at the same time? To address this question we carried out a factor analysis with the variables selected in Step 2. The results are shown in Table 2. It can be seen (top panel) that only the first factor has an Eigenvalue sufficiently high to be retained.<sup>4</sup> In other words, the analysis suggested that our proxies for bonding and bridging attitudes were jointly related with only one latent variable in some meaningful way. It can also be seen (bottom panel) that the five variables that we considered as proxies for bonding attitudes all load positively against the first factor while the four variables that we considered as proxies of bridging attitude all load negatively against the same factor.<sup>5</sup> This finding suggests that the two sets of variables that we identified could in fact represent two sides of the same latent variable. This is a new result in contrast with those in the existing literature. For example, Knudsen et al. (2010) find, on the basis of US data, that two factors should be retained, and that the proxies for bonding and bridging attitudes correspondingly load on the two factors, thus concluding that bonding and bridging are two separate latent variables (see section 2).

# [TABLE 2]

_Step 4_ . As a further test for the preliminary finding of step 3, we carried out a third factor analysis exercise (Table 3). This time we assumed that bonding and bridging attitudes were two sides of the same variable and we constructed proxies for a single latent variable by building variables with low scores corresponding to bonding attitudes and high scores corresponding to bridging attitudes. We constructed five of these variables using some of the same variables we used in step 3 as well as some of the variables discarded in step 2 but useful for this last exercise. The first variable (importance of family vs. importance of friends) was constructed by attributing a value of ‘1’ to those people who equally valued family and friends, a value of ‘2’ to those people who valued family more than friends and a value of ‘3’ to those people who valued friends more than family. We used the same construction procedure for the variables ‘importance of work

> 4Different authors tend to use different cut points to decide how many latent factors should be retained. Here we use a cut point of 0.7, which is a rather standard choice, and also look at the distance between factor loadings.

> 5Note that in table 2 and 3 we report results for the first four factors, even if we retain only the first factor. This is to show how none of the other factors provides a clear interpretation relatively to the variables selected in Step 2.

14
**Table 2** – Main specification

|Depe|Whole<br>sample<br>ndent varia|ble: Willin|Only<br>g to vaccina|with trust<br>te|and norm|s info||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Sti id|0003***|0004***|0004***|0004***|0005***|0012**|0004***|
|rngency nex|.<br>(0.001)|. <br>(0.001)|. <br>(0.001)|. <br>(0.001)|. <br>(0.001)|.<br>(0.004)|.<br>(0.001)|
|1-day change in stringency index (absolute)|||-0.004<br>(0.002)|||||
|7-day change in stringency index (absolute)||||-0.002*<br>(0.001)||||
|14-day change in stringency index (absolute)|||||-0.002**<br>(0.001)|||
|Stringency index^2||||||-0.000**||
|||||||(0.000)||
|Days above stringency score 80|||||||-0.001***<br>(0.000)|
|Vaccination campaign dummy|0.104***|0.092***|0.092***|0.093***|0.092***|0.095***|0.092***|
||(0.021)|(0.019)|(0.019)|(0.019)|(0.018)|(0.019)|(0.019)|
|Daily deaths per million (7 day avg)|0.007|0.001|0.001|0.001|0.001|0.002|0.000|
||(0.007)|(0.008)|(0.008)|(0.008)|(0.007)|(0.008)|(0.008)|
|Know positive case|0.086***|0.081***|0.081***|0.081***|0.080***|0.081***|0.080***|
||(0.010)|(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|
|_Individual attributes_||||||||
|male|0.072***|0.081***|0.081***|0.081***|0.081***|0.081***|0.081***|
||(0.014)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|
|urban|0.014|0.018|0.018|0.018|0.018|0.018|0.018|
||(0.010)|(0.013)|(0.012)|(0.012)|(0.013)|(0.013)|(0.012)|
|age 20-30|-0.096***|-0.105**|-0.105**|-0.105**|-0.105**|-0.105**|-0.105**|
||(0.027)|(0.046)|(0.046)|(0.045)|(0.046)|(0.045)|(0.046)|
|age 31-40|-0.103***|-0.113**|-0.113**|-0.113**|-0.113**|-0.114**|-0.113**|
||(0.027)|(0.045)|(0.045)|(0.045)|(0.046)|(0.045)|(0.045)|
|age 41-50|-0.093***|-0.099**|-0.099**|-0.099**|-0.098**|-0.099**|-0.099**|
||(0.027)|(0.045)|(0.045)|(0.045)|(0.045)|(0.045)|(0.045)|
|age 51-60|-0.074***|-0.079*|-0.079*|-0.079*|-0.079*|-0.079*|-0.079*|
||(0.023)|(0.044)|(0.044)|(0.044)|(0.044)|(0.044)|(0.044)|
|age 61-70|-0.030|-0.034|-0.034|-0.034|-0.034|-0.034|-0.034|
||(0.022)|(0.039)|(0.039)|(0.039)|(0.039)|(0.039)|(0.039)|
|age 71-80|0.005|0.022|0.022|0.022|0.022|0.022|0.022|
||(0.015)|(0.033)|(0.033)|(0.033)|(0.034)|(0.033)|(0.033)|
|Primary education or less|-0.050***|-0.037**|-0.037**|-0.037**|-0.038**|-0.037**|-0.037**|
||(0017)|(0016)|(0016)|(0016)|(0015)|(0016)|(0016)|
|Secondary education|.<br>-0.030***|.<br> -0.030***|.<br> -0.030***|.<br> -0.030***|.<br>-0.030***|.<br>-0.030**|.<br>* -0.030***|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|in good health|-0.003|0.000|0.000|0.000|0.001|0.000|0.001|
||(0.005)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|Constant|0.419***|0.391***|0.383***|0.353***|0.316***|0.132|0.464***|
||(0.045)|(0.071)|(0.074)|(0.084)|(0.091)|(0.142)|(0.078)|
|Observations|232292|61760|61760|61760|61760|61760|61760|
|R-squared|0.058|0.058|0.058|0.058|0.058|0.058|0.058|

Standard errors in parentheses clustered at country level. All estimations include country and month fixed effects.

* p<0.05  ** p<0.01  *** p<0.001

21
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## **2d. Licensing and regulation By type**

## **By granular regions**

<!-- Start of picture text --> Commercial Development Benchmark Europe WEST ECA EAST ECA<br>Banking license Banking license<br>Parts of commercial banking law do not apply Parts of commercial banking law do not apply<br>100% 100%<br>80% 80%<br>Percent Yes 60% Percent Yes 60%<br>40% 40%<br>20% 20%<br>0% 0%<br><!-- End of picture text --> An important aspect of SOFIs’ characteristics is their licensing, especially for institutions that collect retail deposits and extend loans (i.e. perform financial intermediation). A banking license may favor the separation of responsibility for exercising ownership and for regulatory and supervisory duties (Scott, 2007). Among the sample, more Development SOFIs were created by law or act, and have always been state-owned. All Commercial SOFIs in the sample have a banking license and are subject to commercial banking law and the prudential supervision of the banking supervisory authority (with, on average, onsite inspections twice per year). On the contrary, about 60 percent of Development SOFIs have a banking license and about 50 percent are exempted from parts of the commercial banking law. This is not surprising, however, given the sample categories construction - only two Development SOFIs take deposits from the general public and both have a banking license (but are, nevertheless, not subject to all aspects of the regular banking law). The sample categories construction may also explain why West ECA (which is characterized by more Development SOFIs) has a higher incidence of parts of the commercial banking law not applying to the SOFIs.

# **_5.2 Accountability, mandate, and objectives_**

SOFIs are accountable to diverse stakeholders. For instance, a Commercial SOFI is more likely to be legally responsible or accountable directly to its shareholder, whereas a Development SOFI might be accountable to a more diverse set of stakeholders. Moreover, almost one in ten Commercial and Development SOFI is accountable to multiple entities (under the category “Other” in Figure 3). Looking at the granular regions, SOFIs in Benchmark Europe are the most likely to be accountable directly to their shareholders, whilst in West ECA there is high diversity 14
**_5_**

As an initial exercise, table 2 shows the simple correlations between the inequality variables and Barro regressors and other related variables<sup>7</sup> . Exc., ' for per capita GDP, which is negatively correlated with growth, and enrollment rates in secondary, and to a lesser extent primary, education, no variables Table 1: Simple Correlations of Inequality measures.

||aini|Coeffirar|Theil|RTP40|
|---|---|---|---|---|
|alai|1.00|0.97|0.97|0.91|
|Cooffvar||1.00|0.99|0.87|
|The_|||1.00|0.94|
|RTP4O||||1.00|

TABLE 2: Simple Correlations with Barro Regressors and other Variables

|Variable|obs|Corr.w/<br>_GINI_|Tstat|#obs|Corr w/<br>RTP40|T stat|
|---|---|---|---|---|---|---|
|_ASSP7085_|75|-0.031|-0.26|83|_-0.035_|-0.31|
|REVC708S|75|-0.045|-0.39|83|-0.012|-0.11|
|8GoV7088|76|-0.061|_-0.53_|84|_-0.050_|_-0.45_|
|sLNV7088|76|-0.106|-0.92|84|0.002|0.02|
|C57088|8l|0.068|0.61|90|0.003|0.03|
|SODPPC7O|75|-0.319|-2.87|82|-0.255|-2.36|
|LGDPPC70|75|-0.216|-1.89|82|-0.131|-1.18|
|CPRIM60|82|_-0.166_|_-1.51_|89|-0.095|-0.89|
|CSEC60|82|-0.373|-3.59|89|-0.317|-3.11|
|SODPPCIY|72|-0.354|-3.16|77|-0.290|-2.63|
|LODPPCIY|72|-0.225|-1.94|77|-0.162|-1.42|
|_SCONIY_|72|0.083|0.69|77|0.024|0.21|
|_SGOVIY_|72|-0.208|-1.78|77|-0.170|-1.49|
|SNVIY|72|-0.120|-1.01|77|-0.039|-0.34|

' The measures denoted xxxxxlY are measured in the same year as the inequality measures for each countfy. SCONIY is private consumption as percent of GDP, slNvn is investment as percent of GDP, SGoVIY is government consumption as percent of GDP and SODPPCIY is per capita GDP and LGDPPCIY is the log of per capita GDP. AU measures are from Surmmers and Heaton (1991).
**Figure (10): Gender of Workers by Gender of Principal Owner in the Arab Countries**

<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male<br>owned owned owned owned owned owned owned owned owned owned owned owned owned owned owned owned<br>Algeria 2007 Egypt 2008 Lebanon 2009 Libya 2009 Morocco 2007 Syria 2009 Yemen 2010 Jordan 2011<br>Female workers Male workers<br><!-- End of picture text -->

**_Source:_** ICA’s (various series).

Although the level of recorded employment in MSMEs appears to be significant in the Arab world, it is lower as a percentage of total employment in the region than in many other regions. On average, SMEs are responsible for around 30 percent of all private sector employment in the Arab region, compared to 41 percent in Latin America; 49 percent in the OECD; and 49.5 percent in South Asia (Figure 10) this is due to how SMEs are defined. The micro enterprises mainly in the informal sector account for much larger number, making the aggregate percentage of jobs created reach 71 percent.

# **V. KEY CHALLENGES CONFRONTING THE SUSTAINABILITY OF SME GROWTH IN THE ARAB WORLD**

Most Arab countries have put SMEs development as one of their key priority areas. Nevertheless, SMEs are still confronted with key obstacles that have constrained their development and hindered their potential to create private-sector jobs and entrepreneurship opportunities (Figure 11). Constraints include regulatory burdens, lack of access to financial services, and inadequate skills and capacities. There is an increasing awareness that the difficulty of entry into relatively closed private markets holds back the growth and competitiveness of SMEs. There is evidence that smaller firms stagnate in terms of employment growth: small firms stay small. This is largely due to the fact that many of the large entrepreneurs in the region are privileged, and come from the highest political and country leadership spheres, receiving preferential treatment that puts the SMEs at a disadvantage. A comparison between the Arab countries and Latin America demonstrates that firms start out larger when young in the Arab countries but grow more slowly over time so that firms in Latin America are about twice as large after being in operation for 10 years.

14
wage gap appears at the median of the distribution, but at the same time—and consistent with other high-income countries—the largest unexplained gap is found at the top of the distribution, indicating there is a glass-ceiling effect in Russia.

**Figure 1. Gender gap in pay, 1994–2011**

<!-- Start of picture text --> 50 15<br>45<br>10<br>40<br>35<br>5<br>30<br>25 0<br>20<br>‐5<br>15<br>10<br>‐10<br>5<br>0 ‐15<br>Gender pay gap GDP growth rate<br>Growth rate (%)<br>Gender wage gap (%)<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> _Source:_ World Bank (2014). _Notes:_ The gender wage gap for the working-age population (18–60) is defined as the difference between male and female hourly rate pay (2011 prices) as a percentage of the male rate. The GDP growth rate is measured as the annual percentage growth rate of GDP per capita based on constant local currency. Aggregates are based on constant 2005 U.S. dollars.

4
4 liquid form. Moreover, as we show below, price stabilization has proven expensive to farmers.

Even if nominal price stabilization limited price volatility to producers it became fiscally unsustainable after the mid- 1980s. The objective of producer price stabilization then changed from inter-annual stabilization to intra-annual stabilization (within seasons). Since 1990, Cote d'Ivoire has not attempted inter-annual price stabilization and it is unlikely that it will try to do so again.

The objective of remunerative producer prices has not been well met. Figure 1 compares Ivorian producer prices to world prices in real terms; the latter are represented by the International Cocoa Organization (ICCO) price converted to FOB Cote d'Ivoire. Following the collapse in 1990 from CFAF 400 to CFAF 200 per kg, producer prices remained low as a share of FOB. Though nominal prices rose after the 1994 devaluation and the (unrelated) recovery of world prices that began in 1993, nonetheless, real producer prices during the 1990s have been less than one-half of their 1980 levels. The difference between producer and FOB prices was enormous before 1987, became negative in 1988 and 1989, and has widened again since 1993.

_Figure 1. Cote d'Ivoire -- Quarterly cocoa prices (1990 US $/mt)_ <!-- Start of picture text --> 3000<br>2500<br>X Producer -FOB<br>2000<br>1500<br>1000<br>500<br>0<br>83 83 84 85 86 86 87 88 89 89 90 91 92 92 93 94 95 95 96 97<br><!-- End of picture text -->

**_Source:_** Authors'calculations from World Bank (1998).

As a share of FOB, producer prices in Cote d'Ivoire have been low compared to those in other major producing countries. Data from 1995 (Figure 2) show that the share of the producer price in Cote d'Ivoire was smaller than any other major grower, with the exception of Ghana. Similar comparisons for 1997 showed that the producer share of the FOB price in Cote d'Ivoire and Ghana remained between 48-50 percent, while producer shares of the FOB price in other major cocoa producing countries such as Brazil, Dominican Republic, Ecuador, Indonesia, Malaysia and Nigeria were between 82-92 percent.
used in previous studies. We also include in _Xit_ the shares of the population with wage income and non-farm income to control for local economic development. The year dummy variables and tt the province dummy variables help capture unobserved trends occurring for the same years and ττ ii provinces. is the error term. The two coefficients uu 1 and 2 that measure the correlation between iitt per capita expenditure and inequality are of primary interest.εε ββ ββ<sup>11</sup> The summary statistics are provided in Appendix A, Table A.2.

For better comparison, we use three model specifications for equation (1) where we sequentially add the explanatory variables to the two core variables (log of) per capita expenditure and Gini index. For the first model, we add the province FE. For the second model, we add to model 1 the year FE. Finally, for model 3, we add all the remaining _Xit_ control variables, which is our preferred model for interpretation. Table 4 shows that the estimated results for per capita expenditure vary somewhat between model 1 and model 2, which is perhaps due to unobserved trends occurring in different years. But more importantly, once we control for both the province and year FE, overall, the results remain similar between model 2 and model 3.

In particular, Table 4 shows that, holding all other factors constant, a 1 percent rise in a province’s per capita expenditure reduced its poverty rate by 0.26 percentage points (column 3), its poverty gap by 0.10 percentage points (column 6), and its poverty severity index by 0.05 percentage points (column 9). Interestingly, holding fixed the per capita expenditure levels, a 1

> 11 We also estimate a variant of equation (1) where we include the explanatory variables in lagged forms using both province FE and GMM models. The results are qualitatively similar (Dang et al., 2023). We further show the estimation results with additional control variables for public infrastructure such as the share of villages with yearround passable roads, the share of villages with a daily market, and the distance from the village to the nearest town in Appendix A, Tables A.3 and A.4. Since these variables are computed from the commune module of the VHLSSs, we do not have complete data for these control variables for the whole period of our study. Yet, these tables show that provinces with more villages that have year-round passable roads could reduce poverty and have faster economic growth.

12
P POLICY RESFARCH WOIOKING,<sup>PAPEK2883</sup>

# Abstract

Economic developmenit necessarily<sup>chanigesthewelfare</sup> of socioCcololilic groups to various degrees,<sup>dependinig</sup> on differenctes in their social arrangements.<sup>The</sup> challenge for policvyrakers is to select<sup>the</sup><sup>**changes**that</sup> will be most socially desirable. Essama-Nssaih demonistrates the usefulness of distributional<sup>analysisfor</sup> social evaluation and, more specifically,<sup>forwelfare</sup> evaluatioll, uislig data from the 1994<sup>Integrated</sup> Houseihold Survey in Guinea. Because<sup>theinttrinationial</sup> commiiiunity has declared poverty eradication<sup>a</sup> fundamental objective of developmeit,<sup>theautIlorusesa</sup> poverty-focused approach to social<sup>evaluationibasedon</sup>

**_the_** _maximii priniciple._ This principle<sup>offersaullifying</sup> framework for analyzing the socioeconiomic<sup>impactof</sup> public policy by using a wvide variety of<sup>evaluationi</sup> functions, inequality indicators<sup>(liketheextendedGini</sup> coefficient), and poverry indices<sup>(suchasSen'sindexand</sup> the meilimbrs of the Foster-Greer-Thorbecke<sup>family).</sup> The author also examines, within the<sup>contextof</sup> commodity taxation, oiow to identify<sup>sociallydesirable</sup> policy options usinlg both the dominance<sup>criterionand</sup> abbreviated social welfare fulictions.<sup>Heincludes</sup> computer roLitinies for calculating various<sup>welfare indices</sup> and for plotting the relevant concenltrationi<sup>curves.</sup>

This paper-a product of the Poverty Reductio<sup>nGroup,PovertyReductionand EconomilicManagement Net-work-ispart</sup> of a **larger** effort in the network<sup>to</sup><sup>**ulinlerstand**thepovertxandsocialimpa;ctof publicpolicy.Copiesofthe paperare</sup> available free from the World Bank,<sup>181 8 H StreetNW, Washington,DC204.3.3.Pleasecontact OykiaoKootzeniew,room</sup> XIC4-554, telephone 202-473-5075,<sup>fax202-522-3283X</sup> , email address okootzemew\(@wvorldbanik.org.<sup>PolicyResearch</sup> Working Papers are also posted<sup>onthe Web athttp://econ.worldbank.org.Theauthor</sup><sup>**may**becontactedat</sup> bessamilanissah@(- w\vorldbank.org. September<sup>2002.(50pages)</sup>

> _The lPolic Research Workinzg Paper_<sup>_Seriesdisseminates thefindinigsof wuorkinpro)gressto) enicou ragetheexcanigeof ideasabouit_</sup> _developmient issues-An obiective oo_<sup>_the seriesis to get the findinissoit quickl),_</sup> **_vien_** _if tbe presentations are less_<sup>_than_</sup><sup>**_fullid_**</sup><sup>_polished.The_</sup> _papers_ **_carry_** _the_ **_names_** _of the_ **_aothors_**<sup>_a1ndshooild be cited according/v.Thefindings,interpretations,_</sup><sup>**_and_**</sup><sup>_conclusionsexpressed_</sup><sup>**_in_**</sup><sup>_this_</sup> _ps/Pcr are entirely those o/ the authors._<sup>_ITheydonzotnecessar)ilrepresent theviewvof theWorldBank,itsExecitivel)irectors,orthe_</sup> _coitiitr-ics they represenit._

P'roducedi by the Research Advisorv Staff
average, and the choice of weighting: an unweighted average would smooth the series but would “center” the average exchange rate on the middle value of the three. The weights aim to center the average on the latest point of the three—they are the ratio of price inflation rates between each point and the latest point, derived from assumption that if prices rise faster in country A than in the US, the exchange rate between A and the US will adjust accordingly—all other factors being equal.

However, some degree of volatility still remains: a few countries that moved up the income categories fell back within two to three years (Table 4). The number of countries affected by this “round-tripping” is relatively small, and some of this may be unavoidable, particularly when it reflects political change, economic shocks, or conflict. Still, changes resulting purely from large but short-term changes in exchange rates should be minimized; even though a relatively rare occurrence, it can draw sharp criticism when it occurs, especially when the resource allocation decisions of development partners are impacted.

**Table 4: “Volatile” changes in the income classification between FY02 and FY16**

|World Bank fiscal year|02|03|04|05|06|07|08|09|10|11|12|13|14|15|16|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Calendar year of data|00|01|02|03|04|05|06|07|08|09|10|11|12|13|14|
|Albania|LM|LM|LM|LM|LM|LM|LM|LM|LM|UM|UM|LM|UM|UM|UM|
|Antigua and Barbuda|UM|<br>UM|H|UM|UM|H|H|H|H|UM|UM|UM|H|H|H|
|Barbados|H|UM|H|UM|UM|UM|H|H|H|H|H|H|H|H|H|
|Equatorial Guinea|LM|L|L|L|UM|UM|UM|H|H|H|H|H|H|H|H|
|Fiji|LM|LM|LM|LM|LM|LM|LM|UM|UM|UM|LM|LM|UM|UM|UM|
|Hungary|UM|UM|UM|UM|UM|UM|UM|H|H|H|H|H|UM|UM|H|
|Latvia|LM|UM|UM|UM|UM|UM|UM|UM|UM|H|UM|UM|H|H|H|
|Malta|H|UM|H|H|H|H|H|H|H|H|H|H|H|H|H|
|Mauritania|L|L|L|L|L|L|L|L|L|L|LM|L|LM|LM|LM|
|Solomon Islands|L|L|L|L|L|L|L|L|LM|L|LM|LM|LM|LM|LM|
|South Sudan|..|..|..|..|..|..|..|..|..|..|..|LM|L|LM|L|
|Turkey|UM|<br>LM|LM|LM|UM|UM|UM|UM|UM|UM|UM|UM|UM|UM|UM|

Countries are included in this list if they returned to a classification they had previously held during the fifteen year period between FY02 and FY16 for three years or less; H=high, UM=upper middle, LM=lower middle, L=low. _Source_ : http://siteresources.worldbank.org/DATASTATISTICS/Resources/OGHIST.xls, accessed November 30, 2016 One option for changing current practice would be to use a “buffer” around the threshold to help minimize any volatility. So, for example, a country might only be reclassified if it has 19
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