rafmacalaba/datause-encoder-data huggingface dataset:
firms outweighs that created by formal SMEs, however, countries like Yemen, Iraq and Libya are exceptions. The rise of SMEs and their importance in Libya can be partially attributed to the fact that the economy suffers from non-diversification of economic activities; mostly concentrated in construction, oil, and gas sectors.

Figure (7): Percentage of Jobs Created by Firm Size in ICA Enterprise Survey <!-- Start of picture text --> 150<br>100<br>50<br>0<br>MENA Lebanon Egypt Libya Morocco GC Syria Yemen Iraq<br>-50<br>-100<br>Small Medium Large<br><!-- End of picture text -->

**_Source:_** World Bank Enterprise Surveys (2007-2011).

This evidence must be tempered by several cautions. First, not all of the samples are perfectly proportional to the employment-weighted presence of firms in the economies. Second, the samples do not include microenterprises, and clearly a large amount of employment and, potentially, employment growth, takes place among these firms. Third, since our data is from multiple years, we cannot account for the full dynamic effects of exit and entry on employment. But clearly there is a strong indication that SMEs are not fulfilling their potential to produce a small number of high growth firms that account for the great bulk of job growth.

Many countries in the region record a large share of jobs in government services compared with other countries. However, in recent years the public sector has been unable to support job or income growth. Consequently, the number of jobs created in the last decade was considerably less than the number needed to address key challenges, such as high youth unemployment, low labor force participation rates, especially among women, and fast-growing labor forces. In all countries, job quality has been a particular concern. Jobs in government services have been increasingly difficult to get while finding similar quality jobs in the private sector has been hard. The report shows that nongovernment services and manufacturing can serve as engines of both job creation and income growth. Services have been a source of strength both on income and jobs, in levels and growth, especially in oil importers. Manufacturing has contributed to growth in income and jobs, but its size is small on average in MENA relative to other developing countries.

# **C. The Role of SMEs in Job Creation in the Arab World**

SMEs are major contributors to employment and the overall economy in the Arab region<sup>14</sup> . They account for a large share of enterprises, representing between 80 percent and 90 percent of all formal sector enterprises. Moreover, SMEs contribute a large share of private sector employment opportunities, typically accounting for somewhere between 20 and 40 percent of all private

> 14 Ayyagari, Demirgu-Kunt, and V. Maksimovic (2011).

12
# **4.4 Does air pollution cause a reduction in income levels?**

Air pollution has been documented to be detrimental to health and productivity, and thus indirectly people’s incomes. In this section, we use a difference-in-difference design to test the potential impact of air pollution on income levels. We compare pollution levels and economic indicators in downwind and upwind areas, before and after the activation of new coal plants. First stage results confirm that differences in pollution levels are insignificant before activation, but post-activation pollution levels are significantly higher in downwind areas. However, reduced form results do not confirm meaningful differences between the income levels in up- and downwind locations. Thus, these results do not yield robust evidence that air pollution causes reductions in income levels. This result is documented in this section for the sake of completeness, and to inform future efforts for a causal identification with global coverage. Possible factors that can explain this null result are the relatively short time series of data (which may not allow long-term health and productivity effects to materialize), and the fact that health-related expenditures are included in indicators of economic activity thus diluting the impact.

To evaluate the causal effect of power plants’ operation on local economic indicators, we use a difference-in-difference design that compares upwind and downwind pixels before and after coal plant activation. The econometric specifications are as follows: where the sample is at the year-plant-pixel level. The time range is the same as the gridded income dataset, i.e. 1990-2015. To ensure that pre- and post-activation periods are included, we restrict the sample consists to 2,380 coal power units that were activated between 1992-2013 (i.e. disregarding 3,941 units that were activated outside this time frame or have missing commission years). Besides, we include up- and downwind pixels that are located within a 0.5-degree radius around plants, and drop those in crosswind directions. Upwind pixels are defined as those located -45<sup>_◦_</sup> to 45<sup>_◦_</sup> around plants, while downwind areas are located 135<sup>_◦_</sup> to 180<sup>_◦_</sup> and -180<sup>_◦_</sup> to -135<sup>_◦_</sup> . Upwind and downwind areas are visualized in Figure 3.

The dependent variable _Yijt_ in the first stage is SO2 in recipient pixel _i_ near plant _j_ in year _t_ . In the reduced form, _Yijt_ is pixel-level GDP, GDP per capita, or HDI. On the right-hand side, _Downwindij_ is a dummy that is one if pixel _i_ is in the downwind direction of plant _j_ and zero otherwise. _Operatingjt_ is an indicator that equals one if plant _j_ started to operate in or 18
TABLE 4: GDP GROWTH AND CRIME AGAINST FIRMS – INSTRUMENTAL VARIABLES

||% of Losses Due to Crime<br>Over Sales<br>IV Estimates<br>|Real GDP per capita Growth<br>First Stage Results|
|---|---|---|
||Second Stage Results<br>||
||Small and Mediu|m Firms sample|
||1|2|
||-0.1438***||
|Real GDP per Capita Growth|[0.0406]||
|Police per 100,000 persons lagged|-0.0012*|-0.0073|
||[0.0006]|[0.0051]|
||0.1615**|-0.1774|
|Firm with Female Owner|[0.0782]|[0.1701]|
|Malaria Ecology Index||-1.2454<br>[1.8219]|
|% of land in the Tropics||-3.6897***<br>[0.9537]|
|Real GNI per capita in 100s|-0.0090***|-0.0597***|
||[0.0020]|[0.0188]|
|GINI|-0.0269|-0.2665***|
||[0.0180]|[0.0757]|
|Population in Millions, Total|0.0051***|0.0353**|
||[0.0016]|[0.0163]|
|Small firm dummy variable|0.2501***|0.0628|
||[0.0838]|[0.0763]|
|Large City or Capital dummy variable|0.0757|-0.0129|
||[0.1017]|[0.2594]|
|Manufacturing firm dummy variable|-0.1834*|0.0797|
||[0.1013]|[0.1029]|
|Number of Countries|27|27|
|Number of Observations|9160|9160|
|Instruments|Malaria Ecology Index, % of||
||land in the Tropics||
|Under-identification test (Kleibergen-Paap LM statistic) P-<br>value:|0.0210||
|Hansen J statistic (over-identification test of all instruments)|01155||
|p-value:|.||

* significant at 10%; ** significant at 5%; *** significant at 1%, Standard errors in brackets clustered at the country level

26
less developed countries) but also lose highly educated emigrants (to other OECD countries and between each other). This is more similar to what happens to countries of intermediate income level that attract immigrants from poorer countries and send migrants to richer countries. With these overall patterns in mind, we turn to the simulation exercise.

# 4 Simulated wage e¤ects: basic speci...cation

## 4.1 Parameterization

Our model allows us to calculate the wage e¤ects of migration depending on the values of three fundamental parameters �q; �I and �: We take a range of values from the literature. Table 2 summarizes the values of the parameters chosen and the respective sources. We would like to remind the reader that parameters �q and �s are set to 0:6 in all simulations. This choice generates reasonable skill premia and wage di¤erentials between natives and immigrants when immigrants represent around 10 percent of the skill-speci...c labor force.<sup>11</sup> For the parameter �q there are several estimates in the literature. A group of in‡uential papers propose speci...c estimated values for low, intermediate and high levels of substitution. For instance Johnson (1970) and Murphy et al (1998) estimate values for �q around 1.30 (respectively 1.34 and 1.36); Ciccone and Peri (2005) and Krusell et al. (2000) estimate values around 1.50 (respectively 1.50 and 1.66) and Ottaviano and Peri (forthcoming) estimate a value close to 2.

The parameter �I has been the subject of several recent papers and has generated a certain level of debate. This parameter is particularly relevant to determine the e¤ect of immigrants on wages of natives and, as we will see, the choice of this parameter makes some di¤erence in evaluating the e¤ects of migration in certain countries. Borjas et al (2008) and Ottaviano and Peri (forthcoming) use US data and Manacorda et al (forthcoming) use UK data in their estimation.

Finally the parameter �, whose magnitude has been estimated using data from US cities (Moretti 2004a, 2004b) or US states (Acemoglu and Angrist 2000 and Iranzo and Peri 2009) is also subject to a certain level of disagreement between those who ...nd substantial schooling externalities and those who do not.

To preview the main features of the dependence of the simulated e¤ects on parameter values, �q has very little bearing on the impact of immigration and emigration on average wages, but it is critical for the e¤ects on the wage distribution between more and less educated natives. The parameter �I in‡uences the average wage impact of immigration on natives but has no bearings on the impact of emigration. Finally � matters for the impact of immigration and emigration on average wages with no e¤ect on the wage distribution.

## 4.2 Simulation with the basic speci...cation

Table 3 and the three panels of Figure 1 show the long-run impact of immigration (dotted line), emigration (dashed line)) and net migration (solid line) between 1990-2000 on average

> 11Those preference parameters play a minor role in our wage simulations. They enter both the numerator and denominator in the expressions for percentage changes in wages and hence they canel out.

12
|The Internation-<br>al Bureau of Ed-<br>ucation (IBE)—<br>legal dataset|UNESCO|The IBE provides a country database that<br>includes profiles of education systems (in-<br>cluding laws and policies), a selection of<br>official reports from national education au-<br>thorities, information on national curricula,<br>and links to official information sources on<br>education (including Ministries of Educa-<br>tion).|Does not cover issues related to insti-<br>tutions, labor market, property, public<br>goods, social protection, and hate<br>crime or hate speech. Does not allow<br>for cross-country comparison.|
|---|---|---|---|
|European Equal-<br>ity Law Net-<br>work—Legal<br>Developments|European<br>Equality Law<br>Network|The network includes updates on legisla-<br>tive, policy, and case-law developments in<br>the field of nondiscrimination in the 35<br>states that participate in the network.|Does not cover non-European coun-<br>tries. Does not allow for cross-<br>country comparison.|
|WORLD Policy<br>Forum—Data<br>on Education|The WORLD<br>Policy Analy-<br>sis Center at<br>University of<br>California, Los<br>Angeles<br>(UCLA)|<br>The WORLD Policy Forum includes laws<br>and public policies in 191 countries cover-<br>ing poverty, discrimination, education,<br>health, child labor, child marriage, and pa-<br>rental care.|Does not cover institutions, property,<br>public goods, social protection, and<br>hate crime or hate speech.|
|Compendium of<br>blasphemy laws|Human Rights<br>First|Compendium of blasphemy laws covers<br>legislation that seeks to criminalize those<br>who insult or defame religions and related<br>legislation.<br>Includes legislation on freedom of religion<br>and discriminatory laws against religious<br>minorities.|Covers only religious minorities. Does<br>not cover other groups and does not<br>cover labor market, education, prop-<br>erty, public goods, social protections,<br>and hate speech and hate crimes.|
|Sexual Orienta-<br>tion Laws in the<br>World|International<br>Lesbian, Gay,<br>Bisexual,<br>Trans and In-<br>tersex Asso-<br>ciation (ILGA)|The Sexual Orientation Laws in the World<br>dataset includes criminalization, protection,<br>and recognition laws related to sexual mi-<br>norities.|<br>Covers only sexual minorities.|

31 Anti-Discrimination Law and Shared Prosperity
sample is restricted to Grade 4 students at baseline, and as described, the control group of students were not beneficiaries of the program’s expansion. Columns 3 and 5 report the means (and standard deviations) of household and individual characteristics prior to the intervention for each of the control groups, and Columns 4 and 6 report the estimated difference (with standard errors in parenthesis) between treatment and control applicants.

Overall, characteristics differ very little between treatment and control groups. Only a few coefficients in Columns 4 and 6 are statistically significant: Of the 32 differences reported, 5 are significant at the 10% level; among them, 1 is significant at the 1% level, a result that is consistent with pure chance. Again, the results from these tests confirm the validity of the random assignment, since both control and treatment groups are similar in their observed characteristics.

An important feature of the data is that the poverty index and the test score are statistically indistinguishable between the treatment and control groups. Figures 3 and 4 present the density of poverty index and the test score at baseline for treatment and control schools. There is a clear overlap throughout the whole distribution. A Kolmogorov-Smirnov test does not allow us to reject equality of both distributions .

Table 1 also shows that the applicants who are offered scholarships on the basis of merit have more assets, a lower poverty index score, and performed better on the baseline test than students offered a poverty-based scholarship. For instance, the poverty index, which ranges from 0 (wealthiest family in the sample) to 292 (poorest family in the sample), has a mean of 245.13 for students targeted based on poverty and 218.2 for students targeted based on merit. Likewise, the baseline test (ranging from 0 to 25) has a mean of 19.77 for merit-based students and 17.74 for poverty-based students. At the same time, however, the correlation coefficient between the poverty index and the test score for the entire sample of individuals at baseline is remarkably low (for example, the Spearman correlation coefficient is 0.16, and the pairwise correlation is 0.17). We return to this issue when we discuss the tradeoffs between the two targeting approaches. The main finding from Table 1, however, is that the samples are balanced at baseline, allowing us to identify causal effects.

# _Attrition_

Before turning to the estimates of program impact, it is useful to discuss the construction of our sample. Recall that the endline analysis is based on a household survey administered to a random subsample of applicants. The survey company received a list of 4,225 randomly selected applicants to 13
# **Logit Analysis in a Rotating Panel Context and an Application to Self-Employment Decisions***

**Patricio Aroca Gonzalez** _Universidad Cat6lica Del Norte, Chile_

**William F. Maloney** _The World Bank_

Our thanks to Roger Koenker and to the participants at the Eighth Annual Conference on Panel Data for helpful discussions. We also thank the Mexican National Institute of Statistics, Geography<sup>and</sup> Information (INEGI) for the use of the data. INEGI is in no way responsible for any incorrect manipulation of the data or erroneous conclusions drawn from it. Contacts:wmaloneyai)worldbank.org, parocac4)socompa.cecun.uncn.cl
<!-- Start of picture text --> 6<br>| — Observed<br>5 \ — — — - Theoretical<br>/<br>I) 1996 Farm Bill<br>|<br>4||<br>||<br>|<br>|<br>3 | | I<br>i h<br>|<br>|<br>i! 1<br>2 ; 1|1 ;<br>yb I || ,<br>I | | | 4 ", at<br>| s o my ! \ - v<br>é / a f<br>1 ce 1 ho! -<br>0<br>1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text -->
_Africa’s Macroeconomic Story_

**Figure 2: Trend Real GDP per Capita Growth in Developing Countries**

_Percent_ <!-- Start of picture text --> 4<br>2<br>0<br>-2<br>1962 1972 1982 1992 2002 2012<br>All Developing Countries ex. China and India Sub-Saharan Africa<br><!-- End of picture text --> _Source:_ World Bank Global Economic Prospects January 2013. _Note:_ 2012 estimates.

# _Cross-Country Perspective_

Much of the literature has focused on explaining differences in growth outcomes within the region using attributes such as:

- geographic disadvantages (landlocked versus coastal countries)<sup>4</sup> ;

- commodity exporters versus importers (mainly metals, oil and other minerals); and

- fragile states (countries in conflict / post-conflict).

We consider each of these in turn.

> 4 Collier (2008) lists the relatively large number of landlocked countries as being one explanation for Africa’s lower average growth rate compared to other developing regions.

11
4 ownership and exporter status drive firm perceptions as expected. We also test whether firms’ views actually reflect their experience, as evidenced by responses to other questions in surveys. Section V concludes.

# **II What Do Perceptions Tell Us?  A Review of the Literature**

# 2.1 <u>Rankings versus Ratings</u>

Any study of perception-based data needs to start off with a clear understanding of what is being measured. As shown by many studies, including those referenced in Iarossi (2006), responses are sensitive to how the question is framed. One debate is whether ratings or rankings are preferable for measuring “values” or other opinion-based variables. Alwin and Krosnick (1987) find that rating and ranking choices can produce different results, but that the difference is eliminated when “non-discriminating” respondents—that is, those who rate most items similarly are removed from the sample. To reduce the incidence of low discrimination, McCarty and Shrum (2000) suggest that first asking respondents to pick their least and most important values, and then to rate all values provides more robust differentiation than using rankings alone. Alwin and Krosnick (1985) also analyze comparisons of ranking and rating. They conclude that ranking may be preferred as forcing stronger expression and relationships. But they include an important caveat—forcing a ranking may induce spurious differentiation for respondents who genuinely do not have major preferences among different choices. One way of interpreting this literature is that, while the choice of ranking and rating will depend on the precise question at hand, elements of ranking and rating may need to be combined to allow responses that are less constrained yet discriminating.

This has implications for investment climate analyses which usually include coverage of many potential constraints. Whether a single “binding constraint” exists is an empirical question. In this case, rating would therefore seem preferable, to allow responses to reflect an essential degree of flexibility. In addition, simply asking firms to rank constraints provides no information on whether the top-ranked constraint is serious or
<!-- Start of picture text --> Figure 3 Distribution of BOD Load Intensity<br>Indonesian Paper Plants<br>150<br>140 US Std.=-3.8<br>- 130 kg/ton -<br>Co110<br>IL 110 Compliance i0<br>o 90<br>780<br>~7O<br>060<br>'040<br>o~30<br>~20<br>10 _ _ _ _ _ _ _ _ _ _ _<br>0 D * i<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Paper Plants<br>Figure 4 Distribution of BOD Load Intensity<br>Indonesian Textile Plants<br>160-<br>150 l<br>U) 140--<br>.9 130<br>'f 120 Compliance is<br>100 < 12.5 kg/ton Violation is > 12.5 kg/ton<br>0 100 of BODL_*<br>90 of BOD<br>,0 80<br>70<br>m 50<br>W. 40<br>30 20 6 567890 *E 21 1 56171819[1 E 4 5 2 6221111]<br>10 in~*<br>0<br>0 1 2 3 4 5 67 8 910 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27<br>Textile Plants<br><!-- End of picture text --> 5
OTHER POLYNESIA

- 2. Projections NRR-1 by 2010

|Yaor ftpultioan|Dp.<br>ratio|Pwriod|**Gth**<br>rate|C8|CDR|TFP|'IO<br>**Mai_**|o<br>F_ml_|**li**|**x1000**|
|---|---|---|---|---|---|---|---|---|---|---|
|1990<br>85|81.4|1990-94|1.87|28.1|4.9|3.50|68.4|74.3|21|25|
|1995<br>94|71.1|1995 -99|1.96|26.2|4.6|3.00|69.9|75.9|17|21|
|2000<br>103|63.4|2000-04|1.89|23.2|4.3|2.58|71.4|77.5|14|17|
|2005<br>114|60.9|2005-09|1.53|19.7|4.4|2.22|72.3|78.4|12|15|
|2010<br>123|54.1|2010-14|1.35|18.0|4.4|2.09|73.3|79.4|10|13|
|2015<br>131|49.0|2014-19|1.28|17.4|4.6|2.08|74.3|80.4|8|10|
|2020<br>140|45.2|2020-24|1.22|16.8|4.6|2.08|75.4|81.5|6|8|
|2025<br>149|43.9|2025-49|0.85|14.2|5.8|2.07|77.7|83.9|4|5<br>|
|2050<br>184|59.7|2050-74|0.25|12.4|9.9|2.06|80.0|86.4|3|4<br>|
|2075<br>196|69.5|2075-99|0.08|12.0|11.2|2.06<br>|81.2<br>|87.9<br>|2<br>|3<br>|
|2100<br>200|72.3|2100-24|0.04|11.8|11.3|2.06|U.1<br>|88.9<br>|2<br>|3<br>|
|2125<br>202<br>2150<br>203|73.4<br>74.2|2125-49|0.02|11.7|11.5|2.06|82.4|89.3|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75+<br>70-74<br>65-69<br>80-64<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34 22RRU21UgE<br>25-29<br>{§2ES2 %gm 1 ~20-24 2{ m<br>§2g§§2zS Zm%gEUB 1~15-19 1E2%m<br>10-14<br>5.9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 1 0<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income Group, 1985-90

<!-- Start of picture text --> TFR e0 Growth rate<br>Country Region Incor group Country Region Incor gr Country<br><!-- End of picture text --> <!-- Start of picture text --> Country Region Incowa group<br><!-- End of picture text --> 91
# **7. Tables**

**Table 1** – Descriptive statistics

||mean|sd|min|max|observations|
|---|---|---|---|---|---|
|_Individual characteristics_||||||
|age 20-30 yrs.|0.248|0.432|0|1|232292|
|age 31-40 yrs.|0.251|0.434|0|1|232292|
|age 41-50 yrs.|0.205|0.404|0|1|232292|
|age 51-50yrs.|0.165|0.371|0|1|232292|
|age 61-70 yrs.|0.098|0.297|0|1|232292|
|age 71-80 yrs.|0.029|0.167|0|1|232292|
|age 80 above yrs.|0.004|0.065|0|1|232292|
|male|0.589|0.492|0|1|232292|
|urban|0.862|0.345|0|1|232292|
|primary edu. or less|0.045|0.208|0|1|232292|
|secondary edu.|0.257|0.437|0|1|232292|
|tertiary edu.|0.698|0.459|0|1|232292|
|ingood health|0.766|0.423|0|1|232292|
|_Experiences and subjective beliefs_||||||
|will accept vaccine|0.656|0.475|0|1|232292|
|Perception of high risk of infection|0.469|0.499|0|1|98277|
|know positive case|0.596|0.491|0|1|232292|
|trustsgovt. health authorities|0.384|0.486|0|1|85392|
|_Norms_||||||
|No. people out of 100 who will<br>vaccinate|63.2|27.8|0|100|178581|
|No.people out of 100 who wear mask|74.6|29.0|0|100|226156|
|_Country-level circumstances_||||||
|stringency index|64.0|11.78|29.6|88.0|232292|
|New deathsper million(7-dayavg.)|2.01|2.65|0|17.45|232292|

20
<!-- Start of picture text --> ea<br>Years during 26<br>whichat least<br>20%of the<br>countries in the 24<br>sample were in<br>the first year of a 99<br>systemic banking<br>(blackonieislines} 20<br>18<br>16<br>14<br>12<br>Emerging1980s crisismarket Mencan-pesointemational contagiocnsis a n d | 10<br>8<br><!-- End of picture text -->

1970 1972 1974 i976 1978 1980 1982 1984 1986 198 1990 1997 1994 1996 1998 2000 2002 2004 2006