rafmacalaba/datause-encoder-data huggingface dataset:
|Second Local Government Development Project|Martin Onyach-Olaa|
|---|---|
|Second Local Government Development Project|Martin Onyach-Olaa|
|Second Local Government Development Project|Martin Onyach-Olaa|
|Second Local Government Development Project|Martin Onyach-Olaa|
|Second Local Government Development Project|Martin Onyach-Olaa|
|CGAPgrant to Fondation Zakoura|Xavier Reille|
|CGAPgrant to Fondation Zakoura|Xavier Reille|
|CGAPgrant to Fondation Zakoura|Xavier Reille|
|CGAPgrant to Fondation Zakoura|Xavier Reille|
|CGAPgrant to Fondation Zakoura|Xavier Reille|
|CGAPgrant to Fondation Zakoura|Xavier Reille|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|HIV/AIDS and Health(MAPprogram)|Mahamat Goadi Louani|
|Power Sector Generation & RestructuringProject|Arturo S. Rivera|
|Power Sector Generation & RestructuringProject|Arturo S. Rivera|
|Power Sector Generation & RestructuringProject|Arturo S. Rivera|
|Power Sector Generation & RestructuringProject|Arturo S. Rivera|
|Power Sector Generation & RestructuringProject|Arturo S. Rivera|
|Power Sector Generation & RestructuringProject|Arturo S. Rivera|
|Power Sector Generation & RestructuringProject|Arturo S. Rivera|
|Power Sector Generation & RestructuringProject|Arturo S. Rivera|
|Power Sector Generation & RestructuringProject|Arturo S. Rivera|
|Power Sector Generation & RestructuringProject|Arturo S. Rivera|
|EmergencyCommunityEmpowerment and Public Works Project|Norman BentleyPiccioni|
|EmergencyCommunityEmpowerment and Public Works Project|Norman BentleyPiccioni|
|EmergencyCommunityEmpowerment and Public Works Project|Norman BentleyPiccioni|
|EmergencyCommunityEmpowerment and Public Works Project|Norman BentleyPiccioni|
|Reachingthe VeryPoor through Credit Unions-WOCCU/FFH|Syed Mesbahuddin Hashemi|
|Reachingthe VeryPoor through Credit Unions-WOCCU/FFH|Syed Mesbahuddin Hashemi|
|Reachingthe VeryPoor through Credit Unions-WOCCU/FFH|Syed Mesbahuddin Hashemi|
|Reachingthe VeryPoor through Credit Unions-WOCCU/FFH|Syed Mesbahuddin Hashemi|
|Reachingthe VeryPoor through Credit Unions-WOCCU/FFH|Syed Mesbahuddin Hashemi|
|Power Outages|-0.187*|-0.205*|-0.192*|
|---|---|---|---|
|(days)||||
||(0.102)|(0.105)|(0.106)|
|Courts Obstacle<br>|-1.432|-1.080|-1.081|
|(cell average)||||
||(1.322)|(1.471)|(1.466)|
|Crime Losses Y:1<br>|-1.510*|-1.432|-1.378|
|N:0||||
||(0.907)|(0.910)|(0.908)|
|Firm Inspected Y:1|-0.590|-0.564|-0.552|
|N:0||||
||(0.846)|(0.849)|(0.850)|
|How Much Of An|-0.400|-0.415|-0.426|
|Obstacle: Tax||||
|Rates||||
||(0.321)|(0.321)|(0.322)|
|How Much Of An|-0.185|-0.193|-0.173|
|Obstacle: Labor||||
|Regulations?||||
||(0.401)|(0.401)|(0.399)|
|Population (logs)||-46.729|-57.415|
|||(40.569)|(40.654)|
|Urbanization (%)||-1.699|-1.623|
|||(1.360)|(1.355)|
|WBL Index||-5.633|-0.854|
|||(12.997)|(13.437)|
|Women can work||-26.481***|-24.642***|
|the same night<br>||||
|hours as men Y:1||||
|N:0||||
|||(6.178)|(6.282)|
|Women can work||4.173|3.185|
|in the same||||
|industries as men||||
|Y:1 N:0||||
|||(6.741)|(6.843)|
|Ratio of Female to||-1.399|-0.822|
|Male Population||||
|||(1.338)|(1.382)|
|Fertility Rate||-25.246***|-28.560***|
|||(8.963)|(9.345)|
|Starting a Business||0.025|0.038|
|(DTF)||||
|||(0.088)|(0.089)|
|Inflation (%)||<br>1.153**|<br>1.158**|
|||(0.525)|(0.531)|

65
- 19 -

**<u>Table 4. Electricity tariff structure in Albania</u>**

|Year|1st block<br>rate|2nd block<br>rate|Threshold<br>(kWh)|Norma<br>consumption|tive<br> (kWh)1/|
|---|---|---|---|---|---|
||(lek/kWh)|(lek/kWh)||Urban|Rural|
|2002|4.0|8.0|300.0|356.9|244.4|
|2003|4.0|8.1|300.0|356.9|244.4|
|2004|4.5|8.9|220.0|363.8|237.0|
|2005|4.5|9.0|210.0|363.8|237.0|

1/ Average across districts for the summer period. Source: Electricity Regulatory Entity, Albania.

> How much people would respond to changes in marginal prices, _MP_ , depends on the price elasticity of demand. In the literature the estimates vary significantly, depending on country, period and data being analyzed, and methodology adopted. According to a classical literature review (Taylor, 1975), the price elasticity is estimated at –1.02 to –2.00 for residential users and –1.25 to –1.94 for industrial consumers. A more recent meta analysis shows that the average price elasticity of residential electricity demand is –0.35 in the short run and –0.85 over the long run (Espey and Espey, 2004). With regional aggregate data, Bernstein and Griffin (2005) find that the residential electricity elasticities in the United States are –0.24 and –0.32 in the short and long run, respectively. With micro data, Wilder and Willenborg (1975) estimate the elasticity at –1.31 in South Carolina in 1973. Reiss and White (2005) report an elasticity of –0.39 for California households during the period: 1993-1997. With more detailed micro data on an hourly basis, the time-of-day price elasticity is estimated to ranges from –0.13 during peak hours and –0.22 during non-peak hours in Connecticut

> (Hausman _et al._ , 1978).

**Nordin’s difference.** Second, net positive (negative) consumer welfare caused by the increasing (decreasing) block tariff should be included in _X_ 1, but it will be another endogenous variable for the same reason as above mentioned. Note that the price variable in the demand function must be a marginal tariff rate of the final block that each consumer faces, rather than the average price (computed as the total bill divided by total consumption). This is basically because neoclassical economic theory assumes that people consume goods or
# **5. Results**

## **5.1 Characteristics of the migrant population**

## _5.1.1 Demographic characteristics and spatial distribution of migrants_

We begin our analysis by looking at the total number of migrants recorded during 2017 and 2018 in each Libyan province (Figure 4a). Almagerb, Alkufra, and Bengazi had the most migrants registered, and Al Jabalal Al Akhadar, Ghat, and Ubari had the fewest.<sup>9</sup> Figure 4b shows that the number of migrants has increased in almost all provinces from 2017 to 2018, with a huge increase in Almargeb. Finally, Figure 4c documents high heterogeneity in terms of number of migrants across Libyan provinces during the period under analysis. There was a slight increase in the number of migrants between rounds 5 and 16 (January 2017–February 2018). In round 17 (March-April 2018), the data indicate a massive increase in the number of migrants in almost all provinces. After that, the last three rounds of 2018 included in our data set show a reduction in the number of migrants registered at the FMPs, except in Almagerb, where the number of migrants remained very high.

DTM data also allow us to recover some information on the demographic composition of migrants. These are represented in figure 5 at the Libyan level and in figure 6 at the provincial level. The large majority (97%) of migrants are males. In 2017, females and minors were between 3% and 5% of the migrant population, respectively. The most diverse composition of the population is found in the Western regions in 2017 (Aljufra, Ghat, and Nalut). In 2018, the shares of women and minors were significantly higher.

## _5.1.2 Top origin countries and preferred destinations_

In each survey round, for each FMP, DTM data report the three most common migrant nationalities registered and the three most preferred destination countries for the migrants located at that FMP.

Table 2 shows the five nationalities most commonly registered at the FMPs in 2017 and 2018. The order of the nationalities indicates how frequently they were found.

Table 3 shows the five more preferred arrival destinations registered at the FMPs in 2017 and 2018.

> 9 Baseline Assessment data are repeated estimates of net stocks of migrants at the FMP level. This implies that there is the possibility of double counting (counting the same migrant at two different FMPs during a given time period). There is a tradeoff; using the monthly data would minimize the probability of double counting (it is unlikely that the same individual would move between FMPs over such a short time period), but high frequency data may be affected by a high level of variance, which would make difficult to analyze longer-time trends in the evolution of migration flow. For this reason, we decided to use the yearly average of the monthly data at the FMP level (which are themselves an average of data collected each two weeks).

9
Finally, we analyze “official” data from Eurostat, which is calculated by the European Union (EU) using confidential census data for a sample of 9 EU countries, by “EU-industries”, which are broader than 2-digit NACE codes. We do not have data from this sample for non-EU, transition countries or for certain industries. For example, whereas we calculate using the Amadeus database about 600 observations by country and 2-digit NACE industry codes, Eurostat only includes about 250 observations. Eurostat provides entry rates, calculated as the one-year change in the number of firms, and exit rates, calculated as the number of firms exiting the industry, excluding mergers and acquisitions.

Table 11, Panel A shows that our entry rates across countries and industries using the Amadeus database and Eurostat data are significantly correlated at about 67%. In addition, both Amadeus and Eurostat entry rates are highly correlated with Eurostat exit rates. This is consistent with Dunne, Roberts, and Samuelson (1988), who find a strong positive relationship between entry and exit rates across U.S. industries. Panel B shows correlations by 2-digit industry code, averaged across countries. We find that Eurostat entry rates are strongly significantly correlated at the industry-level with Amadeus entry rates and D&B U.S. entry rates at 90% and 60%, respectively. This suggests that (i) our calculations using the Amadeus data are in line with official figures and (ii) industry-level entry rates are broadly similar in both Europe and the U.S. Table 12 shows that our main regression results are robust to the substitution of entry rates from Eurostat.

# **IV. Conclusion**

This paper uses cross-country data to identify the impact of regulations on entry with a view to determining what motives might prompt such regulation. We also examine other potential environmental factors that could affect entry. We use the Amadeus database, which includes financial data on over 3 million firms in Western and Eastern Europe. This data improves upon previously used datasets in that it includes (1) a large number of private, unlisted and publicly traded corporations and (2) all sectors (it is not limited to manufacturing). This database offers a 31
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>50<br>Scenario<br>ne}3 | Detta invest<br>= 25 BW vetta Maxinvest<br>5<br>aod || Healthy Diet Invest<br>2 | Healthy Diet. Maxinvest<br>a°.<br>Qa || Healthy & Sust Diet Invest-<br>6 1° in By Heattny & sust Diet Maxinvest<br>-25<br>SSP2 SSP2 SSP2 SSP2 SSP2 SSP2<br><!-- End of picture text -->

<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>400<br>Investment type<br>7)Q 200 BB Expansion<br>—5 WD Uparade<br>a= 200 BW Efficiency<br>| Maintenance.<br>EEELFESGBSEEEESee ess FEESEFSESFEBEBESESSFsh FELEFEBESEESEResess FEELEEFEBEETSSess as EFELFEELFEBESEESSESess EFEELEEEEBEEBESSSSs F<br>eta 2 a= S22 2 aS 1 oO = of = cz oa 2 DS S2tOQ eo z= S2oftA=<br>Sf >dus THFBGZSE THFBEBSE THEFBEGZGSE CHFBZGE CHLEEDBBS<br>g@2o0Og ,%®20 OgB2ogaiM. Oga2o2%320= B2o030Og 5M ga2aogaog 3? = S8=2oOOg 3?456«<br>=:,Ex8= 8| xzPEwY= zsPeo | rea 5 osztea ggEI ré2PEa3<br>322 B29 gz 322 322 B29<br>=e 2 = ag & 2 2 =o S 2 eS oR a<br>= é rz rs Ls ré ré<br>pmo® <=[icy bioca poo® 2)ia bs[4cy<br><!-- End of picture text -->
# **-** **<u>Table B.7. Descriptive Statistics, all countries years</u>**

||**MHH**<br>|**FHH**<br>|
|---|---|---|
|Heads ae|48.14|55.52|
|g|(13.61)<br>|(14.10)<br>|
||0.36|0.70|
|Head does not complete primary school|(0.48)<br>|(0.46)<br>|
|Head's highest education level is primary|0.38<br>(0.48)<br>|0.19<br>(0.40)<br>|
|Head's hihest education level is secondar|0.12|0.06|
|g y|(0.32)<br>|(0.23)<br>|
|Hd' hiht dti ll i tti|0.15|0.05|
|eas ges eucaon eve s erary|(0.36)<br>|(0.21)<br>|
||0.01|0.04|
|Head is never married|(0.11)|(0.19)|
|Hd i id|0.95|0.17|
|ea s mono marre|(0.22)|(0.37)|
||0.02|0.03|
|Head is poly married|(0.15)|(0.16)|
||0.01|0.25|
|Head is divorced/separated|<br>(0.07)|<br>(0.43)|
|Head is widowed|0.01<br>|0.71<br>|
||(0.11)<br>|(0.45)<br>|
|Hd i ld|0.77|0.19|
|ea s empoye|(0.42)|(0.39)|
||0.03|0.01|
|Head is unemployed|<br>(0.16)|<br>(0.10)|
|Head is homemaker/housewife|0.01<br>|0.55<br>|
||(0.08)<br>|(0.50)<br>|
|d i d|0.00|0.00|
|Hea s stuent|(0.04)|(0.03)|
||0.18|0.27|
|Head is pensioner/retired/disabled|<br>(0.39)|<br>(0.45)|
|Head is other activities|0.02|0.01|
||(0.15)<br>|(0.11)<br>|
|P it ti|2,034,242.98|1,731,419.27|
|er capa consumpon|(2,496,523)|(2,451,185)|
|Per capita transfers|204.12<br>|471.50<br>|
||(805.10)|(1,655.70)|
|Household size|7.39|5.75|
||(4.05)<br>|(3.99)<br>|
||2.85|1.89|
|Number of children|(2.46)|(2.38)|
||0.26|0.29|
|Number of seniors|<br>(0.54)|<br>(0.48)|
|12 dlt hild|0.04|0.16|
|- aus, no c|(0.19)<br>|(0.37)<br>|
||0.10|0.09|
|1-2 adults, 1-2 children|(0.29)|(0.28)|
|1-2 adult, 3 or more children|0.25<br>|0.13<br>|
||(0.43)|(0.34)|
|3 adults or more 0-1 child|0.16|0.25|
|,|(0.37)<br>|(0.43)<br>|
|3 adults or more, 2-3 children|0.17<br>(0.38)|0.19<br>(0.39)|
||028|019|
|3 adults or more, 4 children or more|.<br>(0.45)|.<br>(0.39)|
|Rural area|0.32|0.27|
||(0.47)<br>|(0.45)<br>|
||0.68|0.73|
|Urban area|(0.47)|(0.45)|

Note: Household sampling weights are applied. Standard deviations are in parentheses. FHHs and MHHs are self-reported.

76
3 distribution (Buchinsky, 1994, 1998; Machado and Mata, 2001; Bauer and Haisken-DeNew, 2001; Hartog, Pereira and Vieira, 2001). This reinforces the need to investigate the screening hypothesis across the earnings distribution. An appropriate empirical strategy is to fit the earnings model across different points in the conditional sample distribution, using the quintile regression method. This was first introduced by Koenker and Bassett (1978). Assume _yi_ , _i_ = 1,..., _n_ , is a sample of observations on log earnings, and that _Xi_ is a _K_ x 1 vector comprising the education, experience and other control characteristics contained on the righthand side of equation (1). The quintile regression model can be expressed as: where Quint _θ_ ( _yi_ | _Xi_ ) denotes the quintile _θ_ of log earnings conditional on the regressor vector. Following Koenker and Bassett (1978), the regression quintile _θ_ can be defined as the solution to the problem:

where _ρθ_ (.) is known as the “check function” and is defined as: Estimation is by minimizing the sum of weighted absolute deviations and can be performed using linear programming methods (Buchinsky 1998). An estimated variancecovariance matrix for the chosen system of quintile regressions is obtained using a bootstrap re-sampling method using Stata Release 7 (StataCorp 2001). Quintile regression coefficients can be interpreted by considering the partial derivative of the conditional quintile with respect to a particular regressor. This equates to the marginal change in the _θ_ th conditional quintile due to a marginal change in the regressor. It is however important to note that a sample individual who is in the _θ_ th conditional quintile may no longer remain in that quintile if his or her characteristic measured by the particular regressor changes. So, for example, rates of return to additional years of schooling or experience as captured by the estimated coefficients apply to an individual remaining in a particular conditional quintile.

# **3. Data Source and Description**

The present paper uses data drawn from the 1988, 1992 and 1998 Brazilian household surveys (Pesquisa Nacional por Amostra de Domicílios, PNAD). The PNADs are a series of nationally representative household surveys conducted more or less annually since 1976, using a consistent methodology by the Instituto Brasileiro de Geografia e Estastistica (IBGE). All members of each household over ten years in age are asked detailed questions concerning their labor market activity during one week in September. Each survey covers a nationally representative sample of households. The sample size has risen progressively from 69,066 households in 1988, to 94,171 in 1992 and 112,434 in 1998. The present paper draws from these households the sample of employed men between the ages of 18 and 65, who report earnings and hours of work data and information on human capital and the other controls used for estimation purposes. This results in a sample of 65,002 in 1988, 67,880 in 1992 and 74,003 in 1998. The 63% increase in the number of households sampled over the decade
and liberalization of trade and capital flows, including for foreign direct investment. Extensive restrictions on domestic financial systems were eased, and market access to foreign institutions was increased.

<!-- Start of picture text --> Figure 24: LAC Growth and Inflation<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006<br>-10<br>Growth (GDP per capita) Inflation (GDP deflator)<br>Annual % change<br><!-- End of picture text --> Source: World Development Indicators <!-- Start of picture text --> 180 Figure 25: Public Debt of Selected Latin American Countries<br>160 Total (domestic plus external) gross central government debt (% of GDP)<br>140<br>120 Argentina<br>100<br>Brazil<br>80<br>Chile<br>60<br>40 Mexico<br>20<br>Uruguay<br>0<br>Source: Reinhart and Rogoff (2011).<br><!-- End of picture text --> 82. In the first half of the 1990s, these stabilization policies succeeded in bringing down inflation—an achievement that has endured since. With the debt overhang resolved and reforms under way, private capital flows resumed. In conjunction with more liberalized domestic financial markets, domestic spending rose, real growth accelerated, and social indicators began to improve.

83. Yet, towards the end of the 1990s signs of fragilities became evident with Mexico‘s ― tequila‖ crisis and contagion to other major economies in the region. In parallel, in the wake of the Asian and Russian crises, investor appetite for global risk declined; and the consequent sudden reversals of capital inflows accentuated inherent vulnerabilities in many Latin American economies and led to economic and financial crises in Brazil and Ecuador (1999), Argentina (2001), and Brazil and Uruguay (2002)) and other countries in the region also came under pressure (Figure 26).

84.

35
Table 1. Trial and data summary

|MadangProvince|Control|Treatment|
|---|---|---|
|No. of schools in the trial|15|16|
|of which were sampled in EGRA|||
|at baseline (2011)|11|10|
|at end‐line (2013)|10|12|
|at both baseline and end‐line|6|7|
|Western Highlands Province|Control|Treatment|
|No. of schools in the trial|23|23|
|of which were sampled in EGRA|||
|at baseline (2013)|10|10|
|at end‐line (2014)|23|23|
|at both baseline and end‐line|10|10|

Source: PNG Madang and WHP EGRA datasets for number of schools in the sample; unpublished government reports and sampling data for number of schools in the pilots Sample sizes by grade, province and school treatment status are described in Table 2. Second grade students were sampled only in the Madang province baseline round, and the samples for the Western Highlands Province include 4<sup>th</sup> grade students only at baseline. Each round of EGRA is sampled as a repeated cross-section, and, because of the timing, no cohort was sampled more than once except for the 2<sup>nd</sup> grade students in the Madang baseline; they were in 4<sup>th</sup> grade at the time of the Madang end-line sampling.

Table 2. Number of students sampled in EGRA

||Madan<br>|gProvince<br>|Wester<br>Pr<br>|n Highlands<br>ovince<br>|
|---|---|---|---|---|
||Control|Treatment|Control|Treatment|
|Baseline sample|||||
|No. of 2nd grade students|188|212|0|0|
|No. of 3rd grade students|230|216|223|228|
|No. of 4thgrade students|214|219|211|227|
|End‐line sample|||||
|No. of 2nd grade students|0|0|0|0|
|No. of 3rd grade students|197|240|466|489|
|No. of 4thgrade students|184|254|0|0|

Baseline and end‐line surveys sampled different cohorts of students. Source: PNG EGRA Madang and WHP datasets The data used in this evaluation are that of grade 3 only. The 4<sup>th</sup> grade sample at end-line does not include any students from Western Highlands Province, and while the 4<sup>th</sup> grade sample in Madang province could be used with the 2<sup>nd</sup> grade sample, the sample of students in schools that were 7
Panel A of Table 3 reports the intent to treat effects on business related outcomes. The dependent variable in column 1 is an aggregate index of business knowledge that includes questions on competition and basic business concepts, not necessarily taught during the training. As mentioned, Appendix Table A4 reports the intent to treat impacts for the individual items that are used to construct the aggregate index. The definition of the aggregate variables is reported in Appendix B. Because these aggregates include some variables that are only observed at follow-up, the number of observations for the aggregate is 3,494 instead of 4,161 observations included in the baseline.

Consistent with the assumption of the model, we find that business training (and not being assigned a winner of the lottery) increases business knowledge for all CO members interviewed. This is remarkable because business knowledge was assessed 18 months after the business training was implemented. Given that a substantial amount of time has elapsed from training to testing, it is plausible that the acquired business knowledge will not be forgotten. The next two columns report business creation in the household with (column 2) or without (column 3) the CO member’s involvement in the business. The sample includes again all study CO members. We find no effect of business training on business creation either with or without access to the larger loan. We next examine business failure among business owners at baseline and again find no effect. The point estimate on business training is negative and large, but so is the standard error. The model is silent on business entry as it assumes that all households are simultaneously involved in self-employment and wage work, but it does predict that business failure should be lower among clients offered business training.

Columns 6 and 7 report intent to treat impacts on operations and business practices for the sample of business owners at baseline. We find that the offer of business training leads to improvements in business practices such as recording the sales on a piece of paper as well as separating business from household accounts by recording money taken for household needs. There are also some improvements in business operations, especially among business owners assigned as winners of the lottery.<sup>27</sup> In

> 27 Appendix Table A4 shows that the reason there is no overall improvements in business operations for the sample of CO members offered business training is due to opposing changes in individual items. In particular, business training encouraged entrepreneurs to secure a buyer which led to reductions in marketing and the need to open the business to the public.

18