rafmacalaba/datause-encoder-data huggingface dataset:
can lead to bias and could result in a poorly performing differenced estimator, see Arellano et al. (2002), Wright (2005), Bond et al. (2005).<sup>17</sup> To reduce the weak instruments bias<sup>18</sup> of the Difference-Estimator, Arellano and Bover (1995) and Blundell and Bond (1997) propose a different GMM estimator: the System Estimator. The System Estimator, using some mild stationarity assumptions, relies on a _system_ of equations, namely the regression in differences, (eq. (4)), added to the regression in levels, (eq. (3)). The system estimator exploits the orthogonality condition of equations (5) and (6) for the equation in differences. For the equation in levels, lagged _differences_ of the corresponding variables are used as instruments. Under mean stationary assumptions, these are shown to be appropriate instruments; that is the additional conditions required are that there be no correlation between the _differences_ of the variables and the country specific effects: _E_ [( _yi_ , _t_ + _p_ − _yi_ , _t_ + _q_ ) η _i_ ] = 0 ; _E_ [( _X i_ , _t_ + _p_ − _X i_ , _t_ + _q_ ) η _i_ ] = 0 ∀ _p,q_ (7) The additional moment conditions for the second part of the system, the regressions in levels are: _E_ [( _yi_ , _t_ − _s_ − _yi_ , _t_ − _s_ − 1)( η _i_ + ε _i_ , _t_ )] = 0 _s_ = 1, (8) and The extra orthogonality conditions help in gaining identification in the case where the dependent variable (institutions) comes from a very persistent series. For instance, in the extreme case of a unit root the difference, ( _yi_ , _t_ − _s_<sup>−</sup> _yi_ , _t_ − _s_ − 1) would be a pure innovation and so the best instrument for an AR (1) process. For all the estimates we present a test for the extra assumptions

> 17 Monte Carlo experiments show that the weakness of the instruments can increase the small-sample bias of the GMM estimates and make inference unreliable, Staiger and Stock (1997), Stock et al. (2002).

> 18 It is worth noting that the bias properties under weak instruments, in this case the presence of a highly persistent series, are well known only for the autoregressive coefficient.

25
to value added, employment and number of establishments. The various combinations of these strata generate the cells for each industrysizeregion. Notice the lack of 4-digit stratification, feature to which we shall return in the subsection below.

As a first look at the data, we assess the comparability between the manufacturing censuses and ES using the Quantile-Quantile (QQ) plot. Figure 8 plots the quantiles of the size distribution of firms in the ES against the quantiles based on manufacturing census data. The size quantiles are given in a logarithm of employment. If the points in the plot more or less lie on the same line, then we expect the ES data to reflect the size distribution in the national censuses. Departures from this relationship indicate that the size distribution in the two datasets are<sup>18</sup> <!-- Start of picture text --> Cote d’Ivoire ES 2009 Ethiopia ES 2011<br>2 4 6 8 2 3 4 5 6 7<br>Employment(Census) Employment (Census)<br>Ghana ES 2007 Ghana ES 2013 Kenya ES 2007 Kenya ES 2013<br>2 3 4 5 6 7 2 3 4 5 6 7 2 4 6 8 10 2 4 6 8 10<br>Employment (Census) Employment (Census) Employment (Census) Employment (Census)<br>10 10<br>8 8<br>6 6<br>Employment (ES 2009) 4 Employment (ES 2011) 4<br>2 2<br>8 10 10<br>7<br>6 8 8<br>6<br>5<br>6 6<br>4<br>4<br>Employment (ES 2007) Employment (ES 2013) Employment (ES 2007) Employment (ES 2013)<br>4 4<br>3<br>2 2 2 2<br><!-- End of picture text --> Figure 8: QQ plot of the enterprise censuses vs surveys A visual comparison of the QQ plots shows that the distribution based on the ES and the census data look different in all the countries except for Kenya.<sup>19</sup> The top panel of 8 plots the quantile of size distribution in the 2009 ES for Cˆote d’Ivoire and 2011 ES for Ethiopia vis-a-vis the manufacturing census data in same year. As can be seen from the figure the distribution in the ES differs considerably from that implied by the manufacturing census. Similarly, the quantile of firm size in ES for the year 2007 and 2013 is plotted against a the 2003 National Industrial Census of Ghana. A visual comparison

> 18Note that we choose not to use sampling weights as they are not appropriate to make this comparison since they are at broader strata.

> 19To make it comparable with the ES, firms employing fewer than 5 workers are dropped in our sample.

23
that Jordan gave when they arrived in Washington in 1992, and he was the head of the Kennedy Center.<sup>82</sup> As with McNamara, James Wolfensohn continued the effort to push the World Bank beyond a narrow banking approach, toward a more comprehensive development approach that combined research on economic development with policy goals in a "knowledge bank." The results could be seen in the Economic Development Institute (EDI) and the annual publication of the World Development Reports (WDRs).<sup>83</sup> His development strategy was called the Comprehensive Development Framework (CDF) that sought to achieve development based on a comprehensive and inclusive approach with measurable long-term results, with the recipient nation in the driver’s seat, and in partnership with international institutions, NGOs, and the private sector.<sup>84</sup> Although Wolfensohn had worked in the financial services community prior to his appointment, he regarded the Bank as somewhat different from the for-profit world. When he arrived, appointments within it followed national lines and sought to achieve an implied balance among countries and regions. This made the Bank rigid to a point, as did its compensation system.<sup>85</sup> Thus, as a part of this strategy, Wolfensohn tried to work more effectively with NGOs to achieve results in two main areas: one was the interrelationship of the substantive areas— governance, corruption, finance, infrastructure and then education, health and the environment. Since these items are interrelated, they cannot be addressed apart from each other. The second area of the CFT was the consideration of governance and legal and judicial systems and the financial systems to control. Despite the interrelationships, the development community did not work together but competed within itself.<sup>86</sup> One of his major initiatives was the Heavily Indebted Poor Countries (HIPC) debt forgiveness program. As someone who had spent a lifetime in banking, he felt that a lending business that served low credit borrowers would, by its nature, not receive payment of 100% on every loan.<sup>87</sup> The ultimate success of these programs in American politics, however, required that Wolfensohn work with a Republican Congress to provide the funds. His second five-year term at the Bank began in June 2000. At the end of the year, George W. Bush was elected president, which changed the relationship with the Bank. The Bush administration was more concerned with the IMF than the World Bank, and development issues were a lower priority, at least in the early years of Bush’s tenure.<sup>88</sup> The situation with Treasury improved when John Snow replaced 20
## **Figure 6 Average MSE and Trend of Gini, for LAC Countries**

- A) Average Gini MSE B) Trend of Average Gini MSE

<!-- Start of picture text --> 3.50 3.50<br>3.00<br>3.00<br>2.50<br>2.50 2.00<br>2.00 1.50<br>1.00<br>1.50 0.50<br>0.00<br>1.00<br>0.50<br>0.00<br>2017 2018 2019 2020<br>Old Model New - Rescaling<br>New - No Rescaling 2017 2018 2019 2020<br>Source: Own estimations based on SEDLAC.<br>Distributional effects<br>This section presents the performance of the considered models' variations along the whole income<br>distribution. The Growth Incidence Curves (GICs) for each country/year are calculated using the SEDLAC<br>actual income data and the simulated income vector for each variation. The squared errors correspond<br>to the difference in the annualized growth rate at each percentile of the income distribution. The MSEs<br>correspond to the average squared error (MSE) across countries over time. Figure 7 presents the results.<br>It is worth noting that these results do not include information on the 1-5 and 96-100 percentiles due to<br>the high dispersion in the tails of the actual distribution.<br>Figure 7 Average MSE of GICs for LAC Countries<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>2017 2018 2019 2020<br>Old Model New Rescaling New No Rescaling<br>Old Model New Rescaling New No Rescaling Old Model New Rescaling New No Rescaling Old Model New Rescaling New No Rescaling Old Model New Rescaling New No Rescaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

# **Distributional effects**

This section presents the performance of the considered models' variations along the whole income distribution. The Growth Incidence Curves (GICs) for each country/year are calculated using the SEDLAC actual income data and the simulated income vector for each variation. The squared errors correspond to the difference in the annualized growth rate at each percentile of the income distribution. The MSEs correspond to the average squared error (MSE) across countries over time. Figure 7 presents the results. It is worth noting that these results do not include information on the 1-5 and 96-100 percentiles due to the high dispersion in the tails of the actual distribution.

Source: Own estimations based on SEDLAC.

Results show that differences between the proposed variations of the model are more significant in this case (a higher magnitude in the units of the vertical axis), and that is because more cut-offs are 22
<!-- Start of picture text --> Number of Days without Rain Deviation in Number of Days without Rain<br>0 432 864 1,296 1,728 2,160 2592 3024 3456 3,888 4,320 0 432 864 1296 1,728 2160 2592 3,024 3456 3888 4,320<br>1 nH'1OTf1YTS1fYT4iTY EO14HnI1PTi1TO iTSff1f<br>'\' I11 i)f| I'1 fl1I 'i| 111 i)|| 'I| 11\ i)\' 11' 1|' 1'\ 'f! '\\ '11 1i!<br>6 ' | } ! 1 4 05 i}1 i]i |! |! i}! ! i]1 | '' j<br>3-—0 -3—< }1;<br>S S \ i \ mt \ \ i \<br>Lo} ' Lo}<br>-5 i ! { t ! 1 I ! F<br>' ' 1 | ' ' ' 1 ' ' -054— t ! t ! 1 t {<br>''!!!''''1t'<br>1t i) t i) ' I ! t 1 ' ' | ' 1 ! I '<br>1 1 ' ' ' ' 1 1 ' ' 1 i ' ! ' ' ' 1 i]<br>i ' i ' I ' i) ' ' 1 ' i ' i ' ' i) '<br>ssFf< € €F< §€e £€x ££ SF. § Crs< A< ~ ars ees ee€x £a €&eS< £ €s € FF< ~S<br>Sf& 2 FF& TFS FF& FTF3s SF& SFoO Sfs > a& 2 & S & Ce) & oe 3s s<br>eg gg FF Fs ee ¢ & x<br>Longest Dry Spell<br>2 0pe 4321 8641 «1,296i 1,728] 2,1601 2592I 3,0241 3,456i 3.8884 4,320<br>'t'1i't!1<br>1I ' ! 1 ' I ! !<br>| ' i ' j 1 } } 1<br>= ° ,<br>5 \ \ \ \ y i<br>= f ( f<br>gs° t{ {{ ft{ {( t{ {I {{ t{ i<br>~2 1 1 1 1 1 1 1 i 1<br>1t'!1't!1<br>'t ! ! i ' t ! !<br>1' I 1' '1 1i 1' i)t i)' 'i)<br>-4 i I ' i 1 1 I ! 1<br>7 eS—x 1 aL111s rss _ 1@ 1sKS_L__<2 we1 eS__1___FS<br>kf FF FTF TF LK & c as oe<br>” re ve &<br><!-- End of picture text -->
Tunisian Labor Force Survey, unemployment among youth (aged 29 or below) holding a university degree increased from 34 percent in 2005 to 56 percent in 2011. These figures are suggestive of a long education-to-work transition for university graduates. University graduates constitute a large share of unemployed youth in Tunisia. While tertiary educated youth (15-29 years old) made up less than 16 percent of those employed in the Tunisian labor market in 2010, they accounted for over 34 percent of the unemployed. In this context, the graduates’ employment problem has become one of the main concerns for policymakers in Tunisia.

As part of a Development Policy Loan aiming to support a broad set of employment policies in Tunisia, an innovative entrepreneurship track was introduced into the tertiary curriculum in academic year 2009/10. Up to then, during the last semester of the applied undergraduate curriculum, students took an internship and wrote an academic thesis as graduation requirements. In June 2009, the Ministry of Education and Higher Education passed a reform creating an entrepreneurship track where students would receive business training and coaching to develop a business plan. In August 2009, the Ministries of Education and Higher Education and of Vocational Training and Labor jointly signed an order to allow students to graduate by submitting their business plan instead of a traditional thesis. The newly established entrepreneurship track aimed primarily at increasing self-employment and fostering an entrepreneurship culture among university graduates, as well as more broadly at improving participants’ employment outcomes.

To increase students’ awareness of the new track, communication campaigns took place on campus and through the media to inform students about the newly introduced alternative to the standard curriculum. Once in the entrepreneurship track, students were offered support for developing a business plan through business training and personalized coaching. The entrepreneurship track provided students with: (i) entrepreneurship courses organized by the public employment office; (ii) external private sector coaches, mainly entrepreneurs or professionals in an industry relevant to the student’s business idea; and (iii) supervision from university professors in development and finalization of the business plan. For each student, the final product of the program was a comprehensive business plan that served as an undergraduate thesis. Participants were also given the option to submit their business plan to a competition, with a chance to win seed capital to fund their project.

7
line to be below those for 2005 and 2012 for both men and women, however, that does not seem to have been the case during this crisis.<sup>11</sup> So far, we have presented results on the incidence (Table 1) and persistence (Table 2 and Figure 1) of Nininess. In Table 3, we dig a little deeper and explore the percentage of Ninis in their fifth and final interview, who come from studying during the first interview, as opposed to coming from only working, working and studying, or from already being Ninis (tables available upon request).

Approximately a fifth of male Ninis and a tenth of female Ninis aged 15 to 24 years old were studying one year earlier. Given the social expectations for most of these youths to be at school, it is unsurprising that these figures are driven mainly by youth aged 15 to 18 (among whom 28.7 percent of male Ninis and 21.7 percent of female Ninis were studying a year earlier). In comparison, of those male Ninis in the 19 to 24 age range, only 12.2 percent were studying a year earlier, with the corresponding figure for women being nearly half the size at 5.5 percent.

Table 3. From school to Nini: Percentage of Ninis in fifth interview who were studying during the <u>first interview, Mexico, by gender and age Group</u>

|||_Male_<br>|||_Female_<br>||
|---|---|---|---|---|---|---|
||_(1)_<br>_15 to 18_|_(2)_<br>_19 to 24_|_(3)_<br>_15-24_|_(4)_<br>_15 to 18_|_(5)_<br>_19 to 24_|_(6)_|
|_Variable_|_y.o._|_y.o._|_y.o._|_y.o._|_y.o._|_15-24 y.o._|
|All|28.7|12.2|20.1|21.7|5.5|10.7|
|_Quintiles of H_|_H income per_|_capita_|||||
|Quintile 1|39.7|20.8|29.1|24.3|7.8|13.0|
|Quintile 3|29.5|11.0|20.7|22.2|4.1|9.7|
|Quintile 5|25.7|13.8|17.9|25.3|8.2|12.3|
|Urban|31.7|13.7|22.1|25.6|7.2|12.7|
|Rural|19.4|5.9|13.1|16.1|2.1|7.2|

_Notes_ : We employ a pooled sample of nearly 360,000 observations (persons) that had complete information on the variables of interest for the first and fifth interviews of the rotating panel (see description in text). Weights are used in the estimation. A person is considered a _Nini_ if s/he is not working nor studying. _Source_ : Authors' calculations based on the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), 2005:Q1 - 2013:Q4.

The evidence suggests that male Ninis from disadvantaged socioeconomic backgrounds and urban areas are more likely to have been studying a year earlier than other male Ninis. As

> 11 A deeper analysis of the effects of the recession on labour participation and school enrolment is left for further research.

12
clearly too large to be a true measure of income volatility.

It seems informative to compare our estimates of the permanent component of income risk, _σ_<sup>2</sup> _ε_<sup>, with the estimates obtained by the extensive empirical literature on U.S. labor market risk</sup> using annual income data drawn from the PSID. Most of these studies find an average value of around _._ 0225 for the annual variance _σε_<sup>2(Carroll and Samwick (1997), Gourinchas and Parker</sup> (2002), Hubbard, Skinner and Zeldes (1994), and Storesletten, Telmer and Yaron (2004)), with a value of _σε_<sup>2=</sup><sup>_._0324beingtheupperbound(MeghirandPistaferri,2004).Assuming</sup> that these income shocks are i.i.d. over time (the maintained random walk assumption), this means that these studies have found a quarterly variance of _σε_<sup>2=</sup><sup>_._0056,withonestudy</sup> estimating _σε_<sup>2=</sup><sup>_._008.Thus,theaveragevalueofourestimatesofpermanentincomerisk</sup> is in line with the estimates that have been obtained by the previous literature on U.S. labor market risk, although our estimates lie somewhat on the high end. Notice that our estimates are obtained using a five-quarter rotating panel, whereas Carroll and Samwick (1997), Gourinchas and Parker (2002), Hubbard, Skinner and Zeldes (1994), Meghir and Pistaferri (2004), and Storesletten, Telmer and Yaron (2004) use the PSID data with a panel dimension of many years. Thus, as long as Mexican workers face similar amounts of permanent labor income risk as U.S. workers (or more), this result suggests that most income shocks we label “permanent” in this paper indeed persist for a very long time.

# **III. Trade Policy and Income Risk**

The procedure outlined in the previous section provides us with estimates of individual income risk, _σεjt_<sup>2,foreachindustry(i.e.,manufacturingsector)</sup><sup>_j_andtimeperiod,i.e.,</sup> quarter, _t_ . We now use these time-varying, industry-specific estimates in conjunction with observations on trade policy, _τjt_ , to estimate the relationship between income risk, _σεjt_<sup>2,and</sup> openness, _τjt_ , using a linear regression model. As mentioned before, in this paper we focus 16
**Figure 4:** Number of PFAs in reformed LAC countries, 1998 and 2006

<!-- Start of picture text --> 25<br>21 1998<br>20 2006<br>15<br>14<br>15<br>11<br>9 9<br>8 8<br>10<br>6 6 6<br>5 4 5 4<br>5 2 2 2<br>0<br>ArgentinaBolivia ChileColombiaCosta RicaEl SalvadorMexico PeruUruguay<br><!-- End of picture text --> Sources: SAFJP, SPVS, SAFP, _Superintendencia Financiera de Colombia_ , SUPEN, _Superintendencia de Pensiones_ , Consar, SBS, and _Banco Central de Uruguay_ .

**Figure 5:** Assets managed by the three largest PFAs as shares of total assets in reformed LAC countries, 1998 and 2006

<!-- Start of picture text --> 120<br>100100 100 1998 2006<br>100 89 85 92<br>80 73 77 71 79 77 77<br>62<br>60 5356 55 52<br>48<br>40<br>20<br>0<br>ArgentinaBolivia ChileColombiaCosta RicaEl SalvadorMexico PeruUruguay<br><!-- End of picture text --> Sources: SAFJP, SPVS, SAFP, _Superintendencia Financiera de Colombia_ , SUPEN, _Superintendencia de Pensiones_ , Consar, SBS, and _Banco Central de Uruguay_ .

**Table 2:** Average <u>administrative fees charged in LAC countries in early 2006</u>

|**Country**|**Proportional**<br>**charge on flows**<br>**(% salary)**|**Fixed charge**<br>**on flows (US$)**|**Charge on**<br>**assets under**<br>**management**|**Charge on**<br>**nominal**<br>**returns**|**Charge on**<br>**excess**<br>**returns**|
|---|---|---|---|---|---|
|Argentina|1.27%<sup>a</sup>|--|--<br>|--|--|
|Bolivia|0.50%||0.2285%<sup>b</sup>|--|--|
|Chile|1.60%|$0.90|--|--|--|
|Colombia|1.57%|--|--|--|--|
|Costa Rica|0.14%|--|--|7.50%|--|
|El Salvador|1.40%<br>|--|--|--|--|
|Mexico|1.20%<sup>b</sup>|--|0.34%|--|--|
|Peru|1.99%|--|--|--|--|
|Dominican Republic|0.50%|--|--|--|28.57%<sup>c</sup>|
|Uruguay|2.07%|$0.26|--|--|--|

Notes:<sup>a</sup> The 2007 legal reform sets a charge ceiling of 1 percent.<sup>b</sup> Different charges apply depending on the fund size.<sup>c</sup> The fee applies to the excess return paid over the interest rate of commercial banking cash deposits. Source: Corvera et al. (2006).

**Table** **<u>3:</u>** Reformed LAC <u>countries: 25-year equivalent fee as a share of assets under management (percent)</u>

|**Country**<br>**Min**|**Max**|**Range**|**Weighted average**|**Standard**<br>**deviation**|**Coefficient of**<br>**variation**|
|---|---|---|---|---|---|
|Argentina<br>1.18|2.54|1.36|1.62|0.38|23.40|
|Bolivia<br>0.56|0.56|0.00|0.56|0.00|0.00|
|Chile<br>0.97|1.30|0.32|1.08|0.12|11.16|
|Colombia<br>0.81|1.01|0.21|0.91|0.09|9.86|
|Costa Rica<br>0.59|1.06|0.47|0.99|0.17|16.81|
|Dominican Republic 0.94|1.15|0.20|1.14|0.08|6.71|
|El Salvador<br>0.86|0.86|0.00|0.86|0.00|0.00|
|Mexico<br>0.71|1.64|0.93|1.19|0.26|22.07|
|Peru<br>1.16|1.82|0.66|1.54|0.26|16.95|
|Uruguay<br>0.80|1.31|0.51|1.00|0.23|23.10|

Source: Corvera et al. (2006).

- 13 -
# **<u>Table 6:</u> Impact of the number of Internet Service Providers on the Internet diffusion process**

||<br>|<br> <br>Low and Lo|<br> <br>wer Middle Inc|<br> <br>ome Countries||<br>|<br>Upper Middl<br>|e and High Inc<br>|ome Countries<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||1 ≤ # of|ISPs ≤ 4|# of IS|Ps ≥ 5|All|1 ≤ # of|ISPs ≤ 4|# of IS|Ps ≥ 5|All|
||[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|9]|[10]|
|Log real GDP per capita|3.837**|3.453**|2.162|-0.036|1.166|2.34|-0.234|1.139*|0.496|0.457|
||[1.728]|[1.393]|[1.469]|[1.120]|[0.868]|[2.671]|[1.182]|[0.678]|[0.689]|[0.635]|
|Log real cost|0.074|0.032|-0.175|0|0.027|0.224|0.006|-0.506***|-0.062|-0.058|
||[0.216]|[0.155]|[0.117]|[0.069]|[0.061]|[0.451]|[0.335]|[0.124]|[0.083]|[0.078]|
|Log lines per capita|3.002***|-0.033|2.585***|0.036|0.081|-0.3|0.711|1.298***|-0.175|-0.158|
||[0.573]|[0.448]|[0.433]|[0.401]|[0.304]|[1.067]|[0.511]|[0.412]|[0.368]|[0.329]|
|Log computers per capita|1.842***|0.334|2.040***|-0.013|0.169|2.762***|-0.018|2.766***|-0.17|-0.092|
||[0.357]|[0.244]|[0.225]|[0.314]|[0.202]|[0.409]|[0.293]|[0.155]|[0.318]|[0.258]|
|Lag Internet users||0.494***||0.582***|0.549***||0.300**||0.806***|0.656***|
|||[0.098]||[0.067]|[0.058]||[0.152]||[0.094]|[0.096]|
|Lag Internet users * I**ISP ≥ 5**|||||-0.006<br>[0.047]|||||0.124*<br>[0.064]|
|Constant|-13.174|0.156|-6.777|0.196**|0.166***|-16.976|0.281***|-10.125|0.070*|0.074*|
||[14.199]|[0.098]|[12.082]|[0.082]|[0.063]|[28.375]|[0.103]|[7.014]|[0.042]|[0.038]|
|Method of estimation|GLS|IV|GLS|IV|IV|GLS|IV|GLS|IV|IV|
|Fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number Observations|204|153|257|203|356|57|41|434|365|406|
|R<sup>2</sup>|0.87|-|0.87|-|-|0.89|-|0.91|-|-|

Notes: The dependent variable is the log of Internet users per capita. Robust standard errors in brackets.

* , **, and *** indicate significance at the 10, 5, and 1 percent levels respectively. The regression uses countries with GDP per capita growth in the interval [-4.9 , 9.2 percent] and controls for country fixed effects.

20