rafmacalaba/datause-encoder-data huggingface dataset:
(WTO) and the Paris-based Centre d’Etudes Prospectives et d’Informations Internationales (CEPII). An important contribution of this project has been an exhaustive coverage of preferential trade arrangements (PTAs) across the world, combined with calculation of the AVE of specific duties. Combined with differences in the bilateral composition of trade, the result is that protection varies by sector and partner for each importer. These data have in turn been integrated with the GTAP database for 2001. We have modified these data further to assume full utilization of the 2001 EU Everything but Arms (EBA) initiative as well as AGOA for those African countries benefiting from more liberal rules of origin, as this has been implemented over a period extending beyond the benchmark year of the original protection data. We have also imposed the elimination of ATC quotas on textiles and clothing on the benchmark, an event which occurred on January 1, 2005. This is of course an important dimension of preference erosion in its own right, insofar as the constraint on the most efficient producers under the ATC implied there was an “implicit” preference for the non- or less-constrained developing country exporters. The impacts of the ATC are assessed in greater detail in Francois, Spinanger, and Woertz (2005)—we return to the magnitude of the associated erosion in the concluding section.

The model itself is a relatively standard general equilibrium model, with CobbDouglas consumer demand over broad categories, and CES-based demand within product categories. For primary sectors, this is Armington-based trade (see the discussion in Francois and Reinert 1997), while other sectors are modeled as being monopolistically competitive. Scale elasticity estimates are based on Antweiler and Trefler (2002) and Francois (2001).<sup>23</sup> Factor supplies are fixed nationally, and are allocated between sectors through factor markets.

The experiment itself is relatively straightforward. We eliminate, on a multilateral basis, all OECD import tariffs on all goods. This includes the ad-valorem equivalents (AVEs) of specific tariffs and tariff-rate quotas, and takes into account the prevailing preference programs as reported in CEPII. We also conduct a sub-experiment, where we eliminate these tariffs first for the EU alone. This gives us a chance to identify the full magnitude of preference erosion for a sample of least developed and low-income countries (conceptually represented by area C in Figure 3), and also to identify the share of these

> 23 See Francois and Roland-Holst (1997) for details. The model is documented in Anderson, Francois, and Hoekman (forthcoming) and is available for download at http://www.intereconomics.com/francois.

18
**Table 4: Reported Valuation of Social Security and Assistance Programs in Colombia**

|||**2008**|||**2009**||
|---|---|---|---|---|---|---|
||**Social**<br>**Security**<br>**programs***|**Social**<br>**Assistance**<br>**programs****|**All**<br>**Programs**|**Social**<br>**Security**<br>**programs**|**Social**<br>**Assistance**<br>**Programs**|**All**<br>**Programs**|
|**Formal Salaried**|3.75|4.02|3.94|3.70|3.97|3.89|
|**Formal Self-Employed**|3.66|3.89|3.85|3.57|3.84|3.76|
|**Informal Salaried**|3.79|4.00|3.93|3.76|3.98|3.92|
|**Informal Self-Employed**|3.74|4.00|3.92|3.75|3.97|3.91|
|**Unemployed**|3.68|3.96|3.88|3.71|3.94|3.88|
|**Inactive**|3.77|4.01|3.93|3.77|3.99|3.92|

Source: Authors’ estimates from Fedesarrollo (2008, 2009) FSLS.

(*) Includes pensions, contributive health and non-contributive health insurance; (**) includes ―Familias en Accion‖, ICBF, SENA, public schools, public universities, social interest housing, and ―Cajas de Compensación Familiar‖

# **5. Transition Matrices**

This section looks in detail at transition flows, their taxonomy and their determinants. Initial estimates show that important occupational mobility asymmetries exist. One-third of ―current‖ self-employed workers were salaried workers in their previous job vis-à-vis only 5 percent of ―current‖ salaried workers moving from previous self-employment (see Table 5, Row 1). Gaviria (2004) also reports large asymmetric transitions for Colombia, in fact larger than those presented here: about 42 percent of formal workers changing jobs during the first quarter of 2004 ended up in informal occupations, while about 22 percent of informal workers changing jobs during the same period moved into a formal occupation. These results may differ, in part, due to a different survey used, the _Encuesta Continua de Hogares_ ; a different definition of informality (Gaviria uses the International Labor Organization’s definition based on the size of enterprise); a shorter reference period compared (six months in Gaviria’s paper rather than 12 months used in this paper); and a different year, 2004, a year of moderate growth after 2002 reforms partially liberalized the labor market while increasing formal contributions to pensions.

By looking at the sum of off-diagonal shares, Table 5 shows that some 81 percent of formal selfemployed; 65 percent of informal salaried; 45 percent of informal self-employed and 35 percent of formal 12
|**Table 4: Descriptive Statistics**<br>Firm-level Variables|||||
|---|---|---|---|---|
|Variable|Mean|exp(mean)|Lin SE|N|
|strict_exit|0.16|n.a.|0.5%|22,824|
|weak_exit|0.23|n.a.|0.5%|22,824|
|Age (years)(log)|2.45|11.60|0.009|22,423|
|Labor productivity (05 USD)(log)|10.96|$ 57,267.58|0.018|20,079|
|Female Ownership|0.37|n.a.|0.6%|22,364|
|Manager’s Experience (yrs.)(log)|2.57|13.05|0.009|22,406|
|Bank financing|0.26|n.a.|0.5%|22,824|
|Exporter|0.11|n.a.|0.4%|22,824|
|Foreign Ownership|0.09|n.a.|0.4%|22,823|
|Limited Liability|0.60|n.a.|0.5%|22,824|
|Years between rounds (n_year)|4.32|n.a.|0.001|22,824|
|Manufacturing|0.38|n.a.|n.a.|22,824|
|Retail|0.29|n.a.|n.a.|22,824|
|Other Services|0.32|n.a.|n.a.|22,824|
|No. of competitors (log)|5.58|266.22|0.009|22,824|
|Labor prod. of competition (05 USD)(log)|10.97|$ 58,192.98|0.003|22,824|
|Survey-weighted estimates|||||
|Economy-level Variables|||||
|Variable|Mean|exp(mean)|SD|N|
|GDP per capita (05 USD)(log)|7.81|$ 2,465.44|0.163|47|
|GDP growth [t,t+n]|3.83|n.a.|0.419|47|
|Openness (% GDP)|83.23|n.a.|4.299|47|
|New-firm density|4.38|n.a.|1.494|37|
|Ease of entry (DB)|0.55|n.a.|0.045|47|
|Resolving insolvency<br>* Based on percentile rank of DB "Distance to Fro|0.50<br>ntier" me|n.a.<br>asure; higher score|0.042<br>indicates gr|47<br>eater ease|

Table 5 provides probit estimates of the likelihood that firms exit (strict_exit=1) the market in 47 economies in which there have been multiple, standardized Enterprise Surveys. Each of the estimations utilizes Stata’s svy prefix, which takes advantage of the program’s built-in survey options, and by default utilizes linearized, Taylor standard errors, incorporating survey weights and the ES’s stratified survey design.<sup>18</sup> To account for pooling data across several economies, survey weights are

> 18 For more information, the ES website includes a note on sampling methodology, here: ~ <u>http://www.enterprisesurveys.org/ /media/FPDKM/EnterpriseSurveys/Documents/Methodology/Sampling_Note.p df</u>

20
# **Table A13:  Transparency International Global Corruption Barometer (GCB)**

|**Data Provider**|Transparency International||||||||
|---|---|---|---|---|---|---|---|---|
|**Description**|Nongovernmental organization devote|d to figh|ting cor|ruption|||||
|**Website**|www.transparency.org||||||||
|**Data Source**<br>|Global Corruption Barometer<br>||||||||
|**Type**<br>|Survey<br>||||||||
|**Respondents**|Households||||||||
|**Frequency**|Annual since 2004||||||||
|**Coverage**|Global sample of countries||||||||
|**Public Access**|Country-level aggregate responses and|some b|reakdow|ns are|reported|on TI'|s website||
|**Description**|This survey commissioned by TI c|ollects|data on|house|holds'|experie|nces wit|h petty|
||corruption and their perceptions of th<br>NOT use data from the TI Corruption<br>corruption based on an aggregation of<br>of Corruption indicator.|e overa<br>Percep<br>a subse|ll incide<br>tions In<br>t of the|nce of<br>dex. T<br>data sou|corrupt<br>his is a<br>rces th|ion.<br>compo<br>at we u|Note tha<br>site indi<br>se in our|t we do<br>cator of<br>Control|
||2006|2005|2004|2003|2002|2000|1998|1996|
|**Voice and Acc**|**ountability**||||||||
|NA|..|..|..|..|..|..|..|..|
|**Political Stabi**|**lity**||||||||
|NA|..|..|..|..|..|..|..|..|
|**Government E**|**ffectiveness**||||||||
|NA|..|..|..|..|..|..|..|..|
|**Regulatory Qu**|**ality**||||||||
|NA|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Control of Cor**|**ruption**||||||||
|Frequency of c|orruption<br>X|X|X|..|..|..|..|..|
|Frequency of h|ousehold bribery<br>X|X|X|..|..|..|..|..|
|Extent of grand|corruption<br>..|..|X|..|..|..|..|..|
|Extent of petty|corruption<br>..|..|X|..|..|..|..|..|
|**Country covera**|**ge (CCR)**<br>62|69|61|..|..|..|..|..|
|**Year of Publica**|**tion**<br>**2006**|**2005**|**2004**|**..**|**..**|**..**|**..**|**..**|

51
**Table 5 Water Sources and Sewerage Systems in UAs**<sup>**7**</sup>

|Urban||Sewerage<br>|Sou|rces of Water<br>|
|---|---|---|---|---|
|Agglomeration|Central City|Non-Central City<br>(Proportion of ULBs covered by Sources)|Central City|Non-Central City<br>(Proportion of ULBs<br>covered by Sources)|
|Hyderabad|S, OSD|S, OSD (20%); OSD(70%); BSD(10%)|T, TW|T (60%); T, TW<br>(30%); TW,T (10%)|
|Chennai|S|OSD (100%)|TW,T|TW (28.5%); T<br>(28.5%); W (43%)|
|Kolkata|S, OSD|S, OSD (10%); OSD (62%); OSD,<br>S(10%);OSD,BSD(5%);S (3%); OSD,O|T,TW|T,TW (72.5%); TW,T<br>(12.5%); T (5%); TW|
|Delhi|NA|S (100%)|T,TW|T,TW (100%)|
|Pune|S|S (50%); S/OSD (50%)|T|T (50%); TW/T (25%);<br>TW/T/W/TK (25%)|

Notes: S-Sewer network; OSD-Open Surface Drain; BSD-Box Surface Drain; PT-Pit system; and O-Other types of sewerage. T-Tap water; TW-Tube well; W-Well water; and TK-tank water; ‘ ,’ implies ‘and’ ; / implies ‘or’ **Source: Census of India, 2001, Authors’ Computations**

- Only the central city in Pune has full coverage with tap water, while both Pune and Chennai have full coverage by sewer network; Connectivity through roads as measured by road length per thousand population is the best in Chennai and the worst in Hyderabad; street lighting facilities are better in the central cities of Pune than in other ULBs but the worst in Hyderabad.

- When non central cities are considered, the order of the UAs in terms of status of these services is different from those observed in central cities. While smaller ULBs in Hyderabad are better off in terms of availability of tap water, the conditions are worse in Kolkata. Sewerage facilities are the best in the non central cities of Delhi which has full coverage through sewer network followed by Pune which has  50 percent coverage while the smaller ULBs in Chennai does not have sewer network at all. In terms of comparable indicators on roads and street lighting it is found that the

> 7 The percentages in brackets for non central cities denote the proportion of the non central cities covered by the respective category of water source/sewerage system. For instance [S, OSD (20%)] would mean 20% of the smaller ULBs in Hyderabad UA have a combination of sewer network and open surface drains in which sewer network dominates. It is to be noted that the order in which the categories are mentioned in cases of multiple categories is important; (S, OSD) would mean a combination in which sewer network dominates whereas (OSD, S) would mean a combination in which open surface drains dominates.

12
|**Max**|423,180|1,340,784<br>4,065,104<br>451,891<br>74.150|9,450|3.000<br>37,409|1260.000|0.608|1.949<br>1,801,600|218,546|6,396<br>55.264|1|1|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Min**|1,514|1,259<br>4,662<br>2,136<br>4.715|1,076|0.000<br>332|2.854|-0.019|-0.937<br>1,570|734|2<br>6.494|0|0|
|**2000**<br>**Std.**<br>**Dev.**|89,726|210,303<br>584,973<br>93,424<br>16.147|2,201|0.786<br>10,945|226.000|0.118|0.874<br>329,974|50,835|1,222<br>11.353|0.277|0.200|
|**Mean**|56,424|97,048<br>212,513<br>62,733<br>43.692|5,536|0.388<br>12,175|86.800|0.071|0.686<br>224,655|50,808|378<br>32.519|0.082|0.041|
|**Obs**|49|48<br>49<br>49<br>49|49|49<br>49|49|49|49<br>49|49|49<br>49|49|49|
|**Max**|214,493|315,562<br>2,163,688<br>277,991<br>69.024|9,450|3.000<br>35,132|1140.000|24.061|1.903<br>1,252,325|103,750|5,475<br>49.084|1|1|
|**Min**|659|390<br>3,424<br>1,479<br>3.710|1,076|0.000<br>296|0.256|0.009|-1.273<br>720|188|2<br>3.806|0|0|
|**1990**<br>**Std.**<br>**Dev.**|50,888|57,665<br>312,521<br>52,244<br>16.195|2,367|0.777<br>9,643|194.000|4.064|0.862<br>265,588|25,085|972<br>9.528|.272|.196|
|**Mean**|28,244|28,246<br>139,336<br>34,653<br>33.922|5,166|0.392<br>9,988|69.800|1.078|0.676<br>165,382|22,930|325<br>23.654|0.078|0.039|
|**Obs**|51|51<br>51<br>51<br>51|51|51<br>51|51|51|51<br>51|51|51<br>51|51|51|
|**Variable**|Sales|Total assets<br>Total migration to US<br>Tertiary migration to US<br>Share of tertiary migration to US in<br>total migration to US|Distance in miles|Conflict in 1990s<br>GDP per capita in current US dollars|Population (millions)|Inflation|Governance<br>Total migration to EU|Tertiary migration to EU|Population density<br>Share of tertiary migration to EU in<br>total migration to EU|<br>Legal restrictions on migration|Restrictions on women’s travel|
Compared to other large apparel exporters, only a small part of value in exports is embodied in wages (see Figure 3). Lesotho remains stuck in a low-wage, low-skill, low-linkage segment with many foreign firms (especially from Asia) investing little in skills training or in developing backward linkages to domestic suppliers (Morris and Staritz 2016).

**Figure 3: Textile/apparel labor value added as share of total export value**

<!-- Start of picture text --> Cambodia 14.5<br>Lesotho 16.4<br>Viet Nam 17.0<br>Malaysia 19.1<br>Honduras 21.2<br>El Salvador 26.6<br>Mauritius 29.7<br>Egypt 30.1<br>South Africa 41.7<br>Bangladesh 44.9<br>0 10 20 30 40 50<br><!-- End of picture text --> Source: Own calculations based on Lesotho’s Social-Accounting Matrix and LACEX database According to the country’s recent input-output table (2011), unskilled labor makes up 87% of labor value added in textile and apparel exports (Figure 4). This indicates that sustaining apparel exports – or similar types of jobs – is essential to poverty reduction in Lesotho.

**Figure 4: Direct and indirect contribution of skilled and unskilled workers to labor value added in exports (2011)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Direct - Skilled Direct - Unskilled Indirect - Skilled Indirect - Unskilled<br><!-- End of picture text --> Source: Own calculations based on Lesotho’s Social-Accounting Matrix and LACEX database<sup>6</sup>

> 6 The Labor Content of Exports (or LACEX) database <mark>was developed by Cali et al. (2016) on the basis of a panel of global inputoutput tables and exports from the Global Trade Analysis Project (GTAP) and employment data from the ILO. The database measures the contribution of labor to a given country</mark> <mark>`’` s exports measured as employees</mark> <mark>`’` compensation or wages (LACEX).</mark>

7
<!-- Start of picture text -->
1.0 —————_ L,(q) - Lorenz for observed distribution (t:)<br>— = = £,(q)- Lorenz for alligned distribution (t:) /<br>———__L.(q) - Lorenz for observed distribution (tz) /,<br>=== Lxq@) - Lorenz for alligned distribution (tz)<br>0.8<br>—<br>2 ;<br>8 /<br>ne} //<br>&7<br>raelo}&06 . 7 /;<br>@4<br>“<br>s<br>Oo yt<br>g 0.4 1;<br>& 7<br>fj ha<br>E ‘7<br>=] 7<br>oO Ja<br>0.2 fp<br>‘7<br>Ga<br>(7 S 7 (t1) Average distance between L.(q) and L,(q):a 0.018<br>—< (te) Average distance between L.(q) and L.(q): 0.018<br>0.0<br>0.0 0.2 0.4 0.6 0.8 1.0<br>Proportion of population (q)<br><!-- End of picture text -->
*****

~~~~~~~~~~~~~I' Table 8: Cross-country Regression Results for LDCs (N=45) - Determinants of TFR, IMR & Female Secondary Enrollment, 1987

||Deoendent varia<br>|bles:<br>||
|---|---|---|---|
||I_<br>_TFR|IMR|FEMSEC|
|constant|7.09|125.417|-0.666.|
||(9.83).<br>|(6.508)**<br>|(-0.182)|
|Female Secondary<br>|-0.389|-5.967|1.786|
|Gross Enrol. Rate,1965<br>|(-3.592)^^<br>|.<br>(-2.414)*|(1.999)'|
|Female Secondary<br>|0.016|0.139|-0.00607|
|Gross Enrol. Rate2<br>|(3.526)...|(1.293)|(-0.155)|
|Female Secondary<br>|-0.0002<br>|-0.00129|-0.0003|
|Gross Enrol. Rate*3<br>|(-3.937) * ^|(-0.940)|(-0.567|
|Male Secondary<br>|0.0837|-0.565|-0.739|
|Gross Enrol. Rate 1965<br>|'(0.720)|(-0.235)|(-0.891)|
|Male Secondary<br>|-0.006<br>|0.0588|0.031|
|Gross Enrol. Rate^2<br>|(-1.339)|(0.630)|(0.891)|
|Male Secondary<br>|0.0001|-0.00074|-0.00027|
|Gross Enrol. RateA3<br>|(2.076)-|(-0.692)|(-0.673)|
|**0/**Population w/<br>||-0.442||
|Accessto Water<br>||(-1.866)^||
|Population per<br>|0.0000026|0.00082||
|Physician<br>|(0.161)|(1.924)-||
|Urban Population as|0.014|0.539|0.296|
|% of Total Pop.<br>|(1.120)|(2.564)**|(2.791)...|
|Gross National Income|-0.00069|-0.012|0.0024|
|Per Capita, 1987<br>|(-2.428)*|(-2.407)*|(1.120)|
|Expend. on Education<br>as % of GNP, 1985<br>|||1.769<br>(2.976)|
|adjusted R2|0.66|0.74|0.88|

significant at the 100% level significant at the **5%** level

* significant at the 1 % level

(standard errors corrected for heteroskedasticity) Source: K. Subbarao and L. Raney "Social Gains from Female Education, PHRWD, forthcoming.
The share of ’marginal’ children enrolling in the same school type is proxied by the share of children from the 25% least educated households in each district and year.<sup>12</sup> To distinguish between attraction and congestion effects, we also experiment with a second enrollment measure, namely the share of ’desirable’ children enrolling in the school. This is proxied by the share of children from the 25% most educated households in each district and year.

Finally, to proxy household’s educational preferences, we use the same socio-economic characteristics as in Section II, in particular log food consumption and the years of education of the household head. All other school level variables that may affect parental enrollment choices are captured by a choice-specific unobservable _ξj,t_<sup>_m_.Arandomcomponent</sup><sup>_εm_</sup> _ij,t_<sup>capturestheremaining</sup> idiosyncratic heterogeneity in preferences.

The effect of social interactions in enrollment decisions can be modeled in a two-step procedure (see Bayer & Timmins (2007)) The first step of this procedure consists of estimating the probability that a household enrols in a particular school sector as a function of the individual specific regressors as well as a districtsector-time fixed effect. The results of this analysis are equivalent to the conditional logit model presented in Section II. In the second step, we estimate how this fixed value of enrolling in a particular sector that is common across all households, _δj,t_<sup>_m_,isaffectedbychangesinobservable</sup> school-level attributes and the enrollment behaviour of others.

Having estimated the sector-district-time fixed effects _δj,t_<sup>_m_inthefirststep,thesecond-step</sup>

> 12Ideally, we would like to measure the effect of a change in pupil-teacher ratios (PTR) in the school on enrollment. However, we can only link students to schools at the district level. To examine the extent to which NERs at the district and average pupil-teacher ratios in public schools are linked, we use data from EMIS, which contains detailed information on all public schools in Kenya. Regressing PTR on district NER (in percent) of children from the 25% least educated households, we find a significant positive effect of .34. That is, a one percentage point increase in _σ_ corresponds to a .34 increase in average PTR.

31
access institutions, such as those securing property rights, as hampering firm growth (for example, de Soto, 1989). Further there tends to be an assumption that formality is an all or nothing state. We argue that these views are valid only as special cases of a more general and continuous relation between the firm and society.

WVe recast the question of formality as the firm's decision of how much to participate in the numerous institutions of civil society: federal and local treasuries, governmental programs such as social security (including pensions and health care), the legal system, the banking system, health inspection, firm censuses, trade organizations, civic organizations, _etc._ We argue that a minimal degree of participation in some institutions is a necessary input to growth for many firms, and that participation increases with the success of the business. That is, formality can be viewed as a normal input to production: q = f(L,K,P), where L, is labor, K is capital, and P is participation in (a number of different) societal institutions, and all three inputs are complementary. The benefits of formality, while often overlooked, are numerous. They include, but are not limited to:<sup>6</sup> _1. Enforceable/impersonal contracts and credible signaling._ All entrepreneurs have access tD social relationships to enforce implicit contracts among their friends and family, who form a small number of their potential customers and employees. Participation in the legal system is needlessly expensive for transactions with these individuals. Similarly, old age and health insurance may be easily handled by insuring through their mutual extended network of friends and family. Property rights secured by personal ties may be sufficient if investment is minimal. These characteristics of small scale economic transactions are commonly observed in developing countries, as well as in many ethnic enclaves in developed countries. But this mode of operation is constrained by the ability of the entrepreneur to maintain personal relations with all involved parties, a task increasingly unmanageable as firms expand. Legally recognized, enforceable contracts lend credibility to arrangements, permit entry into long term commitments, diminish risk, and can reduce monitoring costs. For example, in a world of imperfect

> 6 See also de Soto (1989).

3
salient (Bisin and Verdier 2000, Bisin et al 2015, Connor 1972) and lead to a closer identification with their own group. Social identity theory describes how in different settings, individuals categorize themselves into groups, and the different social groups to which an individual belongs are associated with positive or negative connotations (Tajfel and Turner 1979, Tajfel 1982). The theory posits that individuals strive to maintain or enhance their self-esteem and as such, they seek to gain a positive social identity by favorably comparing their in-group to the relevant out-groups.

In sum, whereas both control and treatment group participants may experience improved intergroup attitudes as a result of being exposed to a diverse cohort, those who serve in a different ethnic region may develop increased understanding of the ethnic groups in which they served and/or a stronger attachment to the nation from experiencing the diversity of the country and overcoming a fear of the unknown. In addition, the experience of being a minority among a different group may reinforce their attachment to their ethnic group.

# **6 Results and Discussion**

This section begins with describing the NYSC experience of respondents to show that the treatment assignments created greater interactions with other ethnic groups. Then, it discusses the impact of interethnic exposure on different aspects of national integration. The outcomes include both revealed preference measures such as interethnic migration, knowledge of states, marriage and dating decisions as well as attitudinal measures such as willingness to migrate, national and ethnic identity, and feelings of closeness and trust towards different ethnic groups.

Tables 2-5 report results for the different sets of outcomes. The odd-numbered columns report results from the main specification while even-numbered columns include controls 21