rafmacalaba/datause-encoder-data huggingface dataset:
economic penalty imposed by the need to use sub-optimal roads and trucking services in covering overland road distances and from reducing the obstacles to greater “gravitational pull” from import markets.

# **_5. Conclusions and Policy Implications_**

As highlighted by the Gravity Model–based results analyzed above, intra-regional trade in Central America does not behave like it does in a highly spatially-integrated region _._ Instead, it is still inhibited by a number of inefficiencies that overshadow the formal trade tariff and non–tariff barriers that have historically figured high on the regional integration agenda.

The economic **_distance_** between the markets of Central America is being distorted and magnified by a number of inefficiencies, including the poor road quality, the congestion at the border crossings and metropolitan areas, the inadequate supply of trucking services, and other overland transport inefficiencies. These factors may explain why in Central America the negative effect of distance on trade flows tends to be higher than in a spatially-integrated region.

Similarly, the results show that Central American economies fail to take advantage of **_adjacency_** - not only to the extent that it affects intra-regional trade patterns but also the region’s trade with the U.S. and other external partners. As highlighted by recent supply chain analyses that are able to capture these specific micro-level challenges, the reasons why country adjacency in Central America does not seem to exhibit the positive impact on trade flows that it would be expected to include burdensome customs procedures, delays, and lack of regulatory harmonization (e.g. in terms of phytosanitary standards for agricultural exports).

Finally, the Gravity Model results indicate that Central American countries are experiencing a lower **_“gravitational pull”_** from the nearby economies. Intuitively, this is due to poorly established trade linkages, an atomized shipping industry, little information sharing on cargo and backhaul, and relatively few options and competition for shipping (i.e. absence of coastal shipping, rail services, and low road density).

The overall policy implications of the above estimations are similar to those identified in the recent supply chain analyses carried out in Central America that, instead, highlight the microlevel challenges related to overland transport inefficiencies, burdensome border crossing procedures, and lack of regulatory harmonization posed. As highlighted previously, in order to reduce the inefficiencies that currently offset the positive benefits that Central American countries could obtain from being adjacent to one another, policy and regulatory actions need to be focused on improving the region’s border crossings, while additional investments and institutional coordination initiatives are warranted to reduce the ‘economic’— i.e. the cost- and time-equivalent—overland distance between production and consumption centers within the region.

20
# Part II: Challenges and Opportunities

“ _I would like them to know women are the ones who take care of their families and when they go for workplaces far away from home, it does not mean they enjoy it. Most of their husbands left or died and for them to go out it means it is hard so they should protect women’s needs. Women should earn more money so that they could be able to take care of their family members whom they have left behind_ ”, case study.

In this section, we turn our attention to the challenges faced by migrant workers, and ask what opportunities exist for policies and programs to support these workers and their families. The application of a migration lens – particularly that of the expanding female migrant population ‐ offers a fresh approach and new insights to familiar policy questions regarding employment, urban development, financial inclusion, health, and education.

## 1. Quality of Employment

### _Working conditions of textile workers_

The results of our fieldwork are consistent with previous reports of textile workers’ working conditions.<sup>15,16,17</sup> Commonly experienced workplace problems reported by respondents are verbal abuse and harassment from employers, very cold temperatures, hazardous chemicals and machines, job insecurity if they do not meet their employer’s expectations, and poor conditions, including lack of running water and working toilets at times (“when water gets finished at work sometimes we drink from the toilets. They do not let us go home when there is no water”). Twelve percent of textile workers reported being harassed or threatened in the workplace. However, sexual relations between employers and employees appeared to sometimes be linked to promotion or preferential treatment.<sup>18</sup> Qualitative work points to textile workers’ vulnerability in such relationships: women enter them as a means to employment benefits, but often suffer worse conditions when the relationship sours or the women do not adhere to the employers’ demands. One group noted that such incidences are likely to increase as

> 15 <mark>International Labor Organization. (2013).</mark>

> 16 <mark>Pike and Godfrey (2014).</mark>

> 17 They’Daemane, Moses M. M. (2014).

> 18 Despite explicitly asking about transactional sex in our survey, only 8% of survey respondents reported having sex for money or gifts in the past 12 months. Still, such behavior among female migrants emerged in qualitative work with rural communities and female migrants to be perceived as occurring.

33
Table 1: Descriptives

||Panel|2012|Onshore 2013|Ofshore 2013|2014|2016|
|---|---|---|---|---|---|---|
|Firm Characteristics:|||||||
|Ofshore|0.19|0.20|0.00|1.00|0.21|0.19|
||(0.40)|(0.40)|(0.00)|(0.00)|(0.40)|(0.39)|
|Foreign|0.09|0.10|0.01|0.43|0.10|0.09|
||(0.29)|(0.30)|(0.12)|(0.50)|(0.30)|(0.29)|
|Age (years)|9.83|9.34|10.12|6.83|9.56|10.09|
||(9.81)|(9.31)|(9.82)|(5.71)|(9.16)|(11.23)|
|Non-missing Manufacturing|0.31|0.32|0.29|0.41|0.31|0.30|
||(0.46)|(0.47)|(0.45)|(0.49)|(0.46)|(0.46)|
|Non-missing Sales|0.35|0.34|0.40|0.14|0.35|0.36|
||(0.48)|(0.47)|(0.49)|(0.34)|(0.48)|(0.48)|
|Non-missing Services|0.27|0.26|0.22|0.44|0.27|0.27|
||(0.44)|(0.44)|(0.42)|(0.50)|(0.44)|(0.44)|
|Entry|0.06|0.07|0.06|0.07|0.06|0.05|
||(0.24)|(0.25)|(0.24)|(0.26)|(0.23)|(0.21)|
|Exit|0.02|0.01|0.01|0.02|0.01|0.02|
||(0.12)|(0.11)|(0.11)|(0.14)|(0.12)|(0.12)|
|Social Security Records Outcomes:|||||||
|Non-missing Employment|0.51|0.55|0.55|0.41|0.49|0.46|
||(0.50)|(0.50)|(0.50)|(0.49)|(0.50)|(0.50)|
|Number of employees|17.96|19.03|13.24|40.68|16.79|14.89|
||(108.61)|(113.31)|(71.81)|(208.27)|(104.52)|(102.84)|
|Non-missing Wagebill|0.51|0.55|0.54|0.41|0.48|0.45|
||(0.50)|(0.50)|(0.50)|(0.49)|(0.50)|(0.50)|
|Wagebill, M dinars|0.21|0.19|0.18|0.38|0.22|0.24|
||(1.52)|(1.31)|(1.35)|(1.75)|(1.59)|(1.73)|
|Tax Record Outcomes:|||||||
|Revenue Before Tax, M dinars|1.74|1.66|1.62|1.94|1.65|1.75|
||(15.87)|(14.94)|(14.76)|(15.59)|(14.51)|(16.02)|
|Profts, M dinars|0.03|0.03|0.02|0.03|0.01|0.00|
||(3.85)|(3.14)|(3.41)|(12.00)|(3.48)|(2.88)|
|Non-missing Exports from tax authorities|0.18|0.23|0.11|0.58|0.19|0.14|
||(0.39)|(0.42)|(0.32)|(0.49)|(0.39)|(0.35)|
|Exports from Tax authorities, M dinars|0.51|0.52|0.14|1.92|0.47|0.48|
||(8.59)|(8.97)|(2.64)|(15.55)|(7.06)|(8.10)|
|Customs Records Outcomes:|||||||
|Non-missing Exports Customs|0.08|0.09|0.05|0.23|0.08|0.06|
||(0.27)|(0.29)|(0.22)|(0.42)|(0.27)|(0.24)|
|Exports Customs, M dinars|4.03|3.24|1.67|5.29|3.87|4.52|
||(26.12)|(21.78)|(11.36)|(28.66)|(23.96)|(30.01)|
|Imports Customs, M dinars|2.09|1.75|1.50|3.08|2.01|2.22|
||(12.78)|(10.25)|(8.17)|(15.75)|(11.16)|(14.06)|
|Observations|769241|66040|58198|14983|80725|94219|

Note: Table 1 provides a detailed descriptive analysis of various firm characteristics along with key outcome measures for the cleaned panel as well as in 2012, 2014, and 2016. The table is segmented in 2013 by firm Offshore status, comparing offshore and onshore firms in the year before the tax reform. This table documents the evolution of firm attributes and performance indicators (Social Security Records Outcomes, Tax Record Outcomes, and Customs Records Outcomes) over time.

We also document differences between onshore and offshore firms. Table 1 shows that in 2013, the year before the reform, 43% of offshore firms are foreign-owned vs 1% of onshore, and offshores are younger - less than 7 years on average versus almost 10 years for onshores. In terms of sectors, offshores are much more likely to be in manufacturing (41% versus 29% for onshore) and services (45% versus 22%), while onshore firms are more likely to be in retail and wholesale.

12
# **TABLE 3: WHO ATTRITS?**

Probability of Attriting after first interview and Probability of dropping out anytime after second interview

||Only Present<br>in First Wave||Attrition between W|ave 2 and Wave 5||
|---|---|---|---|---|---|
||Attrit<sup>a</sup>|Dropout<sup>b</sup>|Dropout<sup>b</sup>|Dropout<sup>b</sup>|Dropout<sup>b</sup>|
|Independent Variables:|dF/dX|dF/dX|dF/dX|dF/dX|dF/dX|
|Initial Income of Household|8.05E-08|1.38E-06|8.59E-07|5.86E-07|7.06E-07|
||(9.52)|(32.42)|(26.94)|(13.18)|(22.97)|
|Change in Income between periods 1&2 *|---|7.96E-07|0.003|2.39E-07|0.014|
|||(20.69)|(6.67)|(5.57)|(20.39)|
|Age of Household Head|-0.003|-0.020|-0.020|-0.020|-0.020|
||(14.65)|(37.36)|(36.49)|(37.02)|(36.60)|
|Age Squared of Household Head|3.020E-05|2.135E-04|2.122E-04|2.118E-04|2.123E-04|
||(11.05)|(29.37)|(28.73)|(29.14)|(28.75)|
|Household Size|-0.003|-0.014|-0.013|-0.013|-0.013|
||(20.73)|(34.25)|(30.42)|(31.87)|(30.27)|
|Number of Children in Household|0.002|0.007|0.006|0.006|0.006|
||(9.56)|(15.39)|(12.67)|(13.30)|(12.48)|
|Male|0.005|0.020|0.023|0.021|0.023|
||(12.64)|(19.20)|(20.78)|(19.69)|(20.66)|
|Married|-0.009|-0.041|-0.043|-0.041|-0.042|
||(24.55)|(44.50)|(44.42)|(44.15)|(44.08)|
|Education Dummies<sup>d</sup>|Yes|Yes|Yes|Yes|Yes|
|Number of Observations|1610094|1468273|1423132|1468273|1423132|
|Pseudo R squared|0.0128|0.0145|0.0141|0.0142|0.0144|
|Change in Probability due to 1 SD rise in initial income<sup>c</sup>|7.95E-04|1.37E-02|8.50E-03|5.76E-03|6.96E-03|
|Change in Probability due to 1 SD rise in change in income variable|---|8.02E-03|2.17E-03|2.25E-03|6.39E-03|
|*Change in Income Variable|---|Inc2-Inc1|Ln(Inc2)-Ln(Inc1)|Abs(Inc2-Inc1)|Abs[Ln(Inc2)-Ln(Inc1)]|

# Notes

a Attrit=1 if household only present for 1st interview; Attrit=0 if household present for longer than 1st interview b Dropout=1 if household left anytime after 2nd interview; Dropout=0 if household present for entire panel (5 interviews) Absolute value of Z-statistics in parentheses c Evaluated at the means of all other variables d Coefficients on education dummies are negative and statistically significant for all dummies in all specifications. Omitted group is no schooling.
# **Appendix 2. Reported Valuation of Social Programs in Colombia**

## **Table 2.1 Valuation, knowledge and participation in selected social programs as % of workers who report the following categories**

|**Program**|**How wouldyo**<br>**following pr**|**u value the**<br>**ograms:**|**How much do**<br>**following **|**you know the**<br>**programs:**|**Doyou or someone in**<br>**your householdparticipate?**|
|---|---|---|---|---|---|
||Very good/good|Don't know|Fairlywell|Don’t know|Yes|
|Pension system|44.5|37.0|29.0|71.0|24.6|
|Contributive health|59.7|10.6|56.7|43.3|61.7|
|Noncontributive health care program|60.3|13.5|43.8|56.2|40.4|
|Family Compensation Fund programs|54.1|37.5|60.3|69.7|26.3|
|ICBF (Hogares comunitarios)|56.5|32.7|29.2|70.8|10.5|
|ICBF (Desayunos infantiles)|55.4|38.4|27.4|72.6|14.3|
|SENA traininig programs|67.7|29.0|35.7|64.3|14.2|
|Familias en Acción|33.4|59.0|13.7|86.3|8.0|

Source: Authors’ estimates from ELS 2008 Note: As illustration, the interpretation of the first row in table is as follows: 44.5% of workers report to value as good or very good the pension system in Colombia; 29% of workers report to know it fairly well; and 24.6% report to participate in it.

# **Table 2.2 Valuation of social programs across selected characteristics of workers: % of those with the characteristic that report to value social programs as “good” or “very good”**

|**Program**|**Do you know the**|**program:**|**Occ**|**upation:**|**Social Secu**|**rity Affiliatio**|**n:**|
|---|---|---|---|---|---|---|---|
||Fairlywell|A little|Salaried|Self-employed|Pension & Health|Onlyhealth|Neither|
|Pension system|70.9|70.4|68.6|69.4|67.3|68.6|71.0|
|Contributive health|69.4|63.3|67.2|65.9|67.6|67.2|65.8|
|Noncontributive health care program|76.2|63.1|70.0|68.7|68.4|63.5|70.8|
|Family Compensation Fund programs|91.5|82.3|87.9|82.8|87.6|82.6|85.0|
|ICBF (Hogares comunitarios)|86.7|81.9|84.9|82.8|84.8|77.4|84.3|
|ICBF (Desayunos infantiles)|92.0|88.3|90.5|90.5|90.8|91.0|89.7|
|SENA traininig programs|96.6|94.1|95.4|95.1|95.7|97.0|95.0|
|Familias en Acción|85.5|79.6|79.4|82.1|76.5|80.5|83.4|

Source: Authors’ estimates from ELS 2008 Note: As illustration, the interpretation of the first row in table is as follows: Of those who report to know the pension system fairly well, 70.9% report to value it as good or very good. Of those who report to know the pension system only a little, 70.4% value it as good or very good. Some 68.6% of salaried workers report to value the pension system as good or very good, while among self-employed workers, 69.4% do. Some 67.3%, 68.6% and 71% of those workers affiliated to pensions and health care, only to health and affiliated to neither of them report to value the pension system as good or very good, respectively.

26
population differences (larger countries tend to have more projects). The lack of any relationship between project proliferation and declining bureaucratic quality is consistent with the ambiguous predictions of the model in section 3, but is inconsistent with the descriptive literature, which tends to blame equally project proliferation and donor fragmentation for undermining public sector administrative capacity.

# **5. Conclusions**

This study provides a formal analysis and empirical evidence suggesting that competitive donor practices, where there are many small donors and no dominant donor, erode administrative capacity in recipient country governments. In their need to show results, donors each act to maximize performance of their own projects, and shirk on provision of the public sector human and organizational infrastructure essential for the country’s overall long-term development.<sup>28</sup> Empirical findings show that bureaucratic quality suffers where aid is fragmented more severely across donors. Informal tests of reverse causality strongly indicate that causation runs from fragmentation to lower quality. Proxies for donor altruism tend to be associated with improving bureaucratic quality, consistent with the model’s predictions, although these variables are not always significant.

Even if donor practices contribute to declining bureaucratic quality, this side effect of aid does not necessarily imply that aid overall is counterproductive for development. Donors’ use of human resources, however sub-optimal, may sometimes be more efficient than the government’s use of them. The implication of this paper is that donor practices produce less damaging side effects when donors have a higher share of the aid “market” in a recipient country or when they are more altruistic (motivated more by development concerns).

This analysis should also not be interpreted as implying that donors should refrain from hiring local staff, or that prevailing salaries for highly qualified civil servants are high enough. The point is rather that donors should not unduly distort the market for skilled labor. The resource injection from high donor-paid salaries potentially has a positive net impact on development, despite the adverse impact on the functioning of government. However, those same benefits could be obtained, without the negative consequences, from using the funds to increase the salaries of underpaid civil servants, or through general budget support. Budget support targeted for increasing salaries of senior and technical staff—particularly where the use of merit systems increases the

> 28 Bureaucratic quality has been linked to growth in several cross-country studies, including Evans and Rauch (1999) and Keefer and Knack (1997), who measure bureaucratic quality using ICRG data.

24
Suppose now that each person _i_ with observable traits _xi_ has home and counterfactual foreign wages that are jointly normally distributed as π( _f i_ ,<sup>_h_</sup> _i_ )∼ _N_ [( _f_ , _h_ ) , (σ _f_ , σ _h_ ) , ρ] where ρ is the correlation. Any level set of this distribution ( _f_ , _h_ ) π=Π is an ellipse, as in Figure 2. Individuals to the upper left of the line _h_ = _f_ + _c_ move from foreign to home, and the rest do not. The modal new migrant due to an infinitesimal decrease in _c_ is represented by the tangent point ( _f m_ ,<sup>_h_</sup> _m_ ) . Using the equations of the ellipse and movement threshold, we can calculate precisely the wage gain to this marginal migrant.

Suppose that the marginal migrant’s US wage _h_ m is observed, but his foreign wage _f_ m is not observed and is instead approximated by _f_ , the average for workers in foreign with equal observable traits. In this case if the true wage ratio _Re_ = _ehm_ − _fm_ is estimated by _Ro_ = _ehm_ − _f_ , with a bias of _Ro Re_ = _e fm_ − _f_ . There are two cases: _No selection or negative selection_ : Where there is no selection of migrants or negative selection on unobserved traits in the foreign market ( _f m_ − _f_ ≤ 0 ) , the estimated wage ratio underestimates the true ratio. The left-hand panel of Figure 2 shows one such case. Recent evidence from Fernández-Huertas Moraga (2008) indicates that Mexican migrants to the US are modestly negatively selected on unobserved traits.

_Positive selection_ : Where there is positive selection on unobserved traits ( _f m_ − _f_ > 0 ) , as in the right-hand panel of Figure 2, the estimated wage ratio overstates the true ratio by where Θ ≡ (γ(ρ −γ)) ( 1 − ργ) and γ ≡ σ _f_<sup>σ</sup> _h_ and ρ is the correlation.<sup>15</sup> To begin to evaluate the magnitude of bias (5) note that if γ ≈ 1 (as for three of the four countries in Figure 1) then Θ = − 1 (as long as ρ < 1) and _Ro Re_ = _e_ ( _h_ − _hm_ ) . At γ = .084 (the average γ across the countries) and with positive selection overestimation occurs if and only if _hm_ < _h_ and ρ < .084 .

> 15 Marks (1982) shows that the equation of this ellipse is _fa_ ˆ 2 − _hfb_ ˆˆ + _ah_ ˆ 2 = _Z_ , where _f_ ˆ ≡ ( _f_ − _f_ ) / σ _f_ , _h_ ˆ ≡ ( _h_ − _h_ ) / σ _h_ , _a_ ≡ 1/ ( 1 − ρ 2 ) , _b_ ≡ 2 ρ / ( 1 − ρ 2 ) , and _Z_ is some constant. ˆ ˆ ˆ ˆ ˆ The implicit function theorem yields yields _dh_<sup>ˆ</sup> / _fd_ = ( _bh_ − 2 _fa_ ) ( 2 _ah_ − _fb_ ) , which equals the slope of the tangent line γ ≡ σ _f_<sup>σ</sup> _h_ at any point ( _f m_ ,<sup>_h_</sup> _m_ ) such that that ˆ ˆ _hm f m_ = ( 2 _a_ − _b_ γ) ( _b_ − 2 _a_ γ) . This implies that for the marginal new migrant, _f m_ − _f_ = ( _hm_ − _h_ )Θ , where Θ ≡ ( _b_ γ − 2 _a_ γ 2 ) ( 2 _a_ − _b_ γ) = (γ(ρ−γ)) ( 1 − ργ) .

17
Table 4: Positive price shock and investment decisions

|VARIABLES|(1)<br>d_lninv|(2)<br>d_lninv|(3)<br>d_lninv|
|---|---|---|---|
|d_price|-0.425|7.218**|7.148**|
||(0.748)|(3.112)|(3.252)|
|d_price^2|-0.567*|-0.582*|-0.529*|
||(0.301)|(0.306)|(0.306)|
|d_price*Ln(asset)||-0.445***|-0.446**|
|||(0.167)|(0.175)|
|Ln(asset)|0.179**|0.284***|0.280***|
||(0.0756)|(0.0927)|(0.0935)|
|d_price*Ln(land)||0.364|0.400|
|||(0.246)|(0.254)|
|Ln(land)|0.222***|0.152*|0.276***|
||(0.0570)|(0.0810)|(0.0947)|
|Ln(land)^2|||0.0542***|
||||(0.0175)|
|Average years of schooling|0.0128|0.0150|0.0155|
||(0.0272)|(0.0270)|(0.0268)|
|Household size|0.0878**|0.0841**|0.0759**|
||(0.0334)|(0.0342)|(0.0347)|
|Average age|-0.00242|-0.00311|-0.00330|
||(0.0107)|(0.0107)|(0.0107)|
|Constant|9.124***|7.346***|7.431***|
||(1.247)|(1.518)|(1.519)|
|Observations|762|762|762|
|Sample|All|All|All|
|Provincial dummies included but not||||
|reported|YES|YES|YES|
|R-squared|0.223|0.233|0.239|
|*** p<0.01, ** p<0.05, * p<0.1||||

Standard errors are heteroscedasticity robust and clustered at the village level, and are reported in the parentheses.

31
# **Figure 1: Historical Evolution of Infrastructure in South Africa. Source: Fedderke, Perkins and Luiz (2005).**

<!-- Start of picture text --> Real GDP and public-sector economic infrastructural investment<br>(gross) and fixed capital stock (indices, all measured per capita)<br>260<br>220<br>180<br>140<br>100<br>60<br>1960 1970 1980 1990 2000<br>Infrastructural investment Infrastructural fixed capital stock GDP<br>South Africa: indices of infrastructure<br>200<br>150<br>100<br>50<br>0<br>1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000<br>Railway lines Goods stock (rail) Paved roads<br>Electricity Phone lines, incl. mobile<br>Index (1960 = 100) (all values per capita)<br>Index (1990 = 100)<br><!-- End of picture text --> 44
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