rafmacalaba/datause-encoder-data huggingface dataset:
||Use Hidden<br>Contraception|(8)|-0.017<br>(0.027)<br>[0.535]|19.56|24,898||equals 1 if true; in|(going out without|with the combined|ecifc linear trends,|in square brackets.|
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|ledge and Health -|Acceptable Reaso<br>Domestic Violenc|(6)|-0.310<br>(0.121)<br>[0.010]|19.45|24,052|05, 2011, and 2016).|ns. In columns 1–3, 7,|0 to 5 of acceptable r|is the predicted leve|include birth year an|evel and shown in par|
|ng on Know|Height<br>(z-score)|(5)|0.031<br>(0.142)<br>[0.830]|8.22|19,879|y (in years 20|e eight colum|he count from <br>|�<br>f Schooling_izy_|All regressions|d at the zone l|
|of Schooli|BMI<br>(z-score)|(4)|0.234<br>(0.132)<br>[0.076]|9.70|19,491|Health Surve|of each of th|umn 6 it is t|od).<br>Years o|0 to 1988.|are clustere|
|of Efect of Years|Know about<br>Fam. Planning|(3)|0.023<br>(0.022)<br>[0.287]|19.56|24,898|n Demographic and|described at the top|outcome; and in col|using sex, burning fo|irth cohorts from 197|sion. Standard errors|
|ational Estimates|Read about<br>Fam. Planning|(2)|0.047<br>(0.021)<br>[0.027]|19.55|24,885|ata from the Ethiopia|dependent variable is|value of the described|ing with husband, ref|s include women in b|from a unique regres|
|e C.18: N|Literate|(1)|0.121<br>(0.019)<br>[0.000]|20.27|24,480|based on d|ndex. The|ndardized|dren, argu|All sample|estimate is|
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A.37
- **Galiani, Sebastian, Ivan Lopez Cruz, and Gustavo Torrens** , “Stirring Up a Hornets’ Nest: Geographic Distribution of Crime,” _Journal of Economic Behavior & Organization_ , 2018, _152_ , 17–35.

- **Gonzalez-Navarro, Marco** , “Deterrence and Geographical Externalities in Auto Theft,” _American Economic Journal: Applied Economics_ , 2013, _5_ (4), 92–110.

- **International Maritime Bureau** , “Piracy and Armed Robbery against Ships: Report for the period 1 January - 31 December 2005,” Technical Report, International Chamber of Commerce - International Maritime Bureau 2006.

- , “Piracy and Armed Robbery against Ships: Report for the period 1 January - 30 June 2015,” Technical Report, International Chamber of Commerce - International Maritime Bureau 2015.

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, “Regulation for carriage of AIS,” 2016.

- **Kremer, Michael and Jack Willis** , “Guns, Latrines and Land Reform: Dynamic Pigouvian Taxation,” _American Economic Review_ , 2016, _106_ (5 (May 2016)), 83–88.

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- **MSCHOA** , _Best Management Practices for Protection against Somalia Based Piracy_ , Edinburgh, U.K.: Witherby Publishing Group Ltd, 2011.

46
important, accounting for 17 percent of total exports at the end of the surge, and 25 percent of export growth, in developing countries. The importance of the exchange rate for the extensive margin in developing countries is confirmed using regression analysis for the full sample of annual data from 1980-2006.

These results are consistent with a distortion that disproportionately affects the tradables sector and offers insight into the nature of the distortion. The large real exchange rate depreciation facilitates a reorientation of production towards the most efficient manufacturing industries in developing countries. While the product and market space is well explored in developed markets; there is ample room for expansion along both dimensions in developing countries. We find that in developing countries the extent of depreciation and the importance of new export markets and new export products are much larger than in developed countries. Moreover, only in developing countries is undervaluation an important part of the surge. This is suggestive of a market failure that leads to suboptimal entry in new products and new markets at the “equilibrium” exchange rate. Undervaluation—or, put another way, _a competitive currency_ —is the big push that encourages both more entries and more successes. It performs like a “grand opening sale”, and once customers start coming many of the new relationships are maintained even after the sale is over.

The paper is organized as follows: The next section defines export surges and describes the data. Section III examines the relationship between export surges, GDP surges, and export collapses, to look for cyclical effects. Section IV looks at the conditions that lead to an episode. Section V focuses on the composition of the export 6
# **Table 7: Budgeting**

||(1)|(2)|(3)|
|---|---|---|---|
||Budgeting<br>Composite Score|Q1: Have a<br>Written Budget|Q2: Adhere to<br>Budget|
|Autonomous Trainer Selection (T1)|0.032**|0.031**|0.033**|
||(0.012)|(0.015)|(0.015)|
|Fixed Trainer Selection (T2)|0.010|0.015|0.006|
||(0.011)|(0.014)|(0.013)|
|R-squared|0.044|0.011|0.054|
|N|7885|7885|7885|
|Dependent Variable Mean in Control Group|<br>0.341|0.155|0.526|
|F-test(p-value): T1 = T2|0.117|0.364|0.071|

This table presents analysis for budgeting using household survey data across two endline rounds. All regressions control for survey round dummies as well as stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE then exceeded, the peak volumes of the early 2000s (figure 10). As of 2010 SITARAIL was on track to achieve its highest-ever traffic volume of more than 900 million tonne-km.

**Table 6. Railway indicators for SITARAIL and selected other countries, 2005–09 average**

||**SITARAIL**|TRANSRAIL|CAMRAIL|MADARAIL|
|---|---|---|---|---|
||**Côte d’Ivoire**<br>**Burkina Faso**|Senegal<br>Mali|Cameroon|(Madagascar)|
|Concessioned (1)/ state run (0)|**1**|1|1|1|
|Freight traffic volume (million tonne-km)|**794**|393|1,061|113|
|Passenger traffic volume (million passenger-km)|**210**|91|377|3|
|Total traffic volume (million TU)*|**878**|429|1,212|114|
|**Efficiency**|||||
|Staff: 1,000 TU per staff|**558**|247|547|118|
|Derailments/million TU|**0.01**|0.45|0.15|2.31|
|Mainline locomotive breakdowns per 100,000 km|**6**|15|9|6|
|**Tariffs**|||||
|Average unit tariff, freight, cents/tonne-km|**6.3**|7.0|8.1|6.0|

_Source:_ World Bank 2010; Bullock 2009. Derived from AICD rail operator database (www.infrastructureafrica.org/aicd/tools/data). — = Not available.

_Note:_ * With 2.5 passenger-km equivalent to 1 TU, 1 tonne-km equivalent to 1 TU. TU = traffic unit.

**Figure 10. Evolution of SITARAIL’s freight traffic over time**

<!-- Start of picture text --> 1,000<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Total traffic in million traffic units (TUs)<br><!-- End of picture text --> _Source:_ AICD 2006; WB/AfDB 2009 _. Note:_ **x** = Projection for 2010.

# **Challenges**

In spite of its impressive operational performance, SITARAIL is in financial distress due to the accumulation of losses made during the crisis period and the related financial liabilities it owes to stateowned asset-holding companies. SITARAIL’s total financial war-related losses are equivalent to $38.6 million. They include: (i) physical damage ($5.7 million), (ii) loss of income to employees ($9.7 million), (iii) nonpayment of state-owned asset-holding companies’ debt ($8.6 million), (iv) concession fee 18
# A. IPV Often Index

|p=.039 / b=-0.482<br>Boys, had sex||
|---|---|
|p=.030 / b=-0.378<br>Goal, had sex||
|p=.338<br>Boys, not had sex||
|p=.088 / b=-0.133<br>Goal, not had sex||
|-1<br>-0.5<br>0<br>0.5|1|

# B. IPV in last year Index

|p=.107 / b=-0.282<br>Boys, had sex|
|---|
|p=.023 / b=-0.444<br>Goal, had sex|
|p=.368<br>Boys, not had sex|
|p=.624<br>Goal, not had sex|
|-1<br>-0.5<br>0<br>0.5<br>1|

**Figure 6** Impact of Treatments on IPV, Heterogeneity by ever had sex at baseline

_Notes._ This figure presents estimates of _β_ 1 and _β_ 2 and from equation 1. Panel A presents estimates for the IPV often index and Panel B presents estimates for the IPV in last year index. The indices are centered on females in ELA only communities who were sexually active at baseline and who were not assigned to the _Goal_ treatment. Bolded markers are statistically significant at _p <_ 0 _._ 1 _. p_ -values and coefficient estimates are displayed beside each marker. _Source._ Female baseline and endline data, balanced panel. The sample in the top half is restricted to females who had ever had sex at baseline. The sample in the bottom half is restricted to females who had not ever had sex at baseline.

47
<!-- Start of picture text --> 75%<br>&<br>‘=ra2=]><br>r=<br>8 50%<br>2<br>2<br>onDCccoya<br>=<br>8<br>=3<br>ry°fo<br>& 25%<br>xs}<br>£<br>ag<br>0%<br>2007 2012 2016 2021<br>| Men | Women<br><!-- End of picture text -->
|**Log emigrants**|0.040**|0.011|0.106***|0.016|0.054***|0.055**|0.058***|-0.016|
|---|---|---|---|---|---|---|---|---|
||(0.018)|(0.018)|(0.019)|(0.022)|(0.018)|(0.026)|(0.016)|(0.034)|
|**Log distance**|-0.824***|.|-1.041***|.|-0.863***|.|-1.318***|.|
||(0.062)|.|(0.092)|.|(0.080)|.|(0.081)|.|
|**Contiguity**|0.084|.|0.487|.|0.162|.|-0.112|.|
||(0.085)|.|(0.476)|.|(0.477)|.|(0.206)|.|
|**Common Language**|0.044|.|0.329**|.|0.334**|.|0.080|.|
||(0.087)|.|(0.156)|.|(0.150)|.|(0.155)|.|
|**Colony**|0.406***|.|0.163|.|0.525***|.|0.932*|.|
||(0.134)|.|(0.211)|.|(0.178)|.|(0.508)|.|
|**RTA**|0.403***|0.267***|0.085|0.115|0.245*|0.253|-0.095|0.096|
||(0.074)|(0.059)|(0.198)|(0.216)|(0.148)|(0.178)|(0.165)|(0.267)|
|**Common Legislation**|0.311***|.|0.295***|.|-0.044|.|0.216**|.|
||(0.063)|.|(0.098)|.|(0.081)|.|(0.095)|.|
|**Common Currency**|0.305***|0.199|1.504**|1.093*|1.226**|0.36|0.000|0.831|
||(0.125)|(0.123)|(0.613)|(0.644)|(0.506)|(0.394)|(0.423)|(0.741)|
|**Importer /Exporter time-**<br>**varying dummies (it/jt)**|YES|YES|YES|YES|YES|YES|YES|YES|
|**Asymmetric pair dummies**<br>**(ij)**|.|YES|.|YES|.|YES|.|YES|
|**Observations**|2,088|2,088|4,187|4,187|4,269|4,269|6,174|6,174|
|**R**<sup>**2**</sup>|0.959|0.985|0.788|0.930|0.847|0.894|0.671|0.857|

The dependent variable is the log of bilateral exports. All regressions include importer and exporter fixed effects. Superscripts ***, **, * denote statistical significance at 1, 5 and 10% respectively. Robust standard errors are provided in parentheses.

The only paper, with estimates directly comparable to those presented here however, are those provided by Felbermayr and Jung (2009), since, as argued throughout the paper, cross-sectional results should be viewed with caution given that other bilateral factors, which might drive both trade and migration cannot be accounted for. They estimate a panel of North-South trade and migration for 1990 and 2000 using the 25
instance, Esquivel (1999) shows that, while the pace of convergence across states was relatively fast over 1940–60, it halted and started to reverse over the next 35 years. This divergence was confirmed by subsequent studies focused on 1985–2000 (Chiquiar 2005; Garc ́ıa-Verd ́u 2005; Rodr ́ıguez-Oreggia 2007; Rodr ́ıguez-Pose and S ́anchez-Reaza 2005). In general, regional divergence during these years was linked to trade liberalization and the entry into force of the North American Free Trade Agreement, which bolstered the emergence of club convergence in the states that had benefited the most from these reforms given their initial endowment of relatively high-skilled labor and better public infrastructure.

Empirical evidence on convergence at a higher level of geographical disaggregation, namely, municipalities, has been scarce in Mexico. This is primarily because of the lack of a sample with robust income information and statistical power at that level. A couple of studies have reported dramatic disparities among municipalities in income and poverty in 2000 (L ́opez-Calva et al. 2008; Sz ́ekely et al. 2007) by applying small area estimation techniques to impute incomes from the main household income survey to the population census. Using this technique and logistic regressions, Mexico’s National Council for the Evaluation of Social Development Policy (CONEVAL) has computed rates of and changes in income poverty between 2000 and 2005 and multidimensional poverty between 2010 and 2015 across municipalities. This paper provides the first long-run assessment of regional disparities and paths in income, poverty, and inequality, based on comparable data on municipalities.<sup>2</sup>

# **3 Mapping income, poverty, and inequality in municipalities**

Capturing long-run trends in income, poverty, and inequality among municipalities requires a dataset of intertemporally comparable indicators of well-being that are statistically representative of the population in each municipality. The availability of such a dataset, however, may entail a trade-off between relatively high precision in the measurement of, say, household income and significant geographical detail. One might exploit household surveys designed to capture all sources of income and thus

> 2Two papers have used a preliminary version of this dataset to examine changes across municipalities. Villalobos Barr ́ıa et al. (2016) have analyzed, through Gaussian mixture modeling, the univariate and joint distribution of human development indicators —income, infant mortality, and years of schooling— and the conformation of development clusters in 1990, 2000, and 2010. Using the same preliminary dataset, Enamorado et al. (2016) study the causal effect of inequality on drugrelated homicides and report a sizable decline in inequality in the majority of municipalities over 1990–2010.

6
countries and over time. Bangladesh registers as the country with the lowest, whereas Malaysia features the highest, transportation energy intensity of these eleven countries throughout the study period. Most of the countries demonstrate an overall upward trend in transportation energy intensity, that is, their fuel consumption for transportation per unit of economic output has increased, yet the two largest economies in the region, China and India (especially China), show steadily improving transportation energy intensity over the study period, which should mitigate some of the increased CO2 emissions expected from their swift economic growth. Transportation energy intensity in Pakistan and Sri Lanka at the end of the study period are found to be close to the level at the beginning, although they fluctuate in between. Despite the overall deterioration in transportation energy intensity in most of the other countries, a noticeable improvement can be observed in the last year of the study period for every country other than Bangladesh and China.

**Figure 4: Transportation Energy Intensity in Selected Asian Countries (1980-2005)**

<!-- Start of picture text --> 60<br>Bangladesh<br>China<br>50<br>India<br>Indonesia<br>40<br>Korea<br>30 Malaysia<br>Pakistan<br>20<br>Philippines<br>Sri Lanka<br>10<br>Thailand<br>Vietnam<br>0<br>(ktoe/billion US$)<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> Source: IEA (2007b, c, f, g) 19
year, and health insurance contributions (see below). If health insurance contributions are higher than the pre-health insurance deduction personal income tax, then the health contribution value is decreased to the level of PIT. Finally, there are other important deductions, including a child tax credit, deductions for internet bill payments, and rehabilitation expenses among others. The most significant of these is the child tax credit (CTC), which cannot exceed the pre-CTC PIT value plus social security and health contributions paid.

**Table 2. Poland: Tax and Social Security Contributions**

|Main taxes applicabletoindividualsin Pol|and|||
|---|---|---|---|
|Personal income tax - regular rates||18% and 32%(for incom|e > PLN 85,528)|
|Personal income tax – flat rate (may be app<br>employment ifcertainconditions aremet)|lied to self-|19%||
|Dividends||19%||
|Interest||19%||
|Royalties||18% and 32%||
|Capitalgains||19%||
|VAT||23% (standard rate), red<br>along withsome exempt|uced rates of 8%, 5%, 0%<br>ions.|
|Inheritance tax||3%-20%||
|Special expatriate regime (only selected so<br>income)|urces of|20%||
|Social insurance contributions||||
|Insurance type|Cap on salary<br>|Allocation o|f contribution cost|
||subject to|||
||contribution|Employer|Employee|
|Pension|112,380 PLN|9.76% of remuneration|9.76% of remuneration|
|Disability|per annum|6.5% of remuneration|1.5% of remuneration|
|Sickness||-|2.45% of remuneration|
|Accident|No cap<br>applies|0.67% - 3.33% of<br>remuneration<br>(depending on risk<br>category). 1.67% is the<br>mostcommon rate||
|Health||-|9% (7,75% taxdeductible)|
|Otheremployer’s charges||||
|Labor Fund||2.45% of remuneration||
|Employees’GuaranteedPaymentsFund||0.1% of remuneration||
|Source: Deloitte (2015).||||

Agricultural taxes are levied on ownership or possession of agricultural arable lands or woods. In case of farm land the taxable base is the number of conversion hectares (calculated on the basis of actual area, kind and quality of land and location in one of four tax zones, set depending on 6
<!-- Start of picture text --> Figure 2<br>Structure of Enterprise Employment by Ownership and by Economic Sector for the Period 2000-2007<br><!-- End of picture text --> <!-- Start of picture text --> A. FDI B. Private C. SOE<br>2000 2000 2000<br>Agriculture<br>1500 1500 1500<br>Capital-<br>intensive<br>Labor-<br>1000 1000 1000 intensive<br>Skill-<br>intensive<br>500 500 500<br>Construction/<br>Services<br>0 0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2000 2001 2002 2003 2004 2005 2006 2007 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text -->

# Source: the _Enterprise Survey_ data 2000-2007 (GSO). Notes: The numbers of employment are by thousands.

The manufacturing sector is disaggregated into labor-, capital-, and skill-intensive sectors. The labor-intensive sector includes textiles, apparel, footwear/leather products, wood products, furniture/miscellaneous manufacturing, metal products, and non-metallic mineral products. The capital-intensive sector is defined as mining, food products, basic manufacturing, and chemical, rubber, plastic products. The skill-intensive sector consists of electronics/machinery and transport equipment.

37
* 1 2

commodity exports **is** accounted for by the fall in market share for these items.<sup>11</sup> In other words, had developing countries' market shares remained at their 1965 levels the relative importance of primary stage exports would have risen rather than fallen for the rice and tea processing chains.

While the previous analysis demonstrated that there has been a secular shift in developing countries' exports toward processed commodities, two questions of importance concern the contribution of different developed country markets to this shift and, second, the extent to which different developing countries have participated. Table 3 is addressed to the first issue and examines major developed country commodity imports over 1965-87. Specifically, the table shows the value of 13 countries' (or country groups) 1987 imports of major commodity groups as well as the 1987 share of primary stage (unprocessed) commodities in this total. To indicate the shift in relative importance, the lower third of the table shows the 1965-87 change in the share of primary stage items in total commodity imports.

With one or two exceptions Table 3 shows that the sizable shift toward trade in processed commodities extends to all the developed countries and occurs over all major commodity groups. Due largely to ores, minerals and metals the share of unprocessed items in Australia and New Zealand's total commodity import fell by more than 30-percentage points while the corresponding decline for Japan lAside from the normal reasons often advanced in support of further commodity processing in developing countries there is an additional potentially important consideration relating to food products in that further processing generally means increased preservation or an extension of the useful life of the commodity (grains may be an exception to this general rule). As such, further processing may be a useful way of combatting the major problem of food spoilage in some developing countries. Data compiled by Olechowski (1987), Yeats (1979) and Laird and Yeats (1990) suggest that escalating tariffs and nontariff barriers in developed country markets are one reason why developing countries have difficulties in processing food commodities for export.