rafmacalaba/datause-encoder-data huggingface dataset:
# **II. Education in Bogota**

Colombia is a relatively typical middle income, Latin American country. Child mortality is relatively low at 21 per 1000 births and individuals can expect to live long lives -- life expectance at birth is 72.6 years. The per capita income of Colombia is US$ 2,020, with only 17.8 percent of the population living on less than two dollars per day (World Bank, 2006).

While the central government maintains control of curriculum as well as of the allocation of teachers and their wages, municipalities are primarily responsible for the administration of public education using national funds. The central government provides resources to municipalities, primarily from income and VAT taxes, and close to 90 percent of these funds are required by law to go toward health and education. With these funds, municipalities must develop, maintain, and run the facilities in their jurisdictions. Municipalities that have greater capacity to collect and administer taxes supplement central resources with local funds, usually from property taxes.

The academic year runs from the end of January until the middle of November. The system is divided into three categories: basic primary (grades one through five), basic secondary (grades six through nine) and middle secondary (grades ten and eleven). After finishing the eleventh grade, children can matriculate to either traditional universities or one of many vocational schools. Students usually start school at five to seven years of age, and children are legally required to attend school through the ninth grade, a period referred to as basic education.

Like in most urban areas in middle-income countries, school attendance is highest for younger children. The enrollment rate for students of age between 5 and 13 are close to 100 percent. After 13 years old, the attendance rate starts to decline. The average attendance rate for individuals aged 15 is 92 percent, 16 is 90 percent and 17 is 80 percent. The drop is faster for low-income individuals. For individuals falling into the bottom two categories of the Colombian poverty index (the SISBEN), the attendance rate for 15 year olds is 84 percent, for 16 year olds is 80 percent and for 17 years olds is 65 percent (Fedesarrollo, 2005). Overall, there were 89,000 between 5 and 18 years of age who were not attending school in 2003. Seventy-four percent of these were classified in the bottom two categories of the SISBEN (Fedesarrollo, 2005).

When surveyed, students claim that the major reason for dropping out is the cost of education. Students have to pay to enroll each year; they also have to pay for required items like

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<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>200<br>System<br>150 El Surface irrigation<br>oO BB dip : inigation——<br>= | Sprinkler irrigation<br>= 100<br>a Rainfed P high-input pe<br>BB Rainted high-input<br><!-- End of picture text -->

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<!-- Start of picture text --> a<br>&<br>= | Healthy Diet Maxinvest<br>a a Healthy & Sust Diet Invest<br>.38 20 li | | Healthy & Sust Diet Maxinvest<br>SSP2 SSP2 SSP2 SSP2 SSP2 SSP2<br><!-- End of picture text -->
**FIGURE A.4: TRADE WAR TARIFFS AND EXPORT CHANGES, WITH FIXED EFFECTS**

<!-- Start of picture text --> Panel A Panel B<br>Bystanders' Export Value to US Bystanders' Export Value to China<br>2015-17 2017-19 2015-17 2017-19<br>.2 .3<br>.15 .2<br>.1 .1<br>.05 0<br>0 -.1<br>0 .05 .1 .15 .2 0 .05 .1 .15 .2<br>∆ ln T(US,CH) ∆ ln T(CH,US)<br>Pre-period: β =-0.12 (0.11). Post-period: β =0.20 (0.11). Pre-period: β =-0.01 (0.18). Post-period: β =-0.06 (0.20).<br>Panel C Panel D<br>Bystanders' Export Value to RW Bystanders' Export Value to RW<br>2015-17 2017-19 2015-17 2017-19<br>.1<br>.1<br>.05<br>.05<br>0<br>0<br>-.05<br>-.05<br>-.1<br>-.1<br>0 .05 .1 .15 .2 0 .05 .1 .15 .2<br>∆ ln T(US,CH) ∆ ln T(CH,US)<br>Pre-period: β =-0.00 (0.09). Post-period: β =0.15 (0.09). Pre-period: β =0.12 (0.08). Post-period: β =0.30 (0.08).<br>ln X(i,US) ln X(i,CH)<br>∆ ∆<br>ln X(i,RW) ln X(i,RW)<br>∆ ∆<br><!-- End of picture text --> Notes: The panels show binscatter plots of bystanders’ export growth (on the y-axes) against changes in tariffs due to the trade war (on the x-axes), controlling for country-by-sector fixed effects. Panel A is the regression: ∆ ln _Xiω_<sup>_US_</sup> = _αi_<sup>_US_</sup> , _j_ ( _ω_ )<sup>+</sup><sup>_β_∆ln</sup><sup>_T_</sup> _CH_<sup>_US_+</sup><sup>_ε_</sup> _iω_<sup>_US_. Panel B is the regression: ∆ln</sup><sup>_X_</sup> _iω_<sup>_CH_</sup> = _αi_<sup>_CH_</sup> , _j_ ( _ω_ )<sup>+</sup><sup>_β_∆ln</sup><sup>_T_</sup> _US_<sup>_CH_+</sup><sup>_ε_</sup> _iω_<sup>_CH_. Panel C is the regression:</sup> ∆ ln _Xiω_<sup>_RW_</sup> = _αi_<sup>_RW_</sup> , _j_ ( _ω_ )<sup>+</sup><sup>_β_∆ln</sup><sup>_T_</sup> _CH_<sup>_US_+</sup><sup>_ε_</sup> _iω_<sup>_RW_</sup> . Panel D is the regression: ∆ ln _Xiω_<sup>_RW_</sup> = _ααi_<sup>_RW_</sup> , _j_ ( _ω_ )<sup>+</sup><sup>_β_∆ln</sup><sup>_T_</sup> _US_<sup>_CH_+</sup><sup>_ε_</sup> _iω_<sup>_RW_</sup> . Also reported are regressions with exports prior to the trade war from 2015-17. Below each panel are OLS coefficients (standard errors clustered by product).
dichotomy between humanitarian action and development efforts and the lack of evidence on the impact of refugees on the hosting population. We do not claim that local integration is the optimal solution in all protracted cases. However, the reluctance to integrate refugees locally has precluded the opportunity to test local integration approaches that are fundamentally driven by the economic interactions between refugees and host communities. One also needs to overcome the institutional constraint for such approaches since local integration requires further coordination between the UNHCR and other institutions like UNDP, the World Bank, non‐governmental organizations, development agencies, national and local governments. Overall, there is a dearth of evidence on the effectiveness of different policies in protracted refugee situations, but what appears to be clear is that refugees’ capacities should be considered as an asset in the search for sustainable solutions to protracted refugee situations.

# **Bibliography**

Alderman, H., J. Hoddinott, and B. Kinsey. 2006. “Long‐term consequences of early childhood malnutrition.” _Oxford Economic Papers_ 58(3): 450‐474.

Akresh, R., Verwimp, P. and T. Bundervoet. 2011. “Civil War, Crop Failure, and Child Stunting in Rwanda," _Economic Development and Cultural Change_ , 59(4), pp. 777‐810.

Akresh, R., S. Bhalotra, M. Leone, and U. Osili. 2012a. “War and Stature: Growing up during the Nigerian civil war," _American Economic Review: Papers and Proceedings,_ 102(3), 273{277.

Akresh, R., Lucchetti, L., and H. Thirumurthy. 2012b. "Wars and child health: Evidence from the Eritrean‐ Ethiopian conflict," _Journal of Development Economics_ , Vol. 99, pp. 330‐40.

- Alix‐Garcia, J., A. Bartlett, and D. Saah. 2012. “Displaced Populations, Humanitarian Assistance and Hosts: A Framework for Analyzing Impacts on Semi‐urban Households.” _World Development_ 40 (2): 373–386.

- ———. 2013. “The Landscape of Conflict: IDPs, Aid and Land‐Use Change in Darfur.” _Journal of Economic Geography_ 13 (4): 589–617.

- Alix‐Garcia, J., and D. Saah. 2010. “The Effect of Refugee Inflows on Host Communities: Evidence from Tanzania.” _World Bank Economic Review_ 24 (1): 148–170.

- Aldelman, S., D. Gilligan, K. Lehrer (2012) Childhood interrupted: How conflict‐induced displacement shaped human capital formation in Northern Uganda. Mimeo.

- Baez, J. E. 2011. “Civil Wars beyond Their Borders: The Human Capital and Health Consequences of Hosting Refugees.” _Journal of Development Economics_ 96 (2): 391–408. doi:http://dx.doi.org/10.1016/j.jdeveco.2010.08.011.

Bailey, S. (2013). The Impact of Cash Transfers on Food Consumption in Humanitarian Settings: A review of evidence.

32
owner (Figure 14, Panel B). The rest employ an average of 0.41 (0.36) workers, all of whom are unpaid family member or apprentices (Figure 14, Panel A). By definition, employers hire paid workers, though they also employ a significant share of unpaid workers. The 94.3 percent of employers who are classified as micro firms (defined as firms that employ 1-4 employees), employ an average of 1.85 workers (in addition to the owners), where about 0.8 are unpaid workers. The 5.7 percent of employers who are classified as small firms (defined as firms having five or more employees), have an average of 3.7 paid workers and 2.5 unpaid workers.

**Figure 14. Distribution of workers per firm, by informal firm type**

<!-- Start of picture text --> Panel A: Average Number of Workers per Firm Panel B: Distribution of Labor Force within<br>Type (excluding the owner) Firm Type<br>3.5 100% 5.7%<br>3.0 41.1% 36.3%<br>2.5<br>50% 94.3%<br>1.85<br>2.0<br>58.9% 63.7%<br>1.5<br>0%<br>1.0<br>0.41 0.36 LE-SE HE-SE Employer<br>0.5<br>Owner and 5+ employees<br>0.0 Owner and 1-4 employees<br>LE-SE HE-SE Employer Owner only<br><!-- End of picture text --> Source: Own elaboration based on IES. Notes: Weighted average includes Mozambique, Zambia and Zimbabwe.

The difference in average sales and profits by firm types masks the high variance, and significant overlap, in the distributions. LE-SE firms have the lowest average monthly sales (250 USD PPP adjusted) as compared to HE-SE (600 USD) and employers (800 USD) (Figure 15, Panel A). However, an exploration of the left tail of the distribution shows that while half of LE-SE firms sold, on average, less than 200 USD in the previous month, a significant share of HE-SE (25 percent) and employers (30 percent) also had low sales. At the other extreme, 35 percent and 30 percent of HE-SE and employers, respectively, sold at least 600 USD in the last month on average while the share is 10 percent for LE-SE. When accounting for firm costs, average profits are particularly high for employers, earning an average of 302 USD monthly as compared to 31 USD for LE-SE and 42 USD for HE-SE. However, there is a bimodal distribution among employers, where profits for the larger sub-group is similar to average profits of the other firm types (Figure 15, Panel A).

On a per worker basis, HE-SE stand out as having higher and more concentrated profits, a proxy for productivity, than the other firm types. The average monthly value of sales per worker is 464 USD in HE-SE firms, as compared to 224 USD and 276 USD for LE-SE and employers, respectively (Figure 15, Panel B). LE-SE and employers also have a similar distribution of sales per worker. The similar pattern between these two firm types is because LE-SE combine a low value of sales with a workforce of small size. Employers, on the other 30
crops, such as China, India, Southeast Asia and Eastern Europe, could face food supply reductions of 3.5-6.1% from the baseline by 2020.

# **References**

Narayanan, B. G. and T. L. Walmsley, Editors. 2008. Global Trade, Assistance, and Production: The GTAP 7 Data Base, Center for Global Trade Analysis, Purdue University.

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- Burniaux J-M. and Chateau J., 2010. ”Background Report: An Overview of the OECD ENVLinkages model”, OECD.

- Birur, D., Hertel T., and Tyner W., Impact of Biofuel Production on World Agricultural Markets: A Computable General Equilibrium Analysis, GTAP Working Paper No. 53, Department of Agricultural Economics, Purdue University.

- Choi S., 2004. "The Potential and Cost of Carbon Sequestration in Agricultural Soil: An Empirical Study with a Dynamic Model fo the Midwestern U.S." Ph.D. Thesis, Department of Agricultural, Environmental, and Development Economics, Ohio State University.

- Farrell A.E., R.J. Plevin, B.T. Turner, A.D. Jones, M. O‟Hare and D.M. Kammen. 2006. Ethanol Can Contribute to Energy and Environmental Goals. Science 311: 506–8.

- Jarrett P. and Torres R., 1987. “A revised supply block for the major seven countries in INTERLINK”, OECD Department of Economics and Statistics Working Papers No. 41.

- Ma H., Oxley L., and Gibson J., 2010. “China's energy economy: A survey of the literature”, Economic Systems, Elsevier, vol. 34(2), pages 105-132.

- Ogg, C.W. 2009. Avoiding More Biofuel Surprises: The Fuel, Food and Forest Trade-offs. Journal of Development and Agricultural Economics: 1(1):12-17.

- Rosegrant, M. W., T. Zhu, S. Msangi and T. Sulser. 2008. Global Scenarios for Biofuels: Impacts and Implications. Review of Agricultural Economics 30(3): 495-505

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create capacity for local resource<sup>mobilization (USAID 1989). A brief</sup> analysis of two successful<sup>pro-poor institutions demonstrates ID's potential.</sup>

# _The National Irrigation Administration_

In their chapter "The Impact<sup>of Participation," Romana de los</sup> Reyes and Sylvia Jopillo (1988:90-116)<sup>assess the impact of non-participatory</sup> versus participatory irrigation<sup>systems. They illustrate many of the benefits</sup> that have resulted from adopting<sup>a more participatory institutional approach,</sup> such as relatively higher rice<sup>yields and hectares irrigated, and the greater</sup> representation of landless and<sup>small farmers in water user association</sup> leadership positions. This essay,<sup>however, will focus only on their findings</sup> that pertain to costs and area<sup>irrigated.</sup> First, the researchers show<sup>that although the cost of the</sup> participatory system exceeds that<sup>of the non-participatory system, it only</sup> does so by a very small amount<sup>(merely $2 more per hectare). This results</sup> from the fact that the non-participatory<sup>system costs more per hectare for the</sup> design and construction (see<sup>Table 2 for cost break down of the two systems).</sup> Second, they demonstrate that despite<sup>the higher costs, the benefits</sup> associated with the institutional<sup>activities made them well worth the</sup> investment for three main reasons:

- a) the participatory system mobilized<sup>local resources far</sup> surpassing those of the non-participatory<sup>system;</sup>

- b) loan repayments have been significantly<sup>higher under the</sup> participatory system; and

- c) the area of land irrigated has<sup>expanded more under the</sup> participatory system than<sup>the non-participatory system</sup> especially during the dry season.

These factors also contribute to<sup>sustainability.</sup>
almost half (or more) of our data points.<sup>15</sup> A caveat in interpreting these results, however, is that we cannot be confident that onset of illness coincides perfectly with the start of the reporting period. For instance, if one family member misses work for an entire reporting period – and then the first few days of the next reporting period (but not the whole week) – and another household member compensates for the duration of the illness, this behavior would look like a both a contemporaneous and a lagged response. Similarly, if a family member misses the last part of a reporting period and then the next week, and another household member compensates for this illness, this will look like an anticipation response to an upcoming illness. Still, we believe that the results are suggestive of the time path of response, even if the magnitudes are hard to establish due to this issue.

The event study, presented in figure 2, is consistent with the possibility that some response to the illness of another worker in the household continues into the next week. For instance, conditional on lagged and lead effects, men are 7.9 percentage points more likely to work in weeks in which another family member is unexpectedly ill, and 7.6 percentage points more likely to work in the week after a family member is unexpectedly ill (P = 0.13). The estimated effect of another family member missing work next week (despite having planned to work that week) on men’s contemporaneous labor supply, by contrast, is close to zero and statistically insignificant. A similar pattern emerges on the intensive margin, although the lagged effect of an illness is closer to zero and further from statistical significance (P = 0.46 in the specification for days of work, and P = 0.38 in the specification for hours).

> 15 This interpretation of the coefficients in this graph diverge slightly from a traditional event study design, where the coefficients shown give treatment effects relative to a time period before the event (and the leads and lags are included in the estimation).  Instead, because many respondents face multiple “treatments” (illness of other workers in their household), the coefficients in figure 2 show effects in the week before, during, and after the illness of another household member, relative to weeks in which there was no illness in that week, or the weeks before and after.

22
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between the availability of imported intermediate goods and firms’ decision to upgrade foreign technology embodied in imported capital goods. To test the first implication of the model, we estimate the probability that firm _i_ imports capital goods in year _t_ using the following linear probability model: _Importer_ ( _k_ ) _ist_ = _γ_ 1 _Inputτs,t-1_ + _γ_ 2 _Zs,t-1_ + _γ_ 3 _Xi,t-1_ + _μi_ + _υt_ + _εist_ ( _I_ ) Here _Importer_ ( _k_ ) _ist_ is a dummy variable for firm _i_ producing in industry _s_ having positive imports of capital goods in year _t_ . _Input τ s, t-1_ represents the input tariffs of industry _s_ in year _t −_ 1. We have already shown that we rely on exogenous changes in tariffs that are not correlated with initial firm or industry characteristics. Moreover, we use lagged tariffs values to ensure that contemporaneous firms’ decisions cannot affect past values of tariffs. _Zs,t-1_ is a set of industry level control variables and _Xi,t-1_ is a set of firm level observable characteristics varying over time. All specifications include firm fixed effects, _μi_ , that take into account unobservable and time-invariant firm characteristics and year fixed effects that control for macroeconomic shocks affecting all firms and industries in the same way, _υt_ . Since tariffs vary at the 3-digit industry level over time, the errors are corrected for clustering across 3-digit industry level.

As discussed above, input-tariff changes are not correlated with either initial firm characteristics or industry characteristics during the period 1989-1997. To deal with additional concerns of reverse causality and omitted variables, we introduce different control variables at the industry level which may affect firms’ import decisions of capital goods and could reflect the effects of input-tariff changes. The _γ_ 1 coefficient on input tariffs might then simply be picking up the effects of variations of tariffs on capital goods. The simple model presented in the previous section also predicts that the probability of importing capital goods is a decreasing function of capital goods tariffs. Hence, we first include India’s import tariffs on capital goods to capture the direct effects of variations in tariffs affecting capital equipement products on firms’ decision to import those capital goods. Second, all specifications also 30
21 Table 4: Household Expenditures Per Person and Social Capital, Comparing the Village and Household Level and Using HRDS data for Incomes.

|Column:|1|2|3|4|
|---|---|---|---|---|
|Source of Data:|SCPS|Human Res|ource and Devel|opment Survey|
|Level of data:|Household|Cluster|Hous|ehold|
|Type of estimation':|OLS|IV|IV<sup>5 </sup>(a)|IV (B)|
|Cluster Level Social Capital|.084<br>1.10|.208<br>2.56|.193<br>2.31|.227<br>1.71|
|Household Level Social Capital'|-.020<br>.526||||
|Household size|-.077<br>7.61|.019<br>1.04|-.080<br>10.5|-.079<br>10.3|
|Average adult schooling<sup>2</sup>|.019<br>1.43|-.057<br>1.42|.021<br>2.87|.021<br>2.79|
|Female head of household (1 =yes)|-.041<br>(.448)|.345<br>1.19|-.009<br>_.150_|-.010<br>.173|
|Asset ownership (In)<sup>2</sup>|.253<br>4.40|.245<br>3.88|.143<br>5.26|.143<br>5.20|
|Self-employed in agriculture (I =yes)<br>l|-.193<br> <br>2.36|-.325<br>1.19|-.068<br>1.69|-.069<br>1.68|
|Distance to nearestmnarket(clstr)<sup>2</sup>|-.0036<br>.243|-.004<br>1.05|-.0087<br>2.21|-.0087<br>2.21|
|Agroclimatic zone dummies'|||||
|lRegre-scinn statictics<br>Number of Observations<br>Adjusted R-Squared<br>|846<br>.215|84<br>-|1505<br>-|1505|
|First stage Incremental R<sup>2</sup>|-|.092|.116|.061|
|Instrument test ( p-level)|_|.618|.783|.786|

Notes: 1) Tbe t-statistics are based on Huber corrected standard errors that are heteroskedasticity consistent and account for stratified sampling.

2) If any of these variables were mnissing then a value was imputed for that household and a missing dumrnmy variable is set equal to one.

- 3) Cluster level social capital index excludes household's own response.

- 4) Included in the regressions but not reported are dummy variables for each of six agro-climatic zones and the three missing value dunmny variables.

- 5) The instrument sets are a: trust in strangers, tribcsman, cell leader, village chairman (government), district officials, central govermnent while instrunent set B excludes strangers.