rafmacalaba/datause-encoder-data huggingface dataset:
Children under five were not included in our study. This decision was made because the migration crisis intensified between 2016 and 2019, meaning most Venezuelan children under five in 2022 were born in Colombia, thereby acquiring Colombian nationality.<sup>7</sup> was determined the of all household mem- _Eligibility._ Survey eligibility by nationality bers. Colombian children and adolescents were considered eligible if they were between the ages of 5 and 17, and both the child and their parents were Colombian nationals. Venezuelan children and adolescents were eligible under similar age criteria, provided that both the child and their parents were identified as Venezuelan migrants (not naturalized in Colombia) and had migrated to Colombia between 2016 and 2020, aligning with the period of the migratory crisis.

We specifically excluded children from binational households, which include those with one foreign parent and one native parent, or those who have obtained Colombian citizenship through recent naturalization policies. This approach was taken to focus the study on the of children without the ef- integration process immigrant potential confounding fects of the services and advantages available to those with dual citizenship.

Within each selected household, interviews were conducted with up to two children. In households with more than two children, one child from each age group was chosen a random selection to a broad of through process participate, ensuring representation experiences within the study’s scope.

_Sampling frame._ We use the 2018 population census data as a sampling frame for the Colombian sample. It allowed the identification of residential blocks and households with children in the desired age range. With this source of information, it was possible to identify the number of households and residential blocks with children and adoles-

> 7Although Colombia only grants nationality to children of Colombian nationals, it follows a _jus sanguini_ principle, the Colombian government has introduced reforms, such as the the program _Primero la Ni ̃nez_ to give Colombian nationality to children of Venezuelan parents born at times when diplomatic relations between Colombia and Venezuela were cut and hence, it was not possible to apply for a Venezuelan nationality for this minors in Colombia.

15
# _Evolution and structure of public spending_

In the period from 1990 to 1995, the government put greater emphasis in its budget policy on maintaining public services at the cost of running persistent budget deficits. However, following the adoption of a structural adjustment program from 1995, stability was given greater importance at the cost of reducing the quality of public services.

As noted, the PRSP adopted in 2003 called for a substantial real increase in spending on social sectors, with the ratio expected to reach 12.8 percent of GDP by 2004. While spending on social sectors had followed an upward trend for the period since 1991, its 2004 GDP share fell short of the PRSP target. According to the MAMS database (see below), total spending on health and education, which represent the bulk of social spending, only reached 6.7 percent of GDP in 2004. In 2006, total social spending had increased to 8.4 percent of GDP, still substantially short of the PRSP target (Bulletin of Government Finance Statistics). The majority of this spending is on education (6.1 percent of GDP) and health (1.7 percent of GDP). A significant proportion of the budget is used to subsidize electricity and oil derivatives, mostly diesel. These subsidies rose from 3.1 percent of GDP in 2002 to 6.3 percent in 2004, and peaked at close to 10 percent of GDP in 2006, substantially exceeding the allocation to education.<sup>6</sup> In 2005, the government started to reduce these subsidies on a gradual basis, bringing the petroleum subsidy down to 6.5 percent of GDP in 2009.

The combined health and education share in total government spending increased by one percentage point between 1991 and 2009, from 18.7 to 19.8 (see Table 2.1). As a share of GDP, government spending in health and education increased only very marginally, from 5.5 to 5.7 percent, during the same period. It is interesting that, in the intervening period between 1991 and 2006, the share of health and education in total public expenditures peaked at 25.7 percent in 1994 and, after reaching another high point in 2000 at 22.1 percent, has been on a downward trend, falling to under 20 percent since 2006.

> 6 It should be noted that the fiscal cost of the petroleum subsidy depends on the world price of petroleum products, as the domestic price is fixed.

11
# _3.4 Limitations of the study_

This assessment of the impact of the JCLIS relies on simple comparisons of the average outcomes of interest between participants of the lift irrigation system and their neighbors in the same village or in a neighboring village who do not participate. We use self-reported recall data collected from farmers through in-person interviews with them. JCLIS sites and their beneficiaries were not selected randomly. Site selection was guided by hydrological and topological characteristics. Farmers with land closer to favorable sites may differ from their neighbors in skills, motivation, social capital, and other qualities, any of which can also affect agricultural outcomes. Farmers were moreover required to pay a small one-time Rs. 1,100 fee and join the local water user group to irrigate their land from the JCLIS, and their decision to do so may also differentiate them from others who chose to stay out or wait longer before joining the group. The comparisons made are therefore qualified as suffering from omitted variables bias. Nor did the study benefit from baseline data on variables of interest prior to the intervention. Attempts to collect recall data from farmers with questions about cropping areas, patterns, and yields before the JOHAR irrigation systems were introduced led to unreliable data, particularly for earlier years, and were discontinued. We also considered revisiting farmers who were interviewed in the baseline survey carried out by Oxford Policy Management (OPM), but at the time of our survey (August-September 2021), very few villages in the baseline survey had a functioning JCLIS. We compare beneficiary and nonbeneficiary farmers across economic variables that presumably do not change over time or as a result of the JCLIS irrigation. These include average land holding size and asset holdings. We find that the two are not significantly different across the two groups, providing some evidence of baseline comparability. In an alternate specification, we drop the control farmers from the neighboring villages and compare JCLIS beneficiaries only with their geographical neighbors while controlling for village fixed effects and find results similar to our main specification. Results from this alternate specification are reported in Table A.6 in the appendix. We also try to mitigate the selection bias in our study by using a Coarsened Matching and comparing only the matched sample of treated and control farmers. Results with the matched sample (Table A.7 in the appendix) are also qualitatively similar to the results from our main specification. Lastly, as a robustness check, we leverage remotely sensed data and control for two years of pre-treatment measures of Normalized Difference Vegetation Index (NDVI) for both groups of farmers (at a spatial resolution of 10 meters) in our main analysis. We find that our results remain the same, 7
# **V. Conclusion**

A number of conclusions can be reached from the above analysis. **First, the proliferation of PTAs in recent years, which is an on-going phenomenon with global proportions, has contributed to greater financial services liberalization commitments for many LCR countries and led to an increasingly complex regional ‘commitments map’ (or financial services ‘spaghetti bowl’)** . Most progress in financial services rule-making and market opening – typically via a stand-alone financial services chapter – has been achieved via FTAs; by contrast, LCR countries that have relied on the multilateral framework and on sub-regional customs unions for trade commitments in financial services have not made much progress to-date.

**Second, the inclusion of financial services in LCR PTAs depends greatly on whether it is a North-South or South-South agreement** . As can be seen below in Table 4, the main proponents of including financial services have been developed countries in North-South agreements. This is not surprising: most LCR countries are net importers of financial services, have few – if any – perceived ‘offensive’ interests linked to a “ _demandeur_ ” or domestic constituency, both of which lessen the scope for striking reciprocal bargains within the sector. Conversely, the inclusion of financial services in most North-South agreements likely reflects the fact that the majority of foreign financial institutions in LCR countries are headquartered in developed countries, as well as the relative bargaining powers between the negotiating counterparts. Only two LCR countries have tended to include financial services chapters in South-South agreements – Mexico (primarily in the immediate post-NAFTA period) and Panama (which is an offshore financial center and a net exporter of financial services). A more detailed comparison of financial services chapters under North-South and South-South PTAs, particularly when they involve the same country, would be an interesting follow-up research topic.

**Table 4: Coverage of Financial Services (FS) by Type of FTA in LCR**

||**North-South**|**South-South**|
|---|---|---|
|**Inclusion of FS Chapter**|9|7|
|**No FS Chapter**|4|14|

_Source: Table 3._ _Note: All LCR FTAs (including those not yet implemented), starting with NAFTA, have been categorized either as a North-South (i.e. including at least one developed country) or a South-South trade agreement._

**Third, an analysis of a sample of LCR countries that have recently participated in PTAs yields evidence of significant additional liberalization commitments when compared to the GATS**<sup>70</sup> . This is not unusual given the time elapsed and the (unilateral) market opening undertaken by these countries since the mid-1990s. Additional commitments tend to span all financial sub-sectors, including those that were not well

> 70 Roy M., Marchetti J. and Hoe Lim A. (September 2006) undertake this analysis for a broader set of PTAs and sectors (as well as compare PTA commitments to recent WTO offers) and reach the same conclusion.

39
Next, we examine in Table 4 the relationship between the decision to send out a work migrant and the household’s welfare status prior to the migration event. We find evidence of a positive relationship between welfare and sending a migrant for work. Even after controlling for a number of related factors, such as education level and the size of the household, we still find that households who send work migrants are better off economically. Indeed, we observe in column (1) a positive relationship between a household’s per capita consumption level and its decision to send a work migrant in the next survey wave. While poor households are as likely as non-poor households to send a work migrant (cols. (3) and (4)), the poorest 40 percent of households are 2 percentage points less likely (col. 5) to do so. None of these explanatory variables remains significant when household fixed effects are applied.

**Table 4. Lagged welfare status and sending of work migrant, household level**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|||||||
|Log of per capita expenditure|||||||
|(constant prices)|0.03***|0.00|||||
||(0.01)|(0.01)|||||
|Poor household|||-0.01|0.01|||
||||(0.01)|(0.01)|||
|Poorest 40% of households|||||-0.02**|-0.00|
||||||(0.01)|(0.01)|
|Constant|-0.51***|-0.01|-0.17***|0.01|-0.17***|0.01|
||(0.07)|(0.11)|(0.03)|(0.05)|(0.02)|(0.05)|
|Household fixed effects?|No|Yes|No|Yes|No|Yes|
|Observations|8,302|8,302|8,302|8,302|8,345|8,345|
|R-squared|0.09|0.59|0.09|0.59|0.09|0.59|

_Source: Authors’ calculations with UNPS. Regressions include all lagged covariates reported in Table 3 and year fixed effects. Region fixed effects are included in the specification without household fixed effects. Robust standard errors, clustered at the level of the enumeration area, are in parentheses. Significance levels are reported as follows: * p<0.10, ** p<0.05, *** p<0.01._ 10
origin countries can arise. Future work should seek to more clearly disentangle how such effects occur and what role the institutional and social contexts play.

# REFERENCES

Adams, R.H., and J. Page. 2005. “Do International Migration and Remittances Reduce Poverty in Developing Countries?” _World Development_ 33 (10): 1645–69.

- Akee, R., and D. Kapur. 2012. “Remittances and Rashomon.” Working Paper 285, Center for Global Development.

- Antman, F.M. 2011. “The Intergenerational Effects of Paternal Migration on Schooling and Work: What Can We Learn from Children’s Time Allocations?” _Journal of Development Economics_ 96 (2): 200–8.

- ——— . 2012. “Gender, Educational Attainment, and the Impact of Parental Migration on Children

- Left Behind. _Journal of Population Economics_ 25 (4): 1187–214.

- Bansak, C., and B. Chezum. 2009. “How do Remittances Affect Human Capital Formation of School-Age Boys and Girls?” _The American Economic Review_ : 145–8.

- Barsbai, T., H. Rapoport, A. Steinmayr,  and C. Trebesch. Forthcoming. “The Effect of Labor Migration on the Diffusion of Democracy: Evidence from a Former Soviet Republic.” _American Economic Journal: Applied Economics_ .

- Batista, C., and P.C. Vicente. 2011. “Do Migrants Improve Governance at Home? Evidence from a Voting Experiment.” _The World Bank Economic Review_ 25 (1): 77–104.

- Beine, M., F. Docquier, and M. Schiff. 2013. “International Migration, Transfer of Norms and Home Country Fertility.” _Canadian Journal of Economics_ 46 (4): 1406–30.

- Berry, J.W. 1997. “Immigration, Acculturation, and Adaptation.” _Applied Psychology_ 46 (1): 5– 34.

Böhme, M., R. Persian, and T. Stöhr. 2015. “Alone But Better Off? Adult Child Migration and Health of Elderly Parents in Moldova.” _Journal of Health Economics_ 39: 211–27.

- Böhme, M., T. Stöhr. 2014. “Household Interview Duration Analysis in Capi Survey Management.” _Field Methods_ 26: 390–405.

- Borcan, O., M. Lindahl, and A. Mitrut. 2017. “Fighting Corruption in Education: What Works and Who Benefits?” _American Economic Journal: Economic Policy_ 9 (1): 180–209.

- Calero, C., A.S. Bedi, and R. Sparrow. 2009. “Remittances, Liquidity Constraints and Human Capital Investments in Ecuador.” _World Development_ 37 (6): 1143–54.

- Cameron, L., N. Erkal, L. Gangadharan, and M. Zhang. 2015. “Cultural Integration: Experimental Evidence of Convergence in Immigrants’ Preferences.” _Journal of Economic Behavior and Organization_ 111: 38–58.

- Carasciuc, L. 2001. “Corruption and Quality of Governance: the Case of Moldova.” Monograph, Transparency International.

- Careja, R., and P. Emmenegger. 2012. “Making Democratic Citizens the Effects of Migration Experience on Political Attitudes in Central and Eastern Europe.” _Comparative Political Studies_ 45 (7): 875–902.

Chauvet, L., and M. Mercier. 2014. “Do Return Migrants Transfer Political Norms to Their Origin Country? Evidence from Mali.” _Journal of Comparative Economics_ 42 (3): 630–51.

20
|**Table A2b: Baseline balance for those not**<br>|**respondin**<br>|**g to any 7-y**<br>|**ear follo**<br>|**w-up surve**<br>|**y**<br>|
|---|---|---|---|---|---|
|Overall<br>Mean|Overall<br>S.D.|Control<br>Mean|PI<br>Mean|Traditiona<br>Mean|l p-value<br>equality|
|_Baseline strata variables_||||||
|Monthly profits<br>190|299|153|212|223|0.775|
|Commerce sector<br>0.52|0.50|0.52|0.57|0.49||
|Production sector<br>0.25|0.43|0.21|0.26|0.28||
|Female<br>0.54|0.50|0.47|0.58|0.60||
|_Other baseline variables_||||||
|Age of Owner<br>39.2|11.6|38.1|39.4|40.7|0.995|
|Years schooling<br>7.7|5.5|8.4|7.2|7.2|0.663|
|Firm age<br>10.8|8.7|11.0|11.5|9.9|0.750|
|Monthly sales<br>1193|2060|1161|1439|1033|0.969|
|Weekly sales<br>375|618|373|441|324|0.737|
|Weekly profits<br>63|102|56|63|74|0.848|
|Capital stock<br>1356|4150|1455|1388|1193|0.853|
|Number of employees<br>2.3|3.7|2.3|2.2|2.5|0.896|
|Personal initiative index<br>4.23|0.55|4.29|4.16|4.20|0.590|
|Business practices<br>0.58<br>|0.15|0.59|0.59|0.56|0.767|
|Sample Size<br>250<br>Notes: Baseline (2013) characteristics for e<br>Monetary values are expressed in terms o<br>denote firms randomly assigned to the co<br>traditional business training groups respe<br>means across the three groups.|ntreprene<br>f Septemb<br>ntrol group<br>ctively. P-v|107<br>urs intervie<br>er 2021 USD<br>, personal i<br>alue of equ|65<br>wed at le<br>.**Contro**<br>nitiative t<br>ality tests|78<br>ast once in<br>**l, PI,**and**Tr**<br>raining gro<br>for equalit|2021.<br>**aditional**<br>up, and<br>y of|

30
# **5.1 Impact on Mobile Money Usage and Remittances**

Table 3 reports the effects of the rollout on mobile money usage and remittances. The agent rollout had no effects on mobile money awareness or the probability of using mobile money (columns 1 and 2). It also had no effect on savings (column 3 and 4). The agent rollout did not change the probability of receiving money via any channel or via mobile money (columns 5 and 6), but, conditional on receiving money via any channel, it decreased transportation costs, by 45 percent on average (column 7). This effect is driven by clusters with high Airtel priority rating. In these clusters, average transportation costs for receiving money decreased by 76 percent. The agent rollout may have reduced transportation costs since it brought mobile money agents closer to treatment clusters and thus allowed respondents to walk or bike to an agent instead of having to pay for a motorcycle or mini-bus taxi. However, the estimated effects on transportation costs are not statistically significant when controlling for multiple hypothesis testing.

Table 4 displays the effect of the agent rollout on transactions in Airtel’s mobile money data. Consistent with the results in table 3, we do not find a significant effect of the agent rollout on the number of Airtel Money transactions. Further, appendix figure A4 illustrates that Airtel Money transactions remained low, both in control and treatment areas, after agents were rolled out in treatment clusters in 2017.

Overall, we thus do not find any evidence that the agent rollout increased use of mobile money, savings, or remittances. For households who were already receiving remittances, we find weak evidence that the agent rollout decreased transportation costs for making these transactions. The results suggest that these households used the newly rolled out agents instead of going to previously existing 14
<!-- Start of picture text --> Percent of GDP<br>250 , =Total —Government —Private<br>200<br>150<br>100<br>50 _<br>Q SS SSeS<br>1970 1978 1986 1994 2002 2010 2018<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP<br>180 —Total |—Government —Private<br>120<br>xa<br>.<br>°<br>py" fad he<br>60 ASwok - ees<br>0: | | |<br>1970 1978 1986 1994 2002 2010 2018<br><!-- End of picture text -->
27 (eq. (9)) but now the dependent variable is the primacy rate in year _t_ (116 x 7 = 812 obs.). The variables of interest are again MFGSERV ( _t_ ), DEINDU (change 1980- _t_ ), and NRXGDP ( _t_ -1) (or FMXGDP and AGXGDP). In Cols. (3)-(6), we consider either three lags or four lags of the variables of interest (i.e., two or three extra lags in addition to the contemporaneous lag) and show the overall correlations across the various lags. We control for the urban share in _t_ , include controls for populations in _t_ , and cluster standard errors at the country level. Again, we find no significant correlation.

**City-Level Results.** We use the Functional Urban Area (FUA)-level data of the GHSL database to study whether the _largest cities_ , not just the _largest city_ , grow differently than other cities depending on the country’s “type”. We have population estimates for 7,422 FUAs c. 1975, 1990, 2000 and 2015 in 115 sample countries. We regress the log growth of their population between 1975 and 2015 – log (pop. + 1) 2015 - log (pop. + 1) 1975 – on the (country-level) variables of interest and their interactions with a dummy for whether the FUA is the capital/largest city (2015; we call this dummy “top 1”). We also consider the capital city and the two (top 2) or five (top 5) largest cities, or the capital city only (top 0) as well as add the controls of the long-difference regressions at the country level.

As seen in Web Appx. Table D.6, there are no significant correlations. However, point estimates suggest that the top cities in countries specialized in agricultural exports indeed grow slower relative top cities in industrialized countries. Yet, differences are not significant. We also do not find any significant correlation in a 10-year panel framework with one or two extra lags included (see Web Appx. Table D.7).

The results presented in this section suggest that even if some resources might be associated with urban primacy in some countries, it is not the case in general. While we do not discuss this above, we also find no correlation with deindustrialization. Thus, in resource-rich and deindustrializing countries, consumption cities are found along the urban hierarchy and, as shown in Fig. 4(a), the shares of urban tradables in their cities are lower than those in MFGSERV countries for _all_ population size categories of cities.

# **7. Concluding Discussion: Consumption Cities & Urban Human Capital**

Using census data for a large number of cities and countries, we established the following stylized fact: While some cities have high employment shares of urban tradables and can be characterized as _production cities_ , a relatively high number of cities have high shares of urban non-tradables and can be characterized as _consumption cities_ . This appears to be