rafmacalaba/datause-encoder-data huggingface dataset:
11 where Tot. Ven. Inflow _t_ is the aggregate number of (legal) Venezuelans entering Colombia every year (as recorded by migration authorities), Population<sup>1993</sup> _md_<sup>isthetotalpop-</sup> ulation of municipality _m_ in 1993, which is kept fixed prior to the migration shock to avoid further endogeneity concerns. Venezuelan Share<sup>1993</sup> _md_ is the share of Venezuelans living in municipality _m_ according to the 1993 population census to the total number of Venezuelans living in Colombia.<sup>10</sup> For robustness, we aggregate our outcome variables at the municipality level and reestimate equation 4.1 changing the school fixed effects for municipality fixed effects.

4.1. **Challenges to identification.** Our identification relies on the plausibly exogeneity of our predicted migration shock. We now discuss potential threats to this assumption, and how we have dealt with them.

First, it is worth noting that we are not using the _predicted_ cumulative inflows as an instrument of the _actual_ inflows of Venezuelans to each municipality/year. This is because there are no administrative records of where do the arriving Venezuelan nationals settle on a yearly basis. We therefore use the predicted shock to estimate a reduced-form equation. The magnitude of our results should therefore be interpreted as only capturing the numerator of a standard 2SLS estimator (and thus likely as lower bounds of the latent IV estimate).

The first assumption that we rely on for identification is, therefore, that the _predicted_ migration is strongly correlated with the (unobserved) actual Venezuelan migration.

> 10 We use the 1993 census because by the time of the next census (2005) the Venezuelan political crises -and thus migration to Colombia- had started under the rule of Hugo Chavez. Our results are however largely unchanged if we use the 2005 census to compute the predicted migration flow of each municipality. Indeed, the correlation of the 1993 and the 2005-based measures if 0.93.
**Table 6: Correlations at the Country Level**

||**Formality**|**Weighted**<br>**Formality**|**Schneider**|**Self-**<br>**employment**|**Share of**<br>**Firms with**<br>**Credit**|**% Needs**<br>**Financed with**<br>**External**<br>**Finance**|**Private**<br>**Credit/GDP**|**Getting**<br>**Credit**|**Corruption**|**Corporate**<br>**Tax**|**Ln GDP per**<br>**capita**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Formality**|1|||||||||||
|**Weighted Formality**|0.9349|1||||||||||
|**Schneider**|-0.1178|-0.0436|1|||||||||
|**Self-employment**|-0.0619|-0.241|0.318|1||||||||
|**Share of Firms with Credit**|0.5533|0.2471|-0.0014|0.0835|1|||||||
|**% Needs Financed with**|0.3837|-0.0746|-0.2839|0.0763|0.7156|1||||||
|**External Finance**||||||||||||
|**Private Credit/GDP**|0.3085|0.1075|-0.4552|-0.2855|0.6088|0.4507|1|||||
|**Getting Credit**|-0.0537|0.13|0.3042|0.5693|-0.3621|-0.3128|-0.3755|1||||
|**Absence of corruption**|0.2151|-0.0327|-0.3596|-0.3388|0.2338|0.2391|0.4041|-0.28|1|||
|**Corporate Tax**|-0.1115|-0.1478|0.0355|0.101|-0.2793|-0.2559|-0.0275|0.2056|0.0635|1||
|**Log GDP per capita**|0.2868|0.1201|-0.4799|-0.51|0.5054|0.432|0.6968|-0.5929|0.5061|-0.3798|1|

_Schneider_ is the informal production as percentage of GDP, Schneider, (2002). _Self-employment_ is the percentage of self employed workers with respect to total active population, ILO, Loayza and Rigolini, (2006). _Private credit/GDP_ is Private Credit By Deposit Money Banks over GDP, Beck et al., (2001). _Corruption_ : ICRG index of absence of corruption. _Corporate tax rate_ : Top corporate Tax Rate for All Fund Member Countries, IMF 2006. _Getting Credit_ is a rank index based 4 indexes: strength of legal rights index, depth of credit information index, public credit registry coverage, private credit bureau coverage, Doing Business. _Log of GDP per capita_ is in US$ at constant 2000 prices, WDI.

27
Again, in the 1930s, landless black tenants who benefited from very limited land distribution created ". . .a permanent middle class that ultimately emerged in the 1960s as the backbone of the civil-rights movement in the rural South."<sup>2 0</sup> From this discussion of how the state can facilitate collective action at community level, we turn now to what the state can do to create the conditions for effective governance of community-driven development.

# 3. **The State and local administrations: creating synergies with communities**

The modern state has enormous potential for providing the institutional support, inputs and infrastructure necessary for upward mobility and for triggering local development initiatives. These case studies illustrate how effective alliances can be formed between higher levels of government and communities, to elicit better service delivery from local agencies (Figure 1). This requires careful design of bureaucratic organizations and staff incentives for perforrnance: a large question that has received considerable attention,<sup>2</sup> ' and care to avoid "local capture." We also focus on the relatively neglected area of the incentives of grassroots level staff, who form the direct interface between communities and the state. Here, too, creative mixing of local and supra-local strengths can be highly productive.

We begin by illustrating some key obstacles to performance by grassroots level staff and how they can be averted, drawing on Wade's (1982, 1997) comparison of South Korean and Indian irrigation agencies. This is followed by case studies of redesign of local agency incentives, both more successful and less successful. Section Ell draws out the lessons from these case studies, about the key ingredients for success in generating these state-community synergies.

_The importance of agency incentives and design: illustrations from Indian and South Korean canal irrigation systems_ Several case-studies illustrate how service delivery improves if the highly complementary strengths of communities and the state are blended: using local strengths in information and monitoring capacity to complement technical and other inputs from higher **22** levels of government. One study which has been especially carefully developed and 20 Salamon (1979:129), cited in Herring (1999).

21 See for examnple Manor (1999) and Bardhan (1997).

22 For examnple, the school program in Himachal Pradesh in India blended the technical skills of higher levels of govermment in designing curricula and textbooks, with community-level monitoring of school (Probe Team 1999). Similarly, the highly successful program to eradicate river blindness in Sub-Saharan Africa combined the logistical strengths of the state for aerial spraying, and with those of communities for managing smooth distribution of drugs through civil wars and other cnses (World Bank 2000a).

For less complex tasks, bottom-up collective action is also very powerful, as Hirshman (1 984a,b) has illustrated through empirical examples from Latin Amenca. See also Ostrom, Schroeder, and Wynne (1993), Owen and van Domelen (1998), Barnentos (1999), and World Bank (2000a,b).

8
for intermediate goods, but this kind of disaggregated data is not necessary for this paper and the assumptions necessary for that are not part of the data used in this paper.

# **Results 4**

Before focusing on the trade effect of trade agreements in South Asia, it is a good idea to compare with the literature. The literature usually uses manufacturing data due to data availability and for comparability. Table (1) shows the results of the world average RTA effect using manufacturing trade data. Column 1 shows that without including domestic trade flows the effect is 0.02. When domestic trade flows are taken into account in the estimation the RTA coefficient increases. Column 2 shows that including domestic trade flows without controlling for border effects increases the RTA coefficient but it suffers from an upward bias as explained by Bergstrand et al. (2015). Column 3 uses domestic trade flows and control for border effects and shows a benchmark result of 0.10. This RTAs effect is below those seen in the literature when using small samples. For instance, Bergstrand et al. (2015) reports a mean coefficient of around 0.50 when covering 56 countries and Baier et al. (2019) reports a mean coefficient of 0.49 with a sample of 53 countries. But 0.10 is in line with studies covering most countries in the world economy. Recent examples of these studies are Felbermayr et al. (2022) use a sample of 186 countries and report a RTA coefficient of 0.12, and Larch et al. (2019) that reports a coefficient of 0.11. These results in table (1), and the small part of the literature covering as many countries as possible, show that the inclusion of more countries (that tend to be developing economies) uncovers a lower average RTA effect. These benchmark results will be key in understanding the effects of the heterogeneous trade agreements.

With these benchmark results at hand, I now focus on the RTA effects specific to South Asia countries. I continue to use data for the manufacturing sector to be able to compare the previous results and the literature in general. I am interested in disentangling the effects of the intra-regional RTAs and the RTAs that a South Asian country signs with a third country outside the region. Table (2) introduces these additional effects of RTAs in South Asia. Column 1 is the benchmark average result from table (1). Column 2 adds an interaction term of RTA in which at least one of the countries is from South Asia. It shows that the additional coefficient is 0.11. This leaves the trade creation effects on RTA 7
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potential trade deflection towards Mexico. Nevertheless, when doing so, its coefficient is not significant, as shown in estimates in next section. Thus, the variable is not retained in model (3).

Also included in vector _TD_ is the Rauch (1999) index of differentiation (<sup>_I_</sup> _iRauch_ ) which classifies goods into three categories according to their degree of differentiation. First, “homogeneous goods” include products internationally traded in organized exchanges, with a well-defined price, such as wheat (<sup>_I_</sup> _iRauch_ =1). Second, “reference-priced ” goods include products not traded in organized exchanges, but having reference prices available in specialized publications, such as polyethylene (<sup>_I_</sup> _iRauch_ =2). Third, “differentiated goods” do not satisfy either of the two previous criteria (<sup>_I_</sup> _iRauch_ =3). Because of their nature, homogenous goods can be more easily subject to “trade-deflection” or “re-exporting” than differentiated goods.<sup>14</sup> Then, the less differentiated a good, the bigger the room for trade deflection and the more justifiable restrictive RoO may appear to prevent this problem. The RoO equation in this paper is the first one to control for the degree of differentiation of a good.

# **_ii) Political Economy (_** _PE_ **_)_**

The US MFN tariff for 1993,<sup>_t_</sup> _iMFN_ , _US_ , is included to control for the extent of preferential market access to the US under NAFTA. A positive and significant coefficient would capture situations where RoO are more stringent in products for which greater market access was provided, as found in previous studies. Indeed, US industrial groups that managed to obtain higher MFN tariffs for their products prior to NAFTA were serious candidates to receive enhanced trade protection through additionally stricter RoO under NAFTA.

> 14 To clarify this point, imagine a country member to an FTA that can import a homogenous good, say sugar, and a differentiated good, say car engines, but that can also produce them locally at higher production costs. In addition suppose the country has the lowest MFN tariffs for these goods in the FTA. This fact combined with the fact that both merchandises can be stocked creates an incentive for re-exporting both goods.  However, pretending that a foreign engine is locally made is more difficult _vis-à-vis_ customs certification, than pretending that foreign sugar is locally produced.

13
34 members with junior secondary school, household members with senior secondary school, household members with university education, a rural dummy, per capita household income in ethno-religious group in 1998/99, inhabitants per square meter in ethno-religious group in 1998/99, the square of the number of inhabitants per square meter in ethnoreligious group in 1998/99, fraction of people in top three religions in the ethno-religious group in 1998/99, fraction of population receiving internal remittances (from Ghana) in ethno-religious group in 1998/99, fraction of population receiving international remittances (from African and other countries) in ethno-religious group in 1998/99, and seven regional dummies. A Hausman test rejects in all cases the need for instrumental variables. All expenditure categories defined in Table 6. All regressions are weighted.

*Significant at 0.10.  ** Significant at 0.05. ***Significant at .01.

Source: 2005/06 Ghana GLSS 5 Survey (sub-sample).
employment for the population. About 60 percent of the country’s population relies on agriculture for their livelihoods (Oseni and Winters, 2009). The majority of industries and cities of the country are located in the South, while the North is more rural and less densely populated (Oseni and Winters, 2009), thus, it is not surprising to find a socio-economic divide in the country. In addition, poverty is found to be much more prevalent in Northern Nigeria than it is in the South and concurrently, the majority of the country’s middle class is concentrated in the Southern regions (Corral et al., 2015).

The analysis uses data from two rounds of the Nigeria General Household Survey Panel (GHS- Panel) conducted by the National Bureau of Statistics in 2010-2011 (round 1) and in 2012-2013 (round 2). The GHS-Panel which has a sample of 5,000 households is part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) project which aims to improve agricultural statistics in Sub-Saharan Africa. The GHS-Panel provides information on household agricultural activities, household’s human capital, other economic activities, and access to services and resources. Moreover, since the surveys were administered in two visits, namely: post-planting (Aug-Oct 2010; Sept-Nov 2012) and postharvest (Feb-Apr 2011; Feb-Apr 2013), it contains data on farm and non-farm activities for the corresponding season. High resolution precipitation records for the 2001-2009 periods are derived from the National Oceanic and Atmospheric Administration (NOAA) climate prediction center. The latter are geo-referenced amounts for the households in the GHSPanel sample.

The observational units for this analysis are Nigerian farming households that are present during both survey rounds,<sup>7</sup> in total we have 2,232 farming households<sup>8</sup> . The outcome of interest is a dummy variable that equals 1 if the household diversifies its sources of income. In order to construct this indicator, various modules from the GHS-Panel are relied upon, specifically, the labor and non-farm enterprise modules. These modules permit identification of individuals and the types of activities they are involved in. According to this information, a household is classified as diversified if any household member is involved in any labor activity outside of the household’s farm. More in details, if any individual within the household reports working outside the household’s farm, the household would be classified as diversified. Additionally, if anyone inside the household owns a non-farm enterprise which is in operation, the household is classified as diversified. Finally, if the

> 7Farming households are defined as all households who own land and reported planting crops during the post-planting season.

> 8Households for which land measures were not available were removed. Additionally households in Lagos and Abuja were also removed due to their urban nature.

6
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.89*** 0.91***<br>H<br>Kendall tau-b 0.78*** 0.75*** 0.77***<br>Spearman 0.85*** 0.87*** 0.76***<br>A<br>Kendall tau-b 0.69*** 0.72*** 0.64***<br>Spearman 0.9*** 0.9*** 0.91***<br>M0<br>Kendall tau-b 0.76*** 0.77*** 0.76***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>B2.1. FOSD: Counting Vector B2.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>B2.3. SOSD: Social Sustainability Index B2.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.97*** 0.9***<br>H<br>Kendall tau-b 0.89*** 0.89*** 0.72***<br>Spearman 0.95*** 0.92*** 0.68**<br>A<br>Kendall tau-b 0.89*** 0.78*** 0.56**<br>Spearman 0.93*** 0.93*** 0.9***<br>M0<br>Kendall tau-b 0.83*** 0.83*** 0.72***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .2<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference 0<br>.2<br>-.1<br>0<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _B3. Change in the weights assigned to each dimension. Baseline scenario: Evenly distributed weights across all indicators (25% each). Alternative scenario: Skewed weighting scheme in favor of the Social Cohesion dimension (40% for Social Cohesion, and 20% for the rest of dimensions)._ Peru B3.1. FOSD: Counting Vector B3.2. Dominance Analysis 43
Table 4: Age-specific death rates<sup>and rate ratios before and</sup> after the intervention.

|_______________|PRE-INTER|VENTION||
|---|---|---|---|
|Age|PHC villages|Non-PHC<br>villages|Ratios|
|o|115.9<br>(59/509)|99.1<br>(35/353)|1.169|
|1-4|44.5<br>(75/1685)|27.7<br>(32/1156.5)|1.609|
||POST-INTER|VENTION||
|0|75.2<br>_<br>(41/545-5)|135.2<br>(43/318)<br>|0.556<br>|
|1-4|11.8<br>(21/1775)|28.4<br><br>(35/1232)|.416|

The effect of the bed-nets on<sup>the mortality of the 1-4 year olds</sup> is particularly clear although the<sup>decline in infant mortality is</sup> also significant. There is no<sup>obvious explanation for the higher</sup> initial lpvels of 1-4 year old<sup>morta7ity in the PHC compared to</sup> the non-PHC villages in the pre-intervention<sup>period. There is a</sup> suggestion in the cause of death data<sup>from the two sets of</sup> villages that malaria and severe<sup>diarrhoea/malnutrition with</sup> gastro-enteritis may be more important<sup>causes in the larger PHC</sup> villages. Whatever the reasons for<sup>the initial differences, the</sup> bed-net intervention has reversed<sup>this relationship between the</sup> mortality in the two sets of villages.<sup>Although some of the</sup> numbers are very small, examination<sup>of the rate ratios for single</sup> years of age reveals that the decline<sup>in mortality in the PHC</sup> villages was especially marked for<sup>one year old children. In the</sup> PHC villages, deaths in this age<sup>category fell from 35 to 8 over</sup> the two years; for the non-?HC villages,<sup>the number of deaths of</sup> one year olds rose from 14 to 18.

The results from the morbidity surveillance,<sup>including</sup> measurement of body temperatures and<sup>blood slides for those with</sup> fever, and the studies of the mosquito<sup>populations, will show</sup> that these mortality result,s are directly<sup>associated with a</sup> 11
Table 6: Differences in HH Budget Shares by Sex Child Composition in Albania (2002 - 2012)

||Budget Share||||
|---|---|---|---|---|
|Variables|(All)|Same-sex|Two boys|Two girls|
||(1)|(2)|(3)|(4)|
|HH Food Expenditure (S)|0.548|-0.001|-0.008*|0.006|
|||(0.004)|(0.004)|(0.004)|
|HH Health Expenditure (S)|0.054|-0.003|0.002|-0.007**|
|||(0.002)|(0.003)|(0.003)|
|HH Clothing Expenditure (S)|0.046|0.001|-0.000|0.001|
|||(0.001)|(0.001)|(0.001)|
|Children’s Education Expenditure (S)|0.025|-0.001|-0.002*|0.001|
|||(0.001)|(0.001)|(0.001)|
|Children’s Clothing Expenditure (S)|0.020|0.001*|-0.000|0.002***|
|||(0.000)|(0.000)|(0.000)|
|Children’s Education Expenditure (S.p.c)|0.010|-0.001***|-0.000*|-0.001*|
|||(0.000)|(0.000)|(0.000)|
|Children’s Clothing Expenditure (S.p.c)|0.008|-0.000|-0.000|-0.000|
|||(0.000)|(0.000)|(0.000)|
|Children’s Clothes (S.p.c.)|0.005|-0.000|-0.000|-0.000|
|||(0.000)|(0.000)|(0.000)|
|Children’s Footwear (S.p.c.)|0.003|-0.000|-0.000|-0.000|
|||(0.000)|(0.000)|(0.000)|

_Notes:_ Differences in means (mean of the relevant group minus mean of the rest of the population) and their standard errors (in parentheses). * Indicates statistical significance at 10%. ** Indicates statistical significance at 5%. *** Indicates statistical significance at 1%. The data used are the 2002 LSMS, the 2005 LSMS and the 2012 LSMS. The sample consists of 7480 Albanian mothers aged 20 to 49 years old with two or more children aged 18 or younger. (S) refers to the share of a particular expenditure over total expenditure, while (S.p.c) refers to the respective share divided by the number of children aged 18 or younger still living in the household.

39