rafmacalaba/datause-encoder-data huggingface dataset:
to have positive and statistically significant impact on FDI inflows to the regions (Caughlin and Segev, 1999).

The last specification of the model used a lagged term for the regional GDP to assess the dynamic effect of the regional market on the attractiveness of the FDI. The lagged term is significant at 5% with an impact equal to about one-fourth of the contemporaneous effect of the regional GDP found in previous specifications. This result is also supported by Casi and Resmini’s (2011) work who find, in the case of local regions within European countries, that the higher the number of foreign firms and growth rate of regional GDP in the previous period in the region, the larger the number of new foreign firms that a region is able to attract. Overall, most other signs and significance remain similar to previous specifications confirming the individual effects of each determining factor.

**Table 5** : _Results of the GMM estimation_

|||Sys<br>|tem GMM (Tw<br>|o Step Approac<br>|h)<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|FDIt-1|0.340<sup>***</sup>|0.215<sup>*</sup>|0.269<sup>*</sup>|0.290<sup>**</sup>|0.293<sup>**</sup>|0.332<sup>**</sup>|
||(3.60)|(1.83)|(1.80)|(2.26)|(2.17)|(2.21)|
|GDPt-1||||||0.339<sup>**</sup><br>(2.36)|
|GDP|0.519<sup>***</sup>|0.300<sup>*</sup>|0.321<sup>**</sup>|0.362<sup>**</sup>|0.294<sup>**</sup>||
||(7.14)|(1.85)|(2.33)|(2.31)|(2.04)||
|Unemployment|-0.048<sup>**</sup>|0.007|0.004|-0.002|0.008|0.008|
||(-2.04)|(0.42)|(0.28)|(-0.13)|(0.44)|(0.60)|
|Private Firms||0.373|0.225|0.108|0.217|0.038|
|||(0.97)|(0.62)|(0.27)|(0.63)|(010)|
|Governance Index||0.745<sup>***</sup>|0.532<sup>**</sup>|0.615<sup>***</sup>|0.632<sup>***</sup>|0.694<sup>***</sup>|
|||(3.01)|(2.11)|(2.98)|(3.05)|(2.79)|
|Regional Dev areas|||0.261<sup>***</sup>|0.189<sup>**</sup>|0.256<sup>***</sup>|0.243<sup>**</sup>|
||||(3.12)|(2.56)|(3.16)|(2.15)|
|Port|||0.763<sup>**</sup>||0.676<sup>***</sup>|0.578<sup>***</sup>|
||||(2.54)||(2.80)|(0.286)|
|Highways||||0.488<sup>**</sup>|||
|||||(2.12)|||
|Dummy Revolution||||0.078<br>(0.81)|0.046<br>(0.63)||
|Observations|210|210|210|210|210|210|
|Hansen J-test|0.64|0.49|0.41|0.60|0.59|1.00|
|AR(1)|0.00|0.00|0.01|0.01|0.01|0.01|
|AR(2)|0.09|0.17|0.12|0.08|0.09|0.1|

Notes: Standard errors are in parentheses. All GMM regressions use robust standard errors. For the two‐step GMM, the Windmeijer (2005) finite sample correction for standard errors is employed. *, ** and *** denote significance at the 10% , 5%‐ and 1% level, respectively. The row for the Hansen J‐test reports the p‐values for the null hypothesis of instrument validity. The values reported for AR(1) and AR(2) are the p‐values for first and second order autocorrelated disturbances in the first differences equation. Notice that across various specifications, the Hansen J-test of
at home (Yamanaka 2003). Such networks lower the costs of migration for villagers by providing information about job opportunities outside Nepal, helping potential migrants secure employment, supplying credit to cover reallocation expenses, and ameliorating housing costs upon arrival. Indeed, as Thieme (2003) shows, in Nepal, migrants tend to follow their co-villagers and migrate to the same destinations. They are also likely to fill the same niches in the labor market in the host county. Relying on a similar identification strategy, Woodruff and Zenteno (2001) and McKenzie and Rapoport (2005) analyze the effects of migration on children’s health and schooling outcomes in Mexico; Du et al. (2005) study the relationship between migration and rural poverty in China; and Taylor and Mora (2006) investigate the effect of migration on expenditure patterns of rural households in Mexico. We expect this instrument to affect the probability of international migration and have small or no influence on the probability of migration within Nepal.

To construct an instrument for the domestic migration, we use data from the first round of the NLSS. The variable for this instrument is the proportion of domestic migrants in a district in 1995.<sup>9</sup> The underlying rationale is similar to the one discussed above, and we expect this instrument to have a positive and significant effect on the probability of domestic migration.

Our identification strategy requires that lagged migrant networks influence household consumption only through current migration. The presence of ward or district characteristics or shocks that simultaneously influence migration and household consumption decisions would violate our identification restrictions. For example, better road infrastructure in a ward or its proximity to a large urban center could reduce the costs of migration and, at the same time, affect a household’s returns on productive activities by providing better access to markets. We endeavor to control for timepersistent unobserved factors by including a set of ward-level characteristics in our empirical specification. In particular, we include variables that specify local labor-market conditions, the occupational structure of the population in a ward, and the set of dummies for aggregated educational levels. In addition, we use the ward-level lagged (1995) mean expenditure and expenditure Gini. These variables describe the lagged regional poverty

> 9 We also tried to add the proportion of migrants abroad in a district in 1995 as an instrument. This variable adds no extra identification power to our estimations, most likely because of a low level of variation in foreign migration registered in 1995.

15
firms whose operational links are well-documented through reliable sources like annual reports, filings, investor presentations, press releases, and company websites. This approach ensures that our data reflects the most complete and accurate representation of each firm’s network interactions.

Additionally, we explore the dynamics of external versus internal corporate relationships by examining the number of entities connected to a firm through ownership ties, which include subsidiaries, parent companies, and other affiliates within the same corporate family. This captures all other entities that share the same ultimate parent in a given year.<sup>15</sup> We also consider the relevance of these connections to the agribusiness sector by identifying corporate family entities whose primary industry aligns with agribusiness criteria, providing a more targeted estimate of industry-specific within-corporate family interactions in some of the analyses. We consider this metric a lower estimate of the number of entities that an agribusiness company engages with within its corporate boundaries.

# **3.2.2 Exit Dynamics**

In the final step of the analysis, we investigate the correlation between a firm’s network centrality and its exit probability from the agri-business network. Specifically, we apply a logit regression model to the subset of agribusinesses that are actively monitored and have available sales data:

> 15 Related firms can consist of a firm’s subsidiaries, a firm’s parent, its parent’s parent, or other subsidiaries of its ultimate parent. If a firm changes its ultimate parent in a given year, the firm family to which the firm belonged for the majority of the year is taken as a reference count. Non-firm entity types are excluded from the count: Aircraft, Commodity, Currency, ForEx, Index, Individual, Broadcast Call Sign, Asset-Backed, Bank Branch, Corporate Assets, Emp Stk Ownership Plan, Family of Fds (VC/Pvt EQ), Hedge Fund, Mutual Fd-Closed End, Mutual Fd-ETF, Mutual Fd-Open End, Pension Fund, Real Property, Port, Ship. The following entity types are included: Business Association (JP), College/University, Extinct, Foundation/Endowment, Financing Subsidiary/SPE, Government, Holding Company, Joint Venture, NonProfit Organization, Operating Division, Public Company, Private Company, Subsidiary.

13
# **1. Introduction**

The question of whether or not biofuels help mitigate climate change has attracted a long debate in the literature. If indirect emissions, such as those released through land-use change during the expansion of agricultural lands for biofuels production, are ignored, biofuels, undoubtedly, reduce emissions of greenhouse gases (GHGs) through the replacement of fossil fuels (see e.g., OECD, 2008; RFA, 2008; Goldemberg et al. 2008; Macedo et a. 2008)<sup>2</sup> . On other hand, if land-use change related emissions are accounted for, there is no consensus in the literature on the impact of biofuels on net GHG emissions. Searchinger et al. (2008) argue that the use of current agricultural land for biofuel production will release, mainly through indirect land-use change, as much GHG emissions as corn-based ethanol can mitigate in 167 years through fossil fuel replacement in the US. Ogg (2008) blames European biofuel subsidies for rainforest loss in Indonesia. Fargione et al. (2008) assert that it would take 48 years to repay the ‘carbon debt’ incurred if Conservation Reserve Program land is converted to corn ethanol production in the US; over 300 years to repay if Amazonian rainforest is converted for soybean biodiesel production; and over 400 years to repay if tropical peatland rainforest is converted for palm-oil biodiesel production in Indonesia or Malaysia. Similarly, Danielsen et al. (2009)] show that 75 to 93 years of biofuel use would be necessary before carbon savings could be gained from forest conversion<sup>3</sup> .

In the context of this ongoing debate, this study aims to investigate further the effects of large-scale biofuels expansion on GHG emissions, accounting for both the reduction of fossil fuel based emissions and the increase in emissions through land-use change. In contrast to most existing studies, which employ a partial equilibrium approach, we employ a global computable general equilibrium (CGE) model of the world economy, representing multiple countries or regions. The advantage a CGE model has over a partial equilibrium model is that it captures the interactions between various sectors and agents and reveals the economy-wide

> 2 Menichetti and Otto (2009) review 30 studies that estimate, using a life-cycle approach, the GHG mitigation potential of various biofuel feedstocks. Most studies they considered found net mitigation of GHG emissions when emissions related to land-use change were excluded.

> 3 Please see existing studies, such as Timilsina & Shrestha (2010), for detailed review of literature on the climate change impacts of biofuels.

2
issue as relatively poor but talented fishermen could bypass their lack of funds. According to qualitative interviews conducted in the village, it requires great diligence and attention not to damage an FRP and associated gear, such as nets, during operations. In contrast, a hired crew only seeks to maximize catches and according to respondents it cannot be held liable for any damage to the gear or boat. Thus, all respondents agreed that only boat-owning fishermen can make the operation of an FRP economically viable by optimally resolving the trade-off between maximizing daily catches and harming the gear. Apparently this trade-off is less severe for substantially larger vessel, like the trawlers in Kerala.

We now turn to the marketing of daily fish catches. For catamarans, there is interlinking of credit and marketing in our village and it takes the same form as in Platteau’s study villages. The auctioneer gives a loan for the purchase of the gear, which at the time of our 2004 interview was about Rs. 15,000 and 25,000. In return, the boatowner has to sell all his daily catches through that auctioneer, who keeps 5 percent of the value of the sales. The boatowner does not repay the principal. As a consequence, the commission comprises a compensation for the marketing services as well as an implicit interest payment on the amount owed. It thus comes as no surprise that more succesful boatowners are granted a larger loan. When a boatowner switches auctioneers, the new auctioneer settles the debt with the previous one. Switching of auctioneers does occur occasionally, in both our and Platteau’s data. The superiority of this interlinked share arrangement over separate debt and marketing contracts is a result of, first, limited liability of the fisherman and, second, costless monitoring of the fisherman’s day-to-day success by the auctioneer.

While marketing and financing became de-linked when trawlers were introduced in Kerala, the contract is still observed in Tamil Nadu with a slight modification. In this new contract, in addition to a commission of 7 percent, the auctioneer keeps another 10 percent of daily sales, which he deducts from the principal owed by the boatowner. Unlike a catamaran owner whose level of debt remains constant, an FRP owner asks his auctioneer for additional funds from time to time. If these additional loans are granted, they bare no interest and are added to the fisherman’s outstanding balance. The emergence of this feature of debt reduction and repeated renegotiation can be explained by the following two 11
Table A.3: Land market restrictions and wages IV Regression Results

||1<br>Rea|2<br>l Annual wa|3<br>ge|
|---|---|---|---|
|% Area Under LDO|**-2.030**|**-2.328**|**-1.873**|
||**(2.93)*****|**(2.74)*****|**(2.43)****|
|Area LDO*Travel Time|**0.328**|**0.347**|**0.262**|
||**(2.73)*****|**(2.80)*****|**(1.70)***|
|Travel Time to Large City|-0.073|-0.064|-0.048|
||(2.86)***|(2.09)**|(1.97)**|
|P. vivax (current malaria cases)|0.266|0.283|0.233|
||(2.08)**|(2.15)**|(1.71)*|
|Area of DSD|2.302|2.329|2.126|
||(1.57)|(1.58)|(1.48)|
|% of lab. Forc. w. education>= primary|0.108|0.089|0.108|
||(0.90)|(0.70)|(0.90)|
|% of area under inland water bodies|0.153|0.169|0.138|
||(2.37)**|(2.47)**|(2.04)**|
|Land quality (Excellent )|0.167|0.184|0.115|
||(1.49)|(1.55)|(0.96)|
|Land quality (Very good)|0.173|0.184|0.126|
||(1.95)*|(2.01)**|(1.36)|
|Share of area above 1000 feet of elevation|0.092|0.110|0.106|
||(0.83)|(0.97)|(1.01)|
|Log(slope)|||-0.021|
||||(1.10)|
|Log( Education Level (yr))|0.224|0.224|0.224|
||(16.80)***|(16.80)***|(16.84)***|
|Log(Age)|0.014|0.013|0.014|
||(0.62)|(0.57)|(0.60)|
|Male|0.207|0.208|0.208|
||(15.14)***|(15.10)***|(15.19)***|
|Married (yes=1)|0.142|0.142|0.143|
||(9.85)***|(9.79)***|(9.86)***|
|District Fixed Effect|Yes|Yes|Yes|
|Religion & Ethnicity dummies|Yes|Yes|Yes|
|Sectoral Dummies|Yes|Yes|Yes|
|Weak Identification Test||||
|Kleibergen-Paap wald F|30.32|19.71|38.22|
|Stock-Yogo 5% maximal IV rel. bias|9.53|9.53|9.53|
|Hansen's J Statistics|0.25||0.84|
|P-Value|0.62||0.36|

Robust t statistics in parentheses. Standard errors corrected for clustering * significant at 10%; ** significant at 5%; *** significant at 1%
authorities in improving the system or enlarging its mandate:

- Will the demand from retail investors be sufficient to achieve the program’s objectives? There has not been a formal survey of the demand for such a product. It is assumed that time deposit account holders will be the main users of the Expanded Small Investors Program. This assumption relies on another, stronger assumption: that small investors are rational and will switch to better investment options when they become available. But that may not be true. An investor survey or focus group could be used to measure demand, and a marketing campaign may be required to draw attention to the system and educate investors. The Treasury will be responsible for such efforts.

- With the infrastructure in place, could the system be used to distribute a wider range of instruments—starting with other government securities and eventually moving to private securities? Although the program offers only Treasury bills, there are plans to include all retail government securities and longer-term bonds.

- There are no technological limits on such a system. All limits imposed are related to policy or legal concerns, such as the ceiling on the amount that can be purchased in each transaction and the system’s buy-and-hold nature. Are these limits needed, or could they be removed to make the program more attractive?

- Is there are a need to distinguish between institutional and retail investors for government securities? Although the system of securities dealers helps create markets for these securities and facilitates their distribution, do the dealers’ functions justify their privileges?

- Could the Expanded Small Investors Program be offered to offshore Filipino investors? Doing so would require exploring laws in foreign countries that could block such investments, as well as registration procedures, money laundering concerns, and many other issues. But such an exploration might be worthwhile if it increases the demand for the program.

- Could telecommunications technology be used to expand the program’s outreach? In the Philippines mobile phone use is increasing much faster than Internet use. The program currently uses mobile phone technology only to confirm purchases. But if this technology were fully exploited, it could be used to purchases securities. Such an expansion would require sufficient exploration of other concerns for e-security.

44
|||[100%]|[100%]|||
|---|---|---|---|---|---|
||**Panel Sample**|||**Mean t-t**|**est**|
||<br>**(Transit and do**<br>**not transit)**|**Transit**|**No Transit**|**(Transit Vs**<br>**transit**|**. Not**<br>**)**|
|**Age group (%)**||||||
|12 to 18|12.5|16.7|3.0|18.9|***|
|19 to 35|28.7|25.1|36.8|-12.2|***|
|35 to 64|40.3|39.2|42.8|-3.0|***|
|65 +|18.5|19.0|17.4|2.0|**|
|_Mean(years)_|40.7|41.3|39.3|5.3|***|
|**Sex (%)**||||||
|Male|45.2|43.2|50.1|-6.4|***|
|**Educational Achievment (%)**||||||
|Up to completed primary|26.4|24.2|27.4|4.9|***|
|Up to completed Secondary|49.1|47.3|49.9|2.0|**|
|More than Secondary|24.5|28.5|22.7|-7.4|***|
|_Mean(years)_|9.8|10.4|9.5|-9.4|***|
|**Household Position (%)**||||||
|Head|32.6|31.8|34.7|-1.9|*|
|Spouse|20.4|20.7|19.7|1.6||
|Child|32.8|32.6|33.4|-1.7|*|
|Other relative|12.5|13.2|10.7|3.3|***|
|Other Non-relative|1.7|1.8|1.6|-0.6||
|**Marital Status (%)**||||||
|Married|25.8|26.2|24.8|1.5||
|Cohabitation<br>|19.8<br>|17.9<br>|24.5<br>|-6.7<br>|***<br>|
|Single|41.4|42.3|39.1|2.2|**|
|Separated|6.3|6.0|7.3|-3.2|***|
|Divorced|0.9|1.0|0.6|0.9||
|Widowed|5.7|6.6|3.7|6.4|***|
|**Monthly Individual Income (COP)**||||||
|Mean(in COP)|717,175|794,727|612,618|7.2|***|
|HHper capita income(COP)|588,563|596,372|585,144|0.7||
|**Hosuehold features (%)**||||||
|Own Household(payed orpaying)|68.1|69.5|64.8|3.6|***|
|Type of floor||||||
|_Soil or sand_|1.5|1.5|1.4|0.8||
|_Gross wood_|4.9|5.2|4.2|1.6||
|_Cement or gravel_|24.1|23.2|26.3|-3.3|***|
|_Floor title_|65.1|65.5|64.4|0.9||
|_Carpet, marble, parquet_|3.9|4.2|3.4|2.6|***|
|_Other_|0.5|0.5|0.4|0.2||
|Household with car|15.7|16.3|14.5|2.8|***|
|Size||||||
|_Less than 3_|24.6|25.4|22.9|3.3|***|
|_4 to 5_|45.9|46.2|45.2|0.7||
|_6 to 10_|27.9|26.8|30.4|-4.0|***|
|_More than 10_|1.5|1.6|1.5|0.6||
|**Health Insurance (health module)**||||||
|Contributive Regime|49.2|50.7|46.1|4.3|***|
|<br>Subsidized Regime|36.8|35.3|40.9|-5.1|***|
|ISS(Nueva EPS)|6.1|6.9|4.3|4.0|***|
|Other|2.5|2.8|1.8|3.6|***<br>|
|Do not have insurance|5.0|4.3|6.9|-5.6|***|
|**Pension Insurance**||||||
|Worker has coverage|27.5|27.4|46.3|12.5|***|
|Worker has no coverage|72.5|72.6|53.7|-12.5|***|

Source: Authors’ estimates from Fedesarrollo (2008, 2009) FSLS.

18
case of sons at the 5 percent level, although the numerical magnitude is somewhat smaller compared to the estimate in row (3).<sup>35</sup> The results on educational mobility in Table 4 are based on the sample of children born on or after 1967. As discussed in the data section earlier, this is done to make sure that the children’s education is not disrupted by the cultural revolution. However, the 1967 cut‐off also implies that the 1988 children sample consists of relatively younger cohorts (age range 18‐21) compared to the 2002 sample (age range 18‐35). One might wonder if the difference in the age cohorts between 1988 and 2002 samples is partly responsible for the results in Table 4. To explore this, we use a sub‐sample for 2002 consisting of children of 18‐21 years of age, and the estimates are consistent with the conclusions reached earlier (not reported in the Table 4). For example, the estimates from the Klein and Vella specification 1 (i.e, with all parental variables and county fixed effect used for heteroskedasticity) show that the effects on sons is 1.26 and statistically significant at the 5 percent level, but the effect on daughters is not significant at the 10 percent level (P‐ value 0.50).<sup>36</sup> The evidence from alternative specifications of Klein and Vella (2009) estimator when combined with the evidence from the AET sensitivity analysis provide us very strong confidence in the conclusion that for sons the intergenerational persistence in education has increased significantly from 1988 to 2002, while the effect of parental education on daughters probably has not changed in any fundamental way over the same time period.

> 35 If we pick variables that are significant in the heteroskedastic probit regressions for parental education that correspond to Klein and Vella1 and Klein and Vella2 models in Table 4, and rely on them for heteroskedastcity, the estimated causal effect are consistent with the results reported in Table 4. The results are available from the authors.

> 36 The heteroskedasticity based instrument is strong in the 18‐21 years samples for 2002. The Kleibergen‐ Paap F is 44.47 in sons’ sample and 36.19 in daughters’ sample.

30
**1** ( _time_ 3 _−_ 4), and **1** ( _time_ 4+) are dichotomous variables that take the value of one if the Venezuelan migrant arrived to Colombia 1 to 3, 3 to 4, and 4 or more years ago (at the time of the survey collection: 2022). As before, the omitted category are the Colombian children and adolescents which implies that _β_ 1, _β_ 2, and _β_ 3 measure the gap between the Colombian and Venezuelan minors that arrived at different times to Colombia.

7. The results of this exercise are illustrated in Figure Panel (a) presents the estimates for the body mass index and Panel (b) for the Peabody Vocabulary Test score percentiles. The circles in the figures are illustrating the point estimates and the bars depict the confidence intervals. Both figures suggest that the cognitive and physical development gaps between Venezuelan and Colombian minors are closing with time. Particularly, the estimated gap between for the BMI is reduced from 0.42SD to 0.29SD for Venezuelan groups migrants that arrived 1-3 and 4+ years ago to Colombia. This represents a reduction of 0.13 SD or 30%. Similarly, the gap for the percentiles of the Peabody Vocabulary Test Score shrinks from 15.15 p.p. to 9.12 p.p. for Venezuelan migrants that arrived 1-3 and 4+ years ago to Colombia. A reduction of 6.03 p.p. or 40%. As such, the evidence suggests that gaps are closing in time. Next survey rounds will enable us to assess whether this trend is continuous.

# **VI.B What is the role of service access and regularization programs?**

In this subsection we also explore how service access and regularization programs relate with the cognitive and development gaps between Colombian and native minors. We assess this question using the following specification where all the symbols represent the same variables as in equation (1), and **1** ( _Enrolledij_ ) and **1** (Not Enrolled _ij_ ) are dichotomous variables that take the value of one if the Venezue42