rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

What are the preferred policy responses of host country residents to large migration inflows, and to what extent are these preferences driven by contact with migrants as well as values such as humanitarianism? Forced migration has been central to recent electoral shifts in many host countries (Altındağ and Kaushal 2021; Campo et al. 2021; Dinas et al. 2019; Dustmann et al. 2019; Marbach and Ropers 2019; Rozo and Vargas 2021; Steinmayr 2021). It has also arguably contributed to the erosion of public trust in governments who are perceived to be ineffective in dealing with these inflows and raises questions about the role for policymaking in addressing public concern (Connor 2018; Jeannet et al. 2021; McLaren 2015).

A large body of work examines the patterns and determinants of immigration attitudes (Dinesen and Hjorth 2020), as well as public preferences for immigration levels (Citrin et al. 1997; Facchini and Mayda 2008; Scheve and Slaughter 2001). However, the control of inward flows is just one of several kinds of policies available to policymakers in response to migration. In reality, migration policies—either as proposed by political parties or implemented by governments—are multidimensional and comprise several elements. Existing observational surveys, typically measuring public support for single policy proposals in isolation from other topics that are also relevant for migration, provide limited insights on how host country citizens perceive these bundles of policies and distinguish among their constituent parts (Helbling et al. 2017; Helbling and Leblang 2019). Yet expressed public preferences potentially contribute to the ways that national and regional policymakers think about and act upon this issue, as implementing migration policies relies on a degree of public consent.

We address this problem by considering the case of Venezuelan migration to Colombia. Over the last decade, Venezuela has experienced a multifaceted humanitarian, economic and social crisis that has led to a large outflow of its citizens. Official figures from the R4V platform indicates that, by the end of October 2021, 5.91 million Venezuelans had left the country (about 16% of the country’s population), with over 1.75 million migrating to neighbouring Colombia<sup>1</sup> This case is important for empirical and theory development because both populations share ethnolinguistic, social and cultural features. These similarities potentially present divergent possibilities: on the one hand, if Colombians and Venezuelans possess similar occupational skills—then they may be close substitutes in the labour market, giving rise to more restrictive policy preferences on the basis of greater economic

> 1 Statistics obtained from the R4V website – The platform for interagency coordination for refugees and migrants from Venezuela: http://www.r4v.info

2
- Ranger N., S. Hallegatte, S. Bhattacharya, M. Bachu, S. Priya, K. Dhore, F. Rafique, P. Mathur, N. Naville, F. Henriet, C. Herweijer, S. Pohit, and J. Corfee-Morlot, 2011. “A Preliminary Assessment of the Potential Impact of Climate Change on Flood Risk in Mumbai.” _Climatic Change,_ forthcoming.

- Roman-Amat, B. 2007. “Préparer les forêts françaises au changement climatique.” Rapport à MM. les Ministres de l‟Agriculture et de la Pêche et de l‟Ecologie, du Développement et de l‟Aménagement Durables. Paris.

- Schwartz, P., and D. Randall. 2003. _An abrupt climate change scenario and its implications for United States_ [http://www.edf.org/documents/3566_AbruptClimateChange.pdf]

- Shalizi, Z., and F. Lecocq. 2009. “To Mitigate or to Adapt: Is that the question? Observations on an Appropriate Response to the Climate Change Challenge to Development Strategies.” _World Bank Research Observer_ . **25** (2):295–321.

- Smit, B., I. Burton, R.J.T. Klein, and J. Wandel. 2000. “An Anatomy of Adaptation to Climate Change and Variability.” _Climatic Change_ **45** :223–251.

- UNDP, 2007. _Human Development Report 2007/08_ . Palgrave McMillan, New York.

- UNFCCC, 2007. _Investment and Financial Flows to Address Climate Change._ Climate Change Secretariat, Bonn.

Welzer, H. 2009. _Les guerres du climat, Pourquoi on tue au XXI° siècle_ . Paris: Gallimard.

- World Bank. 2003. _World Development Report 2003: Sustainable Development in a Dynamic World: Transforming Institutions, Growth, and Quality of Life_ . Washington, D.C.

- World Bank, 2006. _Investment Framework for Clean Energy and Development._ World Bank, Washington DC.

- World Bank. 2010. _World Development Report 2010: Development and Climate Change_ . Washington, D.C.

- World Bank. 2010. _The Economics of Adaptation to Climate Change._ Washington, D.C.

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countries, participants in programs promoting self-employment tend to be more educated (almost 1 more year of schooling) and older (Betcherman, Olivas and Dar, 2004). Even though household income is likely to be underestimated in our sample, we find that average household per capita income is slightly higher than average per capita income for a _Jefes_ beneficiary.<sup>31</sup> Finally, the findings also show that in our sample the income of the beneficiary represents a more important source of income for the household (between 48% and 52% in our sample versus approximately 35% for the average _Jefe_ ).

In the empirical work, we consider that an individual _i_ has been affected by the program (i.e., has having received “treatment”) during period _t_ if the he/she has received inputs and equipment for the project _and_ if he has received technical assistance (measured by at least one visit from the tutors). Using this definition and depending on the project status of the individuals in each period, we group the final sample of 476 individuals into: _Never Beneficiaries_ (298 individuals are never MEP beneficiaries in any of the two periods), _Entrants_ (155 individuals are not MEP beneficiaries in 2004 but become beneficiaries in 2005), _Drop-outs_ (3 individuals are MEP beneficiaries in 2004 but are no longer beneficiaries in 2005) and _Always Beneficiaries_ (20 individuals are MEP beneficiaries during the two periods).

Table 4 reports some descriptive statistics for the different groups at the end of 2004 (baseline survey). Column (1) refers to all individuals who have never been program participants, column (2) refers to those who have never been program participants but that have applied to the program, column (3) refers to the entrants and column (4) to those participants during the two periods. The comparison shows that the groups are relatively homogeneous in

> 31There are two major reasons why individual income in our sample is underestimated when compared to the EPH. First, time and cost constraints in the implementation of the survey implied that only the beneficiary was interviewed (as opposed to all household members in EPH). The income of other members could only be inferred by the household aggregate income. Second, the survey collects information on fewer income sources than the EPH.

18
10. Some considerations may be given to quality differences of traded goods, especially nonprimary goods, between intra-CMEA and convertible currency areas. The proxy k could be computed for the whole union and uniformly applied to all Republics.

11. More specifically, for the sample primary products, regardless of their destinations/origins, their intemational prices could be assumed to be equal to the prices in the "Western" market. For the sample manufactured goods, their international prices for trade with Western economies should be the actual transaction prices, while those for inter-Republic and intra-CMEA trade could be assumed to be equal to the quality-adjusted transaction prices in Westem market. These quality-adjusted prices could be estimated, based on the 1988 study by Oblath and Tar on Hungary-USSR trade.

12. Proxy Es could be computed for sample products taken from the final demand side in input-output tables or for sample products from the production side. In the latter case, proxy Es should be computed from the value-added estimates in domestic and international prices, where the imported raw materials as re-evaluated at international prices would be subtracted from the gross output values as re-evaluated at the international prices.

13. Some people may consider that the above method will yield $GNP figures close to those converted at the purchasing power parity (PPP), which would be much higher than those converted at the exchange rates. Generally, the "higher" PPP-converted GNPs would result mainly from using PPPs for non-traded goods and services, which are much "lower" than those for traded goods. Even then, according to 1985 ICP data, the PPPs for tradables are significantly "lower" than the official exchange rates for most market economies (Attachment 1). This suggests that using E* directly for FSU Republics would result in overstated $GNP figures for the Republics compared to market economies. To ensure internationally comparability of $ GNP figures, E* for FSU Republics may have to be adjusted such that the adjusted E* would deviate from the PPPs for tradables to the same extent as for the "average' market economy.

14. One option for adjusting E* is as follows: (a) First, PPPs for tradables are computed for benchmark countries of 1985 ICP; (b) Second, the ratios of the official exchange rates to the above PPPs are computed for the benchmark countries and simply averaged: (Eo/PPP); (c) E* for each FSU Republic is multiplied by (Eo/PPP) from (b) above: E**=(E*)(Eo/PPP).

# _15._ Alternatively, E** could be approximated as follows:

E**=average of [(E*)/PPPi](Eoi) =average of (PPP*i)(Eoi) for comparator economy i-1,2,3...n, where PPP*i =PPP between FSU republic and a comparator economy i and Eoi=prevailing exchange rate vis-a-vis the U.S.dollar in comparator i.

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Table 4. Effect of a 1 Percentage Point Decrease in the Net Gini on Log GDP per capita

||Level of Economic Development:<br>1970 GDP per capita|Level of Economic Development:<br>2010 GDP per capita|
|---|---|---|
||(1)|(2)|
|Brunei|0.71|1.09|
|Cambodia|-0.25|0.31|
|Indonesia|-0.37|0.44|
|Laos|-0.39|0.36|
|Malaysia|-0.10|0.73|
|Philippines|-0.11|0.40|
|Singapore|0.20|1.08|
|Thailand|-0.11|0.63|
|Vietnam|-0.39|0.37|
|ASEAN Average|-0.09|0.60|
|ASEAN Median|-0.11|0.40|

Note: The table reports effects on log GDP per capita of a 1 percentage point decrease in the net Gini. For column (1) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) with the values of countries' GDP per capita in 1970. For column (2) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) with the values of countries' GDP per capita in 2010. Specifically, the estimated relationship for country _c_ and period _t_ is ln(GDP per capita)ct = 157*NetGinict – 24*NetGinict*ln(initial GDP per capita)c.

18
**Table 3: Determinants of Learning, Indonesia, PISA**

||Pisa 2003||||Pisa 2006||||
|---|---|---|---|---|---|---|---|---|
||||Regression|Sample|||Regression|Sample|
||Coef.|S.E.|<br>mean|<br>mean|Coef.|S.E.|<br>mean|<br>mean|
|_Institutions_|||||||||
|School determines pedagogy|38.55|(4.75)*|0.99|0.99|34.20|(8.24)*|0.96|0.95|
|Adequate supply of teachers|0.55|(3.71)|0.47|0.46|8.74|(3.81)*|0.71|0.74|
|_Schools_|||||||||
|Public|36.15|(3.59)*|0.70|0.55|33.26|(3.89)*|0.67|0.60|
|% students repeating grade|-0.90|(0.28)***|1.27|0.90|-1.35|(0.30)*|0.81|0.74|
|Rural area (<3,000)|-15.72|(0.28)*|0.23|0.31|-14.32|(3.59)*|0.20|0.26|
|Urban (3,000 and above)|||0.77|0.69|||0.80|0.74|
|_Student characteristics_|||||||||
|Grade|||||||||
|8<sup>th</sup>|||0.13|0.15|||0.12|0.12|
|9<sup>th</sup>|25.79|(1.78)*|0.46|0.49|25.03|(2.58)*|0.46|0.40|
|10<sup>th</sup>|74.14|(3.87)*|0.39|0.35|75.63|(4.50)*|0.38|0.44|
|11th|87.35|(5.60)*|0.03|0.02|78.19|(6.09)*|0.05|0.04|
|Age|-11.66|(1.87)*|15.71|15.71|-9.45|(2.00)*|15.76|15.78|
|Female|-7.57|(1.30)*|0.51|0.51|-18.60|(2.51)*|0.51|0.49|
|_Family background_|||||||||
|Mother’s education - none|||0.14|0.15|||0.14|0.14|
|Mother -Primary|-3.50|(1.86)***|0.31|0.35|7.14|(2.35)*|0.32|0.35|
|Mother’s education - lower sec|-3.66|(2.19)|0.17|0.15|10.38|(2.66)*|0.20|0.19|
|Mother’s education - upper sec|21.37|(2.79)*|0.20|0.17|15.06|(3.24)*|0.25|0.22|
|Mother’s education -university|-2.45|(3.44)|0.19|0.18|7.94|(4.38)***|0.11|0.11|
|Books at home|||||||||
|None–10 books|||0.33|0.34|||0.21|0.21|
|11–100 books|4.56|(1.22)*|0.59|0.58|1.76|(1.28)|0.69|0.69|
|101-500 books|22.36|(2.07)*|0.09|0.08|1.30|(1.77)|0.10|0.10|
|Computers at home|||||||||
|None|||0.82|0.84|||0.85|0.85|
|One or more than one|16.00|(2.21)*|0.18|0.16|41.42|(3.78)*|0.15|0.15|
|Language speak at home|||||||||
|Test language speak at home|-12.25|(2.18)*|0.32|0.32|-9.66|(3.34)*|0.34|0.34|
|Constant|453.94|(30.79)*|||437.60|(32.33)*|||
|Observation|8,549||||8,688||||
|R<sup>2</sup>|0.26||||0.34||||
|Total Sample|10,761|0.79|||10,647|0.82|||
|_Source_: Program for Internationa|l Student A|ssessment ( PIS|A) 2003 and|2006|||||
|_Notes_: *** 90%; **95%; *99%|||||||||

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variation in survey design has the potential to affect the comparability and reliability of hunger statistics across countries and over time. In this study we explore the implications of survey design on estimates of the number of hungry people.

We explore a unique survey experiment which randomly assigned seven different HCES methods to 3,525 households in Tanzania. This experiment covered urban and rural settings, and reflects the range of Sub-Saharan environments where, according to the FAO, the proportion of hungry people is highest and increasing. Using data from this experiment, we calculate hunger figures ranging from 19 to 68 percent in the same villages at the same time, depending on the survey method. The features of the survey experiment, described below, ensure that any differences in derived hunger numbers are solely attributable to survey design.

Our results suggest that any comparative assessment of hunger prevalence using HCES should clearly take into account differences in survey design. However, hunger numbers are not simply scaled up or down by differences in survey design: the differential likelihood that a household is counted as hungry through one survey design and as not hungry through another survey design is correlated with the household’s size, wealth, location (urban or rural), and the education of its head. Comparing hunger numbers across survey designs is therefore not trivial. Furthermore, the ranking of socio-economic or geographical groups by hunger prevalence within them – an exercise that may be carried out with survey data in order to inform the targeting of nutrition interventions – will depend on the survey design.

The sensitivity of hunger estimates to survey design variations is greater than for other statistics derived from HCES, such as poverty counts and inequality measures (Beegle et al. 2012). The reason is that the surveys differ the most in the ways that they go about measuring food consumption, whereas modules devoted to non-food consumption tend to be more standardized. Therefore, we advocate for more effort spent on harmonizing survey design, in order to obtain comparable hunger numbers within and between countries and over time. The existing idiosyncratic variation in survey designs is not inherent to the survey approach to measurement. In other areas of socio-economic statistics, efforts have succeeded at harmonization in methods - cross-country data on fertility and health are much more comparable because of the standardized approach to measurement taken by the Demographic and Health Surveys.

3
Using plant or "factory" level data for 1998-99, from the Indian Annual Survey of Industries (ASI), we examine location choices in eight manufacturing industries.' These are (with National Industrial Classification [NIC] codes in parenthesis):

1. FoodProcessing(151, 152, 153, 154, 155)

2. Textiles and Textile products, including wearing apparel (171, 172, 173, 181) 3. Leather and leather products (191, 192) 4. Paper products, printing and publishing (210, 221,* 222) 5. Chemical, chemical products, rubber and plastic products (241, 242, 243, 251, 252) 6. Basic Metals and Metal Products (271, 272, 273, 281, 289) 7. Mechanical Machinery and Equipment (291, 292) 8. Electrical and Electronics (including computer) Equipment (292, 300, 31, 32) These plant level data are supplemented by district and urban demographic and amenities data from the 1991 Census of India and detailed, geographically referenced information on the availability and quality of transport infrastructure linking urban areas (CMIE, 1998; ML Infomap, 1998). The Annual Survey of Industries (ASI) data allow us to identify each plant at the district level spatially and at the four digit SIC level sectorally.

This paper is organized in three parts. In Part I, we present the analytic framework and specify the econometric model to examine location decisions at the firm level.<sup>2</sup> In Part II, we discuss results from the econometric analysis. Part m briefly summarizes the contributions and IBy grouping frmns into carefully defined sectors (rather than examining all manufacturing together), we can identify the differential impact of regional characteristics or geographic externalities across industries. For example, in comparison to Food Processing, which is closely linked to the traditional rural industrial base, industries such as Machinery, Metals, and Computers and Electronics are relatively footloose urban industries subject to considerable agglomeration economies.

2The ASI provides information on plants or factories, which are the units of production. These are roughly equivalent to the use of establishment level data. The industry survey does not allow us to identify enterprises to whom individual establishments may be linked.

3
**Table 5: Balance tests of time-invariant and pre-determined municipal attributes**

||Area|Elevation|Latitude|Longitude|Distance<br>to federal<br>capital|Distance<br>to state<br>capital|Share of woman<br>(age >9) that were<br>economically<br>active in 1991|<br>Share of black<br>population in<br>1991|<br>Income per<br>capita in 1991|
|---|---|---|---|---|---|---|---|---|---|
|Thresholds 1-7|-47.643|0.737|0.001|0.000|0.334|-0.326|-0.003|0.000|-0.000|
||[32.080]|[1.460]|[0.009]|[0.010]|[1.006]|[0.950]|[0.005]|[0.000]|[0.000]|
|Obs.|21124|21124|21124|21124|21124|21124|19036|19080|19120|
|Thresholds 1-4|-21.432|0.516|-0.001|0.007|1.664|-0.357|-0.005|0.000|-0.000|
||[24.250]|[1.790]|[0.011]|[0.013]|[1.198]|[1.150]|[0.006]|[0.000]|[0.000]|
|Obs.|18321|18321|18321|18321|18321|18321|16354|16367|16401|

_Notes:_ Estimates from reduced-form regressions relating time-invariant or pre-determined municipal attributes to theoretical transfers observed in the period 2000-2006. Data for 1991 come from the corresponding population census. All regressions include a three order population polynomial, state and year fixed effects. Robust standard errors clustered at the municipal level in brackets. *, **, *** represent significance at the 10%, 5% and 1% level, respectively.
32 One could argue that in such a horse race, continuous variables like party age will explain more variation than dichotomous variables, like New Democracy. However, when we replace the dichotomous “New democracy” measure in Table 4 with a continuous measure of democratic age (the number of years of continuous competitive elections, from the Database of Political Institutions), it is still the case that the party age-election interaction term is large and significant. Columns 5 and 6 of Table 4 display these results. Column 5 shows that in the base sample, the interaction between years of competitive elections and the election variable is negative: in older democracies, political budget cycles are smaller. The column 6 specification adds the party age variables. The party age interaction is significant and large – as before, political budget cycles are dampened in countries with older political parties. The years of election interaction is no longer significant, providing additional evidence that the democratic age effect operates through political parties.<sup>18</sup> Figure 1a graphically displays the results from Column 4 in Table 4. The magnitude of political budget cycles declines significantly with the age of political parties. No significant evidence for the cycles can be found when parties are older than 50 years: the 95% confidence interval around 50 years does not exclude zero. Figure 1b indicates that the sample includes many observations on both sides of the 50 year threshold: 57.4 percent of the observations are below this threshold. The diminishing effect of political budget cycles in countries with older political parties is not an artifact of the rarity of such countries.

> 18 We also perform the reverse exercise and ask whether results are robust to substituting a continuous version of party age with a dichotomous version, a dummy variable equal to one if average party age is older than the median (43 years) and zero otherwise.  Our results in columns 3 and 4 are similar or stronger after making this substitution.  The party age interactions, using the dichotomous party variable, are highly significant and the New Democracy interaction loses significance.
markets (see Annex 2 for specific sources and definitions). Individual country data on trade and producer prices were obtained from the FAO Trade Yearbook and the FAO Produoi:ion Yearbook, respectively.<sup>4</sup> The source of price Index and exchange rate data Is the IMF's International Financial Statistics Yearbook.

Export revenue Is valued f.o.b. In U.S. dollars. Export unit values (EUV) are computed by dividing revenue by the total quantity of exports (In metric tons) of the particular commodity. Therefore, except where noted, EUVs are valued In U.S. doilars per metric ton. Producer prices are valued In local currency per metric ton.0 World prices, as well as export revenue (and therefore EUVs), are deflated to 1980 U.S. dollars with the IMF's "World Consumer Price Index", a GDP-welghted average of country consumer price Indlces (CPI). EUVs are also deflated with the World CPI Instead of the national CPls to reflect the EUVs purchasing power In International markets. In contrast, the producer prices are deflated to 1980 prices by the approprlate natlonal CPI. Because multiple data sources are used In this study, there are some differences In commodity definitlons (see 4 Trade data are available for the period 1961-87. Producer price availability varies by country/commodity case but Is usually 1966-87.

5 FAO producer prices are "prices received by farmers" and refer to the national average of Individual commodities comprlsing all grades, kinds and varieties.